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             Please use the previous external buffer!`)}else n=ge();return this.storageCache.set(n,{gpuData:{id:n,type:0,buffer:e},originalSize:t}),ns("verbose",()=>`[WebGPU] GpuDataManager.registerExternalBuffer(size=${t}) => id=${n}, registered.`),n}unregisterExternalBuffer(e){e!==void 0&&(this.storageCache.delete(e),ns("verbose",()=>`[WebGPU] GpuDataManager.unregisterExternalBuffer() => id=${e}`))}create(e,t=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST){let s=Q(e),n,o=(t&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE,i=(t&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM;if(o||i){let c=(o?this.freeBuffers:this.freeUniformBuffers).get(s);c?c.length>0?n=c.pop():n=this.backend.device.createBuffer({size:s,usage:t}):n=this.backend.device.createBuffer({size:s,usage:t})}else n=this.backend.device.createBuffer({size:s,usage:t});let a={id:ge(),type:0,buffer:n};return this.storageCache.set(a.id,{gpuData:a,originalSize:Number(e)}),ns("verbose",()=>`[WebGPU] GpuDataManager.create(size=${e}) => id=${a.id}`),a}get(e){return this.storageCache.get(e)?.gpuData}release(e){let t=typeof e=="bigint"?Number(e):e,s=this.storageCache.get(t);if(!s){if(this.storageCache.size===0)return 0;throw new Error("releasing data does not exist")}return ns("verbose",()=>`[WebGPU] GpuDataManager.release(id=${t}), gpuDataId=${s.gpuData.id}`),this.storageCache.delete(t),this.buffersPending.push(s.gpuData.buffer),s.originalSize}async download(e,t){let s=this.storageCache.get(Number(e));if(!s)throw new Error("data does not exist");await Ie(this.backend,s.gpuData.buffer,s.originalSize,t)}refreshPendingBuffers(){if(this.buffersPending.length!==0)if(this.backend.sessionStatus==="default"){for(let e of this.buffersPending){let t=In.get(e.size);if((e.usage&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE){let s=this.freeBuffers.get(e.size)||[];t===void 0||s.length>=t?e.destroy():s.push(e)}else if((e.usage&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM){let s=this.freeUniformBuffers.get(e.size)||[];t===void 0||s.length>=t?e.destroy():s.push(e)}else e.destroy()}this.buffersPending=[]}else{let e=this.capturedPendingBuffers.get(this.backend.currentSessionId);e||(e=[],this.capturedPendingBuffers.set(this.backend.currentSessionId,e));for(let t of this.buffersPending)e.push(t);this.buffersPending=[]}}dispose(){this.freeBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.freeUniformBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache.forEach(e=>{e.gpuData.buffer.destroy()}),this.capturedPendingBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.capturedPendingBuffers=new Map}onCreateSession(){this.sessionCount+=1}onReleaseSession(e){let t=this.capturedPendingBuffers.get(e);t&&(t.forEach(s=>{s.destroy()}),this.capturedPendingBuffers.delete(e)),this.sessionCount-=1,this.sessionCount===0&&(ns("warning",()=>"[WebGPU] Clearing webgpu buffer cache"),this.storageCache.forEach(s=>{s.gpuData.buffer.destroy()}),this.storageCache=new Map)}},_t=(...e)=>new Xe(...e)}),vt,Qe,It=y(()=>{vt=class{constructor(e){Object.assign(this,e)}get cacheKey(){return this.key||(this.key=Object.getOwnPropertyNames(this).sort().map(e=>`${this[e]}`).join(";")),this.key}},Qe=e=>new vt(e)}),Xt,gs,Se,Ps,js,Rs,dr,zt=y(()=>{Xt=class{static calcMatMulShape(e,t){return e[1]!==t[0]?void 0:[e[0],t[1]]}},gs=class{static calcShape(e,t,s=!1){let n=e.length,o=t.length;if(n===0)return t;if(o===0)return e;let i=Math.max(e.length,t.length),a=new Array(i);if(s){if(n<2||o<2)return;let c=Xt.calcMatMulShape([e[n-2],e[n-1]],[t[o-2],t[o-1]]);if(c===void 0)return;[a[i-2],a[i-1]]=c}for(let c=s?3:1;c<=i;c++){let p=n-c<0?1:e[n-c],h=o-c<0?1:t[o-c];if(p!==h&&p>1&&h>1)return;let k=Math.max(p,h);if(p&&h)a[i-c]=Math.max(p,h);else{if(k>1)return;a[i-c]=0}}return a}static isValidBroadcast(e,t){let s=e.length,n=t.length;if(s>n)return!1;for(let o=1;o<=s;o++)if(e[s-o]!==1&&e[s-o]!==t[n-o])return!1;return!0}},Se=class xp{static size(t){return xp.getSizeFromDimensionRange(t,0,t.length)}static convertShape(t,s=4){let n=t.length;if(n===0)return[];let o=new Array(n),i=n-1;for(;i>=0;){if(t[i]%s===0){o[i]=t[i]/s;break}if(s%t[i]!==0)throw new Error("cannot convert shape");o[i]=1,s/=t[i],i--}for(i--;i>=0;i--)o[i]=t[i];return o}static sizeFromDimension(t,s){if(s<0||s>t.length)throw new Error(`invalid dimension of ${s} for sizeFromDimension as Tensor has ${t.length} dimensions.`);return xp.getSizeFromDimensionRange(t,s,t.length)}static sizeToDimension(t,s){if(s<0||s>t.length)throw new Error(`invalid dimension of ${s} for sizeToDimension as Tensor has ${t.length} dimensions.`);return xp.getSizeFromDimensionRange(t,0,s)}static getSizeFromDimensionRange(t,s,n){let o=1;for(let i=s;i<n;i++){if(t[i]<0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains negative values in them.");o*=Number(t[i])}return o}static computeStrides(t){let s=t.length;if(s===0)return[];if(s===1)return[1];let n=new Array(s);n[s-1]=1,n[s-2]=t[s-1];for(let o=s-3;o>=0;--o)n[o]=n[o+1]*t[o+1];return n}static normalizeAxis(t,s){if(t<-s&&t>=s)throw new Error("unsupported axis for this operation.");return t<0?t+s:t}static normalizeAxes(t,s){return t.map(n=>this.normalizeAxis(n,s??t.length))}static sortBasedOnPerm(t,s){return s?s.map(n=>t[n]):t.slice().reverse()}static padShape(t,s){let n=t.length;return t.map((o,i)=>o+s[i]+s[i+n])}static areEqual(t,s){return t.length!==s.length?!1:t.every((n,o)=>n===s[o])}},Ps=class Hd{static adjustPoolAttributes(t,s,n,o,i,a){if(!t&&n.length!==s.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(t)for(let c=0;c<s.length-2;c++)c>=n.length?n.push(s[c+2]):n[c]=s[c+2];for(let c=0;c<n.length;c++)if(c<o.length){if(o[c]<0)throw new Error("strides should be greater than or equal to 1")}else o.push(1);for(let c=0;c<n.length;c++)if(c<i.length){if(i[c]<0)throw new Error("dilations should be greater than or equal to 1")}else i.push(1);for(let c=0;c<n.length*2;c++)if(c<a.length){if(a[c]<0)throw new Error("pad should be greater than or equal to 1")}else a.push(0);for(let c=0;c<n.length;c++){if(n[c]<=0)throw new Error("kernel shapes need to be greater than 0");if(a[c]>=n[c]||a[c+n.length]>=n[c])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(t,s,n,o,i,a,c){if(c){if(i.length!==2*(t.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(s.length!==t.length-2)throw new Error("length of strides should be the length of data dimensions");if(o.length!==t.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let p=0;p<t.length-2;p++)Hd.adjustPadAndReturnShape(t[p+(a?1:2)],s[p],n[p],o[p],i,p,p+t.length-2,c)}}static computePoolOutputShape(t,s,n,o,i,a,c){if(s.length<=0)throw new Error("input shape must be of size greater than 0");let p=[s[0],s[1]];return Hd.computeShapeHelper(t,s,p,n,o,i,a,c),p}static computeConvOutputShape(t,s,n,o,i,a,c){if(t.length<=0||s.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let p=[t[0],s[0]];return Hd.computeShapeHelper(!1,t,p,n,o,i,a,c),p}static computeShapeHelper(t,s,n,o,i,a,c,p){if(t)for(let h=0;h<s.length-2;h++)n.push(1);else for(let h=0;h<s.length-2;h++)n.push(Hd.adjustPadAndReturnShape(s[h+2],o[h],i[h],a[h],c,h,h+s.length-2,p))}static adjustPadAndReturnShape(t,s,n,o,i,a,c,p){let h=n*(o-1)+1;if(p&&p!=="NOTSET")switch(p){case"VALID":return i[a]=0,i[c]=0,Math.floor((t-h)/s+1);case"SAME_LOWER":case"SAME_UPPER":if(n!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let k=((t+s-1)/s-1)*s+o-t;return i[a]=Math.floor(p==="SAME_LOWER"?(k+1)/2:k/2),i[c]=k-i[a],Math.floor((t+k-o)/s+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((t+i[a]+i[c]-h)/s+1)}},js=class{static getShapeOfGemmResult(e,t,s,n,o){if(e.length!==2||s.length!==2)throw new Error("shape need to be of size 2");let i,a,c;t?(i=e[1],a=e[0]):(i=e[0],a=e[1]);let p=-1;if(n?(c=s[0],p=1):(c=s[1],p=0),s[p]!==a)throw new Error("dimension mismatch");if(i<=0||c<=0||a<=0)throw new Error("invalid shape specified");if(o&&!gs.isValidBroadcast(o,[i,c]))throw new Error("gemm: invalid bias shape for broadcast");return[i,c,a]}},Rs=-34028234663852886e22,dr=34028234663852886e22}),zs,gr,Zt,us,xt,os,wr,As,Vs,Mt,Es,Oe,gt,tr,qr,Qn,ka,Gt=y(()=>{Ft(),zt(),zs=64,gr=(e,t)=>{if(t===3)throw new Error("vec3 has same alignment as vec4, use vec4 instead");switch(Number(e)){case 10:return t>1?`vec${t}<f16>`:"f16";case 1:return t>1?`vec${t}<f32>`:"f32";case 6:return t>1?`vec${t}<i32>`:"i32";case 12:return t>1?`vec${t}<u32>`:"u32";case 7:if(t>1)throw new Error("currently not supported vecX of uint64 yet");return["vec2<u32>","i32"];case 13:if(t>1)throw new Error("currently not supported vecX of uint64 yet");return["vec2<u32>","u32"];case 9:if(t!==4)throw new Error("bool must be vec4");return["u32","vec4<bool>"];case 22:return"i32";case 21:return"u32";default:throw new Error(`Unknown data type: ${e}`)}},Zt=(e,t=1)=>{let s=gr(e,t);return typeof s=="string"?s:s[0]},us=(e,t=1)=>{let s=gr(e,t);return typeof s=="string"?s:s[1]},xt=(...e)=>{let t=[];return e.forEach(s=>{s.length!==0&&t.push({type:12,data:s},{type:12,data:Se.computeStrides(s)})}),t},os=e=>e%4===0?4:e%2===0?2:1,wr=(e="f32",t,s="0")=>!t||t===1?`${e}(${s})`:`vec${t}<${e}>(${s})`,As=(e,t,s)=>e==="f32"?s:t===1?`f32(${s})`:`vec${t}<f32>(${s})`,Vs=(e,t)=>t===4?`(${e}.x + ${e}.y + ${e}.z + ${e}.w)`:t===2?`(${e}.x + ${e}.y)`:t===3?`(${e}.x + ${e}.y + ${e}.z)`:e,Mt=(e,t,s,n)=>e.startsWith("uniforms.")&&s>4?typeof t=="string"?n==="f16"?`${e}[(${t}) / 8][(${t}) % 8 / 4][(${t}) % 8 % 4]`:`${e}[(${t}) / 4][(${t}) % 4]`:n==="f16"?`${e}[${Math.floor(t/8)}][${Math.floor(t%8/4)}][${t%8%4}]`:`${e}[${Math.floor(t/4)}][${t%4}]`:s>1?`${e}[${t}]`:e,Es=(e,t,s,n,o)=>{let i=typeof s=="number",a=i?s:s.length,c=[...new Array(a).keys()],p=a<2?"u32":a<=4?`vec${a}<u32>`:`array<u32, ${a}>`,h=gr(t,o),k=typeof h=="string"?h:h[1],u=typeof h=="string"?h:h[0],S={indices:p,value:k,storage:u,tensor:t},B=at=>typeof at=="string"?at:`${at}u`,N={offsetToIndices:!1,indicesToOffset:!1,broadcastedIndicesToOffset:!1,set:!1,setByIndices:!1,get:!1,getByIndices:!1},L=i?"uniforms.":"",se=`${L}${e}_shape`,ee=`${L}${e}_strides`,V="";for(let at=0;at<a-1;at++)V+=`
    let dim${at} = current / ${Mt(ee,at,a)};
    let rest${at} = current % ${Mt(ee,at,a)};
    indices[${at}] = dim${at};
    current = rest${at};
    `;V+=`indices[${a-1}] = current;`;let de=a<2?"":`
  fn o2i_${e}(offset: u32) -> ${S.indices} {
    var indices: ${S.indices};
    var current = offset;
    ${V}
    return indices;
  }`,me=at=>(N.offsetToIndices=!0,a<2?at:`o2i_${e}(${at})`),ye=[];if(a>=2)for(let at=a-1;at>=0;at--)ye.push(`${Mt(ee,at,a)} * (indices[${at}])`);let Be=a<2?"":`
  fn i2o_${e}(indices: ${S.indices}) -> u32 {
    return ${ye.join("+")};
  }`,Ee=at=>(N.indicesToOffset=!0,a<2?at:`i2o_${e}(${at})`),tt=(...at)=>a===0?"0u":`${S.indices}(${at.map(B).join(",")})`,pt=(at,Pt)=>a<2?`${at}`:`${Mt(at,Pt,a)}`,Ct=(at,Pt,ps)=>a<2?`${at}=${ps};`:`${Mt(at,Pt,a)}=${ps};`,Dt={},$t=(at,Pt)=>{N.broadcastedIndicesToOffset=!0;let ps=`${Pt.name}broadcastedIndicesTo${e}Offset`;if(ps in Dt)return`${ps}(${at})`;let vs=[];for(let Ts=a-1;Ts>=0;Ts--){let yn=Pt.indicesGet("outputIndices",Ts+Pt.rank-a);vs.push(`${pt(ee,Ts)} * (${yn} % ${pt(se,Ts)})`)}return Dt[ps]=`fn ${ps}(outputIndices: ${Pt.type.indices}) -> u32 {
             return ${vs.length>0?vs.join("+"):"0u"};
           }`,`${ps}(${at})`},bt=(at,Pt)=>(()=>{if(S.storage===S.value)return`${e}[${at}]=${Pt};`;if(S.storage==="vec2<u32>"&&S.value==="i32")return`${e}[${at}]=vec2<u32>(u32(${Pt}), select(0u, 0xFFFFFFFFu, ${Pt} < 0));`;if(S.storage==="vec2<u32>"&&S.value==="u32")return`${e}[${at}]=vec2<u32>(u32(${Pt}), 0u);`;if(S.storage==="u32"&&S.value==="vec4<bool>")return`${e}[${at}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${Pt}));`;throw new Error(`not supported combination of storage type ${S.storage} and value type ${S.value} yet`)})(),Kt=at=>(()=>{if(S.storage===S.value)return`${e}[${at}]`;if(S.storage==="vec2<u32>"&&S.value==="i32")return`i32(${e}[${at}].x)`;if(S.storage==="vec2<u32>"&&S.value==="u32")return`u32(${e}[${at}].x)`;if(S.storage==="u32"&&S.value==="vec4<bool>")return`vec4<bool>(bool(${e}[${at}] & 0xFFu), bool(${e}[${at}] & 0xFF00u), bool(${e}[${at}] & 0xFF0000u), bool(${e}[${at}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${S.storage} and value type ${S.value} yet`)})(),jt=a<2?"":`
  fn get_${e}ByIndices(indices: ${S.indices}) -> ${k} {
    return ${Kt(`i2o_${e}(indices)`)};
  }`,Lt=a<2?"":(()=>{let at=c.map(ps=>`d${ps}: u32`).join(", "),Pt=c.map(ps=>`d${ps}`).join(", ");return`
  fn get_${e}(${at}) -> ${k} {
    return get_${e}ByIndices(${tt(Pt)});
  }`})(),ss=(...at)=>{if(at.length!==a)throw new Error(`indices length must be ${a}`);let Pt=at.map(B).join(",");return a===0?Kt("0u"):a===1?Kt(Pt[0]):(N.get=!0,N.getByIndices=!0,N.indicesToOffset=!0,`get_${e}(${Pt})`)},Jt=at=>a<2?Kt(at):(N.getByIndices=!0,N.indicesToOffset=!0,`get_${e}ByIndices(${at})`),qt=a<2?"":`
  fn set_${e}ByIndices(indices: ${S.indices}, value: ${k}) {
    ${bt(`i2o_${e}(indices)`,"value")}
  }`,Qs=a<2?"":(()=>{let at=c.map(ps=>`d${ps}: u32`).join(", "),Pt=c.map(ps=>`d${ps}`).join(", ");return`
  fn set_${e}(${at}, value: ${k}) {
    set_${e}ByIndices(${tt(Pt)}, value);
  }`})();return{impl:()=>{let at=[],Pt=!1;return N.offsetToIndices&&(at.push(de),Pt=!0),N.indicesToOffset&&(at.push(Be),Pt=!0),N.broadcastedIndicesToOffset&&(Object.values(Dt).forEach(ps=>at.push(ps)),Pt=!0),N.set&&(at.push(Qs),Pt=!0),N.setByIndices&&(at.push(qt),Pt=!0),N.get&&(at.push(Lt),Pt=!0),N.getByIndices&&(at.push(jt),Pt=!0),!i&&Pt&&at.unshift(`const ${se} = ${S.indices}(${s.join(",")});`,`const ${ee} = ${S.indices}(${Se.computeStrides(s).join(",")});`),at.join(`
`)},type:S,offsetToIndices:me,indicesToOffset:Ee,broadcastedIndicesToOffset:$t,indices:tt,indicesGet:pt,indicesSet:Ct,set:(...at)=>{if(at.length!==a+1)throw new Error(`indices length must be ${a}`);let Pt=at[a];if(typeof Pt!="string")throw new Error("value must be string");let ps=at.slice(0,a).map(B).join(",");return a===0?bt("0u",Pt):a===1?bt(ps[0],Pt):(N.set=!0,N.setByIndices=!0,N.indicesToOffset=!0,`set_${e}(${ps}, ${Pt})`)},setByOffset:bt,setByIndices:(at,Pt)=>a<2?bt(at,Pt):(N.setByIndices=!0,N.indicesToOffset=!0,`set_${e}ByIndices(${at}, ${Pt});`),get:ss,getByOffset:Kt,getByIndices:Jt,usage:n,name:e,strides:ee,shape:se,rank:a}},Oe=(e,t,s,n=1)=>Es(e,t,s,"input",n),gt=(e,t,s,n=1)=>Es(e,t,s,"output",n),tr=(e,t,s)=>Es(e,t,s,"atomicOutput",1),qr=(e,t,s,n=1)=>Es(e,t,s,"internal",n),Qn=class{constructor(e,t){this.normalizedDispatchGroup=e,this.limits=t,this.internalVariables=[],this.variables=[],this.uniforms=[],this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(e){return`if (global_idx >= ${typeof e=="number"?`${e}u`:e}) { return; }`}mainStart(e=zs){let t=typeof e=="number"?e:e[0],s=typeof e=="number"?1:e[1],n=typeof e=="number"?1:e[2];if(t>this.limits.maxComputeWorkgroupSizeX||s>this.limits.maxComputeWorkgroupSizeY||n>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${t}, ${s}, ${n}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(t*s*n>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${t}, ${s}, ${n}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let o=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1,i=o?`@builtin(global_invocation_id) global_id : vec3<u32>,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>,
                                             @builtin(local_invocation_id) local_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(num_workgroups) num_workgroups : vec3<u32>`,a=o?`let global_idx = global_id.x;
         let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
             workgroup_id.y * num_workgroups[0] + workgroup_id.x;
         let global_idx = workgroup_index * ${t*s*n}u + local_idx;`;return`@compute @workgroup_size(${t}, ${s}, ${n})
  fn main(${i}) {
    ${a}
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    let workgroup_id_x = workgroup_index % stride;
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    let output_row = workgroup_id_y * ${N}u + local_id.y;
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    }
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  ${$a(o,n,L,se)}

  ${N.mainStart()}
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    let indices = ${se.offsetToIndices("global_idx")};
    let aIndices = perm(indices);

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          let offset = outputIndex * uniforms.reduceSize;

          var bestValue = f32(${Fa[n]});
          let Length = uniforms.reduceSize;
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           bestValue = ${Yn[n]};
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          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${S}u);
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           if (local_idx < currentSize) {
            let candidate = aBestValues[local_idx + interval];
            bestValue = ${Oa[n]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
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                  ${me}
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        ${L.registerUniform("output_size","u32").declareVariables(ee,V)}

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

          ${se.join(`
`)}
          ${de[0]}       // init ops for reduce max/min
          ${de[1]}
          ${me}
          ${de[3]}
          ${de.length===4?V.setByOffset("global_idx","value"):de.slice(4).join(`
`)}
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         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};e.compute(Zn("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},Wo=e=>Qe(e)}),Go,to,al,Ko,ll,On,Ho,ul,qo=y(()=>{Ft(),zt(),Hr(),Gt(),Go=(e,t)=>{let s=e[0],n=e[1],o=e[2],i=e[3],a=e[4],c=e[5];if(a&&c)throw new Error("Attention cannot have both past and attention_bias");if(s.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let p=s.dims[0],h=s.dims[1],k=s.dims[2];if(o.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]!==k)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let u=o.dims[0]/3,S=u,B=S;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let de of t.qkvHiddenSizes)if(de%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");u=t.qkvHiddenSizes[0],S=t.qkvHiddenSizes[1],B=t.qkvHiddenSizes[2]}let N=h;if(u!==S)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==u+S+B)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let L=0;if(a){if(S!==B)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(a.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(a.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(a.dims[1]!==p)throw new Error('Input "past" second dimension must be batch_size');if(a.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(a.dims[4]!==S/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(L=a.dims[3])}let se=N+L,ee=-1,V=0;if(i)throw new Error("Mask not supported");if(a)throw new Error("past is not supported");if(c){if(c.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(c.dims[0]!==p||c.dims[1]!==t.numHeads||c.dims[2]!==h||c.dims[3]!==se)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:h,pastSequenceLength:L,kvSequenceLength:N,totalSequenceLength:se,maxSequenceLength:ee,inputHiddenSize:k,hiddenSize:u,vHiddenSize:B,headSize:Math.floor(u/t.numHeads),vHeadSize:Math.floor(B/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:V,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},to=(e,t,s)=>t&&e?`
      let total_sequence_length_input = u32(${t.getByOffset("0")});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${e?.getByOffset("batchIdx")}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${s?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,al=(e,t,s,n,o,i,a,c)=>{let p=os(a?1:i),h=64,k=i/p;k<h&&(h=32);let u=Math.ceil(i/p/h),S=[{type:12,data:t},{type:12,data:s},{type:12,data:n},{type:12,data:o},{type:12,data:k},{type:12,data:u}],B=Zt(e.dataType,p),N=us(1,p),L=["type"];a&&L.push("type"),c&&L.push("type");let se=ee=>{let V=gt("x",e.dataType,e.dims,p),de=[V],me=a?Oe("seq_lens",a.dataType,a.dims):void 0;me&&de.push(me);let ye=c?Oe("total_sequence_length_input",c.dataType,c.dims):void 0;ye&&de.push(ye);let Be=us(e.dataType),Ee=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${h}>;
  var<workgroup> thread_sum: array<f32, ${h}>;
  ${ee.registerUniforms(Ee).declareVariables(...de)}
  ${ee.mainStart([h,1,1])}
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let sequence_length = uniforms.sequence_length;
    var total_sequence_length = uniforms.total_sequence_length;
    ${to(me,ye,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${h}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${a?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${N}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${N}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(p){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: ${p}`)}})()};
    workgroupBarrier();

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

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

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        x[offset + i] = ${V.type.value}(${Be}(1.0) / ${Be}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${N}(x[offset + i]);
        x[offset + i] = ${V.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${a?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${V.type.value}(${Be}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${B};${p}`,inputDependencies:L},getShaderSource:se,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(i/h),y:o,z:t*s},programUniforms:S})}},Ko=(e,t,s,n,o,i,a,c,p)=>{let h=a+i.kvSequenceLength,k=[i.batchSize,i.numHeads,i.sequenceLength,h],u=e>1&&n,S=i.kvNumHeads?i.kvNumHeads:i.numHeads,B=u?[i.batchSize,S,h,i.headSize]:void 0,N=i.nReps?i.nReps:1,L=i.scale===0?1/Math.sqrt(i.headSize):i.scale,se=os(i.headSize),ee=i.headSize/se,V=12,de={x:Math.ceil(h/V),y:Math.ceil(i.sequenceLength/V),z:i.batchSize*i.numHeads},me=[{type:12,data:i.sequenceLength},{type:12,data:ee},{type:12,data:h},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:1,data:L},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:N}],ye=u&&n&&Se.size(n.dims)>0,Be=["type","type"];ye&&Be.push("type"),o&&Be.push("type"),c&&Be.push("type"),p&&Be.push("type");let Ee=[{dims:k,dataType:t.dataType,gpuDataType:0}];u&&Ee.push({dims:B,dataType:t.dataType,gpuDataType:0});let tt=pt=>{let Ct=Oe("q",t.dataType,t.dims,se),Dt=Oe("key",s.dataType,s.dims,se),$t=[Ct,Dt];if(ye){let qt=Oe("past_key",n.dataType,n.dims,se);$t.push(qt)}o&&$t.push(Oe("attention_bias",o.dataType,o.dims));let bt=c?Oe("seq_lens",c.dataType,c.dims):void 0;bt&&$t.push(bt);let Kt=p?Oe("total_sequence_length_input",p.dataType,p.dims):void 0;Kt&&$t.push(Kt);let jt=gt("output",t.dataType,k),Lt=[jt];u&&Lt.push(gt("present_key",t.dataType,B,se));let ss=us(1,se),Jt=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${V}u;

  var<workgroup> tileQ: array<${Ct.type.storage}, ${V*V}>;
  var<workgroup> tileK: array<${Ct.type.storage}, ${V*V}>;
  ${pt.registerUniforms(Jt).declareVariables(...$t,...Lt)}
  ${pt.mainStart([V,V,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${N===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${N===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let sequence_length = uniforms.M;
    var total_sequence_length = uniforms.N;
    ${to(bt,Kt,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${ye&&u?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${u?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${ss}(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;
      ${ye&&u?`
              if (n + local_id.y < past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
              }`:`
          if (n + local_id.y < uniforms.kv_sequence_length) {
            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
          }`}
      ${u?`if (n + local_id.y < present_sequence_length) {
        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
      }`:""}
      }
      workgroupBarrier();

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

      workgroupBarrier();
    }

    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(se){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: ${se}`)}})()};
        output[outputIdx] = ${jt.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${se};${o!==void 0};${n!==void 0};${e}`,inputDependencies:Be},getRunData:()=>({outputs:Ee,dispatchGroup:de,programUniforms:me}),getShaderSource:tt}},ll=(e,t,s,n,o,i,a=void 0,c=void 0)=>{let p=i+o.kvSequenceLength,h=o.nReps?o.nReps:1,k=o.vHiddenSize*h,u=e>1&&n,S=o.kvNumHeads?o.kvNumHeads:o.numHeads,B=u?[o.batchSize,S,p,o.headSize]:void 0,N=[o.batchSize,o.sequenceLength,k],L=12,se={x:Math.ceil(o.vHeadSize/L),y:Math.ceil(o.sequenceLength/L),z:o.batchSize*o.numHeads},ee=[{type:12,data:o.sequenceLength},{type:12,data:p},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:k},{type:12,data:i},{type:12,data:o.kvSequenceLength},{type:12,data:h}],V=u&&n&&Se.size(n.dims)>0,de=["type","type"];V&&de.push("type"),a&&de.push("type"),c&&de.push("type");let me=[{dims:N,dataType:t.dataType,gpuDataType:0}];u&&me.push({dims:B,dataType:t.dataType,gpuDataType:0});let ye=Be=>{let Ee=Oe("probs",t.dataType,t.dims),tt=Oe("v",s.dataType,s.dims),pt=[Ee,tt];V&&pt.push(Oe("past_value",n.dataType,n.dims));let Ct=a?Oe("seq_lens",a.dataType,a.dims):void 0;a&&pt.push(Ct);let Dt=c?Oe("total_sequence_length_input",c.dataType,c.dims):void 0;c&&pt.push(Dt);let $t=[gt("output",t.dataType,N)];u&&$t.push(gt("present_value",t.dataType,B));let bt=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${L}u;
  var<workgroup> tileQ: array<${Ee.type.value}, ${L*L}>;
  var<workgroup> tileV: array<${Ee.type.value}, ${L*L}>;
  ${Be.registerUniforms(bt).declareVariables(...pt,...$t)}
  ${Be.mainStart([L,L,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${h===1?"headIdx":"headIdx / uniforms.n_reps"};
   let kv_num_heads = ${h===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
   let m = global_id.y;
   let n = global_id.x;
   let sequence_length = uniforms.M;
   var total_sequence_length = uniforms.K;
   ${to(Ct,Dt,!0)}
   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
   ${V&&u?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${u?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${Ee.type.storage}(0);
   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
        ${V&&u?`
        if (w + local_id.y < past_sequence_length) {
          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
        }
      `:`
            if (w + local_id.y < uniforms.kv_sequence_length) {
              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
            }`}
        ${u?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + headIdx * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:de},getRunData:()=>({outputs:me,dispatchGroup:se,programUniforms:ee}),getShaderSource:ye}},On=(e,t,s,n,o,i,a,c,p,h,k=void 0,u=void 0)=>{let S=Math.min(e.outputCount,1+(a?1:0)+(c?1:0)),B=S>1?h.pastSequenceLength:0,N=B+h.kvSequenceLength,L=p&&Se.size(p.dims)>0?p:void 0,se=[t,s];S>1&&a&&Se.size(a.dims)>0&&se.push(a),L&&se.push(L),k&&se.push(k),u&&se.push(u);let ee=e.compute(Ko(S,t,s,a,L,h,B,k,u),{inputs:se,outputs:S>1?[-1,1]:[-1]})[0];e.compute(al(ee,h.batchSize,h.numHeads,B,h.sequenceLength,N,k,u),{inputs:k&&u?[ee,k,u]:[ee],outputs:[]});let V=[ee,n];S>1&&c&&Se.size(c.dims)>0&&V.push(c),k&&V.push(k),u&&V.push(u),e.compute(ll(S,ee,n,c,h,B,k,u),{inputs:V,outputs:S>1?[0,2]:[0]})},Ho=(e,t)=>{let s=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,o=t.inputHiddenSize,i=t.headSize,a=12,c={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:n},{type:12,data:o},{type:12,data:i},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],k=u=>{let S=gt("output_q",p[0].dataType,s),B=gt("output_k",p[0].dataType,s),N=gt("output_v",p[0].dataType,s),L=Oe("input",p[0].dataType,p[0].dims),se=Oe("weight",p[1].dataType,p[1].dims),ee=Oe("bias",p[2].dataType,p[2].dims),V=L.type.storage,de=[{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 = ${a}u;
  var<workgroup> tileInput: array<${V}, ${a*a}>;
  var<workgroup> tileWeightQ: array<${V}, ${a*a}>;
  var<workgroup> tileWeightK: array<${V}, ${a*a}>;
  var<workgroup> tileWeightV: array<${V}, ${a*a}>;
  ${u.registerUniforms(de).declareVariables(L,se,ee,S,B,N)}
  ${u.mainStart([a,a,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

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

    var valueQ = ${V}(0);
    var valueK = ${V}(0);
    var valueV = ${V}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:h}),getShaderSource:k},{inputs:p,outputs:[-1,-1,-1]})},ul=(e,t)=>{let s=Go(e.inputs,t),[n,o,i]=Ho(e,s);return On(e,n,o,i,e.inputs[4],void 0,void 0,void 0,e.inputs[5],s)}}),Xo,dl,cl,Qo,tc=y(()=>{He(),Ft(),zt(),It(),Gt(),Xo=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let s=(n,o,i)=>{let a=o.length;if(a!==n.length)throw new Error(`${i}: num dimensions != ${a}`);o.forEach((c,p)=>{if(c!==n[p])throw new Error(`${i}: dim[${p}] 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);s(e[1].dims,n,"Invalid input scale"),s(e[2].dims,n,"Invalid input B"),s(e[3].dims,n,"Invalid input mean"),s(e[4].dims,n,"Invalid input var")}else s(e[1].dims,[1],"Invalid input scale"),s(e[2].dims,[1],"Invalid input B"),s(e[3].dims,[1],"Invalid input mean"),s(e[4].dims,[1],"Invalid input var")},dl=(e,t)=>{let{epsilon:s,spatial:n,format:o}=t,i=e[0].dims,a=n?os(i[i.length-1]):1,c=o==="NHWC"&&i.length>1?a:1,p=Se.size(i)/a,h=n,k=h?i.length:i,u=Oe("x",e[0].dataType,e[0].dims,a),S=Oe("scale",e[1].dataType,e[1].dims,c),B=Oe("bias",e[2].dataType,e[2].dims,c),N=Oe("inputMean",e[3].dataType,e[3].dims,c),L=Oe("inputVar",e[4].dataType,e[4].dims,c),se=gt("y",e[0].dataType,k,a),ee=()=>{let de="";if(n)de=`let cOffset = ${i.length===1?"0u":o==="NHWC"?`outputIndices[${i.length-1}] / ${a}`:"outputIndices[1]"};`;else if(o==="NCHW")de=`
            ${se.indicesSet("outputIndices","0","0")}
            let cOffset = ${se.indicesToOffset("outputIndices")};`;else{de=`var cIndices = ${S.type.indices}(0);
                       cIndices[0] = outputIndices[${i.length-1}];`;for(let me=1;me<S.rank;me++)de+=`cIndices[${me}] = outputIndices[${me}];`;de+=`let cOffset = ${S.indicesToOffset("cIndices")};`}return de},V=de=>`
  const epsilon = ${s};
  ${de.registerUniform("outputSize","u32").declareVariables(u,S,B,N,L,se)}
  ${de.mainStart()}
  ${de.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${se.offsetToIndices(`global_idx * ${a}`)};
    ${ee()}
    let scale = ${S.getByOffset("cOffset")};
    let bias = ${B.getByOffset("cOffset")};
    let inputMean = ${N.getByOffset("cOffset")};
    let inputVar = ${L.getByOffset("cOffset")};
    let x = ${u.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${se.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${a}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:V,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h?[{type:12,data:p},...xt(i)]:[{type:12,data:p}]})}},cl=e=>Qe(e),Qo=(e,t)=>{let{inputs:s,outputCount:n}=e,o=cl({...t,outputCount:n});if(x.webgpu.validateInputContent&&Xo(s,o),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(dl(s,o))}}),pl,Yo,hl,sc=y(()=>{zt(),Gt(),pl=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")},Yo=e=>{let t=e[0].dims,s=e[0].dims[2],n=Se.size(t)/4,o=e[0].dataType,i=Oe("input",o,t,4),a=Oe("bias",o,[s],4),c=Oe("residual",o,t,4),p=gt("output",o,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:h=>`
  const channels = ${s}u / 4;
  ${h.declareVariables(i,a,c,p)}

  ${h.mainStart()}
    ${h.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${i.getByOffset("global_idx")}
      + ${a.getByOffset("global_idx % channels")} + ${c.getByOffset("global_idx")};
    ${p.setByOffset("global_idx","value")}
  }`}},hl=e=>{pl(e.inputs),e.compute(Yo(e.inputs))}}),Jo,as,ml,Zo,_l,fl,ei,gl,wl,ti,yl,Ml,bl,vl,si,Tl,Fn,ri,so,xl,ni,Pl,El,oi,Cl,kl,ii,Sl,$l,ai,Al,Il,li,Ol,Fl,ui,Dl,ro,di,Ll,zl,Bl,ci,Rl,Nl,pi=y(()=>{Ft(),zt(),It(),Gt(),Jo=(e,t,s,n,o,i,a)=>{let c=Math.ceil(t/4),p="";typeof o=="string"?p=`${o}(a)`:p=o("a");let h=Oe("inputData",s,[c],4),k=gt("outputData",n,[c],4),u=[{name:"vec_size",type:"u32"}];return a&&u.push(...a),`
      ${e.registerUniforms(u).declareVariables(h,k)}

  ${i??""}

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

    let a = ${h.getByOffset("global_idx")};
    ${k.setByOffset("global_idx",p)}
  }`},as=(e,t,s,n,o,i=e.dataType,a,c)=>{let p=[{type:12,data:Math.ceil(Se.size(e.dims)/4)}];return a&&p.push(...a),{name:t,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:h=>Jo(h,Se.size(e.dims),e.dataType,i,s,n,c),getRunData:h=>({outputs:[{dims:e.dims,dataType:i}],dispatchGroup:{x:Math.ceil(Se.size(h[0].dims)/64/4)},programUniforms:p})}},ml=e=>{e.compute(as(e.inputs[0],"Abs","abs"))},Zo=e=>{e.compute(as(e.inputs[0],"Acos","acos"))},_l=e=>{e.compute(as(e.inputs[0],"Acosh","acosh"))},fl=e=>{e.compute(as(e.inputs[0],"Asin","asin"))},ei=e=>{e.compute(as(e.inputs[0],"Asinh","asinh"))},gl=e=>{e.compute(as(e.inputs[0],"Atan","atan"))},wl=e=>{e.compute(as(e.inputs[0],"Atanh","atanh"))},ti=e=>Qe(e),yl=(e,t)=>{let s;switch(t.to){case 10:s="vec4<f16>";break;case 1:s="vec4<f32>";break;case 12:s="vec4<u32>";break;case 6:s="vec4<i32>";break;case 9:s="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(as(e.inputs[0],"Cast",s,void 0,t.cacheKey,t.to))},Ml=e=>{let t,s,n=e.length>=2&&e[1].data!==0,o=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,s=o?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,s=o?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return Qe({min:t,max:s})},bl=(e,t)=>{let s=t||Ml(e.inputs),n=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"Clip",o=>`clamp(${o}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,s.cacheKey,void 0,[{type:e.inputs[0].dataType,data:s.min},{type:e.inputs[0].dataType,data:s.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},vl=e=>{e.compute(as(e.inputs[0],"Ceil","ceil"))},si=e=>{e.compute(as(e.inputs[0],"Cos","cos"))},Tl=e=>{e.compute(as(e.inputs[0],"Cosh","cosh"))},Fn=e=>Qe(e),ri=(e,t)=>{let s=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,`
  const elu_alpha_ = ${s}(${t.alpha});

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

  fn elu_vf32(v: vec4<${s}>) -> vec4<${s}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,t.cacheKey))},so=(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));
}`,xl=e=>{let t=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"Erf",s=>`erf_vf32(${s})`,so(t)))},ni=e=>{e.compute(as(e.inputs[0],"Exp","exp"))},Pl=e=>{e.compute(as(e.inputs[0],"Floor","floor"))},El=e=>{let t=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"Gelu",s=>`0.5 * ${s} * (1.0 + erf_vf32(${s} * 0.7071067811865475))`,so(t)))},oi=(e,t)=>{let s=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${s}>(0.0))`,`const leaky_relu_alpha_ = ${s}(${t.alpha});`,t.cacheKey))},Cl=e=>{e.compute(as(e.inputs[0],"Not",t=>`!${t}`))},kl=e=>{e.compute(as(e.inputs[0],"Neg",t=>`-${t}`))},ii=e=>{e.compute(as(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Sl=e=>{let t=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"Relu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > vec4<${t}>(0.0))`))},$l=e=>{e.compute(as(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},ai=e=>Qe(e),Al=(e,t)=>{let s=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"HardSigmoid",n=>`max(vec4<${s}>(0.0), min(vec4<${s}>(1.0), ${t.alpha} * ${n} + vec4<${s}>(${t.beta})))`,void 0,t.cacheKey))},Il=e=>{e.compute(as(e.inputs[0],"Sin","sin"))},li=e=>{e.compute(as(e.inputs[0],"Sinh","sinh"))},Ol=e=>{e.compute(as(e.inputs[0],"Sqrt","sqrt"))},Fl=e=>{e.compute(as(e.inputs[0],"Tan","tan"))},ui=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Dl=e=>{e.compute(as(e.inputs[0],"Tanh",ui))},ro=(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 ${ui("v")};
}
`,di=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Ll=e=>{let t=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"FastGelu",di,ro(t),void 0,e.inputs[0].dataType))},zl=(e,t)=>{let s=us(e.inputs[0].dataType);return e.compute(as(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${s}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${s}>(${t.alpha});`,t.cacheKey)),0},Bl=e=>{e.compute(as(e.inputs[0],"Log","log"))},ci=(e,t)=>`
const alpha = vec4<${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<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Rl=e=>`quick_gelu_impl(${e})`,Nl=(e,t)=>{let s=us(e.inputs[0].dataType);e.compute(as(e.inputs[0],"QuickGelu",Rl,ci(s,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),jl,hi,Ul,rc=y(()=>{zt(),Gt(),pi(),jl=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")},hi=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let s=Oe("input",e[0].dataType,e[0].dims,4),n=Oe("bias",e[0].dataType,[e[0].dims[2]],4),o=gt("output",e[0].dataType,t,4),i=Se.size(t)/4,a=Zt(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:c=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

  ${c.declareVariables(s,n,o)}

  ${so(a)}

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

    ${o.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Ul=e=>{jl(e.inputs),e.compute(hi(e.inputs))}}),Vl,Wl,ir,Gl,Kl,mi,Hl,ql,_i,Xl,Ql,fi,Yl,nc=y(()=>{Ft(),zt(),Gt(),Vl=(e,t,s,n,o,i,a,c,p,h,k,u)=>{let S,B;typeof c=="string"?S=B=(V,de)=>`${c}((${V}),(${de}))`:typeof c=="function"?S=B=c:(S=c.scalar,B=c.vector);let N=gt("outputData",k,n.length,4),L=Oe("aData",p,t.length,4),se=Oe("bData",h,s.length,4),ee;if(o)if(i){let V=Se.size(t)===1,de=Se.size(s)===1,me=t.length>0&&t[t.length-1]%4===0,ye=s.length>0&&s[s.length-1]%4===0;V||de?ee=N.setByOffset("global_idx",B(V?`${L.type.value}(${L.getByOffset("0")}.x)`:L.getByOffset("global_idx"),de?`${se.type.value}(${se.getByOffset("0")}.x)`:se.getByOffset("global_idx"))):ee=`
            let outputIndices = ${N.offsetToIndices("global_idx * 4u")};
            let offsetA = ${L.broadcastedIndicesToOffset("outputIndices",N)};
            let offsetB = ${se.broadcastedIndicesToOffset("outputIndices",N)};
            ${N.setByOffset("global_idx",B(a||me?L.getByOffset("offsetA / 4u"):`${L.type.value}(${L.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||ye?se.getByOffset("offsetB / 4u"):`${se.type.value}(${se.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else ee=N.setByOffset("global_idx",B(L.getByOffset("global_idx"),se.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let V=(de,me,ye="")=>{let Be=`aData[indexA${me}][componentA${me}]`,Ee=`bData[indexB${me}][componentB${me}]`;return`
            let outputIndices${me} = ${N.offsetToIndices(`global_idx * 4u + ${me}u`)};
            let offsetA${me} = ${L.broadcastedIndicesToOffset(`outputIndices${me}`,N)};
            let offsetB${me} = ${se.broadcastedIndicesToOffset(`outputIndices${me}`,N)};
            let indexA${me} = offsetA${me} / 4u;
            let indexB${me} = offsetB${me} / 4u;
            let componentA${me} = offsetA${me} % 4u;
            let componentB${me} = offsetB${me} % 4u;
            ${de}[${me}] = ${ye}(${S(Be,Ee)});
          `};k===9?ee=`
            var data = vec4<u32>(0);
            ${V("data",0,"u32")}
            ${V("data",1,"u32")}
            ${V("data",2,"u32")}
            ${V("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:ee=`
            ${V("outputData[global_idx]",0)}
            ${V("outputData[global_idx]",1)}
            ${V("outputData[global_idx]",2)}
            ${V("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(L,se,N)}

        ${u??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${ee}
      }`},Wl=(e,t,s,n,o,i,a=s.dataType)=>{let c=s.dims.map(L=>Number(L)??1),p=n.dims.map(L=>Number(L)??1),h=!Se.areEqual(c,p),k=c,u=Se.size(c),S=!1,B=!1,N=[h];if(h){let L=gs.calcShape(c,p,!1);if(!L)throw new Error("Can't perform binary op on the given tensors");k=L.slice(),u=Se.size(k);let se=Se.size(c)===1,ee=Se.size(p)===1,V=c.length>0&&c[c.length-1]%4===0,de=p.length>0&&p[p.length-1]%4===0;N.push(se),N.push(ee),N.push(V),N.push(de);let me=1;for(let ye=1;ye<k.length;ye++){let Be=c[c.length-ye],Ee=p[p.length-ye];if(Be===Ee)me*=Be;else break}me%4===0?(B=!0,S=!0):(se||ee||V||de)&&(S=!0)}else S=!0;return N.push(S),{name:e,shaderCache:{hint:t+N.map(L=>L.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:L=>Vl(L,c,p,k,S,h,B,o,s.dataType,n.dataType,a,i),getRunData:()=>({outputs:[{dims:k,dataType:a}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(Se.size(k)/4)},...xt(c,p,k)]})}},ir=(e,t,s,n,o,i)=>{e.compute(Wl(t,o??"",e.inputs[0],e.inputs[1],s,n,i))},Gl=e=>{ir(e,"Add",(t,s)=>`${t}+${s}`)},Kl=e=>{ir(e,"Div",(t,s)=>`${t}/${s}`)},mi=e=>{ir(e,"Equal",{scalar:(t,s)=>`u32(${t}==${s})`,vector:(t,s)=>`vec4<u32>(${t}==${s})`},void 0,void 0,9)},Hl=e=>{ir(e,"Mul",(t,s)=>`${t}*${s}`)},ql=e=>{let t=Oe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;ir(e,"Pow",{scalar:(s,n)=>`pow_custom(${s},${n})`,vector:(s,n)=>`pow_vector_custom(${s},${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));
    }
      `)},_i=e=>{ir(e,"Sub",(t,s)=>`${t}-${s}`)},Xl=e=>{ir(e,"Greater",{scalar:(t,s)=>`u32(${t}>${s})`,vector:(t,s)=>`vec4<u32>(${t}>${s})`},void 0,void 0,9)},Ql=e=>{ir(e,"Less",{scalar:(t,s)=>`u32(${t}<${s})`,vector:(t,s)=>`vec4<u32>(${t}<${s})`},void 0,void 0,9)},fi=e=>{ir(e,"GreaterOrEqual",{scalar:(t,s)=>`u32(${t}>=${s})`,vector:(t,s)=>`vec4<u32>(${t}>=${s})`},void 0,void 0,9)},Yl=e=>{ir(e,"LessOrEqual",{scalar:(t,s)=>`u32(${t}<=${s})`,vector:(t,s)=>`vec4<u32>(${t}<=${s})`},void 0,void 0,9)}}),gi,Jl,Zl,no,eu,tu,su=y(()=>{Ft(),zt(),It(),Gt(),gi=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let s=0,n=e[s],o=n.dataType,i=n.dims.length;e.forEach((a,c)=>{if(c!==s){if(a.dataType!==o)throw new Error("input tensors should be one type");if(a.dims.length!==i)throw new Error("input tensors should have the same shape");a.dims.forEach((p,h)=>{if(h!==t&&p!==n.dims[h])throw new Error("non concat dimensions must match")})}})},Jl=(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;
  }`,Zl=(e,t)=>{let s=e.length,n=[];for(let o=0;o<s;++o){let i=t.setByOffset("global_idx",e[o].getByIndices("indices"));s===1?n.push(i):o===0?n.push(`if (inputIndex == ${o}u) { ${i} }`):o===s-1?n.push(`else { ${i} }`):n.push(`else if (inputIndex == ${o}) { ${i} }`)}return n.join(`
`)},no=(e,t,s,n)=>{let o=Se.size(s),i=new Array(e.length),a=new Array(e.length),c=0,p=[],h=[],k=[{type:12,data:o}];for(let L=0;L<e.length;++L)c+=e[L].dims[t],i[L]=c,h.push(e[L].dims.length),a[L]=Oe(`input${L}`,n,h[L]),p.push("rank"),k.push({type:12,data:i[L]});for(let L=0;L<e.length;++L)k.push(...xt(e[L].dims));k.push(...xt(s));let u=gt("output",n,s.length),S=u.indicesGet("indices",t),B=Array.from(Array(i.length).keys()).map(L=>`uniforms.sizeInConcatAxis${L}`).join(","),N=L=>`

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

  ${Jl(i.length,B)}

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

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

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

    ${Zl(a,u)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:k}),getShaderSource:N}},eu=(e,t)=>{let s=e.inputs,n=s[0].dims,o=Se.normalizeAxis(t.axis,n.length);gi(s,o);let i=n.slice();i[o]=s.reduce((c,p)=>c+(p.dims.length>o?p.dims[o]:0),0);let a=s.filter(c=>Se.size(c.dims)>0);e.compute(no(a,o,i,s[0].dataType),{inputs:a})},tu=e=>Qe({axis:e.axis})}),Dr,Qr,Lr,wi,Yr=y(()=>{Ft(),zt(),Dr=(e,t,s="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, ${t}(${s}(uniforms.clip_min)), ${t}(${s}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${s}(uniforms.alpha) * value + ${s}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${s}(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 ${e.activation}`)}},Qr=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Lr=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},wi=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[s,n]=e?.activation_params||[.2,.5];return{activation:t,alpha:s,beta:n}}else if(t==="Clip"){let[s,n]=e?.activation_params||[Rs,dr];return{activation:t,clipMax:n,clipMin:s}}else if(t==="LeakyRelu"){let[s]=e?.activation_params||[.01];return{activation:t,alpha:s}}return{activation:t}}}),Ws,yi,Mi=y(()=>{Ws=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},yi=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),ru,oc=y(()=>{ru=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),mn,bi,vi=y(()=>{Ft(),zt(),Gt(),Yr(),mn=(e,t,s,n,o)=>{let i=n-s;return`
      ${Array.from({length:s}).map((a,c)=>`
      if (${Mt(t.shape,c,t.rank)} != 1) {
        ${t.indicesSet(e,c,Mt(o,c+i,n))}
      } else {
        ${t.indicesSet(e,c,0)}
      }`).join("")}
`},bi=(e,t,s,n,o=!1,i)=>{let a=e[0].dims,c=e[1].dims,p=a[a.length-2],h=c[c.length-1],k=a[a.length-1],u=os(h),S=os(k),B=os(p),N=Se.size(s)/u/B,L=e.length>2,se=n?n.slice(0,-2):s.slice(0,-2),ee=[Se.size(se),p,h],V=[{type:12,data:N},{type:12,data:p},{type:12,data:h},{type:12,data:k}];Qr(t,V),V.push(...xt(se,a,c)),L&&V.push(...xt(e[2].dims)),V.push(...xt(ee));let de=me=>{let ye=qr("batch_dims",e[0].dataType,se.length),Be=Oe("a",e[0].dataType,a.length,S),Ee=Oe("b",e[1].dataType,c.length,u),tt=gt("output",e[0].dataType,ee.length,u),pt=Zt(tt.type.tensor),Ct=Dr(t,tt.type.value,pt),Dt=[Be,Ee],$t="";if(L){let jt=o?u:1;Dt.push(Oe("bias",e[2].dataType,e[2].dims.length,jt)),$t=`${o?`value += bias[col / ${jt}];`:`value += ${tt.type.value}(bias[row + i]);`}`}let bt=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Lr(t,bt);let Kt=()=>{let jt=`var a_data: ${Be.type.value};`;for(let Lt=0;Lt<S;Lt++)jt+=`
              let b_data${Lt} = b[(b_offset + (k + ${Lt}) * uniforms.N + col) / ${u}];`;for(let Lt=0;Lt<B;Lt++){jt+=`a_data = a[(a_offset + (row + ${Lt}) * uniforms.K + k) / ${S}];`;for(let ss=0;ss<S;ss++)jt+=`
            values[${Lt}] = fma(${Ee.type.value}(a_data${S===1?"":`[${ss}]`}), b_data${ss}, values[${Lt}]);
`}return jt};return`
  ${me.registerUniforms(bt).registerInternalVariables(ye).declareVariables(...Dt,tt)}
  ${me.mainStart()}
    ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${u})) * ${u};
    var index1 = global_idx / (uniforms.N / ${u});
    let stride1 = uniforms.M / ${B};
    let row = (index1 % stride1) * ${B};
    let batch = index1 / stride1;

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

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

    var b_indices: ${Ee.type.indices};
    ${mn("b_indices",Ee,Ee.rank-2,ye.rank,"batch_indices")}
    ${Ee.indicesSet("b_indices",Ee.rank-2,0)}
    ${Ee.indicesSet("b_indices",Ee.rank-1,0)}
    let b_offset = ${Ee.indicesToOffset("b_indices")};
    var values: array<${tt.type.value}, ${B}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${S}) {
      ${Kt()}
    }
    for (var i = 0u; i < ${B}u; i++) {
      var value = values[i];
      ${$t}
      ${Ct}
      let cur_indices = ${tt.type.indices}(batch, row + i, col);
      let offset = ${tt.indicesToOffset("cur_indices")};
      ${tt.setByOffset(`offset / ${u}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${u};${S};${B};${o}`,inputDependencies:L?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i?i(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(N/64)},programUniforms:V}),getShaderSource:de}}}),Ti,nu,xi,oo,ou,Pi,Ei,io,Ci=y(()=>{Ft(),zt(),Gt(),Yr(),vi(),Mi(),Ti=(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":""});
        `,nu=(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];"}
        }`,xi=(e,t,s="f32",n,o=!1,i=32,a=!1,c=32)=>{let p=t[1]*e[1],h=t[0]*e[0],k=o?p:i,u=o?i:p,S=k/t[0],B=i/t[1];if(!((o&&S===4&&e[1]===4||!o&&(S===3||S===4))&&k%t[0]===0&&i%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${S} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${S} must be 3 or 4.
  tileAWidth ${k} must be divisible by workgroupSize[0]${t[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${S}<${s}>, ${k/S}>, ${u}>;
var<workgroup> mm_Bsub: array<array<vec4<${s}>, ${h/e[0]}>, ${i}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${S};
const tileInner = ${i};

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

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${B};
  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;
          ${Ti(o,n)}
      }

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

          ${nu(o,S)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},oo=(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":""});
            `,ou=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Pi=(e,t,s="f32",n,o=!1,i=32,a=!1,c=32,p=!1)=>{let h=e[1]*t[1],k=e[0]*t[0],u=o?h:i,S=o?i:h;if(!(S%t[1]===0&&u%t[0]===0&&i%t[1]===0))throw new Error(`tileAHight ${S} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}, tileInner ${i} must be divisible by workgroupSize[1]${t[1]}`);let B=S/t[1],N=u/t[0],L=i/t[1],se=p?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${h};
    let globalColStart = i32(workgroupId.x) * ${k};

    // 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 < ${S}; inputRow = inputRow + ${t[1]}) {
        for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) {
          ${oo(o,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${t[1]}) {
            for (var inputCol = localCol; inputCol < ${k}; 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<${s}, 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 = ${o?`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) * ${h};

let tileRowA = i32(localId.y) * ${B};
let tileColA = i32(localId.x) * ${N};
let tileRowB = i32(localId.y) * ${L};
// 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 < ${B}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${N}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${oo(o,n)}
    }
  }

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

  // Compute acc values for a single thread.
  var BCached : array<${s}, 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) {
      ${ou(o)}
      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<${s}, ${u}>, ${S}>;
  var<workgroup> mm_Bsub : array<array<${s}, ${k}>, ${i}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${i};

@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 = ${a?"0":"i32(globalId.z)"};
    ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${a?`${Math.ceil(c/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};

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

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ws(e,h)}) {
      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 + ${o?"bias[colIn]":`${Ws(e,h)}(bias[row])`};`:""}
        ${s}
        ${p.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},io=(e,t,s,n,o=!1,i)=>{let a=e[0].dims,c=e[1].dims,p=a.slice(0,-2),h=c.slice(0,-2),k=n?n.slice(0,-2):s.slice(0,-2),u=Se.size(k),S=a[a.length-2],B=a[a.length-1],N=c[c.length-1],L=B%4===0&&N%4===0,se=S<=8?[4,1,1]:[4,4,1],ee=[8,8,1],V=[Math.ceil(N/ee[0]/se[0]),Math.ceil(S/ee[1]/se[1]),Math.ceil(u/ee[2]/se[2])],de=L?4:1,me=[...p,S,B/de],ye=me.length,Be=[...h,B,N/de],Ee=Be.length,tt=[u,S,N/de],pt=[{type:6,data:S},{type:6,data:N},{type:6,data:B}];Qr(t,pt),pt.push(...xt(k,me,Be));let Ct=["rank","rank"],Dt=e.length>2;Dt&&(pt.push(...xt(e[2].dims)),Ct.push("rank")),pt.push(...xt(tt));let $t=bt=>{let Kt=k.length,jt=qr("batchDims",e[0].dataType,Kt,1),Lt=Zt(e[0].dataType),ss=Oe("a",e[0].dataType,ye,de),Jt=Oe("b",e[1].dataType,Ee,de),qt=gt("result",e[0].dataType,tt.length,de),Qs=[ss,Jt];if(Dt){let Ts=o?de:1;Qs.push(Oe("bias",e[2].dataType,e[2].dims.length,Ts))}let at=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Lr(t,at);let Pt=Zt(qt.type.tensor),ps=Dr(t,qt.type.value,Pt),vs=Ei(de,Dt,ps,[jt,ss,Jt,qt],o);return`
  ${bt.registerUniforms(at).registerInternalVariables(jt).declareVariables(...Qs,qt)}
  ${vs}
  ${L?xi(se,ee,Lt,jt):Pi(se,ee,Lt,jt)}
                   `};return{name:"MatMul",shaderCache:{hint:`${se};${t.activation};${L};${o}`,inputDependencies:Ct},getRunData:()=>({outputs:[{dims:i?i(s):s,dataType:e[0].dataType}],dispatchGroup:{x:V[0],y:V[1],z:V[2]},programUniforms:pt}),getShaderSource:$t}}}),ki,iu,ic=y(()=>{Ft(),er(),Gt(),Yr(),Mi(),oc(),Ci(),ki=(e,t,s,n,o=!1,i,a=4,c=4,p=4,h="f32")=>{let k=pt=>{switch(pt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${pt} is not supported.`)}},u=pt=>{switch(pt){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 ${pt} is not supported.`)}},S=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,B=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,N=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",L=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",se=e?"row":"col",ee=e?"col":"row",V=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${se} / outWidth;
    let outCol = ${se} % outWidth;

    let WRow = ${ee} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${ee} / 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 = ${ee} % inChannels;
    var resData = ${Ws(a,h)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${N} && xCol >= 0 && xCol < ${L}) {
      ${S}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${k(a)}
    }
    return resData;`,de=e?t&&n?`
    let col = colIn * ${a};
    ${V}`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${V}
    }
    return ${Ws(a,h)}(0.0);`:n&&s?`
    let col = colIn * ${a};
    ${V}`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${V}
    }
    return ${Ws(a,h)}(0.0);`,me=`${u(c)}`,ye=Ws(p,h),Be=Ws(e?a:c,h),Ee=Ws(e?c:a,h),tt=Dr(i,ye,h);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Be} {
      ${e?de:me}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ee} {
      ${e?me:de}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${ye}) {
      let col = colIn * ${p};
      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])"};
      ${B}
      ${yi(o)}
      ${tt}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},iu=(e,t,s,n,o,i,a,c,p)=>{let h=t.format==="NHWC",k=h?e[0].dims[3]:e[0].dims[1],u=s[0],S=h?s[2]:s[3],B=h?s[1]:s[2],N=h?s[3]:s[1],L=h&&(k%4===0||k%3===0)&&N%4===0,se=h?N:S*B,ee=h?S*B:N,V=[8,8,1],de=n<=8?[4,1,1]:[4,4,1],me=[Math.ceil(se/V[0]/de[0]),Math.ceil(ee/V[1]/de[1]),Math.ceil(u/V[2]/de[2])];ns("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${me}`);let ye=L?h&&k%4!==0?3:4:1,Be=V[1]*de[1],Ee=V[0]*de[0],tt=Math.max(V[0]*ye,V[1]),pt=n%Be===0,Ct=o%Ee===0,Dt=i%tt===0,$t=L?[ye,4,4]:[1,1,1],bt=[{type:6,data:n},{type:6,data:o},{type:6,data:i},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Qr(t,bt),bt.push(...xt(e[0].dims,e[1].dims));let Kt=["rank","rank"];a&&(bt.push(...xt(e[2].dims)),Kt.push("rank")),bt.push(...xt(s));let jt=Lt=>{let ss=[{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}];Lr(t,ss);let Jt=L?4:1,qt=Zt(e[0].dataType),Qs=`
      fn setOutputAtIndex(flatIndex : i32, value : ${L?`vec4<${qt}>`:qt}) {
        result[flatIndex] = ${L?`vec4<${qt}>`:qt}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${L?`vec4<${qt}>`:qt}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${L?"/ 4":""}, value);
      }`,at=Oe("x",e[0].dataType,e[0].dims.length,ye===3?1:ye),Pt=Oe("w",e[1].dataType,e[1].dims.length,Jt),ps=[at,Pt],vs=gt("result",e[0].dataType,s.length,Jt);if(a){let Ts=Oe("bias",e[2].dataType,e[2].dims.length,Jt);ps.push(Ts),Qs+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${L?`vec4<${qt}>`:qt} {
          return bias[coords.${h?"w":"y"}${L?"/ 4":""}];
        }`}return`
        ${ru("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 };
        ${Lt.registerUniforms(ss).declareVariables(...ps,vs)}
        ${Qs}
        ${ki(h,pt,Ct,Dt,a,t,$t[0],$t[1],$t[2],qt)}
        ${L?xi(de,V,qt,void 0,!h,tt):Pi(de,V,qt,void 0,!h,tt,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${ye};${L};${pt};${Ct};${Dt};${Be};${Ee};${tt}`,inputDependencies:Kt},getRunData:()=>({outputs:[{dims:p?p(s):s,dataType:e[0].dataType}],dispatchGroup:{x:me[0],y:me[1],z:me[2]},programUniforms:bt}),getShaderSource:jt}}}),Si,$i,Dn,au,Ai,ao,lu,uu,ac=y(()=>{Ft(),er(),zt(),Gt(),Yr(),Mi(),Si=e=>{let t=1;for(let s=0;s<e.length;s++)t*=e[s];return t},$i=e=>typeof e=="number"?[e,e,e]:e,Dn=(e,t)=>t<=1?e:e+(e-1)*(t-1),au=(e,t,s,n=1)=>{let o=Dn(t,n);return Math.floor((e[0]*(s-1)-s+o)/2)},Ai=(e,t,s,n,o)=>{o==null&&(o=au(e,t[0],n[0]));let i=[0,0,0,s];for(let a=0;a<3;a++)e[a]+2*o>=t[a]&&(i[a]=Math.trunc((e[a]-t[a]+2*o)/n[a]+1));return i},ao=(e,t,s,n,o,i,a,c,p,h)=>{let k,u,S,B;if(e==="VALID"&&(e=0),typeof e=="number"){k={top:e,bottom:e,left:e,right:e,front:e,back:e};let N=Ai([t,s,n,1],[c,p,h],1,[o,i,a],e);u=N[0],S=N[1],B=N[2]}else if(Array.isArray(e)){if(!e.every((L,se,ee)=>L===ee[0]))throw Error(`Unsupported padding parameter: ${e}`);k={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let N=Ai([t,s,n,1],[c,p,h],1,[o,i,a],e[0]);u=N[0],S=N[1],B=N[2]}else if(e==="SAME_UPPER"){u=Math.ceil(t/o),S=Math.ceil(s/i),B=Math.ceil(n/a);let N=(u-1)*o+c-t,L=(S-1)*i+p-s,se=(B-1)*a+h-n,ee=Math.floor(N/2),V=N-ee,de=Math.floor(L/2),me=L-de,ye=Math.floor(se/2),Be=se-ye;k={top:de,bottom:me,left:ye,right:Be,front:ee,back:V}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:k,outDepth:u,outHeight:S,outWidth:B}},lu=(e,t,s,n,o,i=!1,a="channelsLast")=>{let c,p,h,k,u;if(a==="channelsLast")[c,p,h,k,u]=e;else if(a==="channelsFirst")[c,u,p,h,k]=e;else throw new Error(`Unknown dataFormat ${a}`);let[S,,B,N,L]=t,[se,ee,V]=$i(s),[de,me,ye]=$i(n),Be=Dn(B,de),Ee=Dn(N,me),tt=Dn(L,ye),{padInfo:pt,outDepth:Ct,outHeight:Dt,outWidth:$t}=ao(o,p,h,k,se,ee,V,Be,Ee,tt),bt=i?S*u:S,Kt=[0,0,0,0,0];return a==="channelsFirst"?Kt=[c,bt,Ct,Dt,$t]:a==="channelsLast"&&(Kt=[c,Ct,Dt,$t,bt]),{batchSize:c,dataFormat:a,inDepth:p,inHeight:h,inWidth:k,inChannels:u,outDepth:Ct,outHeight:Dt,outWidth:$t,outChannels:bt,padInfo:pt,strideDepth:se,strideHeight:ee,strideWidth:V,filterDepth:B,filterHeight:N,filterWidth:L,effectiveFilterDepth:Be,effectiveFilterHeight:Ee,effectiveFilterWidth:tt,dilationDepth:de,dilationHeight:me,dilationWidth:ye,inShape:e,outShape:Kt,filterShape:t}},uu=(e,t,s,n,o,i)=>{let a=i==="channelsLast";a?e[0].dims[3]:e[0].dims[1];let c=[64,1,1],p={x:s.map((se,ee)=>ee)},h=[Math.ceil(Si(p.x.map(se=>s[se]))/c[0]),1,1];ns("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let k=1,u=Se.size(s),S=[{type:12,data:u},{type:12,data:n},{type:12,data:o},{type:12,data:t.strides},{type:12,data:t.dilations}];Qr(t,S),S.push(...xt(e[0].dims,e[1].dims));let B=["rank","rank"],N=e.length===3;N&&(S.push(...xt(e[2].dims)),B.push("rank")),S.push(...xt(s));let L=se=>{let ee=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Lr(t,ee);let V=1,de=Zt(e[0].dataType),me=Oe("x",e[0].dataType,e[0].dims.length,k),ye=Oe("W",e[1].dataType,e[1].dims.length,V),Be=[me,ye],Ee=gt("result",e[0].dataType,s.length,V),tt="";if(N){let Dt=Oe("bias",e[2].dataType,e[2].dims.length,V);Be.push(Dt),tt+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${de} {
          return bias[${a?Mt("coords",4,5):Mt("coords",1,5)}];
        }`}let pt=Ws(k,de),Ct=Dr(t,pt,de);return`
            ${tt}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${me.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 ${ye.getByIndices("aIndices")};
            }
          ${se.registerUniforms(ee).declareVariables(...Be,Ee)}
          ${se.mainStart()}
          ${se.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${Ee.offsetToIndices("global_idx")};
              let batch = ${Mt("coords",0,me.rank)};
              let d2 = ${a?Mt("coords",me.rank-1,me.rank):Mt("coords",1,me.rank)};
              let xFRCCorner = vec3<u32>(${a?Mt("coords",1,me.rank):Mt("coords",2,me.rank)},
              ${a?Mt("coords",2,me.rank):Mt("coords",3,me.rank)},
              ${a?Mt("coords",3,me.rank):Mt("coords",4,me.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${a?Mt("uniforms.x_shape",1,me.rank):Mt("uniforms.x_shape",2,me.rank)};
              let xShapeZ = ${a?Mt("uniforms.x_shape",2,me.rank):Mt("uniforms.x_shape",3,me.rank)};
              let xShapeW = ${a?Mt("uniforms.x_shape",3,me.rank):Mt("uniforms.x_shape",4,me.rank)};
              let xShapeU = ${a?Mt("uniforms.x_shape",4,me.rank):Mt("uniforms.x_shape",1,me.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) {
                      ${a?`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) {
                        ${a?`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) {
                      ${a?`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) {
                      ${a?`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);
                    }
                  }
                }
              }
              ${N?"value = value + getBiasByOutputCoords(coords)":""};
              ${Ct}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${a};${k};${N}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:h[0],y:h[1],z:h[2]},programUniforms:S}),getShaderSource:L}}}),du,cu,pu=y(()=>{Ft(),zt(),Gt(),Yr(),du=(e,t,s,n)=>{let o=e.length>2,i=o?"value += b[output_channel];":"",a=e[0].dims,c=e[1].dims,p=t.format==="NHWC",h=p?s[3]:s[1],k=h/t.group,u=p&&k>=4?os(h):1,S=Se.size(s)/u,B=[{type:12,data:S},{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:k}];Qr(t,B),B.push(...xt(a,[c[0],c[1],c[2],c[3]/u]));let N=o?["rank","rank","rank"]:["rank","rank"];B.push(...xt([s[0],s[1],s[2],s[3]/u]));let L=se=>{let ee=gt("output",e[0].dataType,s.length,u),V=Zt(ee.type.tensor),de=Dr(t,ee.type.value,V),me=Oe("x",e[0].dataType,a.length),ye=Oe("w",e[1].dataType,c.length,u),Be=[me,ye];o&&Be.push(Oe("b",e[2].dataType,e[2].dims,u));let Ee=[{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"}];Lr(t,Ee);let tt=p?`
      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 = ${me.get("batch","xHeight","xWidth","input_channel")};
            let wVal = ${ye.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 = ${me.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${ye.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${se.registerUniforms(Ee).declareVariables(...Be,ee)}

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

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

    var value: ${ee.type.value} = ${ee.type.value}(0);
    ${tt}
    ${i}
    ${de}
    ${ee.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${u}`,inputDependencies:N},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:B}),getShaderSource:L}},cu=(e,t,s,n)=>{let o=e.length>2,i=os(s[3]),a=os(s[2]),c=Se.size(s)/i/a,p=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],h=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],k=[s[0],s[1],s[2],s[3]/i],u=[{type:12,data:c},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Qr(t,u),u.push(...xt(p,h,k));let S=(a-1)*t.strides[1]+h[1],B=N=>{let L=gt("output",e[0].dataType,k.length,i),se=Zt(L.type.tensor),ee=Dr(t,L.type.value,se),V=Oe("x",e[0].dataType,p.length,i),de=Oe("w",e[1].dataType,h.length,i),me=[V,de];o&&me.push(Oe("b",e[2].dataType,e[2].dims,i));let ye=o?"value += b[output_channel];":"",Be=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Lr(t,Be),`
  ${N.registerUniforms(Be).declareVariables(...me,L)}
  ${N.mainStart()}
    ${N.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] / ${a}u;
    let col = (index1 % width1) * ${a}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

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

    var x_vals: array<${V.type.value}, ${S}>;
    var values: array<${L.type.value}, ${a}>;
    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 < ${h[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 < ${S}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${V.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${V.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${h[1]}; w_width++) {
          let w_val = ${de.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${a}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${a}u; i++) {
      var value = values[i];
      ${ye}
      ${ee}
      ${L.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${a};${S};${h[0]};${h[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:u}),getShaderSource:B}}}),hu,lo,Ii,uo,Oi,Fi,mu,Di,Li,lc=y(()=>{zt(),ic(),ac(),Ci(),pu(),Yr(),vi(),Fr(),hu=(e,t,s,n,o,i)=>{let a=e[0],c=e.slice(i?1:2,i?3:4),p=c.length,h=t[0],k=t.slice(2).map((S,B)=>S+(S-1)*(s[B]-1)),u=c.map((S,B)=>S+n[B]+n[B+p]).map((S,B)=>Math.floor((S-k[B]+o[B])/o[B]));return u.splice(0,0,a),u.splice(i?3:1,0,h),u},lo=[2,3,1,0],Ii=(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 s=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(s!==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 o=e[0].dims.length-2;if(t.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(t.strides.length!==o)throw new Error(`strides should be ${o}D`);if(t.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},uo=(e,t)=>{let s=e.kernelShape.slice();s.length<t[1].dims.length-2&&s.push(...Array(t[1].dims.length-2-s.length).fill(0));for(let i=2;i<t[1].dims.length;++i)s[i-2]===0&&(s[i-2]=t[1].dims[i]);let n=e.pads.slice();Ps.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,s,n,e.format==="NHWC",e.autoPad);let o=Object.assign({},e);return Object.assign(o,{kernelShape:s,pads:n}),o},Oi=e=>{let t=wi(e),s=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,i=e.group,a=e.kernel_shape,c=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:n,format:s,dilations:o,group:i,kernelShape:a,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},Fi=(e,t,s,n)=>{let o=s.format==="NHWC",i=hu(t[0].dims,t[1].dims,s.dilations,s.pads,s.strides,o);if(s.group!==1){let Be=[t[0]];if(o){let Ee=e.kernelCustomData.wT??e.compute(nr(t[1],lo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ee),Be.push(Ee)}else Be.push(t[1]);t.length===3&&Be.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&t[1].dims[0]===s.group&&t[1].dims[1]===1&&s.dilations[0]===1&&s.dilations[1]===1?e.compute(cu(Be,s,i,n),{inputs:Be}):e.compute(du(Be,s,i,n),{inputs:Be});return}let a=t.length===3,c=t[0].dims[o?1:2],p=t[0].dims[o?2:3],h=t[0].dims[o?3:1],k=t[1].dims[2],u=t[1].dims[3],S=i[o?1:2],B=i[o?2:3],N=i[o?3:1],L=o&&k===c&&u===p&&s.pads[0]===0&&s.pads[1]===0;if(L||k===1&&u===1&&s.dilations[0]===1&&s.dilations[1]===1&&s.strides[0]===1&&s.strides[1]===1&&s.pads[0]===0&&s.pads[1]===0){let Be=i[0],Ee,tt,pt,Ct=[];if(o){let bt=e.kernelCustomData.wT??e.compute(nr(t[1],lo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];if(s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=bt),L){let Kt=c*p*h;Ee=t[0].reshape([1,Be,Kt]),tt=bt.reshape([1,Kt,N]),pt=[1,Be,N]}else Ee=t[0].reshape([Be,c*p,h]),tt=bt.reshape([1,h,N]),pt=[Be,S*B,N];Ct.push(Ee),Ct.push(tt)}else Ee=t[0].reshape([Be,h,c*p]),tt=t[1].reshape([1,N,h]),pt=[Be,N,S*B],Ct.push(tt),Ct.push(Ee);a&&Ct.push(t[2]);let Dt=pt[2],$t=Ct[0].dims[Ct[0].dims.length-1];Dt<8&&$t<8?e.compute(bi(Ct,s,i,pt,o,n),{inputs:Ct}):e.compute(io(Ct,s,i,pt,o,n),{inputs:Ct});return}let se=!0,ee=e.kernelCustomData.wT??e.compute(nr(t[1],lo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ee);let V=[t[0],ee];a&&V.push(t[2]);let de=o?S*B:N,me=o?N:S*B,ye=k*u*h;e.compute(iu(V,s,i,de,me,ye,a,se,n),{inputs:V})},mu=(e,t)=>{let s=t.format==="NHWC",n=[e.inputs[0].reshape(s?[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 o=[0,t.pads[0],0,t.pads[1]],i=[1].concat(t.strides),a=[1].concat(t.dilations),c=[1].concat(t.kernelShape),p=uo({...t,pads:o,strides:i,dilations:a,kernelShape:c},n);Fi(e,n,p,h=>s?[h[0],h[2],h[3]]:[h[0],h[1],h[3]])},Di=(e,t,s)=>{let n=s.format==="NHWC"?"channelsLast":"channelsFirst",o=uo(s,t),i=s.autoPad==="NOTSET"?s.pads:s.autoPad,a=lu(t[0].dims,t[1].dims,s.strides,s.dilations,i,!1,n);e.compute(uu(t,o,a.outShape,[a.filterDepth,a.filterHeight,a.filterWidth],[a.padInfo.front,a.padInfo.top,a.padInfo.left],n))},Li=(e,t)=>{if(Ii(e.inputs,t),e.inputs[0].dims.length===3)mu(e,t);else if(e.inputs[0].dims.length===5)Di(e,e.inputs,t);else{let s=uo(t,e.inputs);Fi(e,e.inputs,s)}}}),zi,uc=y(()=>{Ft(),er(),zt(),Gt(),zi=(e,t,s)=>{let n=e.length>2,o=t.outputShape,i=t.format==="NHWC",a=t.group,c=e[1].dims,p=c[2]/a,h=c[3],k=i?os(h):1,u=Se.size(o)/k,S=[Math.ceil(u/64),1,1];ns("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${S}`);let B=["rank","rank"],N=[t.strides[0],t.strides[1]],L=[t.kernelShape[i?1:2],t.kernelShape[i?2:3]],se=[t.dilations[0],t.dilations[1]],ee=[L[0]+(t.dilations[0]<=1?0:(t.kernelShape[i?1:2]-1)*(t.dilations[0]-1)),L[1]+(t.dilations[1]<=1?0:(t.kernelShape[i?2:3]-1)*(t.dilations[1]-1))],V=[ee[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),ee[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],de=[{type:12,data:u},{type:12,data:N},{type:12,data:L},{type:12,data:se},{type:12,data:ee},{type:6,data:V},{type:12,data:p},{type:12,data:h},...xt(e[0].dims,e[1].dims)];n&&(de.push(...xt(e[2].dims)),B.push("rank")),de.push(...xt(o));let me=ye=>{let Be=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:N.length},{name:"filter_dims",type:"u32",length:L.length},{name:"dilations",type:"u32",length:L.length},{name:"effective_filter_dims",type:"u32",length:ee.length},{name:"pads",type:"i32",length:V.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],Ee=Zt(e[0].dataType),tt=i?1:2,pt=i?2:3,Ct=i?3:1,Dt=Oe("W",e[1].dataType,e[1].dims.length,k),$t=Oe("Dy",e[0].dataType,e[0].dims.length),bt=[$t,Dt];n&&bt.push(Oe("bias",e[2].dataType,[o[Ct]].length,k));let Kt=gt("result",e[0].dataType,o.length,k),jt=`
            let outputIndices = ${Kt.offsetToIndices(`global_idx * ${k}`)};
            let batch = ${Kt.indicesGet("outputIndices",0)};
            let d1 = ${Kt.indicesGet("outputIndices",Ct)};
            let r = ${Kt.indicesGet("outputIndices",tt)};
            let c = ${Kt.indicesGet("outputIndices",pt)};
            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 = ${Kt.type.value}(0.0);
            for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${Ee}(dyRCorner) + ${Ee}(wR)) / ${Ee}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${Ee}(uniforms.Dy_shape[${tt}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);

              for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${Ee}(dyCCorner) + ${Ee}(wC)) / ${Ee}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${Ee}(uniforms.Dy_shape[${pt}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {
                  let xValue = ${i?$t.get("batch","idyR","idyC","inputChannel"):$t.get("batch","inputChannel","idyR","idyC")};
                  let w_offset = ${Dt.indicesToOffset(`${Dt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
                  let wValue = ${Dt.getByOffset(`w_offset / ${k}`)};
                  dotProd = dotProd + xValue * wValue;
                  inputChannel = inputChannel + 1;
                }
              }
            }
            let value = dotProd${n?` + bias[d1 / ${k}]`:""};
            ${Kt.setByOffset("global_idx","value")};
          `;return`
    ${ye.registerUniforms(Be).declareVariables(...bt,Kt)}
      ${ye.mainStart()}
      ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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                ${u.mainStart()}
                  ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${c.offsetToIndices("global_idx")};
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                  ${c.setByOffset("global_idx","sum")};
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            ${h.registerUniforms(c.map(me=>({name:`${zn(me)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...o,a)}

            ${h.mainStart()}
            ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
            var outputIndices = ${a.offsetToIndices("global_idx")};
            ${o.map((me,ye)=>`var input${ye}Indices: ${o[ye].type.indices};`).join(`
`)}
            ${de.join(`
`)};
            ${a.setByOffset("global_idx","sum")};
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          let offset${ee} = ${S.broadcastedIndicesToOffset(`outputIndices${ee}`,B)};
          let index${ee} = offset${ee} / 4u;
          let component${ee} = offset${ee} % 4u;
          ${se}[${ee}] = ${V}(${S.getByOffset(`index${ee}`)}[component${ee}]);
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        let outputOffset = global_idx * ${c};
        var data = vec4<u32>(0);
        ${L("data",0,"u32")}
        ${L("data",1,"u32")}
        ${L("data",2,"u32")}
        ${L("data",3,"u32")}
        ${B.setByOffset("global_idx","data")}
      }`}else N=`
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        let inputOffset = ${S.broadcastedIndicesToOffset("outputIndices",B)};
        let data = ${B.type.value}(${S.getByOffset(`inputOffset / ${a}`)});
        ${B.setByOffset("global_idx","data")}
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    ${u.registerUniform("vec_size","u32").declareVariables(S,B)}
    ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
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      let bias${B}_offset: u32 = (global_idx * 4 + ${B}) % uniforms.bias_size;
      let bias${B} = ${p.getByOffset(`bias${B}_offset / 4`)}[bias${B}_offset % 4];`,S=o?`
      let bias = ${p.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${u(0)}${u(1)}${u(2)}${u(3)}
      let bias = ${c.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(k).declareVariables(c,p,h)}

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    ${a.mainStart(zs)}
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      let x = ${c.getByOffset("global_idx")};
      ${S}
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          var idx${V} = ${N.getByIndices(`indicesIndices${V}`)};
          if (idx${V} < 0) {
            idx${V} = idx${V} + uniforms.axisDimLimit;
          }
          var dataIndices${V} : ${B.type.indices};
        `;for(let ye=0,Be=0;ye<o;ye++)ye===i?(me+=`${o>1?`dataIndices${V}[${ye}]`:`dataIndices${V}`} = u32(idx${V});`,Be+=de):(me+=`${o>1?`dataIndices${V}[${ye}]`:`dataIndices${V}`} = ${a.length>1?`outputIndices${V}[${Be}]`:`outputIndices${V}`};`,Be++);return me},ee;if(e[0].dataType===9){let V=(de,me,ye="")=>`
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          let offset${me} = ${B.indicesToOffset(`dataIndices${me}`)};
          let index${me} = offset${me} / 4u;
          let component${me} = offset${me} % 4u;
          ${de}[${me}] = ${ye}(${B.getByOffset(`index${me}`)}[component${me}]);
        `;ee=`
        let outputOffset = global_idx * ${p};
        var value = vec4<u32>(0);
        ${V("value",0,"u32")}
        ${V("value",1,"u32")}
        ${V("value",2,"u32")}
        ${V("value",3,"u32")}
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      `}else ee=`
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      ${se("")};
      let value = ${B.getByIndices("dataIndices")};
      ${L.setByOffset("global_idx","value")};
      `;return`
      ${S.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(B,N,L)}
      ${S.mainStart()}
        ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${ee}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:u}},Ru=e=>Qe({axis:e.axis}),_n=(e,t)=>{let s=e.inputs;zu(s),e.compute(Bu(e.inputs,t))}}),Nu,ju,Uu,_c=y(()=>{Ft(),zt(),Gt(),Nu=(e,t,s,n,o,i,a,c,p)=>{let h=[{type:12,data:i},{type:12,data:n},{type:12,data:o},{type:12,data:s},{type:12,data:a},{type:12,data:c},{type:12,data:p}],k=[i];h.push(...xt(t.dims,k));let u=S=>{let B=Oe("indices_data",t.dataType,t.dims.length),N=gt("input_slice_offsets_data",12,1,1),L=[B,N],se=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:o.length},{name:"sizes_from_slice_dims_data",type:"u32",length:s.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
  ${S.registerUniforms(se).declareVariables(...L)}
  ${S.mainStart()}
    ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

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

    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
  }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${s.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:k,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:h}),getShaderSource:u},{inputs:[t],outputs:[-1]})[0]},ju=(e,t)=>{let s=e.inputs,n=s[0].dims,o=s[0].dataType,i=s[1].dims,a=i[i.length-1],c=Se.sizeToDimension(i,i.length-1),p=Se.sizeFromDimension(n,t.batchDims+a),h=Se.sizeToDimension(n,t.batchDims),k=Se.sizeFromDimension(n,t.batchDims),u=c/h,S=new Array(a),B=p;for(let me=0;me<a;++me)S[a-1-me]=B,B*=n[t.batchDims+a-1-me];let N=Nu(e,s[1],S,t.batchDims,n,c,u,k,a),L=t.batchDims+a;if(L>n.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let se=i.slice(0,-1).concat(n.slice(L)),ee=Se.size(se),V=[{type:12,data:ee},{type:12,data:p},...xt(s[0].dims,N.dims,se)],de=me=>{let ye=Oe("data",s[0].dataType,s[0].dims.length),Be=Oe("slice_offsets",12,N.dims.length),Ee=gt("output",s[0].dataType,se.length);return`
          ${me.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(ye,Be,Ee)}
            ${me.mainStart()}
            ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`};e.compute({name:"GatherND",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:se,dataType:o}],dispatchGroup:{x:Math.ceil(ee/64)},programUniforms:V}),getShaderSource:de},{inputs:[s[0],N]})},Uu=e=>({batchDims:e.batch_dims,cacheKey:""})}),_o,fc,Vu,Wu,gc=y(()=>{Ft(),zt(),It(),Gt(),_o=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let s=Se.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,o=e[0],i=e[2],a=e.length===4?e[3]:void 0;if(i.dims.length!==o.dims.length||!o.dims.map((c,p)=>p===s?Math.ceil(c/n)===i.dims[p]:c===i.dims[p]).reduce((c,p)=>c&&p,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(a){if(a.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(a.dims.length!==i.dims.length||!a.dims.map((c,p)=>c===i.dims[p]).reduce((c,p)=>c&&p,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},fc=(e,t)=>{let s=e[0].dims,n=e[1].dims,o=s.length,i=Se.normalizeAxis(t.gatherAxis,o),a=Se.normalizeAxis(t.quantizeAxis,o),c=s.slice(0);c.splice(i,1,...n);let p=Se.size(c),h=e[2].dataType,k=e[0].dataType===22,u=[{type:12,data:p},{type:12,data:a},{type:12,data:i},{type:12,data:t.blockSize},...xt(...e.map((B,N)=>B.dims),c)],S=B=>{let N=Oe("data",e[0].dataType,e[0].dims.length),L=Oe("inputIndices",e[1].dataType,e[1].dims.length),se=Oe("scales",e[2].dataType,e[2].dims.length),ee=e.length>3?Oe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,V=gt("output",h,c.length),de=[N,L,se];ee&&de.push(ee);let me=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${B.registerUniforms(me).declareVariables(...de,V)}
        ${B.mainStart()}
        let output_indices = ${V.offsetToIndices("global_idx")};
        var indices_indices = ${L.type.indices}(0);
        ${n.length>1?`
          for (var i: u32 = 0; i < ${n.length}; i++) {
            let index = ${V.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${L.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${V.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${N.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${V.indicesGet("output_indices","i")};
          ${N.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${L.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${s[i]};
        }
        ${N.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${c.length}; i++) {
          let index = ${V.indicesGet("output_indices",`i + ${n.length} - 1`)};
          ${N.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${N.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${N.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${k?"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 = ${se.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${se.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${se.getByIndices("scale_indices")};
        ${ee?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${ee.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${ee.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${k?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${us(h)}(quantized_data - zero_point) * scale;
        ${V.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((B,N)=>N!==1).map(B=>B.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(B,N)=>"rank")},getRunData:()=>({outputs:[{dims:c,dataType:h}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:u}),getShaderSource:S}},Vu=(e,t)=>{let s=e.inputs;_o(s,t),e.compute(fc(e.inputs,t))},Wu=e=>Qe({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Gu,Ku,Gi,Hu,wc=y(()=>{Ft(),zt(),It(),Gt(),Gu=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.`)},Ku=(e,t)=>{let s=e[0].dims,n=e[0].dataType,o=s.length,i=e[1].dims,a=e[1].dataType,c=Se.normalizeAxis(t.axis,o),p=s[c],h=i.slice(0),k=Se.size(h),u=Oe("input",n,o),S=Oe("indicesInput",a,i.length),B=gt("output",n,h.length),N=[{type:12,data:k},{type:6,data:p},{type:12,data:c}];return N.push(...xt(s,i,h)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:N}),getShaderSource:L=>`
      ${L.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,S,B)}
      ${L.mainStart()}
      ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${B.setByOffset("global_idx","value")};
  }`}},Gi=e=>Qe({axis:e.axis}),Hu=(e,t)=>{let s=e.inputs;Gu(s),e.compute(Ku(e.inputs,t))}}),Ki,qu,Xu,Hi,yc=y(()=>{Ft(),zt(),Gt(),Ki=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")},qu=(e,t)=>{let s=e[0].dims.slice(),n=e[1].dims.slice(),[o,i,a]=js.getShapeOfGemmResult(s,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),c=[o,i];if(!c)throw new Error("Can't use gemm on the given tensors");let p=16,h=Math.ceil(i/p),k=Math.ceil(o/p),u=!0,S=Se.size(c),B=[{type:12,data:u?h:S},{type:12,data:o},{type:12,data:i},{type:12,data:a},{type:1,data:t.alpha},{type:1,data:t.beta}],N=["type","type"];e.length===3&&(B.push(...xt(e[2].dims)),N.push("rank")),B.push(...xt(c));let L=ee=>{let V="";t.transA&&t.transB?V="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?V="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?V="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(V="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let de=t.alpha===1?"":"value *= uniforms.alpha;",me=Oe("a",e[0].dataType,e[0].dims),ye=Oe("b",e[1].dataType,e[1].dims),Be=me.type.value,Ee=null,tt=[me,ye];e.length===3&&(Ee=Oe("c",e[2].dataType,e[2].dims.length),tt.push(Ee));let pt=gt("output",e[0].dataType,c.length);tt.push(pt);let Ct=[{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`
  ${ee.registerUniforms(Ct).declareVariables(...tt)}

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

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

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

    ${de}
    ${Ee!=null?`let cOffset = ${Ee.broadcastedIndicesToOffset("vec2(m, n)",pt)}; value += ${Be}(uniforms.beta) * ${Ee.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`},se=ee=>{let V=Oe("a",e[0].dataType,e[0].dims),de=Oe("b",e[1].dataType,e[1].dims),me=null,ye=[V,de];e.length===3&&(me=Oe("c",e[2].dataType,e[2].dims.length),ye.push(me));let Be=gt("output",e[0].dataType,c.length);ye.push(Be);let Ee=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],tt="",pt="";t.transA&&t.transB?(pt=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${V.type.value}(0);
      }

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

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

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

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${de.type.value}(0);
      }
      `,tt="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let Ct=t.alpha===1?"":"value *= uniforms.alpha;";return`
  ${ee.registerUniforms(Ee).declareVariables(...ye)}
  var<workgroup> tile_a: array<array<${V.type.storage}, ${p}>, ${p}>;
  var<workgroup> tile_b: array<array<${de.type.storage}, ${p}>, ${p}>;
  ${ee.mainStart([p,p,1])}
    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${p};
    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${p};
    let num_tiles = (uniforms.K - 1) / ${p} + 1;
    var k_start = 0u;
    var value = ${Be.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${pt}
      k_start = k_start + ${p};
      workgroupBarrier();

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

    ${Ct}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${me!=null?`let cOffset = ${me.broadcastedIndicesToOffset("vec2(m, n)",Be)}; value += ${Be.type.value}(uniforms.beta) * ${me.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`};return u?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:N},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:h*k},programUniforms:B}),getShaderSource:se}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:N},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:B}),getShaderSource:L}},Xu=e=>{let t=e.transA,s=e.transB,n=e.alpha,o=e.beta;return{transA:t,transB:s,alpha:n,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Hi=(e,t)=>{Ki(e.inputs),e.compute(qu(e.inputs,t))}}),yr,mr,Jr,Zr,Qu,Yu,qi,fo,Mc,Ju,Zu,Xi,ed,td,sd=y(()=>{Ft(),zt(),It(),Gt(),[yr,mr,Jr,Zr]=[0,1,2,3],Qu=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},Yu=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,qi=e=>`
  fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} {
    var v: vec4<f32>;
    var coeffs = gs_get_cubic_coeffs(x);
    for (var i = 0; i < 4; i++) {
      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
    }
    coeffs = gs_get_cubic_coeffs(y);
    let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
    return pixel;
  }
`,fo=e=>`
  fn gs_denormalize(n: f32, length: i32) -> f32 {
    ${e.alignCorners===0?`
    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
    `:`
    // alignCorners: true => [-1, 1] to [0, length - 1]
    return (n + 1.0) / 2.0 * (f32(length - 1));
    `}
  }
`,Mc=e=>`
  ${e.paddingMode==="reflection"?`
      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
        var dx = 0.0;
        var fx = f32(x);
        let range = x_max - x_min;
        if (fx < x_min) {
          dx = x_min - fx;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_min + r;
          } else {
            fx = x_max - r;
          }
        } else if (fx > x_max) {
          dx = fx - x_max;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_max - r;
          } else {
            fx = x_min + r;
          }
        }
        return u32(fx);
      }`:""}
`,Ju=(e,t,s)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${t} {
     var pixel = ${t}(0);
     var indices = vec4<u32>(0);
     indices[${yr}] = batch;
     indices[${mr}] = channel;`+(()=>{switch(s.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${Jr}] = u32(r);
            indices[${Zr}] = u32(c);
          }
        `;case"border":return`
          indices[${Jr}] = u32(clamp(r, 0, H - 1));
          indices[${Zr}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${Jr}] = gs_reflect(r, border[1], border[3]);
          indices[${Zr}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${s.paddingMode} is not supported`)}})()+`
    return ${e.getByIndices("indices")};
  }
`,Zu=(e,t,s)=>(()=>{switch(s.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${yr}], indices[${mr}], border);
        `;case"bilinear":return`
          let x1 = i32(floor(x));
          let y1 = i32(floor(y));
          let x2 = x1 + 1;
          let y2 = y1 + 1;

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

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

          let dx = x - f32(x0 + 1);
          let dy = y - f32(y0 + 1);
          let result = gs_bicubic_interpolate(p, dx, dy);
        `;default:throw new Error(`mode ${s.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,Xi=(e,t)=>{let s=Oe("x",e[0].dataType,e[0].dims.length),n=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=Oe("grid",e[1].dataType,n.length,2),i=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format==="NHWC"&&(i=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[yr,mr,Jr,Zr]=[0,3,1,2]);let a=gt("output",e[0].dataType,i.length),c=s.type.value,p=Se.size(i),h=[{type:12,data:p},...xt(e[0].dims,n,i)],k=u=>`
  ${u.registerUniform("output_size","u32").declareVariables(s,o,a)}
  ${Yu}
  ${qi(c)}
  ${fo(t)}
  ${Mc(t)}
  ${Ju(s,c,t)}

  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${Jr}]);
      let W_in = i32(uniforms.x_shape[${Zr}]);

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

      let indices = ${a.offsetToIndices("global_idx")};
      var grid_indices = vec3<u32>(indices[${yr}], indices[${Jr}], indices[${Zr}]);
      let nxy = ${o.getByIndices("grid_indices")};
      var x = gs_denormalize(f32(nxy[0]), W_in);
      var y = gs_denormalize(f32(nxy[1]), H_in);

      ${Zu(a,c,t)}
  }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:u=>{let S=Se.size(i);return{outputs:[{dims:i,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:h}},getShaderSource:k}},ed=(e,t)=>{Qu(e.inputs),e.compute(Xi(e.inputs,t))},td=e=>Qe({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Hs,rd,Qi,nd,bc,fn,Yi,od=y(()=>{Ft(),zt(),It(),Hr(),qo(),Gt(),Fr(),Hs=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,rd=(e,t)=>{let s=e[0],n=Hs(e,1),o=Hs(e,2),i=Hs(e,3),a=Hs(e,4),c=Hs(e,5),p=Hs(e,6),h=Hs(e,7);if(s.dims.length!==3&&s.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let k=s.dims[0],u=s.dims[1],S=s.dims.length===3?s.dims[2]:t.numHeads*s.dims[4],B=u,N=0,L=0,se=Math.floor(S/t.numHeads);if(p&&h&&Se.size(p.dims)&&Se.size(h.dims)){if(p.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(p.dims[0]!==k||p.dims[1]!==t.numHeads||p.dims[3]!==se)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[0]!==k||h.dims[1]!==t.numHeads||h.dims[3]!==se)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[2]!==h.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(h.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');N=p.dims[2],L=p.dims[2]}else if(p&&Se.size(p.dims)||h&&Se.size(h.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let ee;if(n&&Se.size(n.dims)>0){if(s.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(s.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]!==s.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');ee=2,B=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==se)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');ee=5,B=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==se)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');ee=0,B=n.dims[2]}}else{if(s.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(s.dims[2]!==t.numHeads||s.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');ee=3}if(i&&Se.size(i.dims)>0){if(i.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 V=N+B,de=0;if(a&&Se.size(a.dims)>0){de=8;let Ee=a.dims;throw Ee.length===1?Ee[0]===k?de=1:Ee[0]===3*k+2&&(de=3):Ee.length===2&&Ee[0]===k&&Ee[1]===V&&(de=5),de===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let me=!1,ye=S;if(o&&Se.size(o.dims)>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(s.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(B!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');ye=o.dims[2]}else{if(B!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');ye=o.dims[1]*o.dims[3],me=!0}}let Be=!1;if(a&&Se.size(a.dims)>0)throw new Error("Key padding mask is not supported");if(c&&Se.size(c.dims)>0){if(c.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(c.dims[0]!==k||c.dims[1]!==t.numHeads||c.dims[2]!==u||c.dims[3]!==V)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:k,sequenceLength:u,pastSequenceLength:N,kvSequenceLength:B,totalSequenceLength:V,maxSequenceLength:L,inputHiddenSize:0,hiddenSize:S,vHiddenSize:ye,headSize:se,vHeadSize:Math.floor(ye/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:de,scale:t.scale,broadcastResPosBias:Be,passPastInKv:me,qkvFormat:ee}},Qi=e=>Qe({...e}),nd=Qe({perm:[0,2,1,3]}),bc=(e,t,s,n,o,i,a)=>{let c=[n,o,i],p=Se.size(c),h=[{type:12,data:p},{type:12,data:a},{type:12,data:i}],k=u=>{let S=gt("qkv_with_bias",t.dataType,c),B=Oe("qkv",t.dataType,c),N=Oe("bias",s.dataType,c),L=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${u.registerUniforms(L).declareVariables(B,N,S)}
  ${u.mainStart()}
    ${u.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:c,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:k},{inputs:[t,s],outputs:[-1]})[0]},fn=(e,t,s,n,o,i,a,c)=>{let p=i;if(a&&Se.size(a.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=bc(e,i,a,t,n,s*o,c),p=p.reshape([t,n,s,o]),s===1||n===1?p:e.compute(nr(p,nd.perm),{inputs:[p],outputs:[-1]})[0]}else return i.dims.length===3&&(p=i.reshape([t,n,s,o])),s===1||n===1?p:e.compute(nr(p,nd.perm),{inputs:[p],outputs:[-1]})[0]},Yi=(e,t)=>{let s=rd(e.inputs,t),n=e.inputs[0],o=Hs(e.inputs,1),i=Hs(e.inputs,2),a=Hs(e.inputs,3),c=Hs(e.inputs,4),p=Hs(e.inputs,5),h=Hs(e.inputs,6),k=Hs(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if(o?.dims.length===5)throw new Error("Packed KV is not implemented");let u=o&&i&&o.dims.length===4&&i.dims.length===4,S=fn(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,n,a,0);if(u)return On(e,S,o,i,c,void 0,h,k,p,s);if(!o||!i)throw new Error("key and value must be provided");let B=fn(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.headSize,o,a,s.hiddenSize),N=fn(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.vHeadSize,i,a,2*s.hiddenSize);On(e,S,B,N,c,void 0,h,k,p,s)}}),id,Ji,ad,ld,go,ud,dd,Zi=y(()=>{Ft(),zt(),It(),Gt(),id=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Ji=(e,t)=>{let s=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>s.push(Number(o))),n=s.length),Qe({numOutputs:n,axis:t.axis,splitSizes:s})},ad=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${Mt("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,ld=e=>{let t=e.length,s=[];for(let n=0;n<t;++n){let o=e[n].setByIndices("indices","input[global_idx]");t===1?s.push(o):n===0?s.push(`if (output_number == ${n}u) { ${o} }`):n===t-1?s.push(`else { ${o} }`):s.push(`else if (output_number == ${n}) { ${o} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${s.join(`
`)}
      }`},go=(e,t)=>{let s=e[0].dims,n=Se.size(s),o=e[0].dataType,i=Se.normalizeAxis(t.axis,s.length),a=new Array(t.numOutputs),c=Oe("input",o,s.length),p=new Array(t.numOutputs),h=[],k=[],u=0,S=[{type:12,data:n}];for(let N=0;N<t.numOutputs;N++){u+=t.splitSizes[N],p[N]=u;let L=s.slice();L[i]=t.splitSizes[N],k.push(L),a[N]=gt(`output${N}`,o,L.length),h.push({dims:k[N],dataType:e[0].dataType})}S.push({type:12,data:p},...xt(s,...k));let B=N=>`
  ${N.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(c,...a)}
  ${ad(p.length)}
  ${ld(a)}

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

    var indices = ${c.offsetToIndices("global_idx")};
    var index = ${c.indicesGet("indices",i)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Mt("uniforms.size_in_split_axis","output_number - 1u",p.length)};
      ${c.indicesSet("indices",i,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:B,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:S})}},ud=(e,t)=>{id(e.inputs);let s=e.inputs.length===1?t:Ji(e.inputs,t);e.compute(go(e.inputs,s),{inputs:[0]})},dd=e=>{let t=e.axis,s=e.splitSizes,n=e.numOutputs<0?s.length:e.numOutputs;if(n!==s.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Qe({axis:t,numOutputs:n,splitSizes:s})}}),cd,pd,ea,hd,vc=y(()=>{It(),qo(),od(),Zi(),Fr(),cd=(e,t)=>{if(t.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let s=e[0],n=e[1],o=e[2],i=e[3],a=e[4];if(t.localWindowSize!==-1)throw new Error("Local attention is not supported");if(t.softcap!==0)throw new Error("Softcap is not supported");if(t.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(t.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(s.dims.length!==3&&s.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let c=!1,p=s.dims[0],h=s.dims[1],k=s.dims.length===3?c?s.dims[2]/3:s.dims[2]:t.numHeads*s.dims[4],u=h,S=0,B=!n||n.dims.length===0,N=Math.floor(B?k/(t.numHeads+2*t.kvNumHeads):k/t.numHeads);B&&(k=N*t.numHeads);let L=i&&i.dims.length!==0,se=a&&a.dims.length!==0;if(L&&i.dims.length===4&&i.dims[0]===p&&i.dims[1]!==t.kvNumHeads&&i.dims[2]===t.kvNumHeads&&i.dims[3]===N)throw new Error("BSNH pastKey/pastValue is not supported");if(L&&se){if(i.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(a.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');S=i.dims[2]}else if(L||se)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let ee=1;if(n&&n.dims.length>0){if(s.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(s.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(s.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');u=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==N)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');u=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==N)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');u=n.dims[2]}}else{if(s.dims.length!==3&&s.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(s.dims.length===5&&(s.dims[2]!==t.numHeads||s.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');ee=3}let V=0,de=!1,me=t.kvNumHeads?N*t.kvNumHeads:k;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(s.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(u!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');me=o.dims[2]}else{if(u!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');me=o.dims[1]*o.dims[3],de=!0}}let ye=e.length>4?e[5]:void 0;if(ye&&ye.dims.length!==1&&ye.dims[0]!==p)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:p,sequenceLength:h,pastSequenceLength:S,kvSequenceLength:u,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:k,vHiddenSize:me,headSize:N,vHeadSize:Math.floor(me/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:V,scale:t.scale,broadcastResPosBias:!1,passPastInKv:de,qkvFormat:ee}},pd=Qe({perm:[0,2,1,3]}),ea=(e,t,s)=>{let n=t,o=s.kvNumHeads;return t.dims.length===3&&s.kvSequenceLength!==0&&(n=t.reshape([s.batchSize,s.kvSequenceLength,o,s.headSize]),n=e.compute(nr(n,pd.perm),{inputs:[n],outputs:[-1]})[0]),n},hd=(e,t)=>{let s=cd(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let n=e.inputs[0],o=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,i=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,a=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,c=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,p=e.inputs.length>4?e.inputs[5]:void 0,h=e.inputs.length>5?e.inputs[6]:void 0,k=s.kvNumHeads?s.kvNumHeads:s.numHeads,u=Qe({axis:2,numOutputs:3,splitSizes:[s.numHeads*s.headSize,k*s.headSize,k*s.headSize]}),[S,B,N]=!o&&!i?e.compute(go([n],u),{inputs:[n],outputs:[-1,-1,-1]}):[n,o,i],L=fn(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,S,void 0,0);On(e,L,ea(e,B,s),ea(e,N,s),void 0,void 0,a,c,void 0,s,p,h)}}),ta,md,_d,fd,gd=y(()=>{Ft(),zt(),Fr(),Gt(),ta=(e,t,s,n,o,i,a,c)=>{let p=os(i),h=p===1?"f32":`vec${p}f`,k=p===1?"vec2f":`mat2x${p}f`,u=o*a,S=64;u===1&&(S=256);let B=[o,a,i/p],N=[o,a,2],L=["rank","type","type"],se=[];se.push(...xt(B,N));let ee=V=>{let de=Oe("x",t.dataType,3,p),me=Oe("scale",s.dataType,s.dims),ye=Oe("bias",n.dataType,n.dims),Be=gt("output",1,3,2),Ee=[de,me,ye,Be];return`
  var<workgroup> workgroup_shared : array<${k}, ${S}>;
  const workgroup_size = ${S}u;
  ${V.declareVariables(...Ee)}
  ${V.mainStart(S)}
    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 = ${h}(0);
    var squared_sum = ${h}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${h}(${de.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${k}(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 = ${Vs("workgroup_shared[0][0]",p)} / f32(hight * ${p});
      let squared_sum_final = ${Vs("workgroup_shared[0][1]",p)} / f32(hight * ${p});

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${c}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${c};${S}`,inputDependencies:L},getRunData:()=>({outputs:[{dims:N,dataType:1}],dispatchGroup:{x:u},programUniforms:se}),getShaderSource:ee},{inputs:[t,s,n],outputs:[-1]})[0]},md=(e,t,s)=>{let n=t[0].dims,o=n,i=2,a=n[0],c=n[1],p=Se.sizeFromDimension(n,i),h=os(p),k=Se.size(o)/h,u=ta(e,t[0],t[1],t[2],a,p,c,s.epsilon),S=[a,c,p/h],B=[a,c],N=["type","none"],L=se=>{let ee=Oe("x",t[0].dataType,S.length,h),V=Oe("scale_shift",1,B.length,2),de=gt("output",t[0].dataType,S.length,h),me=[ee,V,de];return`
  ${se.registerUniform("output_size","u32").declareVariables(...me)}
  ${se.mainStart()}
  ${se.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${de.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${V.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${ee.getByOffset("global_idx")} * ${de.type.value}(scale_shift.x) + ${de.type.value}(scale_shift.y);
      ${de.setByOffset("global_idx","value")};
  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${h}`,inputDependencies:N},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:[{type:12,data:k},...xt(S,B,S)]}),getShaderSource:L},{inputs:[t[0],u]})},_d=(e,t,s)=>{let n=t[0].dims,o=n,i=n[0],a=n[n.length-1],c=Se.sizeFromDimension(n,1)/a,p=os(a),h=Se.size(o)/p,k=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],u=["type","type"],S=!1,B=[0,n.length-1];for(let ee=0;ee<n.length-2;ee++)S=S||n[ee+1]!==1,B.push(ee+1);S=S&&n[n.length-1]!==1;let N=S?e.compute(nr(e.inputs[0],B),{inputs:[e.inputs[0]],outputs:[-1]})[0]:e.inputs[0].reshape(Array.from({length:n.length},(ee,V)=>n[B[V]])),L=ta(e,N,t[1],t[2],i,c,a,s.epsilon),se=ee=>{let V=Zt(t[0].dataType),de=p===1?"vec2f":`mat${p}x2f`,me=Ee=>{let tt=Ee===0?"x":"y",pt=p===1?"f32":`vec${p}f`;switch(p){case 1:return`${V}(${pt}(scale.${tt}))`;case 2:return`vec2<${V}>(${pt}(scale[0].${tt}, scale[1].${tt}))`;case 4:return`vec4<${V}>(${pt}(scale[0].${tt}, scale[1].${tt}, scale[2].${tt}, scale[3].${tt}))`;default:throw new Error(`Not supported compoents ${p}`)}},ye=Oe("input",t[0].dataType,t[0].dims,p),Be=gt("output",t[0].dataType,o,p);return`
  @group(0) @binding(0) var<storage, read> input : array<${ye.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${de}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${Be.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${ee.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], ${me(0)}, ${me(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:se},{inputs:[t[0],L]})},fd=(e,t)=>{t.format==="NHWC"?_d(e,e.inputs,t):md(e,e.inputs,t)}}),wd,yd,sa,Tc=y(()=>{Ft(),zt(),Gt(),wd=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},yd=(e,t,s)=>{let n=t.simplified,o=e[0].dims,i=e[1],a=!n&&e[2],c=o,p=Se.normalizeAxis(t.axis,o.length),h=Se.sizeToDimension(o,p),k=Se.sizeFromDimension(o,p),u=Se.size(i.dims),S=a?Se.size(a.dims):0;if(u!==k||a&&S!==k)throw new Error(`Size of X.shape()[axis:] == ${k}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${u} and bias size of ${S}`);let B=[];for(let ye=0;ye<o.length;++ye)ye<p?B.push(o[ye]):B.push(1);let N=os(k),L=["type","type"],se=[{type:12,data:h},{type:1,data:k},{type:12,data:Math.floor(k/N)},{type:1,data:t.epsilon}];a&&L.push("type");let ee=s>1,V=s>2,de=ye=>{let Be=Zt(e[0].dataType),Ee=[Oe("x",e[0].dataType,e[0].dims,N),Oe("scale",i.dataType,i.dims,N)];a&&Ee.push(Oe("bias",a.dataType,a.dims,N)),Ee.push(gt("output",e[0].dataType,c,N)),ee&&Ee.push(gt("mean_data_output",1,B)),V&&Ee.push(gt("inv_std_output",1,B));let tt=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${ye.registerUniforms(tt).declareVariables(...Ee)}
  ${ye.mainStart()}
    ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${wr("f32",N)};
    var mean_square_vector = ${wr("f32",N)};

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

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

    ${ee?"mean_data_output[global_idx] = mean":""};
    ${V?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},me=[{dims:c,dataType:e[0].dataType}];return ee&&me.push({dims:B,dataType:1}),V&&me.push({dims:B,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${N};${s};${n}`,inputDependencies:L},getRunData:()=>({outputs:me,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:se}),getShaderSource:de}},sa=(e,t)=>{wd(e.inputs),e.compute(yd(e.inputs,t,e.outputCount))}}),Md,ms,Cp=y(()=>{zt(),vi(),Ci(),Md=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.")},ms=e=>{Md(e.inputs);let t=gs.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let s=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];if(s<8&&n<8)e.compute(bi(e.inputs,{activation:""},t));else{let o=t[t.length-2],i=Se.size(e.inputs[0].dims.slice(0,-2)),a=Se.size(e.inputs[1].dims.slice(0,-2));if(i!==1&&o===1&&a===1){let c=e.inputs[0].reshape([1,i,n]),p=e.inputs[1].reshape([1,n,s]),h=[1,i,s],k=[c,p];e.compute(io(k,{activation:""},t,h),{inputs:k})}else e.compute(io(e.inputs,{activation:""},t))}}}),xc,Pc,ra,bd,vd,Ec=y(()=>{Ft(),zt(),It(),Gt(),xc=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let s=e[0],n=s.dims.length;if(s.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((t.k+t.blockSize-1)/t.blockSize),i=t.blockSize/8*t.bits,a=e[1];if(!Se.areEqual(a.dims,[t.n,o,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let c=e[2].dims;if(Se.size(c)!==t.n*o)throw new Error("scales input size error.");if(e.length===4){let p=e[3].dims,h=t.bits>4?t.n*o:t.n*Math.floor((o+1)/2);if(Se.size(p)!==h)throw new Error("zeroPoints input size error.")}},Pc=(e,t)=>{let s=e[0].dims,n=s.length,o=s[n-2],i=t.k,a=t.n,c=s.slice(0,n-2),p=Se.size(c),h=e[1].dims[2]/4,k=e[0].dataType,u=os(t.k),S=os(h),B=os(a),N=c.concat([o,a]),L=o>1&&a/B%2===0?2:1,se=Se.size(N)/B/L,ee=64,V=[],de=[p,o,i/u],me=Se.convertShape(e[1].dims).slice();me.splice(-1,1,h/S),V.push(...xt(de)),V.push(...xt(me)),V.push(...xt(e[2].dims)),e.length===4&&V.push(...xt(Se.convertShape(e[3].dims)));let ye=[p,o,a/B];V.push(...xt(ye));let Be=Ee=>{let tt=de.length,pt=Oe("a",e[0].dataType,tt,u),Ct=Oe("b",12,me.length,S),Dt=Oe("scales",e[2].dataType,e[2].dims.length),$t=[pt,Ct,Dt],bt=e.length===4?Oe("zero_points",12,e[3].dims.length):void 0;bt&&$t.push(bt);let Kt=ye.length,jt=gt("output",e[0].dataType,Kt,B),Lt=Zt(e[0].dataType),ss=(()=>{switch(u){case 1:return`array<${Lt}, 8>`;case 2:return`mat4x2<${Lt}>`;case 4:return`mat2x4<${Lt}>`;default:throw new Error(`${u}-component is not supported.`)}})(),Jt=()=>{let at=`
          // reuse a data
            var input_offset = ${pt.indicesToOffset(`${pt.type.indices}(batch, row, word_offset)`)};
            var a_data: ${ss};
            for (var j: u32 = 0; j < ${8/u}; j++) {
              a_data[j] = ${pt.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let Pt=0;Pt<B*L;Pt++)at+=`
            b_value = ${S===1?`b${Pt}_data`:`b${Pt}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${ss}(${Array.from({length:4},(ps,vs)=>`${Lt}(b_value_lower[${vs}]), ${Lt}(b_value_upper[${vs}])`).join(", ")});
            b_dequantized_values = ${u===1?`${ss}(${Array.from({length:8},(ps,vs)=>`(b_quantized_values[${vs}] - ${bt?`zero_point${Pt}`:"zero_point"}) * scale${Pt}`).join(", ")});`:`(b_quantized_values - ${ss}(${Array(8).fill(`${bt?`zero_point${Pt}`:"zero_point"}`).join(",")})) * scale${Pt};`};
            workgroup_shared[local_id.x * ${L} + ${Math.floor(Pt/B)}]${B>1?`[${Pt%B}]`:""} += ${Array.from({length:8/u},(ps,vs)=>`${u===1?`a_data[${vs}] * b_dequantized_values[${vs}]`:`dot(a_data[${vs}], b_dequantized_values[${vs}])`}`).join(" + ")};
          `;return at},qt=()=>{let at=`
            var col_index = col * ${B};
            ${bt?`
            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 = ${Lt}(8);`}
            `;for(let Pt=0;Pt<B*L;Pt++)at+=`
            let scale${Pt} = ${Dt.getByOffset("col_index * nBlocksPerCol + block")};
            ${bt?`
            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 = ${bt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${Pt} = ${Lt}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return at},Qs=()=>{let at=`col_index = col * ${B};`;for(let Pt=0;Pt<B*L;Pt++)at+=`
            let b${Pt}_data = ${Ct.getByIndices(`${Ct.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return at+=`
            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: ${ss};
            var b_dequantized_values: ${ss};`,at};return`
        var<workgroup> workgroup_shared: array<${jt.type.value}, ${L*ee}>;
        ${Ee.declareVariables(...$t,jt)}
        ${Ee.mainStart([ee,1,1])}
          let output_indices = ${jt.offsetToIndices(`(global_idx / ${ee}) * ${L}`)};
          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 += ${ee}) {
            //process one block
            var word_offset: u32 = block * ${t.blockSize/u};
            ${qt()}
            for (var word: u32 = 0; word < ${h}; word += ${S}) {
              ${Qs()}
              for (var i: u32 = 0; i < ${S}; i++) {
                ${Jt()}
                word_offset += ${8/u};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${L}) {
            var output_value: ${jt.type.value} = ${jt.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${ee}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${L};
            }
            ${jt.setByIndices(`${jt.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${u};${S};${B};${L};${ee}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:N,dataType:k}],dispatchGroup:{x:se},programUniforms:V}),getShaderSource:Be}},ra=(e,t)=>{let s=e[0].dims,n=s.length,o=s[n-2],i=t.k,a=t.n,c=s.slice(0,n-2),p=Se.size(c),h=e[1].dims[2]/4,k=e[0].dataType,u=os(t.k),S=os(h),B=c.concat([o,a]),N=128,L=a%8===0?8:a%4===0?4:1,se=N/L,ee=se*S*8,V=ee/u,de=ee/t.blockSize,me=Se.size(B)/L,ye=[],Be=[p,o,i/u],Ee=Se.convertShape(e[1].dims).slice();Ee.splice(-1,1,h/S),ye.push(...xt(Be)),ye.push(...xt(Ee)),ye.push(...xt(e[2].dims)),e.length===4&&ye.push(...xt(Se.convertShape(e[3].dims)));let tt=[p,o,a];ye.push(...xt(tt));let pt=Ct=>{let Dt=Be.length,$t=Oe("a",e[0].dataType,Dt,u),bt=Oe("b",12,Ee.length,S),Kt=Oe("scales",e[2].dataType,e[2].dims.length),jt=[$t,bt,Kt],Lt=e.length===4?Oe("zero_points",12,e[3].dims.length):void 0;Lt&&jt.push(Lt);let ss=tt.length,Jt=gt("output",e[0].dataType,ss),qt=Zt(e[0].dataType),Qs=()=>{switch(u){case 1:return`
          let a_data0 = vec4<${qt}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${qt}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${qt}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${qt}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${u}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${$t.type.value}, ${V}>;
        var<workgroup> inter_results: array<array<${Jt.type.value}, ${se}>, ${L}>;
        ${Ct.declareVariables(...jt,Jt)}
        ${Ct.mainStart([se,L,1])}
          let output_indices = ${Jt.offsetToIndices(`workgroup_index * ${L}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let n_blocks_per_col = uniforms.b_shape[1];
          let num_tiles =  (n_blocks_per_col - 1) / ${de} + 1;

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

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

          if (local_idx < ${L}) {
            var output_value: ${Jt.type.value} = ${Jt.type.value}(0);
            for (var b = 0u; b < ${se}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${Jt.setByIndices(`${Jt.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${u};${S};${se};${L}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:k}],dispatchGroup:{x:me},programUniforms:ye}),getShaderSource:pt}},bd=(e,t)=>{xc(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(ra(e.inputs,t)):e.compute(Pc(e.inputs,t))},vd=e=>Qe(e)}),Td,na,oa,Cc,xd,Pd,ia,kc,Sc,$c=y(()=>{Ft(),zt(),Gt(),Td=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].")}},na=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
            k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,s)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Mt("uniforms.x_shape",o,t)})) {
              break;
            }
            offset += k * i32(${Mt("uniforms.x_strides",o,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},oa=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,s)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Mt("uniforms.x_shape",o,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Mt("uniforms.x_shape",o,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Mt("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Cc=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,s)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Mt("uniforms.x_shape",o,t)})) {
                  k = i32(${Mt("uniforms.x_shape",o,t)}) - 1;
                }
                offset += k * i32(${Mt("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},xd=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,s)};
                if (k < 0)  {
                  k += i32(${Mt("uniforms.x_shape",o,t)}]);
                }
                if (k >= i32(${Mt("uniforms.x_shape",o,t)})) {
                  k -= i32(${Mt("uniforms.x_shape",o,t)});
                }
                offset += k * i32(${Mt("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Pd=(e,t,s)=>{switch(s.mode){case 0:return na(e,t,s.pads.length);case 1:return oa(e,t,s.pads.length);case 2:return Cc(e,t,s.pads.length);case 3:return xd(e,t,s.pads.length);default:throw new Error("Invalid mode")}},ia=(e,t)=>{let s=Se.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,o=Se.size(s),i=[{type:12,data:o},{type:6,data:t.pads}],a=e.length>=3&&e[2].data;t.mode===0&&i.push({type:a?e[2].dataType:1,data:t.value}),i.push(...xt(e[0].dims,s));let c=["rank"],p=h=>{let k=gt("output",e[0].dataType,s.length),u=Oe("x",e[0].dataType,n.length),S=u.type.value,B=Pd(k,n.length,t),N=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&N.push({name:"constant_value",type:a?S:"f32"}),`
            ${h.registerUniforms(N).declareVariables(u,k)}
            ${h.mainStart()}
            ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${S}(0);
            ${B}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${a}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Se.size(s)/64)},programUniforms:i}),getShaderSource:p}},kc=(e,t)=>{if(e.length>1){let s=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,o=e[0].dims.length,i=new Int32Array(2*o).fill(0);if(e.length>=4){let c=e[3].getBigInt64Array();for(let p=0;p<c.length;p++)i[Number(c[p])]=Number(s[p]),i[Number(c[p])+o]=Number(s[p+c.length])}else s.forEach((c,p)=>i[Number(p)]=Number(c));let a=[];return i.forEach(c=>a.push(c)),{mode:t.mode,value:n,pads:a}}else return t},Sc=(e,t)=>{Td(e.inputs);let s=kc(e.inputs,t);e.compute(ia(e.inputs,s),{inputs:[0]})}}),Bn,aa,la,ua,da,Ed,Cd,ca,pa,kd,Sd,$d,Ad,Id,ha,Od,Fd,Dd,Ac,kp=y(()=>{He(),Ft(),zt(),Gt(),Bn=e=>{if(x.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},aa=(e,t,s)=>{let n=t.format==="NHWC",o=e.dims.slice();n&&o.splice(1,0,o.pop());let i=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),c=t.strides.slice(),p=i?t.dilations.slice():[],h=t.pads.slice();Ps.adjustPoolAttributes(s,o,a,c,p,h);let k=Ps.computePoolOutputShape(s,o,c,p,a,h,t.autoPad),u=Object.assign({},t);i?Object.assign(u,{kernelShape:a,strides:c,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(u,{kernelShape:a,strides:c,pads:h,cacheKey:t.cacheKey});let S=k.slice();return S.push(S.splice(1,1)[0]),[u,n?S:k]},la=(e,t)=>{let s=t.format==="NHWC",n=Se.size(e),o=Se.size(t.kernelShape),i=[{type:12,data:n},{type:12,data:o}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let c=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],k=t.pads[t.pads.length-1],u=!!(h+k);i.push({type:12,data:c},{type:12,data:p},{type:12,data:h},{type:12,data:k}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let S=!1;if(t.kernelShape.length===2){let B=t.kernelShape[t.kernelShape.length-2],N=t.strides[t.strides.length-2],L=t.pads[t.pads.length/2-2],se=t.pads[t.pads.length-2];S=!!(L+se),i.push({type:12,data:B},{type:12,data:N},{type:12,data:L},{type:12,data:se}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,a,!0,u,S]}else{if(s)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let c=Se.computeStrides(t.kernelShape);i.push({type:12,data:c},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:c.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,k)=>h+k);return[i,a,!!p,!1,!1]}},ua=(e,t,s,n,o,i,a,c,p,h,k,u)=>{let S=o.format==="NHWC",B=t.type.value,N=gt("output",t.type.tensor,n);if(o.kernelShape.length<=2){let L="",se="",ee="",V=s-(S?2:1);if(k?L=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${V}] = indices[${V}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${V}] < 0 || xIndices[${V}]
                      >= uniforms.x_shape[${V}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${i}
                }`:L=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${V}] = indices[${V}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${i}
                }`,o.kernelShape.length===2){let de=s-(S?3:2);u?se=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${de}] = indices[${de}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${de}] < 0 || xIndices[${de}] >= uniforms.x_shape[${de}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:se=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${de}] = indices[${de}] * uniforms.sh - uniforms.phStart + j;
                `,ee=`
              }
            `}return`
            ${e.registerUniforms(p).declareVariables(t,N)}

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

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

              var value = ${B}(${c});
              var pad = 0;
              ${se}
              ${L}
              ${ee}
              ${a}

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

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

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

              var value = ${B}(${c});
              var pad = 0;
              var isPad = false;

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

                isPad = false;
                for (var j = ${s-L}u; j < ${s}u; j++) {
                  xIndices[j] = indices[j] * ${Mt("uniforms.strides",`j - ${s-L}u`,L)}
                    + offsets[j - ${s-L}u] - ${Mt("uniforms.pads","j - 2u",se)};
                  ${ee}
              }
              ${a}

              output[global_idx] = value;
            }`}},da=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Ed=e=>`${da(e)};${e.countIncludePad}`,Cd=e=>`${da(e)};${e.storageOrder};${e.dilations}`,ca=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}),pa=(e,t,s,n)=>{let[o,i]=aa(t,n,s),a=Oe("x",t.dataType,t.dims.length),c=a.type.value,p="value += x_val;",h="";o.countIncludePad?h+=`value /= ${c}(uniforms.kernelSize);`:h+=`value /= ${c}(i32(uniforms.kernelSize) - pad);`;let[k,u,S,B,N]=la(i,o);k.push(...xt(t.dims,i));let L=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${S};${B};${N}`,inputDependencies:L},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Se.size(i)/64)},programUniforms:k}),getShaderSource:se=>ua(se,a,t.dims.length,i.length,o,p,h,0,u,S,B,N)}},kd=e=>{let t=e.count_include_pad!==0,s=ca(e);if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...s,cacheKey:""};return{...n,cacheKey:Ed(n)}},Sd=(e,t)=>{Bn(e.inputs),e.compute(pa("AveragePool",e.inputs[0],!1,t))},$d={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Ad=e=>{let t=e.format;return{format:t,...$d,cacheKey:t}},Id=(e,t)=>{Bn(e.inputs),e.compute(pa("GlobalAveragePool",e.inputs[0],!0,t))},ha=(e,t,s,n)=>{let[o,i]=aa(t,n,s),a=`
      value = max(x_val, value);
    `,c="",p=Oe("x",t.dataType,t.dims.length),h=["rank"],[k,u,S,B,N]=la(i,o);return k.push(...xt(t.dims,i)),{name:e,shaderCache:{hint:`${n.cacheKey};${S};${B};${N}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Se.size(i)/64)},programUniforms:k}),getShaderSource:L=>ua(L,p,t.dims.length,i.length,o,a,c,t.dataType===10?-65504:-1e5,u,S,B,N)}},Od=(e,t)=>{Bn(e.inputs),e.compute(ha("MaxPool",e.inputs[0],!1,t))},Fd=e=>{let t=e.storage_order,s=e.dilations,n=ca(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let o={storageOrder:t,dilations:s,...n,cacheKey:""};return{...o,cacheKey:Cd(o)}},Dd=e=>{let t=e.format;return{format:t,...$d,cacheKey:t}},Ac=(e,t)=>{Bn(e.inputs),e.compute(ha("GlobalMaxPool",e.inputs[0],!0,t))}}),Ic,Oc,Fc,Dc,Sp=y(()=>{Ft(),zt(),It(),Gt(),Ic=(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((s,n)=>s===e[2].dims[n]).reduce((s,n)=>s&&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((o,i)=>i===t.axis||o===e[0].dims[i]).reduce((o,i)=>o&&i,!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 s=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSize<Math.ceil(s/n)||t.blockSize>Math.ceil(s/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Oc=(e,t)=>{let s=Se.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,o=n===3,i=e[0].dims,a=e[1].dataType,c=Se.size(i),p=n===3||n===2,h=p?[Math.ceil(Se.size(e[0].dims)/4)]:e[0].dims,k=e[1].dims,u=e.length>2?e[2]:void 0,S=u?p?[Math.ceil(Se.size(u.dims)/4)]:u.dims:void 0,B=k.length===0||k.length===1&&k[0]===1,N=B===!1&&k.length===1,L=os(c),se=B&&(!p||L===4),ee=se?L:1,V=se&&!p?L:1,de=Oe("input",p?12:n,h.length,V),me=Oe("scale",a,k.length),ye=u?Oe("zero_point",p?12:n,S.length):void 0,Be=gt("output",a,i.length,ee),Ee=[de,me];ye&&Ee.push(ye);let tt=[h,k];u&&tt.push(S);let pt=[{type:12,data:c/ee},{type:12,data:s},{type:12,data:t.blockSize},...xt(...tt,i)],Ct=Dt=>{let $t=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${Dt.registerUniforms($t).declareVariables(...Ee,Be)}
      ${Dt.mainStart()}
          ${Dt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${Be.offsetToIndices("global_idx")};

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

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

          // Set zero-point input
          ${ye?B?p?`
                let zero_point_input = ${ye.getByOffset("0")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${ye.getByOffset("0")}`:N?p?`
                let zero_point_index = ${Be.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${ye.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${Be.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${ye.getByOffset("zero_point_index")};`:p?`
                let zero_point_offset = ${me.indicesToOffset("scale_indices")};
                let zero_point_input = ${ye.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${ye.getByIndices("scale_indices")};`:`let zero_point_value = ${p?o?"i32":"u32":de.type.value}(0);`};
      // Compute and write output
      ${Be.setByOffset("global_idx",`${Be.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:ye?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Ct,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(c/ee/64),y:1,z:1},programUniforms:pt})}},Fc=(e,t)=>{Ic(e.inputs,t),e.compute(Oc(e.inputs,t))},Dc=e=>Qe({axis:e.axis,blockSize:e.blockSize})}),Lc,zc,Bc,$p=y(()=>{He(),Ft(),Gt(),Lc=(e,t,s)=>{let n=e===t,o=e<t&&s<0,i=e>t&&s>0;if(n||o||i)throw new Error("Range these inputs' contents are invalid.")},zc=(e,t,s,n)=>{let o=Math.abs(Math.ceil((t-e)/s)),i=[o],a=o,c=[{type:12,data:a},{type:n,data:e},{type:n,data:s},...xt(i)],p=h=>{let k=gt("output",n,i.length),u=k.type.value,S=[{name:"outputSize",type:"u32"},{name:"start",type:u},{name:"delta",type:u}];return`
        ${h.registerUniforms(S).declareVariables(k)}
        ${h.mainStart()}
        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${u}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c})}},Bc=e=>{let t=0,s=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],s=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],s=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),x.webgpu.validateInputContent&&Lc(t,s,n),e.compute(zc(t,s,n,e.inputs[0].dataType),{inputs:[]})}}),Rc,Nc,jc,Uc,Ap=y(()=>{Ft(),zt(),It(),Gt(),Rc=(e,t,s,n)=>{if(e!=="none"&&n!=="i32"&&n!=="u32"&&n!=="f32")throw new Error(`Input ${n} is not supported with reduction ${e}.`);let o=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,i=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${t}, oldValue, newValue);
                  if res.exchanged {
                    break;
                  }
                  oldValue = res.old_value;
                }
              }`;switch(e){case"none":return`${t}=${s};`;case"add":return n==="i32"||n==="u32"?`atomicAdd(&${t}, bitcast<${n}>(${s}));`:`
              ${o}bitcast<${n}>(oldValue) + (${s})${i}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${s}));`:`
                ${o}max(bitcast<f32>(oldValue), (${s}))${i}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${s}));`:`${o}min(bitcast<${n}>(oldValue), (${s}))${i}`;case"mul":return`${o}(bitcast<${n}>(oldValue) * (${s}))${i}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Nc=(e,t)=>{let s=e[0].dims,n=e[1].dims,o=s,i=1,a=Math.ceil(Se.size(n)/i),c=n[n.length-1],p=Se.sizeFromDimension(s,c),h=[{type:12,data:a},{type:12,data:c},{type:12,data:p},...xt(e[1].dims,e[2].dims,o)],k=u=>{let S=Oe("indices",e[1].dataType,e[1].dims.length),B=Oe("updates",e[2].dataType,e[2].dims.length,i),N=t.reduction!=="none"&&t.reduction!==""?tr("output",e[0].dataType,o.length):gt("output",e[0].dataType,o.length,i);return`
      ${u.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(S,B,N)}
      ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
  var data_offset = 0u;
  let indices_start = uniforms.last_index_dimension * global_idx;
  let indices_end = indices_start + uniforms.last_index_dimension;
  for (var i = indices_start; i < indices_end; i++) {
    var index = i32(indices[i].x);
    ${e[0].dims.length===1?`
    let element_count_dim = uniforms.output_strides;
    let dim_value = uniforms.output_shape;`:`
    let element_count_dim = uniforms.output_strides[i - indices_start];
    let dim_value = uniforms.output_shape[i - indices_start + uniforms.last_index_dimension];`}
    if (index >= 0) {
      if (index >= i32(dim_value)) {
        index = i32(dim_value - 1);
      }
    } else {
      if (index < -i32(dim_value)) {
        index = 0;
      } else {
        index += i32(dim_value);
      }
    }
    data_offset += u32((u32(index) * element_count_dim));
  }

  for (var i = 0u; i < uniforms.num_updates_elements; i++) {
    let value = updates[uniforms.num_updates_elements * global_idx + i];
    ${Rc(t.reduction,"output[data_offset + i]","value",N.type.value)}
  }

      }`};return{name:"ScatterND",shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:k}},jc=e=>Qe({reduction:e.reduction}),Uc=(e,t)=>{e.compute(Nc(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),Vc,Wc,Gc,Kc,Hc,qc,Xc,Qc,Yc,Jc,Zc,Ld,ep,tp,sp,Ht,zd,Ns,Us,qs=y(()=>{Ft(),zt(),It(),Gt(),Vc=(e,t)=>{if(e.every(s=>s>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")}},Wc=(e,t,s)=>{t.every(o=>o>=0&&o<s||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(s).fill(1);return t.forEach((o,i)=>n[o]=e[i]),n},Gc=(e,t,s,n,o,i)=>{let[a,c,p]=s>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(k=>i.push(k));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(c>0&&e.length>c&&e[c].dims.length===1&&e[c].dims[0]>0){if(e[c].getFloat32Array().forEach(k=>n.push(k)),n.length!==0&&n.length!==h&&s>=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");Vc(n,t),t.axes.length>0&&Wc(n,t.axes,h).forEach((k,u)=>n[u]=k)}if(p>0&&e.length>p&&e[p].dims.length===1&&e[p].dims[0]>0&&(e[p].getBigInt64Array().forEach(k=>o.push(Number(k))),o.length!==0&&o.length!==h&&s>=18&&o.length!==t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(n.length!==0&&n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(o.length!==0&&o.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 o<"u"&&n.length>0&&o.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},Kc=(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`)}})()+"}",Hc=(e,t,s)=>`fn getNearestPixelFromOriginal(xOriginal: ${s}, isDownSample: bool) -> ${s} {`+(()=>{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`)}})()+"}",qc=(e,t,s)=>{let n=new Array(s).fill(0).concat(new Array(s).fill(1)),o=e.length===0?n:e.slice();return t.length>0?(t.forEach((i,a)=>{n[i]=o[a],n[a+s]=o[t.length+a]}),n):o},Xc=(e,t,s,n)=>{let o=[];if(s.length>0)if(n.length>0){if(e.forEach(i=>o.push(i)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((i,a)=>o[i]=s[a])}else s.forEach(i=>o.push(i));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((i,a)=>Math.round(i*t[a]))}return o},Qc=(e,t,s)=>{let n=(()=>{switch(s.keepAspectRatioPolicy){case"not_larger":return s.axes.length>0?Math.min(...s.axes.map(i=>t[i]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return s.axes.length>0?Math.max(...s.axes.map(i=>t[i]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${s.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let o=e.slice();return s.axes.length>0?(s.axes.forEach(i=>t[i]=n),s.axes.forEach(i=>o[i]=Math.round(e[i]*t[i]))):(t.fill(n,0,t.length),o.forEach((i,a)=>o[a]=Math.round(i*t[a]))),o},Yc=(e,t,s,n,o)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${s.length}> {
      var original_indices: array<${e.type.value}, ${s.length}>;
      for (var i:u32 = 0; i < ${s.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${Mt("uniforms.scales","i",n)};
        var roi_low = ${Mt("uniforms.roi","i",o)};
        var roi_hi = ${Mt("uniforms.roi",`i + ${t.length}`,o)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${Mt("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${Mt("uniforms.output_shape","i",s.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,Jc=(e,t,s,n,o,i,a)=>`
    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 = ${Mt("uniforms.scales","i",o)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Mt("uniforms.roi","i",i)};
          var roi_hi = ${Mt("uniforms.roi",`i + ${s.length}`,i)};
          var input_shape_i = ${Mt("uniforms.input_shape","i",s.length)};
          var output_shape_i = ${Mt("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${a} || (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;
    }`,Zc=(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 >= ${Mt("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,Ld=(e,t,s,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",s,"batch")};
`:"",ep=(e,t,s,n,o)=>{let[i,a,c,p]=s.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",a,`max(0, min(row, ${s[a]} - 1))`)};
      ${e.indicesSet("input_indices",c,`max(0, min(col, ${s[c]} - 1))`)};
      ${Ld(e,p,i,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

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

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

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

      var x111: ${k} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${k} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${k} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${k} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${k} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${k} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${k} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${k} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${k} = abs(depth - ${k}(depth1));
      var dx2: ${k} = abs(${k}(depth2) - depth);
      var dy1: ${k} = abs(height - ${k}(height1));
      var dy2: ${k} = abs(${k}(height2) - height);
      var dz1: ${k} = abs(width - ${k}(width1));
      var dz2: ${k} = abs(${k}(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);
    }`},Ht=(e,t,s,n,o,i)=>{let a=e.dims,c=qc(i,t.axes,a.length),p=Xc(a,n,o,t.axes),h=n.slice();n.length===0&&(h=a.map((V,de)=>V===0?1:p[de]/V),t.keepAspectRatioPolicy!=="stretch"&&(p=Qc(a,h,t)));let k=gt("output",e.dataType,p.length),u=Oe("input",e.dataType,a.length),S=Se.size(p),B=a.length===p.length&&a.every((V,de)=>V===p[de]),N=t.coordinateTransformMode==="tf_crop_and_resize",L=t.extrapolationValue,se=u.type.value,ee=V=>`
      ${B?"":`
      ${Kc(t.coordinateTransformMode,se)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${Zc(u,a)};
              ${Hc(t.nearestMode,s,se)};
              ${Jc(u,k,a,p,h.length,c.length,N)};
              `;case"linear":return`
              ${Yc(k,a,p,h.length,c.length)};
              ${(()=>{if(a.length===2||a.length===4)return`${ep(u,k,a,N,L)}`;if(a.length===3||a.length===5)return`${sp(u,k,a,N,L)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(a.length===2||a.length===4)return`${tp(u,k,a,p,h,c,t.cubicCoeffA,N,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")}})()};
      `}
      ${V.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",c.length).declareVariables(u,k)}
      ${V.mainStart()}
        ${V.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${B?"output[global_idx] = input[global_idx];":`
        let output_indices = ${k.offsetToIndices("global_idx")};
        var input_indices: ${u.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${u.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${s}|${h.length>0?h:""}|${o.length>0?o:""}|${c.length>0?c:""}|${B}|${a}`,inputDependencies:["rank"]},getShaderSource:ee,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:[{type:12,data:S},{type:1,data:h},{type:1,data:c},...xt(a,p)]})}},zd=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Ns=(e,t)=>{let s=[],n=[],o=[],i=zd(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Gc(e.inputs,t,i,s,n,o),e.compute(Ht(e.inputs[0],t,i,s,n,o),{inputs:[0]})},Us=e=>{let t=e.antialias,s=e.axes,n=e.coordinateTransformMode,o=e.cubicCoeffA,i=e.excludeOutside!==0,a=e.extrapolationValue,c=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return Qe({antialias:t,axes:s,coordinateTransformMode:n,cubicCoeffA:o,excludeOutside:i,extrapolationValue:a,keepAspectRatioPolicy:c,mode:p,nearestMode:h})}}),en,rp,Bd,np=y(()=>{Ft(),zt(),It(),Gt(),en=(e,t)=>{let[s,n,o,i]=e,{numHeads:a,rotaryEmbeddingDim:c}=t;if(s.dims.length!==3&&s.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${s.dims.length}`);if(!Se.areEqual(n.dims,[])&&!Se.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(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(!Se.areEqual(o.dims,i.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(c>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=s.dims[0],h=s.dims[s.dims.length-2],k=o.dims[0],u=Se.sizeFromDimension(s.dims,1)/h,S=c===0?o.dims[1]*2:u/a;if(c>S)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(p!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(h!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(S/2!==o.dims[1]&&c/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(h>k)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},rp=(e,t)=>{let{interleaved:s,numHeads:n,rotaryEmbeddingDim:o,scale:i}=t,a=e[0].dims[0],c=Se.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=c/p,k=e[2].dims[1],u=o===0?k*2:h/n,S=new Array(a,p,h/u,u-k),B=Se.computeStrides(S),N=[{type:1,data:i},{type:12,data:S},{type:12,data:B},...e[0].dims.length===3?new Array({type:12,data:[c,h,u,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[c,u,p*u,1]}):[],...xt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],L=se=>{let ee=Oe("input",e[0].dataType,e[0].dims.length),V=Oe("position_ids",e[1].dataType,e[1].dims.length),de=Oe("cos_cache",e[2].dataType,e[2].dims.length),me=Oe("sin_cache",e[3].dataType,e[3].dims.length),ye=gt("output",e[0].dataType,e[0].dims.length);return se.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:S.length},{name:"global_strides",type:"u32",length:B.length},{name:"input_output_strides",type:"u32",length:B.length}]),`
        ${se.declareVariables(ee,V,de,me,ye)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${V.broadcastedIndicesToOffset("bsnh.xy",gt("",V.type.tensor,2))};
            let position_id =
                u32(${V.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${s});
            let j = i + select(half_rotary_emb_dim, 1, ${s});
            let re = ${ee.getByOffset("i")} * ${de.get("position_id","bsnh[3]")} -
                ${ee.getByOffset("j")} * ${me.get("position_id","bsnh[3]")};
            ${ye.setByOffset("i","re")}
            let im = ${ee.getByOffset("i")} * ${me.get("position_id","bsnh[3]")} +
                ${ee.getByOffset("j")} * ${de.get("position_id","bsnh[3]")};
            ${ye.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${ye.setByOffset("k",ee.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:Qe({interleaved:s}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:L,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Se.size(S)/zs)},programUniforms:N})}},Bd=(e,t)=>{en(e.inputs,t),e.compute(rp(e.inputs,t))}}),f,_,Y,xe=y(()=>{Ft(),zt(),Gt(),f=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],s=e[1],n=e[2];if(t.dataType!==s.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(s.dims.length!==3&&s.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=t.dims[t.dims.length-1],i=t.dims[t.dims.length-2];if(s.dims[s.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(s.dims[s.dims.length-2]!==i)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==o)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},_=(e,t,s,n)=>{let o=t.simplified,i=e[0].dims,a=Se.size(i),c=i,p=a,h=i.slice(-1)[0],k=n?i.slice(0,-1).concat(1):[],u=!o&&e.length>3,S=e.length>4,B=n&&s>1,N=n&&s>2,L=s>3,se=64,ee=os(h),V=[{type:12,data:p},{type:12,data:ee},{type:12,data:h},{type:1,data:t.epsilon}],de=ye=>{let Be=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Ee=[Oe("x",e[0].dataType,e[0].dims,ee),Oe("skip",e[1].dataType,e[1].dims,ee),Oe("gamma",e[2].dataType,e[2].dims,ee)];u&&Ee.push(Oe("beta",e[3].dataType,e[3].dims,ee)),S&&Ee.push(Oe("bias",e[4].dataType,e[4].dims,ee)),Ee.push(gt("output",e[0].dataType,c,ee)),B&&Ee.push(gt("mean_output",1,k)),N&&Ee.push(gt("inv_std_output",1,k)),L&&Ee.push(gt("input_skip_bias_sum",e[0].dataType,c,ee));let tt=Zt(e[0].dataType),pt=Zt(1,ee);return`

      ${ye.registerUniforms(Be).declareVariables(...Ee)}
      var<workgroup> sum_shared : array<${pt}, ${se}>;
      var<workgroup> sum_squared_shared : array<${pt}, ${se}>;

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

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

        var reduce_size : u32 = ${se};
        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 = ${Vs("sum",ee)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Vs("square_sum",ee)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon);
        ${B?"mean_output[global_idx] = mean;":""}
        ${N?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${o?"":`- ${tt}(mean)`}) *
            ${tt}(inv_std_dev) * gamma[offset1d + i]
            ${u?"+ beta[offset1d + i]":""};
        }
      }`},me=[{dims:c,dataType:e[0].dataType}];return s>1&&me.push({dims:k,dataType:1}),s>2&&me.push({dims:k,dataType:1}),s>3&&me.push({dims:i,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${ee};${B};${N};${L}`,inputDependencies:e.map((ye,Be)=>"type")},getShaderSource:de,getRunData:()=>({outputs:me,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:V})}},Y=(e,t)=>{f(e.inputs);let s=[0];e.outputCount>1&&s.push(-3),e.outputCount>2&&s.push(-3),e.outputCount>3&&s.push(3),e.compute(_(e.inputs,t,e.outputCount,!1),{outputs:s})}}),ke,Fe,st,rt,ct,kt,Yt,Ut,Bt=y(()=>{Ft(),zt(),It(),Gt(),ke=(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((s,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`)})},Fe=(e,t)=>{let s=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>s.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>s.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return s},st=(e,t)=>{if(e.length>1){let s=Fe(e,1),n=Fe(e,2),o=Fe(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),Qe({starts:s,ends:n,axes:o})}else return t},rt=(e,t,s,n,o)=>{let i=e;return e<0&&(i+=s[n[t]]),o[t]<0?Math.max(0,Math.min(i,s[n[t]]-1)):Math.max(0,Math.min(i,s[n[t]]))},ct=(e,t,s)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${s.length}; i >= 0; i--) {
            let input_shape_i = ${Mt("uniforms.input_shape","i",s.length)};
            let steps_i = ${Mt("uniforms.steps","i",s.length)};
            let signs_i = ${Mt("uniforms.signs","i",s.length)};
            let starts_i = ${Mt("uniforms.starts","i",s.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;
      }`,kt=(e,t)=>{let s=e[0].dims,n=Se.size(s),o=t.axes.length>0?Se.normalizeAxes(t.axes,s.length):[...Array(s.length).keys()],i=Fe(e,4);i.forEach(ee=>ee!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(o.length).fill(1));let a=t.starts.map((ee,V)=>rt(ee,V,s,o,i)),c=t.ends.map((ee,V)=>rt(ee,V,s,o,i));if(o.length!==a.length||o.length!==c.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==s.length)for(let ee=0;ee<s.length;++ee)o.includes(ee)||(a.splice(ee,0,0),c.splice(ee,0,s[ee]),i.splice(ee,0,1));let p=i.map(ee=>Math.sign(ee));i.forEach((ee,V,de)=>{if(ee<0){let me=(c[V]-a[V])/ee,ye=a[V],Be=ye+me*i[V];a[V]=Be,c[V]=ye,de[V]=-ee}});let h=s.slice(0);o.forEach((ee,V)=>{h[ee]=Math.ceil((c[ee]-a[ee])/i[ee])});let k={dims:h,dataType:e[0].dataType},u=gt("output",e[0].dataType,h.length),S=Oe("input",e[0].dataType,e[0].dims.length),B=Se.size(h),N=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:p.length},{name:"steps",type:"u32",length:i.length}],L=[{type:12,data:B},{type:12,data:a},{type:6,data:p},{type:12,data:i},...xt(e[0].dims,h)],se=ee=>`
      ${ee.registerUniforms(N).declareVariables(S,u)}
        ${ct(S,u,s)}
        ${ee.mainStart()}
          ${ee.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${u.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${u.setByOffset("global_idx",S.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${a.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:se,getRunData:()=>({outputs:[k],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:L})}},Yt=(e,t)=>{ke(e.inputs,t);let s=st(e.inputs,t);e.compute(kt(e.inputs,s),{inputs:[0]})},Ut=e=>{let t=e.starts,s=e.ends,n=e.axes;return Qe({starts:t,ends:s,axes:n})}}),At,bs,Vt,Wt,_s=y(()=>{Ft(),zt(),It(),Fr(),Gt(),At=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},bs=(e,t)=>{let s=e.inputs[0],n=s.dims,o=Se.size(n),i=n.length,a=Se.normalizeAxis(t.axis,i),c=a<n.length-1,p,h=[];c?(h=Array.from({length:i},(Ee,tt)=>tt),h[a]=i-1,h[i-1]=a,p=e.compute(nr(s,h),{inputs:[s],outputs:[-1]})[0]):p=s;let k=p.dims,u=k[i-1],S=o/u,B=os(u),N=u/B,L=64;S===1&&(L=256);let se=(Ee,tt)=>tt===4?`max(max(${Ee}.x, ${Ee}.y), max(${Ee}.z, ${Ee}.w))`:tt===2?`max(${Ee}.x, ${Ee}.y)`:tt===3?`max(max(${Ee}.x, ${Ee}.y), ${Ee}.z)`:Ee,ee=Oe("x",p.dataType,p.dims,B),V=gt("result",p.dataType,p.dims,B),de=ee.type.value,me=Zt(p.dataType)==="f32"?`var threadMax = ${de}(-3.402823e+38f);`:`var threadMax = ${de}(-65504.0h);`,ye=Ee=>`
      var<workgroup> rowMaxShared : ${de};
      var<workgroup> rowSumShared : ${de};
      var<workgroup> threadShared : array<${de}, ${L}>;

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

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

        // find the rows max
        ${me}
        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 = ${de}(${se("threadShared[0]",B)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${de}(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 = ${de}(${Vs("threadShared[0]",B)});
        }
        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);
        }
      }`,Be=e.compute({name:"Softmax",shaderCache:{hint:`${B};${L}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:k,dataType:p.dataType}],dispatchGroup:{x:S},programUniforms:[{type:6,data:N}]}),getShaderSource:ye},{inputs:[p],outputs:[c?-1:0]})[0];c&&e.compute(nr(Be,h),{inputs:[Be]})},Vt=(e,t)=>{At(e.inputs),bs(e,t)},Wt=e=>Qe({axis:e.axis})}),ws,es,Is,Os,ks,Bs=y(()=>{Ft(),zt(),Gt(),ws=e=>Array.from(e.getBigInt64Array(),Number),es=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(ws(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")},Is=(e,t)=>{let s=[];for(let n=0;n<e.length;++n)s.push(e[n]*t[n]);return s},Os=(e,t)=>{let s=e[0].dims,n=t??ws(e[1]),o=Is(s,n),i=Se.size(o),a=e[0].dataType,c=Oe("input",a,s.length),p=gt("output",a,o.length),h=k=>`
      const inputShape = ${c.indices(...s)};
      ${k.registerUniform("output_size","u32").declareVariables(c,p)}
      ${k.mainStart()}
      ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${p.offsetToIndices("global_idx")};
      var input_indices: ${c.type.indices};
      for (var i = 0; i < ${s.length}; i++) {
        let input_dim_i = ${c.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${p.indicesGet("output_indices","i")}  % input_dim_i;

        ${c.indicesSet("input_indices","i","input_dim_value")}
      }
      ${p.setByOffset("global_idx",c.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},...xt(e[0].dims,o)]}),getShaderSource:h}},ks=e=>{es(e.inputs),e.compute(Os(e.inputs),{inputs:[0]})}}),Xs,ar,ma,lr=y(()=>{Ft(),zt(),Gt(),Xs=(e,t,s,n,o)=>{let i=gt("output_data",o,s.length,4),a=Oe("a_data",t[1].dataType,t[1].dims.length,4),c=Oe("b_data",t[2].dataType,t[2].dims.length,4),p=Oe("c_data",t[0].dataType,t[0].dims.length,4),h,k=(u,S,B)=>`select(${S}, ${u}, ${B})`;if(!n)h=i.setByOffset("global_idx",k(a.getByOffset("global_idx"),c.getByOffset("global_idx"),p.getByOffset("global_idx")));else{let u=(S,B,N="")=>{let L=`a_data[index_a${B}][component_a${B}]`,se=`b_data[index_b${B}][component_b${B}]`,ee=`bool(c_data[index_c${B}] & (0xffu << (component_c${B} * 8)))`;return`
            let output_indices${B} = ${i.offsetToIndices(`global_idx * 4u + ${B}u`)};
            let offset_a${B} = ${a.broadcastedIndicesToOffset(`output_indices${B}`,i)};
            let offset_b${B} = ${c.broadcastedIndicesToOffset(`output_indices${B}`,i)};
            let offset_c${B} = ${p.broadcastedIndicesToOffset(`output_indices${B}`,i)};
            let index_a${B} = offset_a${B} / 4u;
            let index_b${B} = offset_b${B} / 4u;
            let index_c${B} = offset_c${B} / 4u;
            let component_a${B} = offset_a${B} % 4u;
            let component_b${B} = offset_b${B} % 4u;
            let component_c${B} = offset_c${B} % 4u;
            ${S}[${B}] = ${N}(${k(L,se,ee)});
          `};o===9?h=`
            var data = vec4<u32>(0);
            ${u("data",0,"u32")}
            ${u("data",1,"u32")}
            ${u("data",2,"u32")}
            ${u("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:h=`
            ${u("output_data[global_idx]",0)}
            ${u("output_data[global_idx]",1)}
            ${u("output_data[global_idx]",2)}
            ${u("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(p,a,c,i)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${h}
      }`},ar=e=>{let t=e[1].dims,s=e[2].dims,n=e[0].dims,o=e[1].dataType,i=!(Se.areEqual(t,s)&&Se.areEqual(s,n)),a=t,c=Se.size(t);if(i){let h=gs.calcShape(gs.calcShape(t,s,!1),n,!1);if(!h)throw new Error("Can't perform where op on the given tensors");a=h,c=Se.size(a)}let p=Math.ceil(c/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:h=>Xs(h,e,a,i,o),getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:p},...xt(n,t,s,a)]})}},ma=e=>{e.compute(ar(e.inputs))}}),xr,wo=y(()=>{ec(),qo(),tc(),sc(),rc(),nc(),su(),lc(),dc(),cc(),pc(),ho(),hc(),Ep(),mc(),_c(),gc(),wc(),yc(),sd(),vc(),gd(),Tc(),Cp(),Ec(),od(),$c(),kp(),Sp(),$p(),Ap(),eo(),qs(),np(),xe(),Bt(),_s(),Zi(),Bs(),Fr(),pi(),lr(),xr=new Map([["Abs",[ml]],["Acos",[Zo]],["Acosh",[_l]],["Add",[Gl]],["ArgMax",[Vo,Wo]],["ArgMin",[il,Wo]],["Asin",[fl]],["Asinh",[ei]],["Atan",[gl]],["Atanh",[wl]],["Attention",[ul]],["AveragePool",[Sd,kd]],["BatchNormalization",[Qo]],["BiasAdd",[hl]],["BiasSplitGelu",[Ul]],["Cast",[yl,ti]],["Ceil",[vl]],["Clip",[bl]],["Concat",[eu,tu]],["Conv",[Li,Oi]],["ConvTranspose",[yu,Ni]],["Cos",[si]],["Cosh",[Tl]],["CumSum",[Ui,bu]],["DepthToSpace",[zr,xu]],["DequantizeLinear",[Fc,Dc]],["Div",[Kl]],["Einsum",[$u,Au]],["Elu",[ri,Fn]],["Equal",[mi]],["Erf",[xl]],["Exp",[ni]],["Expand",[mo]],["FastGelu",[Lu]],["Floor",[Pl]],["FusedConv",[Li,Oi]],["Gather",[_n,Ru]],["GatherElements",[Hu,Gi]],["GatherBlockQuantized",[Vu,Wu]],["GatherND",[ju,Uu]],["Gelu",[El]],["Gemm",[Hi,Xu]],["GlobalAveragePool",[Id,Ad]],["GlobalMaxPool",[Ac,Dd]],["Greater",[Xl]],["GreaterOrEqual",[fi]],["GridSample",[ed,td]],["GroupQueryAttention",[hd]],["HardSigmoid",[Al,ai]],["InstanceNormalization",[fd]],["LayerNormalization",[sa]],["LeakyRelu",[oi,Fn]],["Less",[Ql]],["LessOrEqual",[Yl]],["Log",[Bl]],["MatMul",[ms]],["MatMulNBits",[bd,vd]],["MaxPool",[Od,Fd]],["Mul",[Hl]],["MultiHeadAttention",[Yi,Qi]],["Neg",[kl]],["Not",[Cl]],["Pad",[Sc]],["Pow",[ql]],["QuickGelu",[Nl,Fn]],["Range",[Bc]],["Reciprocal",[ii]],["ReduceMin",[sl]],["ReduceMean",[Za]],["ReduceMax",[No]],["ReduceSum",[jo]],["ReduceProd",[rl]],["ReduceL1",[Ro]],["ReduceL2",[el]],["ReduceLogSum",[ol]],["ReduceLogSumExp",[tl]],["ReduceSumSquare",[nl]],["Relu",[Sl]],["Resize",[Ns,Us]],["RotaryEmbedding",[Bd]],["ScatterND",[Uc,jc]],["Sigmoid",[$l]],["Sin",[Il]],["Sinh",[li]],["Slice",[Yt,Ut]],["SkipLayerNormalization",[Y]],["Split",[ud,dd]],["Sqrt",[Ol]],["Softmax",[Vt,Wt]],["Sub",[_i]],["Tan",[Fl]],["Tanh",[Dl]],["ThresholdedRelu",[zl,Fn]],["Tile",[ks]],["Transpose",[Ia,Ao]],["Where",[ma]]])}),_a,fa=y(()=>{He(),er(),Gt(),_a=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,s,n,o){We(e.programInfo.name);let i=this.backend.device,a=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let c=[];for(let h of t)c.push({binding:c.length,resource:{buffer:h.buffer}});for(let h of s)c.push({binding:c.length,resource:{buffer:h.buffer}});o&&c.push({binding:c.length,resource:o});let p=i.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:c,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let h={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:p,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(h)}a.setPipeline(e.computePipeline),a.setBindGroup(0,p),a.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(),ze(e.programInfo.name)}dispose(){}build(e,t){We(e.name);let s=this.backend.device,n=[];[{feature:"shader-f16",extension:"f16"},{feature:"subgroups",extension:"subgroups"},{feature:"subgroups-f16",extension:"subgroups_f16"}].forEach(h=>{s.features.has(h.feature)&&n.push(`enable ${h.extension};`)});let o=ka(t,this.backend.device.limits),i=e.getShaderSource(o),a=`${n.join(`
`)}
${o.additionalImplementations}
${i}`,c=s.createShaderModule({code:a,label:e.name});ns("verbose",()=>`[WebGPU] ${e.name} shader code: ${a}`);let p=s.createComputePipeline({compute:{module:c,entryPoint:"main"},layout:"auto",label:e.name});return ze(e.name),{programInfo:e,computePipeline:p,uniformVariablesInfo:o.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,s=typeof e=="number"?1:e.y||1,n=typeof e=="number"?1:e.z||1,o=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=o&&s<=o&&n<=o)return[t,s,n];let i=t*s*n,a=Math.ceil(Math.sqrt(i));if(a>o){if(a=Math.ceil(Math.cbrt(i)),a>o)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[a,a,a]}else return[a,a,1]}}}),Rn,op,ys,Fs,Br,gn=y(()=>{He(),Ft(),er(),An(),ft(),wo(),fa(),Rn=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let s=[];for(let n=0;n<e.length;++n){let o=e[n].dataType;switch(t[n]){case"none":{s.push("");break}case"type":{s.push(`${o}`);break}case"rank":{let i=e[n].dims.length;s.push(`${o};${i}`);break}case"dims":{let i=e[n].dims.join(",");s.push(`${o};${i}`);break}default:throw new Error(`unsupported input dependency: ${t[n]}`)}}return s.join("|")},op=(e,t,s)=>{let n=e.name;return e.shaderCache?.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+s+`:${Rn(t,e.shaderCache?.inputDependencies??new Array(t.length).fill("dims"))}`,n},ys=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},Fs=class{constructor(e){this.subgroupsSupported=e.features.has("subgroups"),this.subgroupsF16Supported=e.features.has("subgroups");let t=e.limits;!this.subgroupsSupported||!t.minSubgroupSize||!t.maxSubgroupSize?this.subgroupSizeRange=void 0:this.subgroupSizeRange=[t.minSubgroupSize,t.maxSubgroupSize]}},Br=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 s=[],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:s},o=i=>t.features.has(i)&&s.push(i)&&!0;o("chromium-experimental-timestamp-query-inside-passes")||o("timestamp-query"),o("shader-f16"),o("subgroups")&&o("subgroups-f16"),this.device=await t.requestDevice(n),this.deviceInfo=new Fs(this.device),this.adapterInfo=new ys(t.info||await t.requestAdapterInfo()),this.gpuDataManager=_t(this),this.programManager=new _a(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,pn(e.logLevel,!!e.debug),this.device.onuncapturederror=i=>{i.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${i.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;We(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{let t=new BigUint64Array(e.getMappedRange()),s=this.pendingQueries.get(e);for(let n=0;n<t.length/2;n++){let o=s[n],i=o.kernelId,a=this.kernels.get(i),c=a.kernelType,p=a.kernelName,h=o.programName,k=o.inputTensorViews,u=o.outputTensorViews,S=t[n*2],B=t[n*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=S);let N=Number(S-this.queryTimeBase),L=Number(B-this.queryTimeBase);if(!Number.isSafeInteger(N)||!Number.isSafeInteger(L))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:k.map(se=>({dims:se.dims,dataType:fr(se.dataType)})),outputsMetadata:u.map(se=>({dims:se.dims,dataType:fr(se.dataType)})),kernelId:i,kernelType:c,kernelName:p,programName:h,startTime:N,endTime:L});else{let se="";k.forEach((V,de)=>{se+=`input[${de}]: [${V.dims}] | ${fr(V.dataType)}, `});let ee="";u.forEach((V,de)=>{ee+=`output[${de}]: [${V.dims}] | ${fr(V.dataType)}, `}),console.log(`[profiling] kernel "${i}|${c}|${p}|${h}" ${se}${ee}execution time: ${L-N} ns`)}je("GPU",`${h}::${S}::${B}`)}e.unmap(),this.pendingQueries.delete(e)}),ze()}run(e,t,s,n,o,i){We(e.name);let a=[];for(let V=0;V<t.length;++V){let de=t[V].data;if(de===0)continue;let me=this.gpuDataManager.get(de);if(!me)throw new Error(`no GPU data for input: ${de}`);a.push(me)}let{outputs:c,dispatchGroup:p,programUniforms:h}=e.getRunData(t),k=s.length===0?c.map((V,de)=>de):s;if(k.length!==c.length)throw new Error(`Output size ${k.length} must be equal to ${c.length}.`);let u=[],S=[];for(let V=0;V<c.length;++V){if(!Number.isInteger(k[V])||k[V]<-3||k[V]>=i)throw new Error(`Invalid output index: ${k[V]}`);if(k[V]===-3)continue;let de=k[V]===-1,me=k[V]===-2,ye=de||me?o(c[V].dataType,c[V].dims):n(k[V],c[V].dataType,c[V].dims);if(u.push(ye),ye.data===0)continue;let Be=this.gpuDataManager.get(ye.data);if(!Be)throw new Error(`no GPU data for output: ${ye.data}`);if(de&&this.temporaryData.push(Be),me){let Ee=this.kernelPersistentData.get(this.currentKernelId);Ee||(Ee=[],this.kernelPersistentData.set(this.currentKernelId,Ee)),Ee.push(Be)}S.push(Be)}if(a.length!==t.length||S.length!==u.length){if(S.length===0)return ze(e.name),u;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let B;if(h){let V=0,de=[];h.forEach(Ee=>{let tt=typeof Ee.data=="number"?[Ee.data]:Ee.data;if(tt.length===0)return;let pt=Ee.type===10?2:4,Ct,Dt;Ee.type===10?(Dt=tt.length>4?16:tt.length>2?8:tt.length*pt,Ct=tt.length>4?16:pt*tt.length):(Dt=tt.length<=2?tt.length*pt:16,Ct=16),V=Math.ceil(V/Dt)*Dt,de.push(V);let $t=Ee.type===10?8:4;V+=tt.length>4?Math.ceil(tt.length/$t)*Ct:tt.length*pt});let me=16;V=Math.ceil(V/me)*me;let ye=new ArrayBuffer(V);h.forEach((Ee,tt)=>{let pt=de[tt],Ct=typeof Ee.data=="number"?[Ee.data]:Ee.data;if(Ee.type===6)new Int32Array(ye,pt,Ct.length).set(Ct);else if(Ee.type===12)new Uint32Array(ye,pt,Ct.length).set(Ct);else if(Ee.type===10)new Uint16Array(ye,pt,Ct.length).set(Ct);else if(Ee.type===1)new Float32Array(ye,pt,Ct.length).set(Ct);else throw new Error(`Unsupported uniform type: ${fr(Ee.type)}`)});let Be=this.gpuDataManager.create(V,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Be.buffer,0,ye,0,V),this.gpuDataManager.release(Be.id),B={offset:0,size:V,buffer:Be.buffer}}let N=this.programManager.normalizeDispatchGroupSize(p),L=N[1]===1&&N[2]===1,se=op(e,t,L),ee=this.programManager.getArtifact(se);if(ee||(ee=this.programManager.build(e,N),this.programManager.setArtifact(se,ee),ns("info",()=>`[artifact] key: ${se}, programName: ${e.name}`)),h&&ee.uniformVariablesInfo){if(h.length!==ee.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${ee.uniformVariablesInfo.length}, got ${h.length} in program "${ee.programInfo.name}".`);for(let V=0;V<h.length;V++){let de=h[V],me=de.type,ye=typeof de.data=="number"?1:de.data.length,[Be,Ee]=ee.uniformVariablesInfo[V];if(me!==Be||ye!==Ee)throw new Error(`Uniform variable ${V} mismatch: expect type ${Be} with size ${Ee}, got type ${me} with size ${ye} in program "${ee.programInfo.name}".`)}}if(ns("info",()=>`[ProgramManager] run "${e.name}" (key=${se}) with ${N[0]}x${N[1]}x${N[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let V={kernelId:this.currentKernelId,programName:ee.programInfo.name,inputTensorViews:t,outputTensorViews:u};this.pendingKernels.push(V),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(V)}return this.programManager.run(ee,a,S,N,B),ze(e.name),u}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,s,n){let o=xr.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let i={kernelType:e,kernelName:n,kernelEntry:o[0],attributes:[o[1],s]};this.kernels.set(t,i)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let s of t)this.gpuDataManager.release(s.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,s){let n=this.kernels.get(e);if(!n)throw new Error(`kernel not created: ${e}`);let o=n.kernelType,i=n.kernelName,a=n.kernelEntry,c=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${i}" is not allowed to be called recursively`);this.currentKernelId=e,c[0]&&(c[1]=c[0](c[1]),c[0]=void 0),ns("info",()=>`[WebGPU] Start to run kernel "[${o}] ${i}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),a(t,c[1]),0}catch(h){return s.push(Promise.resolve(`[WebGPU] Kernel "[${o}] ${i}" failed. ${h}`)),1}finally{p&&s.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${o}] ${i}": ${h.message}`:null));for(let h of this.temporaryData)this.gpuDataManager.release(h.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,s,n){let o=this.sessionExternalDataMapping.get(e);o||(o=new Map,this.sessionExternalDataMapping.set(e,o));let i=o.get(t),a=this.gpuDataManager.registerExternalBuffer(s,n,i);return o.set(t,[a,s]),a}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(s=>this.gpuDataManager.unregisterExternalBuffer(s[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw new Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,s){return async()=>{let n=await Ie(this,e,t);return hn(n.buffer,s)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.mode==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){ns("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(){ns("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){ns("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),s=e.length;this.pendingKernels=[];for(let n=0;n<s;n++){let o=this.getComputePassEncoder(),i=e[n];this.writeTimestamp(this.pendingDispatchNumber*2),o.setPipeline(i.computePipeline),o.setBindGroup(0,i.bindGroup),o.dispatchWorkgroups(...i.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(t[n]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),wn,Nn,ga,yo,Mo,Rd,wa,bo,Nd=y(()=>{er(),wn=1,Nn=()=>wn++,ga=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),yo=(e,t)=>{let s=ga.get(e);if(!s)throw new Error("Unsupported data type.");return t.length>0?Math.ceil(t.reduce((n,o)=>n*o)*s/8):0},Mo=class{constructor(e){this.sessionId=e.sessionId,this.mlContext=e.context,this.mlTensor=e.tensor,this.dataType=e.dataType,this.tensorShape=e.shape}get tensor(){return this.mlTensor}get type(){return this.dataType}get shape(){return this.tensorShape}get byteLength(){return yo(this.dataType,this.tensorShape)}destroy(){ns("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(e){this.mlContext.writeTensor(this.mlTensor,e)}async read(e){return e?this.mlContext.readTensor(this.mlTensor,e):this.mlContext.readTensor(this.mlTensor)}sameTypeAndShape(e,t){return this.dataType===e&&this.tensorShape.length===t.length&&this.tensorShape.every((s,n)=>s===t[n])}},Rd=class{constructor(e,t){this.tensorManager=e,this.wrapper=t}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(e,t,s){if(this.wrapper){if(this.wrapper.sameTypeAndShape(e,t))return this.wrapper.tensor;if(s){if(this.wrapper.byteLength!==yo(e,t))throw new Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let n=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(e,t,n,!0,!0),s&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(e){if(this.wrapper)if(e.byteLength===this.wrapper.byteLength){this.wrapper.write(e);return}else ns("verbose",()=>"Data size does not match tensor size. Releasing tensor."),this.releaseTensor();this.activeUpload?this.activeUpload.set(e):this.activeUpload=new Uint8Array(e)}async download(e){if(this.activeUpload)if(e){e instanceof ArrayBuffer?new Uint8Array(e).set(this.activeUpload):new Uint8Array(e.buffer,e.byteOffset,e.byteLength).set(this.activeUpload);return}else return this.activeUpload.buffer;if(!this.wrapper)throw new Error("Tensor has not been created.");return e?this.wrapper.read(e):this.wrapper.read()}},wa=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=Nn();return this.tensorTrackersById.set(e,new Rd(this)),e}releaseTensorId(e){let t=this.tensorTrackersById.get(e);t&&(this.tensorTrackersById.delete(e),t.tensorWrapper&&this.releaseTensor(t.tensorWrapper))}async ensureTensor(e,t,s,n){ns("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${t}, shape: ${s}, copyOld: ${n}}`);let o=this.tensorTrackersById.get(e);if(!o)throw new Error("Tensor not found.");return o.ensureTensor(t,s,n)}upload(e,t){let s=this.tensorTrackersById.get(e);if(!s)throw new Error("Tensor not found.");s.upload(t)}async download(e,t){ns("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${t?.byteLength}}`);let s=this.tensorTrackersById.get(e);if(!s)throw new Error("Tensor not found.");return s.download(t)}releaseTensorsForSession(e){for(let t of this.freeTensors)t.sessionId===e&&t.destroy();this.freeTensors=this.freeTensors.filter(t=>t.sessionId!==e)}registerTensor(e,t,s,n){let o=Nn(),i=new Mo({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:s,shape:n});return this.tensorTrackersById.set(o,new Rd(this,i)),this.externalTensors.add(i),o}async getCachedTensor(e,t,s,n,o){let i=this.backend.currentSessionId;for(let[p,h]of this.freeTensors.entries())if(h.sameTypeAndShape(e,t)){ns("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, shape: ${t}}`);let k=this.freeTensors.splice(p,1)[0];return k.sessionId=i,k}let a=this.backend.currentContext;ns("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let c=await a.createTensor({dataType:e,shape:t,dimensions:t,usage:s,writable:n,readable:o});return new Mo({sessionId:i,context:a,tensor:c,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},bo=(...e)=>new wa(...e)}),ya,Ma,vo,Ip=y(()=>{Ft(),St(),An(),Nd(),er(),ya=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),Ma=(e,t)=>{if(e===t)return!0;if(e===void 0||t===void 0)return!1;let s=Object.keys(e).sort(),n=Object.keys(t).sort();return s.length===n.length&&s.every((o,i)=>o===n[i]&&e[o]===t[o])},vo=class{constructor(e){this.tensorManager=bo(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],pn(e.logLevel,!!e.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(e){this.activeSessionId=e}async createMLContext(e){if(e instanceof GPUDevice){let s=this.mlContextCache.findIndex(n=>n.gpuDevice===e);if(s!==-1)return this.mlContextCache[s].mlContext;{let n=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:n}),n}}else if(e===void 0){let s=this.mlContextCache.findIndex(n=>n.options===void 0&&n.gpuDevice===void 0);if(s!==-1)return this.mlContextCache[s].mlContext;{let n=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:n}),n}}let t=this.mlContextCache.findIndex(s=>Ma(s.options,e));if(t!==-1)return this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:s}),s}}get currentContext(){let e=this.getMLContext(this.currentSessionId);if(!e)throw new Error(`No MLContext found for session ${this.currentSessionId}`);return e}registerMLContext(e,t){this.mlContextBySessionId.set(e,t);let s=this.sessionIdsByMLContext.get(t);s||(s=new Set,this.sessionIdsByMLContext.set(t,s)),s.add(e)}onReleaseSession(e){let t=this.mlContextBySessionId.get(e);if(!t)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let s=this.sessionIdsByMLContext.get(t);if(s.delete(e),s.size===0){this.sessionIdsByMLContext.delete(t);let n=this.mlContextCache.findIndex(o=>o.mlContext===t);n!==-1&&this.mlContextCache.splice(n,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){ns("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,s,n){let o=ya.get(t);if(!o)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,o,s,n)}uploadTensor(e,t){if(!dt().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");ns("verbose",()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${t.byteLength}}`),this.tensorManager.upload(e,t)}async downloadTensor(e,t){return this.tensorManager.download(e,t)}createMLTensorDownloader(e,t){return async()=>{let s=await this.tensorManager.download(e);return hn(s,t)}}registerMLTensor(e,t,s){let n=ya.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let o=this.tensorManager.registerTensor(this.currentContext,e,n,s);return ns("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${s}} -> {tensorId: ${o}}`),o}registerMLConstant(e,t,s,n,o,i){if(!i)throw new Error("External mounted files are not available.");let a=e;e.startsWith("./")&&(a=e.substring(2));let c=i.get(a);if(!c)throw new Error(`File with name ${a} not found in preloaded 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t=Se.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=Se.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=Se.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=Se.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(Se.size(t)!==Se.size(this.dims))throw new Error("Invalid new shape");return new n_(this.module,this.dataType,this.data,t)}},tn=class{constructor(e,t,s){this.module=e,this.backend=t,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=t.adapterInfo,this.deviceInfo=t.deviceInfo;let n=e.PTR_SIZE,o=s/e.PTR_SIZE,i=n===4?"i32":"i64";this.opKernelContext=Number(e.getValue(n*o++,i));let a=Number(e.getValue(n*o++,i));this.outputCount=Number(e.getValue(n*o++,i)),this.customDataOffset=Number(e.getValue(n*o++,"*")),this.customDataSize=Number(e.getValue(n*o++,i));let c=[];for(let p=0;p<a;p++){let h=Number(e.getValue(n*o++,i)),k=Number(e.getValue(n*o++,"*")),u=Number(e.getValue(n*o++,i)),S=[];for(let B=0;B<u;B++)S.push(Number(e.getValue(n*o++,i)));c.push(new ba(e,h,k,S))}this.inputs=c}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,t){let s=t?.inputs?.map(a=>typeof a=="number"?this.inputs[a]:a)??this.inputs,n=t?.outputs??[],o=(a,c,p)=>new ba(this.module,c,this.output(a,p),p),i=(a,c)=>{let p=Zs(a,c);if(!p)throw new Error(`Unsupported data type: ${a}`);let h=p>0?this.backend.gpuDataManager.create(p).id:0;return new ba(this.module,a,h,c)};return this.backend.run(e,s,n,o,i,this.outputCount)}output(e,t){let s=this.module.stackSave();try{let n=this.module.PTR_SIZE,o=n===4?"i32":"i64",i=this.module.stackAlloc((1+t.length)*n);this.module.setValue(i,t.length,o);for(let a=0;a<t.length;a++)this.module.setValue(i+n*(a+1),t[a],o);return this.module._JsepOutput(this.opKernelContext,e,i)}catch(n){throw new Error(`Failed to generate kernel's output[${e}] with dims [${t}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(s)}}},_r=async(e,t,s,n)=>{let o=t.jsepInit;if(!o)throw new Error("Failed to initialize JSEP. 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vh(),await Th(e)}async createInferenceSessionHandler(e,t){let s=new Ah;return await s.loadModel(e,t),Promise.resolve(s)}},Oh=new Wp});He(),He(),He();var d_="1.21.0-dev.20241205-d27fecd3d3",c_=$e;{let e=(u_(),T(Ih)).wasmBackend;K("webgpu",e,5),K("webnn",e,5),K("cpu",e,10),K("wasm",e,10)}Object.defineProperty(x.versions,"web",{value:d_,enumerable:!0});/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */},"./src/backends/onnx.js":(Ce,A,r)=>{var g;r.r(A),r.d(A,{Tensor:()=>U.Tensor,createInferenceSession:()=>ae,deviceToExecutionProviders:()=>K,isONNXProxy:()=>H,isONNXTensor:()=>R});var O=r("./src/env.js"),j=r("?2ce3"),te=r("./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs"),U=r("./node_modules/onnxruntime-common/dist/esm/index.js");const y=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"}}),P=[];let b,T;const v=Symbol.for("onnxruntime");if(v in globalThis)T=globalThis[v];else if(O.apis.IS_NODE_ENV){switch(T=j??(g||(g=r.t(j,2))),process.platform){case"win32":P.push("dml");break;case"linux":process.arch==="x64"&&P.push("cuda");break}P.push("cpu"),b=["cpu"]}else T=te,O.apis.IS_WEBNN_AVAILABLE&&P.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),O.apis.IS_WEBGPU_AVAILABLE&&P.push("webgpu"),P.push("wasm"),b=["wasm"];const z=T.InferenceSession;function K(D=null){if(!D)return b;switch(D){case"auto":return P;case"gpu":return P.filter($=>["webgpu","cuda","dml","webnn-gpu"].includes($))}if(P.includes(D))return[y[D]??D];throw new Error(`Unsupported device: "${D}". Should be one of: ${P.join(", ")}.`)}let re=null;async function ae(D,$,w){re&&await re;const C=z.create(D,$);re??=C;const x=await C;return x.config=w,x}function R(D){return D instanceof T.Tensor}const G=T?.env;G?.wasm&&(G.wasm.wasmPaths=`https://cdn.jsdelivr.net/npm/@huggingface/transformers@${O.env.version}/dist/`,G.wasm.proxy=!1,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(G.wasm.numThreads=1)),G?.webgpu&&(G.webgpu.powerPreference="high-performance");function H(){return G?.wasm?.proxy}O.env.backends.onnx=G},"./src/base/feature_extraction_utils.js":(Ce,A,r)=>{r.r(A),r.d(A,{FeatureExtractor:()=>te,validate_audio_inputs:()=>U});var g=r("./src/utils/constants.js"),O=r("./src/utils/generic.js"),j=r("./src/utils/hub.js");class te extends O.Callable{constructor(P){super(),this.config=P}static async from_pretrained(P,b){const T=await(0,j.getModelJSON)(P,g.FEATURE_EXTRACTOR_NAME,!0,b);return new this(T)}}function U(y,P){if(!(y instanceof Float32Array||y instanceof Float64Array))throw new Error(`${P} expects input to be a Float32Array or a Float64Array, but got ${y?.constructor?.name??typeof y} instead. 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No instance will be fused."),J=new Set);const le=$.class_queries_logits??$.logits,fe=($.masks_queries_logits??$.pred_masks).sigmoid();let[Pe,be,De]=le.dims;if(De-=1,q!==null&&q.length!==Pe)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Ge=[];for(let Ne=0;Ne<Pe;++Ne){let lt=q!==null?q[Ne]:null,ue=le[Ne],Z=fe[Ne],[he,ve,Le]=K(ue,Z,w,De);if(Le.length===0){let[ne,qe]=lt??Z.dims.slice(-2),Ae=new O.Tensor("int32",new Int32Array(ne*qe).fill(-1),[ne,qe]);Ge.push({segmentation:Ae,segments_info:[]});continue}let[Ze,Ke]=ae(he,ve,Le,C,x,J,lt);Ge.push({segmentation:Ze,segments_info:Ke})}return Ge}function H($,w=.5,C=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class D extends g.Callable{constructor(w){super(),this.image_mean=w.image_mean??w.mean,this.image_std=w.image_std??w.std,this.resample=w.resample??2,this.do_rescale=w.do_rescale??!0,this.rescale_factor=w.rescale_factor??.00392156862745098,this.do_normalize=w.do_normalize,this.do_thumbnail=w.do_thumbnail,this.size=w.size??w.image_size,this.do_resize=w.do_resize??this.size!==void 0,this.size_divisibility=w.size_divisibility??w.size_divisor,this.do_center_crop=w.do_center_crop,this.crop_size=w.crop_size,this.do_convert_rgb=w.do_convert_rgb??!0,this.do_crop_margin=w.do_crop_margin,this.pad_size=w.pad_size,this.do_pad=w.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=w.do_flip_channel_order??!1,this.config=w}async thumbnail(w,C,x=2){const J=w.height,q=w.width,le=C.height,ce=C.width;let fe=Math.min(J,le),Pe=Math.min(q,ce);return fe===J&&Pe===q?w:(J>q?Pe=Math.floor(q*fe/J):q>J&&(fe=Math.floor(J*Pe/q)),await w.resize(Pe,fe,{resample:x}))}async crop_margin(w,C=200){const x=w.clone().grayscale(),J=(0,j.min)(x.data)[0],le=(0,j.max)(x.data)[0]-J;if(le===0)return w;const ce=C/255;let fe=x.width,Pe=x.height,be=0,De=0;const Ge=x.data;for(let Ne=0;Ne<x.height;++Ne){const lt=Ne*x.width;for(let ue=0;ue<x.width;++ue)(Ge[lt+ue]-J)/le<ce&&(fe=Math.min(fe,ue),Pe=Math.min(Pe,Ne),be=Math.max(be,ue),De=Math.max(De,Ne))}return w=await w.crop([fe,Pe,be,De]),w}pad_image(w,C,x,{mode:J="constant",center:q=!1,constant_values:le=0}={}){const[ce,fe,Pe]=C;let be,De;if(typeof x=="number"?(be=x,De=x):x==="square"?be=De=Math.max(ce,fe):(be=x.width,De=x.height),be!==fe||De!==ce){const Ge=new Float32Array(be*De*Pe);if(Array.isArray(le))for(let ue=0;ue<Ge.length;++ue)Ge[ue]=le[ue%Pe];else le!==0&&Ge.fill(le);const[Ne,lt]=q?[Math.floor((be-fe)/2),Math.floor((De-ce)/2)]:[0,0];for(let ue=0;ue<ce;++ue){const Z=(ue+lt)*be,he=ue*fe;for(let ve=0;ve<fe;++ve){const Le=(Z+ve+Ne)*Pe,Ze=(he+ve)*Pe;for(let Ke=0;Ke<Pe;++Ke)Ge[Le+Ke]=w[Ze+Ke]}}if(J==="symmetric"){if(q)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const ue=ce-1,Z=fe-1;for(let he=0;he<De;++he){const ve=he*be,Le=(0,te.calculateReflectOffset)(he,ue)*fe;for(let Ze=0;Ze<be;++Ze){if(he<ce&&Ze<fe)continue;const Ke=(ve+Ze)*Pe,ne=(Le+(0,te.calculateReflectOffset)(Ze,Z))*Pe;for(let qe=0;qe<Pe;++qe)Ge[Ke+qe]=w[ne+qe]}}}w=Ge,C=[De,be,Pe]}return[w,C]}rescale(w){for(let C=0;C<w.length;++C)w[C]=this.rescale_factor*w[C]}get_resize_output_image_size(w,C){const[x,J]=w.size;let q,le;if(this.do_thumbnail){const{height:ce,width:fe}=C;q=Math.min(ce,fe)}else Number.isInteger(C)?(q=C,le=this.config.max_size??q):C!==void 0&&(q=C.shortest_edge,le=C.longest_edge);if(q!==void 0||le!==void 0){const ce=q===void 0?1:Math.max(q/x,q/J),fe=x*ce,Pe=J*ce,be=le===void 0?1:Math.min(le/fe,le/Pe);let De=Math.floor(Number((fe*be).toFixed(2))),Ge=Math.floor(Number((Pe*be).toFixed(2)));return this.size_divisibility!==void 0&&([De,Ge]=b([De,Ge],this.size_divisibility)),[De,Ge]}else if(C!==void 0&&C.width!==void 0&&C.height!==void 0){let ce=C.width,fe=C.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Pe=fe/J,be=ce/x;Math.abs(1-be)<Math.abs(1-Pe)?Pe=be:be=Pe,fe=P(Pe*J,this.config.ensure_multiple_of),ce=P(be*x,this.config.ensure_multiple_of)}return[ce,fe]}else{if(this.size_divisibility!==void 0)return b([x,J],this.size_divisibility);if(C.min_pixels!==void 0&&C.max_pixels!==void 0){const{min_pixels:ce,max_pixels:fe}=C,Pe=this.config.patch_size*this.config.merge_size;return R(J,x,Pe,ce,fe)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(C)}`)}}async resize(w){const[C,x]=this.get_resize_output_image_size(w,this.size);return await w.resize(C,x,{resample:this.resample})}async preprocess(w,{do_normalize:C=null,do_pad:x=null,do_convert_rgb:J=null,do_convert_grayscale:q=null,do_flip_channel_order:le=null}={}){this.do_crop_margin&&(w=await this.crop_margin(w));const[ce,fe]=w.size;if(J??this.do_convert_rgb?w=w.rgb():q&&(w=w.grayscale()),this.do_resize&&(w=await this.resize(w)),this.do_thumbnail&&(w=await this.thumbnail(w,this.size,this.resample)),this.do_center_crop){let Ne,lt;Number.isInteger(this.crop_size)?(Ne=this.crop_size,lt=this.crop_size):(Ne=this.crop_size.width,lt=this.crop_size.height),w=await w.center_crop(Ne,lt)}const Pe=[w.height,w.width];let be=Float32Array.from(w.data),De=[w.height,w.width,w.channels];if(this.do_rescale&&this.rescale(be),C??this.do_normalize){let Ne=this.image_mean;Array.isArray(this.image_mean)||(Ne=new Array(w.channels).fill(Ne));let lt=this.image_std;if(Array.isArray(this.image_std)||(lt=new Array(w.channels).fill(Ne)),Ne.length!==w.channels||lt.length!==w.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Ne.length}) and \`image_std\` (${lt.length}) must match the number of channels in the image (${w.channels}).`);for(let ue=0;ue<be.length;ue+=w.channels)for(let Z=0;Z<w.channels;++Z)be[ue+Z]=(be[ue+Z]-Ne[Z])/lt[Z]}if(x??this.do_pad){if(this.pad_size)[be,De]=this.pad_image(be,[w.height,w.width,w.channels],this.pad_size);else if(this.size_divisibility){const[Ne,lt]=b([De[1],De[0]],this.size_divisibility);[be,De]=this.pad_image(be,De,{width:Ne,height:lt})}}if(le??this.do_flip_channel_order){if(De[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Ne=0;Ne<be.length;Ne+=3){const lt=be[Ne];be[Ne]=be[Ne+2],be[Ne+2]=lt}}const Ge=new O.Tensor("float32",be,De).permute(2,0,1);return{original_size:[fe,ce],reshaped_input_size:Pe,pixel_values:Ge}}async _call(w,...C){Array.isArray(w)||(w=[w]);const x=await Promise.all(w.map(q=>this.preprocess(q)));return{pixel_values:(0,O.stack)(x.map(q=>q.pixel_values),0),original_sizes:x.map(q=>q.original_size),reshaped_input_sizes:x.map(q=>q.reshaped_input_size)}}static async from_pretrained(w,C){const x=await(0,U.getModelJSON)(w,y.IMAGE_PROCESSOR_NAME,!0,C);return new this(x)}}},"./src/base/processing_utils.js":(Ce,A,r)=>{r.r(A),r.d(A,{Processor:()=>te});var g=r("./src/utils/constants.js"),O=r("./src/utils/generic.js"),j=r("./src/utils/hub.js");class te extends O.Callable{static classes=["image_processor_class","tokenizer_class","feature_extractor_class"];static uses_processor_config=!1;constructor(y,P){super(),this.config=y,this.components=P}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(y,P={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return 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v={};switch(b.model_type){case"llava":case"paligemma":case"florence2":case"llava_onevision":case"idefics3":v=te(b.text_config);break;case"moondream1":v=te(b.phi_config);break;case"musicgen":v=te(b.decoder);break;case"multi_modality":v=te(b.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":T.num_heads="n_head",T.num_layers="n_layer",T.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":T.num_heads="num_attention_heads",T.num_layers="num_hidden_layers",T.hidden_size="hidden_size";break;case"llama":case"olmo":case"olmo2":case"mobilellm":case"granite":case"cohere":case"mistral":case"starcoder2":case"qwen2":case"qwen2_vl":case"phi":case"phi3":case"phi3_v":T.num_heads="num_key_value_heads",T.num_layers="num_hidden_layers",T.hidden_size="hidden_size",T.num_attention_heads="num_attention_heads";break;case"gemma":case"gemma2":T.num_heads="num_key_value_heads",T.num_layers="num_hidden_layers",T.dim_kv="head_dim";break;case"openelm":T.num_heads="num_kv_heads",T.num_layers="num_transformer_layers",T.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":T.num_heads="num_heads",T.num_layers="num_layers",T.hidden_size="hidden_size";break;case"bloom":T.num_heads="n_head",T.num_layers="n_layer",T.hidden_size="hidden_size";break;case"mpt":T.num_heads="n_heads",T.num_layers="n_layers",T.hidden_size="d_model";break;case"exaone":T.num_heads="num_key_value_heads",T.num_layers="num_layers",T.dim_kv="head_dim",T.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":T.num_decoder_layers="num_decoder_layers",T.num_decoder_heads="num_heads",T.decoder_dim_kv="d_kv",T.num_encoder_layers="num_layers",T.num_encoder_heads="num_heads",T.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":T.num_decoder_layers="decoder_layers",T.num_decoder_heads="decoder_attention_heads",T.decoder_hidden_size="d_model",T.num_encoder_layers="encoder_layers",T.num_encoder_heads="encoder_attention_heads",T.encoder_hidden_size="d_model";break;case"speecht5":T.num_decoder_layers="decoder_layers",T.num_decoder_heads="decoder_attention_heads",T.decoder_hidden_size="hidden_size",T.num_encoder_layers="encoder_layers",T.num_encoder_heads="encoder_attention_heads",T.encoder_hidden_size="hidden_size";break;case"trocr":T.num_encoder_layers=T.num_decoder_layers="decoder_layers",T.num_encoder_heads=T.num_decoder_heads="decoder_attention_heads",T.encoder_hidden_size=T.decoder_hidden_size="d_model";break;case"musicgen_decoder":case"moonshine":T.num_encoder_layers=T.num_decoder_layers="num_hidden_layers",T.num_encoder_heads=T.num_decoder_heads="num_attention_heads",T.encoder_hidden_size=T.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const K=te(b.decoder),re="num_decoder_layers"in K,ae=(0,g.pick)(b,["model_type","is_encoder_decoder"]);return re?(ae.num_decoder_layers=K.num_decoder_layers,ae.num_decoder_heads=K.num_decoder_heads,ae.decoder_hidden_size=K.decoder_hidden_size,ae.num_encoder_layers=K.num_encoder_layers,ae.num_encoder_heads=K.num_encoder_heads,ae.encoder_hidden_size=K.encoder_hidden_size):(ae.num_layers=K.num_layers,ae.num_heads=K.num_heads,ae.hidden_size=K.hidden_size),ae}const z={...v,...(0,g.pick)(b,["model_type","multi_query","is_encoder_decoder"])};for(const K in T)z[K]=b[T[K]];return z}function U(b,{prefix:T="past_key_values",batch_size:v=1}={}){const z={},K=b.normalized_config;if(K.is_encoder_decoder&&"num_encoder_heads"in K&&"num_decoder_heads"in K){const re=K.encoder_dim_kv??K.encoder_hidden_size/K.num_encoder_heads,ae=K.decoder_dim_kv??K.decoder_hidden_size/K.num_decoder_heads,R=[v,K.num_encoder_heads,0,re],G=[v,K.num_decoder_heads,0,ae];for(let H=0;H<K.num_decoder_layers;++H)z[`${T}.${H}.encoder.key`]=R,z[`${T}.${H}.encoder.value`]=R,z[`${T}.${H}.decoder.key`]=G,z[`${T}.${H}.decoder.value`]=G}else{const re=K.num_heads,ae=K.num_layers,R=K.dim_kv??K.hidden_size/(K.num_attention_heads??re);if(K.model_type==="falcon"){const G=[v*re,0,R];for(let H=0;H<ae;++H)z[`${T}.${H}.key`]=G,z[`${T}.${H}.value`]=G}else if(K.multi_query){const G=[v*re,0,2*R];for(let H=0;H<ae;++H)z[`${T}.${H}.key_value`]=G}else if(K.model_type==="bloom"){const G=[v*re,R,0],H=[v*re,0,R];for(let D=0;D<ae;++D)z[`${T}.${D}.key`]=G,z[`${T}.${D}.value`]=H}else if(K.model_type==="openelm")for(let G=0;G<ae;++G){const H=[v,re[G],0,R];z[`${T}.${G}.key`]=H,z[`${T}.${G}.value`]=H}else{const G=[v,re,0,R];for(let H=0;H<ae;++H)z[`${T}.${H}.key`]=G,z[`${T}.${H}.value`]=G}}return z}class y{model_type=null;is_encoder_decoder=!1;max_position_embeddings;"transformers.js_config";constructor(T){Object.assign(this,T),this.normalized_config=te(this)}static async from_pretrained(T,{progress_callback:v=null,config:z=null,cache_dir:K=null,local_files_only:re=!1,revision:ae="main"}={}){z&&!(z instanceof y)&&(z=new y(z));const R=z??await j(T,{progress_callback:v,config:z,cache_dir:K,local_files_only:re,revision:ae});return new this(R)}}class P{static async from_pretrained(...T){return y.from_pretrained(...T)}}},"./src/env.js":(Ce,A,r)=>{r.r(A),r.d(A,{apis:()=>ae,env:()=>w});var g=r("?569f"),O=r("?3f59"),j=r("?154a");const te="3.2.0",U=typeof window<"u"&&typeof window.document<"u",y=typeof self<"u"&&self.constructor?.name==="DedicatedWorkerGlobalScope",P=typeof self<"u"&&"caches"in self,b=typeof navigator<"u"&&"gpu"in navigator,T=typeof navigator<"u"&&"ml"in navigator,v=typeof process<"u",z=v&&process?.release?.name==="node",K=!C(g),re=!C(O),ae=Object.freeze({IS_BROWSER_ENV:U,IS_WEBWORKER_ENV:y,IS_WEB_CACHE_AVAILABLE:P,IS_WEBGPU_AVAILABLE:b,IS_WEBNN_AVAILABLE:T,IS_PROCESS_AVAILABLE:v,IS_NODE_ENV:z,IS_FS_AVAILABLE:K,IS_PATH_AVAILABLE:re}),R=K&&re;let G="./";if(R){const x=Object(import.meta).url;x?G=O.dirname(O.dirname(j.fileURLToPath(x))):typeof __dirname<"u"&&(G=O.dirname(__dirname))}const H=R?O.join(G,"/.cache/"):null,D="/models/",$=R?O.join(G,D):D,w={version:te,backends:{onnx:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!(U||y),localModelPath:$,useFS:K,useBrowserCache:P,useFSCache:K,cacheDir:H,useCustomCache:!1,customCache:null};function C(x){return Object.keys(x).length===0}},"./src/generation/configuration_utils.js":(Ce,A,r)=>{r.r(A),r.d(A,{GenerationConfig:()=>O});var g=r("./src/utils/core.js");class O{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;streamer=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(te){Object.assign(this,(0,g.pick)(te,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(Ce,A,r)=>{r.r(A),r.d(A,{ClassifierFreeGuidanceLogitsProcessor:()=>R,ForcedBOSTokenLogitsProcessor:()=>y,ForcedEOSTokenLogitsProcessor:()=>P,LogitsProcessor:()=>j,LogitsProcessorList:()=>U,LogitsWarper:()=>te,MinLengthLogitsProcessor:()=>K,MinNewTokensLengthLogitsProcessor:()=>re,NoBadWordsLogitsProcessor:()=>ae,NoRepeatNGramLogitsProcessor:()=>v,RepetitionPenaltyLogitsProcessor:()=>z,SuppressTokensAtBeginLogitsProcessor:()=>b,TemperatureLogitsWarper:()=>G,TopKLogitsWarper:()=>D,TopPLogitsWarper:()=>H,WhisperTimeStampLogitsProcessor:()=>T});var g=r("./src/utils/generic.js");r("./src/utils/tensor.js");var O=r("./src/utils/maths.js");class j extends g.Callable{_call(w,C){throw Error("`_call` should be implemented in a subclass")}}class te extends g.Callable{_call(w,C){throw Error("`_call` should be implemented in a subclass")}}class U extends g.Callable{constructor(){super(),this.processors=[]}push(w){this.processors.push(w)}extend(w){this.processors.push(...w)}_call(w,C){let x=C;for(const J of this.processors)x=J(w,x);return x}[Symbol.iterator](){return this.processors.values()}}class y extends j{constructor(w){super(),this.bos_token_id=w}_call(w,C){for(let x=0;x<w.length;++x)if(w[x].length===1){const J=C[x].data;J.fill(-1/0),J[this.bos_token_id]=0}return C}}class P extends j{constructor(w,C){super(),this.max_length=w,this.eos_token_id=Array.isArray(C)?C:[C]}_call(w,C){for(let x=0;x<w.length;++x)if(w[x].length===this.max_length-1){const J=C[x].data;J.fill(-1/0);for(const q of this.eos_token_id)J[q]=0}return C}}class b extends j{constructor(w,C){super(),this.begin_suppress_tokens=w,this.begin_index=C}_call(w,C){for(let x=0;x<w.length;++x)if(w[x].length===this.begin_index){const J=C[x].data;for(const q of this.begin_suppress_tokens)J[q]=-1/0}return C}}class T extends j{constructor(w,C){super(),this.eos_token_id=Array.isArray(w.eos_token_id)?w.eos_token_id[0]:w.eos_token_id,this.no_timestamps_token_id=w.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=C.length,C.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=w.max_initial_timestamp_index}_call(w,C){for(let x=0;x<w.length;++x){const J=C[x].data;if(J[this.no_timestamps_token_id]=-1/0,w[x].length===this.begin_index-1){J.fill(-1/0),J[this.timestamp_begin]=0;continue}const q=w[x].slice(this.begin_index),le=q.length>=1&&q[q.length-1]>=this.timestamp_begin,ce=q.length<2||q[q.length-2]>=this.timestamp_begin;if(le&&(ce?J.subarray(this.timestamp_begin).fill(-1/0):J.subarray(0,this.eos_token_id).fill(-1/0)),w[x].length===this.begin_index&&this.max_initial_timestamp_index!==null){const De=this.timestamp_begin+this.max_initial_timestamp_index;J.subarray(De+1).fill(-1/0)}const fe=(0,O.log_softmax)(J),Pe=Math.log(fe.subarray(this.timestamp_begin).map(Math.exp).reduce((De,Ge)=>De+Ge)),be=(0,O.max)(fe.subarray(0,this.timestamp_begin))[0];Pe>be&&J.subarray(0,this.timestamp_begin).fill(-1/0)}return C}}class v extends j{constructor(w){super(),this.no_repeat_ngram_size=w}getNgrams(w){const C=w.length,x=[];for(let q=0;q<C+1-this.no_repeat_ngram_size;++q){const le=[];for(let ce=0;ce<this.no_repeat_ngram_size;++ce)le.push(w[q+ce]);x.push(le.map(Number))}const J=new Map;for(const q of x){const le=q.slice(0,q.length-1),ce=JSON.stringify(le),fe=J.get(ce)??[];fe.push(q[q.length-1]),J.set(ce,fe)}return J}getGeneratedNgrams(w,C){const x=C.slice(C.length+1-this.no_repeat_ngram_size,C.length);return w.get(JSON.stringify(x.map(Number)))??[]}calcBannedNgramTokens(w){const C=[];if(w.length+1<this.no_repeat_ngram_size)return C;{const x=this.getNgrams(w);return this.getGeneratedNgrams(x,w)}}_call(w,C){for(let x=0;x<w.length;++x){const J=C[x].data,q=this.calcBannedNgramTokens(w[x]);for(const le of q)J[le]=-1/0}return C}}class z extends j{constructor(w){super(),this.penalty=w}_call(w,C){for(let x=0;x<w.length;++x){const J=C[x].data;for(const q of new Set(w[x])){const le=Number(q);J[le]<0?J[le]*=this.penalty:J[le]/=this.penalty}}return C}}class K extends j{constructor(w,C){super(),this.min_length=w,this.eos_token_id=Array.isArray(C)?C:[C]}_call(w,C){for(let x=0;x<w.length;++x)if(w[x].length<this.min_length){const J=C[x].data;for(const q of this.eos_token_id)J[q]=-1/0}return C}}class re extends j{constructor(w,C,x){super(),this.prompt_length_to_skip=w,this.min_new_tokens=C,this.eos_token_id=Array.isArray(x)?x:[x]}_call(w,C){for(let x=0;x<w.length;++x)if(w[x].length-this.prompt_length_to_skip<this.min_new_tokens){const q=C[x].data;for(const le of this.eos_token_id)q[le]=-1/0}return C}}class ae extends j{constructor(w,C){super(),this.bad_words_ids=w,this.eos_token_id=Array.isArray(C)?C:[C]}_call(w,C){for(let x=0;x<w.length;++x){const J=C[x].data,q=w[x];for(const le of this.bad_words_ids){let ce=!0;for(let fe=1;fe<=le.length-1&&le.length<q.length;++fe)if(le.at(-fe-1)!=q.at(-fe)){ce=!1;break}ce&&(J[le.at(-1)]=-1/0)}}return C}}class R extends j{constructor(w){if(super(),w<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${w}.`);this.guidance_scale=w}_call(w,C){if(C.dims[0]!==2*w.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 ${C.dims[0]} for the logits and ${w.length} for the input ids.`);const x=w.length,J=C.slice([0,x],null),q=C.slice([x,C.dims[0]],null);for(let le=0;le<q.data.length;++le)q.data[le]+=(J.data[le]-q.data[le])*this.guidance_scale;return q}}class G extends te{constructor(w){super(),this.temperature=w}_call(w,C){const x=C.data;for(let J=0;J<x.length;++J)x[J]/=this.temperature;return C}}class H extends te{constructor(w,{filter_value:C=-1/0,min_tokens_to_keep:x=1}={}){if(super(),w<0||w>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${w}`);if(!Number.isInteger(x)||x<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${x}`);this.top_p=w,this.filter_value=C,this.min_tokens_to_keep=x}}class D extends te{constructor(w,{filter_value:C=-1/0,min_tokens_to_keep:x=1}={}){if(super(),!Number.isInteger(w)||w<0)throw new Error(`\`top_k\` must be a positive integer, but is ${w}`);this.top_k=Math.max(w,x),this.filter_value=C}}},"./src/generation/logits_sampler.js":(Ce,A,r)=>{r.r(A),r.d(A,{LogitsSampler:()=>te});var g=r("./src/utils/generic.js"),O=r("./src/utils/tensor.js"),j=r("./src/utils/maths.js");r("./src/generation/configuration_utils.js");class te extends g.Callable{constructor(T){super(),this.generation_config=T}async _call(T){return this.sample(T)}async sample(T){throw Error("sample should be implemented in subclasses.")}getLogits(T,v){let z=T.dims.at(-1),K=T.data;if(v===-1)K=K.slice(-z);else{let re=v*z;K=K.slice(re,re+z)}return K}randomSelect(T){let v=0;for(let K=0;K<T.length;++K)v+=T[K];let z=Math.random()*v;for(let K=0;K<T.length;++K)if(z-=T[K],z<=0)return K;return 0}static getSampler(T){if(T.do_sample)return new y(T);if(T.num_beams>1)return new P(T);if(T.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${T.num_return_sequences}.`);return new U(T)}}class U extends te{async sample(T){const v=(0,j.max)(T.data)[1];return[[BigInt(v),0]]}}class y extends te{async sample(T){let v=T.dims.at(-1);this.generation_config.top_k>0&&(v=Math.min(this.generation_config.top_k,v));const[z,K]=await(0,O.topk)(T,v),re=(0,j.softmax)(z.data);return Array.from({length:this.generation_config.num_beams},()=>{const ae=this.randomSelect(re);return[K.data[ae],Math.log(re[ae])]})}}class P extends te{async sample(T){let v=T.dims.at(-1);this.generation_config.top_k>0&&(v=Math.min(this.generation_config.top_k,v));const[z,K]=await(0,O.topk)(T,v),re=(0,j.softmax)(z.data);return Array.from({length:this.generation_config.num_beams},(ae,R)=>[K.data[R],Math.log(re[R])])}}},"./src/generation/stopping_criteria.js":(Ce,A,r)=>{r.r(A),r.d(A,{EosTokenCriteria:()=>U,InterruptableStoppingCriteria:()=>y,MaxLengthCriteria:()=>te,StoppingCriteria:()=>O,StoppingCriteriaList:()=>j});var g=r("./src/utils/generic.js");class O extends g.Callable{_call(b,T){throw Error("StoppingCriteria needs to be subclassed")}}class j extends g.Callable{constructor(){super(),this.criteria=[]}push(b){this.criteria.push(b)}extend(b){b instanceof j?b=b.criteria:b instanceof O&&(b=[b]),this.criteria.push(...b)}_call(b,T){const v=new Array(b.length).fill(!1);for(const z of this.criteria){const K=z(b,T);for(let re=0;re<v.length;++re)v[re]||=K[re]}return v}[Symbol.iterator](){return this.criteria.values()}}class te extends O{constructor(b,T=null){super(),this.max_length=b,this.max_position_embeddings=T}_call(b){return b.map(T=>T.length>=this.max_length)}}class U extends O{constructor(b){super(),Array.isArray(b)||(b=[b]),this.eos_token_id=b}_call(b,T){return b.map(v=>{const z=v.at(-1);return this.eos_token_id.some(K=>z==K)})}}class y extends O{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(b,T){return new Array(b.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(Ce,A,r)=>{r.r(A),r.d(A,{BaseStreamer:()=>te,TextStreamer:()=>y,WhisperTextStreamer:()=>P});var g=r("./src/utils/core.js"),O=r("./src/tokenizers.js"),j=r("./src/env.js");class te{put(T){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const U=j.apis.IS_PROCESS_AVAILABLE?b=>process.stdout.write(b):b=>console.log(b);class y extends te{constructor(T,{skip_prompt:v=!1,callback_function:z=null,token_callback_function:K=null,decode_kwargs:re={},...ae}={}){super(),this.tokenizer=T,this.skip_prompt=v,this.callback_function=z??U,this.token_callback_function=K,this.decode_kwargs={...re,...ae},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(T){if(T.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 v=T[0];this.token_callback_function?.(v),this.token_cache=(0,g.mergeArrays)(this.token_cache,v);const z=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let K;z.endsWith(`
`)?(K=z.slice(this.print_len),this.token_cache=[],this.print_len=0):z.length>0&&(0,O.is_chinese_char)(z.charCodeAt(z.length-1))?(K=z.slice(this.print_len),this.print_len+=K.length):(K=z.slice(this.print_len,z.lastIndexOf(" ")+1),this.print_len+=K.length),this.on_finalized_text(K,!1)}end(){let T;this.token_cache.length>0?(T=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):T="",this.next_tokens_are_prompt=!0,this.on_finalized_text(T,!0)}on_finalized_text(T,v){T.length>0&&this.callback_function?.(T),v&&this.callback_function===U&&j.apis.IS_PROCESS_AVAILABLE&&this.callback_function?.(`
`)}}class P extends y{constructor(T,{skip_prompt:v=!1,callback_function:z=null,token_callback_function:K=null,on_chunk_start:re=null,on_chunk_end:ae=null,on_finalize:R=null,time_precision:G=.02,skip_special_tokens:H=!0,decode_kwargs:D={}}={}){super(T,{skip_prompt:v,callback_function:z,token_callback_function:K,decode_kwargs:{skip_special_tokens:H,...D}}),this.timestamp_begin=T.timestamp_begin,this.on_chunk_start=re,this.on_chunk_end=ae,this.on_finalize=R,this.time_precision=G,this.waiting_for_timestamp=!1}put(T){if(T.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const v=T[0];if(v.length===1){const z=Number(v[0])-this.timestamp_begin;if(z>=0){const K=z*this.time_precision;this.waiting_for_timestamp?this.on_chunk_end?.(K):this.on_chunk_start?.(K),this.waiting_for_timestamp=!this.waiting_for_timestamp,T=[[]]}}return 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v.Tensor("int64",BigInt64Array.from(f.flat().map(_=>BigInt(_))),[f.length,f[0].length])}else return new v.Tensor("int64",BigInt64Array.from(f.map(_=>BigInt(_))),[1,f.length])}function be(f){return new v.Tensor("bool",[f],[1])}async function De(f,_){let{encoder_outputs:Y,input_ids:xe,decoder_input_ids:ke,...Fe}=_;if(!Y){const rt=(0,U.pick)(_,f.sessions.model.inputNames);Y=(await Ge(f,rt)).last_hidden_state}return Fe.input_ids=ke,Fe.encoder_hidden_states=Y,f.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(Fe.encoder_attention_mask=_.attention_mask),await Ne(f,Fe,!0)}async function Ge(f,_){const Y=f.sessions.model,xe=(0,U.pick)(_,Y.inputNames);if(Y.inputNames.includes("inputs_embeds")&&!xe.inputs_embeds){if(!_.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");xe.inputs_embeds=await f.encode_text({input_ids:_.input_ids})}return Y.inputNames.includes("token_type_ids")&&!xe.token_type_ids&&(xe.token_type_ids=new v.Tensor("int64",new BigInt64Array(xe.input_ids.data.length),xe.input_ids.dims)),await ce(Y,xe)}async function Ne(f,_,Y=!1){const xe=f.sessions[Y?"decoder_model_merged":"model"],{past_key_values:ke,...Fe}=_;if(xe.inputNames.includes("use_cache_branch")&&(Fe.use_cache_branch=be(!!ke)),xe.inputNames.includes("position_ids")&&Fe.attention_mask&&!Fe.position_ids){const rt=f.config.model_type==="paligemma"?1:0;Fe.position_ids=he(Fe,ke,rt)}f.addPastKeyValues(Fe,ke);const st=(0,U.pick)(Fe,xe.inputNames);return await ce(xe,st)}function lt({image_token_id:f,inputs_embeds:_,image_features:Y,input_ids:xe,attention_mask:ke}){const Fe=xe.tolist().map(kt=>kt.reduce((Yt,Ut,Bt)=>(Ut==f&&Yt.push(Bt),Yt),[])),st=Fe.reduce((kt,Yt)=>kt+Yt.length,0),rt=Y.dims[0];if(st!==rt)throw new Error(`Image features and image tokens do not match: tokens: ${st}, features ${rt}`);let ct=0;for(let kt=0;kt<Fe.length;++kt){const Yt=Fe[kt],Ut=_[kt];for(let Bt=0;Bt<Yt.length;++Bt)Ut[Yt[Bt]].data.set(Y[ct++].data)}return{inputs_embeds:_,attention_mask:ke}}async function ue(f,{input_ids:_=null,attention_mask:Y=null,pixel_values:xe=null,position_ids:ke=null,inputs_embeds:Fe=null,past_key_values:st=null,generation_config:rt=null,logits_processor:ct=null,...kt}){if(!Fe){if(Fe=await f.encode_text({input_ids:_,...kt}),xe&&_.dims[1]!==1){const Ut=await f.encode_image({pixel_values:xe,...kt});({inputs_embeds:Fe,attention_mask:Y}=f._merge_input_ids_with_image_features({image_features:Ut,inputs_embeds:Fe,input_ids:_,attention_mask:Y}))}else if(st&&xe&&_.dims[1]===1){const Ut=_.dims[1],Bt=Object.values(st)[0].dims.at(-2);Y=(0,v.cat)([(0,v.ones)([_.dims[0],Bt]),Y.slice(null,[Y.dims[1]-Ut,Y.dims[1]])],1)}}if(!ke&&f.config.model_type==="qwen2_vl"){const{image_grid_thw:Ut,video_grid_thw:Bt}=kt;[ke]=f.get_rope_index(_,Ut,Bt,Y)}return await Ne(f,{inputs_embeds:Fe,past_key_values:st,attention_mask:Y,position_ids:ke,generation_config:rt,logits_processor:ct},!0)}function Z(f,_=0){const[Y,xe]=f.dims,ke=f.data,Fe=new BigInt64Array(ke.length);for(let st=0;st<Y;++st){const rt=st*xe;let ct=BigInt(_);for(let kt=0;kt<xe;++kt){const Yt=rt+kt;ke[Yt]===0n?Fe[Yt]=BigInt(1):(Fe[Yt]=ct,ct+=ke[Yt])}}return{data:Fe,dims:f.dims}}function he(f,_=null,Y=0){const{input_ids:xe,inputs_embeds:ke,attention_mask:Fe}=f,{data:st,dims:rt}=Z(Fe,Y);let ct=new v.Tensor("int64",st,rt);if(_){const kt=-(xe??ke).dims.at(1);ct=ct.slice(null,[kt,null])}return ct}function ve(f,_,Y,xe){if(Y.past_key_values){const ke=Object.values(Y.past_key_values)[0].dims.at(-2),{input_ids:Fe,attention_mask:st}=Y;if(!(st&&st.dims[1]>Fe.dims[1])){if(ke<Fe.dims[1])Y.input_ids=Fe.slice(null,[ke,null]);else if(f.config.image_token_index!=null&&Fe.data.some(rt=>rt==f.config.image_token_index)){const rt=f.config.num_image_tokens;if(!rt)throw new Error("`num_image_tokens` is missing in the model configuration.");const ct=Fe.dims[1]-(ke-rt);Y.input_ids=Fe.slice(null,[-ct,null]),Y.attention_mask=(0,v.ones)([1,ke+ct])}}}return Y}function Le(f,_,Y,xe){return Y.past_key_values&&(_=_.map(ke=>[ke.at(-1)])),{...Y,decoder_input_ids:Pe(_)}}function Ze(f,..._){return f.config.is_encoder_decoder?Le(f,..._):ve(f,..._)}function Ke(f,_,Y,xe){const ke=!!Y.past_key_values;return xe.guidance_scale!==null&&xe.guidance_scale>1&&(ke?Y.input_ids=(0,v.cat)([Y.input_ids,Y.input_ids],0):(Y.input_ids=(0,v.cat)([Y.input_ids,(0,v.full_like)(Y.input_ids,BigInt(xe.pad_token_id))],0),Y.attention_mask=(0,v.cat)([Y.attention_mask,(0,v.full_like)(Y.attention_mask,0n)],0))),(ke||!Y.pixel_values)&&(Y.pixel_values=(0,v.full)([0,0,3,384,384],1)),ke&&(Y.images_seq_mask=new v.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),Y.images_emb_mask=new v.Tensor("bool",new Array(0).fill(!1),[1,1,0])),Y}class ne extends te.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(_,Y,xe){super(),this.config=_,this.sessions=Y,this.configs=xe;const ke=C.get(this.constructor),Fe=$.get(ke);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Fe){case D.DecoderOnly:this.can_generate=!0,this._forward=Ne,this._prepare_inputs_for_generation=ve;break;case D.Seq2Seq:case D.Vision2Seq:case D.Musicgen:this.can_generate=!0,this._forward=De,this._prepare_inputs_for_generation=Le;break;case D.EncoderDecoder:this._forward=De;break;case D.ImageTextToText:this.can_generate=!0,this._forward=ue,this._prepare_inputs_for_generation=Ze;break;case D.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=Ze;break;case D.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=Ke;break;default:this._forward=Ge;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const _=[];for(const Y of Object.values(this.sessions))Y?.handler?.dispose&&_.push(Y.handler.dispose());return await Promise.all(_)}static async from_pretrained(_,{progress_callback:Y=null,config:xe=null,cache_dir:ke=null,local_files_only:Fe=!1,revision:st="main",model_file_name:rt=null,subfolder:ct="onnx",device:kt=null,dtype:Yt=null,use_external_data_format:Ut=null,session_options:Bt={}}={}){let At={progress_callback:Y,config:xe,cache_dir:ke,local_files_only:Fe,revision:st,model_file_name:rt,subfolder:ct,device:kt,dtype:Yt,use_external_data_format:Ut,session_options:Bt};const bs=C.get(this),Vt=$.get(bs);xe=At.config=await g.AutoConfig.from_pretrained(_,At);let Wt;if(Vt===D.DecoderOnly)Wt=await Promise.all([J(_,{model:At.model_file_name??"model"},At),q(_,{generation_config:"generation_config.json"},At)]);else if(Vt===D.Seq2Seq||Vt===D.Vision2Seq)Wt=await Promise.all([J(_,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},At),q(_,{generation_config:"generation_config.json"},At)]);else if(Vt===D.MaskGeneration)Wt=await Promise.all([J(_,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},At)]);else if(Vt===D.EncoderDecoder)Wt=await Promise.all([J(_,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},At)]);else if(Vt===D.ImageTextToText){const _s={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};xe.is_encoder_decoder&&(_s.model="encoder_model"),Wt=await Promise.all([J(_,_s,At),q(_,{generation_config:"generation_config.json"},At)])}else if(Vt===D.Musicgen)Wt=await Promise.all([J(_,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},At),q(_,{generation_config:"generation_config.json"},At)]);else if(Vt===D.MultiModality)Wt=await Promise.all([J(_,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},At),q(_,{generation_config:"generation_config.json"},At)]);else if(Vt===D.Phi3V)Wt=await Promise.all([J(_,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},At),q(_,{generation_config:"generation_config.json"},At)]);else{if(Vt!==D.EncoderOnly){const _s=bs??xe?.model_type;_s!=="custom"&&console.warn(`Model type for '${_s}' not found, assuming encoder-only architecture. Please report this at ${P.GITHUB_ISSUE_URL}.`)}Wt=await Promise.all([J(_,{model:At.model_file_name??"model"},At)])}return new this(xe,...Wt)}async _call(_){return await this.forward(_)}async forward(_){return await this._forward(this,_)}get generation_config(){return this.configs?.generation_config??null}_get_logits_warper(_){const Y=new b.LogitsProcessorList;return _.temperature!==null&&_.temperature!==1&&Y.push(new b.TemperatureLogitsWarper(_.temperature)),_.top_k!==null&&_.top_k!==0&&Y.push(new b.TopKLogitsWarper(_.top_k)),_.top_p!==null&&_.top_p<1&&Y.push(new b.TopPLogitsWarper(_.top_p)),Y}_get_logits_processor(_,Y,xe=null){const ke=new b.LogitsProcessorList;if(_.repetition_penalty!==null&&_.repetition_penalty!==1&&ke.push(new b.RepetitionPenaltyLogitsProcessor(_.repetition_penalty)),_.no_repeat_ngram_size!==null&&_.no_repeat_ngram_size>0&&ke.push(new b.NoRepeatNGramLogitsProcessor(_.no_repeat_ngram_size)),_.bad_words_ids!==null&&ke.push(new b.NoBadWordsLogitsProcessor(_.bad_words_ids,_.eos_token_id)),_.min_length!==null&&_.eos_token_id!==null&&_.min_length>0&&ke.push(new b.MinLengthLogitsProcessor(_.min_length,_.eos_token_id)),_.min_new_tokens!==null&&_.eos_token_id!==null&&_.min_new_tokens>0&&ke.push(new b.MinNewTokensLengthLogitsProcessor(Y,_.min_new_tokens,_.eos_token_id)),_.forced_bos_token_id!==null&&ke.push(new b.ForcedBOSTokenLogitsProcessor(_.forced_bos_token_id)),_.forced_eos_token_id!==null&&ke.push(new b.ForcedEOSTokenLogitsProcessor(_.max_length,_.forced_eos_token_id)),_.begin_suppress_tokens!==null){const Fe=Y>1||_.forced_bos_token_id===null?Y:Y+1;ke.push(new b.SuppressTokensAtBeginLogitsProcessor(_.begin_suppress_tokens,Fe))}return _.guidance_scale!==null&&_.guidance_scale>1&&ke.push(new b.ClassifierFreeGuidanceLogitsProcessor(_.guidance_scale)),xe!==null&&ke.extend(xe),ke}_prepare_generation_config(_,Y,xe=T.GenerationConfig){const ke={...this.config};for(const st of["decoder","generator","text_config"])st in ke&&Object.assign(ke,ke[st]);const Fe=new xe(ke);return Object.assign(Fe,this.generation_config??{}),_&&Object.assign(Fe,_),Y&&Object.assign(Fe,(0,U.pick)(Y,Object.getOwnPropertyNames(Fe))),Fe}_get_stopping_criteria(_,Y=null){const xe=new re.StoppingCriteriaList;return _.max_length!==null&&xe.push(new re.MaxLengthCriteria(_.max_length,this.config.max_position_embeddings??null)),_.eos_token_id!==null&&xe.push(new re.EosTokenCriteria(_.eos_token_id)),Y&&xe.extend(Y),xe}_validate_model_class(){if(!this.can_generate){const _=[oa,ia,na,ra],Y=C.get(this.constructor),xe=new Set,ke=this.config.model_type;for(const st of _){const rt=st.get(ke);rt&&xe.add(rt[0])}let Fe=`The current model class (${Y}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw xe.size>0&&(Fe+=` Please use the following class instead: ${[...xe].join(", ")}`),Error(Fe)}}prepare_inputs_for_generation(..._){return this._prepare_inputs_for_generation(this,..._)}_update_model_kwargs_for_generation({generated_input_ids:_,outputs:Y,model_inputs:xe,is_encoder_decoder:ke}){return xe.past_key_values=this.getPastKeyValues(Y,xe.past_key_values),xe.input_ids=new v.Tensor("int64",_.flat(),[_.length,1]),ke||(xe.attention_mask=(0,v.cat)([xe.attention_mask,(0,v.ones)([xe.attention_mask.dims[0],1])],1)),xe.position_ids=null,xe}_prepare_model_inputs({inputs:_,bos_token_id:Y,model_kwargs:xe}){const ke=(0,U.pick)(xe,this.forward_params),Fe=this.main_input_name;if(Fe in ke){if(_)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else ke[Fe]=_;return{inputs_tensor:ke[Fe],model_inputs:ke,model_input_name:Fe}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:_,model_inputs:Y,model_input_name:xe,generation_config:ke}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!Y.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:st,pixel_values:rt,attention_mask:ct,...kt}=Y,Yt=await this._prepare_inputs_embeds(Y);Y={...kt,...(0,U.pick)(Yt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Fe}=await Ge(this,Y);if(ke.guidance_scale!==null&&ke.guidance_scale>1)Fe=(0,v.cat)([Fe,(0,v.full_like)(Fe,0)],0),"attention_mask"in Y&&(Y.attention_mask=(0,v.cat)([Y.attention_mask,(0,v.zeros_like)(Y.attention_mask)],0));else if(Y.decoder_input_ids){const st=Pe(Y.decoder_input_ids).dims[0];if(st!==Fe.dims[0]){if(Fe.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Fe.dims[0]}) than the decoder inputs (${st}).`);Fe=(0,v.cat)(Array.from({length:st},()=>Fe),0)}}return Y.encoder_outputs=Fe,Y}_prepare_decoder_input_ids_for_generation({batch_size:_,model_input_name:Y,model_kwargs:xe,decoder_start_token_id:ke,bos_token_id:Fe,generation_config:st}){let{decoder_input_ids:rt,...ct}=xe;if(!(rt instanceof v.Tensor)){if(rt)Array.isArray(rt[0])||(rt=Array.from({length:_},()=>rt));else if(ke??=Fe,this.config.model_type==="musicgen")rt=Array.from({length:_*this.config.decoder.num_codebooks},()=>[ke]);else if(Array.isArray(ke)){if(ke.length!==_)throw new Error(`\`decoder_start_token_id\` expcted to have length ${_} but got ${ke.length}`);rt=ke}else rt=Array.from({length:_},()=>[ke]);rt=Pe(rt)}return xe.decoder_attention_mask=(0,v.ones_like)(rt),{input_ids:rt,model_inputs:ct}}async generate({inputs:_=null,generation_config:Y=null,logits_processor:xe=null,stopping_criteria:ke=null,streamer:Fe=null,...st}){this._validate_model_class(),Y=this._prepare_generation_config(Y,st);let{inputs_tensor:rt,model_inputs:ct,model_input_name:kt}=this._prepare_model_inputs({inputs:_,model_kwargs:st});const Yt=this.config.is_encoder_decoder;Yt&&("encoder_outputs"in ct||(ct=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:rt,model_inputs:ct,model_input_name:kt,generation_config:Y})));let Ut;Yt?{input_ids:Ut,model_inputs:ct}=this._prepare_decoder_input_ids_for_generation({batch_size:ct[kt].dims.at(0),model_input_name:kt,model_kwargs:ct,decoder_start_token_id:Y.decoder_start_token_id,bos_token_id:Y.bos_token_id,generation_config:Y}):Ut=ct[kt];let Bt=Ut.dims.at(-1);Y.max_new_tokens!==null&&(Y.max_length=Bt+Y.max_new_tokens);const At=this._get_logits_processor(Y,Bt,xe),bs=this._get_stopping_criteria(Y,ke),Vt=ct[kt].dims.at(0),Wt=ae.LogitsSampler.getSampler(Y),_s=new Array(Vt).fill(0),ws=Ut.tolist();Fe&&Fe.put(ws);let es,Is={};for(;;){if(ct=this.prepare_inputs_for_generation(ws,ct,Y),es=await this.forward(ct),Y.output_attentions&&Y.return_dict_in_generate){const lr=this.getAttentions(es);for(const xr in lr)xr in Is||(Is[xr]=[]),Is[xr].push(lr[xr])}const Bs=es.logits.slice(null,-1,null),Xs=At(ws,Bs),ar=[];for(let lr=0;lr<Xs.dims.at(0);++lr){const xr=Xs[lr],wo=await Wt(xr);for(const[_a,fa]of wo){const Rn=BigInt(_a);_s[lr]+=fa,ws[lr].push(Rn),ar.push([Rn]);break}}if(Fe&&Fe.put(ar),bs(ws).every(lr=>lr))break;ct=this._update_model_kwargs_for_generation({generated_input_ids:ar,outputs:es,model_inputs:ct,is_encoder_decoder:Yt})}Fe&&Fe.end();const Os=this.getPastKeyValues(es,ct.past_key_values,!0),ks=new v.Tensor("int64",ws.flat(),[ws.length,ws[0].length]);if(Y.return_dict_in_generate)return{sequences:ks,past_key_values:Os,...Is};for(const Bs of Object.values(es))Bs.location==="gpu-buffer"&&Bs.dispose();return ks}getPastKeyValues(_,Y,xe=!1){const ke=Object.create(null);for(const Fe in _)if(Fe.startsWith("present")){const st=Fe.replace("present","past_key_values"),rt=Fe.includes("encoder");if(rt&&Y?ke[st]=Y[st]:ke[st]=_[Fe],Y&&(!rt||xe)){const ct=Y[st];ct.location==="gpu-buffer"&&ct.dispose()}}return ke}getAttentions(_){const Y={};for(const xe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const ke in _)ke.startsWith(xe)&&(xe in Y||(Y[xe]=[]),Y[xe].push(_[ke]));return Y}addPastKeyValues(_,Y){if(Y)Object.assign(_,Y);else{const ke=(this.sessions.decoder_model_merged??this.sessions.model)?.config?.kv_cache_dtype??"float32",Fe=ke==="float16"?new Uint16Array:[],st=(_[this.main_input_name]??_.attention_mask)?.dims?.[0]??1,rt=(0,g.getKeyValueShapes)(this.config,{batch_size:st});for(const ct in rt)_[ct]=new v.Tensor(ke,Fe,rt[ct])}}async encode_image({pixel_values:_}){const Y=(await ce(this.sessions.vision_encoder,{pixel_values:_})).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:_}){return(await ce(this.sessions.embed_tokens,{input_ids:_})).inputs_embeds}}class qe{}class Ae extends qe{constructor({last_hidden_state:_,hidden_states:Y=null,attentions:xe=null}){super(),this.last_hidden_state=_,this.hidden_states=Y,this.attentions=xe}}class oe extends ne{}class Me extends oe{}class je extends oe{async _call(_){return new Us(await super._call(_))}}class Re extends oe{async _call(_){return new Ht(await super._call(_))}}class We extends oe{async _call(_){return new Ns(await super._call(_))}}class ze extends oe{async _call(_){return new qs(await super._call(_))}}class Ye extends ne{}class nt extends Ye{}class wt extends ne{}class ut extends wt{}class ht extends wt{async _call(_){return new Us(await super._call(_))}}class I extends wt{async _call(_){return new Ht(await super._call(_))}}class ie extends wt{async _call(_){return new Ns(await super._call(_))}}class X extends wt{async _call(_){return new qs(await super._call(_))}}class _e extends ne{}class $e extends _e{}class He extends _e{async _call(_){return new Us(await super._call(_))}}class et extends _e{async _call(_){return new Ht(await super._call(_))}}class ot extends _e{async _call(_){return new Ns(await super._call(_))}}class yt extends _e{async _call(_){return new qs(await super._call(_))}}class mt extends ne{}class Qt extends mt{}class ts extends mt{async _call(_){return new Us(await super._call(_))}}class xs extends mt{async _call(_){return new Ht(await super._call(_))}}class hs extends mt{async _call(_){return new Ns(await super._call(_))}}class $s extends mt{async _call(_){return new qs(await super._call(_))}}class Ms extends ne{}class Ks extends Ms{}class sr extends Ms{async _call(_){return new Us(await super._call(_))}}class Rr extends Ms{async _call(_){return new Ht(await super._call(_))}}class Cr extends Ms{async _call(_){return new Ns(await super._call(_))}}class an extends Ms{async _call(_){return new qs(await super._call(_))}}class Ot extends ne{}class Nr extends Ot{}class br extends Ot{async _call(_){return new Us(await super._call(_))}}class kr extends Ot{async _call(_){return new Ht(await super._call(_))}}class vr extends Ot{async _call(_){return new Ns(await super._call(_))}}class Sr extends Ot{async _call(_){return new qs(await super._call(_))}}class Js extends ne{}class ur extends Js{}class Tr extends Js{async _call(_){return new Us(await super._call(_))}}class jr extends Js{async _call(_){return new Ht(await super._call(_))}}class rr extends Js{async _call(_){return new Ns(await super._call(_))}}class it extends Js{async _call(_){return new qs(await super._call(_))}}class dt extends ne{}class St extends dt{}class cs extends dt{async _call(_){return new Ht(await super._call(_))}}class Ur extends dt{async _call(_){return new Ns(await super._call(_))}}class rs extends dt{async _call(_){return new qs(await super._call(_))}}class Vr extends dt{async _call(_){return new Us(await super._call(_))}}class $r extends ne{}class qn extends $r{}class Tn extends $r{async _call(_){return new Us(await super._call(_))}}class Wr extends $r{async _call(_){return new Ht(await super._call(_))}}class xn extends $r{async _call(_){return new Ns(await super._call(_))}}class Ar extends ne{}class Pn extends Ar{}class Xn extends Ar{async _call(_){return new Us(await super._call(_))}}class Ir extends Ar{async _call(_){return new Ht(await super._call(_))}}class fr extends Ar{async _call(_){return new qs(await super._call(_))}}class Zs extends ne{}class ln extends Zs{}class Gr extends Zs{async _call(_){return new Us(await super._call(_))}}class un extends Zs{async _call(_){return new Ht(await super._call(_))}}class Kr extends Zs{async _call(_){return new Ns(await super._call(_))}}class dn extends Zs{async _call(_){return new qs(await super._call(_))}}class Ft extends ne{}class cn extends Ft{}class En extends Ft{async _call(_){return new Us(await super._call(_))}}class Cn extends Ft{async _call(_){return new Ht(await super._call(_))}}class kn extends Ft{async _call(_){return new qs(await super._call(_))}}class Or extends ne{}class Sn extends Or{}class pn extends Or{async _call(_){return new Ht(await super._call(_))}}class $n extends Or{async _call(_){return new qs(await super._call(_))}}class ns extends Or{async _call(_){return new Us(await super._call(_))}}class er extends ne{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]}class hn extends er{}class An extends er{}class Hr extends ne{}class In extends Hr{}class Te extends Hr{}class M extends ne{}class Q extends M{}class pe extends M{}class ge extends ne{}class Ie extends ge{}class Xe extends ge{}class _t extends ge{async _call(_){return new Ht(await super._call(_))}}class ft extends ne{}class vt extends ft{}class Qe extends ft{}class It extends ft{async _call(_){return new Ht(await super._call(_))}}class Xt extends ft{}class gs extends ne{}class Se extends gs{}class Ps extends gs{}class js extends ne{}class Rs extends js{}class dr extends js{}class zt extends ne{}class zs extends zt{}class gr extends zt{async _call(_){return new Us(await super._call(_))}}class Zt extends zt{async _call(_){return new Ht(await super._call(_))}}class us extends zt{async _call(_){return new Ns(await super._call(_))}}class xt extends zt{async _call(_){return new qs(await super._call(_))}}class os extends ne{}class wr extends os{}class As extends os{async _call(_){return new Us(await super._call(_))}}class Vs extends os{async _call(_){return new Ht(await super._call(_))}}class Mt extends os{async _call(_){return new Ns(await super._call(_))}}class Es extends os{async _call(_){return new qs(await super._call(_))}}class Oe extends ne{}class gt extends Oe{}class tr extends Oe{async _call(_){return new Us(await super._call(_))}}class qr extends Oe{async _call(_){return new Ht(await super._call(_))}}class Qn extends Oe{async _call(_){return new Ns(await super._call(_))}}class ka extends Oe{async _call(_){return new qs(await super._call(_))}}class Gt extends ne{}class Sa extends Gt{}class ko extends Gt{}class So extends ne{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]}class $a extends So{}class Aa extends So{_prepare_generation_config(_,Y){return super._prepare_generation_config(_,Y,G.WhisperGenerationConfig)}_retrieve_init_tokens(_){const Y=[_.decoder_start_token_id];let xe=_.language;const ke=_.task;if(_.is_multilingual){xe||(console.warn("No language specified - defaulting to English (en)."),xe="en");const st=`<|${(0,H.whisper_language_to_code)(xe)}|>`;Y.push(_.lang_to_id[st]),Y.push(_.task_to_id[ke??"transcribe"])}else if(xe||ke)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!_.return_timestamps&&_.no_timestamps_token_id&&Y.at(-1)!==_.no_timestamps_token_id?Y.push(_.no_timestamps_token_id):_.return_timestamps&&Y.at(-1)===_.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),Y.pop()),Y.filter(Fe=>Fe!=null)}async generate({inputs:_=null,generation_config:Y=null,logits_processor:xe=null,stopping_criteria:ke=null,...Fe}){Y=this._prepare_generation_config(Y,Fe);const st=Fe.decoder_input_ids??this._retrieve_init_tokens(Y);if(Y.return_timestamps&&(xe??=new b.LogitsProcessorList,xe.push(new b.WhisperTimeStampLogitsProcessor(Y,st))),Y.begin_suppress_tokens&&(xe??=new b.LogitsProcessorList,xe.push(new b.SuppressTokensAtBeginLogitsProcessor(Y.begin_suppress_tokens,st.length))),Y.return_token_timestamps){if(!Y.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.");Y.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),Y.output_attentions=!0,Y.return_dict_in_generate=!0}const rt=await super.generate({inputs:_,generation_config:Y,logits_processor:xe,decoder_input_ids:st,...Fe});return Y.return_token_timestamps&&(rt.token_timestamps=this._extract_token_timestamps(rt,Y.alignment_heads,Y.num_frames)),rt}_extract_token_timestamps(_,Y,xe=null,ke=.02){if(!_.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`.");xe==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 Fe=this.config.median_filter_width;Fe===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Fe=7);const st=_.cross_attentions,rt=Array.from({length:this.config.decoder_layers},(Vt,Wt)=>(0,v.cat)(st.map(_s=>_s[Wt]),2)),ct=(0,v.stack)(Y.map(([Vt,Wt])=>{if(Vt>=rt.length)throw new Error(`Layer index ${Vt} is out of bounds for cross attentions (length ${rt.length}).`);return xe?rt[Vt].slice(null,Wt,null,[0,xe]):rt[Vt].slice(null,Wt)})).transpose(1,0,2,3),[kt,Yt]=(0,v.std_mean)(ct,-2,0,!0),Ut=ct.clone();for(let Vt=0;Vt<Ut.dims[0];++Vt){const Wt=Ut[Vt];for(let _s=0;_s<Wt.dims[0];++_s){const ws=Wt[_s],es=kt[Vt][_s][0].data,Is=Yt[Vt][_s][0].data;for(let Os=0;Os<ws.dims[0];++Os){let ks=ws[Os].data;for(let Bs=0;Bs<ks.length;++Bs)ks[Bs]=(ks[Bs]-Is[Bs])/es[Bs];ks.set((0,K.medianFilter)(ks,Fe))}}}const Bt=[(0,v.mean)(Ut,1)],At=_.sequences.dims,bs=new v.Tensor("float32",new Float32Array(At[0]*At[1]),At);for(let Vt=0;Vt<At[0];++Vt){const Wt=Bt[Vt].neg().squeeze_(0),[_s,ws]=(0,K.dynamic_time_warping)(Wt.tolist()),es=Array.from({length:_s.length-1},(ks,Bs)=>_s[Bs+1]-_s[Bs]),Is=(0,U.mergeArrays)([1],es).map(ks=>!!ks),Os=[];for(let ks=0;ks<Is.length;++ks)Is[ks]&&Os.push(ws[ks]*ke);bs[Vt].data.set(Os,1)}return bs}}class $o extends ne{requires_attention_mask=!1;main_input_name="input_values";forward_params=["input_values","decoder_input_ids","past_key_values"]}class nr extends $o{}class Ia extends $o{}class Ao extends ne{main_input_name="pixel_values";forward_params=["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"]}class Fr extends ne{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Yn extends Fr{_merge_input_ids_with_image_features({inputs_embeds:_,image_features:Y,input_ids:xe,attention_mask:ke}){const Fe=this.config.image_token_index,rt=xe.tolist().map(Bt=>Bt.findIndex(At=>At==Fe)),ct=rt.every(Bt=>Bt===-1),kt=rt.every(Bt=>Bt!==-1);if(!ct&&!kt)throw new Error("Every input should contain either 0 or 1 image token.");if(ct)return{inputs_embeds:_,attention_mask:ke};const Yt=[],Ut=[];for(let Bt=0;Bt<rt.length;++Bt){const At=rt[Bt],bs=_[Bt],Vt=Y[Bt],Wt=ke[Bt];Yt.push((0,v.cat)([bs.slice([0,At]),Vt,bs.slice([At+1,bs.dims[0]])],0)),Ut.push((0,v.cat)([Wt.slice([0,At]),(0,v.ones)([Vt.dims[0]]),Wt.slice([At+1,Wt.dims[0]])],0))}return{inputs_embeds:(0,v.stack)(Yt,0),attention_mask:(0,v.stack)(Ut,0)}}}class Oa extends Yn{}class Fa extends Yn{}class Da extends ne{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds"}class La extends Da{_merge_input_ids_with_image_features({inputs_embeds:_,image_features:Y,input_ids:xe,attention_mask:ke}){return{inputs_embeds:(0,v.cat)([Y,_],1),attention_mask:(0,v.cat)([(0,v.ones)(Y.dims.slice(0,2)),ke],1)}}async _prepare_inputs_embeds({input_ids:_,pixel_values:Y,inputs_embeds:xe,attention_mask:ke}){if(!_&&!Y)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let Fe,st;return _&&(Fe=await this.encode_text({input_ids:_})),Y&&(st=await this.encode_image({pixel_values:Y})),Fe&&st?{inputs_embeds:xe,attention_mask:ke}=this._merge_input_ids_with_image_features({inputs_embeds:Fe,image_features:st,input_ids:_,attention_mask:ke}):xe=Fe||st,{inputs_embeds:xe,attention_mask:ke}}async forward({input_ids:_,pixel_values:Y,attention_mask:xe,decoder_input_ids:ke,decoder_attention_mask:Fe,encoder_outputs:st,past_key_values:rt,inputs_embeds:ct,decoder_inputs_embeds:kt}){if(ct||({inputs_embeds:ct,attention_mask:xe}=await this._prepare_inputs_embeds({input_ids:_,pixel_values:Y,inputs_embeds:ct,attention_mask:xe})),!st){let{last_hidden_state:Bt}=await Ge(this,{inputs_embeds:ct,attention_mask:xe});st=Bt}if(!kt){if(!ke)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");kt=await this.encode_text({input_ids:ke})}return await Ne(this,{inputs_embeds:kt,attention_mask:Fe,encoder_attention_mask:xe,encoder_hidden_states:st,past_key_values:rt},!0)}}class za extends ne{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Ba extends za{_merge_input_ids_with_image_features(_){const Y=_.image_features.dims.at(-1),xe=_.image_features.view(-1,Y);return lt({image_token_id:this.config.image_token_index,..._,image_features:xe})}}class Ra extends ne{forward_params=["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"]}class Io extends Ra{async encode_image({pixel_values:_,pixel_attention_mask:Y}){return(await ce(this.sessions.vision_encoder,{pixel_values:_,pixel_attention_mask:Y})).image_features}_merge_input_ids_with_image_features(_){const Y=_.image_features.dims.at(-1),xe=_.image_features.view(-1,Y);return lt({image_token_id:this.config.image_token_id,..._,image_features:xe})}}class Na extends ne{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"]}class or extends Na{async forward({input_ids:_=null,attention_mask:Y=null,pixel_values:xe=null,image_sizes:ke=null,position_ids:Fe=null,inputs_embeds:st=null,past_key_values:rt=null,generation_config:ct=null,logits_processor:kt=null,...Yt}){if(!st){let Bt;if(xe&&_.dims[1]!==1){if(!ke)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:Bt}=await ce(this.sessions.vision_encoder,{pixel_values:xe,image_sizes:ke}))}else{const At=this.config.normalized_config.hidden_size;Bt=new v.Tensor("float32",[],[0,At])}({inputs_embeds:st}=await ce(this.sessions.prepare_inputs_embeds,{input_ids:_,image_features:Bt}))}return await Ne(this,{inputs_embeds:st,past_key_values:rt,attention_mask:Y,position_ids:Fe,generation_config:ct,logits_processor:kt},!1)}}class Xr extends ne{}class ja extends Xr{}class Yd extends Xr{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"text_model",...Y})}}class Ua extends Xr{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"text_model",...Y})}}class Jd extends Xr{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"vision_model",...Y})}}class Va extends Xr{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"vision_model",...Y})}}class Oo extends ne{}class Wa extends Oo{}class Ga extends Oo{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"text_model",...Y})}}class Ka extends Xr{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"vision_model",...Y})}}class Zd extends ne{}class cr extends Zd{}class Jn extends ne{}class Zn extends Jn{async forward(_){const Y=!_.input_ids,xe=!_.pixel_values;if(Y&&xe)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(Y&&(_.input_ids=(0,v.ones)([_.pixel_values.dims[0],1])),xe){const{image_size:kt}=this.config.vision_config;_.pixel_values=(0,v.full)([0,3,kt,kt],0)}const{text_embeddings:ke,image_embeddings:Fe,l2norm_text_embeddings:st,l2norm_image_embeddings:rt}=await super.forward(_),ct={};return Y||(ct.text_embeddings=ke,ct.l2norm_text_embeddings=st),xe||(ct.image_embeddings=Fe,ct.l2norm_image_embeddings=rt),ct}}class Fo extends Jn{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"text_model",...Y})}}class pr extends Jn{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"vision_model",...Y})}}class Do extends ne{}class Ha extends Do{}class qa extends Do{}class Lo extends ne{}class Xa extends Lo{}class Qa extends Lo{}class zo extends ne{}class Ya extends zo{}class Ja extends zo{}class Bo extends ne{}class hr extends Bo{}class Za extends Bo{}class Ro extends ne{}class el extends Ro{}class tl extends Ro{}class No extends ne{}class sl extends No{}class rl extends No{}class jo extends ne{}class nl extends jo{}class ol extends jo{}class eo extends ne{}class Uo extends eo{}class il extends eo{}class Vo extends ne{}class Wo extends Vo{}class ec extends Vo{}class Go extends ne{}class to extends Go{}class al extends Go{}class Ko extends ne{}class ll extends Ko{}class On extends Ko{}class Ho extends ne{}class ul extends Ho{}class qo extends Ho{}class Xo extends ne{}class dl extends Xo{}class cl extends Xo{}class Qo extends ne{}class tc extends Qo{}class pl extends Qo{}class Yo extends ne{}class hl extends Yo{}class sc extends Yo{}class Jo extends ne{}class as extends Jo{}class ml extends Jo{}class Zo extends ne{}class _l extends Zo{}class fl extends Zo{}class ei extends ne{}class gl extends ei{}class wl extends ei{}class ti extends ne{}class yl extends ti{}class Ml extends ti{}class bl extends ne{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]}class vl extends bl{get_rope_index(_,Y,xe,ke){const{vision_config:Fe,image_token_id:st,video_token_id:rt,vision_start_token_id:ct}=this.config,kt=Fe.spatial_merge_size??2,Yt=[];if(Y||xe){let Ut=_.tolist();ke||(ke=(0,v.ones_like)(_));const Bt=ke.tolist(),At=Array.from({length:3},ws=>Array.from({length:_.dims[0]},es=>Array.from({length:_.dims[1]},Is=>1))),bs=Y?Y.tolist():[],Vt=xe?xe.tolist():[];let Wt=0,_s=0;for(let ws=0;ws<Ut.length;++ws){const es=Ut[ws].filter((ys,Fs)=>Bt[ws][Fs]==1),Os=es.reduce((ys,Fs,Br)=>(Fs==ct&&ys.push(Br),ys),[]).map(ys=>es[ys+1]),ks=Os.filter(ys=>ys==st).length,Bs=Os.filter(ys=>ys==rt).length;let Xs=[],ar=0,ma=ks,lr=Bs;for(let ys=0;ys<Os.length;++ys){const Fs=es.findIndex((tn,_r)=>_r>ar&&tn==st),Br=es.findIndex((tn,_r)=>_r>ar&&tn==rt),gn=ma>0&&Fs!==-1?Fs:es.length+1,wn=lr>0&&Br!==-1?Br:es.length+1;let Nn,ga,yo,Mo;gn<wn?([ga,yo,Mo]=bs[Wt],++Wt,--ma,Nn=gn):([ga,yo,Mo]=Vt[_s],++_s,--lr,Nn=wn);const[Rd,wa,bo]=[Number(ga),Math.floor(Number(yo)/kt),Math.floor(Number(Mo)/kt)],Nd=Nn-ar,ya=Xs.length>0?(0,K.max)(Xs.at(-1))[0]+1:0;Xs.push(Array.from({length:3*Nd},(tn,_r)=>ya+_r%Nd));const Ma=Nd+ya,vo=Rd*wa*bo,Ip=Array.from({length:vo},(tn,_r)=>Ma+Math.floor(_r/(wa*bo))),ip=Array.from({length:vo},(tn,_r)=>Ma+Math.floor(_r/bo)%wa),ba=Array.from({length:vo},(tn,_r)=>Ma+_r%bo);Xs.push([Ip,ip,ba].flat()),ar=Nn+vo}if(ar<es.length){const ys=Xs.length>0?(0,K.max)(Xs.at(-1))[0]+1:0,Fs=es.length-ar;Xs.push(Array.from({length:3*Fs},(Br,gn)=>ys+gn%Fs))}const xr=Xs.reduce((ys,Fs)=>ys+Fs.length,0),wo=new Array(xr);let _a=0;for(let ys=0;ys<3;++ys)for(let Fs=0;Fs<Xs.length;++Fs){const Br=Xs[Fs],gn=Br.length/3;for(let wn=ys*gn;wn<(ys+1)*gn;++wn)wo[_a++]=Br[wn]}let fa=0;const Rn=Bt[ws];for(let ys=0;ys<Rn.length;++ys)if(Rn[ys]==1){for(let Fs=0;Fs<3;++Fs)At[Fs][ws][ys]=wo[Fs*xr/3+fa];++fa}const op=(0,K.max)(wo)[0];Yt.push(op+1-Ut[ws].length)}return[new v.Tensor("int64",At.flat(1/0),[3,_.dims[0],_.dims[1]]),new v.Tensor("int64",Yt,[Yt.length,1])]}else if(ke){const{data:Ut,dims:Bt}=Z(ke),At=BigInt64Array.from({length:3*Ut.length},(Vt,Wt)=>Ut[Wt%Ut.length]),bs=Array.from({length:Bt[0]},(Vt,Wt)=>(0,K.max)(Ut.subarray(Bt[1]*Wt,Bt[1]*(Wt+1)))[0]+1+Bt[1]);return[new v.Tensor("int64",At,[3,...Bt]),new v.Tensor("int64",bs,[bs.length,1])]}else{const[Ut,Bt]=_.dims,At=BigInt64Array.from({length:3*Ut*Bt},(bs,Vt)=>BigInt(Math.floor(Vt%Bt/Ut)));return[new v.Tensor("int64",At,[3,..._.dims]),(0,v.zeros)([Ut,1])]}}async encode_image({pixel_values:_,image_grid_thw:Y}){return(await ce(this.sessions.vision_encoder,{pixel_values:_,grid_thw:Y})).image_features}_merge_input_ids_with_image_features(_){return lt({image_token_id:this.config.image_token_id,..._})}prepare_inputs_for_generation(_,Y,xe){if(Y.attention_mask&&!Y.position_ids)if(!Y.past_key_values)[Y.position_ids,Y.rope_deltas]=this.get_rope_index(Y.input_ids,Y.image_grid_thw,Y.video_grid_thw,Y.attention_mask);else{Y.pixel_values=null;const ke=BigInt(Object.values(Y.past_key_values)[0].dims.at(-2)),Fe=Y.rope_deltas.map(st=>ke+st);Y.position_ids=(0,v.stack)([Fe,Fe,Fe],0)}return Y}}class si extends ne{}class Tl extends si{}class Fn extends si{}class ri extends ne{}class so extends ri{}class xl extends ri{}class ni extends ne{}class Pl extends ni{}class El extends ni{}class oi extends ne{}class Cl extends oi{}class kl extends oi{}class ii extends ne{}class Sl extends ii{}class $l extends ii{}class ai extends ne{}class Al extends ai{}class Il extends ai{async _call(_){return new Ht(await super._call(_))}}class li extends ne{}class Ol extends li{}class Fl extends li{async _call(_){return new Ht(await super._call(_))}}class ui extends ne{}class Dl extends ui{}class ro extends ne{}class di extends ro{}class Ll extends ro{async _call(_){return new Ht(await super._call(_))}}class zl extends ne{}class Bl extends zl{}class ci extends ne{}class Rl extends ci{}class Nl extends ci{async _call(_){return new Ht(await super._call(_))}}class pi extends ne{}class jl extends pi{}class hi extends ne{}class Ul extends hi{}class rc extends hi{async _call(_){return new Ht(await super._call(_))}}class Vl extends ne{}class Wl extends Vl{async _call(_){return new Bd(await super._call(_))}}class ir extends ne{}class Gl extends ir{}class Kl extends ir{async _call(_){return new Ht(await super._call(_))}}class mi extends ne{}class Hl extends mi{}class ql extends mi{async _call(_){return new Ht(await super._call(_))}}class _i extends ne{}class Xl extends _i{}class Ql extends _i{}class fi extends ne{}class Yl extends fi{}class nc extends fi{}class gi extends ne{}class Jl extends gi{}class Zl extends gi{async _call(_){return new Ht(await super._call(_))}}class no extends ne{}class eu extends no{}class tu extends no{async _call(_){return new Dr(await super._call(_))}}class su extends no{async _call(_){return new Qr(await super._call(_))}}class Dr extends qe{constructor({logits:_,pred_boxes:Y}){super(),this.logits=_,this.pred_boxes=Y}}class Qr extends qe{constructor({logits:_,pred_boxes:Y,pred_masks:xe}){super(),this.logits=_,this.pred_boxes=Y,this.pred_masks=xe}}class Lr extends ne{}class wi extends Lr{}class Yr extends Lr{async _call(_){return new Ws(await super._call(_))}}class Ws extends qe{constructor({logits:_,pred_boxes:Y}){super(),this.logits=_,this.pred_boxes=Y}}class yi extends ne{}class Mi extends yi{}class ru extends yi{async _call(_){return new oc(await super._call(_))}}class oc extends Dr{}class mn extends ne{}class bi extends mn{}class vi extends mn{async _call(_){return new Ht(await super._call(_))}}class Ti extends ne{}class nu extends Ti{}class xi extends Ti{async _call(_){return new Ht(await super._call(_))}}class oo extends ne{}class ou extends oo{}class Pi extends oo{async _call(_){return new Ht(await super._call(_))}}class Ei extends ne{}class io extends Ei{}class Ci extends Ei{async _call(_){return new Ht(await super._call(_))}}class ki extends ne{}class iu extends ki{}class ic extends ki{}class Si extends ne{}class $i extends Si{}class Dn extends Si{}class au extends ne{}class Ai extends au{}class ao extends ne{}class lu extends ao{}class uu extends ao{}class ac extends ao{}class du extends ne{}class cu extends du{}class pu extends ne{}class hu extends pu{}class lo extends pu{}class Ii extends ne{}class uo extends Ii{}class Oi extends Ii{}class Fi extends ne{}class mu extends Fi{}class Di extends ne{}class Li extends Di{}class lc extends Di{async _call(_){return new Ht(await super._call(_))}}class zi extends ne{}class uc extends zi{}class _u extends zi{async _call(_){return new Ht(await super._call(_))}}class Bi extends ne{}class fu extends Bi{}class Ri extends Bi{async _call(_){return new Ht(await super._call(_))}}class Ni extends ne{}class gu extends Ni{}class ji extends Ni{async _call(_){return new wu(await super._call(_))}}class wu extends qe{constructor({logits:_,pred_boxes:Y}){super(),this.logits=_,this.pred_boxes=Y}}class yu extends ne{}class dc extends yu{async get_image_embeddings({pixel_values:_}){return await Ge(this,{pixel_values:_})}async forward(_){if((!_.image_embeddings||!_.image_positional_embeddings)&&(_={..._,...await this.get_image_embeddings(_)}),!_.input_labels&&_.input_points){const xe=_.input_points.dims.slice(0,-1),ke=xe.reduce((Fe,st)=>Fe*st,1);_.input_labels=new v.Tensor("int64",new BigInt64Array(ke).fill(1n),xe)}const Y={image_embeddings:_.image_embeddings,image_positional_embeddings:_.image_positional_embeddings};return _.input_points&&(Y.input_points=_.input_points),_.input_labels&&(Y.input_labels=_.input_labels),_.input_boxes&&(Y.input_boxes=_.input_boxes),await ce(this.sessions.prompt_encoder_mask_decoder,Y)}async _call(_){return new Mu(await super._call(_))}}class Mu extends qe{constructor({iou_scores:_,pred_masks:Y}){super(),this.iou_scores=_,this.pred_masks=Y}}class Ui extends ne{}class bu extends Ui{}class cc extends Ui{}class Vi extends ne{}class vu extends Vi{}class Tu extends Vi{}class zr extends ne{}class xu extends zr{}class pc extends zr{async _call(_){return new en(await super._call(_))}}class co extends zr{async _call(_){return new Ht(await super._call(_))}}class Ln extends zr{async _call(_){return new Ns(await super._call(_))}}class po extends ne{}class Pu extends po{}class Eu extends po{async _call(_){return new Ns(await super._call(_))}}class Cu extends ne{}class ku extends Cu{}class zn extends ne{}class Su extends zn{}class $u extends zn{async _call(_){return new en(await super._call(_))}}class Au extends zn{async _call(_){return new Ht(await super._call(_))}}class ho extends ne{}class Iu extends ho{}class Wi extends ho{async _call(_){return new en(await super._call(_))}}class Ou extends ho{async _call(_){return new Ht(await super._call(_))}}class Fu extends ho{async _call(_){return new Ns(await super._call(_))}}class mo extends ne{}class hc extends mo{}class Du extends mo{async _call(_){return new en(await super._call(_))}}class Lu extends mo{async _call(_){return new Ht(await super._call(_))}}class Ep extends ne{}class zu extends zr{}class Bu extends zr{async _call(_){return new en(await super._call(_))}}class Ru extends zr{async _call(_){return new Ht(await super._call(_))}}class _n extends ne{}class mc extends _n{}class Nu extends _n{async _call(_){return new en(await super._call(_))}}class ju extends _n{async _call(_){return new Ht(await super._call(_))}}class Uu extends _n{async _call(_){return new zd(await super._call(_))}}class _c extends _n{async _call(_){return new Ns(await super._call(_))}}class _o extends ne{}class fc extends _o{}class Vu extends _o{}class Wu extends _o{async generate_speech(_,Y,{threshold:xe=.5,minlenratio:ke=0,maxlenratio:Fe=20,vocoder:st=null}={}){const rt={input_ids:_},{encoder_outputs:ct,encoder_attention_mask:kt}=await Ge(this,rt),Yt=ct.dims[1]/this.config.reduction_factor,Ut=Math.floor(Yt*Fe),Bt=Math.floor(Yt*ke),At=this.config.num_mel_bins;let bs=[],Vt=null,Wt=null,_s=0;for(;;){++_s;const Is=be(!!Wt);let Os;Wt?Os=Wt.output_sequence_out:Os=new v.Tensor("float32",new Float32Array(At),[1,1,At]);let ks={use_cache_branch:Is,output_sequence:Os,encoder_attention_mask:kt,speaker_embeddings:Y,encoder_hidden_states:ct};this.addPastKeyValues(ks,Vt),Wt=await ce(this.sessions.decoder_model_merged,ks),Vt=this.getPastKeyValues(Wt,Vt);const{prob:Bs,spectrum:Xs}=Wt;if(bs.push(Xs),_s>=Bt&&(Array.from(Bs.data).filter(ar=>ar>=xe).length>0||_s>=Ut))break}const ws=(0,v.cat)(bs),{waveform:es}=await ce(st.sessions.model,{spectrogram:ws});return{spectrogram:ws,waveform:es}}}class gc extends ne{main_input_name="spectrogram"}class Gu extends ne{}class Ku extends Gu{}class Gi extends ne{}class Hu extends Gi{}class wc extends Gi{}class Ki extends ne{}class qu extends Ki{}class Xu extends Ki{}class Hi extends ne{}class yc extends Hi{}class yr extends Hi{}class mr extends ne{}class Jr extends mr{}class Zr extends mr{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"text_model",...Y})}}class Qu extends mr{static async from_pretrained(_,Y={}){return super.from_pretrained(_,{model_file_name:"audio_model",...Y})}}class Yu extends ne{}class qi extends Yu{async _call(_){return new np(await super._call(_))}}class fo extends ne{}class Mc extends fo{}class Ju extends fo{}class Zu extends fo{}class Xi extends ne{}class ed extends Xi{}class td extends Xi{}class sd extends ne{}class Hs extends sd{}class rd extends sd{async _call(_){return new Ht(await super._call(_))}}class Qi extends ne{}class nd extends Qi{}class bc extends Qi{}class fn extends ne{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(_){const[Y,xe]=_.dims,ke=this.config.decoder.num_codebooks,Fe=xe-ke;let st=0;for(let kt=0;kt<_.size;++kt){if(_.data[kt]===this.config.decoder.pad_token_id)continue;const Yt=kt%xe,Ut=Math.floor(kt/xe)%ke,Bt=Yt-Ut;Bt>0&&Bt<=Fe&&(_.data[st++]=_.data[kt])}const rt=Math.floor(Y/ke),ct=st/(rt*ke);return new v.Tensor(_.type,_.data.slice(0,st),[rt,ke,ct])}prepare_inputs_for_generation(_,Y,xe){let ke=structuredClone(_);for(let st=0;st<ke.length;++st)for(let rt=0;rt<ke[st].length;++rt)st%this.config.decoder.num_codebooks>=rt&&(ke[st][rt]=BigInt(this.config.decoder.pad_token_id));return xe.guidance_scale!==null&&xe.guidance_scale>1&&(ke=ke.concat(ke)),super.prepare_inputs_for_generation(ke,Y,xe)}async generate(_){const Y=await super.generate(_),xe=this._apply_and_filter_by_delay_pattern_mask(Y).unsqueeze_(0),{audio_values:ke}=await ce(this.sessions.encodec_decode,{audio_codes:xe});return ke}}class Yi extends ne{}class od extends Yi{}class id extends Yi{async _call(_){return new Ht(await super._call(_))}}class Ji extends ne{}class ad extends Ji{}class ld extends Ji{async _call(_){return new Ht(await super._call(_))}}class go extends ne{}class ud extends go{}class dd extends go{async _call(_){return new Ht(await super._call(_))}}class Zi extends ne{}class cd extends Zi{}class pd extends Zi{async _call(_){return new Ht(await super._call(_))}}class ea extends ne{}class hd extends ea{}class vc extends ne{}class ta extends vc{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(..._){super(..._),this._generation_mode="text"}async forward(_){const Y=this._generation_mode??"text";let xe;if(Y==="text"||!_.past_key_values){const ct=this.sessions.prepare_inputs_embeds,kt=(0,U.pick)(_,ct.inputNames);xe=await ce(ct,kt)}else{const ct=this.sessions.gen_img_embeds,kt=(0,U.pick)({image_ids:_.input_ids},ct.inputNames);xe=await ce(ct,kt)}const ke={..._,...xe},Fe=await Ne(this,ke),st=this.sessions[Y==="text"?"lm_head":"gen_head"];if(!st)throw new Error(`Unable to find "${st}" generation head`);const rt=await ce(st,(0,U.pick)(Fe,st.inputNames));return{...xe,...Fe,...rt}}async generate(_){return this._generation_mode="text",super.generate(_)}async generate_images(_){this._generation_mode="image";const Y=(_.inputs??_[this.main_input_name]).dims[1],ke=(await super.generate(_)).slice(null,[Y,null]),Fe=this.sessions.image_decode,{decoded_image:st}=await ce(Fe,{generated_tokens:ke}),rt=st.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),ct=[];for(const kt of rt){const Yt=z.RawImage.fromTensor(kt);ct.push(Yt)}return ct}}class md extends qe{constructor({char_logits:_,bpe_logits:Y,wp_logits:xe}){super(),this.char_logits=_,this.bpe_logits=Y,this.wp_logits=xe}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class _d extends ne{}class fd extends _d{async _call(_){return new md(await super._call(_))}}class gd extends ne{}class wd extends gd{}class yd extends gd{}class sa extends ne{}class Tc extends sa{}class Md extends sa{}class ms{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(_,{progress_callback:Y=null,config:xe=null,cache_dir:ke=null,local_files_only:Fe=!1,revision:st="main",model_file_name:rt=null,subfolder:ct="onnx",device:kt=null,dtype:Yt=null,use_external_data_format:Ut=null,session_options:Bt={}}={}){const At={progress_callback:Y,config:xe,cache_dir:ke,local_files_only:Fe,revision:st,model_file_name:rt,subfolder:ct,device:kt,dtype:Yt,use_external_data_format:Ut,session_options:Bt};if(At.config=await g.AutoConfig.from_pretrained(_,At),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const bs of this.MODEL_CLASS_MAPPINGS){const Vt=bs.get(At.config.model_type);if(Vt)return await Vt[1].from_pretrained(_,At)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${At.config.model_type}", attempting to construct from base class.`),await ne.from_pretrained(_,At);throw Error(`Unsupported model type: ${At.config.model_type}`)}}const Cp=new Map([["bert",["BertModel",Me]],["nomic_bert",["NomicBertModel",nt]],["roformer",["RoFormerModel",ut]],["electra",["ElectraModel",Qt]],["esm",["EsmModel",qn]],["convbert",["ConvBertModel",$e]],["camembert",["CamembertModel",Ks]],["deberta",["DebertaModel",Nr]],["deberta-v2",["DebertaV2Model",ur]],["mpnet",["MPNetModel",ln]],["albert",["AlbertModel",Sn]],["distilbert",["DistilBertModel",St]],["roberta",["RobertaModel",zs]],["xlm",["XLMModel",wr]],["xlm-roberta",["XLMRobertaModel",gt]],["clap",["ClapModel",Jr]],["clip",["CLIPModel",ja]],["clipseg",["CLIPSegModel",Ha]],["chinese_clip",["ChineseCLIPModel",cr]],["siglip",["SiglipModel",Wa]],["jina_clip",["JinaCLIPModel",Zn]],["mobilebert",["MobileBertModel",Pn]],["squeezebert",["SqueezeBertModel",cn]],["wav2vec2",["Wav2Vec2Model",xu]],["wav2vec2-bert",["Wav2Vec2BertModel",hc]],["unispeech",["UniSpeechModel",Su]],["unispeech-sat",["UniSpeechSatModel",Iu]],["hubert",["HubertModel",zu]],["wavlm",["WavLMModel",mc]],["audio-spectrogram-transformer",["ASTModel",Sa]],["vits",["VitsModel",qi]],["pyannote",["PyAnnoteModel",Pu]],["wespeaker-resnet",["WeSpeakerResNetModel",ku]],["detr",["DetrModel",eu]],["rt_detr",["RTDetrModel",wi]],["table-transformer",["TableTransformerModel",Mi]],["vit",["ViTModel",Al]],["ijepa",["IJepaModel",Ol]],["pvt",["PvtModel",di]],["vit_msn",["ViTMSNModel",Rl]],["vit_mae",["ViTMAEModel",Bl]],["groupvit",["GroupViTModel",jl]],["fastvit",["FastViTModel",Ul]],["mobilevit",["MobileViTModel",Gl]],["mobilevitv2",["MobileViTV2Model",Hl]],["owlvit",["OwlViTModel",Xl]],["owlv2",["Owlv2Model",Yl]],["beit",["BeitModel",Jl]],["deit",["DeiTModel",bi]],["hiera",["HieraModel",nu]],["convnext",["ConvNextModel",Li]],["convnextv2",["ConvNextV2Model",uc]],["dinov2",["Dinov2Model",fu]],["resnet",["ResNetModel",ou]],["swin",["SwinModel",io]],["swin2sr",["Swin2SRModel",iu]],["donut-swin",["DonutSwinModel",mu]],["yolos",["YolosModel",gu]],["dpt",["DPTModel",$i]],["glpn",["GLPNModel",uo]],["hifigan",["SpeechT5HifiGan",gc]],["efficientnet",["EfficientNetModel",Hs]],["decision_transformer",["DecisionTransformerModel",hd]],["patchtst",["PatchTSTForPrediction",wd]],["patchtsmixer",["PatchTSMixerForPrediction",Tc]],["mobilenet_v1",["MobileNetV1Model",od]],["mobilenet_v2",["MobileNetV2Model",ad]],["mobilenet_v3",["MobileNetV3Model",ud]],["mobilenet_v4",["MobileNetV4Model",cd]],["maskformer",["MaskFormerModel",hu]],["mgp-str",["MgpstrForSceneTextRecognition",fd]]]),xc=new Map([["t5",["T5Model",hn]],["longt5",["LongT5Model",In]],["mt5",["MT5Model",Q]],["bart",["BartModel",Ie]],["mbart",["MBartModel",vt]],["marian",["MarianModel",bu]],["whisper",["WhisperModel",$a]],["m2m_100",["M2M100Model",vu]],["blenderbot",["BlenderbotModel",Se]],["blenderbot-small",["BlenderbotSmallModel",Rs]]]),Pc=new Map([["bloom",["BloomModel",Pl]],["jais",["JAISModel",Ya]],["gpt2",["GPT2Model",Xa]],["gptj",["GPTJModel",sl]],["gpt_bigcode",["GPTBigCodeModel",nl]],["gpt_neo",["GPTNeoModel",hr]],["gpt_neox",["GPTNeoXModel",el]],["codegen",["CodeGenModel",Uo]],["llama",["LlamaModel",Wo]],["exaone",["ExaoneModel",to]],["olmo",["OlmoModel",ul]],["olmo2",["Olmo2Model",dl]],["mobilellm",["MobileLLMModel",ll]],["granite",["GraniteModel",tc]],["cohere",["CohereModel",hl]],["gemma",["GemmaModel",as]],["gemma2",["Gemma2Model",_l]],["openelm",["OpenELMModel",gl]],["qwen2",["Qwen2Model",yl]],["phi",["PhiModel",Tl]],["phi3",["Phi3Model",so]],["mpt",["MptModel",Cl]],["opt",["OPTModel",Sl]],["mistral",["MistralModel",Hu]],["starcoder2",["Starcoder2Model",qu]],["falcon",["FalconModel",yc]],["stablelm",["StableLmModel",ed]]]),ra=new Map([["speecht5",["SpeechT5ForSpeechToText",Vu]],["whisper",["WhisperForConditionalGeneration",Aa]],["moonshine",["MoonshineForConditionalGeneration",Ia]]]),bd=new Map([["speecht5",["SpeechT5ForTextToSpeech",Wu]]]),vd=new Map([["vits",["VitsModel",qi]],["musicgen",["MusicgenForConditionalGeneration",fn]]]),Ec=new Map([["bert",["BertForSequenceClassification",Re]],["roformer",["RoFormerForSequenceClassification",I]],["electra",["ElectraForSequenceClassification",xs]],["esm",["EsmForSequenceClassification",Wr]],["convbert",["ConvBertForSequenceClassification",et]],["camembert",["CamembertForSequenceClassification",Rr]],["deberta",["DebertaForSequenceClassification",kr]],["deberta-v2",["DebertaV2ForSequenceClassification",jr]],["mpnet",["MPNetForSequenceClassification",un]],["albert",["AlbertForSequenceClassification",pn]],["distilbert",["DistilBertForSequenceClassification",cs]],["roberta",["RobertaForSequenceClassification",Zt]],["xlm",["XLMForSequenceClassification",Vs]],["xlm-roberta",["XLMRobertaForSequenceClassification",qr]],["bart",["BartForSequenceClassification",_t]],["mbart",["MBartForSequenceClassification",It]],["mobilebert",["MobileBertForSequenceClassification",Ir]],["squeezebert",["SqueezeBertForSequenceClassification",Cn]]]),Td=new Map([["bert",["BertForTokenClassification",We]],["roformer",["RoFormerForTokenClassification",ie]],["electra",["ElectraForTokenClassification",hs]],["esm",["EsmForTokenClassification",xn]],["convbert",["ConvBertForTokenClassification",ot]],["camembert",["CamembertForTokenClassification",Cr]],["deberta",["DebertaForTokenClassification",vr]],["deberta-v2",["DebertaV2ForTokenClassification",rr]],["mpnet",["MPNetForTokenClassification",Kr]],["distilbert",["DistilBertForTokenClassification",Ur]],["roberta",["RobertaForTokenClassification",us]],["xlm",["XLMForTokenClassification",Mt]],["xlm-roberta",["XLMRobertaForTokenClassification",Qn]]]),na=new Map([["t5",["T5ForConditionalGeneration",An]],["longt5",["LongT5ForConditionalGeneration",Te]],["mt5",["MT5ForConditionalGeneration",pe]],["bart",["BartForConditionalGeneration",Xe]],["mbart",["MBartForConditionalGeneration",Qe]],["marian",["MarianMTModel",cc]],["m2m_100",["M2M100ForConditionalGeneration",Tu]],["blenderbot",["BlenderbotForConditionalGeneration",Ps]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",dr]]]),oa=new Map([["bloom",["BloomForCausalLM",El]],["gpt2",["GPT2LMHeadModel",Qa]],["jais",["JAISLMHeadModel",Ja]],["gptj",["GPTJForCausalLM",rl]],["gpt_bigcode",["GPTBigCodeForCausalLM",ol]],["gpt_neo",["GPTNeoForCausalLM",Za]],["gpt_neox",["GPTNeoXForCausalLM",tl]],["codegen",["CodeGenForCausalLM",il]],["llama",["LlamaForCausalLM",ec]],["exaone",["ExaoneForCausalLM",al]],["olmo",["OlmoForCausalLM",qo]],["olmo2",["Olmo2ForCausalLM",cl]],["mobilellm",["MobileLLMForCausalLM",On]],["granite",["GraniteForCausalLM",pl]],["cohere",["CohereForCausalLM",sc]],["gemma",["GemmaForCausalLM",ml]],["gemma2",["Gemma2ForCausalLM",fl]],["openelm",["OpenELMForCausalLM",wl]],["qwen2",["Qwen2ForCausalLM",Ml]],["phi",["PhiForCausalLM",Fn]],["phi3",["Phi3ForCausalLM",xl]],["mpt",["MptForCausalLM",kl]],["opt",["OPTForCausalLM",$l]],["mbart",["MBartForCausalLM",Xt]],["mistral",["MistralForCausalLM",wc]],["starcoder2",["Starcoder2ForCausalLM",Xu]],["falcon",["FalconForCausalLM",yr]],["trocr",["TrOCRForCausalLM",Ku]],["stablelm",["StableLmForCausalLM",td]],["phi3_v",["Phi3VForCausalLM",or]]]),Cc=new Map([["multi_modality",["MultiModalityCausalLM",ta]]]),xd=new Map([["bert",["BertForMaskedLM",je]],["roformer",["RoFormerForMaskedLM",ht]],["electra",["ElectraForMaskedLM",ts]],["esm",["EsmForMaskedLM",Tn]],["convbert",["ConvBertForMaskedLM",He]],["camembert",["CamembertForMaskedLM",sr]],["deberta",["DebertaForMaskedLM",br]],["deberta-v2",["DebertaV2ForMaskedLM",Tr]],["mpnet",["MPNetForMaskedLM",Gr]],["albert",["AlbertForMaskedLM",ns]],["distilbert",["DistilBertForMaskedLM",Vr]],["roberta",["RobertaForMaskedLM",gr]],["xlm",["XLMWithLMHeadModel",As]],["xlm-roberta",["XLMRobertaForMaskedLM",tr]],["mobilebert",["MobileBertForMaskedLM",Xn]],["squeezebert",["SqueezeBertForMaskedLM",En]]]),Pd=new Map([["bert",["BertForQuestionAnswering",ze]],["roformer",["RoFormerForQuestionAnswering",X]],["electra",["ElectraForQuestionAnswering",$s]],["convbert",["ConvBertForQuestionAnswering",yt]],["camembert",["CamembertForQuestionAnswering",an]],["deberta",["DebertaForQuestionAnswering",Sr]],["deberta-v2",["DebertaV2ForQuestionAnswering",it]],["mpnet",["MPNetForQuestionAnswering",dn]],["albert",["AlbertForQuestionAnswering",$n]],["distilbert",["DistilBertForQuestionAnswering",rs]],["roberta",["RobertaForQuestionAnswering",xt]],["xlm",["XLMForQuestionAnswering",Es]],["xlm-roberta",["XLMRobertaForQuestionAnswering",ka]],["mobilebert",["MobileBertForQuestionAnswering",fr]],["squeezebert",["SqueezeBertForQuestionAnswering",kn]]]),ia=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ao]],["idefics3",["Idefics3ForConditionalGeneration",Io]]]),kc=new Map([["llava",["LlavaForConditionalGeneration",Yn]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Oa]],["moondream1",["Moondream1ForConditionalGeneration",Fa]],["florence2",["Florence2ForConditionalGeneration",La]],["qwen2-vl",["Qwen2VLForConditionalGeneration",vl]],["idefics3",["Idefics3ForConditionalGeneration",Io]],["paligemma",["PaliGemmaForConditionalGeneration",Ba]]]),Sc=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ao]]]),$c=new Map([["vit",["ViTForImageClassification",Il]],["ijepa",["IJepaForImageClassification",Fl]],["pvt",["PvtForImageClassification",Ll]],["vit_msn",["ViTMSNForImageClassification",Nl]],["fastvit",["FastViTForImageClassification",rc]],["mobilevit",["MobileViTForImageClassification",Kl]],["mobilevitv2",["MobileViTV2ForImageClassification",ql]],["beit",["BeitForImageClassification",Zl]],["deit",["DeiTForImageClassification",vi]],["hiera",["HieraForImageClassification",xi]],["convnext",["ConvNextForImageClassification",lc]],["convnextv2",["ConvNextV2ForImageClassification",_u]],["dinov2",["Dinov2ForImageClassification",Ri]],["resnet",["ResNetForImageClassification",Pi]],["swin",["SwinForImageClassification",Ci]],["segformer",["SegformerForImageClassification",Ju]],["efficientnet",["EfficientNetForImageClassification",rd]],["mobilenet_v1",["MobileNetV1ForImageClassification",id]],["mobilenet_v2",["MobileNetV2ForImageClassification",ld]],["mobilenet_v3",["MobileNetV3ForImageClassification",dd]],["mobilenet_v4",["MobileNetV4ForImageClassification",pd]]]),Bn=new Map([["detr",["DetrForObjectDetection",tu]],["rt_detr",["RTDetrForObjectDetection",Yr]],["table-transformer",["TableTransformerForObjectDetection",ru]],["yolos",["YolosForObjectDetection",ji]]]),aa=new Map([["owlvit",["OwlViTForObjectDetection",Ql]],["owlv2",["Owlv2ForObjectDetection",nc]]]),la=new Map([["detr",["DetrForSegmentation",su]],["clipseg",["CLIPSegForImageSegmentation",qa]]]),ua=new Map([["segformer",["SegformerForSemanticSegmentation",Zu]],["sapiens",["SapiensForSemanticSegmentation",lu]]]),da=new Map([["detr",["DetrForSegmentation",su]],["maskformer",["MaskFormerForInstanceSegmentation",lo]]]),Ed=new Map([["sam",["SamModel",dc]]]),Cd=new Map([["wav2vec2",["Wav2Vec2ForCTC",pc]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Du]],["unispeech",["UniSpeechForCTC",$u]],["unispeech-sat",["UniSpeechSatForCTC",Wi]],["wavlm",["WavLMForCTC",Nu]],["hubert",["HubertForCTC",Bu]]]),ca=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",co]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Lu]],["unispeech",["UniSpeechForSequenceClassification",Au]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Ou]],["wavlm",["WavLMForSequenceClassification",ju]],["hubert",["HubertForSequenceClassification",Ru]],["audio-spectrogram-transformer",["ASTForAudioClassification",ko]]]),pa=new Map([["wavlm",["WavLMForXVector",Uu]]]),kd=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Fu]],["wavlm",["WavLMForAudioFrameClassification",_c]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Ln]],["pyannote",["PyAnnoteForAudioFrameClassification",Eu]]]),Sd=new Map([["vitmatte",["VitMatteForImageMatting",Wl]]]),$d=new Map([["patchtst",["PatchTSTForPrediction",yd]],["patchtsmixer",["PatchTSMixerForPrediction",Md]]]),Ad=new Map([["swin2sr",["Swin2SRForImageSuperResolution",ic]]]),Id=new Map([["dpt",["DPTForDepthEstimation",Dn]],["depth_anything",["DepthAnythingForDepthEstimation",Ai]],["glpn",["GLPNForDepthEstimation",Oi]],["sapiens",["SapiensForDepthEstimation",uu]],["depth_pro",["DepthProForDepthEstimation",cu]]]),ha=new Map([["sapiens",["SapiensForNormalEstimation",ac]]]),Od=new Map([["vitpose",["VitPoseForPoseEstimation",Dl]]]),Fd=new Map([["clip",["CLIPVisionModelWithProjection",Va]],["siglip",["SiglipVisionModel",Ka]],["jina_clip",["JinaCLIPVisionModel",pr]]]),Dd=[[Cp,D.EncoderOnly],[xc,D.EncoderDecoder],[Pc,D.DecoderOnly],[Ec,D.EncoderOnly],[Td,D.EncoderOnly],[na,D.Seq2Seq],[ra,D.Seq2Seq],[oa,D.DecoderOnly],[Cc,D.MultiModality],[xd,D.EncoderOnly],[Pd,D.EncoderOnly],[ia,D.Vision2Seq],[kc,D.ImageTextToText],[$c,D.EncoderOnly],[la,D.EncoderOnly],[da,D.EncoderOnly],[ua,D.EncoderOnly],[Sd,D.EncoderOnly],[$d,D.EncoderOnly],[Ad,D.EncoderOnly],[Id,D.EncoderOnly],[ha,D.EncoderOnly],[Od,D.EncoderOnly],[Bn,D.EncoderOnly],[aa,D.EncoderOnly],[Ed,D.MaskGeneration],[Cd,D.EncoderOnly],[ca,D.EncoderOnly],[bd,D.Seq2Seq],[vd,D.EncoderOnly],[pa,D.EncoderOnly],[kd,D.EncoderOnly],[Fd,D.EncoderOnly]];for(const[f,_]of Dd)for(const[Y,xe]of f.values())$.set(Y,_),C.set(xe,Y),w.set(Y,xe);const Ac=[["MusicgenForConditionalGeneration",fn,D.Musicgen],["Phi3VForCausalLM",or,D.Phi3V],["CLIPTextModelWithProjection",Ua,D.EncoderOnly],["SiglipTextModel",Ga,D.EncoderOnly],["JinaCLIPTextModel",Fo,D.EncoderOnly],["ClapTextModelWithProjection",Zr,D.EncoderOnly],["ClapAudioModelWithProjection",Qu,D.EncoderOnly]];for(const[f,_,Y]of Ac)$.set(f,Y),C.set(_,f),w.set(f,_);class kp extends ms{static MODEL_CLASS_MAPPINGS=Dd.map(_=>_[0]);static BASE_IF_FAIL=!0}class Ic extends ms{static MODEL_CLASS_MAPPINGS=[Ec]}class Oc extends ms{static MODEL_CLASS_MAPPINGS=[Td]}class Fc extends ms{static MODEL_CLASS_MAPPINGS=[na]}class Dc extends ms{static MODEL_CLASS_MAPPINGS=[ra]}class Sp extends ms{static MODEL_CLASS_MAPPINGS=[bd]}class Lc extends ms{static MODEL_CLASS_MAPPINGS=[vd]}class zc extends ms{static MODEL_CLASS_MAPPINGS=[oa]}class Bc extends ms{static MODEL_CLASS_MAPPINGS=[xd]}class $p extends ms{static MODEL_CLASS_MAPPINGS=[Pd]}class Rc extends ms{static MODEL_CLASS_MAPPINGS=[ia]}class Nc extends ms{static MODEL_CLASS_MAPPINGS=[$c]}class jc extends ms{static MODEL_CLASS_MAPPINGS=[la]}class Uc extends ms{static MODEL_CLASS_MAPPINGS=[ua]}class Ap extends ms{static MODEL_CLASS_MAPPINGS=[da]}class Vc extends ms{static MODEL_CLASS_MAPPINGS=[Bn]}class Wc extends ms{static MODEL_CLASS_MAPPINGS=[aa]}class Gc extends ms{static MODEL_CLASS_MAPPINGS=[Ed]}class Kc extends ms{static MODEL_CLASS_MAPPINGS=[Cd]}class Hc extends ms{static MODEL_CLASS_MAPPINGS=[ca]}class qc extends ms{static MODEL_CLASS_MAPPINGS=[pa]}class Xc extends ms{static MODEL_CLASS_MAPPINGS=[kd]}class Qc extends ms{static MODEL_CLASS_MAPPINGS=[Sc]}class Yc extends ms{static MODEL_CLASS_MAPPINGS=[Sd]}class Jc extends ms{static MODEL_CLASS_MAPPINGS=[Ad]}class Zc extends ms{static MODEL_CLASS_MAPPINGS=[Id]}class Ld extends ms{static MODEL_CLASS_MAPPINGS=[ha]}class ep extends ms{static MODEL_CLASS_MAPPINGS=[Od]}class tp extends ms{static MODEL_CLASS_MAPPINGS=[Fd]}class sp extends qe{constructor({logits:_,past_key_values:Y,encoder_outputs:xe,decoder_attentions:ke=null,cross_attentions:Fe=null}){super(),this.logits=_,this.past_key_values=Y,this.encoder_outputs=xe,this.decoder_attentions=ke,this.cross_attentions=Fe}}class Ht extends qe{constructor({logits:_}){super(),this.logits=_}}class zd extends qe{constructor({logits:_,embeddings:Y}){super(),this.logits=_,this.embeddings=Y}}class Ns extends qe{constructor({logits:_}){super(),this.logits=_}}class Us extends qe{constructor({logits:_}){super(),this.logits=_}}class qs extends qe{constructor({start_logits:_,end_logits:Y}){super(),this.start_logits=_,this.end_logits=Y}}class en extends qe{constructor({logits:_}){super(),this.logits=_}}class rp extends qe{constructor({logits:_,past_key_values:Y}){super(),this.logits=_,this.past_key_values=Y}}class Bd extends qe{constructor({alphas:_}){super(),this.alphas=_}}class np extends qe{constructor({waveform:_,spectrogram:Y}){super(),this.waveform=_,this.spectrogram=Y}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(Ce,A,r)=>{r.r(A),r.d(A,{ASTFeatureExtractor:()=>j});var g=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var O=r("./src/utils/audio.js");class j extends g.FeatureExtractor{constructor(U){super(U);const y=this.config.sampling_rate,P=(0,O.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(y/2),y,null,"kaldi",!0);for(let b=0;b<P.length;++b)P[b].push(0);this.mel_filters=P,this.window=(0,O.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(U,y){return(0,O.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:y,transpose:!0})}async _call(U){(0,g.validate_audio_inputs)(U,"ASTFeatureExtractor");const y=await this._extract_fbank_features(U,this.config.max_length);if(this.config.do_normalize){const P=this.std*2,b=y.data;for(let T=0;T<b.length;++T)b[T]=(b[T]-this.mean)/P}return{input_values:y.unsqueeze_(0)}}}},"./src/models/auto/feature_extraction_auto.js":(Ce,A,r)=>{r.r(A),r.d(A,{AutoFeatureExtractor:()=>te});var g=r("./src/utils/constants.js"),O=r("./src/utils/hub.js");r("./src/base/feature_extraction_utils.js");var j=r("./src/models/feature_extractors.js");class te{static async from_pretrained(y,P={}){const b=await(0,O.getModelJSON)(y,g.FEATURE_EXTRACTOR_NAME,!0,P),T=b.feature_extractor_type,v=j[T];if(!v)throw new Error(`Unknown feature_extractor_type: '${T}'. Please report this at ${g.GITHUB_ISSUE_URL}.`);return new v(b)}}},"./src/models/auto/image_processing_auto.js":(Ce,A,r)=>{r.r(A),r.d(A,{AutoImageProcessor:()=>U});var g=r("./src/utils/constants.js"),O=r("./src/utils/hub.js"),j=r("./src/base/image_processors_utils.js"),te=r("./src/models/image_processors.js");class U{static async from_pretrained(P,b={}){const T=await(0,O.getModelJSON)(P,g.IMAGE_PROCESSOR_NAME,!0,b),v=T.image_processor_type??T.feature_extractor_type;let z=te[v];return z||(v!==void 0&&console.warn(`Image processor type '${v}' not found, assuming base ImageProcessor. Please report this at ${g.GITHUB_ISSUE_URL}.`),z=j.ImageProcessor),new z(T)}}},"./src/models/auto/processing_auto.js":(Ce,A,r)=>{r.r(A),r.d(A,{AutoProcessor:()=>P});var g=r("./src/utils/constants.js"),O=r("./src/utils/hub.js"),j=r("./src/base/processing_utils.js"),te=r("./src/models/processors.js"),U=r("./src/models/image_processors.js"),y=r("./src/models/feature_extractors.js");class P{static async from_pretrained(T,v={}){const z=await(0,O.getModelJSON)(T,g.IMAGE_PROCESSOR_NAME,!0,v),{image_processor_type:K,feature_extractor_type:re,processor_class:ae}=z;if(ae&&te[ae])return te[ae].from_pretrained(T,v);if(!K&&!re)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const R={};if(K){const H=U[K];if(!H)throw new Error(`Unknown image_processor_type: '${K}'.`);R.image_processor=new H(z)}if(re){const H=U[re];if(H)R.image_processor=new H(z);else{const D=y[re];if(!D)throw new Error(`Unknown feature_extractor_type: '${re}'.`);R.feature_extractor=new D(z)}}const G={};return new j.Processor(G,R)}}},"./src/models/beit/image_processing_beit.js":(Ce,A,r)=>{r.r(A),r.d(A,{BeitFeatureExtractor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(Ce,A,r)=>{r.r(A),r.d(A,{BitImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(Ce,A,r)=>{r.r(A),r.d(A,{ChineseCLIPFeatureExtractor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(Ce,A,r)=>{r.r(A),r.d(A,{ClapFeatureExtractor:()=>j});var g=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var O=r("./src/utils/audio.js");class j extends g.FeatureExtractor{constructor(U){super(U),this.mel_filters=(0,O.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,O.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,O.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(U,y,P,b){let T;const v=U.length-y;if(v>0)if(P==="rand_trunc"){const z=Math.floor(Math.random()*(v+1));U=U.subarray(z,z+y),T=await this._extract_fbank_features(U,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${P}" not implemented`);else{if(v<0){let z=new Float64Array(y);if(z.set(U),b==="repeat")for(let K=U.length;K<y;K+=U.length)z.set(U.subarray(0,Math.min(U.length,y-K)),K);else if(b==="repeatpad")for(let K=U.length;K<-v;K+=U.length)z.set(U,K);U=z}if(P==="fusion")throw new Error(`Truncation strategy "${P}" not implemented`);T=await this._extract_fbank_features(U,this.mel_filters_slaney,this.config.nb_max_samples)}return T.unsqueeze_(0)}async _extract_fbank_features(U,y,P=null){return(0,O.spectrogram)(U,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:y,log_mel:"dB",max_num_frames:P,do_pad:!1,transpose:!0})}async _call(U,{max_length:y=null}={}){return(0,g.validate_audio_inputs)(U,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(U,y??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}},"./src/models/clip/image_processing_clip.js":(Ce,A,r)=>{r.r(A),r.d(A,{CLIPFeatureExtractor:()=>j,CLIPImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}class j extends O{}},"./src/models/convnext/image_processing_convnext.js":(Ce,A,r)=>{r.r(A),r.d(A,{ConvNextFeatureExtractor:()=>j,ConvNextImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{constructor(U){super(U),this.crop_pct=this.config.crop_pct??.875}async resize(U){const y=this.size?.shortest_edge;if(y===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(y<384){const P=Math.floor(y/this.crop_pct),[b,T]=this.get_resize_output_image_size(U,{shortest_edge:P});U=await U.resize(b,T,{resample:this.resample}),U=await U.center_crop(y,y)}else U=await U.resize(y,y,{resample:this.resample});return U}}class j extends O{}},"./src/models/deit/image_processing_deit.js":(Ce,A,r)=>{r.r(A),r.d(A,{DeiTFeatureExtractor:()=>j,DeiTImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}class j extends O{}},"./src/models/detr/image_processing_detr.js":(Ce,A,r)=>{r.r(A),r.d(A,{DetrFeatureExtractor:()=>te,DetrImageProcessor:()=>j});var g=r("./src/base/image_processors_utils.js"),O=r("./src/utils/tensor.js");class j extends g.ImageProcessor{async _call(y){const P=await super._call(y),b=[P.pixel_values.dims[0],64,64],T=(0,O.full)(b,1n);return{...P,pixel_mask:T}}post_process_object_detection(...y){return(0,g.post_process_object_detection)(...y)}post_process_panoptic_segmentation(...y){return(0,g.post_process_panoptic_segmentation)(...y)}post_process_instance_segmentation(...y){return(0,g.post_process_instance_segmentation)(...y)}}class te extends j{}},"./src/models/donut/image_processing_donut.js":(Ce,A,r)=>{r.r(A),r.d(A,{DonutFeatureExtractor:()=>j,DonutImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{pad_image(U,y,P,b={}){const[T,v,z]=y;let K=this.image_mean;Array.isArray(this.image_mean)||(K=new Array(z).fill(K));let re=this.image_std;Array.isArray(re)||(re=new Array(z).fill(K));const ae=K.map((R,G)=>-R/re[G]);return super.pad_image(U,y,P,{center:!0,constant_values:ae,...b})}}class j extends O{}},"./src/models/dpt/image_processing_dpt.js":(Ce,A,r)=>{r.r(A),r.d(A,{DPTFeatureExtractor:()=>j,DPTImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}class j extends O{}},"./src/models/efficientnet/image_processing_efficientnet.js":(Ce,A,r)=>{r.r(A),r.d(A,{EfficientNetImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{constructor(te){super(te),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(U=>U*U))}}},"./src/models/feature_extractors.js":(Ce,A,r)=>{r.r(A),r.d(A,{ASTFeatureExtractor:()=>g.ASTFeatureExtractor,ClapFeatureExtractor:()=>O.ClapFeatureExtractor,ImageFeatureExtractor:()=>v.ImageProcessor,MoonshineFeatureExtractor:()=>j.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>te.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>U.SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor:()=>y.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>P.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>b.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>T.WhisperFeatureExtractor});var g=r("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),O=r("./src/models/clap/feature_extraction_clap.js"),j=r("./src/models/moonshine/feature_extraction_moonshine.js"),te=r("./src/models/pyannote/feature_extraction_pyannote.js"),U=r("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),y=r("./src/models/speecht5/feature_extraction_speecht5.js"),P=r("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),b=r("./src/models/wespeaker/feature_extraction_wespeaker.js"),T=r("./src/models/whisper/feature_extraction_whisper.js"),v=r("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(Ce,A,r)=>{r.r(A),r.d(A,{Florence2Processor:()=>te});var g=r("./src/base/processing_utils.js"),O=r("./src/models/auto/image_processing_auto.js"),j=r("./src/tokenizers.js");class te extends g.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=O.AutoImageProcessor;constructor(y,P){super(y,P);const{tasks_answer_post_processing_type:b,task_prompts_without_inputs:T,task_prompts_with_input:v}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(b??{})),this.task_prompts_without_inputs=new Map(Object.entries(T??{})),this.task_prompts_with_input=new Map(Object.entries(v??{})),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(y){typeof y=="string"&&(y=[y]);const P=[];for(const b of y)if(this.task_prompts_without_inputs.has(b))P.push(this.task_prompts_without_inputs.get(b));else{for(const[T,v]of this.task_prompts_with_input)if(b.includes(T)){P.push(v.replaceAll("{input}",b).replaceAll(T,""));break}P.length!==y.length&&P.push(b)}return P}post_process_generation(y,P,b){const T=this.tasks_answer_post_processing_type.get(P)??"pure_text";y=y.replaceAll("<s>","").replaceAll("</s>","");let v;switch(T){case"pure_text":v=y;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const z=T==="ocr"?"quad_boxes":"bboxes",K=y.matchAll(this.regexes[z]),re=[],ae=[];for(const[R,G,...H]of K)re.push(G?G.trim():re.at(-1)??""),ae.push(H.map((D,$)=>(Number(D)+.5)/this.size_per_bin*b[$%2]));v={labels:re,[z]:ae};break;default:throw new Error(`Task "${P}" (of type "${T}") not yet implemented.`)}return{[P]:v}}async _call(y,P=null,b={}){if(!y&&!P)throw new Error("Either text or images must be provided");const T=await this.image_processor(y,b),v=P?this.tokenizer(P,b):{};return{...T,...v}}}},"./src/models/glpn/image_processing_glpn.js":(Ce,A,r)=>{r.r(A),r.d(A,{GLPNFeatureExtractor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}},"./src/models/idefics3/image_processing_idefics3.js":(Ce,A,r)=>{r.r(A),r.d(A,{Idefics3ImageProcessor:()=>j});var g=r("./src/base/image_processors_utils.js"),O=r("./src/utils/tensor.js");class j extends g.ImageProcessor{constructor(U){super(U),this.do_image_splitting=U.do_image_splitting??!0,this.max_image_size=U.max_image_size}get_resize_for_vision_encoder(U,y){let[P,b]=U.dims.slice(-2);const T=b/P;return b>=P?(b=Math.ceil(b/y)*y,P=Math.floor(b/T),P=Math.ceil(P/y)*y):(P=Math.ceil(P/y)*y,b=Math.floor(P*T),b=Math.ceil(b/y)*y),{height:P,width:b}}async _call(U,{do_image_splitting:y=null,return_row_col_info:P=!1}={}){let b;if(!Array.isArray(U))b=[[U]];else{if(U.length===0||!U[0])throw new Error("No images provided.");Array.isArray(U[0])?b=U:b=[U]}let T=[],v=[],z=[];const K=[],re=[];for(const C of b){let x=await Promise.all(C.map(le=>this.preprocess(le)));K.push(...x.map(le=>le.original_size)),re.push(...x.map(le=>le.reshaped_input_size)),x.forEach(le=>le.pixel_values.unsqueeze_(0));const{longest_edge:J}=this.max_image_size;let q;if(y??this.do_image_splitting){let le=new Array(x.length),ce=new Array(x.length);q=await Promise.all(x.map(async(fe,Pe)=>{const be=this.get_resize_for_vision_encoder(fe.pixel_values,J),De=await(0,O.interpolate_4d)(fe.pixel_values,{size:[be.height,be.width]}),{frames:Ge,num_splits_h:Ne,num_splits_w:lt}=await this.split_image(De,this.max_image_size);return le[Pe]=Ne,ce[Pe]=lt,(0,O.cat)(Ge,0)})),v.push(le),z.push(ce)}else{const le=[J,J];q=await Promise.all(x.map(ce=>(0,O.interpolate_4d)(ce.pixel_values,{size:le}))),v.push(new Array(x.length).fill(0)),z.push(new Array(x.length).fill(0))}T.push((0,O.cat)(q,0))}const ae=T.length,[R,G,H,D]=T[0].dims;let $,w;if(ae===1)$=T[0].unsqueeze_(0),w=(0,O.full)([ae,R,H,D],!0);else{const C=Math.max(...T.map(q=>q.dims.at(0)));w=(0,O.full)([ae,C,H,D],!0);const x=w.data,J=C*H*D;for(let q=0;q<ae;++q){const le=T[q].dims[0];if(le<C){T[q]=(0,O.cat)([T[q],(0,O.full)([C-le,G,H,D],0)],0);const ce=q*J+le*H*D,fe=(q+1)*J;x.fill(!1,ce,fe)}}$=(0,O.stack)(T,0)}return{pixel_values:$,pixel_attention_mask:w,original_sizes:K,reshaped_input_sizes:re,...P?{rows:v,cols:z}:{}}}async split_image(U,{longest_edge:y}){const P=y,b=y,T=[],[v,z]=U.dims.slice(-2);let K=0,re=0;if(v>P||z>b){K=Math.ceil(v/P),re=Math.ceil(z/b);const ae=Math.ceil(v/K),R=Math.ceil(z/re);for(let D=0;D<K;++D)for(let $=0;$<re;++$){let w,C,x,J;D===K-1?(C=v-ae,J=v):(C=D*ae,J=(D+1)*ae),$===re-1?(w=z-R,x=z):(w=$*R,x=($+1)*R);const q=[C,w],le=[J,x],ce=await(0,O.slice)(U,q,le,[2,3]);T.push(ce)}const G=P,H=b;(v!==G||z!==H)&&(U=await(0,O.interpolate_4d)(U,{size:[G,H]}))}return T.push(U),{frames:T,num_splits_h:K,num_splits_w:re}}}},"./src/models/idefics3/processing_idefics3.js":(Ce,A,r)=>{r.r(A),r.d(A,{Idefics3Processor:()=>b});var g=r("./src/base/processing_utils.js"),O=r("./src/models/auto/image_processing_auto.js"),j=r("./src/tokenizers.js");r("./src/utils/image.js");var te=r("./src/utils/core.js");function U(T,v,z,K,re,ae){let R="";for(let G=0;G<v;++G){for(let H=0;H<z;++H)R+=K+`<row_${G+1}_col_${H+1}>`+re.repeat(T);R+=`
`}return R+=`
${K}${ae}`+re.repeat(T)+`${K}`,R}function y(T,v,z,K){return`${v}${K}`+z.repeat(T)+`${v}`}function P(T,v,z,K,re,ae){return T===0&&v===0?y(z,K,re,ae):U(z,T,v,K,re,ae)}class b extends g.Processor{static image_processor_class=O.AutoImageProcessor;static tokenizer_class=j.AutoTokenizer;static uses_processor_config=!0;fake_image_token="<fake_token_around_image>";image_token="<image>";global_img_token="<global-img>";async _call(v,z=null,K={}){K.return_row_col_info??=!0;let re;z&&(re=await this.image_processor(z,K)),Array.isArray(v)||(v=[v]);const ae=re.rows??[new Array(v.length).fill(0)],R=re.cols??[new Array(v.length).fill(0)],G=this.config.image_seq_len,H=[],D=[];for(let w=0;w<v.length;++w){const C=v[w],x=ae[w],J=R[w];H.push((0,te.count)(C,this.image_token));const q=x.map((fe,Pe)=>P(fe,J[Pe],G,this.fake_image_token,this.image_token,this.global_img_token)),le=C.split(this.image_token);if(le.length===0)throw new Error("The image token should be present in the text.");let ce=le[0];for(let fe=0;fe<q.length;++fe)ce+=q[fe]+le[fe+1];D.push(ce)}return{...this.tokenizer(D),...re}}}},"./src/models/image_processors.js":(Ce,A,r)=>{r.r(A),r.d(A,{BeitFeatureExtractor:()=>g.BeitFeatureExtractor,BitImageProcessor:()=>O.BitImageProcessor,CLIPFeatureExtractor:()=>te.CLIPFeatureExtractor,CLIPImageProcessor:()=>te.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>j.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>U.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>U.ConvNextImageProcessor,DPTFeatureExtractor:()=>T.DPTFeatureExtractor,DPTImageProcessor:()=>T.DPTImageProcessor,DeiTFeatureExtractor:()=>y.DeiTFeatureExtractor,DeiTImageProcessor:()=>y.DeiTImageProcessor,DetrFeatureExtractor:()=>P.DetrFeatureExtractor,DetrImageProcessor:()=>P.DetrImageProcessor,DonutFeatureExtractor:()=>b.DonutFeatureExtractor,DonutImageProcessor:()=>b.DonutImageProcessor,EfficientNetImageProcessor:()=>v.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>z.GLPNFeatureExtractor,Idefics3ImageProcessor:()=>K.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>ae.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>R.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>G.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>H.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>H.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>D.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>D.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>$.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>$.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>w.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>w.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>C.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>C.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>x.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>x.MobileViTImageProcessor,NougatImageProcessor:()=>J.NougatImageProcessor,OwlViTFeatureExtractor:()=>le.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>le.OwlViTImageProcessor,Owlv2ImageProcessor:()=>q.Owlv2ImageProcessor,Phi3VImageProcessor:()=>ce.Phi3VImageProcessor,PvtImageProcessor:()=>fe.PvtImageProcessor,Qwen2VLImageProcessor:()=>Pe.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>be.RTDetrImageProcessor,SamImageProcessor:()=>De.SamImageProcessor,SegformerFeatureExtractor:()=>Ge.SegformerFeatureExtractor,SegformerImageProcessor:()=>Ge.SegformerImageProcessor,SiglipImageProcessor:()=>Ne.SiglipImageProcessor,Swin2SRImageProcessor:()=>lt.Swin2SRImageProcessor,VLMImageProcessor:()=>re.VLMImageProcessor,ViTFeatureExtractor:()=>ue.ViTFeatureExtractor,ViTImageProcessor:()=>ue.ViTImageProcessor,VitMatteImageProcessor:()=>Z.VitMatteImageProcessor,VitPoseImageProcessor:()=>he.VitPoseImageProcessor,YolosFeatureExtractor:()=>ve.YolosFeatureExtractor,YolosImageProcessor:()=>ve.YolosImageProcessor});var g=r("./src/models/beit/image_processing_beit.js"),O=r("./src/models/bit/image_processing_bit.js"),j=r("./src/models/chinese_clip/image_processing_chinese_clip.js"),te=r("./src/models/clip/image_processing_clip.js"),U=r("./src/models/convnext/image_processing_convnext.js"),y=r("./src/models/deit/image_processing_deit.js"),P=r("./src/models/detr/image_processing_detr.js"),b=r("./src/models/donut/image_processing_donut.js"),T=r("./src/models/dpt/image_processing_dpt.js"),v=r("./src/models/efficientnet/image_processing_efficientnet.js"),z=r("./src/models/glpn/image_processing_glpn.js"),K=r("./src/models/idefics3/image_processing_idefics3.js"),re=r("./src/models/janus/image_processing_janus.js"),ae=r("./src/models/jina_clip/image_processing_jina_clip.js"),R=r("./src/models/llava_onevision/image_processing_llava_onevision.js"),G=r("./src/models/mask2former/image_processing_mask2former.js"),H=r("./src/models/maskformer/image_processing_maskformer.js"),D=r("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),$=r("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),w=r("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),C=r("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),x=r("./src/models/mobilevit/image_processing_mobilevit.js"),J=r("./src/models/nougat/image_processing_nougat.js"),q=r("./src/models/owlv2/image_processing_owlv2.js"),le=r("./src/models/owlvit/image_processing_owlvit.js"),ce=r("./src/models/phi3_v/image_processing_phi3_v.js"),fe=r("./src/models/pvt/image_processing_pvt.js"),Pe=r("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),be=r("./src/models/rt_detr/image_processing_rt_detr.js"),De=r("./src/models/sam/image_processing_sam.js"),Ge=r("./src/models/segformer/image_processing_segformer.js"),Ne=r("./src/models/siglip/image_processing_siglip.js"),lt=r("./src/models/swin2sr/image_processing_swin2sr.js"),ue=r("./src/models/vit/image_processing_vit.js"),Z=r("./src/models/vitmatte/image_processing_vitmatte.js"),he=r("./src/models/vitpose/image_processing_vitpose.js"),ve=r("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(Ce,A,r)=>{r.r(A),r.d(A,{VLMImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{constructor(te){super({do_pad:!0,pad_size:{width:te.image_size,height:te.image_size},...te}),this.constant_values=this.config.background_color.map(U=>U*this.rescale_factor)}pad_image(te,U,y,P){return super.pad_image(te,U,y,{constant_values:this.constant_values,center:!0,...P})}}},"./src/models/janus/processing_janus.js":(Ce,A,r)=>{r.r(A),r.d(A,{VLChatProcessor:()=>P});var g=r("./src/base/processing_utils.js"),O=r("./src/models/auto/image_processing_auto.js"),j=r("./src/tokenizers.js"),te=r("./src/utils/core.js"),U=r("./src/utils/tensor.js"),y=r("./src/utils/image.js");class P extends g.Processor{static image_processor_class=O.AutoImageProcessor;static tokenizer_class=j.AutoTokenizer;static uses_processor_config=!0;constructor(T,v){super(T,v),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(T,{images:v=null,chat_template:z="default"}={}){v?Array.isArray(v)||(v=[v]):v=await Promise.all(T.filter(q=>q.images).flatMap(q=>q.images).map(q=>y.RawImage.read(q)));const K=this.tokenizer,re=K.apply_chat_template(T,{tokenize:!1,add_generation_prompt:!0,chat_template:z}),ae=q=>K.encode(q,{add_special_tokens:!1}),R=re.split(this.image_tag),G=R.length-1;if(v.length!==G)throw new Error(`Number of images provided (${v.length}) does not match number of "${this.image_tag}" image tags (${G})`);const[H,D,$]=K.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let w=ae(R[0]),C=new Array(w.length).fill(!1);for(let q=1;q<R.length;++q){const le=new Array(this.num_image_tokens).fill(H),ce=ae(R[q]);w=(0,te.mergeArrays)(w,[D],le,[$],ce);const fe=new Array(this.num_image_tokens).fill(!0);C=(0,te.mergeArrays)(C,[!1],fe,[!1],new Array(ce.length).fill(!1))}const x=[1,w.length],J={input_ids:new U.Tensor("int64",w,x),attention_mask:new U.Tensor("int64",new Array(w.length).fill(1),x),images_seq_mask:new U.Tensor("bool",C,x),images_emb_mask:new U.Tensor("bool",new Array(G*this.num_image_tokens).fill(!0),[1,G,this.num_image_tokens])};if(v&&v.length>0){const q=await this.image_processor(v);return q.pixel_values.unsqueeze_(0),{...J,...q}}return J}}},"./src/models/jina_clip/image_processing_jina_clip.js":(Ce,A,r)=>{r.r(A),r.d(A,{JinaCLIPImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{constructor(te){const{resize_mode:U,fill_color:y,interpolation:P,size:b,...T}=te,v=U==="squash"?{width:b,height:b}:U==="shortest"?{shortest_edge:b}:{longest_edge:b},z=P==="bicubic"?3:2;super({...T,size:v,resample:z,do_center_crop:!0,crop_size:b,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(Ce,A,r)=>{r.r(A),r.d(A,{JinaCLIPProcessor:()=>te});var g=r("./src/base/processing_utils.js"),O=r("./src/models/auto/image_processing_auto.js"),j=r("./src/tokenizers.js");class te extends g.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=O.AutoImageProcessor;async _call(y=null,P=null,b={}){if(!y&&!P)throw new Error("Either text or images must be provided");const T=y?this.tokenizer(y,b):{},v=P?await this.image_processor(P,b):{};return{...T,...v}}}},"./src/models/llava_onevision/image_processing_llava_onevision.js":(Ce,A,r)=>{r.r(A),r.d(A,{LlavaOnevisionImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{}},"./src/models/mask2former/image_processing_mask2former.js":(Ce,A,r)=>{r.r(A),r.d(A,{Mask2FormerImageProcessor:()=>O});var g=r("./src/models/maskformer/image_processing_maskformer.js");class O extends g.MaskFormerImageProcessor{}},"./src/models/maskformer/image_processing_maskformer.js":(Ce,A,r)=>{r.r(A),r.d(A,{MaskFormerFeatureExtractor:()=>j,MaskFormerImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{post_process_panoptic_segmentation(...U){return(0,g.post_process_panoptic_segmentation)(...U)}post_process_instance_segmentation(...U){return(0,g.post_process_instance_segmentation)(...U)}}class j extends O{}},"./src/models/mgp_str/processing_mgp_str.js":(Ce,A,r)=>{r.r(A),r.d(A,{MgpstrProcessor:()=>y});var g=r("./src/base/processing_utils.js"),O=r("./src/models/auto/image_processing_auto.js"),j=r("./src/tokenizers.js"),te=r("./src/utils/maths.js");const U={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class y extends g.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=O.AutoImageProcessor;get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(b,T){if(!U.hasOwnProperty(T))throw new Error(`Format ${T} is not supported.`);const[v,z]=U[T],K=this[v].bind(this),[re,ae]=b.dims,R=[],G=[],H=b.tolist();for(let $=0;$<re;++$){const w=H[$],C=[],x=[];for(let q=1;q<ae;++q){const[le,ce]=(0,te.max)((0,te.softmax)(w[q]));if(x.push(le),ce==z)break;C.push(ce)}const J=x.length>0?x.reduce((q,le)=>q*le,1):0;G.push(C),R.push(J)}return[K(G),R]}char_decode(b){return this.char_tokenizer.batch_decode(b).map(T=>T.replaceAll(" ",""))}bpe_decode(b){return this.bpe_tokenizer.batch_decode(b)}wp_decode(b){return this.wp_tokenizer.batch_decode(b).map(T=>T.replaceAll(" ",""))}batch_decode([b,T,v]){const[z,K]=this._decode_helper(b,"char"),[re,ae]=this._decode_helper(T,"bpe"),[R,G]=this._decode_helper(v,"wp"),H=[],D=[];for(let $=0;$<z.length;++$){const[w,C]=(0,te.max)([K[$],ae[$],G[$]]);H.push([z[$],re[$],R[$]][C]),D.push(w)}return{generated_text:H,scores:D,char_preds:z,bpe_preds:re,wp_preds:R}}static async from_pretrained(...b){const T=await super.from_pretrained(...b),v=await j.AutoTokenizer.from_pretrained("Xenova/gpt2"),z=await j.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return T.components={image_processor:T.image_processor,char_tokenizer:T.tokenizer,bpe_tokenizer:v,wp_tokenizer:z},T}async _call(b,T=null){const v=await this.image_processor(b);return T&&(v.labels=this.tokenizer(T).input_ids),v}}},"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":(Ce,A,r)=>{r.r(A),r.d(A,{MobileNetV1FeatureExtractor:()=>j,MobileNetV1ImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends 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Promise.all(y.map(v=>this.preprocess(v,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,O.stack)(P.map((v,z)=>(0,O.cat)([v.pixel_values,b[z].pixel_values],0)),0),original_sizes:P.map(v=>v.original_size),reshaped_input_sizes:P.map(v=>v.reshaped_input_size)}}}},"./src/models/vitpose/image_processing_vitpose.js":(Ce,A,r)=>{r.r(A),r.d(A,{VitPoseImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{post_process_pose_estimation(te,U,{threshold:y=null}={}){const P=te.tolist(),[b,T,v,z]=te.dims,K=[];for(let re=0;re<b;++re){const ae=P[re],R=U[re],G=[];for(let H=0;H<R.length;++H){const D=R[H],$=[],w=[],C=[],x=D.at(-2)/z,J=D.at(-1)/v;for(let q=0;q<ae.length;++q){let[le,ce]=[0,0],fe=0,Pe=-1/0;const be=ae[q];for(let Ge=0;Ge<be.length;++Ge){const Ne=be[Ge];for(let lt=0;lt<Ne.length;++lt){const ue=Ne[lt];fe+=ue,Pe=Math.max(Pe,ue),le+=(lt+.5)*ue,ce+=Ge*ue}}if(y!=null&&Pe<y)continue;const De=[x*le/fe,J*ce/fe];$.push(De),C.push(q),w.push(Pe)}G.push({bbox:D,scores:w,labels:C,keypoints:$})}K.push(G)}return K}}},"./src/models/wav2vec2/feature_extraction_wav2vec2.js":(Ce,A,r)=>{r.r(A),r.d(A,{Wav2Vec2FeatureExtractor:()=>j});var g=r("./src/base/feature_extraction_utils.js"),O=r("./src/utils/tensor.js");class j extends g.FeatureExtractor{_zero_mean_unit_var_norm(U){const P=U.reduce((T,v)=>T+v,0)/U.length,b=U.reduce((T,v)=>T+(v-P)**2,0)/U.length;return U.map(T=>(T-P)/Math.sqrt(b+1e-7))}async _call(U){(0,g.validate_audio_inputs)(U,"Wav2Vec2FeatureExtractor"),U instanceof Float64Array&&(U=new Float32Array(U));let y=U;this.config.do_normalize&&(y=this._zero_mean_unit_var_norm(y));const P=[1,y.length];return{input_values:new O.Tensor("float32",y,P),attention_mask:new O.Tensor("int64",new BigInt64Array(y.length).fill(1n),P)}}}},"./src/models/wav2vec2/processing_wav2vec2.js":(Ce,A,r)=>{r.r(A),r.d(A,{Wav2Vec2ProcessorWithLM:()=>j});var g=r("./src/base/processing_utils.js"),O=r("./src/models/auto/feature_extraction_auto.js");class j extends g.Processor{static feature_extractor_class=O.AutoFeatureExtractor;async _call(U){return await this.feature_extractor(U)}}},"./src/models/wespeaker/feature_extraction_wespeaker.js":(Ce,A,r)=>{r.r(A),r.d(A,{WeSpeakerFeatureExtractor:()=>j});var g=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var O=r("./src/utils/audio.js");class j extends g.FeatureExtractor{constructor(U){super(U);const y=this.config.sampling_rate,P=(0,O.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(y/2),y,null,"kaldi",!0);for(let b=0;b<P.length;++b)P[b].push(0);this.mel_filters=P,this.window=(0,O.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(U){return U=U.map(y=>y*32768),(0,O.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){(0,g.validate_audio_inputs)(U,"WeSpeakerFeatureExtractor");const y=(await this._extract_fbank_features(U)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const P=y.mean(1).data,b=y.data,[T,v,z]=y.dims;for(let K=0;K<T;++K){const re=K*v*z,ae=K*z;for(let R=0;R<v;++R){const G=re+R*z;for(let H=0;H<z;++H)b[G+H]-=P[ae+H]}}}return{input_features:y}}}},"./src/models/whisper/common_whisper.js":(Ce,A,r)=>{r.r(A),r.d(A,{WHISPER_LANGUAGE_MAPPING:()=>O,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>j,whisper_language_to_code:()=>te});const g=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],O=new Map(g),j=new Map([...g.map(([U,y])=>[y,U]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function te(U){U=U.toLowerCase();let y=j.get(U);if(y===void 0)if(O.has(U))y=U;else{const b=U.length===2?O.keys():O.values();throw new Error(`Language "${U}" is not supported. Must be one of: ${JSON.stringify(b)}`)}return y}},"./src/models/whisper/feature_extraction_whisper.js":(Ce,A,r)=>{r.r(A),r.d(A,{WhisperFeatureExtractor:()=>te});var g=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var O=r("./src/utils/audio.js"),j=r("./src/utils/maths.js");class te extends g.FeatureExtractor{constructor(y){super(y),this.config.mel_filters??=(0,O.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,O.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(y){const P=await(0,O.spectrogram)(y,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}),b=P.data,T=(0,j.max)(b)[0];for(let v=0;v<b.length;++v)b[v]=(Math.max(b[v],T-8)+4)/4;return P}async _call(y){(0,g.validate_audio_inputs)(y,"WhisperFeatureExtractor");let P;return y.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),P=y.slice(0,this.config.n_samples)):(P=new Float32Array(this.config.n_samples),P.set(y)),{input_features:(await this._extract_fbank_features(P)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":(Ce,A,r)=>{r.r(A),r.d(A,{WhisperGenerationConfig:()=>O});var g=r("./src/generation/configuration_utils.js");class O extends g.GenerationConfig{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}},"./src/models/whisper/processing_whisper.js":(Ce,A,r)=>{r.r(A),r.d(A,{WhisperProcessor:()=>te});var g=r("./src/models/auto/feature_extraction_auto.js"),O=r("./src/tokenizers.js"),j=r("./src/base/processing_utils.js");class te extends j.Processor{static tokenizer_class=O.AutoTokenizer;static feature_extractor_class=g.AutoFeatureExtractor;async _call(y){return await this.feature_extractor(y)}}},"./src/models/yolos/image_processing_yolos.js":(Ce,A,r)=>{r.r(A),r.d(A,{YolosFeatureExtractor:()=>j,YolosImageProcessor:()=>O});var g=r("./src/base/image_processors_utils.js");class O extends g.ImageProcessor{post_process_object_detection(...U){return(0,g.post_process_object_detection)(...U)}}class j extends O{}},"./src/ops/registry.js":(Ce,A,r)=>{r.r(A),r.d(A,{TensorOpRegistry:()=>te});var g=r("./src/backends/onnx.js"),O=r("./src/utils/tensor.js");const j=async(U,y,P)=>{const b=await(0,g.createInferenceSession)(new Uint8Array(U),y);return async T=>{const v=Object.fromEntries(Object.entries(T).map(([K,re])=>[K,re.ort_tensor])),z=await b.run(v);return Array.isArray(P)?P.map(K=>new O.Tensor(z[K])):new O.Tensor(z[P])}};class te{static session_options={};static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=j([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=j([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=j([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=j([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=j([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=j([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=j([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}},"./src/pipelines.js":(Ce,A,r)=>{r.r(A),r.d(A,{AudioClassificationPipeline:()=>ce,AutomaticSpeechRecognitionPipeline:()=>Pe,DepthEstimationPipeline:()=>Le,DocumentQuestionAnsweringPipeline:()=>Z,FeatureExtractionPipeline:()=>q,FillMaskPipeline:()=>H,ImageClassificationPipeline:()=>De,ImageFeatureExtractionPipeline:()=>le,ImageSegmentationPipeline:()=>Ge,ImageToImagePipeline:()=>ve,ImageToTextPipeline:()=>be,ObjectDetectionPipeline:()=>lt,Pipeline:()=>re,QuestionAnsweringPipeline:()=>G,SummarizationPipeline:()=>$,Text2TextGenerationPipeline:()=>D,TextClassificationPipeline:()=>ae,TextGenerationPipeline:()=>x,TextToAudioPipeline:()=>he,TokenClassificationPipeline:()=>R,TranslationPipeline:()=>w,ZeroShotAudioClassificationPipeline:()=>fe,ZeroShotClassificationPipeline:()=>J,ZeroShotImageClassificationPipeline:()=>Ne,ZeroShotObjectDetectionPipeline:()=>ue,pipeline:()=>ne});var g=r("./src/tokenizers.js"),O=r("./src/models.js"),j=r("./src/models/auto/processing_auto.js");r("./src/base/processing_utils.js");var te=r("./src/utils/generic.js"),U=r("./src/utils/core.js"),y=r("./src/utils/maths.js"),P=r("./src/utils/audio.js"),b=r("./src/utils/tensor.js"),T=r("./src/utils/image.js");async function v(Ae){return Array.isArray(Ae)||(Ae=[Ae]),await Promise.all(Ae.map(oe=>T.RawImage.read(oe)))}async function z(Ae,oe){return Array.isArray(Ae)||(Ae=[Ae]),await Promise.all(Ae.map(Me=>typeof Me=="string"||Me instanceof URL?(0,P.read_audio)(Me,oe):Me instanceof Float64Array?new Float32Array(Me):Me))}function K(Ae,oe){oe&&(Ae=Ae.map(ze=>ze|0));const[Me,je,Re,We]=Ae;return{xmin:Me,ymin:je,xmax:Re,ymax:We}}class re extends te.Callable{constructor({task:oe,model:Me,tokenizer:je=null,processor:Re=null}){super(),this.task=oe,this.model=Me,this.tokenizer=je,this.processor=Re}async dispose(){await this.model.dispose()}}class ae extends re{constructor(oe){super(oe)}async _call(oe,{top_k:Me=1}={}){const je=this.tokenizer(oe,{padding:!0,truncation:!0}),Re=await this.model(je),We=this.model.config.problem_type==="multi_label_classification"?nt=>nt.sigmoid():nt=>new b.Tensor("float32",(0,y.softmax)(nt.data),nt.dims),ze=this.model.config.id2label,Ye=[];for(const nt of Re.logits){const wt=We(nt),ut=await(0,b.topk)(wt,Me),ht=ut[0].tolist(),ie=ut[1].tolist().map((X,_e)=>({label:ze?ze[X]:`LABEL_${X}`,score:ht[_e]}));Me===1?Ye.push(...ie):Ye.push(ie)}return Array.isArray(oe)||Me===1?Ye:Ye[0]}}class R extends re{constructor(oe){super(oe)}async _call(oe,{ignore_labels:Me=["O"]}={}){const je=Array.isArray(oe),Re=this.tokenizer(je?oe:[oe],{padding:!0,truncation:!0}),ze=(await this.model(Re)).logits,Ye=this.model.config.id2label,nt=[];for(let wt=0;wt<ze.dims[0];++wt){const ut=Re.input_ids[wt],ht=ze[wt],I=[];for(let ie=0;ie<ht.dims[0];++ie){const X=ht[ie],_e=(0,y.max)(X.data)[1],$e=Ye?Ye[_e]:`LABEL_${_e}`;if(Me.includes($e))continue;const He=this.tokenizer.decode([ut[ie].item()],{skip_special_tokens:!0});if(He==="")continue;const et=(0,y.softmax)(X.data);I.push({entity:$e,score:et[_e],index:ie,word:He})}nt.push(I)}return je?nt:nt[0]}}class G extends re{constructor(oe){super(oe)}async _call(oe,Me,{top_k:je=1}={}){const Re=this.tokenizer(oe,{text_pair:Me,padding:!0,truncation:!0}),{start_logits:We,end_logits:ze}=await this.model(Re),Ye=Re.input_ids.tolist(),nt=Re.attention_mask.tolist(),wt=this.tokenizer.all_special_ids,ut=[];for(let ht=0;ht<We.dims[0];++ht){const I=Ye[ht],ie=I.findIndex(ot=>ot==this.tokenizer.sep_token_id);nt[ht].map((ot,yt)=>ot==1&&(yt===0||yt>ie&&wt.findIndex(mt=>mt==I[yt])===-1));const X=We[ht].tolist(),_e=ze[ht].tolist();for(let ot=1;ot<X.length;++ot)(nt[ht]==0||ot<=ie||wt.findIndex(yt=>yt==I[ot])!==-1)&&(X[ot]=-1/0,_e[ot]=-1/0);const $e=(0,y.softmax)(X).map((ot,yt)=>[ot,yt]),He=(0,y.softmax)(_e).map((ot,yt)=>[ot,yt]);$e[0][0]=0,He[0][0]=0;const et=(0,U.product)($e,He).filter(ot=>ot[0][1]<=ot[1][1]).map(ot=>[ot[0][1],ot[1][1],ot[0][0]*ot[1][0]]).sort((ot,yt)=>yt[2]-ot[2]);for(let ot=0;ot<Math.min(et.length,je);++ot){const[yt,mt,Qt]=et[ot],ts=I.slice(yt,mt+1),xs=this.tokenizer.decode(ts,{skip_special_tokens:!0});ut.push({answer:xs,score:Qt})}}return je===1?ut[0]:ut}}class H extends re{constructor(oe){super(oe)}async _call(oe,{top_k:Me=5}={}){const je=this.tokenizer(oe,{padding:!0,truncation:!0}),{logits:Re}=await this.model(je),We=[],ze=je.input_ids.tolist();for(let Ye=0;Ye<ze.length;++Ye){const nt=ze[Ye],wt=nt.findIndex(X=>X==this.tokenizer.mask_token_id);if(wt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const ut=Re[Ye][wt],ht=await(0,b.topk)(new b.Tensor("float32",(0,y.softmax)(ut.data),ut.dims),Me),I=ht[0].tolist(),ie=ht[1].tolist();We.push(ie.map((X,_e)=>{const $e=nt.slice();return $e[wt]=X,{score:I[_e],token:Number(X),token_str:this.tokenizer.model.vocab[X],sequence:this.tokenizer.decode($e,{skip_special_tokens:!0})}}))}return Array.isArray(oe)?We:We[0]}}class D extends re{_key="generated_text";constructor(oe){super(oe)}async _call(oe,Me={}){Array.isArray(oe)||(oe=[oe]),this.model.config.prefix&&(oe=oe.map(nt=>this.model.config.prefix+nt));const je=this.model.config.task_specific_params;je&&je[this.task]&&je[this.task].prefix&&(oe=oe.map(nt=>je[this.task].prefix+nt));const Re=this.tokenizer,We={padding:!0,truncation:!0};let ze;this instanceof w&&"_build_translation_inputs"in Re?ze=Re._build_translation_inputs(oe,We,Me):ze=Re(oe,We);const Ye=await this.model.generate({...ze,...Me});return Re.batch_decode(Ye,{skip_special_tokens:!0}).map(nt=>({[this._key]:nt}))}}class $ extends D{_key="summary_text";constructor(oe){super(oe)}}class w extends D{_key="translation_text";constructor(oe){super(oe)}}function C(Ae){return Array.isArray(Ae)&&Ae.every(oe=>"role"in oe&&"content"in oe)}class x extends re{constructor(oe){super(oe)}async _call(oe,Me={}){let je=!1,Re=!1,We;if(typeof oe=="string")We=oe=[oe];else if(Array.isArray(oe)&&oe.every(ie=>typeof ie=="string"))je=!0,We=oe;else{if(C(oe))oe=[oe];else if(Array.isArray(oe)&&oe.every(C))je=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Re=!0,We=oe.map(ie=>this.tokenizer.apply_chat_template(ie,{tokenize:!1,add_generation_prompt:!0}))}const ze=Me.add_special_tokens??!1,Ye=Re?!1:Me.return_full_text??!0;this.tokenizer.padding_side="left";const nt=this.tokenizer(We,{add_special_tokens:ze,padding:!0,truncation:!0}),wt=await this.model.generate({...nt,...Me}),ut=this.tokenizer.batch_decode(wt,{skip_special_tokens:!0});let ht;!Ye&&nt.input_ids.dims.at(-1)>0&&(ht=this.tokenizer.batch_decode(nt.input_ids,{skip_special_tokens:!0}).map(ie=>ie.length));const I=Array.from({length:oe.length},ie=>[]);for(let ie=0;ie<ut.length;++ie){const X=Math.floor(ie/wt.dims[0]*oe.length);ht&&(ut[ie]=ut[ie].slice(ht[X])),I[X].push({generated_text:Re?[...oe[X],{role:"assistant",content:ut[ie]}]:ut[ie]})}return!je&&I.length===1?I[0]:I}}class J extends re{constructor(oe){super(oe),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([Me,je])=>[Me.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(oe,Me,{hypothesis_template:je="This example is {}.",multi_label:Re=!1}={}){const We=Array.isArray(oe);We||(oe=[oe]),Array.isArray(Me)||(Me=[Me]);const ze=Me.map(wt=>je.replace("{}",wt)),Ye=Re||Me.length===1,nt=[];for(const wt of oe){const ut=[];for(const ie of ze){const X=this.tokenizer(wt,{text_pair:ie,padding:!0,truncation:!0}),_e=await this.model(X);Ye?ut.push([_e.logits.data[this.contradiction_id],_e.logits.data[this.entailment_id]]):ut.push(_e.logits.data[this.entailment_id])}const I=(Ye?ut.map(ie=>(0,y.softmax)(ie)[1]):(0,y.softmax)(ut)).map((ie,X)=>[ie,X]).sort((ie,X)=>X[0]-ie[0]);nt.push({sequence:wt,labels:I.map(ie=>Me[ie[1]]),scores:I.map(ie=>ie[0])})}return We?nt:nt[0]}}class q extends re{constructor(oe){super(oe)}async _call(oe,{pooling:Me="none",normalize:je=!1,quantize:Re=!1,precision:We="binary"}={}){const ze=this.tokenizer(oe,{padding:!0,truncation:!0}),Ye=await this.model(ze);let nt=Ye.last_hidden_state??Ye.logits??Ye.token_embeddings;if(Me!=="none")if(Me==="mean")nt=(0,b.mean_pooling)(nt,ze.attention_mask);else if(Me==="cls")nt=nt.slice(null,0);else throw Error(`Pooling method '${Me}' not supported.`);return je&&(nt=nt.normalize(2,-1)),Re&&(nt=(0,b.quantize_embeddings)(nt,We)),nt}}class le extends re{constructor(oe){super(oe)}async _call(oe,{pool:Me=null}={}){const je=await v(oe),{pixel_values:Re}=await this.processor(je),We=await this.model({pixel_values:Re});let ze;if(Me){if(!("pooler_output"in We))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");ze=We.pooler_output}else ze=We.last_hidden_state??We.logits??We.image_embeds;return ze}}class ce extends re{constructor(oe){super(oe)}async _call(oe,{top_k:Me=5}={}){const je=this.processor.feature_extractor.config.sampling_rate,Re=await z(oe,je),We=this.model.config.id2label,ze=[];for(const Ye of Re){const nt=await this.processor(Ye),ut=(await this.model(nt)).logits[0],ht=await(0,b.topk)(new b.Tensor("float32",(0,y.softmax)(ut.data),ut.dims),Me),I=ht[0].tolist(),X=ht[1].tolist().map((_e,$e)=>({label:We?We[_e]:`LABEL_${_e}`,score:I[$e]}));ze.push(X)}return Array.isArray(oe)?ze:ze[0]}}class fe extends re{constructor(oe){super(oe)}async _call(oe,Me,{hypothesis_template:je="This is a sound of {}."}={}){const Re=!Array.isArray(oe);Re&&(oe=[oe]);const We=Me.map(ut=>je.replace("{}",ut)),ze=this.tokenizer(We,{padding:!0,truncation:!0}),Ye=this.processor.feature_extractor.config.sampling_rate,nt=await z(oe,Ye),wt=[];for(const ut of nt){const ht=await this.processor(ut),I=await this.model({...ze,...ht}),ie=(0,y.softmax)(I.logits_per_audio.data);wt.push([...ie].map((X,_e)=>({score:X,label:Me[_e]})))}return Re?wt[0]:wt}}class Pe extends re{constructor(oe){super(oe)}async _call(oe,Me={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(oe,Me);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(oe,Me);case"moonshine":return this._call_moonshine(oe,Me);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(oe,Me){Me.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Me.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const je=!Array.isArray(oe);je&&(oe=[oe]);const Re=this.processor.feature_extractor.config.sampling_rate,We=await z(oe,Re),ze=[];for(const Ye of We){const nt=await this.processor(Ye),ut=(await this.model(nt)).logits[0],ht=[];for(const ie of ut)ht.push((0,y.max)(ie.data)[1]);const I=this.tokenizer.decode(ht);ze.push({text:I})}return je?ze[0]:ze}async _call_whisper(oe,Me){const je=Me.return_timestamps??!1,Re=Me.chunk_length_s??0,We=Me.force_full_sequences??!1;let ze=Me.stride_length_s??null;const Ye={...Me};je==="word"&&(Ye.return_token_timestamps=!0,Ye.return_timestamps=!1);const nt=!Array.isArray(oe);nt&&(oe=[oe]);const wt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,ut=this.processor.feature_extractor.config.hop_length,ht=this.processor.feature_extractor.config.sampling_rate,I=await z(oe,ht),ie=[];for(const X of I){let _e=[];if(Re>0){if(ze===null)ze=Re/6;else if(Re<=ze)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const et=ht*Re,ot=ht*ze,yt=et-2*ot;let mt=0;for(;;){const Qt=mt+et,ts=X.subarray(mt,Qt),xs=await this.processor(ts),hs=mt===0,$s=Qt>=X.length;if(_e.push({stride:[ts.length,hs?0:ot,$s?0:ot],input_features:xs.input_features,is_last:$s}),$s)break;mt+=yt}}else _e=[{stride:[X.length,0,0],input_features:(await this.processor(X)).input_features,is_last:!0}];for(const et of _e){Ye.num_frames=Math.floor(et.stride[0]/ut);const ot=await this.model.generate({inputs:et.input_features,...Ye});je==="word"?(et.tokens=ot.sequences.tolist()[0],et.token_timestamps=ot.token_timestamps.tolist()[0].map(yt=>(0,y.round)(yt,2))):et.tokens=ot[0].tolist(),et.stride=et.stride.map(yt=>yt/ht)}const[$e,He]=this.tokenizer._decode_asr(_e,{time_precision:wt,return_timestamps:je,force_full_sequences:We});ie.push({text:$e,...He})}return nt?ie[0]:ie}async _call_moonshine(oe,Me){const je=!Array.isArray(oe);je&&(oe=[oe]);const Re=this.processor.feature_extractor.config.sampling_rate,We=await z(oe,Re),ze=[];for(const Ye of We){const nt=await this.processor(Ye),wt=Math.floor(Ye.length/Re)*6,ut=await this.model.generate({max_new_tokens:wt,...Me,...nt}),ht=this.processor.batch_decode(ut,{skip_special_tokens:!0})[0];ze.push({text:ht})}return je?ze[0]:ze}}class be extends re{constructor(oe){super(oe)}async _call(oe,Me={}){const je=Array.isArray(oe),Re=await v(oe),{pixel_values:We}=await this.processor(Re),ze=[];for(const Ye of We){Ye.dims=[1,...Ye.dims];const nt=await this.model.generate({inputs:Ye,...Me}),wt=this.tokenizer.batch_decode(nt,{skip_special_tokens:!0}).map(ut=>({generated_text:ut.trim()}));ze.push(wt)}return je?ze:ze[0]}}class De extends re{constructor(oe){super(oe)}async _call(oe,{top_k:Me=5}={}){const je=await v(oe),{pixel_values:Re}=await this.processor(je),We=await this.model({pixel_values:Re}),ze=this.model.config.id2label,Ye=[];for(const nt of We.logits){const wt=await(0,b.topk)(new b.Tensor("float32",(0,y.softmax)(nt.data),nt.dims),Me),ut=wt[0].tolist(),I=wt[1].tolist().map((ie,X)=>({label:ze?ze[ie]:`LABEL_${ie}`,score:ut[X]}));Ye.push(I)}return Array.isArray(oe)?Ye:Ye[0]}}class Ge extends re{constructor(oe){super(oe),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(oe,{threshold:Me=.5,mask_threshold:je=.5,overlap_mask_area_threshold:Re=.8,label_ids_to_fuse:We=null,target_sizes:ze=null,subtask:Ye=null}={}){if(Array.isArray(oe)&&oe.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const wt=await v(oe),ut=wt.map(He=>[He.height,He.width]),{pixel_values:ht,pixel_mask:I}=await this.processor(wt),ie=await this.model({pixel_values:ht,pixel_mask:I});let X=null;if(Ye!==null)X=this.subtasks_mapping[Ye];else for(let[He,et]of Object.entries(this.subtasks_mapping))if(et in this.processor.image_processor){X=this.processor.image_processor[et].bind(this.processor.image_processor),Ye=He;break}const _e=this.model.config.id2label,$e=[];if(Ye==="panoptic"||Ye==="instance"){const He=X(ie,Me,je,Re,We,ze??ut)[0],et=He.segmentation;for(const ot of He.segments_info){const yt=new Uint8ClampedArray(et.data.length);for(let Qt=0;Qt<et.data.length;++Qt)et.data[Qt]===ot.id&&(yt[Qt]=255);const mt=new T.RawImage(yt,et.dims[1],et.dims[0],1);$e.push({score:ot.score,label:_e[ot.label_id],mask:mt})}}else if(Ye==="semantic"){const{segmentation:He,labels:et}=X(ie,ze??ut)[0];for(const ot of et){const yt=new Uint8ClampedArray(He.data.length);for(let Qt=0;Qt<He.data.length;++Qt)He.data[Qt]===ot&&(yt[Qt]=255);const mt=new T.RawImage(yt,He.dims[1],He.dims[0],1);$e.push({score:null,label:_e[ot],mask:mt})}}else throw Error(`Subtask ${Ye} not supported.`);return $e}}class Ne extends re{constructor(oe){super(oe)}async _call(oe,Me,{hypothesis_template:je="This is a photo of {}"}={}){const Re=Array.isArray(oe),We=await v(oe),ze=Me.map(I=>je.replace("{}",I)),Ye=this.tokenizer(ze,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:nt}=await this.processor(We),wt=await this.model({...Ye,pixel_values:nt}),ut=this.model.config.model_type==="siglip"?I=>I.sigmoid().data:I=>(0,y.softmax)(I.data),ht=[];for(const I of wt.logits_per_image){const X=[...ut(I)].map((_e,$e)=>({score:_e,label:Me[$e]}));X.sort((_e,$e)=>$e.score-_e.score),ht.push(X)}return Re?ht:ht[0]}}class lt extends re{constructor(oe){super(oe)}async _call(oe,{threshold:Me=.9,percentage:je=!1}={}){const Re=Array.isArray(oe);if(Re&&oe.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const We=await v(oe),ze=je?null:We.map(ie=>[ie.height,ie.width]),{pixel_values:Ye,pixel_mask:nt}=await this.processor(We),wt=await this.model({pixel_values:Ye,pixel_mask:nt}),ut=this.processor.image_processor.post_process_object_detection(wt,Me,ze),ht=this.model.config.id2label,I=ut.map(ie=>ie.boxes.map((X,_e)=>({score:ie.scores[_e],label:ht[ie.classes[_e]],box:K(X,!je)})));return Re?I:I[0]}}class ue extends re{constructor(oe){super(oe)}async _call(oe,Me,{threshold:je=.1,top_k:Re=null,percentage:We=!1}={}){const ze=Array.isArray(oe),Ye=await v(oe),nt=this.tokenizer(Me,{padding:!0,truncation:!0}),wt=await this.processor(Ye),ut=[];for(let ht=0;ht<Ye.length;++ht){const I=Ye[ht],ie=We?null:[[I.height,I.width]],X=wt.pixel_values[ht].unsqueeze_(0),_e=await this.model({...nt,pixel_values:X}),$e=this.processor.image_processor.post_process_object_detection(_e,je,ie,!0)[0];let He=$e.boxes.map((et,ot)=>({score:$e.scores[ot],label:Me[$e.classes[ot]],box:K(et,!We)})).sort((et,ot)=>ot.score-et.score);Re!==null&&(He=He.slice(0,Re)),ut.push(He)}return ze?ut:ut[0]}}class Z extends re{constructor(oe){super(oe)}async _call(oe,Me,je={}){const Re=(await v(oe))[0],{pixel_values:We}=await this.processor(Re),ze=`<s_docvqa><s_question>${Me}</s_question><s_answer>`,Ye=this.tokenizer(ze,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,nt=await this.model.generate({inputs:We,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ye,...je}),ut=this.tokenizer.batch_decode(nt)[0].match(/<s_answer>(.*?)<\/s_answer>/);let ht=null;return ut&&ut.length>=2&&(ht=ut[1].trim()),[{answer:ht}]}}class he extends re{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(oe){super(oe),this.vocoder=oe.vocoder??null}async _call(oe,{speaker_embeddings:Me=null}={}){return this.processor?this._call_text_to_spectrogram(oe,{speaker_embeddings:Me}):this._call_text_to_waveform(oe)}async _call_text_to_waveform(oe){const Me=this.tokenizer(oe,{padding:!0,truncation:!0}),{waveform:je}=await this.model(Me),Re=this.model.config.sampling_rate;return{audio:je.data,sampling_rate:Re}}async _call_text_to_spectrogram(oe,{speaker_embeddings:Me}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await O.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Me=="string"||Me instanceof URL)&&(Me=new Float32Array(await(await fetch(Me)).arrayBuffer())),Me instanceof Float32Array)Me=new b.Tensor("float32",Me,[1,Me.length]);else if(!(Me instanceof b.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:je}=this.tokenizer(oe,{padding:!0,truncation:!0}),{waveform:Re}=await this.model.generate_speech(je,Me,{vocoder:this.vocoder}),We=this.processor.feature_extractor.config.sampling_rate;return{audio:Re.data,sampling_rate:We}}}class ve extends re{constructor(oe){super(oe)}async _call(oe){const Me=await v(oe),je=await this.processor(Me),Re=await this.model(je),We=[];for(const ze of Re.reconstruction){const Ye=ze.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");We.push(T.RawImage.fromTensor(Ye))}return We.length>1?We:We[0]}}class Le extends re{constructor(oe){super(oe)}async _call(oe){const Me=await v(oe),je=await this.processor(Me),{predicted_depth:Re}=await this.model(je),We=[];for(let ze=0;ze<Me.length;++ze){const Ye=(0,b.interpolate)(Re[ze],Me[ze].size.reverse(),"bilinear",!1),nt=Ye.mul_(255/(0,y.max)(Ye.data)[0]).to("uint8");We.push({predicted_depth:Re[ze],depth:T.RawImage.fromTensor(nt)})}return We.length>1?We:We[0]}}const Ze=Object.freeze({"text-classification":{tokenizer:g.AutoTokenizer,pipeline:ae,model:O.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:g.AutoTokenizer,pipeline:R,model:O.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:g.AutoTokenizer,pipeline:G,model:O.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:g.AutoTokenizer,pipeline:H,model:O.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:g.AutoTokenizer,pipeline:$,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:g.AutoTokenizer,pipeline:w,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:g.AutoTokenizer,pipeline:D,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:g.AutoTokenizer,pipeline:x,model:O.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:g.AutoTokenizer,pipeline:J,model:O.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:ce,model:O.AutoModelForAudioClassification,processor:j.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:g.AutoTokenizer,pipeline:fe,model:O.AutoModel,processor:j.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:g.AutoTokenizer,pipeline:Pe,model:[O.AutoModelForSpeechSeq2Seq,O.AutoModelForCTC],processor:j.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:g.AutoTokenizer,pipeline:he,model:[O.AutoModelForTextToWaveform,O.AutoModelForTextToSpectrogram],processor:[j.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:g.AutoTokenizer,pipeline:be,model:O.AutoModelForVision2Seq,processor:j.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:De,model:O.AutoModelForImageClassification,processor:j.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Ge,model:[O.AutoModelForImageSegmentation,O.AutoModelForSemanticSegmentation,O.AutoModelForUniversalSegmentation],processor:j.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:g.AutoTokenizer,pipeline:Ne,model:O.AutoModel,processor:j.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:lt,model:O.AutoModelForObjectDetection,processor:j.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:g.AutoTokenizer,pipeline:ue,model:O.AutoModelForZeroShotObjectDetection,processor:j.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:g.AutoTokenizer,pipeline:Z,model:O.AutoModelForDocumentQuestionAnswering,processor:j.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:ve,model:O.AutoModelForImageToImage,processor:j.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Le,model:O.AutoModelForDepthEstimation,processor:j.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:g.AutoTokenizer,pipeline:q,model:O.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:j.AutoProcessor,pipeline:le,model:[O.AutoModelForImageFeatureExtraction,O.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Ke=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function ne(Ae,oe=null,{progress_callback:Me=null,config:je=null,cache_dir:Re=null,local_files_only:We=!1,revision:ze="main",device:Ye=null,dtype:nt=null,model_file_name:wt=null,session_options:ut={}}={}){Ae=Ke[Ae]??Ae;const ht=Ze[Ae.split("_",1)[0]];if(!ht)throw Error(`Unsupported pipeline: ${Ae}. Must be one of [${Object.keys(Ze)}]`);oe||(oe=ht.default.model,console.log(`No model specified. Using default model: "${oe}".`));const I={progress_callback:Me,config:je,cache_dir:Re,local_files_only:We,revision:ze,device:Ye,dtype:nt,model_file_name:wt,session_options:ut},ie=new Map([["tokenizer",ht.tokenizer],["model",ht.model],["processor",ht.processor]]),X=await qe(ie,oe,I);X.task=Ae,(0,U.dispatchCallback)(Me,{status:"ready",task:Ae,model:oe});const _e=ht.pipeline;return new _e(X)}async function qe(Ae,oe,Me){const je=Object.create(null),Re=[];for(const[We,ze]of Ae.entries()){if(!ze)continue;let Ye;Array.isArray(ze)?Ye=new Promise(async(nt,wt)=>{let ut;for(const ht of ze){if(ht===null){nt(null);return}try{nt(await ht.from_pretrained(oe,Me));return}catch(I){if(I.message?.includes("Unsupported model type"))ut=I;else if(I.message?.includes("Could not locate file"))ut=I;else{wt(I);return}}}wt(ut)}):Ye=ze.from_pretrained(oe,Me),je[We]=Ye,Re.push(Ye)}await Promise.all(Re);for(const[We,ze]of Object.entries(je))je[We]=await ze;return je}},"./src/tokenizers.js":(Ce,A,r)=>{r.r(A),r.d(A,{AlbertTokenizer:()=>br,AutoTokenizer:()=>In,BartTokenizer:()=>rs,BertTokenizer:()=>Nr,BlenderbotSmallTokenizer:()=>$n,BlenderbotTokenizer:()=>pn,BloomTokenizer:()=>Tn,CLIPTokenizer:()=>Cn,CamembertTokenizer:()=>it,CodeGenTokenizer:()=>En,CodeLlamaTokenizer:()=>Ar,CohereTokenizer:()=>An,ConvBertTokenizer:()=>Tr,DebertaTokenizer:()=>Sr,DebertaV2Tokenizer:()=>Js,DistilBertTokenizer:()=>rr,ElectraTokenizer:()=>St,EsmTokenizer:()=>Zs,FalconTokenizer:()=>Ir,GPT2Tokenizer:()=>Ur,GPTNeoXTokenizer:()=>fr,GemmaTokenizer:()=>Gr,Grok1Tokenizer:()=>un,HerbertTokenizer:()=>ur,LlamaTokenizer:()=>xn,M2M100Tokenizer:()=>Ft,MBart50Tokenizer:()=>$r,MBartTokenizer:()=>Vr,MPNetTokenizer:()=>Xn,MarianTokenizer:()=>Or,MgpstrTokenizer:()=>Hr,MobileBertTokenizer:()=>kr,NllbTokenizer:()=>dn,NougatTokenizer:()=>er,PreTrainedTokenizer:()=>Ot,Qwen2Tokenizer:()=>ln,RoFormerTokenizer:()=>jr,RobertaTokenizer:()=>qn,SiglipTokenizer:()=>kn,SpeechT5Tokenizer:()=>ns,SqueezeBertTokenizer:()=>vr,T5Tokenizer:()=>cs,TokenizerModel:()=>le,VitsTokenizer:()=>hn,Wav2Vec2CTCTokenizer:()=>Sn,WhisperTokenizer:()=>cn,XLMRobertaTokenizer:()=>Pn,XLMTokenizer:()=>dt,is_chinese_char:()=>H});var g=r("./src/utils/generic.js"),O=r("./src/utils/core.js"),j=r("./src/utils/hub.js"),te=r("./src/utils/maths.js"),U=r("./src/utils/tensor.js"),y=r("./src/utils/data-structures.js"),P=r("./node_modules/@huggingface/jinja/dist/index.js"),b=r("./src/models/whisper/common_whisper.js");r("./src/utils/constants.js");async function T(Te,M){const Q=await Promise.all([(0,j.getModelJSON)(Te,"tokenizer.json",!0,M),(0,j.getModelJSON)(Te,"tokenizer_config.json",!0,M)]);return M.legacy!==null&&(Q[1].legacy=M.legacy),Q}function v(Te,M){const Q=[];let pe=0;for(const ge of Te.matchAll(M)){const Ie=ge[0];pe<ge.index&&Q.push(Te.slice(pe,ge.index)),Ie.length>0&&Q.push(Ie),pe=ge.index+Ie.length}return pe<Te.length&&Q.push(Te.slice(pe)),Q}function z(Te,M=!0){if(Te.Regex!==void 0){let Q=Te.Regex.replace(/\\([#&~])/g,"$1");for(const[pe,ge]of J)Q=Q.replaceAll(pe,ge);return new RegExp(Q,"gu")}else if(Te.String!==void 0){const Q=(0,O.escapeRegExp)(Te.String);return new RegExp(M?Q:`(${Q})`,"gu")}else return console.warn("Unknown pattern type:",Te),null}function K(Te){return new Map(Object.entries(Te))}function re(Te){const M=Te.dims;switch(M.length){case 1:return Te.tolist();case 2:if(M[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return Te.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${M.length}.`)}}function ae(Te){return Te.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 R(Te){return Te.replace(/\p{M}/gu,"")}function G(Te){return R(Te.toLowerCase())}function H(Te){return Te>=19968&&Te<=40959||Te>=13312&&Te<=19903||Te>=131072&&Te<=173791||Te>=173824&&Te<=177983||Te>=177984&&Te<=178207||Te>=178208&&Te<=183983||Te>=63744&&Te<=64255||Te>=194560&&Te<=195103}function D(Te,M,Q){const pe=[];let ge=0;for(;ge<Te.length;){if(pe.push(Te[ge]),(M.get(Te[ge])??Q)!==Q){++ge;continue}for(;++ge<Te.length&&(M.get(Te[ge])??Q)===Q;)M.get(pe.at(-1))!==Q&&(pe[pe.length-1]+=Te[ge])}return pe}function $(Te){return Te.match(/\S+/g)||[]}const w="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",C=new RegExp(`^[${w}]+$`,"gu"),x=".,!?…。，、।۔،",J=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${x}])]+`,` ?[^\\s${x}]+`]]);class q{constructor(M){this.content=M.content,this.id=M.id,this.single_word=M.single_word??!1,this.lstrip=M.lstrip??!1,this.rstrip=M.rstrip??!1,this.special=M.special??!1,this.normalized=M.normalized??null}}class le extends g.Callable{constructor(M){super(),this.config=M,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(M,...Q){switch(M.type){case"WordPiece":return new ce(M);case"Unigram":return new fe(M,...Q);case"BPE":return new De(M);default:if(M.vocab)return Array.isArray(M.vocab)?new fe(M,...Q):new Ge(M,...Q);throw new Error(`Unknown TokenizerModel type: ${M.type}`)}}_call(M){return M=this.encode(M),this.fuse_unk&&(M=D(M,this.tokens_to_ids,this.unk_token_id)),M}encode(M){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(M){return M.map(Q=>this.tokens_to_ids.get(Q)??this.unk_token_id)}convert_ids_to_tokens(M){return M.map(Q=>this.vocab[Q]??this.unk_token)}}class ce extends le{constructor(M){super(M),this.tokens_to_ids=K(M.vocab),this.unk_token_id=this.tokens_to_ids.get(M.unk_token),this.unk_token=M.unk_token,this.max_input_chars_per_word=M.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,pe]of this.tokens_to_ids)this.vocab[pe]=Q}encode(M){const Q=[];for(const pe of M){const ge=[...pe];if(ge.length>this.max_input_chars_per_word){Q.push(this.unk_token);continue}let Ie=!1,Xe=0;const _t=[];for(;Xe<ge.length;){let ft=ge.length,vt=null;for(;Xe<ft;){let Qe=ge.slice(Xe,ft).join("");if(Xe>0&&(Qe=this.config.continuing_subword_prefix+Qe),this.tokens_to_ids.has(Qe)){vt=Qe;break}--ft}if(vt===null){Ie=!0;break}_t.push(vt),Xe=ft}Ie?Q.push(this.unk_token):Q.push(..._t)}return Q}}class fe extends le{constructor(M,Q){super(M);const pe=M.vocab.length;this.vocab=new Array(pe),this.scores=new Array(pe);for(let ge=0;ge<pe;++ge){const Ie=M.vocab[ge];this.vocab[ge]=Ie[0],this.scores[ge]=Ie[1]}this.unk_token_id=M.unk_id,this.unk_token=this.vocab[M.unk_id],this.tokens_to_ids=new Map(this.vocab.map((ge,Ie)=>[ge,Ie])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Q.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 y.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(M){const Q=M.chars,pe=1;let ge=0;for(;ge<Q.length;){let Ie=!1;const Xe=Q.slice(ge).join(""),_t=this.trie.commonPrefixSearch(Xe);for(const ft of _t){const vt=this.tokens_to_ids.get(ft),Qe=this.scores[vt],It=(0,O.len)(ft);M.insert(ge,It,Qe,vt),!Ie&&It===pe&&(Ie=!0)}Ie||M.insert(ge,pe,this.unk_score,this.unk_token_id),ge+=pe}}tokenize(M){const Q=new y.TokenLattice(M,this.bos_token_id,this.eos_token_id);return this.populateNodes(Q),Q.tokens()}encode(M){const Q=[];for(const pe of M){const ge=this.tokenize(pe);Q.push(...ge)}return Q}}const Pe=(()=>{const Te=[...Array.from({length:94},(ge,Ie)=>Ie+33),...Array.from({length:12},(ge,Ie)=>Ie+161),...Array.from({length:82},(ge,Ie)=>Ie+174)],M=Te.slice();let Q=0;for(let ge=0;ge<256;++ge)Te.includes(ge)||(Te.push(ge),M.push(256+Q),Q+=1);const pe=M.map(ge=>String.fromCharCode(ge));return Object.fromEntries(Te.map((ge,Ie)=>[ge,pe[Ie]]))})(),be=(0,O.reverseDictionary)(Pe);class De extends le{constructor(M){super(M),this.tokens_to_ids=K(M.vocab),this.unk_token_id=this.tokens_to_ids.get(M.unk_token),this.unk_token=M.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[pe,ge]of this.tokens_to_ids)this.vocab[ge]=pe;const Q=Array.isArray(M.merges[0]);this.merges=Q?M.merges:M.merges.map(pe=>pe.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((pe,ge)=>[JSON.stringify(pe),ge])),this.end_of_word_suffix=M.end_of_word_suffix,this.continuing_subword_suffix=M.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(M){if(M.length===0)return[];const Q=this.cache.get(M);if(Q!==void 0)return Q;const pe=Array.from(M);this.end_of_word_suffix&&(pe[pe.length-1]+=this.end_of_word_suffix);let ge=[];if(pe.length>1){const Ie=new y.PriorityQueue((ft,vt)=>ft.score<vt.score);let Xe={token:pe[0],bias:0,prev:null,next:null},_t=Xe;for(let ft=1;ft<pe.length;++ft){const vt={bias:ft/pe.length,token:pe[ft],prev:_t,next:null};_t.next=vt,this._add_node(Ie,_t),_t=vt}for(;!Ie.isEmpty();){const ft=Ie.pop();if(ft.deleted||!ft.next||ft.next.deleted)continue;if(ft.deleted=!0,ft.next.deleted=!0,ft.prev){const Qe={...ft.prev};ft.prev.deleted=!0,ft.prev=Qe,Qe.prev?Qe.prev.next=Qe:Xe=Qe}const vt={token:ft.token+ft.next.token,bias:ft.bias,prev:ft.prev,next:ft.next.next};vt.prev?(vt.prev.next=vt,this._add_node(Ie,vt.prev)):Xe=vt,vt.next&&(vt.next.prev=vt,this._add_node(Ie,vt))}for(let ft=Xe;ft!==null;ft=ft.next)ge.push(ft.token)}else ge=pe;if(this.continuing_subword_suffix)for(let Ie=0;Ie<ge.length-1;++Ie)ge[Ie]+=this.continuing_subword_suffix;return this.cache.set(M,ge),ge}_add_node(M,Q){const pe=this.bpe_ranks.get(JSON.stringify([Q.token,Q.next.token]));pe!==void 0&&(Q.score=pe+Q.bias,M.push(Q))}encode(M){const Q=[];for(const pe of M){if(this.ignore_merges&&this.tokens_to_ids.has(pe)){Q.push(pe);continue}const ge=this.bpe(pe);for(const Ie of ge)if(this.tokens_to_ids.has(Ie))Q.push(Ie);else if(this.byte_fallback){const Xe=Array.from(this.text_encoder.encode(Ie)).map(_t=>`<0x${_t.toString(16).toUpperCase().padStart(2,"0")}>`);Xe.every(_t=>this.tokens_to_ids.has(_t))?Q.push(...Xe):Q.push(this.unk_token)}else Q.push(this.unk_token)}return Q}}class Ge extends le{constructor(M,Q){super(M),this.tokens_to_ids=K(Q.target_lang?M.vocab[Q.target_lang]:M.vocab),this.bos_token=Q.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Q.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=Q.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=Q.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[pe,ge]of this.tokens_to_ids)this.vocab[ge]=pe}encode(M){return M}}class Ne extends g.Callable{constructor(M){super(),this.config=M}static fromConfig(M){if(M===null)return null;switch(M.type){case"BertNormalizer":return new qe(M);case"Precompiled":return new hs(M);case"Sequence":return new ne(M);case"Replace":return new lt(M);case"NFC":return new ue(M);case"NFKC":return new Z(M);case"NFKD":return new he(M);case"Strip":return new ve(M);case"StripAccents":return new Le(M);case"Lowercase":return new Ze(M);case"Prepend":return new Ke(M);default:throw new Error(`Unknown Normalizer type: ${M.type}`)}}normalize(M){throw Error("normalize should be implemented in subclass.")}_call(M){return this.normalize(M)}}class lt extends Ne{normalize(M){const Q=z(this.config.pattern);return Q===null?M:M.replaceAll(Q,this.config.content)}}class ue extends Ne{normalize(M){return M=M.normalize("NFC"),M}}class Z extends Ne{normalize(M){return M=M.normalize("NFKC"),M}}class he extends Ne{normalize(M){return M=M.normalize("NFKD"),M}}class ve extends Ne{normalize(M){return this.config.strip_left&&this.config.strip_right?M=M.trim():(this.config.strip_left&&(M=M.trimStart()),this.config.strip_right&&(M=M.trimEnd())),M}}class Le extends Ne{normalize(M){return M=R(M),M}}class Ze extends Ne{normalize(M){return M=M.toLowerCase(),M}}class Ke extends Ne{normalize(M){return M=this.config.prepend+M,M}}class ne extends Ne{constructor(M){super(M),this.normalizers=M.normalizers.map(Q=>Ne.fromConfig(Q))}normalize(M){return this.normalizers.reduce((Q,pe)=>pe.normalize(Q),M)}}class qe extends Ne{_tokenize_chinese_chars(M){const Q=[];for(let pe=0;pe<M.length;++pe){const ge=M[pe],Ie=ge.charCodeAt(0);H(Ie)?(Q.push(" "),Q.push(ge),Q.push(" ")):Q.push(ge)}return Q.join("")}stripAccents(M){return M.normalize("NFD").replace(/\p{Mn}/gu,"")}_is_control(M){switch(M){case"	":case`
`:case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(M)}}_clean_text(M){const Q=[];for(const pe of M){const ge=pe.charCodeAt(0);ge===0||ge===65533||this._is_control(pe)||(/^\s$/.test(pe)?Q.push(" "):Q.push(pe))}return Q.join("")}normalize(M){return this.config.clean_text&&(M=this._clean_text(M)),this.config.handle_chinese_chars&&(M=this._tokenize_chinese_chars(M)),this.config.lowercase?(M=M.toLowerCase(),this.config.strip_accents!==!1&&(M=this.stripAccents(M))):this.config.strip_accents&&(M=this.stripAccents(M)),M}}class Ae extends g.Callable{static fromConfig(M){if(M===null)return null;switch(M.type){case"BertPreTokenizer":return new oe(M);case"Sequence":return new $s(M);case"Whitespace":return new Ms(M);case"WhitespaceSplit":return new Ks(M);case"Metaspace":return new ts(M);case"ByteLevel":return new Me(M);case"Split":return new je(M);case"Punctuation":return new Re(M);case"Digits":return new We(M);case"Replace":return new sr(M);default:throw new Error(`Unknown PreTokenizer type: ${M.type}`)}}pre_tokenize_text(M,Q){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(M,Q){return(Array.isArray(M)?M.map(pe=>this.pre_tokenize_text(pe,Q)):this.pre_tokenize_text(M,Q)).flat()}_call(M,Q){return this.pre_tokenize(M,Q)}}class oe extends Ae{constructor(M){super(),this.pattern=new RegExp(`[^\\s${w}]+|[${w}]`,"gu")}pre_tokenize_text(M,Q){return M.trim().match(this.pattern)||[]}}class Me extends Ae{constructor(M){super(),this.config=M,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=Pe,this.text_encoder=new TextEncoder}pre_tokenize_text(M,Q){return this.add_prefix_space&&!M.startsWith(" ")&&(M=" "+M),(this.use_regex?M.match(this.pattern)||[]:[M]).map(ge=>Array.from(this.text_encoder.encode(ge),Ie=>this.byte_encoder[Ie]).join(""))}}class je extends Ae{constructor(M){super(),this.config=M,this.pattern=z(this.config.pattern,this.config.invert)}pre_tokenize_text(M,Q){return this.pattern===null?[]:this.config.invert?M.match(this.pattern)||[]:this.config.behavior?.toLowerCase()==="removed"?M.split(this.pattern).filter(pe=>pe):v(M,this.pattern)}}class Re extends Ae{constructor(M){super(),this.config=M,this.pattern=new RegExp(`[^${w}]+|[${w}]+`,"gu")}pre_tokenize_text(M,Q){return M.match(this.pattern)||[]}}class We extends Ae{constructor(M){super(),this.config=M;const Q=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(Q,"gu")}pre_tokenize_text(M,Q){return M.match(this.pattern)||[]}}class ze extends g.Callable{constructor(M){super(),this.config=M}static fromConfig(M){if(M===null)return null;switch(M.type){case"TemplateProcessing":return new wt(M);case"ByteLevel":return new ut(M);case"RobertaProcessing":return new nt(M);case"BertProcessing":return new Ye(M);case"Sequence":return new ht(M);default:throw new Error(`Unknown PostProcessor type: ${M.type}`)}}post_process(M,...Q){throw Error("post_process should be implemented in subclass.")}_call(M,...Q){return this.post_process(M,...Q)}}class Ye extends ze{constructor(M){super(M),this.cls=M.cls[0],this.sep=M.sep[0]}post_process(M,Q=null,{add_special_tokens:pe=!0}={}){pe&&(M=(0,O.mergeArrays)([this.cls],M,[this.sep]));let ge=new Array(M.length).fill(0);if(Q!==null){const Ie=pe&&this instanceof nt?[this.sep]:[],Xe=pe?[this.sep]:[];M=(0,O.mergeArrays)(M,Ie,Q,Xe),ge=(0,O.mergeArrays)(ge,new Array(Q.length+Ie.length+Xe.length).fill(1))}return{tokens:M,token_type_ids:ge}}}class nt extends Ye{}class wt extends ze{constructor(M){super(M),this.single=M.single,this.pair=M.pair}post_process(M,Q=null,{add_special_tokens:pe=!0}={}){const ge=Q===null?this.single:this.pair;let Ie=[],Xe=[];for(const _t of ge)"SpecialToken"in _t?pe&&(Ie.push(_t.SpecialToken.id),Xe.push(_t.SpecialToken.type_id)):"Sequence"in _t&&(_t.Sequence.id==="A"?(Ie=(0,O.mergeArrays)(Ie,M),Xe=(0,O.mergeArrays)(Xe,new Array(M.length).fill(_t.Sequence.type_id))):_t.Sequence.id==="B"&&(Ie=(0,O.mergeArrays)(Ie,Q),Xe=(0,O.mergeArrays)(Xe,new Array(Q.length).fill(_t.Sequence.type_id))));return{tokens:Ie,token_type_ids:Xe}}}class ut extends ze{post_process(M,Q=null){return Q&&(M=(0,O.mergeArrays)(M,Q)),{tokens:M}}}class ht extends ze{constructor(M){super(M),this.processors=M.processors.map(Q=>ze.fromConfig(Q))}post_process(M,Q=null,pe={}){let ge;for(const Ie of this.processors)if(Ie instanceof ut)M=Ie.post_process(M).tokens,Q&&(Q=Ie.post_process(Q).tokens);else{const Xe=Ie.post_process(M,Q,pe);M=Xe.tokens,ge=Xe.token_type_ids}return{tokens:M,token_type_ids:ge}}}class I extends g.Callable{constructor(M){super(),this.config=M,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=M.trim_offsets}static fromConfig(M){if(M===null)return null;switch(M.type){case"WordPiece":return new He(M);case"Metaspace":return new xs(M);case"ByteLevel":return new et(M);case"Replace":return new ie(M);case"ByteFallback":return new X(M);case"Fuse":return new _e(M);case"Strip":return new $e(M);case"Sequence":return new yt(M);case"CTC":return new ot(M);case"BPEDecoder":return new mt(M);default:throw new Error(`Unknown Decoder type: ${M.type}`)}}_call(M){return this.decode(M)}decode(M){return this.decode_chain(M).join("")}decode_chain(M){throw Error("`decode_chain` should be implemented in subclass.")}}class ie extends I{decode_chain(M){const Q=z(this.config.pattern);return Q===null?M:M.map(pe=>pe.replaceAll(Q,this.config.content))}}class X extends I{constructor(M){super(M),this.text_decoder=new TextDecoder}decode_chain(M){const Q=[];let pe=[];for(const ge of M){let Ie=null;if(ge.length===6&&ge.startsWith("<0x")&&ge.endsWith(">")){const Xe=parseInt(ge.slice(3,5),16);isNaN(Xe)||(Ie=Xe)}if(Ie!==null)pe.push(Ie);else{if(pe.length>0){const Xe=this.text_decoder.decode(Uint8Array.from(pe));Q.push(Xe),pe=[]}Q.push(ge)}}if(pe.length>0){const ge=this.text_decoder.decode(Uint8Array.from(pe));Q.push(ge),pe=[]}return Q}}class _e extends I{decode_chain(M){return[M.join("")]}}class $e extends I{constructor(M){super(M),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(M){return M.map(Q=>{let pe=0;for(let Ie=0;Ie<this.start&&Q[Ie]===this.content;++Ie){pe=Ie+1;continue}let ge=Q.length;for(let Ie=0;Ie<this.stop;++Ie){const Xe=Q.length-Ie-1;if(Q[Xe]===this.content){ge=Xe;continue}else break}return Q.slice(pe,ge)})}}class He extends I{constructor(M){super(M),this.cleanup=M.cleanup}decode_chain(M){return M.map((Q,pe)=>(pe!==0&&(Q.startsWith(this.config.prefix)?Q=Q.replace(this.config.prefix,""):Q=" "+Q),this.cleanup&&(Q=ae(Q)),Q))}}class et extends I{constructor(M){super(M),this.byte_decoder=be,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(M){const Q=M.join(""),pe=new Uint8Array([...Q].map(Ie=>this.byte_decoder[Ie]));return this.text_decoder.decode(pe)}decode_chain(M){const Q=[];let pe=[];for(const ge of M)this.added_tokens.find(Ie=>Ie.content===ge)!==void 0?(pe.length>0&&(Q.push(this.convert_tokens_to_string(pe)),pe=[]),Q.push(ge)):pe.push(ge);return pe.length>0&&Q.push(this.convert_tokens_to_string(pe)),Q}}class ot extends I{constructor(M){super(M),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(M){if(M.length===0)return"";const Q=[M[0]];for(let Ie=1;Ie<M.length;++Ie)M[Ie]!==Q.at(-1)&&Q.push(M[Ie]);let ge=Q.filter(Ie=>Ie!==this.pad_token).join("");return this.cleanup&&(ge=ae(ge).replaceAll(this.word_delimiter_token," ").trim()),ge}decode_chain(M){return[this.convert_tokens_to_string(M)]}}class yt extends I{constructor(M){super(M),this.decoders=M.decoders.map(Q=>I.fromConfig(Q))}decode_chain(M){return this.decoders.reduce((Q,pe)=>pe.decode_chain(Q),M)}}class mt extends I{constructor(M){super(M),this.suffix=this.config.suffix}decode_chain(M){return M.map((Q,pe)=>Q.replaceAll(this.suffix,pe===M.length-1?"":" "))}}class Qt extends I{decode_chain(M){let Q="";for(let pe=1;pe<M.length;pe+=2)Q+=M[pe];return[Q]}}class ts extends Ae{constructor(M){super(),this.addPrefixSpace=M.add_prefix_space,this.replacement=M.replacement,this.strRep=M.str_rep||this.replacement,this.prepend_scheme=M.prepend_scheme??"always"}pre_tokenize_text(M,{section_index:Q=void 0}={}){let pe=M.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!pe.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&Q===0)&&(pe=this.strRep+pe),[pe]}}class xs extends I{constructor(M){super(M),this.addPrefixSpace=M.add_prefix_space,this.replacement=M.replacement}decode_chain(M){const Q=[];for(let pe=0;pe<M.length;++pe){let ge=M[pe].replaceAll(this.replacement," ");this.addPrefixSpace&&pe==0&&ge.startsWith(" ")&&(ge=ge.substring(1)),Q.push(ge)}return Q}}class hs extends Ne{constructor(M){super(M),this.charsmap=M.precompiled_charsmap}normalize(M){return M=M.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),M=M.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),M.includes("～")?M=M.split("～").map(pe=>pe.normalize("NFKC")).join("～"):M=M.normalize("NFKC"),M}}class $s extends Ae{constructor(M){super(),this.tokenizers=M.pretokenizers.map(Q=>Ae.fromConfig(Q))}pre_tokenize_text(M,Q){return this.tokenizers.reduce((pe,ge)=>ge.pre_tokenize(pe,Q),[M])}}class Ms extends Ae{constructor(M){super()}pre_tokenize_text(M,Q){return M.match(/\w+|[^\w\s]+/g)||[]}}class Ks extends Ae{constructor(M){super()}pre_tokenize_text(M,Q){return $(M)}}class sr extends Ae{constructor(M){super(),this.config=M,this.pattern=z(this.config.pattern),this.content=this.config.content}pre_tokenize_text(M,Q){return this.pattern===null?[M]:[M.replaceAll(this.pattern,this.config.content)]}}const Rr=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Cr(Te,M,Q,pe){for(const ge of Object.keys(Te)){const Ie=M-Te[ge].length,Xe=Q(ge),_t=new Array(Ie).fill(Xe);Te[ge]=pe==="right"?(0,O.mergeArrays)(Te[ge],_t):(0,O.mergeArrays)(_t,Te[ge])}}function an(Te,M){for(const Q of Object.keys(Te))Te[Q].length=M}class Ot extends g.Callable{return_token_type_ids=!1;padding_side="right";constructor(M,Q){super(),this._tokenizer_config=Q,this.normalizer=Ne.fromConfig(M.normalizer),this.pre_tokenizer=Ae.fromConfig(M.pre_tokenizer),this.model=le.fromConfig(M.model,Q),this.post_processor=ze.fromConfig(M.post_processor),this.decoder=I.fromConfig(M.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const pe of M.added_tokens){const ge=new q(pe);this.added_tokens.push(ge),this.model.tokens_to_ids.set(ge.content,ge.id),this.model.vocab[ge.id]=ge.content,ge.special&&(this.special_tokens.push(ge.content),this.all_special_ids.push(ge.id))}if(this.additional_special_tokens=Q.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((pe,ge)=>ge.content.length-pe.content.length).map(pe=>`${pe.lstrip?"\\s*":""}(${(0,O.escapeRegExp)(pe.content)})${pe.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.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=Q.model_max_length,this.remove_space=Q.remove_space,this.clean_up_tokenization_spaces=Q.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Q.do_lowercase_and_remove_accent??!1,Q.padding_side&&(this.padding_side=Q.padding_side),this.legacy=!1,this.chat_template=Q.chat_template??null,Array.isArray(this.chat_template)){const pe=Object.create(null);for(const{name:ge,template:Ie}of this.chat_template){if(typeof ge!="string"||typeof Ie!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');pe[ge]=Ie}this.chat_template=pe}this._compiled_template_cache=new Map}getToken(...M){for(const Q of M){const pe=this._tokenizer_config[Q];if(pe)if(typeof pe=="object"){if(pe.__type==="AddedToken")return pe.content;throw Error(`Unknown token: ${pe}`)}else return pe}return null}static async from_pretrained(M,{progress_callback:Q=null,config:pe=null,cache_dir:ge=null,local_files_only:Ie=!1,revision:Xe="main",legacy:_t=null}={}){const ft=await T(M,{progress_callback:Q,config:pe,cache_dir:ge,local_files_only:Ie,revision:Xe,legacy:_t});return new this(...ft)}_call(M,{text_pair:Q=null,add_special_tokens:pe=!0,padding:ge=!1,truncation:Ie=null,max_length:Xe=null,return_tensor:_t=!0,return_token_type_ids:ft=null}={}){const vt=Array.isArray(M);let Qe;if(vt){if(M.length===0)throw Error("text array must be non-empty");if(Q!==null){if(Array.isArray(Q)){if(M.length!==Q.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Qe=M.map((Xt,gs)=>this._encode_plus(Xt,{text_pair:Q[gs],add_special_tokens:pe,return_token_type_ids:ft}))}else Qe=M.map(Xt=>this._encode_plus(Xt,{add_special_tokens:pe,return_token_type_ids:ft}))}else{if(M==null)throw Error("text may not be null or undefined");if(Array.isArray(Q))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Qe=[this._encode_plus(M,{text_pair:Q,add_special_tokens:pe,return_token_type_ids:ft})]}if(Xe===null?ge==="max_length"?Xe=this.model_max_length:Xe=(0,te.max)(Qe.map(Xt=>Xt.input_ids.length))[0]:Ie||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."),Xe=Math.min(Xe,this.model_max_length??1/0),ge||Ie)for(let Xt=0;Xt<Qe.length;++Xt)Qe[Xt].input_ids.length!==Xe&&(Qe[Xt].input_ids.length>Xe?Ie&&an(Qe[Xt],Xe):ge&&Cr(Qe[Xt],Xe,gs=>gs==="input_ids"?this.pad_token_id:0,this.padding_side));const It={};if(_t){if(!(ge&&Ie)&&Qe.some(gs=>{for(const Se of Object.keys(gs))if(gs[Se].length!==Qe[0][Se]?.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 Xt=[Qe.length,Qe[0].input_ids.length];for(const gs of Object.keys(Qe[0]))It[gs]=new U.Tensor("int64",BigInt64Array.from(Qe.flatMap(Se=>Se[gs]).map(BigInt)),Xt)}else{for(const Xt of Object.keys(Qe[0]))It[Xt]=Qe.map(gs=>gs[Xt]);if(!vt)for(const Xt of Object.keys(It))It[Xt]=It[Xt][0]}return It}_encode_text(M){return M===null?null:(this.added_tokens_regex?M.split(this.added_tokens_regex).filter(ge=>ge):[M]).map((ge,Ie)=>{if(this.added_tokens.find(_t=>_t.content===ge)!==void 0)return ge;{if(this.remove_space===!0&&(ge=ge.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(ge=G(ge)),this.normalizer!==null&&(ge=this.normalizer(ge)),ge.length===0)return[];const _t=this.pre_tokenizer!==null?this.pre_tokenizer(ge,{section_index:Ie}):[ge];return this.model(_t)}}).flat()}_encode_plus(M,{text_pair:Q=null,add_special_tokens:pe=!0,return_token_type_ids:ge=null}={}){const{tokens:Ie,token_type_ids:Xe}=this._tokenize_helper(M,{pair:Q,add_special_tokens:pe}),_t=this.model.convert_tokens_to_ids(Ie),ft={input_ids:_t,attention_mask:new Array(_t.length).fill(1)};return(ge??this.return_token_type_ids)&&Xe&&(ft.token_type_ids=Xe),ft}_tokenize_helper(M,{pair:Q=null,add_special_tokens:pe=!1}={}){const ge=this._encode_text(M),Ie=this._encode_text(Q);return this.post_processor?this.post_processor(ge,Ie,{add_special_tokens:pe}):{tokens:(0,O.mergeArrays)(ge??[],Ie??[])}}tokenize(M,{pair:Q=null,add_special_tokens:pe=!1}={}){return this._tokenize_helper(M,{pair:Q,add_special_tokens:pe}).tokens}encode(M,{text_pair:Q=null,add_special_tokens:pe=!0,return_token_type_ids:ge=null}={}){return this._encode_plus(M,{text_pair:Q,add_special_tokens:pe,return_token_type_ids:ge}).input_ids}batch_decode(M,Q={}){return M instanceof U.Tensor&&(M=M.tolist()),M.map(pe=>this.decode(pe,Q))}decode(M,Q={}){if(M instanceof U.Tensor&&(M=re(M)),!Array.isArray(M)||M.length===0||!(0,O.isIntegralNumber)(M[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(M,Q)}decode_single(M,{skip_special_tokens:Q=!1,clean_up_tokenization_spaces:pe=null}){let ge=this.model.convert_ids_to_tokens(M);Q&&(ge=ge.filter(Xe=>!this.special_tokens.includes(Xe)));let Ie=this.decoder?this.decoder(ge):ge.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Ie=Ie.replaceAll(this.decoder.end_of_word_suffix," "),Q&&(Ie=Ie.trim())),(pe??this.clean_up_tokenization_spaces)&&(Ie=ae(Ie)),Ie}get_chat_template({chat_template:M=null,tools:Q=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const pe=this.chat_template;if(M!==null&&Object.hasOwn(pe,M))M=pe[M];else if(M===null)if(Q!==null&&"tool_use"in pe)M=pe.tool_use;else if("default"in pe)M=pe.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(pe).sort()}.`)}else if(M===null)if(this.chat_template)M=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 M}apply_chat_template(M,{tools:Q=null,documents:pe=null,chat_template:ge=null,add_generation_prompt:Ie=!1,tokenize:Xe=!0,padding:_t=!1,truncation:ft=!1,max_length:vt=null,return_tensor:Qe=!0,return_dict:It=!1,tokenizer_kwargs:Xt={},...gs}={}){if(ge=this.get_chat_template({chat_template:ge,tools:Q}),typeof ge!="string")throw Error(`chat_template must be a string, but got ${typeof ge}`);let Se=this._compiled_template_cache.get(ge);Se===void 0&&(Se=new P.Template(ge),this._compiled_template_cache.set(ge,Se));const Ps=Object.create(null);for(const Rs of Rr){const dr=this.getToken(Rs);dr&&(Ps[Rs]=dr)}const js=Se.render({messages:M,add_generation_prompt:Ie,tools:Q,documents:pe,...Ps,...gs});if(Xe){const Rs=this._call(js,{add_special_tokens:!1,padding:_t,truncation:ft,max_length:vt,return_tensor:Qe,...Xt});return It?Rs:Rs.input_ids}return js}}class Nr extends Ot{return_token_type_ids=!0}class br extends Ot{return_token_type_ids=!0}class kr extends Ot{return_token_type_ids=!0}class vr extends Ot{return_token_type_ids=!0}class Sr extends Ot{return_token_type_ids=!0}class Js extends Ot{return_token_type_ids=!0}class ur extends Ot{return_token_type_ids=!0}class Tr extends Ot{return_token_type_ids=!0}class jr extends Ot{return_token_type_ids=!0}class rr extends Ot{}class it extends Ot{}class dt extends Ot{return_token_type_ids=!0;constructor(M,Q){super(M,Q),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class St extends Ot{return_token_type_ids=!0}class cs extends Ot{}class Ur extends Ot{}class rs extends Ot{}class Vr extends Ot{constructor(M,Q){super(M,Q),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(pe=>this.languageRegex.test(pe)),this.lang_to_token=pe=>pe}_build_translation_inputs(M,Q,pe){return Kr(this,M,Q,pe)}}class $r extends Vr{}class qn extends Ot{}class Tn extends Ot{}const Wr="▁";class xn extends Ot{padding_side="left";constructor(M,Q){super(M,Q),this.legacy=Q.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new ts({replacement:Wr,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(M){if(M===null)return null;if(this.legacy||M.length===0)return super._encode_text(M);let Q=super._encode_text(Wr+M.replaceAll(Wr," "));return Q.length>1&&Q[0]===Wr&&this.special_tokens.includes(Q[1])&&(Q=Q.slice(1)),Q}}class Ar extends Ot{}class Pn extends Ot{}class Xn extends Ot{}class Ir extends Ot{}class fr extends Ot{}class Zs extends Ot{}class ln extends Ot{}class Gr extends Ot{}class un extends Ot{}function Kr(Te,M,Q,pe){if(!("language_codes"in Te)||!Array.isArray(Te.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Te)||!(Te.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Te)||typeof Te.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ge=pe.src_lang,Ie=pe.tgt_lang;if(!Te.language_codes.includes(Ie))throw new Error(`Target language code "${Ie}" is not valid. Must be one of: {${Te.language_codes.join(", ")}}`);if(ge!==void 0){if(!Te.language_codes.includes(ge))throw new Error(`Source language code "${ge}" is not valid. Must be one of: {${Te.language_codes.join(", ")}}`);for(const Xe of Te.post_processor.config.single)if("SpecialToken"in Xe&&Te.languageRegex.test(Xe.SpecialToken.id)){Xe.SpecialToken.id=Te.lang_to_token(ge);break}}return pe.forced_bos_token_id=Te.model.convert_tokens_to_ids([Te.lang_to_token(Ie)])[0],Te._call(M,Q)}class dn extends Ot{constructor(M,Q){super(M,Q),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(pe=>this.languageRegex.test(pe)),this.lang_to_token=pe=>pe}_build_translation_inputs(M,Q,pe){return Kr(this,M,Q,pe)}}class Ft extends Ot{constructor(M,Q){super(M,Q),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(pe=>this.languageRegex.test(pe)).map(pe=>pe.slice(2,-2)),this.lang_to_token=pe=>`__${pe}__`}_build_translation_inputs(M,Q,pe){return Kr(this,M,Q,pe)}}class cn extends Ot{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(M,{return_timestamps:Q=!1,return_language:pe=!1,time_precision:ge=null,force_full_sequences:Ie=!0}={}){if(ge===null)throw Error("Must specify time_precision");let Xe=null;const _t=Q==="word";function ft(){return{language:Xe,timestamp:[null,null],text:""}}const vt=[];let Qe=ft(),It=0;const Xt=this.timestamp_begin,Se=Xt+1500;let Ps=[],js=[],Rs=!1,dr=null;const zt=new Set(this.all_special_ids);for(const Zt of M){const us=Zt.tokens,xt=_t?Zt.token_timestamps:null;let os=null,wr=Xt;if("stride"in Zt){const[Mt,Es,Oe]=Zt.stride;if(It-=Es,dr=Mt-Oe,Es&&(wr=Es/ge+Xt),Oe)for(let gt=us.length-1;gt>=0;--gt){const tr=Number(us[gt]);if(tr>=Xt){if(os!==null&&(tr-Xt)*ge<dr)break;os=tr}}}let As=[],Vs=[];for(let Mt=0;Mt<us.length;++Mt){const Es=Number(us[Mt]);if(zt.has(Es)){const Oe=this.decode([Es]),gt=b.WHISPER_LANGUAGE_MAPPING.get(Oe.slice(2,-2));if(gt!==void 0){if(Xe!==null&&gt!==Xe&&!Q){Ps.push(As);const tr=this.findLongestCommonSequence(Ps)[0],qr=this.decode(tr);Qe.text=qr,vt.push(Qe),Ps=[],As=[],Qe=ft()}Xe=Qe.language=gt}}else if(Es>=Xt&&Es<=Se){const Oe=(Es-Xt)*ge+It,gt=(0,te.round)(Oe,2);if(os!==null&&Es>=os)Rs=!0;else if(Rs||Ps.length>0&&Es<wr)Rs=!1;else if(Qe.timestamp[0]===null)Qe.timestamp[0]=gt;else if(gt!==Qe.timestamp[0]){Qe.timestamp[1]=gt,Ps.push(As),_t&&js.push(Vs);const[tr,qr]=this.findLongestCommonSequence(Ps,js),Qn=this.decode(tr);Qe.text=Qn,_t&&(Qe.words=this.collateWordTimestamps(tr,qr,Xe)),vt.push(Qe),Ps=[],As=[],js=[],Vs=[],Qe=ft()}}else if(As.push(Es),_t){let Oe=(0,te.round)(xt[Mt]+It,2),gt;if(Mt+1<xt.length){gt=(0,te.round)(xt[Mt+1]+It,2);const tr=this.decode([Es]);C.test(tr)&&(gt=(0,te.round)(Math.min(Oe+ge,gt),2))}else gt=null;Vs.push([Oe,gt])}}if("stride"in Zt){const[Mt,Es,Oe]=Zt.stride;It+=Mt-Oe}As.length>0?(Ps.push(As),_t&&js.push(Vs)):Ps.every(Mt=>Mt.length===0)&&(Qe=ft(),Ps=[],As=[],js=[],Vs=[])}if(Ps.length>0){if(Ie&&Q)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[Zt,us]=this.findLongestCommonSequence(Ps,js),xt=this.decode(Zt);Qe.text=xt,_t&&(Qe.words=this.collateWordTimestamps(Zt,us,Xe)),vt.push(Qe)}let zs=Object.create(null);const gr=vt.map(Zt=>Zt.text).join("");if(Q||pe){for(let Zt=0;Zt<vt.length;++Zt){const us=vt[Zt];Q||delete us.timestamp,pe||delete us.language}if(_t){const Zt=[];for(const us of vt)for(const xt of us.words)Zt.push(xt);zs={chunks:Zt}}else zs={chunks:vt}}return[gr,zs]}findLongestCommonSequence(M,Q=null){let pe=M[0],ge=pe.length,Ie=[];const Xe=Array.isArray(Q)&&Q.length>0;let _t=Xe?[]:null,ft=Xe?Q[0]:null;for(let vt=1;vt<M.length;++vt){const Qe=M[vt];let It=0,Xt=[ge,ge,0,0];const gs=Qe.length;for(let zs=1;zs<ge+gs;++zs){const gr=Math.max(0,ge-zs),Zt=Math.min(ge,ge+gs-zs),us=pe.slice(gr,Zt),xt=Math.max(0,zs-ge),os=Math.min(gs,zs),wr=Qe.slice(xt,os);if(us.length!==wr.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let As;Xe?As=us.filter((Es,Oe)=>Es===wr[Oe]&&ft[gr+Oe]<=Q[vt][xt+Oe]).length:As=us.filter((Es,Oe)=>Es===wr[Oe]).length;const Vs=zs/1e4,Mt=As/zs+Vs;As>1&&Mt>It&&(It=Mt,Xt=[gr,Zt,xt,os])}const[Se,Ps,js,Rs]=Xt,dr=Math.floor((Ps+Se)/2),zt=Math.floor((Rs+js)/2);Ie.push(...pe.slice(0,dr)),pe=Qe.slice(zt),ge=pe.length,Xe&&(_t.push(...ft.slice(0,dr)),ft=Q[vt].slice(zt))}return Ie.push(...pe),Xe?(_t.push(...ft),[Ie,_t]):[Ie,[]]}collateWordTimestamps(M,Q,pe){const[ge,Ie,Xe]=this.combineTokensIntoWords(M,pe),_t=[];for(let ft=0;ft<ge.length;++ft){const vt=Xe[ft];_t.push({text:ge[ft],timestamp:[Q[vt.at(0)][0],Q[vt.at(-1)][1]]})}return _t}combineTokensIntoWords(M,Q,pe=`"'“¡¿([{-`,ge=`"'.。,，!！?？:：”)]}、`){Q=Q??"english";let Ie,Xe,_t;return["chinese","japanese","thai","lao","myanmar"].includes(Q)?[Ie,Xe,_t]=this.splitTokensOnUnicode(M):[Ie,Xe,_t]=this.splitTokensOnSpaces(M),this.mergePunctuations(Ie,Xe,_t,pe,ge)}decode(M,Q){let pe;return Q?.decode_with_timestamps?(M instanceof U.Tensor&&(M=re(M)),pe=this.decodeWithTimestamps(M,Q)):pe=super.decode(M,Q),pe}decodeWithTimestamps(M,Q){const pe=Q?.time_precision??.02,ge=Array.from(this.all_special_ids).at(-1)+1;let Ie=[[]];for(let Xe of M)if(Xe=Number(Xe),Xe>=ge){const _t=((Xe-ge)*pe).toFixed(2);Ie.push(`<|${_t}|>`),Ie.push([])}else Ie[Ie.length-1].push(Xe);return Ie=Ie.map(Xe=>typeof Xe=="string"?Xe:super.decode(Xe,Q)),Ie.join("")}splitTokensOnUnicode(M){const Q=this.decode(M,{decode_with_timestamps:!0}),pe="�",ge=[],Ie=[],Xe=[];let _t=[],ft=[],vt=0;for(let Qe=0;Qe<M.length;++Qe){const It=M[Qe];_t.push(It),ft.push(Qe);const Xt=this.decode(_t,{decode_with_timestamps:!0});(!Xt.includes(pe)||Q[vt+Xt.indexOf(pe)]===pe)&&(ge.push(Xt),Ie.push(_t),Xe.push(ft),_t=[],ft=[],vt+=Xt.length)}return[ge,Ie,Xe]}splitTokensOnSpaces(M){const[Q,pe,ge]=this.splitTokensOnUnicode(M),Ie=[],Xe=[],_t=[],ft=new RegExp(`^[${w}]$`,"gu");for(let vt=0;vt<Q.length;++vt){const Qe=Q[vt],It=pe[vt],Xt=ge[vt],gs=It[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),Se=Qe.startsWith(" "),Ps=Qe.trim(),js=ft.test(Ps);if(gs||Se||js||Ie.length===0)Ie.push(Qe),Xe.push(It),_t.push(Xt);else{const Rs=Ie.length-1;Ie[Rs]+=Qe,Xe[Rs].push(...It),_t[Rs].push(...Xt)}}return[Ie,Xe,_t]}mergePunctuations(M,Q,pe,ge,Ie){const Xe=structuredClone(M),_t=structuredClone(Q),ft=structuredClone(pe);let vt=Xe.length-2,Qe=Xe.length-1;for(;vt>=0;)Xe[vt].startsWith(" ")&&ge.includes(Xe[vt].trim())?(Xe[Qe]=Xe[vt]+Xe[Qe],_t[Qe]=(0,O.mergeArrays)(_t[vt],_t[Qe]),ft[Qe]=(0,O.mergeArrays)(ft[vt],ft[Qe]),Xe[vt]="",_t[vt]=[],ft[vt]=[]):Qe=vt,--vt;for(vt=0,Qe=1;Qe<Xe.length;)!Xe[vt].endsWith(" ")&&Ie.includes(Xe[Qe])?(Xe[vt]+=Xe[Qe],_t[vt]=(0,O.mergeArrays)(_t[vt],_t[Qe]),ft[vt]=(0,O.mergeArrays)(ft[vt],ft[Qe]),Xe[Qe]="",_t[Qe]=[],ft[Qe]=[]):vt=Qe,++Qe;return[Xe.filter(It=>It),_t.filter(It=>It.length>0),ft.filter(It=>It.length>0)]}}class En extends Ot{}class Cn extends Ot{}class kn extends Ot{}class Or extends Ot{constructor(M,Q){super(M,Q),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(pe=>this.languageRegex.test(pe)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(M){if(M===null)return null;const[Q,...pe]=M.trim().split(this.languageRegex);if(pe.length===0)return super._encode_text(Q);if(pe.length===2){const[ge,Ie]=pe;return this.supported_language_codes.includes(ge)||console.warn(`Unsupported language code "${ge}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,O.mergeArrays)([ge],super._encode_text(Ie))}}}class Sn extends Ot{}class pn extends Ot{}class $n extends Ot{}class ns extends Ot{}class er extends Ot{}class hn extends Ot{constructor(M,Q){super(M,Q),this.decoder=new Qt({})}}class An extends Ot{}class Hr extends Ot{}class In{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:cs,DistilBertTokenizer:rr,CamembertTokenizer:it,DebertaTokenizer:Sr,DebertaV2Tokenizer:Js,BertTokenizer:Nr,HerbertTokenizer:ur,ConvBertTokenizer:Tr,RoFormerTokenizer:jr,XLMTokenizer:dt,ElectraTokenizer:St,MobileBertTokenizer:kr,SqueezeBertTokenizer:vr,AlbertTokenizer:br,GPT2Tokenizer:Ur,BartTokenizer:rs,MBartTokenizer:Vr,MBart50Tokenizer:$r,RobertaTokenizer:qn,WhisperTokenizer:cn,CodeGenTokenizer:En,CLIPTokenizer:Cn,SiglipTokenizer:kn,MarianTokenizer:Or,BloomTokenizer:Tn,NllbTokenizer:dn,M2M100Tokenizer:Ft,LlamaTokenizer:xn,CodeLlamaTokenizer:Ar,XLMRobertaTokenizer:Pn,MPNetTokenizer:Xn,FalconTokenizer:Ir,GPTNeoXTokenizer:fr,EsmTokenizer:Zs,Wav2Vec2CTCTokenizer:Sn,BlenderbotTokenizer:pn,BlenderbotSmallTokenizer:$n,SpeechT5Tokenizer:ns,NougatTokenizer:er,VitsTokenizer:hn,Qwen2Tokenizer:ln,GemmaTokenizer:Gr,Grok1Tokenizer:un,CohereTokenizer:An,MgpstrTokenizer:Hr,PreTrainedTokenizer:Ot};static async from_pretrained(M,{progress_callback:Q=null,config:pe=null,cache_dir:ge=null,local_files_only:Ie=!1,revision:Xe="main",legacy:_t=null}={}){const[ft,vt]=await T(M,{progress_callback:Q,config:pe,cache_dir:ge,local_files_only:Ie,revision:Xe,legacy:_t}),Qe=vt.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let It=this.TOKENIZER_CLASS_MAPPING[Qe];return It||(console.warn(`Unknown tokenizer class "${Qe}", attempting to construct from base class.`),It=Ot),new It(ft,vt)}}},"./src/utils/audio.js":(Ce,A,r)=>{r.r(A),r.d(A,{hamming:()=>b,hanning:()=>P,mel_filter_bank:()=>R,read_audio:()=>U,spectrogram:()=>w,window_function:()=>C});var g=r("./src/utils/hub.js"),O=r("./src/utils/maths.js"),j=r("./src/utils/core.js"),te=r("./src/utils/tensor.js");async function U(x,J){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. 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Will expand buffer when needed.");let w=parseInt($??"0"),C=new Uint8Array(w),x=0;const J=H.body.getReader();async function q(){const{done:le,value:ce}=await J.read();if(le)return;let fe=x+ce.length;if(fe>w){w=fe;let be=new Uint8Array(w);be.set(C),C=be}C.set(ce,x),x=fe;const Pe=x/w*100;return D({progress:Pe,loaded:x,total:w}),q()}return await q(),C}function G(...H){return H=H.map((D,$)=>($&&(D=D.replace(new RegExp("^/"),"")),$!==H.length-1&&(D=D.replace(new RegExp("/$"),"")),D)),H.join("/")}},"./src/utils/image.js":(Ce,A,r)=>{r.r(A),r.d(A,{RawImage:()=>K,load_image:()=>re});var g=r("./src/utils/core.js"),O=r("./src/utils/hub.js"),j=r("./src/env.js"),te=r("./src/utils/tensor.js"),U=r("?2b25");let y,P,b;const T=j.apis.IS_BROWSER_ENV||j.apis.IS_WEBWORKER_ENV;if(T)y=(ae,R)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(ae,R)},b=self.createImageBitmap,P=self.ImageData;else if(U)b=async ae=>{const G=(await ae.metadata()).channels,{data:H,info:D}=await ae.rotate().raw().toBuffer({resolveWithObject:!0}),$=new K(new Uint8ClampedArray(H),D.width,D.height,D.channels);return G!==void 0&&G!==D.channels&&$.convert(G),$};else throw new Error("Unable to load image processing library.");const v={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},z=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class K{constructor(R,G,H,D){this.data=R,this.width=G,this.height=H,this.channels=D}get size(){return[this.width,this.height]}static async read(R){if(R instanceof K)return R;if(typeof R=="string"||R instanceof URL)return await this.fromURL(R);throw new Error(`Unsupported input type: ${typeof R}`)}static fromCanvas(R){if(!T)throw new Error("fromCanvas() is only supported in browser environments.");const H=R.getContext("2d").getImageData(0,0,R.width,R.height).data;return new K(H,R.width,R.height,4)}static async fromURL(R){const G=await(0,O.getFile)(R);if(G.status!==200)throw new Error(`Unable to read image from "${R}" (${G.status} ${G.statusText})`);const H=await G.blob();return this.fromBlob(H)}static async fromBlob(R){if(T){const G=await b(R),H=y(G.width,G.height).getContext("2d");return H.drawImage(G,0,0),new this(H.getImageData(0,0,G.width,G.height).data,G.width,G.height,4)}else{const G=U(await R.arrayBuffer());return await b(G)}}static fromTensor(R,G="CHW"){if(R.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${R.dims.length} dimensions.`);if(G==="CHW")R=R.transpose(1,2,0);else if(G!=="HWC")throw new Error(`Unsupported channel format: ${G}`);if(!(R.data instanceof Uint8ClampedArray||R.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${R.type}`);switch(R.dims[2]){case 1:case 2:case 3:case 4:return new K(R.data,R.dims[1],R.dims[0],R.dims[2]);default:throw new Error(`Unsupported number of channels: ${R.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const R=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let G=0,H=0;G<this.data.length;G+=this.channels){const D=this.data[G],$=this.data[G+1],w=this.data[G+2];R[H++]=Math.round(.2989*D+.587*$+.114*w)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(R,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const R=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let G=0,H=0;G<this.data.length;++G)R[H++]=this.data[G],R[H++]=this.data[G],R[H++]=this.data[G];break;case 4:for(let G=0,H=0;G<this.data.length;G+=4)R[H++]=this.data[G],R[H++]=this.data[G+1],R[H++]=this.data[G+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(R,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const R=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let G=0,H=0;G<this.data.length;++G)R[H++]=this.data[G],R[H++]=this.data[G],R[H++]=this.data[G],R[H++]=255;break;case 3:for(let G=0,H=0;G<this.data.length;G+=3)R[H++]=this.data[G],R[H++]=this.data[G+1],R[H++]=this.data[G+2],R[H++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(R,this.width,this.height,4)}putAlpha(R){if(R.width!==this.width||R.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${R.width}x${R.height}`);if(R.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${R.channels}`);const G=this.data,H=R.data,D=this.width*this.height;if(this.channels===3){const $=new Uint8ClampedArray(D*4);for(let w=0,C=0,x=0;w<D;++w)$[x++]=G[C++],$[x++]=G[C++],$[x++]=G[C++],$[x++]=H[w];return this._update($,this.width,this.height,4)}else if(this.channels===4){for(let $=0;$<D;++$)G[4*$+3]=H[$];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(R,G,{resample:H=2}={}){if(this.width===R&&this.height===G)return this;let D=v[H]??H;const $=(0,g.isNullishDimension)(R),w=(0,g.isNullishDimension)(G);if($&&w)return this;if($?R=G/this.height*this.width:w&&(G=R/this.width*this.height),T){const C=this.channels,x=this.toCanvas(),J=y(R,G).getContext("2d");return J.drawImage(x,0,0,R,G),new K(J.getImageData(0,0,R,G).data,R,G,4).convert(C)}else{let C=this.toSharp();switch(D){case"box":case"hamming":(D==="box"||D==="hamming")&&(console.warn(`Resampling method ${D} is not yet supported. Using bilinear instead.`),D="bilinear");case"nearest":case"bilinear":case"bicubic":C=C.affine([R/this.width,0,0,G/this.height],{interpolator:D});break;case"lanczos":C=C.resize({width:R,height:G,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${D} is not supported.`)}return await b(C)}}async pad([R,G,H,D]){if(R=Math.max(R,0),G=Math.max(G,0),H=Math.max(H,0),D=Math.max(D,0),R===0&&G===0&&H===0&&D===0)return this;if(T){const $=this.channels,w=this.toCanvas(),C=this.width+R+G,x=this.height+H+D,J=y(C,x).getContext("2d");return J.drawImage(w,0,0,this.width,this.height,R,H,this.width,this.height),new K(J.getImageData(0,0,C,x).data,C,x,4).convert($)}else{const $=this.toSharp().extend({left:R,right:G,top:H,bottom:D});return await b($)}}async crop([R,G,H,D]){if(R=Math.max(R,0),G=Math.max(G,0),H=Math.min(H,this.width-1),D=Math.min(D,this.height-1),R===0&&G===0&&H===this.width-1&&D===this.height-1)return this;const $=H-R+1,w=D-G+1;if(T){const C=this.channels,x=this.toCanvas(),J=y($,w).getContext("2d");return J.drawImage(x,R,G,$,w,0,0,$,w),new K(J.getImageData(0,0,$,w).data,$,w,4).convert(C)}else{const C=this.toSharp().extract({left:R,top:G,width:$,height:w});return await b(C)}}async center_crop(R,G){if(this.width===R&&this.height===G)return this;const H=(this.width-R)/2,D=(this.height-G)/2;if(T){const $=this.channels,w=this.toCanvas(),C=y(R,G).getContext("2d");let x=0,J=0,q=0,le=0;return H>=0?x=H:q=-H,D>=0?J=D:le=-D,C.drawImage(w,x,J,R,G,q,le,R,G),new K(C.getImageData(0,0,R,G).data,R,G,4).convert($)}else{let $=this.toSharp();if(H>=0&&D>=0)$=$.extract({left:Math.floor(H),top:Math.floor(D),width:R,height:G});else if(H<=0&&D<=0){const w=Math.floor(-D),C=Math.floor(-H);$=$.extend({top:w,left:C,right:R-this.width-C,bottom:G-this.height-w})}else{let w=[0,0],C=0;D<0?(w[0]=Math.floor(-D),w[1]=G-this.height-w[0]):C=Math.floor(D);let x=[0,0],J=0;H<0?(x[0]=Math.floor(-H),x[1]=R-this.width-x[0]):J=Math.floor(H),$=$.extend({top:w[0],bottom:w[1],left:x[0],right:x[1]}).extract({left:J,top:C,width:R,height:G})}return await b($)}}async toBlob(R="image/png",G=1){if(!T)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:R,quality:G})}toTensor(R="CHW"){let G=new te.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(R!=="HWC")if(R==="CHW")G=G.permute(2,0,1);else throw new Error(`Unsupported channel format: ${R}`);return G}toCanvas(){if(!T)throw new Error("toCanvas() is only supported in browser environments.");const R=this.clone().rgba(),G=y(R.width,R.height),H=new P(R.data,R.width,R.height);return G.getContext("2d").putImageData(H,0,0),G}split(){const{data:R,width:G,height:H,channels:D}=this,$=R.constructor,w=R.length/D,C=Array.from({length:D},()=>new $(w));for(let x=0;x<w;++x){const J=D*x;for(let q=0;q<D;++q)C[q][x]=R[J+q]}return C.map(x=>new K(x,G,H,1))}_update(R,G,H,D=null){return this.data=R,this.width=G,this.height=H,D!==null&&(this.channels=D),this}clone(){return new K(this.data.slice(),this.width,this.height,this.channels)}convert(R){if(this.channels===R)return this;switch(R){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: ${this.channels}`)}return this}async save(R){if(T){if(j.apis.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const G=R.split(".").pop().toLowerCase(),H=z.get(G)??"image/png",D=await this.toBlob(H),$=URL.createObjectURL(D),w=document.createElement("a");w.href=$,w.download=R,w.click(),w.remove()}else{if(j.env.useFS)return await this.toSharp().toFile(R);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(T)throw new Error("toSharp() is only supported in 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O(D,$,w){const C=new Array(w.length),x=new Array(w.length);for(let le=w.length-1,ce=1;le>=0;--le)x[le]=ce,C[le]=$[w[le]],ce*=C[le];const J=w.map((le,ce)=>x[w.indexOf(ce)]),q=new D.constructor(D.length);for(let le=0;le<D.length;++le){let ce=0;for(let fe=$.length-1,Pe=le;fe>=0;--fe)ce+=Pe%$[fe]*J[fe],Pe=Math.floor(Pe/$[fe]);q[ce]=D[le]}return[q,C]}function j(D){const $=T(D)[0],w=D.map(J=>Math.exp(J-$)),C=w.reduce((J,q)=>J+q,0);return w.map(J=>J/C)}function te(D){const $=T(D)[0];let w=0;for(let J=0;J<D.length;++J)w+=Math.exp(D[J]-$);const C=Math.log(w);return D.map(J=>J-$-C)}function U(D,$){let w=0;for(let C=0;C<D.length;++C)w+=D[C]*$[C];return w}function y(D,$){const w=U(D,$),C=P(D),x=P($);return w/(C*x)}function P(D){return Math.sqrt(D.reduce(($,w)=>$+w*w,0))}function b(D){if(D.length===0)throw Error("Array must not be empty");let $=D[0],w=0;for(let C=1;C<D.length;++C)D[C]<$&&($=D[C],w=C);return[$,w]}function T(D){if(D.length===0)throw Error("Array must not be empty");let 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ue=Ne,Z=ue+De,he=Z+De,ve=he+De,Le=$[ue],Ze=$[ue+1],Ke=$[Z],ne=$[Z+1],qe=$[he],Ae=$[he+1],oe=$[ve],Me=$[ve+1],je=be[lt],Re=C*be[lt+1],We=Ke*je-ne*Re,ze=Ke*Re+ne*je,Ye=be[2*lt],nt=C*be[2*lt+1],wt=qe*Ye-Ae*nt,ut=qe*nt+Ae*Ye,ht=be[3*lt],I=C*be[3*lt+1],ie=oe*ht-Me*I,X=oe*I+Me*ht,_e=Le+wt,$e=Ze+ut,He=Le-wt,et=Ze-ut,ot=We+ie,yt=ze+X,mt=C*(We-ie),Qt=C*(ze-X);$[ue]=_e+ot,$[ue+1]=$e+yt,$[Z]=He+Qt,$[Z+1]=et-mt,$[he]=_e-ot,$[he+1]=$e-yt,$[ve]=He-Qt,$[ve+1]=et+mt}}}}_singleTransform2($,w,C,x,J){const q=$[x],le=$[x+1],ce=$[x+J],fe=$[x+J+1];w[C]=q+ce,w[C+1]=le+fe,w[C+2]=q-ce,w[C+3]=le-fe}_singleTransform4($,w,C,x,J,q){const le=J*2,ce=J*3,fe=$[x],Pe=$[x+1],be=$[x+J],De=$[x+J+1],Ge=$[x+le],Ne=$[x+le+1],lt=$[x+ce],ue=$[x+ce+1],Z=fe+Ge,he=Pe+Ne,ve=fe-Ge,Le=Pe-Ne,Ze=be+lt,Ke=De+ue,ne=q*(be-lt),qe=q*(De-ue);w[C]=Z+Ze,w[C+1]=he+Ke,w[C+2]=ve+qe,w[C+3]=Le-ne,w[C+4]=Z-Ze,w[C+5]=he-Ke,w[C+6]=ve-qe,w[C+7]=Le+ne}_realTransform4($,w,C){const x=this._csize;let q=1<<this._width,le=x/q<<1,ce,fe;const Pe=this._bitrev;if(le===4)for(ce=0,fe=0;ce<x;ce+=le,++fe){const Ge=Pe[fe];this._singleRealTransform2(w,$,ce,Ge>>>1,q>>>1)}else for(ce=0,fe=0;ce<x;ce+=le,++fe){const Ge=Pe[fe];this._singleRealTransform4(w,$,ce,Ge>>>1,q>>>1,C)}const be=this.table;for(q>>=2;q>=2;q>>=2){le=x/q<<1;const Ge=le>>>1,Ne=Ge>>>1,lt=Ne>>>1;for(ce=0;ce<x;ce+=le)for(let ue=0,Z=0;ue<=lt;ue+=2,Z+=q){const he=ce+ue,ve=he+Ne,Le=ve+Ne,Ze=Le+Ne,Ke=$[he],ne=$[he+1],qe=$[ve],Ae=$[ve+1],oe=$[Le],Me=$[Le+1],je=$[Ze],Re=$[Ze+1],We=Ke,ze=ne,Ye=be[Z],nt=C*be[Z+1],wt=qe*Ye-Ae*nt,ut=qe*nt+Ae*Ye,ht=be[2*Z],I=C*be[2*Z+1],ie=oe*ht-Me*I,X=oe*I+Me*ht,_e=be[3*Z],$e=C*be[3*Z+1],He=je*_e-Re*$e,et=je*$e+Re*_e,ot=We+ie,yt=ze+X,mt=We-ie,Qt=ze-X,ts=wt+He,xs=ut+et,hs=C*(wt-He),$s=C*(ut-et);if($[he]=ot+ts,$[he+1]=yt+xs,$[ve]=mt+$s,$[ve+1]=Qt-hs,ue===0){$[Le]=ot-ts,$[Le+1]=yt-xs;continue}if(ue===lt)continue;const Ms=ce+Ne-ue,Ks=ce+Ge-ue;$[Ms]=mt-C*$s,$[Ms+1]=-Qt-C*hs,$[Ks]=ot-C*ts,$[Ks+1]=-yt+C*xs}}const De=x>>>1;for(let Ge=2;Ge<De;Ge+=2)$[x-Ge]=$[Ge],$[x-Ge+1]=-$[Ge+1]}_singleRealTransform2($,w,C,x,J){const q=$[x],le=$[x+J];w[C]=q+le,w[C+1]=0,w[C+2]=q-le,w[C+3]=0}_singleRealTransform4($,w,C,x,J,q){const le=J*2,ce=J*3,fe=$[x],Pe=$[x+J],be=$[x+le],De=$[x+ce],Ge=fe+be,Ne=fe-be,lt=Pe+De,ue=q*(Pe-De);w[C]=Ge+lt,w[C+1]=0,w[C+2]=Ne,w[C+3]=-ue,w[C+4]=Ge-lt,w[C+5]=0,w[C+6]=Ne,w[C+7]=ue}}class K{constructor($){const w=2*($-1),C=2*(2*$-1),x=2**Math.ceil(Math.log2(C));this.bufferSize=x,this._a=w;const J=new Float64Array(C),q=new Float64Array(x);this._chirpBuffer=new Float64Array(x),this._buffer1=new Float64Array(x),this._buffer2=new Float64Array(x),this._outBuffer1=new Float64Array(x),this._outBuffer2=new Float64Array(x);const le=-2*Math.PI/$,ce=Math.cos(le),fe=Math.sin(le);for(let Pe=0;Pe<C>>1;++Pe){const be=(Pe+1-$)**2/2,De=Math.sqrt(ce**2+fe**2)**be,Ge=be*Math.atan2(fe,ce),Ne=2*Pe;J[Ne]=De*Math.cos(Ge),J[Ne+1]=De*Math.sin(Ge),q[Ne]=J[Ne],q[Ne+1]=-J[Ne+1]}this._slicedChirpBuffer=J.subarray(w,C),this._f=new z(x>>1),this._f.transform(this._chirpBuffer,q)}_transform($,w,C){const x=this._buffer1,J=this._buffer2,q=this._outBuffer1,le=this._outBuffer2,ce=this._chirpBuffer,fe=this._slicedChirpBuffer,Pe=this._a;if(C)for(let be=0;be<fe.length;be+=2){const De=be+1,Ge=be>>1,Ne=w[Ge];x[be]=Ne*fe[be],x[De]=Ne*fe[De]}else for(let be=0;be<fe.length;be+=2){const De=be+1;x[be]=w[be]*fe[be]-w[De]*fe[De],x[De]=w[be]*fe[De]+w[De]*fe[be]}this._f.transform(q,x);for(let be=0;be<ce.length;be+=2){const De=be+1;J[be]=q[be]*ce[be]-q[De]*ce[De],J[De]=q[be]*ce[De]+q[De]*ce[be]}this._f.inverseTransform(le,J);for(let be=0;be<le.length;be+=2){const De=le[be+Pe],Ge=le[be+Pe+1],Ne=fe[be],lt=fe[be+1];$[be]=De*Ne-Ge*lt,$[be+1]=De*lt+Ge*Ne}}transform($,w){this._transform($,w,!1)}realTransform($,w){this._transform($,w,!0)}}class re{constructor($){this.fft_length=$,this.isPowerOfTwo=v($),this.isPowerOfTwo?(this.fft=new z($),this.outputBufferSize=2*$):(this.fft=new K($),this.outputBufferSize=this.fft.bufferSize)}realTransform($,w){this.fft.realTransform($,w)}transform($,w){this.fft.transform($,w)}}function ae(D,$){if($%2===0||$<=0)throw new Error("Window size must be a positive odd number");const w=new D.constructor(D.length),C=new D.constructor($),x=Math.floor($/2);for(let J=0;J<D.length;++J){let q=0;for(let le=-x;le<=x;++le){let ce=J+le;ce<0?ce=Math.abs(ce):ce>=D.length&&(ce=2*(D.length-1)-ce),C[q++]=D[ce]}C.sort(),w[J]=C[x]}return w}function R(D,$){const w=Math.pow(10,$);return Math.round(D*w)/w}function G(D){const $=Math.round(D);return Math.abs(D)%1===.5?$%2===0?$:$-1:$}function H(D){const $=D.length,w=D[0].length,C=[$+1,w+1],x=Array.from({length:C[0]},()=>Array(C[1]).fill(1/0));x[0][0]=0;const J=Array.from({length:C[0]},()=>Array(C[1]).fill(-1));for(let Pe=1;Pe<C[1];++Pe)for(let be=1;be<C[0];++be){const De=x[be-1][Pe-1],Ge=x[be-1][Pe],Ne=x[be][Pe-1];let lt,ue;De<Ge&&De<Ne?(lt=De,ue=0):Ge<De&&Ge<Ne?(lt=Ge,ue=1):(lt=Ne,ue=2),x[be][Pe]=D[be-1][Pe-1]+lt,J[be][Pe]=ue}for(let Pe=0;Pe<C[1];++Pe)J[0][Pe]=2;for(let Pe=0;Pe<C[0];++Pe)J[Pe][0]=1;let q=$,le=w,ce=[],fe=[];for(;q>0||le>0;)switch(ce.push(q-1),fe.push(le-1),J[q][le]){case 0:--q,--le;break;case 1:--q;break;case 2:--le;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${q}, ${le}]. Please file a bug report.`)}return ce.reverse(),fe.reverse(),[ce,fe]}},"./src/utils/tensor.js":(Ce,A,r)=>{r.r(A),r.d(A,{Tensor:()=>U,cat:()=>w,full:()=>ce,full_like:()=>fe,interpolate:()=>b,interpolate_4d:()=>T,layer_norm:()=>G,matmul:()=>v,mean:()=>J,mean_pooling:()=>R,ones:()=>Pe,ones_like:()=>be,permute:()=>P,quantize_embeddings:()=>lt,rand:()=>Ne,rfft:()=>z,slice:()=>ae,stack:()=>C,std_mean:()=>x,topk:()=>K,zeros:()=>De,zeros_like:()=>Ge});var g=r("./src/utils/maths.js"),O=r("./src/backends/onnx.js"),j=r("./src/ops/registry.js");const te=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array,uint4:Uint8Array,int4:Int8Array});class U{get dims(){return this.ort_tensor.dims}set dims(Z){this.ort_tensor.dims=Z}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}ort_tensor;constructor(...Z){return(0,O.isONNXTensor)(Z[0])?this.ort_tensor=Z[0]:this.ort_tensor=new O.Tensor(Z[0],Z[1],Z[2]),new Proxy(this,{get:(he,ve)=>{if(typeof ve=="string"){let Le=Number(ve);if(Number.isInteger(Le))return he._getitem(Le)}return he[ve]},set:(he,ve,Le)=>he[ve]=Le})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[Z,...he]=this.dims;if(he.length>0){const ve=he.reduce((Le,Ze)=>Le*Ze);for(let Le=0;Le<Z;++Le)yield this._subarray(Le,ve,he)}else yield*this.data}_getitem(Z){const[he,...ve]=this.dims;if(Z=$(Z,he),ve.length>0){const Le=ve.reduce((Ze,Ke)=>Ze*Ke);return this._subarray(Z,Le,ve)}else return new U(this.type,[this.data[Z]],ve)}indexOf(Z){const he=this.data;for(let ve=0;ve<he.length;++ve)if(he[ve]==Z)return ve;return-1}_subarray(Z,he,ve){const Le=Z*he,Ze=(Z+1)*he,Ke="subarray"in this.data?this.data.subarray(Le,Ze):this.data.slice(Le,Ze);return new 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U(this.type,this.data.slice(),this.dims.slice())}slice(...Z){const he=[],ve=[];for(let Ae=0;Ae<this.dims.length;++Ae){let oe=Z[Ae];if(oe==null)ve.push([0,this.dims[Ae]]),he.push(this.dims[Ae]);else if(typeof oe=="number")oe=$(oe,this.dims[Ae],Ae),ve.push([oe,oe+1]);else if(Array.isArray(oe)&&oe.length===2){let[Me,je]=oe;if(Me=Me===null?0:$(Me,this.dims[Ae],Ae,!1),je=je===null?this.dims[Ae]:$(je,this.dims[Ae],Ae,!1),Me>je)throw new Error(`Invalid slice: ${oe}`);const Re=[Math.max(Me,0),Math.min(je,this.dims[Ae])];ve.push(Re),he.push(Re[1]-Re[0])}else throw new Error(`Invalid slice: ${oe}`)}const Le=ve.map(([Ae,oe])=>oe-Ae),Ze=Le.reduce((Ae,oe)=>Ae*oe),Ke=this.data,ne=new Ke.constructor(Ze),qe=this.stride();for(let Ae=0;Ae<Ze;++Ae){let oe=0;for(let Me=Le.length-1,je=Ae;Me>=0;--Me){const Re=Le[Me];oe+=(je%Re+ve[Me][0])*qe[Me],je=Math.floor(je/Re)}ne[Ae]=Ke[oe]}return new U(this.type,ne,he)}permute(...Z){return P(this,Z)}transpose(...Z){return this.permute(...Z)}sum(Z=null,he=!1){return this.norm(1,Z,he)}norm(Z="fro",he=null,ve=!1){if(Z==="fro")Z=2;else if(typeof Z=="string")throw Error(`Unsupported norm: ${Z}`);const Le=this.data;if(he===null){let ne=Le.reduce((qe,Ae)=>qe+Ae**Z,0)**(1/Z);return new U(this.type,[ne],[])}he=$(he,this.dims.length);const Ze=this.dims.slice();Ze[he]=1;const Ke=new Le.constructor(Le.length/this.dims[he]);for(let ne=0;ne<Le.length;++ne){let qe=0;for(let Ae=this.dims.length-1,oe=ne,Me=1;Ae>=0;--Ae){const je=this.dims[Ae];if(Ae!==he){const Re=oe%je;qe+=Re*Me,Me*=Ze[Ae]}oe=Math.floor(oe/je)}Ke[qe]+=Le[ne]**Z}if(Z!==1)for(let ne=0;ne<Ke.length;++ne)Ke[ne]=Ke[ne]**(1/Z);return ve||Ze.splice(he,1),new U(this.type,Ke,Ze)}normalize_(Z=2,he=1){he=$(he,this.dims.length);const ve=this.norm(Z,he,!0),Le=this.data,Ze=ve.data;for(let Ke=0;Ke<Le.length;++Ke){let ne=0;for(let qe=this.dims.length-1,Ae=Ke,oe=1;qe>=0;--qe){const Me=this.dims[qe];if(qe!==he){const je=Ae%Me;ne+=je*oe,oe*=this.dims[qe]}Ae=Math.floor(Ae/Me)}Le[Ke]/=Ze[ne]}return this}normalize(Z=2,he=1){return this.clone().normalize_(Z,he)}stride(){return q(this.dims)}squeeze(Z=null){return new U(this.type,this.data,H(this.dims,Z))}squeeze_(Z=null){return this.dims=H(this.dims,Z),this}unsqueeze(Z=null){return new U(this.type,this.data,D(this.dims,Z))}unsqueeze_(Z=null){return this.dims=D(this.dims,Z),this}flatten_(Z=0,he=-1){he=(he+this.dims.length)%this.dims.length;let ve=this.dims.slice(0,Z),Le=this.dims.slice(Z,he+1),Ze=this.dims.slice(he+1);return this.dims=[...ve,Le.reduce((Ke,ne)=>Ke*ne,1),...Ze],this}flatten(Z=0,he=-1){return this.clone().flatten_(Z,he)}view(...Z){let he=-1;for(let Le=0;Le<Z.length;++Le)if(Z[Le]===-1){if(he!==-1)throw new Error("Only one dimension can be inferred");he=Le}const ve=this.data;if(he!==-1){const Le=Z.reduce((Ze,Ke,ne)=>ne!==he?Ze*Ke:Ze,1);Z[he]=ve.length/Le}return new U(this.type,ve,Z)}neg_(){const Z=this.data;for(let he=0;he<Z.length;++he)Z[he]=-Z[he];return this}neg(){return this.clone().neg_()}clamp_(Z,he){const ve=this.data;for(let Le=0;Le<ve.length;++Le)ve[Le]=Math.min(Math.max(ve[Le],Z),he);return this}clamp(Z,he){return this.clone().clamp_(Z,he)}round_(){const Z=this.data;for(let he=0;he<Z.length;++he)Z[he]=Math.round(Z[he]);return this}round(){return this.clone().round_()}mean(Z=null,he=!1){return J(this,Z,he)}to(Z){if(this.type===Z)return this;if(!te.hasOwnProperty(Z))throw new Error(`Unsupported type: ${Z}`);let he;const ve=["int64","uint64"].includes(this.type),Le=["int64","uint64"].includes(Z);return ve&&!Le?he=Number:!ve&&Le&&(he=BigInt),new U(Z,te[Z].from(this.data,he),this.dims)}}function y(ue,Z){const he=ue.length,ve=Z.reduce((Ze,Ke)=>Ze*Ke);if(he!==ve)throw Error(`cannot reshape array of size ${he} into shape (${Z})`);let Le=ue;for(let Ze=Z.length-1;Ze>=0;Ze--)Le=Le.reduce((Ke,ne)=>{let qe=Ke[Ke.length-1];return qe.length<Z[Ze]?qe.push(ne):Ke.push([ne]),Ke},[[]]);return Le[0]}function P(ue,Z){const[he,ve]=(0,g.permute_data)(ue.data,ue.dims,Z);return new U(ue.type,he,ve)}function 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Of=d.pipeline;d.quantize_embeddings;d.rand;d.read_audio;d.rfft;d.round;d.slice;d.softmax;d.spectrogram;d.stack;d.std_mean;d.topk;d.window_function;d.zeros;d.zeros_like;const Ca=16e3,gh=Ca/1e3,Ff=.3,Df=.1,Lf=400,zf=Lf*gh,Bf=80,mh=Bf*gh,Rf=250*gh,Nf=30,jf=512,Uf=Math.ceil(mh/jf);async function Vf(){try{return navigator.gpu?(await navigator.gpu.requestAdapter(),!0):!1}catch{return!1}}const _h=await Vf()?"webgpu":"wasm";self.postMessage({type:"info",message:"Loading models..."});const Wf=await If.from_pretrained("onnx-community/silero-vad",{config:{model_type:"custom"},dtype:"fp32"}).catch(Ce=>{throw self.postMessage({error:Ce}),Ce}),Gf={webgpu:{encoder_model:"fp32",decoder_model_merged:"q4"},wasm:{encoder_model:"fp32",decoder_model_merged:"q8"}};self.postMessage({type:"info",message:`Using device: "${_h}"`});const o_=await Of("automatic-speech-recognition","onnx-community/moonshine-base-ONNX",{device:_h,dtype:Gf[_h]}).catch(Ce=>{throw self.postMessage({error:Ce}),Ce});await o_(new Float32Array(Ca));self.postMessage({type:"status",status:"ready",message:"Ready!"});let Pp=Promise.resolve();const Ea=new Float32Array(Nf*Ca);let vn=0;const Kf=new fh("int64",[Ca],[]);let e_=new fh("float32",new Float32Array(2*1*128),[2,1,128]),qd=!1;async function Hf(Ce){const A=new fh("float32",Ce,[1,Ce.length]),{stateN:r,output:g}=await(Pp=Pp.then(j=>Wf({input:A,sr:Kf,state:e_})));e_=r;const O=g.data[0];return O>Ff||qd&&O>=Df}const qf=async(Ce,A)=>{const{text:r}=await(Pp=Pp.then(g=>o_(Ce)));self.postMessage({type:"output",buffer:Ce,message:r,...A})};let Xd=0;const i_=(Ce=0)=>{self.postMessage({type:"status",status:"recording_end",message:"Transcribing...",duration:"until_next"}),Ea.fill(0,Ce),vn=Ce,qd=!1,Xd=0},t_=Ce=>{const r=Date.now()-(Xd+mh)/Ca*1e3,g=r-vn/Ca*1e3,O=r-g,j=Ce?.length??0,te=Ea.slice(0,vn+mh),U=Qd.reduce((b,T)=>b+T.length,0),y=new Float32Array(U+te.length);let P=0;for(const b of Qd)y.set(b,P),P+=b.length;y.set(te,P),qf(y,{start:g,end:r,duration:O}),Ce&&Ea.set(Ce,0),i_(j)};let Qd=[];self.onmessage=async Ce=>{const{buffer:A}=Ce.data,r=qd,g=await Hf(A);if(!r&&!g){Qd.length>=Uf&&Qd.shift(),Qd.push(A);return}const O=Ea.length-vn;if(A.length>=O){Ea.set(A.subarray(0,O),vn),vn+=O;const j=A.subarray(O);t_(j);return}else Ea.set(A,vn),vn+=A.length;if(g){qd||self.postMessage({type:"status",status:"recording_start",message:"Listening...",duration:"until_next"}),qd=!0,Xd=0;return}if(Xd+=A.length,!(Xd<zf)){if(vn<Rf){i_();return}t_()}};