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             Please use the previous external buffer!`)}else n=_t();return this.storageCache.set(n,{gpuData:{id:n,type:0,buffer:e},originalSize:t}),is("verbose",()=>`[WebGPU] GpuDataManager.registerExternalBuffer(size=${t}) => id=${n}, registered.`),n}unregisterExternalBuffer(e){e!==void 0&&(this.storageCache.delete(e),is("verbose",()=>`[WebGPU] GpuDataManager.unregisterExternalBuffer() => id=${e}`))}create(e,t=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST){let s=ht(e),n,o=(t&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE,a=(t&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM;if(o||a){let u=(o?this.freeBuffers:this.freeUniformBuffers).get(s);u?u.length>0?n=u.pop():n=this.backend.device.createBuffer({size:s,usage:t}):n=this.backend.device.createBuffer({size:s,usage:t})}else n=this.backend.device.createBuffer({size:s,usage:t});let i={id:_t(),type:0,buffer:n};return this.storageCache.set(i.id,{gpuData:i,originalSize:Number(e)}),is("verbose",()=>`[WebGPU] GpuDataManager.create(size=${e}) => id=${i.id}`),i}get(e){var t;return(t=this.storageCache.get(e))==null?void 0:t.gpuData}release(e){let t=typeof e=="bigint"?Number(e):e,s=this.storageCache.get(t);if(!s){if(this.storageCache.size===0)return 0;throw new Error("releasing data does not exist")}return is("verbose",()=>`[WebGPU] GpuDataManager.release(id=${t}), gpuDataId=${s.gpuData.id}`),this.storageCache.delete(t),this.buffersPending.push(s.gpuData.buffer),s.originalSize}async download(e,t){let s=this.storageCache.get(Number(e));if(!s)throw new Error("data does not exist");await Tt(this.backend,s.gpuData.buffer,s.originalSize,t)}refreshPendingBuffers(){if(this.buffersPending.length!==0)if(this.backend.sessionStatus==="default"){for(let e of this.buffersPending){let t=ve.get(e.size);if((e.usage&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE){let s=this.freeBuffers.get(e.size)||[];t===void 0||s.length>=t?e.destroy():s.push(e)}else if((e.usage&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM){let s=this.freeUniformBuffers.get(e.size)||[];t===void 0||s.length>=t?e.destroy():s.push(e)}else e.destroy()}this.buffersPending=[]}else{let e=this.capturedPendingBuffers.get(this.backend.currentSessionId);e||(e=[],this.capturedPendingBuffers.set(this.backend.currentSessionId,e));for(let t of this.buffersPending)e.push(t);this.buffersPending=[]}}dispose(){this.freeBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.freeUniformBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache.forEach(e=>{e.gpuData.buffer.destroy()}),this.capturedPendingBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.capturedPendingBuffers=new Map}onCreateSession(){this.sessionCount+=1}onReleaseSession(e){let t=this.capturedPendingBuffers.get(e);t&&(t.forEach(s=>{s.destroy()}),this.capturedPendingBuffers.delete(e)),this.sessionCount-=1,this.sessionCount===0&&(is("warning",()=>"[WebGPU] Clearing webgpu buffer cache"),this.storageCache.forEach(s=>{s.gpuData.buffer.destroy()}),this.storageCache=new Map)}},_s=(...e)=>new Kt(...e)}),Fs,zt,rs=g(()=>{Fs=class{constructor(e){Object.assign(this,e)}get cacheKey(){return this.key||(this.key=Object.getOwnPropertyNames(this).sort().map(e=>`${this[e]}`).join(";")),this.key}},zt=e=>new Fs(e)}),rr,Ws,ze,Zs,Ir,Ss,Xs,Ot=g(()=>{rr=class{static calcMatMulShape(e,t){return e[1]!==t[0]?void 0:[e[0],t[1]]}},Ws=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 a=Math.max(e.length,t.length),i=new Array(a);if(s){if(n<2||o<2)return;let u=rr.calcMatMulShape([e[n-2],e[n-1]],[t[o-2],t[o-1]]);if(u===void 0)return;[i[a-2],i[a-1]]=u}for(let u=s?3:1;u<=a;u++){let p=n-u<0?1:e[n-u],h=o-u<0?1:t[o-u];if(p!==h&&p>1&&h>1)return;let C=Math.max(p,h);if(p&&h)i[a-u]=Math.max(p,h);else{if(C>1)return;i[a-u]=0}}return i}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}},ze=class Hp{static size(t){return Hp.getSizeFromDimensionRange(t,0,t.length)}static convertShape(t,s=4){let n=t.length;if(n===0)return[];let o=new Array(n),a=n-1;for(;a>=0;){if(t[a]%s===0){o[a]=t[a]/s;break}if(s%t[a]!==0)throw new Error("cannot convert shape");o[a]=1,s/=t[a],a--}for(a--;a>=0;a--)o[a]=t[a];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 Hp.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 Hp.getSizeFromDimensionRange(t,0,s)}static getSizeFromDimensionRange(t,s,n){let o=1;for(let a=s;a<n;a++){if(t[a]<0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains negative values in them.");o*=Number(t[a])}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,a)=>o+s[a]+s[a+n])}static areEqual(t,s){return t.length!==s.length?!1:t.every((n,o)=>n===s[o])}},Zs=class Rc{static adjustPoolAttributes(t,s,n,o,a,i){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 u=0;u<s.length-2;u++)u>=n.length?n.push(s[u+2]):n[u]=s[u+2];for(let u=0;u<n.length;u++)if(u<o.length){if(o[u]<0)throw new Error("strides should be greater than or equal to 1")}else o.push(1);for(let u=0;u<n.length;u++)if(u<a.length){if(a[u]<0)throw new Error("dilations should be greater than or equal to 1")}else a.push(1);for(let u=0;u<n.length*2;u++)if(u<i.length){if(i[u]<0)throw new Error("pad should be greater than or equal to 1")}else i.push(0);for(let u=0;u<n.length;u++){if(n[u]<=0)throw new Error("kernel shapes need to be greater than 0");if(i[u]>=n[u]||i[u+n.length]>=n[u])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(t,s,n,o,a,i,u){if(u){if(a.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++)Rc.adjustPadAndReturnShape(t[p+(i?1:2)],s[p],n[p],o[p],a,p,p+t.length-2,u)}}static computePoolOutputShape(t,s,n,o,a,i,u){if(s.length<=0)throw new Error("input shape must be of size greater than 0");let p=[s[0],s[1]];return Rc.computeShapeHelper(t,s,p,n,o,a,i,u),p}static computeConvOutputShape(t,s,n,o,a,i,u){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 Rc.computeShapeHelper(!1,t,p,n,o,a,i,u),p}static computeShapeHelper(t,s,n,o,a,i,u,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(Rc.adjustPadAndReturnShape(s[h+2],o[h],a[h],i[h],u,h,h+s.length-2,p))}static adjustPadAndReturnShape(t,s,n,o,a,i,u,p){let h=n*(o-1)+1;if(p&&p!=="NOTSET")switch(p){case"VALID":return a[i]=0,a[u]=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 C=((t+s-1)/s-1)*s+o-t;return a[i]=Math.floor(p==="SAME_LOWER"?(C+1)/2:C/2),a[u]=C-a[i],Math.floor((t+C-o)/s+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((t+a[i]+a[u]-h)/s+1)}},Ir=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 a,i,u;t?(a=e[1],i=e[0]):(a=e[0],i=e[1]);let p=-1;if(n?(u=s[0],p=1):(u=s[1],p=0),s[p]!==i)throw new Error("dimension mismatch");if(a<=0||u<=0||i<=0)throw new Error("invalid shape specified");if(o&&!Ws.isValidBroadcast(o,[a,u]))throw new Error("gemm: invalid bias shape for broadcast");return[a,u,i]}},Ss=-34028234663852886e22,Xs=34028234663852886e22}),or,_r,fs,$s,Mt,Xt,zs,As,Gs,St,tn,Qe,At,ja,Wo,Ua,Va,Jt=g(()=>{Lt(),Ot(),or=64,_r=(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: ${e}`)}},fs=(e,t=1)=>{let s=_r(e,t);return typeof s=="string"?s:s[0]},$s=(e,t=1)=>{let s=_r(e,t);return typeof s=="string"?s:s[1]},Mt=(...e)=>{let t=[];return e.forEach(s=>{s.length!==0&&t.push({type:12,data:s},{type:12,data:ze.computeStrides(s)})}),t},Xt=e=>e%4===0?4:e%2===0?2:1,zs=(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})`,Gs=(e,t)=>t===4?`(${e}.x + ${e}.y + ${e}.z + ${e}.w)`:t===2?`(${e}.x + ${e}.y)`:t===3?`(${e}.x + ${e}.y + ${e}.z)`:e,St=(e,t,s,n)=>e.startsWith("uniforms.")&&s>4?typeof t=="string"?n==="f16"?`${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}[${t}]`:e,tn=(e,t,s,n,o)=>{let a=typeof s=="number",i=a?s:s.length,u=[...new Array(i).keys()],p=i<2?"u32":i<=4?`vec${i}<u32>`:`array<u32, ${i}>`,h=_r(t,o),C=typeof h=="string"?h:h[1],k=typeof h=="string"?h:h[0],d={indices:p,value:C,storage:k,tensor:t},z=it=>typeof it=="string"?it:`${it}u`,B={offsetToIndices:!1,indicesToOffset:!1,broadcastedIndicesToOffset:!1,set:!1,setByIndices:!1,get:!1,getByIndices:!1},V=a?"uniforms.":"",Z=`${V}${e}_shape`,ee=`${V}${e}_strides`,Y="";for(let it=0;it<i-1;it++)Y+=`
    let dim${it} = current / ${St(ee,it,i)};
    let rest${it} = current % ${St(ee,it,i)};
    indices[${it}] = dim${it};
    current = rest${it};
    `;Y+=`indices[${i-1}] = current;`;let me=i<2?"":`
  fn o2i_${e}(offset: u32) -> ${d.indices} {
    var indices: ${d.indices};
    var current = offset;
    ${Y}
    return indices;
  }`,pe=it=>(B.offsetToIndices=!0,i<2?it:`o2i_${e}(${it})`),Me=[];if(i>=2)for(let it=i-1;it>=0;it--)Me.push(`${St(ee,it,i)} * (indices[${it}])`);let Ie=i<2?"":`
  fn i2o_${e}(indices: ${d.indices}) -> u32 {
    return ${Me.join("+")};
  }`,Le=it=>(B.indicesToOffset=!0,i<2?it:`i2o_${e}(${it})`),et=(...it)=>i===0?"0u":`${d.indices}(${it.map(z).join(",")})`,dt=(it,Pt)=>i<2?`${it}`:`${St(it,Pt,i)}`,Et=(it,Pt,hs)=>i<2?`${it}=${hs};`:`${St(it,Pt,i)}=${hs};`,qt={},Bt=(it,Pt)=>{B.broadcastedIndicesToOffset=!0;let hs=`${Pt.name}broadcastedIndicesTo${e}Offset`;if(hs in qt)return`${hs}(${it})`;let Ns=[];for(let qs=i-1;qs>=0;qs--){let Dr=Pt.indicesGet("outputIndices",qs+Pt.rank-i);Ns.push(`${dt(ee,qs)} * (${Dr} % ${dt(Z,qs)})`)}return qt[hs]=`fn ${hs}(outputIndices: ${Pt.type.indices}) -> u32 {
             return ${Ns.length>0?Ns.join("+"):"0u"};
           }`,`${hs}(${it})`},It=(it,Pt)=>(()=>{if(d.storage===d.value)return`${e}[${it}]=${Pt};`;if(d.storage==="vec2<u32>"&&d.value==="i32")return`${e}[${it}]=vec2<u32>(u32(${Pt}), select(0u, 0xFFFFFFFFu, ${Pt} < 0));`;if(d.storage==="vec2<u32>"&&d.value==="u32")return`${e}[${it}]=vec2<u32>(u32(${Pt}), 0u);`;if(d.storage==="u32"&&d.value==="vec4<bool>")return`${e}[${it}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${Pt}));`;throw new Error(`not supported combination of storage type ${d.storage} and value type ${d.value} yet`)})(),ts=it=>(()=>{if(d.storage===d.value)return`${e}[${it}]`;if(d.storage==="vec2<u32>"&&d.value==="i32")return`i32(${e}[${it}].x)`;if(d.storage==="vec2<u32>"&&d.value==="u32")return`u32(${e}[${it}].x)`;if(d.storage==="u32"&&d.value==="vec4<bool>")return`vec4<bool>(bool(${e}[${it}] & 0xFFu), bool(${e}[${it}] & 0xFF00u), bool(${e}[${it}] & 0xFF0000u), bool(${e}[${it}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${d.storage} and value type ${d.value} yet`)})(),wt=i<2?"":`
  fn get_${e}ByIndices(indices: ${d.indices}) -> ${C} {
    return ${ts(`i2o_${e}(indices)`)};
  }`,Ht=i<2?"":(()=>{let it=u.map(hs=>`d${hs}: u32`).join(", "),Pt=u.map(hs=>`d${hs}`).join(", ");return`
  fn get_${e}(${it}) -> ${C} {
    return get_${e}ByIndices(${et(Pt)});
  }`})(),ps=(...it)=>{if(it.length!==i)throw new Error(`indices length must be ${i}`);let Pt=it.map(z).join(",");return i===0?ts("0u"):i===1?ts(Pt[0]):(B.get=!0,B.getByIndices=!0,B.indicesToOffset=!0,`get_${e}(${Pt})`)},Ut=it=>i<2?ts(it):(B.getByIndices=!0,B.indicesToOffset=!0,`get_${e}ByIndices(${it})`),Qt=i<2?"":`
  fn set_${e}ByIndices(indices: ${d.indices}, value: ${C}) {
    ${It(`i2o_${e}(indices)`,"value")}
  }`,gs=i<2?"":(()=>{let it=u.map(hs=>`d${hs}: u32`).join(", "),Pt=u.map(hs=>`d${hs}`).join(", ");return`
  fn set_${e}(${it}, value: ${C}) {
    set_${e}ByIndices(${et(Pt)}, value);
  }`})();return{impl:()=>{let it=[],Pt=!1;return B.offsetToIndices&&(it.push(me),Pt=!0),B.indicesToOffset&&(it.push(Ie),Pt=!0),B.broadcastedIndicesToOffset&&(Object.values(qt).forEach(hs=>it.push(hs)),Pt=!0),B.set&&(it.push(gs),Pt=!0),B.setByIndices&&(it.push(Qt),Pt=!0),B.get&&(it.push(Ht),Pt=!0),B.getByIndices&&(it.push(wt),Pt=!0),!a&&Pt&&it.unshift(`const ${Z} = ${d.indices}(${s.join(",")});`,`const ${ee} = ${d.indices}(${ze.computeStrides(s).join(",")});`),it.join(`
`)},type:d,offsetToIndices:pe,indicesToOffset:Le,broadcastedIndicesToOffset:Bt,indices:et,indicesGet:dt,indicesSet:Et,set:(...it)=>{if(it.length!==i+1)throw new Error(`indices length must be ${i}`);let Pt=it[i];if(typeof Pt!="string")throw new Error("value must be string");let hs=it.slice(0,i).map(z).join(",");return i===0?It("0u",Pt):i===1?It(hs[0],Pt):(B.set=!0,B.setByIndices=!0,B.indicesToOffset=!0,`set_${e}(${hs}, ${Pt})`)},setByOffset:It,setByIndices:(it,Pt)=>i<2?It(it,Pt):(B.setByIndices=!0,B.indicesToOffset=!0,`set_${e}ByIndices(${it}, ${Pt});`),get:ps,getByOffset:ts,getByIndices:Ut,usage:n,name:e,strides:ee,shape:Z,rank:i}},Qe=(e,t,s,n=1)=>tn(e,t,s,"input",n),At=(e,t,s,n=1)=>tn(e,t,s,"output",n),ja=(e,t,s)=>tn(e,t,s,"atomicOutput",1),Wo=(e,t,s,n=1)=>tn(e,t,s,"internal",n),Ua=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=or){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,a=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>`,i=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}, ${n})
  fn main(${a}) {
    ${i}
  `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let s=e.usage==="input"?"read":"read_write",n=e.usage==="atomicOutput"?"atomic<i32>":e.type.storage;return`@group(0) @binding(${t}) var<storage, ${s}> ${e.name}: array<${n}>;`}declareVariables(...e){return e.map(t=>this.declareVariable(t,this.variableIndex++)).join(`
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    let stride = (uniforms.output_shape[1] - 1) / ${B} + 1;
    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${B}u + local_id.x;
    let input_row = workgroup_id_x * ${B}u + local_id.y;
    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
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    }
    workgroupBarrier();

    let output_col = workgroup_id_x * ${B}u + local_id.x;
    let output_row = workgroup_id_y * ${B}u + local_id.y;
    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
      ${ee.setByIndices(`${ee.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
    }
  }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let V=ze.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(u[1]/B),y:Math.ceil(u[0]/B)},programUniforms:[{type:12,data:V},...Mt(i,u)]}},getShaderSource:h}}return h=B=>{let V=Qe("a",s,i.length),Z=At("output",s,u.length);return`
  ${B.registerUniform("output_size","u32").declareVariables(V,Z)}

  ${Ga(o,n,V,Z)}

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

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

    ${Z.setByOffset("global_idx",V.getByIndices("aIndices"))}
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          var<workgroup> aBestValues : array<f32, ${d}>;
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        ${z}
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         }
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          let outputIndex = global_idx / ${d};
          let offset = outputIndex * uniforms.reduceSize;

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

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

         if (local_idx == 0u) {
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         }
        }`;return{name:e,shaderCache:{hint:`${t};${d}`,inputDependencies:["type"]},getShaderSource:B,getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:p},programUniforms:[{type:12,data:h}]})}},pr=(e,t,s,n)=>{let o=e.inputs.length===1?s:Yo(e.inputs,s),a=o.axes;a.length===0&&!o.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((z,B)=>B));let i=ze.normalizeAxes(a,e.inputs[0].dims.length),u=i,p=e.inputs[0],h=Qo(u,e.inputs[0].dims.length);h.length>0&&(p=e.compute(cr(e.inputs[0],h),{inputs:[0],outputs:[-1]})[0],u=Ya(u.length,p.dims.length));let[C,k]=Ja(p.dims,u),d=C;o.keepDims&&(d=Za(C,i)),e.compute(tl(t,o.cacheKey,[p],n,e.inputs[0].dataType,d,k),{inputs:[p]})},sn=(e,t)=>{pr(e,"ReduceMeanShared",t,"mean")},sl=(e,t)=>{pr(e,"ReduceL1Shared",t,"l1")},Vc=(e,t)=>{pr(e,"ReduceL2Shared",t,"l2")},rl=(e,t)=>{pr(e,"ReduceLogSumExpShared",t,"logSumExp")},Wc=(e,t)=>{pr(e,"ReduceMaxShared",t,"max")},nl=(e,t)=>{pr(e,"ReduceMinShared",t,"min")},Xo=(e,t)=>{pr(e,"ReduceProdShared",t,"prod")},ol=(e,t)=>{pr(e,"ReduceSumShared",t,"sum")},il=(e,t)=>{pr(e,"ReduceSumSquareShared",t,"sumSquare")},al=(e,t)=>{pr(e,"ReduceLogSumShared",t,"logSum")}}),fr,lo,uo,Yo,gr,Jo,ll,ul,Zo,dl,cl,ei,pl,hl,ti,wr,ml,si,_l,fl,ri,gl,wl,ni,yl,Ml,co=g(()=>{Lt(),Ot(),rs(),Jt(),Gc(),fr=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},lo=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],uo=(e,t,s,n,o,a,i=!1,u=!1)=>{let p=[],h=s[0].dims,C=h.length,k=ze.normalizeAxes(o,C),d=!u&&k.length===0;h.forEach((V,Z)=>{d||k.indexOf(Z)>=0?i&&p.push(1):p.push(V)});let z=p.length,B=ze.size(p);return{name:e,shaderCache:t,getShaderSource:V=>{let Z=[],ee=Qe("_A",s[0].dataType,C),Y=At("output",a,z),me=n(ee,Y,k),pe=me[2];for(let Me=0,Ie=0;Me<C;Me++)d||k.indexOf(Me)>=0?(i&&Ie++,pe=`for(var j${Me}: u32 = 0; j${Me} < ${h[Me]}; j${Me}++) {
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                  ${ee.indicesSet("input_indices",Me,`j${Me}`)}
                  ${pe}
                }`):(Z.push(`${ee.indicesSet("input_indices",Me,Y.indicesGet("output_indices",Ie))};`),Ie++);return`

        ${V.registerUniform("output_size","u32").declareVariables(ee,Y)}

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

          ${Z.join(`
`)}
          ${me[0]}       // init ops for reduce max/min
          ${me[1]}
          ${pe}
          ${me[3]}
          ${me.length===4?Y.setByOffset("global_idx","value"):me.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(B/64)},programUniforms:[{type:12,data:B},...Mt(h,p)]})}},Yo=(e,t)=>{let s=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>s.push(Number(n))),zt({axes:s,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},gr=(e,t,s,n)=>{let o=e.inputs,a=o.length===1?s:Yo(o,s);e.compute(uo(t,{hint:a.cacheKey,inputDependencies:["rank"]},[o[0]],a.noopWithEmptyAxes&&a.axes.length===0?lo:n,a.axes,o[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},Jo=(e,t)=>{fr(e.inputs),gr(e,"ReduceLogSum",t,(s,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${s.getByIndices("input_indices")};`,"value = log(value);"])},ll=(e,t)=>{fr(e.inputs),gr(e,"ReduceL1",t,(s,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${s.getByIndices("input_indices")});`,""])},ul=(e,t)=>{fr(e.inputs),gr(e,"ReduceL2",t,(s,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${s.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},Zo=(e,t)=>{fr(e.inputs),gr(e,"ReduceLogSumExp",t,(s,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${s.getByIndices("input_indices")});`,"value = log(value);"])},dl=(e,t)=>{fr(e.inputs),gr(e,"ReduceMax",t,(s,n,o)=>{let a=[];for(let i=0;i<s.rank;i++)(o.indexOf(i)>=0||o.length===0)&&a.push(s.indicesSet("input_indices",i,0));return[`${a.join(`
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var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
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         best_index = i32(last_index);
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var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};e.compute(uo("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},ai=e=>zt(e)}),li,po,vl,ui,xl,Un,di,Tl,ci=g(()=>{Lt(),Ot(),ue(),Jt(),li=(e,t)=>{let s=e[0],n=e[1],o=e[2],a=e[3],i=e[4],u=e[5];if(i&&u)throw new Error("Attention cannot have both past and attention_bias");if(s.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let p=s.dims[0],h=s.dims[1],C=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]!==C)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 k=o.dims[0]/3,d=k,z=d;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let me of t.qkvHiddenSizes)if(me%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");k=t.qkvHiddenSizes[0],d=t.qkvHiddenSizes[1],z=t.qkvHiddenSizes[2]}let B=h;if(k!==d)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==k+d+z)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let V=0;if(i){if(d!==z)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==p)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==d/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(V=i.dims[3])}let Z=B+V,ee=-1,Y=0;if(a)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(u){if(u.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==p||u.dims[1]!==t.numHeads||u.dims[2]!==h||u.dims[3]!==Z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:h,pastSequenceLength:V,kvSequenceLength:B,totalSequenceLength:Z,maxSequenceLength:ee,inputHiddenSize:C,hiddenSize:k,vHiddenSize:z,headSize:Math.floor(k/t.numHeads),vHeadSize:Math.floor(z/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:Y,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},po=(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==null?void 0:e.getByOffset("batchIdx")}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${s?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,vl=(e,t,s,n,o,a,i,u)=>{let p=Xt(i?1:a),h=64,C=a/p;C<h&&(h=32);let k=Math.ceil(a/p/h),d=[{type:12,data:t},{type:12,data:s},{type:12,data:n},{type:12,data:o},{type:12,data:C},{type:12,data:k}],z=fs(e.dataType,p),B=$s(1,p),V=["type"];i&&V.push("type"),u&&V.push("type");let Z=ee=>{let Y=At("x",e.dataType,e.dims,p),me=[Y],pe=i?Qe("seq_lens",i.dataType,i.dims):void 0;pe&&me.push(pe);let Me=u?Qe("total_sequence_length_input",u.dataType,u.dims):void 0;Me&&me.push(Me);let Ie=$s(e.dataType),Le=[{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(Le).declareVariables(...me)}
  ${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;
    ${po(pe,Me,!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 = ${i?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${B}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${B}(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 = ${B}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      sum_vector += exp(${B}(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] = ${Y.type.value}(${Ie}(1.0) / ${Ie}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${B}(x[offset + i]);
        x[offset + i] = ${Y.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${i?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${Y.type.value}(${Ie}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${z};${p}`,inputDependencies:V},getShaderSource:Z,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(a/h),y:o,z:t*s},programUniforms:d})}},ui=(e,t,s,n,o,a,i,u,p)=>{let h=i+a.kvSequenceLength,C=[a.batchSize,a.numHeads,a.sequenceLength,h],k=e>1&&n,d=a.kvNumHeads?a.kvNumHeads:a.numHeads,z=k?[a.batchSize,d,h,a.headSize]:void 0,B=a.nReps?a.nReps:1,V=a.scale===0?1/Math.sqrt(a.headSize):a.scale,Z=Xt(a.headSize),ee=a.headSize/Z,Y=12,me={x:Math.ceil(h/Y),y:Math.ceil(a.sequenceLength/Y),z:a.batchSize*a.numHeads},pe=[{type:12,data:a.sequenceLength},{type:12,data:ee},{type:12,data:h},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:V},{type:12,data:i},{type:12,data:a.kvSequenceLength},{type:12,data:B}],Me=k&&n&&ze.size(n.dims)>0,Ie=["type","type"];Me&&Ie.push("type"),o&&Ie.push("type"),u&&Ie.push("type"),p&&Ie.push("type");let Le=[{dims:C,dataType:t.dataType,gpuDataType:0}];k&&Le.push({dims:z,dataType:t.dataType,gpuDataType:0});let et=dt=>{let Et=Qe("q",t.dataType,t.dims,Z),qt=Qe("key",s.dataType,s.dims,Z),Bt=[Et,qt];if(Me){let Qt=Qe("past_key",n.dataType,n.dims,Z);Bt.push(Qt)}o&&Bt.push(Qe("attention_bias",o.dataType,o.dims));let It=u?Qe("seq_lens",u.dataType,u.dims):void 0;It&&Bt.push(It);let ts=p?Qe("total_sequence_length_input",p.dataType,p.dims):void 0;ts&&Bt.push(ts);let wt=At("output",t.dataType,C),Ht=[wt];k&&Ht.push(At("present_key",t.dataType,z,Z));let ps=$s(1,Z),Ut=[{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 = ${Y}u;

  var<workgroup> tileQ: array<${Et.type.storage}, ${Y*Y}>;
  var<workgroup> tileK: array<${Et.type.storage}, ${Y*Y}>;
  ${dt.registerUniforms(Ut).declareVariables(...Bt,...Ht)}
  ${dt.mainStart([Y,Y,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${B===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${B===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;
    ${po(It,ts,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${Me&&k?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${k?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${ps}(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;
      ${Me&&k?`
              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];
          }`}
      ${k?`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 += ${ps}(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(Z){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: ${Z}`)}})()};
        output[outputIdx] = ${wt.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${Z};${o!==void 0};${n!==void 0};${e}`,inputDependencies:Ie},getRunData:()=>({outputs:Le,dispatchGroup:me,programUniforms:pe}),getShaderSource:et}},xl=(e,t,s,n,o,a,i=void 0,u=void 0)=>{let p=a+o.kvSequenceLength,h=o.nReps?o.nReps:1,C=o.vHiddenSize*h,k=e>1&&n,d=o.kvNumHeads?o.kvNumHeads:o.numHeads,z=k?[o.batchSize,d,p,o.headSize]:void 0,B=[o.batchSize,o.sequenceLength,C],V=12,Z={x:Math.ceil(o.vHeadSize/V),y:Math.ceil(o.sequenceLength/V),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:C},{type:12,data:a},{type:12,data:o.kvSequenceLength},{type:12,data:h}],Y=k&&n&&ze.size(n.dims)>0,me=["type","type"];Y&&me.push("type"),i&&me.push("type"),u&&me.push("type");let pe=[{dims:B,dataType:t.dataType,gpuDataType:0}];k&&pe.push({dims:z,dataType:t.dataType,gpuDataType:0});let Me=Ie=>{let Le=Qe("probs",t.dataType,t.dims),et=Qe("v",s.dataType,s.dims),dt=[Le,et];Y&&dt.push(Qe("past_value",n.dataType,n.dims));let Et=i?Qe("seq_lens",i.dataType,i.dims):void 0;i&&dt.push(Et);let qt=u?Qe("total_sequence_length_input",u.dataType,u.dims):void 0;u&&dt.push(qt);let Bt=[At("output",t.dataType,B)];k&&Bt.push(At("present_value",t.dataType,z));let It=[{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 = ${V}u;
  var<workgroup> tileQ: array<${Le.type.value}, ${V*V}>;
  var<workgroup> tileV: array<${Le.type.value}, ${V*V}>;
  ${Ie.registerUniforms(It).declareVariables(...dt,...Bt)}
  ${Ie.mainStart([V,V,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;
   ${po(Et,qt,!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
   ${Y&&k?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${k?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${Le.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;
        ${Y&&k?`
        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];
            }`}
        ${k?`
            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:me},getRunData:()=>({outputs:pe,dispatchGroup:Z,programUniforms:ee}),getShaderSource:Me}},Un=(e,t,s,n,o,a,i,u,p,h,C=void 0,k=void 0)=>{let d=Math.min(e.outputCount,1+(i?1:0)+(u?1:0)),z=d>1?h.pastSequenceLength:0,B=z+h.kvSequenceLength,V=p&&ze.size(p.dims)>0?p:void 0,Z=[t,s];d>1&&i&&ze.size(i.dims)>0&&Z.push(i),V&&Z.push(V),C&&Z.push(C),k&&Z.push(k);let ee=e.compute(ui(d,t,s,i,V,h,z,C,k),{inputs:Z,outputs:d>1?[-1,1]:[-1]})[0];e.compute(vl(ee,h.batchSize,h.numHeads,z,h.sequenceLength,B,C,k),{inputs:C&&k?[ee,C,k]:[ee],outputs:[]});let Y=[ee,n];d>1&&u&&ze.size(u.dims)>0&&Y.push(u),C&&Y.push(C),k&&Y.push(k),e.compute(xl(d,ee,n,u,h,z,C,k),{inputs:Y,outputs:d>1?[0,2]:[0]})},di=(e,t)=>{let s=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,o=t.inputHiddenSize,a=t.headSize,i=12,u={x:Math.ceil(t.headSize/i),y:Math.ceil(t.sequenceLength/i),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:n},{type:12,data:o},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],C=k=>{let d=At("output_q",p[0].dataType,s),z=At("output_k",p[0].dataType,s),B=At("output_v",p[0].dataType,s),V=Qe("input",p[0].dataType,p[0].dims),Z=Qe("weight",p[1].dataType,p[1].dims),ee=Qe("bias",p[2].dataType,p[2].dims),Y=V.type.storage,me=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${i}u;
  var<workgroup> tileInput: array<${Y}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${Y}, ${i*i}>;
  var<workgroup> tileWeightK: array<${Y}, ${i*i}>;
  var<workgroup> tileWeightV: array<${Y}, ${i*i}>;
  ${k.registerUniforms(me).declareVariables(V,Z,ee,d,z,B)}
  ${k.mainStart([i,i,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

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

    var valueQ = ${Y}(0);
    var valueK = ${Y}(0);
    var valueV = ${Y}(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:u,programUniforms:h}),getShaderSource:C},{inputs:p,outputs:[-1,-1,-1]})},Tl=(e,t)=>{let s=li(e.inputs,t),[n,o,a]=di(e,s);return Un(e,n,o,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],s)}}),pi,El,Pl,hi,Hc=g(()=>{Re(),Lt(),Ot(),rs(),Jt(),pi=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let s=(n,o,a)=>{let i=o.length;if(i!==n.length)throw new Error(`${a}: num dimensions != ${i}`);o.forEach((u,p)=>{if(u!==n[p])throw new Error(`${a}: 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")},El=(e,t)=>{let{epsilon:s,spatial:n,format:o}=t,a=e[0].dims,i=n?Xt(a[a.length-1]):1,u=o==="NHWC"&&a.length>1?i:1,p=ze.size(a)/i,h=n,C=h?a.length:a,k=Qe("x",e[0].dataType,e[0].dims,i),d=Qe("scale",e[1].dataType,e[1].dims,u),z=Qe("bias",e[2].dataType,e[2].dims,u),B=Qe("inputMean",e[3].dataType,e[3].dims,u),V=Qe("inputVar",e[4].dataType,e[4].dims,u),Z=At("y",e[0].dataType,C,i),ee=()=>{let me="";if(n)me=`let cOffset = ${a.length===1?"0u":o==="NHWC"?`outputIndices[${a.length-1}] / ${i}`:"outputIndices[1]"};`;else if(o==="NCHW")me=`
            ${Z.indicesSet("outputIndices","0","0")}
            let cOffset = ${Z.indicesToOffset("outputIndices")};`;else{me=`var cIndices = ${d.type.indices}(0);
                       cIndices[0] = outputIndices[${a.length-1}];`;for(let pe=1;pe<d.rank;pe++)me+=`cIndices[${pe}] = outputIndices[${pe}];`;me+=`let cOffset = ${d.indicesToOffset("cIndices")};`}return me},Y=me=>`
  const epsilon = ${s};
  ${me.registerUniform("outputSize","u32").declareVariables(k,d,z,B,V,Z)}
  ${me.mainStart()}
  ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${Z.offsetToIndices(`global_idx * ${i}`)};
    ${ee()}
    let scale = ${d.getByOffset("cOffset")};
    let bias = ${z.getByOffset("cOffset")};
    let inputMean = ${B.getByOffset("cOffset")};
    let inputVar = ${V.getByOffset("cOffset")};
    let x = ${k.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${Z.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${i}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:Y,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h?[{type:12,data:p},...Mt(a)]:[{type:12,data:p}]})}},Pl=e=>zt(e),hi=(e,t)=>{let{inputs:s,outputCount:n}=e,o=Pl({...t,outputCount:n});if(F.webgpu.validateInputContent&&pi(s,o),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(El(s,o))}}),Cl,mi,kl,qc=g(()=>{Ot(),Jt(),Cl=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")},mi=e=>{let t=e[0].dims,s=e[0].dims[2],n=ze.size(t)/4,o=e[0].dataType,a=Qe("input",o,t,4),i=Qe("bias",o,[s],4),u=Qe("residual",o,t,4),p=At("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(a,i,u,p)}

  ${h.mainStart()}
    ${h.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${a.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")};
    ${p.setByOffset("global_idx","value")}
  }`}},kl=e=>{Cl(e.inputs),e.compute(mi(e.inputs))}}),_i,ds,Sl,fi,$l,Al,gi,Il,Ol,wi,Fl,Dl,yi,Ll,zl,Mi,Vn,Bl,ho,Rl,bi,Nl,jl,vi,Ul,Vl,xi,Wl,Gl,Ti,Kl,Hl,Ei,ql,Ql,mo,Xl,Pi,_o,Yl,Jl,Zl,eu,Ci,tu,ki=g(()=>{Lt(),Ot(),rs(),Jt(),_i=(e,t,s,n,o,a,i)=>{let u=Math.ceil(t/4),p="";typeof o=="string"?p=`${o}(a)`:p=o("a");let h=Qe("inputData",s,[u],4),C=At("outputData",n,[u],4),k=[{name:"vec_size",type:"u32"}];return i&&k.push(...i),`
      ${e.registerUniforms(k).declareVariables(h,C)}

  ${a??""}

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

    let a = ${h.getByOffset("global_idx")};
    ${C.setByOffset("global_idx",p)}
  }`},ds=(e,t,s,n,o,a=e.dataType,i,u)=>{let p=[{type:12,data:Math.ceil(ze.size(e.dims)/4)}];return i&&p.push(...i),{name:t,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:h=>_i(h,ze.size(e.dims),e.dataType,a,s,n,u),getRunData:h=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(ze.size(h[0].dims)/64/4)},programUniforms:p})}},Sl=e=>{e.compute(ds(e.inputs[0],"Abs","abs"))},fi=e=>{e.compute(ds(e.inputs[0],"Acos","acos"))},$l=e=>{e.compute(ds(e.inputs[0],"Acosh","acosh"))},Al=e=>{e.compute(ds(e.inputs[0],"Asin","asin"))},gi=e=>{e.compute(ds(e.inputs[0],"Asinh","asinh"))},Il=e=>{e.compute(ds(e.inputs[0],"Atan","atan"))},Ol=e=>{e.compute(ds(e.inputs[0],"Atanh","atanh"))},wi=e=>zt(e),Fl=(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): ${t.to}`)}e.compute(ds(e.inputs[0],"Cast",s,void 0,t.cacheKey,t.to))},Dl=e=>{let t,s,n=e.length>=2&&e[1].data!==0,o=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,s=o?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,s=o?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return zt({min:t,max:s})},yi=(e,t)=>{let s=t||Dl(e.inputs),n=$s(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Clip",o=>`clamp(${o}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,s.cacheKey,void 0,[{type:e.inputs[0].dataType,data:s.min},{type:e.inputs[0].dataType,data:s.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},Ll=e=>{e.compute(ds(e.inputs[0],"Ceil","ceil"))},zl=e=>{e.compute(ds(e.inputs[0],"Cos","cos"))},Mi=e=>{e.compute(ds(e.inputs[0],"Cosh","cosh"))},Vn=e=>zt(e),Bl=(e,t)=>{let s=$s(e.inputs[0].dataType);e.compute(ds(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))},ho=(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));
}`,Rl=e=>{let t=$s(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Erf",s=>`erf_vf32(${s})`,ho(t)))},bi=e=>{e.compute(ds(e.inputs[0],"Exp","exp"))},Nl=e=>{e.compute(ds(e.inputs[0],"Floor","floor"))},jl=e=>{let t=$s(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Gelu",s=>`0.5 * ${s} * (1.0 + erf_vf32(${s} * 0.7071067811865475))`,ho(t)))},vi=(e,t)=>{let s=$s(e.inputs[0].dataType);e.compute(ds(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))},Ul=e=>{e.compute(ds(e.inputs[0],"Not",t=>`!${t}`))},Vl=e=>{e.compute(ds(e.inputs[0],"Neg",t=>`-${t}`))},xi=e=>{e.compute(ds(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Wl=e=>{let t=$s(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Relu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > vec4<${t}>(0.0))`))},Gl=e=>{e.compute(ds(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},Ti=e=>zt(e),Kl=(e,t)=>{let s=$s(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"HardSigmoid",n=>`max(vec4<${s}>(0.0), min(vec4<${s}>(1.0), ${t.alpha} * ${n} + vec4<${s}>(${t.beta})))`,void 0,t.cacheKey))},Hl=e=>{e.compute(ds(e.inputs[0],"Sin","sin"))},Ei=e=>{e.compute(ds(e.inputs[0],"Sinh","sinh"))},ql=e=>{e.compute(ds(e.inputs[0],"Sqrt","sqrt"))},Ql=e=>{e.compute(ds(e.inputs[0],"Tan","tan"))},mo=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Xl=e=>{e.compute(ds(e.inputs[0],"Tanh",mo))},Pi=(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 ${mo("v")};
}
`,_o=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Yl=e=>{let t=$s(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"FastGelu",_o,Pi(t),void 0,e.inputs[0].dataType))},Jl=(e,t)=>{let s=$s(e.inputs[0].dataType);return e.compute(ds(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${s}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${s}>(${t.alpha});`,t.cacheKey)),0},Zl=e=>{e.compute(ds(e.inputs[0],"Log","log"))},eu=(e,t)=>`
const alpha = vec4<${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<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Ci=e=>`quick_gelu_impl(${e})`,tu=(e,t)=>{let s=$s(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"QuickGelu",Ci,eu(s,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),su,ru,Si,Qc=g(()=>{Ot(),Jt(),ki(),su=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")},ru=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let s=Qe("input",e[0].dataType,e[0].dims,4),n=Qe("bias",e[0].dataType,[e[0].dims[2]],4),o=At("output",e[0].dataType,t,4),a=ze.size(t)/4,i=fs(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:u=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

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

  ${ho(i)}

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

    ${o.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Si=e=>{su(e.inputs),e.compute(ru(e.inputs))}}),nu,ou,yr,$i,iu,au,lu,uu,Ai,du,cu,Ii,pu,Xc=g(()=>{Lt(),Ot(),Jt(),nu=(e,t,s,n,o,a,i,u,p,h,C,k)=>{let d,z;typeof u=="string"?d=z=(Y,me)=>`${u}((${Y}),(${me}))`:typeof u=="function"?d=z=u:(d=u.scalar,z=u.vector);let B=At("outputData",C,n.length,4),V=Qe("aData",p,t.length,4),Z=Qe("bData",h,s.length,4),ee;if(o)if(a){let Y=ze.size(t)===1,me=ze.size(s)===1,pe=t.length>0&&t[t.length-1]%4===0,Me=s.length>0&&s[s.length-1]%4===0;Y||me?ee=B.setByOffset("global_idx",z(Y?`${V.type.value}(${V.getByOffset("0")}.x)`:V.getByOffset("global_idx"),me?`${Z.type.value}(${Z.getByOffset("0")}.x)`:Z.getByOffset("global_idx"))):ee=`
            let outputIndices = ${B.offsetToIndices("global_idx * 4u")};
            let offsetA = ${V.broadcastedIndicesToOffset("outputIndices",B)};
            let offsetB = ${Z.broadcastedIndicesToOffset("outputIndices",B)};
            ${B.setByOffset("global_idx",z(i||pe?V.getByOffset("offsetA / 4u"):`${V.type.value}(${V.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||Me?Z.getByOffset("offsetB / 4u"):`${Z.type.value}(${Z.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else ee=B.setByOffset("global_idx",z(V.getByOffset("global_idx"),Z.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let Y=(me,pe,Me="")=>{let Ie=`aData[indexA${pe}][componentA${pe}]`,Le=`bData[indexB${pe}][componentB${pe}]`;return`
            let outputIndices${pe} = ${B.offsetToIndices(`global_idx * 4u + ${pe}u`)};
            let offsetA${pe} = ${V.broadcastedIndicesToOffset(`outputIndices${pe}`,B)};
            let offsetB${pe} = ${Z.broadcastedIndicesToOffset(`outputIndices${pe}`,B)};
            let indexA${pe} = offsetA${pe} / 4u;
            let indexB${pe} = offsetB${pe} / 4u;
            let componentA${pe} = offsetA${pe} % 4u;
            let componentB${pe} = offsetB${pe} % 4u;
            ${me}[${pe}] = ${Me}(${d(Ie,Le)});
          `};C===9?ee=`
            var data = vec4<u32>(0);
            ${Y("data",0,"u32")}
            ${Y("data",1,"u32")}
            ${Y("data",2,"u32")}
            ${Y("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:ee=`
            ${Y("outputData[global_idx]",0)}
            ${Y("outputData[global_idx]",1)}
            ${Y("outputData[global_idx]",2)}
            ${Y("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(V,Z,B)}

        ${k??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${ee}
      }`},ou=(e,t,s,n,o,a,i=s.dataType)=>{let u=s.dims.map(V=>Number(V)??1),p=n.dims.map(V=>Number(V)??1),h=!ze.areEqual(u,p),C=u,k=ze.size(u),d=!1,z=!1,B=[h];if(h){let V=Ws.calcShape(u,p,!1);if(!V)throw new Error("Can't perform binary op on the given tensors");C=V.slice(),k=ze.size(C);let Z=ze.size(u)===1,ee=ze.size(p)===1,Y=u.length>0&&u[u.length-1]%4===0,me=p.length>0&&p[p.length-1]%4===0;B.push(Z),B.push(ee),B.push(Y),B.push(me);let pe=1;for(let Me=1;Me<C.length;Me++){let Ie=u[u.length-Me],Le=p[p.length-Me];if(Ie===Le)pe*=Ie;else break}pe%4===0?(z=!0,d=!0):(Z||ee||Y||me)&&(d=!0)}else d=!0;return B.push(d),{name:e,shaderCache:{hint:t+B.map(V=>V.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:V=>nu(V,u,p,C,d,h,z,o,s.dataType,n.dataType,i,a),getRunData:()=>({outputs:[{dims:C,dataType:i}],dispatchGroup:{x:Math.ceil(k/64/4)},programUniforms:[{type:12,data:Math.ceil(ze.size(C)/4)},...Mt(u,p,C)]})}},yr=(e,t,s,n,o,a)=>{e.compute(ou(t,o??"",e.inputs[0],e.inputs[1],s,n,a))},$i=e=>{yr(e,"Add",(t,s)=>`${t}+${s}`)},iu=e=>{yr(e,"Div",(t,s)=>`${t}/${s}`)},au=e=>{yr(e,"Equal",{scalar:(t,s)=>`u32(${t}==${s})`,vector:(t,s)=>`vec4<u32>(${t}==${s})`},void 0,void 0,9)},lu=e=>{yr(e,"Mul",(t,s)=>`${t}*${s}`)},uu=e=>{let t=Qe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;yr(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));
    }
      `)},Ai=e=>{yr(e,"Sub",(t,s)=>`${t}-${s}`)},du=e=>{yr(e,"Greater",{scalar:(t,s)=>`u32(${t}>${s})`,vector:(t,s)=>`vec4<u32>(${t}>${s})`},void 0,void 0,9)},cu=e=>{yr(e,"Less",{scalar:(t,s)=>`u32(${t}<${s})`,vector:(t,s)=>`vec4<u32>(${t}<${s})`},void 0,void 0,9)},Ii=e=>{yr(e,"GreaterOrEqual",{scalar:(t,s)=>`u32(${t}>=${s})`,vector:(t,s)=>`vec4<u32>(${t}>=${s})`},void 0,void 0,9)},pu=e=>{yr(e,"LessOrEqual",{scalar:(t,s)=>`u32(${t}<=${s})`,vector:(t,s)=>`vec4<u32>(${t}<=${s})`},void 0,void 0,9)}}),Oi,hu,mu,Fi,_u,fu,gu=g(()=>{Lt(),Ot(),rs(),Jt(),Oi=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let s=0,n=e[s],o=n.dataType,a=n.dims.length;e.forEach((i,u)=>{if(u!==s){if(i.dataType!==o)throw new Error("input tensors should be one type");if(i.dims.length!==a)throw new Error("input tensors should have the same shape");i.dims.forEach((p,h)=>{if(h!==t&&p!==n.dims[h])throw new Error("non concat dimensions must match")})}})},hu=(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;
  }`,mu=(e,t)=>{let s=e.length,n=[];for(let o=0;o<s;++o){let a=t.setByOffset("global_idx",e[o].getByIndices("indices"));s===1?n.push(a):o===0?n.push(`if (inputIndex == ${o}u) { ${a} }`):o===s-1?n.push(`else { ${a} }`):n.push(`else if (inputIndex == ${o}) { ${a} }`)}return n.join(`
`)},Fi=(e,t,s,n)=>{let o=ze.size(s),a=new Array(e.length),i=new Array(e.length),u=0,p=[],h=[],C=[{type:12,data:o}];for(let V=0;V<e.length;++V)u+=e[V].dims[t],a[V]=u,h.push(e[V].dims.length),i[V]=Qe(`input${V}`,n,h[V]),p.push("rank"),C.push({type:12,data:a[V]});for(let V=0;V<e.length;++V)C.push(...Mt(e[V].dims));C.push(...Mt(s));let k=At("output",n,s.length),d=k.indicesGet("indices",t),z=Array.from(Array(a.length).keys()).map(V=>`uniforms.sizeInConcatAxis${V}`).join(","),B=V=>`

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

  ${hu(a.length,z)}

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

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

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

    ${mu(i,k)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:C}),getShaderSource:B}},_u=(e,t)=>{let s=e.inputs,n=s[0].dims,o=ze.normalizeAxis(t.axis,n.length);Oi(s,o);let a=n.slice();a[o]=s.reduce((u,p)=>u+(p.dims.length>o?p.dims[o]:0),0);let i=s.filter(u=>ze.size(u.dims)>0);e.compute(Fi(i,o,a,s[0].dataType),{inputs:i})},fu=e=>zt({axis:e.axis})}),rn,nn,Or,Di,on=g(()=>{Lt(),Ot(),rn=(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 ${e.activation}`)}},nn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Or=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Di=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[s,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:s,beta:n}}else if(t==="Clip"){let[s,n]=(e==null?void 0:e.activation_params)||[Ss,Xs];return{activation:t,clipMax:n,clipMin:s}}else if(t==="LeakyRelu"){let[s]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:s}}return{activation:t}}}),Ks,Li,zi=g(()=>{Ks=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Li=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),Bi,Yc=g(()=>{Bi=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),Wn,Ri,fo=g(()=>{Lt(),Ot(),Jt(),on(),Wn=(e,t,s,n,o)=>{let a=n-s;return`
      ${Array.from({length:s}).map((i,u)=>`
      if (${St(t.shape,u,t.rank)} != 1) {
        ${t.indicesSet(e,u,St(o,u+a,n))}
      } else {
        ${t.indicesSet(e,u,0)}
      }`).join("")}
`},Ri=(e,t,s,n,o=!1,a)=>{let i=e[0].dims,u=e[1].dims,p=i[i.length-2],h=u[u.length-1],C=i[i.length-1],k=Xt(h),d=Xt(C),z=Xt(p),B=ze.size(s)/k/z,V=e.length>2,Z=n?n.slice(0,-2):s.slice(0,-2),ee=[ze.size(Z),p,h],Y=[{type:12,data:B},{type:12,data:p},{type:12,data:h},{type:12,data:C}];nn(t,Y),Y.push(...Mt(Z,i,u)),V&&Y.push(...Mt(e[2].dims)),Y.push(...Mt(ee));let me=pe=>{let Me=Wo("batch_dims",e[0].dataType,Z.length),Ie=Qe("a",e[0].dataType,i.length,d),Le=Qe("b",e[1].dataType,u.length,k),et=At("output",e[0].dataType,ee.length,k),dt=fs(et.type.tensor),Et=rn(t,et.type.value,dt),qt=[Ie,Le],Bt="";if(V){let wt=o?k:1;qt.push(Qe("bias",e[2].dataType,e[2].dims.length,wt)),Bt=`${o?`value += bias[col / ${wt}];`:`value += ${et.type.value}(bias[row + i]);`}`}let It=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Or(t,It);let ts=()=>{let wt=`var a_data: ${Ie.type.value};`;for(let Ht=0;Ht<d;Ht++)wt+=`
              let b_data${Ht} = b[(b_offset + (k + ${Ht}) * uniforms.N + col) / ${k}];`;for(let Ht=0;Ht<z;Ht++){wt+=`a_data = a[(a_offset + (row + ${Ht}) * uniforms.K + k) / ${d}];`;for(let ps=0;ps<d;ps++)wt+=`
            values[${Ht}] = fma(${Le.type.value}(a_data${d===1?"":`[${ps}]`}), b_data${ps}, values[${Ht}]);
`}return wt};return`
  ${pe.registerUniforms(It).registerInternalVariables(Me).declareVariables(...qt,et)}
  ${pe.mainStart()}
    ${pe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${k})) * ${k};
    var index1 = global_idx / (uniforms.N / ${k});
    let stride1 = uniforms.M / ${z};
    let row = (index1 % stride1) * ${z};
    let batch = index1 / stride1;

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

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

    var b_indices: ${Le.type.indices};
    ${Wn("b_indices",Le,Le.rank-2,Me.rank,"batch_indices")}
    ${Le.indicesSet("b_indices",Le.rank-2,0)}
    ${Le.indicesSet("b_indices",Le.rank-1,0)}
    let b_offset = ${Le.indicesToOffset("b_indices")};
    var values: array<${et.type.value}, ${z}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${d}) {
      ${ts()}
    }
    for (var i = 0u; i < ${z}u; i++) {
      var value = values[i];
      ${Bt}
      ${Et}
      let cur_indices = ${et.type.indices}(batch, row + i, col);
      let offset = ${et.indicesToOffset("cur_indices")};
      ${et.setByOffset(`offset / ${k}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${k};${d};${z};${o}`,inputDependencies:V?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(B/64)},programUniforms:Y}),getShaderSource:me}}}),wu,yu,Ni,go,Mu,ji,Ui,wo,Vi=g(()=>{Lt(),Ot(),Jt(),on(),fo(),zi(),wu=(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":""});
        `,yu=(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];"}
        }`,Ni=(e,t,s="f32",n,o=!1,a=32,i=!1,u=32)=>{let p=t[1]*e[1],h=t[0]*e[0],C=o?p:a,k=o?a:p,d=C/t[0],z=a/t[1];if(!((o&&d===4&&e[1]===4||!o&&(d===3||d===4))&&C%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${d} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${d} must be 3 or 4.
  tileAWidth ${C} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${d}<${s}>, ${C/d}>, ${k}>;
var<workgroup> mm_Bsub: array<array<vec4<${s}>, ${h/e[0]}>, ${a}>;

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

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

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

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

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

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

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

          ${yu(o,d)}
      }

      workgroupBarrier();
  }

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

    // 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 < ${d}; inputRow = inputRow + ${t[1]}) {
        for (var inputCol = localCol; inputCol < ${k}; inputCol = inputCol + ${t[0]}) {
          ${go(o,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) {
            for (var inputCol = localCol; inputCol < ${C}; 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) * ${z};
let tileColA = i32(localId.x) * ${B};
let tileRowB = i32(localId.y) * ${V};
// 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 < ${z}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${B}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${go(o,n)}
    }
  }

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${V}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${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) {
      ${Mu(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}, ${k}>, ${d}>;
  var<workgroup> mm_Bsub : array<array<${s}, ${C}>, ${a}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${a};

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

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

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ks(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 + ${o?"bias[colIn]":`${Ks(e,h)}(bias[row])`};`:""}
        ${s}
        ${p.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},wo=(e,t,s,n,o=!1,a)=>{let i=e[0].dims,u=e[1].dims,p=i.slice(0,-2),h=u.slice(0,-2),C=n?n.slice(0,-2):s.slice(0,-2),k=ze.size(C),d=i[i.length-2],z=i[i.length-1],B=u[u.length-1],V=z%4===0&&B%4===0,Z=d<=8?[4,1,1]:[4,4,1],ee=[8,8,1],Y=[Math.ceil(B/ee[0]/Z[0]),Math.ceil(d/ee[1]/Z[1]),Math.ceil(k/ee[2]/Z[2])],me=V?4:1,pe=[...p,d,z/me],Me=pe.length,Ie=[...h,z,B/me],Le=Ie.length,et=[k,d,B/me],dt=[{type:6,data:d},{type:6,data:B},{type:6,data:z}];nn(t,dt),dt.push(...Mt(C,pe,Ie));let Et=["rank","rank"],qt=e.length>2;qt&&(dt.push(...Mt(e[2].dims)),Et.push("rank")),dt.push(...Mt(et));let Bt=It=>{let ts=C.length,wt=Wo("batchDims",e[0].dataType,ts,1),Ht=fs(e[0].dataType),ps=Qe("a",e[0].dataType,Me,me),Ut=Qe("b",e[1].dataType,Le,me),Qt=At("result",e[0].dataType,et.length,me),gs=[ps,Ut];if(qt){let qs=o?me:1;gs.push(Qe("bias",e[2].dataType,e[2].dims.length,qs))}let it=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Or(t,it);let Pt=fs(Qt.type.tensor),hs=rn(t,Qt.type.value,Pt),Ns=Ui(me,qt,hs,[wt,ps,Ut,Qt],o);return`
  ${It.registerUniforms(it).registerInternalVariables(wt).declareVariables(...gs,Qt)}
  ${Ns}
  ${V?Ni(Z,ee,Ht,wt):ji(Z,ee,Ht,wt)}
                   `};return{name:"MatMul",shaderCache:{hint:`${Z};${t.activation};${V};${o}`,inputDependencies:Et},getRunData:()=>({outputs:[{dims:a?a(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Y[0],y:Y[1],z:Y[2]},programUniforms:dt}),getShaderSource:Bt}}}),Wi,bu,Jc=g(()=>{Lt(),Pe(),Jt(),on(),zi(),Yc(),Vi(),Wi=(e,t,s,n,o=!1,a,i=4,u=4,p=4,h="f32")=>{let C=dt=>{switch(dt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${dt} is not supported.`)}},k=dt=>{switch(dt){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${dt} is not supported.`)}},d=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,z=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,B=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",V=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",Z=e?"row":"col",ee=e?"col":"row",Y=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${Z} / outWidth;
    let outCol = ${Z} % 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 = ${Ks(i,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 < ${B} && xCol >= 0 && xCol < ${V}) {
      ${d}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${C(i)}
    }
    return resData;`,me=e?t&&n?`
    let col = colIn * ${i};
    ${Y}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${Y}
    }
    return ${Ks(i,h)}(0.0);`:n&&s?`
    let col = colIn * ${i};
    ${Y}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${Y}
    }
    return ${Ks(i,h)}(0.0);`,pe=e?n&&s?k(u):`
    let col = colIn * ${u};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${k(u)}
    }
    return ${Ks(u,h)}(0.0);`:`
    let col = colIn * ${u};
    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
      ${k(u)}
    }
    return ${Ks(u,h)}(0.0);`,Me=Ks(p,h),Ie=Ks(e?i:u,h),Le=Ks(e?u:i,h),et=rn(a,Me,h);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ie} {
      ${e?me:pe}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${Me}) {
      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])"};
      ${z}
      ${Li(o)}
      ${et}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},bu=(e,t,s,n,o,a,i,u,p)=>{let h=t.format==="NHWC",C=h?e[0].dims[3]:e[0].dims[1],k=s[0],d=h?s[2]:s[3],z=h?s[1]:s[2],B=h?s[3]:s[1],V=h&&(C%4===0||C%3===0)&&B%4===0,Z=h?B:d*z,ee=h?d*z:B,Y=[8,8,1],me=n<=8?[4,1,1]:[4,4,1],pe=[Math.ceil(Z/Y[0]/me[0]),Math.ceil(ee/Y[1]/me[1]),Math.ceil(k/Y[2]/me[2])];is("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${pe}`);let Me=V?h&&C%4!==0?3:4:1,Ie=Y[1]*me[1],Le=Y[0]*me[0],et=Math.max(Y[0]*Me,Y[1]),dt=n%Ie===0,Et=o%Le===0,qt=a%et===0,Bt=V?[Me,4,4]:[1,1,1],It=[{type:6,data:n},{type:6,data:o},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];nn(t,It),It.push(...Mt(e[0].dims,e[1].dims));let ts=["rank","rank"];i&&(It.push(...Mt(e[2].dims)),ts.push("rank")),It.push(...Mt(s));let wt=Ht=>{let ps=[{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}];Or(t,ps);let Ut=V?4:1,Qt=fs(e[0].dataType),gs=`
      fn setOutputAtIndex(flatIndex : i32, value : ${V?`vec4<${Qt}>`:Qt}) {
        result[flatIndex] = ${V?`vec4<${Qt}>`:Qt}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${V?`vec4<${Qt}>`:Qt}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${V?"/ 4":""}, value);
      }`,it=Qe("x",e[0].dataType,e[0].dims.length,Me===3?1:Me),Pt=Qe("w",e[1].dataType,e[1].dims.length,Ut),hs=[it,Pt],Ns=At("result",e[0].dataType,s.length,Ut);if(i){let qs=Qe("bias",e[2].dataType,e[2].dims.length,Ut);hs.push(qs),gs+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${V?`vec4<${Qt}>`:Qt} {
          return bias[coords.${h?"w":"y"}${V?"/ 4":""}];
        }`}return`
        ${Bi("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 };
        ${Ht.registerUniforms(ps).declareVariables(...hs,Ns)}
        ${gs}
        ${Wi(h,dt,Et,qt,i,t,Bt[0],Bt[1],Bt[2],Qt)}
        ${V?Ni(me,Y,Qt,void 0,!h,et):ji(me,Y,Qt,void 0,!h,et,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${Me};${V};${dt};${Et};${qt};${Ie};${Le};${et}`,inputDependencies:ts},getRunData:()=>({outputs:[{dims:p?p(s):s,dataType:e[0].dataType}],dispatchGroup:{x:pe[0],y:pe[1],z:pe[2]},programUniforms:It}),getShaderSource:wt}}}),Gi,Ki,Gn,Hi,qi,vu,Qi,xu,Zc=g(()=>{Lt(),Pe(),Ot(),Jt(),on(),zi(),Gi=e=>{let t=1;for(let s=0;s<e.length;s++)t*=e[s];return t},Ki=e=>typeof e=="number"?[e,e,e]:e,Gn=(e,t)=>t<=1?e:e+(e-1)*(t-1),Hi=(e,t,s,n=1)=>{let o=Gn(t,n);return Math.floor((e[0]*(s-1)-s+o)/2)},qi=(e,t,s,n,o)=>{o==null&&(o=Hi(e,t[0],n[0]));let a=[0,0,0,s];for(let i=0;i<3;i++)e[i]+2*o>=t[i]&&(a[i]=Math.trunc((e[i]-t[i]+2*o)/n[i]+1));return a},vu=(e,t,s,n,o,a,i,u,p,h)=>{let C,k,d,z;if(e==="VALID"&&(e=0),typeof e=="number"){C={top:e,bottom:e,left:e,right:e,front:e,back:e};let B=qi([t,s,n,1],[u,p,h],1,[o,a,i],e);k=B[0],d=B[1],z=B[2]}else if(Array.isArray(e)){if(!e.every((V,Z,ee)=>V===ee[0]))throw Error(`Unsupported padding parameter: ${e}`);C={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let B=qi([t,s,n,1],[u,p,h],1,[o,a,i],e[0]);k=B[0],d=B[1],z=B[2]}else if(e==="SAME_UPPER"){k=Math.ceil(t/o),d=Math.ceil(s/a),z=Math.ceil(n/i);let B=(k-1)*o+u-t,V=(d-1)*a+p-s,Z=(z-1)*i+h-n,ee=Math.floor(B/2),Y=B-ee,me=Math.floor(V/2),pe=V-me,Me=Math.floor(Z/2),Ie=Z-Me;C={top:me,bottom:pe,left:Me,right:Ie,front:ee,back:Y}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:C,outDepth:k,outHeight:d,outWidth:z}},Qi=(e,t,s,n,o,a=!1,i="channelsLast")=>{let u,p,h,C,k;if(i==="channelsLast")[u,p,h,C,k]=e;else if(i==="channelsFirst")[u,k,p,h,C]=e;else throw new Error(`Unknown dataFormat ${i}`);let[d,,z,B,V]=t,[Z,ee,Y]=Ki(s),[me,pe,Me]=Ki(n),Ie=Gn(z,me),Le=Gn(B,pe),et=Gn(V,Me),{padInfo:dt,outDepth:Et,outHeight:qt,outWidth:Bt}=vu(o,p,h,C,Z,ee,Y,Ie,Le,et),It=a?d*k:d,ts=[0,0,0,0,0];return i==="channelsFirst"?ts=[u,It,Et,qt,Bt]:i==="channelsLast"&&(ts=[u,Et,qt,Bt,It]),{batchSize:u,dataFormat:i,inDepth:p,inHeight:h,inWidth:C,inChannels:k,outDepth:Et,outHeight:qt,outWidth:Bt,outChannels:It,padInfo:dt,strideDepth:Z,strideHeight:ee,strideWidth:Y,filterDepth:z,filterHeight:B,filterWidth:V,effectiveFilterDepth:Ie,effectiveFilterHeight:Le,effectiveFilterWidth:et,dilationDepth:me,dilationHeight:pe,dilationWidth:Me,inShape:e,outShape:ts,filterShape:t}},xu=(e,t,s,n,o,a)=>{let i=a==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],p={x:s.map((Z,ee)=>ee)},h=[Math.ceil(Gi(p.x.map(Z=>s[Z]))/u[0]),1,1];is("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let C=1,k=ze.size(s),d=[{type:12,data:k},{type:12,data:n},{type:12,data:o},{type:12,data:t.strides},{type:12,data:t.dilations}];nn(t,d),d.push(...Mt(e[0].dims,e[1].dims));let z=["rank","rank"],B=e.length===3;B&&(d.push(...Mt(e[2].dims)),z.push("rank")),d.push(...Mt(s));let V=Z=>{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}];Or(t,ee);let Y=1,me=fs(e[0].dataType),pe=Qe("x",e[0].dataType,e[0].dims.length,C),Me=Qe("W",e[1].dataType,e[1].dims.length,Y),Ie=[pe,Me],Le=At("result",e[0].dataType,s.length,Y),et="";if(B){let qt=Qe("bias",e[2].dataType,e[2].dims.length,Y);Ie.push(qt),et+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${me} {
          return bias[${i?St("coords",4,5):St("coords",1,5)}];
        }`}let dt=Ks(C,me),Et=rn(t,dt,me);return`
            ${et}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${pe.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 ${Me.getByIndices("aIndices")};
            }
          ${Z.registerUniforms(ee).declareVariables(...Ie,Le)}
          ${Z.mainStart()}
          ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${Le.offsetToIndices("global_idx")};
              let batch = ${St("coords",0,pe.rank)};
              let d2 = ${i?St("coords",pe.rank-1,pe.rank):St("coords",1,pe.rank)};
              let xFRCCorner = vec3<u32>(${i?St("coords",1,pe.rank):St("coords",2,pe.rank)},
              ${i?St("coords",2,pe.rank):St("coords",3,pe.rank)},
              ${i?St("coords",3,pe.rank):St("coords",4,pe.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${i?St("uniforms.x_shape",1,pe.rank):St("uniforms.x_shape",2,pe.rank)};
              let xShapeZ = ${i?St("uniforms.x_shape",2,pe.rank):St("uniforms.x_shape",3,pe.rank)};
              let xShapeW = ${i?St("uniforms.x_shape",3,pe.rank):St("uniforms.x_shape",4,pe.rank)};
              let xShapeU = ${i?St("uniforms.x_shape",4,pe.rank):St("uniforms.x_shape",1,pe.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

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

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

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

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${i?`let xValues = vec4<f32>(
                               getX(batch, xF, xR, xC, d1),
                               getX(batch, xF, xR, xC, d1 + 1),
                               getX(batch, xF, xR, xC, d1 + 2),
                               getX(batch, xF, xR, xC, d1 + 3));
                            `:`let xValues = vec4<f32>(
                               getX(batch, d1, xF, xR, xC),
                               getX(batch, d1 + 1, xF, xR, xC),
                               getX(batch, d1 + 2, xF, xR, xC),
                               getX(batch, d1 + 3, xF, xR, xC));
                            `}
                            let wValues = vec4<f32>(
                              getW(d2, d1, wF, wR, wC),
                              getW(d2, d1 + 1, wF, wR, wC),
                              getW(d2, d1 + 2, wF, wR, wC),
                              getW(d2, d1 + 3, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                    if (inputDepthVec4Remainder == 1) {
                        ${i?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
                    } else if (inputDepthVec4Remainder == 2) {
                      ${i?`let xValues = vec2<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
                      `:`let xValues = vec2<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
                    `}
                    let wValues = vec2<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
                      value += dot(xValues, wValues);
                    } else if (inputDepthVec4Remainder == 3) {
                      ${i?`let xValues = vec3<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
                      `:`let xValues = vec3<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
                    `}
                    let wValues = vec3<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                  }
                }
              }
              ${B?"value = value + getBiasByOutputCoords(coords)":""};
              ${Et}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${i};${C};${B}`,inputDependencies:z},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:h[0],y:h[1],z:h[2]},programUniforms:d}),getShaderSource:V}}}),Tu,Eu,Xi=g(()=>{Lt(),Ot(),Jt(),on(),Tu=(e,t,s,n)=>{let o=e.length>2,a=o?"value += b[output_channel];":"",i=e[0].dims,u=e[1].dims,p=t.format==="NHWC",h=p?s[3]:s[1],C=h/t.group,k=p&&C>=4?Xt(h):1,d=ze.size(s)/k,z=[{type:12,data:d},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:C}];nn(t,z),z.push(...Mt(i,[u[0],u[1],u[2],u[3]/k]));let B=o?["rank","rank","rank"]:["rank","rank"];z.push(...Mt([s[0],s[1],s[2],s[3]/k]));let V=Z=>{let ee=At("output",e[0].dataType,s.length,k),Y=fs(ee.type.tensor),me=rn(t,ee.type.value,Y),pe=Qe("x",e[0].dataType,i.length),Me=Qe("w",e[1].dataType,u.length,k),Ie=[pe,Me];o&&Ie.push(Qe("b",e[2].dataType,e[2].dims,k));let Le=[{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"}];Or(t,Le);let et=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 = ${pe.get("batch","xHeight","xWidth","input_channel")};
            let wVal = ${Me.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 = ${pe.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${Me.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${Z.registerUniforms(Le).declareVariables(...Ie,ee)}

  ${Z.mainStart()}
    ${Z.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 * ${k} / uniforms.output_channels_per_group;
    var in_channel_offset = group_id * uniforms.w_shape[${p?2:1}];

    var value: ${ee.type.value} = ${ee.type.value}(0);
    ${et}
    ${a}
    ${me}
    ${ee.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${k}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:z}),getShaderSource:V}},Eu=(e,t,s,n)=>{let o=e.length>2,a=Xt(s[3]),i=Xt(s[2]),u=ze.size(s)/a/i,p=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],h=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],C=[s[0],s[1],s[2],s[3]/a],k=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];nn(t,k),k.push(...Mt(p,h,C));let d=(i-1)*t.strides[1]+h[1],z=B=>{let V=At("output",e[0].dataType,C.length,a),Z=fs(V.type.tensor),ee=rn(t,V.type.value,Z),Y=Qe("x",e[0].dataType,p.length,a),me=Qe("w",e[1].dataType,h.length,a),pe=[Y,me];o&&pe.push(Qe("b",e[2].dataType,e[2].dims,a));let Me=o?"value += b[output_channel];":"",Ie=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Or(t,Ie),`
  ${B.registerUniforms(Ie).declareVariables(...pe,V)}
  ${B.mainStart()}
    ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${i}u;
    let col = (index1 % width1) * ${i}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

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

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

    for (var i = 0u; i < ${i}u; i++) {
      var value = values[i];
      ${Me}
      ${ee}
      ${V.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${i};${d};${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(u/64)},programUniforms:k}),getShaderSource:z}}}),Pu,yo,Cu,Mo,Yi,bo,ku,Su,vo,ep=g(()=>{Ot(),Jc(),Zc(),Vi(),Xi(),on(),fo(),Kr(),Pu=(e,t,s,n,o,a)=>{let i=e[0],u=e.slice(a?1:2,a?3:4),p=u.length,h=t[0],C=t.slice(2).map((d,z)=>d+(d-1)*(s[z]-1)),k=u.map((d,z)=>d+n[z]+n[z+p]).map((d,z)=>Math.floor((d-C[z]+o[z])/o[z]));return k.splice(0,0,i),k.splice(a?3:1,0,h),k},yo=[2,3,1,0],Cu=(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 ${o*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Mo=(e,t)=>{let s=e.kernelShape.slice();s.length<t[1].dims.length-2&&s.push(...Array(t[1].dims.length-2-s.length).fill(0));for(let a=2;a<t[1].dims.length;++a)s[a-2]===0&&(s[a-2]=t[1].dims[a]);let n=e.pads.slice();Zs.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,s,n,e.format==="NHWC",e.autoPad);let o=Object.assign({},e);return Object.assign(o,{kernelShape:s,pads:n}),o},Yi=e=>{let t=Di(e),s=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,a=e.group,i=e.kernel_shape,u=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:n,format:s,dilations:o,group:a,kernelShape:i,pads:u,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},bo=(e,t,s,n)=>{let o=s.format==="NHWC",a=Pu(t[0].dims,t[1].dims,s.dilations,s.pads,s.strides,o);if(s.group!==1){let Ie=[t[0]];if(o){let Le=e.kernelCustomData.wT??e.compute(cr(t[1],yo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Le),Ie.push(Le)}else Ie.push(t[1]);t.length===3&&Ie.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(Eu(Ie,s,a,n),{inputs:Ie}):e.compute(Tu(Ie,s,a,n),{inputs:Ie});return}let i=t.length===3,u=t[0].dims[o?1:2],p=t[0].dims[o?2:3],h=t[0].dims[o?3:1],C=t[1].dims[2],k=t[1].dims[3],d=a[o?1:2],z=a[o?2:3],B=a[o?3:1],V=o&&C===u&&k===p&&s.pads[0]===0&&s.pads[1]===0;if(V||C===1&&k===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 Ie=a[0],Le,et,dt,Et=[];if(o){let It=e.kernelCustomData.wT??e.compute(cr(t[1],yo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];if(s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=It),V){let ts=u*p*h;Le=t[0].reshape([1,Ie,ts]),et=It.reshape([1,ts,B]),dt=[1,Ie,B]}else Le=t[0].reshape([Ie,u*p,h]),et=It.reshape([1,h,B]),dt=[Ie,d*z,B];Et.push(Le),Et.push(et)}else Le=t[0].reshape([Ie,h,u*p]),et=t[1].reshape([1,B,h]),dt=[Ie,B,d*z],Et.push(et),Et.push(Le);i&&Et.push(t[2]);let qt=dt[2],Bt=Et[0].dims[Et[0].dims.length-1];qt<8&&Bt<8?e.compute(Ri(Et,s,a,dt,o,n),{inputs:Et}):e.compute(wo(Et,s,a,dt,o,n),{inputs:Et});return}let Z=!0,ee=e.kernelCustomData.wT??e.compute(cr(t[1],yo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ee);let Y=[t[0],ee];i&&Y.push(t[2]);let me=o?d*z:B,pe=o?B:d*z,Me=C*k*h;e.compute(bu(Y,s,a,me,pe,Me,i,Z,n),{inputs:Y})},ku=(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]],a=[1].concat(t.strides),i=[1].concat(t.dilations),u=[1].concat(t.kernelShape),p=Mo({...t,pads:o,strides:a,dilations:i,kernelShape:u},n);bo(e,n,p,h=>s?[h[0],h[2],h[3]]:[h[0],h[1],h[3]])},Su=(e,t,s)=>{let n=s.format==="NHWC"?"channelsLast":"channelsFirst",o=Mo(s,t),a=s.autoPad==="NOTSET"?s.pads:s.autoPad,i=Qi(t[0].dims,t[1].dims,s.strides,s.dilations,a,!1,n);e.compute(xu(t,o,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],n))},vo=(e,t)=>{if(Cu(e.inputs,t),e.inputs[0].dims.length===3)ku(e,t);else if(e.inputs[0].dims.length===5)Su(e,e.inputs,t);else{let s=Mo(t,e.inputs);bo(e,e.inputs,s)}}}),$u,tp=g(()=>{Lt(),Pe(),Ot(),Jt(),$u=(e,t,s)=>{let n=e.length>2,o=t.outputShape,a=t.format==="NHWC",i=t.group,u=e[1].dims,p=u[2]/i,h=u[3],C=a?Xt(p):1,k=a?Xt(h):1,d=a?h===1?C:k:1,z=ze.size(o)/k,B=[Math.ceil(z/64),1,1];is("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${B}`);let V=["rank","rank"],Z=[t.strides[0],t.strides[1]],ee=[t.kernelShape[a?1:2],t.kernelShape[a?2:3]],Y=[t.dilations[0],t.dilations[1]],me=[ee[0]+(t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)),ee[1]+(t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1))],pe=[me[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),me[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Me=[{type:12,data:z},{type:12,data:Z},{type:12,data:ee},{type:12,data:Y},{type:12,data:me},{type:6,data:pe},{type:12,data:p},{type:12,data:h},...Mt(e[0].dims,e[1].dims)];n&&(Me.push(...Mt(e[2].dims)),V.push("rank")),Me.push(...Mt(o));let Ie=Le=>{let et=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:Z.length},{name:"filter_dims",type:"u32",length:ee.length},{name:"dilations",type:"u32",length:ee.length},{name:"effective_filter_dims",type:"u32",length:me.length},{name:"pads",type:"i32",length:pe.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],dt=fs(e[0].dataType),Et=a?1:2,qt=a?2:3,Bt=a?3:1,It=Qe("W",e[1].dataType,e[1].dims.length,d),ts=Qe("Dy",e[0].dataType,e[0].dims.length,C),wt=[ts,It];n&&wt.push(Qe("bias",e[2].dataType,[o[Bt]].length,k));let Ht=At("result",e[0].dataType,o.length,k),ps=()=>{let Qt="";if(C===1)Qt+=`
        let w_offset = ${It.indicesToOffset(`${It.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
        let wValue = ${It.getByOffset(`w_offset / ${d}`)};
        dotProd = dotProd + xValue * wValue;`;else if(h===1)Qt+=`
          let wValue = ${It.getByOffset(`${It.indicesToOffset(`${It.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)} / ${d}`)};
          dotProd = dotProd + dot(xValue, wValue);`;else for(let gs=0;gs<C;gs++)Qt+=`
            let wValue${gs} = ${It.getByOffset(`${It.indicesToOffset(`${It.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${gs}, wOutChannel)`)} / ${d}`)};
            dotProd = dotProd + xValue[${gs}] * wValue${gs};`;return Qt},Ut=`
            let outputIndices = ${Ht.offsetToIndices(`global_idx * ${k}`)};
            let batch = ${Ht.indicesGet("outputIndices",0)};
            let d1 = ${Ht.indicesGet("outputIndices",Bt)};
            let r = ${Ht.indicesGet("outputIndices",Et)};
            let c = ${Ht.indicesGet("outputIndices",qt)};
            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 = ${Ht.type.value}(0.0);
            var wR: u32 = 0;
            if (uniforms.dilations.x == 1) {
              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
            }
            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${dt}(dyRCorner) + ${dt}(wR)) / ${dt}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${dt}(uniforms.Dy_shape[${Et}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);
              var wC: u32 = 0;
              if (uniforms.dilations.y == 1) {
                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
              }

              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${dt}(dyCCorner) + ${dt}(wC)) / ${dt}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${dt}(uniforms.Dy_shape[${qt}]) ||
                    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 + ${C}) {
                  let xValue = ${a?ts.getByOffset(`${ts.indicesToOffset(`${ts.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${C}`):ts.get("batch","inputChannel","idyR","idyC")};
                  ${ps()}
                  inputChannel = inputChannel + ${C};
                }
                wC = wC + uniforms.strides.y - 1;
              }
              wR = wR + uniforms.strides[0] - 1;
            }
            let value = dotProd${n?` + bias[d1 / ${k}]`:""};
            ${Ht.setByOffset("global_idx","value")};
          `;return`
    ${Le.registerUniforms(et).declareVariables(...wt,Ht)}
      ${Le.mainStart()}
      ${Le.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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        ${Y("value",2,"u32")}
        ${Y("value",3,"u32")}
        ${V.setByOffset("global_idx","value")}
      `}else ee=`
      let outputIndices = ${V.offsetToIndices("global_idx")};
      ${Z("")};
      let value = ${z.getByIndices("dataIndices")};
      ${V.setByOffset("global_idx","value")};
      `;return`
      ${d.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(z,B,V)}
      ${d.mainStart()}
        ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${ee}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:C}),getShaderSource:k}},Ju=e=>zt({axis:e.axis}),Zu=(e,t)=>{let s=e.inputs;Yu(s),e.compute(Hn(e.inputs,t))}}),ed,Co,td,up=g(()=>{Lt(),Ot(),Jt(),ed=(e,t,s,n,o,a,i,u,p)=>{let h=[{type:12,data:a},{type:12,data:n},{type:12,data:o},{type:12,data:s},{type:12,data:i},{type:12,data:u},{type:12,data:p}],C=[a];h.push(...Mt(t.dims,C));let k=d=>{let z=Qe("indices_data",t.dataType,t.dims.length),B=At("input_slice_offsets_data",12,1,1),V=[z,B],Z=[{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`
  ${d.registerUniforms(Z).declareVariables(...V)}
  ${d.mainStart()}
    ${d.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:C,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:k},{inputs:[t],outputs:[-1]})[0]},Co=(e,t)=>{let s=e.inputs,n=s[0].dims,o=s[0].dataType,a=s[1].dims,i=a[a.length-1],u=ze.sizeToDimension(a,a.length-1),p=ze.sizeFromDimension(n,t.batchDims+i),h=ze.sizeToDimension(n,t.batchDims),C=ze.sizeFromDimension(n,t.batchDims),k=u/h,d=new Array(i),z=p;for(let pe=0;pe<i;++pe)d[i-1-pe]=z,z*=n[t.batchDims+i-1-pe];let B=ed(e,s[1],d,t.batchDims,n,u,k,C,i),V=t.batchDims+i;if(V>n.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let Z=a.slice(0,-1).concat(n.slice(V)),ee=ze.size(Z),Y=[{type:12,data:ee},{type:12,data:p},...Mt(s[0].dims,B.dims,Z)],me=pe=>{let Me=Qe("data",s[0].dataType,s[0].dims.length),Ie=Qe("slice_offsets",12,B.dims.length),Le=At("output",s[0].dataType,Z.length);return`
          ${pe.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(Me,Ie,Le)}
            ${pe.mainStart()}
            ${pe.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:Z,dataType:o}],dispatchGroup:{x:Math.ceil(ee/64)},programUniforms:Y}),getShaderSource:me},{inputs:[s[0],B]})},td=e=>({batchDims:e.batch_dims,cacheKey:""})}),sd,dp,rd,nd,cp=g(()=>{Lt(),Ot(),rs(),Jt(),sd=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let s=ze.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,o=e[0],a=e[2],i=e.length===4?e[3]:void 0;if(a.dims.length!==o.dims.length||!o.dims.map((u,p)=>p===s?Math.ceil(u/n)===a.dims[p]:u===a.dims[p]).reduce((u,p)=>u&&p,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==a.dims.length||!i.dims.map((u,p)=>u===a.dims[p]).reduce((u,p)=>u&&p,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},dp=(e,t)=>{let s=e[0].dims,n=e[1].dims,o=s.length,a=ze.normalizeAxis(t.gatherAxis,o),i=ze.normalizeAxis(t.quantizeAxis,o),u=s.slice(0);u.splice(a,1,...n);let p=ze.size(u),h=e[2].dataType,C=e[0].dataType===22,k=[{type:12,data:p},{type:12,data:i},{type:12,data:a},{type:12,data:t.blockSize},...Mt(...e.map((z,B)=>z.dims),u)],d=z=>{let B=Qe("data",e[0].dataType,e[0].dims.length),V=Qe("inputIndices",e[1].dataType,e[1].dims.length),Z=Qe("scales",e[2].dataType,e[2].dims.length),ee=e.length>3?Qe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,Y=At("output",h,u.length),me=[B,V,Z];ee&&me.push(ee);let pe=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${z.registerUniforms(pe).declareVariables(...me,Y)}
        ${z.mainStart()}
        let output_indices = ${Y.offsetToIndices("global_idx")};
        var indices_indices = ${V.type.indices}(0);
        ${n.length>1?`
          for (var i: u32 = 0; i < ${n.length}; i++) {
            let index = ${Y.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${V.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${Y.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${B.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${Y.indicesGet("output_indices","i")};
          ${B.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${V.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${s[a]};
        }
        ${B.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) {
          let index = ${Y.indicesGet("output_indices",`i + ${n.length} - 1`)};
          ${B.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${B.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${B.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${C?"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 = ${Z.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${Z.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${Z.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 = ${C?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${$s(h)}(quantized_data - zero_point) * scale;
        ${Y.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((z,B)=>B!==1).map(z=>z.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(z,B)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:h}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:k}),getShaderSource:d}},rd=(e,t)=>{let s=e.inputs;sd(s,t),e.compute(dp(e.inputs,t))},nd=e=>zt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Tn,od,id,ad,pp=g(()=>{Lt(),Ot(),rs(),Jt(),Tn=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.`)},od=(e,t)=>{let s=e[0].dims,n=e[0].dataType,o=s.length,a=e[1].dims,i=e[1].dataType,u=ze.normalizeAxis(t.axis,o),p=s[u],h=a.slice(0),C=ze.size(h),k=Qe("input",n,o),d=Qe("indicesInput",i,a.length),z=At("output",n,h.length),B=[{type:12,data:C},{type:6,data:p},{type:12,data:u}];return B.push(...Mt(s,a,h)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:B}),getShaderSource:V=>`
      ${V.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(k,d,z)}
      ${V.mainStart()}
      ${V.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${z.setByOffset("global_idx","value")};
  }`}},id=e=>zt({axis:e.axis}),ad=(e,t)=>{let s=e.inputs;Tn(s),e.compute(od(e.inputs,t))}}),ld,ud,dd,ko,Yp=g(()=>{Lt(),Ot(),Jt(),ld=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")},ud=(e,t)=>{let s=e[0].dims.slice(),n=e[1].dims.slice(),[o,a,i]=Ir.getShapeOfGemmResult(s,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[o,a];if(!u)throw new Error("Can't use gemm on the given tensors");let p=16,h=Math.ceil(a/p),C=Math.ceil(o/p),k=!0,d=ze.size(u),z=[{type:12,data:k?h:d},{type:12,data:o},{type:12,data:a},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],B=["type","type"];e.length===3&&(z.push(...Mt(e[2].dims)),B.push("rank")),z.push(...Mt(u));let V=ee=>{let Y="";t.transA&&t.transB?Y="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?Y="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?Y="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(Y="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let me=t.alpha===1?"":"value *= uniforms.alpha;",pe=Qe("a",e[0].dataType,e[0].dims),Me=Qe("b",e[1].dataType,e[1].dims),Ie=pe.type.value,Le=null,et=[pe,Me];e.length===3&&(Le=Qe("c",e[2].dataType,e[2].dims.length),et.push(Le));let dt=At("output",e[0].dataType,u.length);et.push(dt);let Et=[{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(Et).declareVariables(...et)}

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

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

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

    ${me}
    ${Le!=null?`let cOffset = ${Le.broadcastedIndicesToOffset("vec2(m, n)",dt)}; value += ${Ie}(uniforms.beta) * ${Le.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`},Z=ee=>{let Y=Qe("a",e[0].dataType,e[0].dims),me=Qe("b",e[1].dataType,e[1].dims),pe=null,Me=[Y,me];e.length===3&&(pe=Qe("c",e[2].dataType,e[2].dims.length),Me.push(pe));let Ie=At("output",e[0].dataType,u.length);Me.push(Ie);let Le=[{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"}],et="",dt="";t.transA&&t.transB?(dt=`
      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] = ${Y.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] = ${me.type.value}(0);
      }
      `,et="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):t.transA&&!t.transB?(dt=`
      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] = ${Y.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] = ${me.type.value}(0);
      }
      `,et="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!t.transA&&t.transB?(dt=`
      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] = ${Y.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] = ${me.type.value}(0);
      }
      `,et="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!t.transA&&!t.transB&&(dt=`
      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] = ${Y.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] = ${me.type.value}(0);
      }
      `,et="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let Et=t.alpha===1?"":"value *= uniforms.alpha;";return`
  ${ee.registerUniforms(Le).declareVariables(...Me)}
  var<workgroup> tile_a: array<array<${Y.type.storage}, ${p}>, ${p}>;
  var<workgroup> tile_b: array<array<${me.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 = ${Ie.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${dt}
      k_start = k_start + ${p};
      workgroupBarrier();

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

    ${Et}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${pe!=null?`let cOffset = ${pe.broadcastedIndicesToOffset("vec2(m, n)",Ie)}; value += ${Ie.type.value}(uniforms.beta) * ${pe.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`};return k?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:h*C},programUniforms:z}),getShaderSource:Z}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:z}),getShaderSource:V}},dd=e=>{let t=e.transA,s=e.transB,n=e.alpha,o=e.beta;return{transA:t,transB:s,alpha:n,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},ko=(e,t)=>{ld(e.inputs),e.compute(ud(e.inputs,t))}}),Tr,Fr,an,ln,cd,aa,pd,hd,la,md,_d,ua,fd,gd,da=g(()=>{Lt(),Ot(),rs(),Jt(),[Tr,Fr,an,ln]=[0,1,2,3],cd=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 ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},aa=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,pd=e=>`
  fn gs_bicubic_interpolate(p: mat4x4<${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;
  }
`,hd=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));
    `}
  }
`,la=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);
      }`:""}
`,md=(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 = ${t}(0);
     var indices = vec4<u32>(0);
     indices[${Tr}] = batch;
     indices[${Fr}] = channel;`+(()=>{switch(s.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${an}] = u32(r);
            indices[${ln}] = u32(c);
          }
        `;case"border":return`
          indices[${an}] = u32(clamp(r, 0, H - 1));
          indices[${ln}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${an}] = gs_reflect(r, border[1], border[3]);
          indices[${ln}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${s.paddingMode} is not supported`)}})()+`
    return ${e.getByIndices("indices")};
  }
`,_d=(e,t,s)=>(()=>{switch(s.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Tr}], indices[${Fr}], 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[${Tr}], indices[${Fr}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Tr}], indices[${Fr}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Tr}], indices[${Fr}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Tr}], indices[${Fr}], 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[${Tr}], indices[${Fr}], 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")}`,ua=(e,t)=>{let s=Qe("x",e[0].dataType,e[0].dims.length),n=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=Qe("grid",e[1].dataType,n.length,2),a=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format==="NHWC"&&(a=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[Tr,Fr,an,ln]=[0,3,1,2]);let i=At("output",e[0].dataType,a.length),u=s.type.value,p=ze.size(a),h=[{type:12,data:p},...Mt(e[0].dims,n,a)],C=k=>`
  ${k.registerUniform("output_size","u32").declareVariables(s,o,i)}
  ${aa}
  ${pd(u)}
  ${hd(t)}
  ${la(t)}
  ${md(s,u,t)}

  ${k.mainStart()}
    ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${an}]);
      let W_in = i32(uniforms.x_shape[${ln}]);

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

      ${_d(i,u,t)}
  }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:k=>{let d=ze.size(a);return{outputs:[{dims:a,dataType:k[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}},getShaderSource:C}},fd=(e,t)=>{cd(e.inputs),e.compute(ua(e.inputs,t))},gd=e=>zt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),nr,wd,yd,ca,pa,un,hp,Md=g(()=>{Lt(),Ot(),rs(),ue(),ci(),Jt(),Kr(),nr=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,wd=(e,t)=>{let s=e[0],n=nr(e,1),o=nr(e,2),a=nr(e,3),i=nr(e,4),u=nr(e,5),p=nr(e,6),h=nr(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 C=s.dims[0],k=s.dims[1],d=s.dims.length===3?s.dims[2]:t.numHeads*s.dims[4],z=k,B=0,V=0,Z=Math.floor(d/t.numHeads);if(p&&h&&ze.size(p.dims)&&ze.size(h.dims)){if(p.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(p.dims[0]!==C||p.dims[1]!==t.numHeads||p.dims[3]!==Z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[0]!==C||h.dims[1]!==t.numHeads||h.dims[3]!==Z)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');B=p.dims[2],V=p.dims[2]}else if(p&&ze.size(p.dims)||h&&ze.size(h.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let ee;if(n&&ze.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,z=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==Z)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,z=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==Z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');ee=0,z=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(a&&ze.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let Y=B+z,me=0;if(i&&ze.size(i.dims)>0){me=8;let Le=i.dims;throw Le.length===1?Le[0]===C?me=1:Le[0]===3*C+2&&(me=3):Le.length===2&&Le[0]===C&&Le[1]===Y&&(me=5),me===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let pe=!1,Me=d;if(o&&ze.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(z!==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(z!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');Me=o.dims[1]*o.dims[3],pe=!0}}let Ie=!1;if(i&&ze.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(u&&ze.size(u.dims)>0){if(u.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(u.dims[0]!==C||u.dims[1]!==t.numHeads||u.dims[2]!==k||u.dims[3]!==Y)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:C,sequenceLength:k,pastSequenceLength:B,kvSequenceLength:z,totalSequenceLength:Y,maxSequenceLength:V,inputHiddenSize:0,hiddenSize:d,vHiddenSize:Me,headSize:Z,vHeadSize:Math.floor(Me/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:me,scale:t.scale,broadcastResPosBias:Ie,passPastInKv:pe,qkvFormat:ee}},yd=e=>zt({...e}),ca=zt({perm:[0,2,1,3]}),pa=(e,t,s,n,o,a,i)=>{let u=[n,o,a],p=ze.size(u),h=[{type:12,data:p},{type:12,data:i},{type:12,data:a}],C=k=>{let d=At("qkv_with_bias",t.dataType,u),z=Qe("qkv",t.dataType,u),B=Qe("bias",s.dataType,u),V=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${k.registerUniforms(V).declareVariables(z,B,d)}
  ${k.mainStart()}
    ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:C},{inputs:[t,s],outputs:[-1]})[0]},un=(e,t,s,n,o,a,i,u)=>{let p=a;if(i&&ze.size(i.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=pa(e,a,i,t,n,s*o,u),p=p.reshape([t,n,s,o]),s===1||n===1?p:e.compute(cr(p,ca.perm),{inputs:[p],outputs:[-1]})[0]}else return a.dims.length===3&&(p=a.reshape([t,n,s,o])),s===1||n===1?p:e.compute(cr(p,ca.perm),{inputs:[p],outputs:[-1]})[0]},hp=(e,t)=>{let s=wd(e.inputs,t),n=e.inputs[0],o=nr(e.inputs,1),a=nr(e.inputs,2),i=nr(e.inputs,3),u=nr(e.inputs,4),p=nr(e.inputs,5),h=nr(e.inputs,6),C=nr(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((o==null?void 0:o.dims.length)===5)throw new Error("Packed KV is not implemented");let k=o&&a&&o.dims.length===4&&a.dims.length===4,d=un(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,n,i,0);if(k)return Un(e,d,o,a,u,void 0,h,C,p,s);if(!o||!a)throw new Error("key and value must be provided");let z=un(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.headSize,o,i,s.hiddenSize),B=un(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.vHeadSize,a,i,2*s.hiddenSize);Un(e,d,z,B,u,void 0,h,C,p,s)}}),bd,ha,vd,xd,So,Td,Ed,ma=g(()=>{Lt(),Ot(),rs(),Jt(),bd=e=>{if(!e||e.length<1)throw new Error("too few inputs")},ha=(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),zt({numOutputs:n,axis:t.axis,splitSizes:s})},vd=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${St("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,xd=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) {
        ${s.join(`
`)}
      }`},So=(e,t)=>{let s=e[0].dims,n=ze.size(s),o=e[0].dataType,a=ze.normalizeAxis(t.axis,s.length),i=new Array(t.numOutputs),u=Qe("input",o,s.length),p=new Array(t.numOutputs),h=[],C=[],k=0,d=[{type:12,data:n}];for(let B=0;B<t.numOutputs;B++){k+=t.splitSizes[B],p[B]=k;let V=s.slice();V[a]=t.splitSizes[B],C.push(V),i[B]=At(`output${B}`,o,V.length),h.push({dims:C[B],dataType:e[0].dataType})}d.push({type:12,data:p},...Mt(s,...C));let z=B=>`
  ${B.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(u,...i)}
  ${vd(p.length)}
  ${xd(i)}

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

    var indices = ${u.offsetToIndices("global_idx")};
    var index = ${u.indicesGet("indices",a)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${St("uniforms.size_in_split_axis","output_number - 1u",p.length)};
      ${u.indicesSet("indices",a,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:z,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:d})}},Td=(e,t)=>{bd(e.inputs);let s=e.inputs.length===1?t:ha(e.inputs,t);e.compute(So(e.inputs,s),{inputs:[0]})},Ed=e=>{let t=e.axis,s=e.splitSizes,n=e.numOutputs<0?s.length:e.numOutputs;if(n!==s.length)throw new Error("numOutputs and splitSizes lengh must be equal");return zt({axis:t,numOutputs:n,splitSizes:s})}}),mp,_p,$o,_a,fp=g(()=>{rs(),ci(),Md(),ma(),Kr(),mp=(e,t)=>{if(t.doRotary)throw new Error("GroupQuerryAttention do_rotary attribute is not supported");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],a=e[3],i=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 u=!1,p=s.dims[0],h=s.dims[1],C=s.dims.length===3?u?s.dims[2]/3:s.dims[2]:t.numHeads*s.dims[4],k=h,d=0,z=!n||n.dims.length===0,B=Math.floor(z?C/(t.numHeads+2*t.kvNumHeads):C/t.numHeads);z&&(C=B*t.numHeads);let V=a&&a.dims.length!==0,Z=i&&i.dims.length!==0;if(V&&a.dims.length===4&&a.dims[0]===p&&a.dims[1]!==t.kvNumHeads&&a.dims[2]===t.kvNumHeads&&a.dims[3]===B)throw new Error("BSNH pastKey/pastValue is not supported");if(V&&Z){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');d=a.dims[2]}else if(V||Z)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"');k=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==B)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.');k=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==B)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');k=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 Y=0,me=!1,pe=t.kvNumHeads?B*t.kvNumHeads:C;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(k!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');pe=o.dims[2]}else{if(k!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');pe=o.dims[1]*o.dims[3],me=!0}}let Me=e.length>4?e[5]:void 0;if(Me&&Me.dims.length!==1&&Me.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:d,kvSequenceLength:k,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:C,vHiddenSize:pe,headSize:B,vHeadSize:Math.floor(pe/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:Y,scale:t.scale,broadcastResPosBias:!1,passPastInKv:me,qkvFormat:ee}},_p=zt({perm:[0,2,1,3]}),$o=(e,t,s)=>{let n=t,o=s.kvNumHeads;return t.dims.length===3&&s.kvSequenceLength!==0&&(n=t.reshape([s.batchSize,s.kvSequenceLength,o,s.headSize]),n=e.compute(cr(n,_p.perm),{inputs:[n],outputs:[-1]})[0]),n},_a=(e,t)=>{var Z;let s=mp(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((Z=e.inputs[1])==null?void 0:Z.dims.length)===5)throw new Error("Packed KV is not implemented");let n=e.inputs[0],o=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,a=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,i=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,u=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,p=e.inputs.length>4?e.inputs[5]:void 0,h=e.inputs.length>5?e.inputs[6]:void 0,C=s.kvNumHeads?s.kvNumHeads:s.numHeads,k=zt({axis:2,numOutputs:3,splitSizes:[s.numHeads*s.headSize,C*s.headSize,C*s.headSize]}),[d,z,B]=!o&&!a?e.compute(So([n],k),{inputs:[n],outputs:[-1,-1,-1]}):[n,o,a],V=un(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,d,void 0,0);Un(e,V,$o(e,z,s),$o(e,B,s),void 0,void 0,i,u,void 0,s,p,h)}}),fa,ga,Pd,Cd,kd=g(()=>{Lt(),Ot(),Kr(),Jt(),fa=(e,t,s,n,o,a,i,u)=>{let p=Xt(a),h=p===1?"f32":`vec${p}f`,C=p===1?"vec2f":`mat2x${p}f`,k=o*i,d=64;k===1&&(d=256);let z=[o,i,a/p],B=[o,i,2],V=["rank","type","type"],Z=[];Z.push(...Mt(z,B));let ee=Y=>{let me=Qe("x",t.dataType,3,p),pe=Qe("scale",s.dataType,s.dims),Me=Qe("bias",n.dataType,n.dims),Ie=At("output",1,3,2),Le=[me,pe,Me,Ie];return`
  var<workgroup> workgroup_shared : array<${C}, ${d}>;
  const workgroup_size = ${d}u;
  ${Y.declareVariables(...Le)}
  ${Y.mainStart(d)}
    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 = ${h}(${me.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${C}(sum, squared_sum);
    workgroupBarrier();

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

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${u}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${u};${d}`,inputDependencies:V},getRunData:()=>({outputs:[{dims:B,dataType:1}],dispatchGroup:{x:k},programUniforms:Z}),getShaderSource:ee},{inputs:[t,s,n],outputs:[-1]})[0]},ga=(e,t,s)=>{let n=t[0].dims,o=n,a=2,i=n[0],u=n[1],p=ze.sizeFromDimension(n,a),h=Xt(p),C=ze.size(o)/h,k=fa(e,t[0],t[1],t[2],i,p,u,s.epsilon),d=[i,u,p/h],z=[i,u],B=["type","none"],V=Z=>{let ee=Qe("x",t[0].dataType,d.length,h),Y=Qe("scale_shift",1,z.length,2),me=At("output",t[0].dataType,d.length,h),pe=[ee,Y,me];return`
  ${Z.registerUniform("output_size","u32").declareVariables(...pe)}
  ${Z.mainStart()}
  ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${me.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${Y.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${ee.getByOffset("global_idx")} * ${me.type.value}(scale_shift.x) + ${me.type.value}(scale_shift.y);
      ${me.setByOffset("global_idx","value")};
  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${h}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:[{type:12,data:C},...Mt(d,z,d)]}),getShaderSource:V},{inputs:[t[0],k]})},Pd=(e,t,s)=>{let n=t[0].dims,o=n,a=n[0],i=n[n.length-1],u=ze.sizeFromDimension(n,1)/i,p=Xt(i),h=ze.size(o)/p,C=[{type:12,data:u},{type:12,data:Math.floor(i/p)}],k=["type","type"],d=!1,z=[0,n.length-1];for(let ee=0;ee<n.length-2;ee++)d=d||n[ee+1]!==1,z.push(ee+1);d=d&&n[n.length-1]!==1;let B=d?e.compute(cr(e.inputs[0],z),{inputs:[e.inputs[0]],outputs:[-1]})[0]:e.inputs[0].reshape(Array.from({length:n.length},(ee,Y)=>n[z[Y]])),V=fa(e,B,t[1],t[2],a,u,i,s.epsilon),Z=ee=>{let Y=fs(t[0].dataType),me=p===1?"vec2f":`mat${p}x2f`,pe=Le=>{let et=Le===0?"x":"y",dt=p===1?"f32":`vec${p}f`;switch(p){case 1:return`${Y}(${dt}(scale.${et}))`;case 2:return`vec2<${Y}>(${dt}(scale[0].${et}, scale[1].${et}))`;case 4:return`vec4<${Y}>(${dt}(scale[0].${et}, scale[1].${et}, scale[2].${et}, scale[3].${et}))`;default:throw new Error(`Not supported compoents ${p}`)}},Me=Qe("input",t[0].dataType,t[0].dims,p),Ie=At("output",t[0].dataType,o,p);return`
  @group(0) @binding(0) var<storage, read> input : array<${Me.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${me}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${Ie.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], ${pe(0)}, ${pe(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:C}),getShaderSource:Z},{inputs:[t[0],V]})},Cd=(e,t)=>{t.format==="NHWC"?Pd(e,e.inputs,t):ga(e,e.inputs,t)}}),Sd,$d,wa,gp=g(()=>{Lt(),Ot(),Jt(),Sd=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},$d=(e,t,s)=>{let n=t.simplified,o=e[0].dims,a=e[1],i=!n&&e[2],u=o,p=ze.normalizeAxis(t.axis,o.length),h=ze.sizeToDimension(o,p),C=ze.sizeFromDimension(o,p),k=ze.size(a.dims),d=i?ze.size(i.dims):0;if(k!==C||i&&d!==C)throw new Error(`Size of X.shape()[axis:] == ${C}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${k} and bias size of ${d}`);let z=[];for(let Me=0;Me<o.length;++Me)Me<p?z.push(o[Me]):z.push(1);let B=Xt(C),V=["type","type"],Z=[{type:12,data:h},{type:1,data:C},{type:12,data:Math.floor(C/B)},{type:1,data:t.epsilon}];i&&V.push("type");let ee=s>1,Y=s>2,me=Me=>{let Ie=fs(e[0].dataType),Le=[Qe("x",e[0].dataType,e[0].dims,B),Qe("scale",a.dataType,a.dims,B)];i&&Le.push(Qe("bias",i.dataType,i.dims,B)),Le.push(At("output",e[0].dataType,u,B)),ee&&Le.push(At("mean_data_output",1,z)),Y&&Le.push(At("inv_std_output",1,z));let et=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${Me.registerUniforms(et).declareVariables(...Le)}
  ${Me.mainStart()}
    ${Me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${zs("f32",B)};
    var mean_square_vector = ${zs("f32",B)};

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

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

    ${ee?"mean_data_output[global_idx] = mean":""};
    ${Y?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},pe=[{dims:u,dataType:e[0].dataType}];return ee&&pe.push({dims:z,dataType:1}),Y&&pe.push({dims:z,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${B};${s};${n}`,inputDependencies:V},getRunData:()=>({outputs:pe,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:Z}),getShaderSource:me}},wa=(e,t)=>{Sd(e.inputs),e.compute($d(e.inputs,t,e.outputCount))}}),Ad,Id,wp=g(()=>{Ot(),fo(),Vi(),Ad=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.")},Id=e=>{Ad(e.inputs);let t=Ws.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(Ri(e.inputs,{activation:""},t));else{let o=t[t.length-2],a=ze.size(e.inputs[0].dims.slice(0,-2)),i=ze.size(e.inputs[1].dims.slice(0,-2));if(a!==1&&o===1&&i===1){let u=e.inputs[0].reshape([1,a,n]),p=e.inputs[1].reshape([1,n,s]),h=[1,a,s],C=[u,p];e.compute(wo(C,{activation:""},t,h),{inputs:C})}else e.compute(wo(e.inputs,{activation:""},t))}}}),Od,Fd,Dd,Ld,zd,Bd=g(()=>{Lt(),Ot(),rs(),Jt(),Od=(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),a=t.blockSize/8*t.bits,i=e[1];if(!ze.areEqual(i.dims,[t.n,o,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if(ze.size(u)!==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(ze.size(p)!==h)throw new Error("zeroPoints input size error.")}},Fd=(e,t)=>{let s=e[0].dims,n=s.length,o=s[n-2],a=t.k,i=t.n,u=s.slice(0,n-2),p=ze.size(u),h=e[1].dims[2]/4,C=e[0].dataType,k=Xt(t.k),d=Xt(h),z=Xt(i),B=u.concat([o,i]),V=o>1&&i/z%2===0?2:1,Z=ze.size(B)/z/V,ee=64,Y=[],me=[p,o,a/k],pe=ze.convertShape(e[1].dims).slice();pe.splice(-1,1,h/d),Y.push(...Mt(me)),Y.push(...Mt(pe)),Y.push(...Mt(e[2].dims)),e.length===4&&Y.push(...Mt(ze.convertShape(e[3].dims)));let Me=[p,o,i/z];Y.push(...Mt(Me));let Ie=Le=>{let et=me.length,dt=Qe("a",e[0].dataType,et,k),Et=Qe("b",12,pe.length,d),qt=Qe("scales",e[2].dataType,e[2].dims.length),Bt=[dt,Et,qt],It=e.length===4?Qe("zero_points",12,e[3].dims.length):void 0;It&&Bt.push(It);let ts=Me.length,wt=At("output",e[0].dataType,ts,z),Ht=fs(e[0].dataType),ps=(()=>{switch(k){case 1:return`array<${Ht}, 8>`;case 2:return`mat4x2<${Ht}>`;case 4:return`mat2x4<${Ht}>`;default:throw new Error(`${k}-component is not supported.`)}})(),Ut=()=>{let it=`
          // reuse a data
            var input_offset = ${dt.indicesToOffset(`${dt.type.indices}(batch, row, word_offset)`)};
            var a_data: ${ps};
            for (var j: u32 = 0; j < ${8/k}; j++) {
              a_data[j] = ${dt.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let Pt=0;Pt<z*V;Pt++)it+=`
            b_value = ${d===1?`b${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 = ${ps}(${Array.from({length:4},(hs,Ns)=>`${Ht}(b_value_lower[${Ns}]), ${Ht}(b_value_upper[${Ns}])`).join(", ")});
            b_dequantized_values = ${k===1?`${ps}(${Array.from({length:8},(hs,Ns)=>`(b_quantized_values[${Ns}] - ${It?`zero_point${Pt}`:"zero_point"}) * scale${Pt}`).join(", ")});`:`(b_quantized_values - ${ps}(${Array(8).fill(`${It?`zero_point${Pt}`:"zero_point"}`).join(",")})) * scale${Pt};`};
            workgroup_shared[local_id.x * ${V} + ${Math.floor(Pt/z)}]${z>1?`[${Pt%z}]`:""} += ${Array.from({length:8/k},(hs,Ns)=>`${k===1?`a_data[${Ns}] * b_dequantized_values[${Ns}]`:`dot(a_data[${Ns}], b_dequantized_values[${Ns}])`}`).join(" + ")};
          `;return it},Qt=()=>{let it=`
            var col_index = col * ${z};
            ${It?`
            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 = ${Ht}(8);`}
            `;for(let Pt=0;Pt<z*V;Pt++)it+=`
            let scale${Pt} = ${qt.getByOffset("col_index * nBlocksPerCol + block")};
            ${It?`
            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 = ${It.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${Pt} = ${Ht}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return it},gs=()=>{let it=`col_index = col * ${z};`;for(let Pt=0;Pt<z*V;Pt++)it+=`
            let b${Pt}_data = ${Et.getByIndices(`${Et.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return it+=`
            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: ${ps};
            var b_dequantized_values: ${ps};`,it};return`
        var<workgroup> workgroup_shared: array<${wt.type.value}, ${V*ee}>;
        ${Le.declareVariables(...Bt,wt)}
        ${Le.mainStart([ee,1,1])}
          let output_indices = ${wt.offsetToIndices(`(global_idx / ${ee}) * ${V}`)};
          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/k};
            ${Qt()}
            for (var word: u32 = 0; word < ${h}; word += ${d}) {
              ${gs()}
              for (var i: u32 = 0; i < ${d}; i++) {
                ${Ut()}
                word_offset += ${8/k};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${V}) {
            var output_value: ${wt.type.value} = ${wt.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 += ${V};
            }
            ${wt.setByIndices(`${wt.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${k};${d};${z};${V};${ee}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:C}],dispatchGroup:{x:Z},programUniforms:Y}),getShaderSource:Ie}},Dd=(e,t)=>{let s=e[0].dims,n=s.length,o=s[n-2],a=t.k,i=t.n,u=s.slice(0,n-2),p=ze.size(u),h=e[1].dims[2]/4,C=e[0].dataType,k=Xt(t.k),d=Xt(h),z=u.concat([o,i]),B=128,V=i%8===0?8:i%4===0?4:1,Z=B/V,ee=Z*d*8,Y=ee/k,me=ee/t.blockSize,pe=ze.size(z)/V,Me=[],Ie=[p,o,a/k],Le=ze.convertShape(e[1].dims).slice();Le.splice(-1,1,h/d),Me.push(...Mt(Ie)),Me.push(...Mt(Le)),Me.push(...Mt(e[2].dims)),e.length===4&&Me.push(...Mt(ze.convertShape(e[3].dims)));let et=[p,o,i];Me.push(...Mt(et));let dt=Et=>{let qt=Ie.length,Bt=Qe("a",e[0].dataType,qt,k),It=Qe("b",12,Le.length,d),ts=Qe("scales",e[2].dataType,e[2].dims.length),wt=[Bt,It,ts],Ht=e.length===4?Qe("zero_points",12,e[3].dims.length):void 0;Ht&&wt.push(Ht);let ps=et.length,Ut=At("output",e[0].dataType,ps),Qt=fs(e[0].dataType),gs=()=>{switch(k){case 1:return`
          let a_data0 = vec4<${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<${Qt}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${k}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${Bt.type.value}, ${Y}>;
        var<workgroup> inter_results: array<array<${Ut.type.value}, ${Z}>, ${V}>;
        ${Et.declareVariables(...wt,Ut)}
        ${Et.mainStart([Z,V,1])}
          let output_indices = ${Ut.offsetToIndices(`workgroup_index * ${V}`)};
          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) / ${me} + 1;

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

            // each thread process one block
            let b_row = col + local_id.y;
            let block = tile * ${me} + local_id.x;
            ${Ht?`
            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 = ${Ht.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 = ${ts.getByOffset("b_row * n_blocks_per_col + block")};
            let b_data = ${It.getByIndices(`${It.type.indices}(b_row, block, 0)`)};
            var word_offset = local_id.x * ${t.blockSize/k};
            for (var i: u32 = 0; i < ${d}; i++) {
              ${gs()}
              let b_value = ${d===1?"b_data":"b_data[i]"};
              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
              let b_quantized_values = mat2x4<${Qt}>(${Array.from({length:4},(it,Pt)=>`${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] += ${Array.from({length:2},(it,Pt)=>`${`dot(a_data${Pt}, b_dequantized_values[${Pt}])`}`).join(" + ")};
              word_offset += ${8/k};
            }
            workgroupBarrier();
          }

          if (local_idx < ${V}) {
            var output_value: ${Ut.type.value} = ${Ut.type.value}(0);
            for (var b = 0u; b < ${Z}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${Ut.setByIndices(`${Ut.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${k};${d};${Z};${V}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:z,dataType:C}],dispatchGroup:{x:pe},programUniforms:Me}),getShaderSource:dt}},Ld=(e,t)=>{Od(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(Dd(e.inputs,t)):e.compute(Fd(e.inputs,t))},zd=e=>zt(e)}),Rd,Nd,ya,jd,Ud,ws,yp,Mp,bp,Vd=g(()=>{Lt(),Ot(),Jt(),Rd=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Nd=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
            k = i32(${e.indicesGet("indices",o)}) - ${St("uniforms.pads",o,s)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${St("uniforms.x_shape",o,t)})) {
              break;
            }
            offset += k * i32(${St("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;
            ${n}
            value = x[offset];
          }
      `},ya=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${St("uniforms.pads",o,s)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${St("uniforms.x_shape",o,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${St("uniforms.x_shape",o,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${St("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},jd=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${St("uniforms.pads",o,s)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${St("uniforms.x_shape",o,t)})) {
                  k = i32(${St("uniforms.x_shape",o,t)}) - 1;
                }
                offset += k * i32(${St("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Ud=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${St("uniforms.pads",o,s)};
                if (k < 0)  {
                  k += i32(${St("uniforms.x_shape",o,t)}]);
                }
                if (k >= i32(${St("uniforms.x_shape",o,t)})) {
                  k -= i32(${St("uniforms.x_shape",o,t)});
                }
                offset += k * i32(${St("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},ws=(e,t,s)=>{switch(s.mode){case 0:return Nd(e,t,s.pads.length);case 1:return ya(e,t,s.pads.length);case 2:return jd(e,t,s.pads.length);case 3:return Ud(e,t,s.pads.length);default:throw new Error("Invalid mode")}},yp=(e,t)=>{let s=ze.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,o=ze.size(s),a=[{type:12,data:o},{type:6,data:t.pads}],i=e.length>=3&&e[2].data;t.mode===0&&a.push({type:i?e[2].dataType:1,data:t.value}),a.push(...Mt(e[0].dims,s));let u=["rank"],p=h=>{let C=At("output",e[0].dataType,s.length),k=Qe("x",e[0].dataType,n.length),d=k.type.value,z=ws(C,n.length,t),B=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&B.push({name:"constant_value",type:i?d:"f32"}),`
            ${h.registerUniforms(B).declareVariables(k,C)}
            ${h.mainStart()}
            ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${d}(0);
            ${z}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${i}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(ze.size(s)/64)},programUniforms:a}),getShaderSource:p}},Mp=(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,a=new Int32Array(2*o).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let p=0;p<u.length;p++)a[Number(u[p])]=Number(s[p]),a[Number(u[p])+o]=Number(s[p+u.length])}else s.forEach((u,p)=>a[Number(p)]=Number(u));let i=[];return a.forEach(u=>i.push(u)),{mode:t.mode,value:n,pads:i}}else return t},bp=(e,t)=>{Rd(e.inputs);let s=Mp(e.inputs,t);e.compute(yp(e.inputs,s),{inputs:[0]})}}),qn,Ma,ba,va,Ao,xa,vp,Ta,Ea,Pa,xp,Wd,Gd,Kd,Ca,Hd,qd,Qd,Xd,Tp=g(()=>{Re(),Lt(),Ot(),Jt(),qn=e=>{if(F.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Ma=(e,t,s)=>{let n=t.format==="NHWC",o=e.dims.slice();n&&o.splice(1,0,o.pop());let a=Object.hasOwnProperty.call(t,"dilations"),i=t.kernelShape.slice(),u=t.strides.slice(),p=a?t.dilations.slice():[],h=t.pads.slice();Zs.adjustPoolAttributes(s,o,i,u,p,h);let C=Zs.computePoolOutputShape(s,o,u,p,i,h,t.autoPad),k=Object.assign({},t);a?Object.assign(k,{kernelShape:i,strides:u,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(k,{kernelShape:i,strides:u,pads:h,cacheKey:t.cacheKey});let d=C.slice();return d.push(d.splice(1,1)[0]),[k,n?d:C]},ba=(e,t)=>{let s=t.format==="NHWC",n=ze.size(e),o=ze.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:o}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],C=t.pads[t.pads.length-1],k=!!(h+C);a.push({type:12,data:u},{type:12,data:p},{type:12,data:h},{type:12,data:C}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let d=!1;if(t.kernelShape.length===2){let z=t.kernelShape[t.kernelShape.length-2],B=t.strides[t.strides.length-2],V=t.pads[t.pads.length/2-2],Z=t.pads[t.pads.length-2];d=!!(V+Z),a.push({type:12,data:z},{type:12,data:B},{type:12,data:V},{type:12,data:Z}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,i,!0,k,d]}else{if(s)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=ze.computeStrides(t.kernelShape);a.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:u.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,C)=>h+C);return[a,i,!!p,!1,!1]}},va=(e,t,s,n,o,a,i,u,p,h,C,k)=>{let d=o.format==="NHWC",z=t.type.value,B=At("output",t.type.tensor,n);if(o.kernelShape.length<=2){let V="",Z="",ee="",Y=s-(d?2:1);if(C?V=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${Y}] = indices[${Y}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${Y}] < 0 || xIndices[${Y}]
                      >= uniforms.x_shape[${Y}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`:V=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${Y}] = indices[${Y}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`,o.kernelShape.length===2){let me=s-(d?3:2);k?Z=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${me}] = indices[${me}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${me}] < 0 || xIndices[${me}] >= uniforms.x_shape[${me}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:Z=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${me}] = indices[${me}] * uniforms.sh - uniforms.phStart + j;
                `,ee=`
              }
            `}return`
            ${e.registerUniforms(p).declareVariables(t,B)}

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

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

              var value = ${z}(${u});
              var pad = 0;
              ${Z}
              ${V}
              ${ee}
              ${i}

              output[global_idx] = value;
            }`}else{if(d)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let V=o.kernelShape.length,Z=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")}];
                ${a}
              }`:ee=`
              }
              let x_val = x[${t.indicesToOffset("xIndices")}];
              ${a}
            `,`
            ${e.registerUniforms(p).declareVariables(t,B)}

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

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

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

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

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

              output[global_idx] = value;
            }`}},Ao=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,xa=e=>`${Ao(e)};${e.countIncludePad}`,vp=e=>`${Ao(e)};${e.storageOrder};${e.dilations}`,Ta=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}),Ea=(e,t,s,n)=>{let[o,a]=Ma(t,n,s),i=Qe("x",t.dataType,t.dims.length),u=i.type.value,p="value += x_val;",h="";o.countIncludePad?h+=`value /= ${u}(uniforms.kernelSize);`:h+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[C,k,d,z,B]=ba(a,o);C.push(...Mt(t.dims,a));let V=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${d};${z};${B}`,inputDependencies:V},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(ze.size(a)/64)},programUniforms:C}),getShaderSource:Z=>va(Z,i,t.dims.length,a.length,o,p,h,0,k,d,z,B)}},Pa=e=>{let t=e.count_include_pad!==0,s=Ta(e);if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...s,cacheKey:""};return{...n,cacheKey:xa(n)}},xp=(e,t)=>{qn(e.inputs),e.compute(Ea("AveragePool",e.inputs[0],!1,t))},Wd={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Gd=e=>{let t=e.format;return{format:t,...Wd,cacheKey:t}},Kd=(e,t)=>{qn(e.inputs),e.compute(Ea("GlobalAveragePool",e.inputs[0],!0,t))},Ca=(e,t,s,n)=>{let[o,a]=Ma(t,n,s),i=`
      value = max(x_val, value);
    `,u="",p=Qe("x",t.dataType,t.dims.length),h=["rank"],[C,k,d,z,B]=ba(a,o);return C.push(...Mt(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${d};${z};${B}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(ze.size(a)/64)},programUniforms:C}),getShaderSource:V=>va(V,p,t.dims.length,a.length,o,i,u,t.dataType===10?-65504:-1e5,k,d,z,B)}},Hd=(e,t)=>{qn(e.inputs),e.compute(Ca("MaxPool",e.inputs[0],!1,t))},qd=e=>{let t=e.storage_order,s=e.dilations,n=Ta(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:vp(o)}},Qd=e=>{let t=e.format;return{format:t,...Wd,cacheKey:t}},Xd=(e,t)=>{qn(e.inputs),e.compute(Ca("GlobalMaxPool",e.inputs[0],!0,t))}}),Yd,Jd,Zd,ec,Jp=g(()=>{Lt(),Ot(),rs(),Jt(),Yd=(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,a)=>a===t.axis||o===e[0].dims[a]).reduce((o,a)=>o&&a,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let 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)].")}},Jd=(e,t)=>{let s=ze.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,o=n===3,a=e[0].dims,i=e[1].dataType,u=ze.size(a),p=n===3||n===2,h=p?[Math.ceil(ze.size(e[0].dims)/4)]:e[0].dims,C=e[1].dims,k=e.length>2?e[2]:void 0,d=k?p?[Math.ceil(ze.size(k.dims)/4)]:k.dims:void 0,z=C.length===0||C.length===1&&C[0]===1,B=z===!1&&C.length===1,V=Xt(u),Z=z&&(!p||V===4),ee=Z?V:1,Y=Z&&!p?V:1,me=Qe("input",p?12:n,h.length,Y),pe=Qe("scale",i,C.length),Me=k?Qe("zero_point",p?12:n,d.length):void 0,Ie=At("output",i,a.length,ee),Le=[me,pe];Me&&Le.push(Me);let et=[h,C];k&&et.push(d);let dt=[{type:12,data:u/ee},{type:12,data:s},{type:12,data:t.blockSize},...Mt(...et,a)],Et=qt=>{let Bt=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${qt.registerUniforms(Bt).declareVariables(...Le,Ie)}
      ${qt.mainStart()}
          ${qt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${Ie.offsetToIndices("global_idx")};

          // Set input x
          ${p?`
            let input = ${me.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 = ${me.getByOffset("global_idx")};`};

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

          // Set zero-point input
          ${Me?z?p?`
                let zero_point_input = ${Me.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 = ${Me.getByOffset("0")}`:B?p?`
                let zero_point_index = ${Ie.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${Me.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 = ${Ie.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${Me.getByOffset("zero_point_index")};`:p?`
                let zero_point_offset = ${pe.indicesToOffset("scale_indices")};
                let zero_point_input = ${Me.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 = ${Me.getByIndices("scale_indices")};`:`let zero_point_value = ${p?o?"i32":"u32":me.type.value}(0);`};
      // Compute and write output
      ${Ie.setByOffset("global_idx",`${Ie.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:Me?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Et,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(u/ee/64),y:1,z:1},programUniforms:dt})}},Zd=(e,t)=>{Yd(e.inputs,t),e.compute(Jd(e.inputs,t))},ec=e=>zt({axis:e.axis,blockSize:e.blockSize})}),tc,sc,rc,Ep=g(()=>{Re(),Lt(),Jt(),tc=(e,t,s)=>{let n=e===t,o=e<t&&s<0,a=e>t&&s>0;if(n||o||a)throw new Error("Range these inputs' contents are invalid.")},sc=(e,t,s,n)=>{let o=Math.abs(Math.ceil((t-e)/s)),a=[o],i=o,u=[{type:12,data:i},{type:n,data:e},{type:n,data:s},...Mt(a)],p=h=>{let C=At("output",n,a.length),k=C.type.value,d=[{name:"outputSize",type:"u32"},{name:"start",type:k},{name:"delta",type:k}];return`
        ${h.registerUniforms(d).declareVariables(C)}
        ${h.mainStart()}
        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${k}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:u})}},rc=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]),F.webgpu.validateInputContent&&tc(t,s,n),e.compute(sc(t,s,n,e.inputs[0].dataType),{inputs:[]})}}),nc,oc,Pp,ka,Cp=g(()=>{Lt(),Ot(),rs(),Jt(),nc=(e,t,s,n)=>{if(e!=="none"&&n!=="i32"&&n!=="u32"&&n!=="f32")throw new Error(`Input ${n} is not supported with reduction ${e}.`);let o=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,a=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${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) + (${s})${a}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${s}));`:`
                ${o}max(bitcast<f32>(oldValue), (${s}))${a}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${s}));`:`${o}min(bitcast<${n}>(oldValue), (${s}))${a}`;case"mul":return`${o}(bitcast<${n}>(oldValue) * (${s}))${a}`;default:throw new Error(`Reduction ${e} is not supported.`)}},oc=(e,t)=>{let s=e[0].dims,n=e[1].dims,o=s,a=1,i=Math.ceil(ze.size(n)/a),u=n[n.length-1],p=ze.sizeFromDimension(s,u),h=[{type:12,data:i},{type:12,data:u},{type:12,data:p},...Mt(e[1].dims,e[2].dims,o)],C=k=>{let d=Qe("indices",e[1].dataType,e[1].dims.length),z=Qe("updates",e[2].dataType,e[2].dims.length,a),B=t.reduction!=="none"&&t.reduction!==""?ja("output",e[0].dataType,o.length):At("output",e[0].dataType,o.length,a);return`
      ${k.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(d,z,B)}
      ${k.mainStart()}
        ${k.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];
    ${nc(t.reduction,"output[data_offset + i]","value",B.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(i/64)},programUniforms:h}),getShaderSource:C}},Pp=e=>zt({reduction:e.reduction}),ka=(e,t)=>{e.compute(oc(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),ic,ac,lc,Sa,uc,dc,cc,pc,hc,mc,_c,fc,$a,gc,wc,yc,Mc,bc,vc,xc,kp=g(()=>{Lt(),Ot(),rs(),Jt(),ic=(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")}},ac=(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,a)=>n[o]=e[a]),n},lc=(e,t,s,n,o,a)=>{let[i,u,p]=s>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(C=>a.push(C));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length===1&&e[u].dims[0]>0){if(e[u].getFloat32Array().forEach(C=>n.push(C)),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");ic(n,t),t.axes.length>0&&ac(n,t.axes,h).forEach((C,k)=>n[k]=C)}if(p>0&&e.length>p&&e[p].dims.length===1&&e[p].dims[0]>0&&(e[p].getBigInt64Array().forEach(C=>o.push(Number(C))),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")},Sa=(e,t,s,n)=>`
  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
  // offset-by-one error later in floor().
  let big = (${e}) * (${t});
  let whole = ${n}(big / (${s}));
  let fract = ${n}(big % (${s})) / ${n}(${s});
  return whole + fract;
`,uc=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`
          if (xScale < 1.0 || floor(xScale) != xScale) {
            return ${t}(xResized) / ${t}(xScale);
          } else {
            ${Sa("xResized","lengthOriginal","lengthResized",t)}
          }
        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${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 {
                    ${Sa("xResized","lengthOriginal - 1","lengthResized - 1",t)}
                  }`;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`)}})()+"}",dc=(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`)}})()+"}",cc=(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((a,i)=>{n[a]=o[i],n[i+s]=o[t.length+i]}),n):o},pc=(e,t,s,n)=>{let o=[];if(s.length>0)if(n.length>0){if(e.forEach(a=>o.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,i)=>o[a]=s[i])}else s.forEach(a=>o.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((a,i)=>Math.round(a*t[i]))}return o},hc=(e,t,s)=>{let n=(()=>{switch(s.keepAspectRatioPolicy){case"not_larger":return s.axes.length>0?Math.min(...s.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return s.axes.length>0?Math.max(...s.axes.map(a=>t[a]),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(a=>t[a]=n),s.axes.forEach(a=>o[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),o.forEach((a,i)=>o[i]=Math.round(a*t[i]))),o},mc=(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 = ${St("uniforms.scales","i",n)};
        var roi_low = ${St("uniforms.roi","i",o)};
        var roi_hi = ${St("uniforms.roi",`i + ${t.length}`,o)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${St("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${St("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;
    }`,_c=(e,t,s,n,o,a,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${St("uniforms.scales","i",o)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${St("uniforms.roi","i",a)};
          var roi_hi = ${St("uniforms.roi",`i + ${s.length}`,a)};
          var input_shape_i = ${St("uniforms.input_shape","i",s.length)};
          var output_shape_i = ${St("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet("input_indices","i","input_index")}
      }
      return input_indices;
    }`,fc=(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 >= ${St("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,$a=(e,t,s,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",s,"batch")};
`:"",gc=(e,t,s,n,o)=>{let[a,i,u,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",i,`max(0, min(row, ${s[i]} - 1))`)};
      ${e.indicesSet("input_indices",u,`max(0, min(col, ${s[u]} - 1))`)};
      ${$a(e,p,a,2)}
      return ${e.getByIndices("input_indices")};
    }

    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var row:${h} = originalIndices[${i}];
      var col:${h} = originalIndices[${u}];
      ${n?`if (row < 0 || row > (${s[i]} - 1) || col < 0 || col > (${s[u]} - 1)) {
        return ${o};
      }`:""};
      row = max(0, min(row, ${s[i]} - 1));
      col = max(0, min(col, ${s[u]} - 1));
      var row1: u32 = u32(row);
      var col1: u32 = u32(col);
      var row2: u32 = u32(row + 1);
      var col2: u32 = u32(col + 1);
      var channel: u32 = ${s.length>2?`u32(originalIndices[${p}])`:"0"};
      var batch: u32 =  ${s.length>2?`u32(originalIndices[${a}])`:"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);
    }`},wc=(e,t,s,n,o,a,i,u,p,h)=>{let C=s.length===2,[k,d]=C?[0,1]:[2,3],z=e.type.value,B=V=>{let Z=V===k?"row":"col";return`
      fn ${Z}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${z} {
        var output_index = ${t.indicesGet("output_indices",V)};
        var originalIdx: ${z} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[V]},
        ${n[V]}, ${s[V]}, ${a[V]}, ${a[V]} + ${s.length});
        var fractOriginalIdx: ${z} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

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

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

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

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

      var x111: ${C} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${C} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${C} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${C} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${C} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${C} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${C} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${C} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${C} = abs(depth - ${C}(depth1));
      var dx2: ${C} = abs(${C}(depth2) - depth);
      var dy1: ${C} = abs(height - ${C}(height1));
      var dy2: ${C} = abs(${C}(height2) - height);
      var dz1: ${C} = abs(width - ${C}(width1));
      var dz2: ${C} = abs(${C}(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);
    }`},Mc=(e,t,s,n,o,a)=>{let i=e.dims,u=cc(a,t.axes,i.length),p=pc(i,n,o,t.axes),h=n.slice();n.length===0&&(h=i.map((Y,me)=>Y===0?1:p[me]/Y),t.keepAspectRatioPolicy!=="stretch"&&(p=hc(i,h,t)));let C=At("output",e.dataType,p.length),k=Qe("input",e.dataType,i.length),d=ze.size(p),z=i.length===p.length&&i.every((Y,me)=>Y===p[me]),B=t.coordinateTransformMode==="tf_crop_and_resize",V=t.extrapolationValue,Z=k.type.value,ee=Y=>`
      ${z?"":`
      ${uc(t.coordinateTransformMode,Z)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${fc(k,i)};
              ${dc(t.nearestMode,s,Z)};
              ${_c(k,C,i,p,h.length,u.length,B)};
              `;case"linear":return`
              ${mc(C,i,p,h.length,u.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${gc(k,C,i,B,V)}`;if(i.length===3||i.length===5)return`${yc(k,C,i,B,V)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${wc(k,C,i,p,h,u,t.cubicCoeffA,B,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")}})()};
      `}
      ${Y.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",u.length).declareVariables(k,C)}
      ${Y.mainStart()}
        ${Y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${z?"output[global_idx] = input[global_idx];":`
        let output_indices = ${C.offsetToIndices("global_idx")};
        var input_indices: ${k.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${k.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${s}|${h.length>0?t.mode==="cubic"?h:h.length:""}|${o.length>0?o:""}|${u.length>0?u:""}|${z}|${t.mode==="nearest"?i.length:i}`,inputDependencies:["rank"]},getShaderSource:ee,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:[{type:12,data:d},{type:1,data:h},{type:1,data:u},...Mt(i,p)]})}},bc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},vc=(e,t)=>{let s=[],n=[],o=[],a=bc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");lc(e.inputs,t,a,s,n,o),e.compute(Mc(e.inputs[0],t,a,s,n,o),{inputs:[0]})},xc=e=>{let t=e.antialias,s=e.axes,n=e.coordinateTransformMode,o=e.cubicCoeffA,a=e.excludeOutside!==0,i=e.extrapolationValue,u=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return zt({antialias:t,axes:s,coordinateTransformMode:n,cubicCoeffA:o,excludeOutside:a,extrapolationValue:i,keepAspectRatioPolicy:u,mode:p,nearestMode:h})}}),Tc,Ec,Pc,Sp=g(()=>{Lt(),Ot(),rs(),Jt(),Tc=(e,t)=>{let[s,n,o,a]=e,{numHeads:i,rotaryEmbeddingDim:u}=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(!ze.areEqual(n.dims,[])&&!ze.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(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!ze.areEqual(o.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=s.dims[0],h=s.dims[s.dims.length-2],C=o.dims[0],k=ze.sizeFromDimension(s.dims,1)/h,d=u===0?o.dims[1]*2:k/i;if(u>d)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(d/2!==o.dims[1]&&u/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>C)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Ec=(e,t)=>{let{interleaved:s,numHeads:n,rotaryEmbeddingDim:o,scale:a}=t,i=e[0].dims[0],u=ze.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=u/p,C=e[2].dims[1],k=o===0?C*2:h/n,d=new Array(i,p,h/k,k-C),z=ze.computeStrides(d),B=[{type:1,data:a},{type:12,data:d},{type:12,data:z},...e[0].dims.length===3?new Array({type:12,data:[u,h,k,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,k,p*k,1]}):[],...Mt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],V=Z=>{let ee=Qe("input",e[0].dataType,e[0].dims.length),Y=Qe("position_ids",e[1].dataType,e[1].dims.length),me=Qe("cos_cache",e[2].dataType,e[2].dims.length),pe=Qe("sin_cache",e[3].dataType,e[3].dims.length),Me=At("output",e[0].dataType,e[0].dims.length);return Z.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:d.length},{name:"global_strides",type:"u32",length:z.length},{name:"input_output_strides",type:"u32",length:z.length}]),`
        ${Z.declareVariables(ee,Y,me,pe,Me)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${Y.broadcastedIndicesToOffset("bsnh.xy",At("",Y.type.tensor,2))};
            let position_id =
                u32(${Y.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")} * ${me.get("position_id","bsnh[3]")} -
                ${ee.getByOffset("j")} * ${pe.get("position_id","bsnh[3]")};
            ${Me.setByOffset("i","re")}
            let im = ${ee.getByOffset("i")} * ${pe.get("position_id","bsnh[3]")} +
                ${ee.getByOffset("j")} * ${me.get("position_id","bsnh[3]")};
            ${Me.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${Me.setByOffset("k",ee.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:zt({interleaved:s}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:V,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(ze.size(d)/or)},programUniforms:B})}},Pc=(e,t)=>{Tc(e.inputs,t),e.compute(Ec(e.inputs,t))}}),Cc,kc,$p,Zt=g(()=>{Lt(),Ot(),Jt(),Cc=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],a=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]!==a)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==o)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},kc=(e,t,s,n)=>{let o=t.simplified,a=e[0].dims,i=ze.size(a),u=a,p=i,h=a.slice(-1)[0],C=n?a.slice(0,-1).concat(1):[],k=!o&&e.length>3,d=e.length>4,z=n&&s>1,B=n&&s>2,V=s>3,Z=64,ee=Xt(h),Y=[{type:12,data:p},{type:12,data:ee},{type:12,data:h},{type:1,data:t.epsilon}],me=Me=>{let Ie=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Le=[Qe("x",e[0].dataType,e[0].dims,ee),Qe("skip",e[1].dataType,e[1].dims,ee),Qe("gamma",e[2].dataType,e[2].dims,ee)];k&&Le.push(Qe("beta",e[3].dataType,e[3].dims,ee)),d&&Le.push(Qe("bias",e[4].dataType,e[4].dims,ee)),Le.push(At("output",e[0].dataType,u,ee)),z&&Le.push(At("mean_output",1,C)),B&&Le.push(At("inv_std_output",1,C)),V&&Le.push(At("input_skip_bias_sum",e[0].dataType,u,ee));let et=fs(e[0].dataType),dt=fs(1,ee);return`

      ${Me.registerUniforms(Ie).declareVariables(...Le)}
      var<workgroup> sum_shared : array<${dt}, ${Z}>;
      var<workgroup> sum_squared_shared : array<${dt}, ${Z}>;

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

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

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

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

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${o?"":`- ${et}(mean)`}) *
            ${et}(inv_std_dev) * gamma[offset1d + i]
            ${k?"+ beta[offset1d + i]":""};
        }
      }`},pe=[{dims:u,dataType:e[0].dataType}];return s>1&&pe.push({dims:C,dataType:1}),s>2&&pe.push({dims:C,dataType:1}),s>3&&pe.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${ee};${z};${B};${V}`,inputDependencies:e.map((Me,Ie)=>"type")},getShaderSource:me,getRunData:()=>({outputs:pe,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:Y})}},$p=(e,t)=>{Cc(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(kc(e.inputs,t,e.outputCount,!1),{outputs:s})}}),Sc,Ds,Ys,er,dn,Ap,$c,Ac,_=g(()=>{Lt(),Ot(),rs(),Jt(),Sc=(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`)})},Ds=(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},Ys=(e,t)=>{if(e.length>1){let s=Ds(e,1),n=Ds(e,2),o=Ds(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),zt({starts:s,ends:n,axes:o})}else return t},er=(e,t,s,n,o)=>{let a=e;return e<0&&(a+=s[n[t]]),o[t]<0?Math.max(0,Math.min(a,s[n[t]]-1)):Math.max(0,Math.min(a,s[n[t]]))},dn=(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 = ${St("uniforms.input_shape","i",s.length)};
            let steps_i = ${St("uniforms.steps","i",s.length)};
            let signs_i = ${St("uniforms.signs","i",s.length)};
            let starts_i = ${St("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;
      }`,Ap=(e,t)=>{let s=e[0].dims,n=ze.size(s),o=t.axes.length>0?ze.normalizeAxes(t.axes,s.length):[...Array(s.length).keys()],a=Ds(e,4);a.forEach(ee=>ee!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(o.length).fill(1));let i=t.starts.map((ee,Y)=>er(ee,Y,s,o,a)),u=t.ends.map((ee,Y)=>er(ee,Y,s,o,a));if(o.length!==i.length||o.length!==u.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)||(i.splice(ee,0,0),u.splice(ee,0,s[ee]),a.splice(ee,0,1));let p=a.map(ee=>Math.sign(ee));a.forEach((ee,Y,me)=>{if(ee<0){let pe=(u[Y]-i[Y])/ee,Me=i[Y],Ie=Me+pe*a[Y];i[Y]=Ie,u[Y]=Me,me[Y]=-ee}});let h=s.slice(0);o.forEach((ee,Y)=>{h[ee]=Math.ceil((u[ee]-i[ee])/a[ee])});let C={dims:h,dataType:e[0].dataType},k=At("output",e[0].dataType,h.length),d=Qe("input",e[0].dataType,e[0].dims.length),z=ze.size(h),B=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:p.length},{name:"steps",type:"u32",length:a.length}],V=[{type:12,data:z},{type:12,data:i},{type:6,data:p},{type:12,data:a},...Mt(e[0].dims,h)],Z=ee=>`
      ${ee.registerUniforms(B).declareVariables(d,k)}
        ${dn(d,k,s)}
        ${ee.mainStart()}
          ${ee.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${k.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${k.setByOffset("global_idx",d.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${i.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:Z,getRunData:()=>({outputs:[C],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:V})}},$c=(e,t)=>{Sc(e.inputs,t);let s=Ys(e.inputs,t);e.compute(Ap(e.inputs,s),{inputs:[0]})},Ac=e=>{let t=e.starts,s=e.ends,n=e.axes;return zt({starts:t,ends:s,axes:n})}}),T,j,fe,Fe,De=g(()=>{Lt(),Ot(),rs(),Kr(),Jt(),T=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},j=(e,t)=>{let s=e.inputs[0],n=s.dims,o=ze.size(n),a=n.length,i=ze.normalizeAxis(t.axis,a),u=i<n.length-1,p,h=[];u?(h=Array.from({length:a},(Le,et)=>et),h[i]=a-1,h[a-1]=i,p=e.compute(cr(s,h),{inputs:[s],outputs:[-1]})[0]):p=s;let C=p.dims,k=C[a-1],d=o/k,z=Xt(k),B=k/z,V=64;d===1&&(V=256);let Z=(Le,et)=>et===4?`max(max(${Le}.x, ${Le}.y), max(${Le}.z, ${Le}.w))`:et===2?`max(${Le}.x, ${Le}.y)`:et===3?`max(max(${Le}.x, ${Le}.y), ${Le}.z)`:Le,ee=Qe("x",p.dataType,p.dims,z),Y=At("result",p.dataType,p.dims,z),me=ee.type.value,pe=fs(p.dataType)==="f32"?`var threadMax = ${me}(-3.402823e+38f);`:`var threadMax = ${me}(-65504.0h);`,Me=Le=>`
      var<workgroup> rowMaxShared : ${me};
      var<workgroup> rowSumShared : ${me};
      var<workgroup> threadShared : array<${me}, ${V}>;

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

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${me}) {
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      }
      ${Le.registerUniform("packedCols","i32").declareVariables(ee,Y)}
      ${Le.mainStart(V)}
        let gindex = i32(global_idx);
        let lindex = i32(local_idx);
        const wg = ${V};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
        ${pe}
        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 = ${me}(${Z("threadShared[0]",z)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${me}(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 = ${me}(${Gs("threadShared[0]",z)});
        }
        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);
        }
      }`,Ie=e.compute({name:"Softmax",shaderCache:{hint:`${z};${V}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:C,dataType:p.dataType}],dispatchGroup:{x:d},programUniforms:[{type:6,data:B}]}),getShaderSource:Me},{inputs:[p],outputs:[u?-1:0]})[0];u&&e.compute(cr(Ie,h),{inputs:[Ie]})},fe=(e,t)=>{T(e.inputs),j(e,t)},Fe=e=>zt({axis:e.axis})}),Ze,rt,ft,bt,Rt,Wt=g(()=>{Lt(),Ot(),Jt(),Ze=e=>Array.from(e.getBigInt64Array(),Number),rt=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(Ze(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")},ft=(e,t)=>{let s=[];for(let n=0;n<e.length;++n)s.push(e[n]*t[n]);return s},bt=(e,t)=>{let s=e[0].dims,n=t??Ze(e[1]),o=ft(s,n),a=ze.size(o),i=e[0].dataType,u=Qe("input",i,s.length),p=At("output",i,o.length),h=C=>`
      const inputShape = ${u.indices(...s)};
      ${C.registerUniform("output_size","u32").declareVariables(u,p)}
      ${C.mainStart()}
      ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${p.offsetToIndices("global_idx")};
      var input_indices: ${u.type.indices};
      for (var i = 0; i < ${s.length}; i++) {
        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${p.indicesGet("output_indices","i")}  % input_dim_i;

        ${u.indicesSet("input_indices","i","input_dim_value")}
      }
      ${p.setByOffset("global_idx",u.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...Mt(e[0].dims,o)]}),getShaderSource:h}},Rt=e=>{rt(e.inputs),e.compute(bt(e.inputs),{inputs:[0]})}}),Dt,Gt,es,ns=g(()=>{Lt(),Ot(),Jt(),Dt=(e,t,s,n,o)=>{let a=At("output_data",o,s.length,4),i=Qe("a_data",t[1].dataType,t[1].dims.length,4),u=Qe("b_data",t[2].dataType,t[2].dims.length,4),p=Qe("c_data",t[0].dataType,t[0].dims.length,4),h,C=(k,d,z)=>`select(${d}, ${k}, ${z})`;if(!n)h=a.setByOffset("global_idx",C(i.getByOffset("global_idx"),u.getByOffset("global_idx"),p.getByOffset("global_idx")));else{let k=(d,z,B="")=>{let V=`a_data[index_a${z}][component_a${z}]`,Z=`b_data[index_b${z}][component_b${z}]`,ee=`bool(c_data[index_c${z}] & (0xffu << (component_c${z} * 8)))`;return`
            let output_indices${z} = ${a.offsetToIndices(`global_idx * 4u + ${z}u`)};
            let offset_a${z} = ${i.broadcastedIndicesToOffset(`output_indices${z}`,a)};
            let offset_b${z} = ${u.broadcastedIndicesToOffset(`output_indices${z}`,a)};
            let offset_c${z} = ${p.broadcastedIndicesToOffset(`output_indices${z}`,a)};
            let index_a${z} = offset_a${z} / 4u;
            let index_b${z} = offset_b${z} / 4u;
            let index_c${z} = offset_c${z} / 4u;
            let component_a${z} = offset_a${z} % 4u;
            let component_b${z} = offset_b${z} % 4u;
            let component_c${z} = offset_c${z} % 4u;
            ${d}[${z}] = ${B}(${C(V,Z,ee)});
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            var data = vec4<u32>(0);
            ${k("data",0,"u32")}
            ${k("data",1,"u32")}
            ${k("data",2,"u32")}
            ${k("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:h=`
            ${k("output_data[global_idx]",0)}
            ${k("output_data[global_idx]",1)}
            ${k("output_data[global_idx]",2)}
            ${k("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(p,i,u,a)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${h}
      }`},Gt=e=>{let t=e[1].dims,s=e[2].dims,n=e[0].dims,o=e[1].dataType,a=!(ze.areEqual(t,s)&&ze.areEqual(s,n)),i=t,u=ze.size(t);if(a){let h=Ws.calcShape(Ws.calcShape(t,s,!1),n,!1);if(!h)throw new Error("Can't perform where op on the given tensors");i=h,u=ze.size(i)}let p=Math.ceil(u/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:h=>Dt(h,e,i,a,o),getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:p},...Mt(n,t,s,i)]})}},es=e=>{e.compute(Gt(e.inputs))}}),Yt,as=g(()=>{Kc(),ci(),Hc(),qc(),Qc(),Xc(),gu(),ep(),sp(),rp(),np(),op(),ip(),ap(),lp(),up(),cp(),pp(),Yp(),da(),fp(),kd(),gp(),wp(),Bd(),Md(),Vd(),Tp(),Jp(),Ep(),Cp(),co(),kp(),Sp(),Zt(),_(),De(),ma(),Wt(),Kr(),ki(),ns(),Yt=new Map([["Abs",[Sl]],["Acos",[fi]],["Acosh",[$l]],["Add",[$i]],["ArgMax",[ii,ai]],["ArgMin",[bl,ai]],["Asin",[Al]],["Asinh",[gi]],["Atan",[Il]],["Atanh",[Ol]],["Attention",[Tl]],["AveragePool",[xp,Pa]],["BatchNormalization",[hi]],["BiasAdd",[kl]],["BiasSplitGelu",[Si]],["Cast",[Fl,wi]],["Ceil",[Ll]],["Clip",[yi]],["Concat",[_u,fu]],["Conv",[vo,Yi]],["ConvTranspose",[Fu,ea]],["Cos",[zl]],["Cosh",[Mi]],["CumSum",[Du,Lu]],["DepthToSpace",[Ru,Nu]],["DequantizeLinear",[Zd,ec]],["Div",[iu]],["Einsum",[Wu,Gu]],["Elu",[Bl,Vn]],["Equal",[au]],["Erf",[Rl]],["Exp",[bi]],["Expand",[Qu]],["FastGelu",[Xu]],["Floor",[Nl]],["FusedConv",[vo,Yi]],["Gather",[Zu,Ju]],["GatherElements",[ad,id]],["GatherBlockQuantized",[rd,nd]],["GatherND",[Co,td]],["Gelu",[jl]],["Gemm",[ko,dd]],["GlobalAveragePool",[Kd,Gd]],["GlobalMaxPool",[Xd,Qd]],["Greater",[du]],["GreaterOrEqual",[Ii]],["GridSample",[fd,gd]],["GroupQueryAttention",[_a]],["HardSigmoid",[Kl,Ti]],["InstanceNormalization",[Cd]],["LayerNormalization",[wa]],["LeakyRelu",[vi,Vn]],["Less",[cu]],["LessOrEqual",[pu]],["Log",[Zl]],["MatMul",[Id]],["MatMulNBits",[Ld,zd]],["MaxPool",[Hd,qd]],["Mul",[lu]],["MultiHeadAttention",[hp,yd]],["Neg",[Vl]],["Not",[Ul]],["Pad",[bp]],["Pow",[uu]],["QuickGelu",[tu,Vn]],["Range",[rc]],["Reciprocal",[xi]],["ReduceMin",[gl]],["ReduceMean",[ml]],["ReduceMax",[ri]],["ReduceSum",[ni]],["ReduceProd",[wl]],["ReduceL1",[si]],["ReduceL2",[_l]],["ReduceLogSum",[Ml]],["ReduceLogSumExp",[fl]],["ReduceSumSquare",[yl]],["Relu",[Wl]],["Resize",[vc,xc]],["RotaryEmbedding",[Pc]],["ScatterND",[ka,Pp]],["Sigmoid",[Gl]],["Sin",[Hl]],["Sinh",[Ei]],["Slice",[$c,Ac]],["SkipLayerNormalization",[$p]],["Split",[Td,Ed]],["Sqrt",[ql]],["Softmax",[fe,Fe]],["Sub",[Ai]],["Tan",[Ql]],["Tanh",[Xl]],["ThresholdedRelu",[Jl,Vn]],["Tile",[Rt]],["Transpose",[Ha,qo]],["Where",[es]]])}),Es,xs=g(()=>{Re(),Pe(),Jt(),Es=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){Ne(e.programInfo.name);let a=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let u=[];for(let h of t)u.push({binding:u.length,resource:{buffer:h.buffer}});for(let h of s)u.push({binding:u.length,resource:{buffer:h.buffer}});o&&u.push({binding:u.length,resource:o});let p=a.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:u,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)}i.setPipeline(e.computePipeline),i.setBindGroup(0,p),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),je(e.programInfo.name)}dispose(){}build(e,t){Ne(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 ${h.extension};`)});let o=Va(t,this.backend.device.limits),a=e.getShaderSource(o),i=`${n.join(`
`)}
${o.additionalImplementations}
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V=Number(z-this.queryTimeBase),Z=Number(B-this.queryTimeBase);if(!Number.isSafeInteger(V)||!Number.isSafeInteger(Z))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:k.map(ee=>({dims:ee.dims,dataType:mr(ee.dataType)})),outputsMetadata:d.map(ee=>({dims:ee.dims,dataType:mr(ee.dataType)})),kernelId:i,kernelType:p,kernelName:h,programName:C,startTime:V,endTime:Z});else{let ee="";k.forEach((me,pe)=>{ee+=`input[${pe}]: [${me.dims}] | ${mr(me.dataType)}, `});let Y="";d.forEach((me,pe)=>{Y+=`output[${pe}]: [${me.dims}] | ${mr(me.dataType)}, `}),console.log(`[profiling] kernel "${i}|${p}|${h}|${C}" ${ee}${Y}execution time: ${Z-V} ns`)}Ve("GPU",`${C}::${z}::${B}`)}e.unmap(),this.pendingQueries.delete(e)}),je()}run(e,t,s,n,o,a){Ne(e.name);let i=[];for(let Y=0;Y<t.length;++Y){let me=t[Y].data;if(me===0)continue;let pe=this.gpuDataManager.get(me);if(!pe)throw new Error(`no GPU data for input: ${me}`);i.push(pe)}let{outputs:u,dispatchGroup:p,programUniforms:h}=e.getRunData(t),C=s.length===0?u.map((Y,me)=>me):s;if(C.length!==u.length)throw new Error(`Output size ${C.length} must be equal to ${u.length}.`);let k=[],d=[];for(let Y=0;Y<u.length;++Y){if(!Number.isInteger(C[Y])||C[Y]<-3||C[Y]>=a)throw new Error(`Invalid output index: ${C[Y]}`);if(C[Y]===-3)continue;let me=C[Y]===-1,pe=C[Y]===-2,Me=me||pe?o(u[Y].dataType,u[Y].dims):n(C[Y],u[Y].dataType,u[Y].dims);if(k.push(Me),Me.data===0)continue;let Ie=this.gpuDataManager.get(Me.data);if(!Ie)throw new Error(`no GPU data for output: ${Me.data}`);if(me&&this.temporaryData.push(Ie),pe){let Le=this.kernelPersistentData.get(this.currentKernelId);Le||(Le=[],this.kernelPersistentData.set(this.currentKernelId,Le)),Le.push(Ie)}d.push(Ie)}if(i.length!==t.length||d.length!==k.length){if(d.length===0)return je(e.name),k;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let z;if(h){let Y=0,me=[];h.forEach(Le=>{let et=typeof Le.data=="number"?[Le.data]:Le.data;if(et.length===0)return;let dt=Le.type===10?2:4,Et,qt;Le.type===10?(qt=et.length>4?16:et.length>2?8:et.length*dt,Et=et.length>4?16:dt*et.length):(qt=et.length<=2?et.length*dt:16,Et=16),Y=Math.ceil(Y/qt)*qt,me.push(Y);let Bt=Le.type===10?8:4;Y+=et.length>4?Math.ceil(et.length/Bt)*Et:et.length*dt});let pe=16;Y=Math.ceil(Y/pe)*pe;let Me=new ArrayBuffer(Y);h.forEach((Le,et)=>{let dt=me[et],Et=typeof Le.data=="number"?[Le.data]:Le.data;if(Le.type===6)new Int32Array(Me,dt,Et.length).set(Et);else if(Le.type===12)new Uint32Array(Me,dt,Et.length).set(Et);else if(Le.type===10)new Uint16Array(Me,dt,Et.length).set(Et);else if(Le.type===1)new Float32Array(Me,dt,Et.length).set(Et);else throw new Error(`Unsupported uniform type: ${mr(Le.type)}`)});let Ie=this.gpuDataManager.create(Y,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Ie.buffer,0,Me,0,Y),this.gpuDataManager.release(Ie.id),z={offset:0,size:Y,buffer:Ie.buffer}}let B=this.programManager.normalizeDispatchGroupSize(p),V=B[1]===1&&B[2]===1,Z=Ts(e,t,V),ee=this.programManager.getArtifact(Z);if(ee||(ee=this.programManager.build(e,B),this.programManager.setArtifact(Z,ee),is("info",()=>`[artifact] key: ${Z}, 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 Y=0;Y<h.length;Y++){let me=h[Y],pe=me.type,Me=typeof me.data=="number"?1:me.data.length,[Ie,Le]=ee.uniformVariablesInfo[Y];if(pe!==Ie||Me!==Le)throw new Error(`Uniform variable ${Y} mismatch: expect type ${Ie} with size ${Le}, got type ${pe} with size ${Me} in program "${ee.programInfo.name}".`)}}if(is("info",()=>`[ProgramManager] run "${e.name}" (key=${Z}) with ${B[0]}x${B[1]}x${B[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let Y={kernelId:this.currentKernelId,programName:ee.programInfo.name,inputTensorViews:t,outputTensorViews:k};this.pendingKernels.push(Y),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(Y)}return this.programManager.run(ee,i,d,B,z),je(e.name),k}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=Yt.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:o[0],attributes:[o[1],s]};this.kernels.set(t,a)}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: ${e}`);let o=n.kernelType,a=n.kernelName,i=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),is("info",()=>`[WebGPU] Start to run kernel "[${o}] ${a}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),i(t,u[1]),0}catch(h){return s.push(Promise.resolve(`[WebGPU] Kernel "[${o}] ${a}" failed. ${h}`)),1}finally{p&&s.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${o}] ${a}": ${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 a=o.get(t),i=this.gpuDataManager.registerExternalBuffer(s,n,a);return o.set(t,[i,s]),i}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 Tt(this,e,t);return E(n.buffer,s)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){is("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(){is("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){is("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(),a=e[n];this.writeTimestamp(this.pendingDispatchNumber*2),o.setPipeline(a.computePipeline),o.setBindGroup(0,a.bindGroup),o.dispatchWorkgroups(...a.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(t[n]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}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()}}}),En,Io,ur,Mr,Oo,Qn,Fo,Do,Ps=g(()=>{Pe(),En=1,Io=()=>En++,ur=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),Mr=(e,t)=>{let s=ur.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},Oo=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 Mr(this.dataType,this.tensorShape)}destroy(){is("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)}canReuseTensor(e,t,s){return this.mlContext===e&&this.dataType===t&&this.tensorShape.length===s.length&&this.tensorShape.every((n,o)=>n===s[o])}},Qn=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,n){if(this.wrapper){if(this.wrapper.canReuseTensor(e,t,s))return this.wrapper.tensor;if(n){if(this.wrapper.byteLength!==Mr(t,s))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 o=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(t,s,o,!0,!0),n&&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 is("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()}},Fo=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=Io();return this.tensorTrackersById.set(e,new Qn(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){is("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(this.backend.currentContext,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){is("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${t==null?void 0: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=Io(),a=new Oo({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:s,shape:n});return this.tensorTrackersById.set(o,new Qn(this,a)),this.externalTensors.add(a),o}async getCachedTensor(e,t,s,n,o){let a=this.backend.currentSessionId,i=this.backend.currentContext;for(let[p,h]of this.freeTensors.entries())if(h.canReuseTensor(i,e,t)){is("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, shape: ${t}}`);let C=this.freeTensors.splice(p,1)[0];return C.sessionId=a,C}is("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let u=await i.createTensor({dataType:e,shape:t,dimensions:t,usage:s,writable:n,readable:o});return new Oo({sessionId:a,context:i,tensor:u,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},Do=(...e)=>new Fo(...e)}),Bs,qr,cn,Xn=g(()=>{Lt(),ar(),Q(),Ps(),Pe(),Bs=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),qr=(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,a)=>o===n[a]&&e[o]===t[o])},cn=class{constructor(e){this.tensorManager=Do(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],xn(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=>qr(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){is("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,s,n){let o=Bs.get(t);if(!o)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,o,s,n)}uploadTensor(e,t){if(!bs().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");is("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 E(s,t)}}registerMLTensor(e,t,s){let n=Bs.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let o=this.tensorManager.registerTensor(this.currentContext,e,n,s);return is("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${s}} -> {tensorId: ${o}}`),o}registerMLConstant(e,t,s,n,o,a){if(!a)throw new Error("External mounted files are not available.");let i=e;e.startsWith("./")&&(i=e.substring(2));let u=a.get(i);if(!u)throw new Error(`File with name ${i} not found in preloaded files.`);if(t+s>u.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let p=u.slice(t,t+s).buffer,h;switch(o.dataType){case"float32":h=new Float32Array(p);break;case"float16":h=new Uint16Array(p);break;case"int32":h=new Int32Array(p);break;case"uint32":h=new Uint32Array(p);break;case"int64":h=new BigInt64Array(p);break;case"uint64":h=new BigUint64Array(p);break;case"int8":h=new Int8Array(p);break;case"int4":case"uint4":case"uint8":h=new Uint8Array(p);break;default:throw new Error(`Unsupported data type: ${o.dataType} in creating WebNN Constant from external data.`)}return is("verbose",()=>`[WebNN] registerMLConstant {dataType: ${o.dataType}, shape: ${o.shape}}}`),n.constant(o,h)}flush(){}}}),Lo={};v(Lo,{init:()=>Ia});var Yn,Aa,Ia,Zp=g(()=>{Lt(),Er(),Pe(),Ot(),Xn(),Yn=class g_{constructor(t,s,n,o){this.module=t,this.dataType=s,this.data=n,this.dims=o}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=ze.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=ze.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=ze.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=ze.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(ze.size(t)!==ze.size(this.dims))throw new Error("Invalid new shape");return new g_(this.module,this.dataType,this.data,t)}},Aa=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,a=n===4?"i32":"i64";this.opKernelContext=Number(e.getValue(n*o++,a));let i=Number(e.getValue(n*o++,a));this.outputCount=Number(e.getValue(n*o++,a)),this.customDataOffset=Number(e.getValue(n*o++,"*")),this.customDataSize=Number(e.getValue(n*o++,a));let u=[];for(let p=0;p<i;p++){let h=Number(e.getValue(n*o++,a)),C=Number(e.getValue(n*o++,"*")),k=Number(e.getValue(n*o++,a)),d=[];for(let z=0;z<k;z++)d.push(Number(e.getValue(n*o++,a)));u.push(new Yn(e,h,C,d))}this.inputs=u}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,t){var i;let s=((i=t==null?void 0:t.inputs)==null?void 0:i.map(u=>typeof 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le(e[0],e[2],e[1]);case"gpu-buffer":{let t=e[0];if(!yn(t))throw new Error(`not supported data type: ${t} for deserializing GPU tensor`);let{gpuBuffer:s,download:n,dispose:o}=e[2];return le.fromGpuBuffer(s,{dataType:t,dims:e[1],download:n,dispose:o})}case"ml-tensor":{let t=e[0];if(!Mn(t))throw new Error(`not supported data type: ${t} for deserializing MLTensor tensor`);let{mlTensor:s,download:n,dispose:o}=e[2];return le.fromMLTensor(s,{dataType:t,dims:e[1],download:n,dispose:o})}default:throw new Error(`invalid data location: ${e[3]}`)}},Wh=class{async fetchModelAndCopyToWasmMemory(e){return zh(await vn(e))}async loadModel(e,t){Ne();let s;typeof e=="string"?s=await this.fetchModelAndCopyToWasmMemory(e):s=e,[this.sessionId,this.inputNames,this.outputNames]=await Bh(s,t),je()}async dispose(){return Rh(this.sessionId)}async run(e,t,s){Ne();let n=[],o=[];Object.entries(e).forEach(k=>{let d=k[0],z=k[1],B=this.inputNames.indexOf(d);if(B===-1)throw new Error(`invalid input '${d}'`);n.push(z),o.push(B)});let a=[],i=[];Object.entries(t).forEach(k=>{let d=k[0],z=k[1],B=this.outputNames.indexOf(d);if(B===-1)throw new Error(`invalid output '${d}'`);a.push(z),i.push(B)});let u=n.map((k,d)=>sh(k,()=>`input "${this.inputNames[o[d]]}"`)),p=a.map((k,d)=>k?sh(k,()=>`output "${this.outputNames[i[d]]}"`):null),h=await Nh(this.sessionId,o,u,i,p,s),C={};for(let k=0;k<h.length;k++)C[this.outputNames[i[k]]]=a[k]??Vh(h[k]);return je(),C}startProfiling(){}endProfiling(){jh(this.sessionId)}}}),Gh={};v(Gh,{OnnxruntimeWebAssemblyBackend:()=>nh,initializeFlags:()=>rh,wasmBackend:()=>Kh});var rh,nh,Kh,M_=g(()=>{Re(),Uh(),y_(),rh=()=>{if((typeof F.wasm.initTimeout!="number"||F.wasm.initTimeout<0)&&(F.wasm.initTimeout=0),F.wasm.simd===!1&&console.warn('Deprecated property "env.wasm.simd" is set to false. non-SIMD build is no longer provided, and this setting will be ignored.'),typeof F.wasm.proxy!="boolean"&&(F.wasm.proxy=!1),typeof F.wasm.trace!="boolean"&&(F.wasm.trace=!1),typeof F.wasm.numThreads!="number"||!Number.isInteger(F.wasm.numThreads)||F.wasm.numThreads<=0)if(typeof self<"u"&&!self.crossOriginIsolated)F.wasm.numThreads=1;else{let e=typeof navigator>"u"?R("node:os").cpus().length:navigator.hardwareConcurrency;F.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},nh=class{async init(e){rh(),await Dh(),await Lh(e)}async createInferenceSessionHandler(e,t){let s=new Wh;return await s.loadModel(e,t),Promise.resolve(s)}},Kh=new nh});Re(),Re(),Re();var b_="1.21.0-dev.20250206-d981b153d3",v_=Se;{let e=(M_(),y(Gh)).wasmBackend;H("webgpu",e,5),H("webnn",e,5),H("cpu",e,10),H("wasm",e,10)}Object.defineProperty(F.versions,"web",{value:b_,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":(Ee,$,r)=>{var f;r.r($),r.d($,{Tensor:()=>R.Tensor,createInferenceSession:()=>ne,deviceToExecutionProviders:()=>H,isONNXProxy:()=>q,isONNXTensor:()=>W});var I=r("./src/env.js"),N=r("?2ce3"),X=r("./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs?3a96"),R=r("./node_modules/onnxruntime-common/dist/esm/index.js");const g=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"}}),v=[];let M,y;const b=Symbol.for("onnxruntime");if(b in globalThis)y=globalThis[b];else if(I.apis.IS_NODE_ENV){switch(y=N??(f||(f=r.t(N,2))),process.platform){case"win32":v.push("dml");break;case"linux":process.arch==="x64"&&v.push("cuda");break}v.push("cpu"),M=["cpu"]}else y=X,I.apis.IS_WEBNN_AVAILABLE&&v.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),I.apis.IS_WEBGPU_AVAILABLE&&v.push("webgpu"),v.push("wasm"),M=["wasm"];const O=y.InferenceSession;function H(A=null){if(!A)return M;switch(A){case"auto":return v;case"gpu":return v.filter(S=>["webgpu","cuda","dml","webnn-gpu"].includes(S))}if(v.includes(A))return[g[A]??A];throw new Error(`Unsupported device: "${A}". Should be one of: ${v.join(", ")}.`)}let se=null;async function ne(A,S,w){se&&await se;const x=O.create(A,S);se??(se=x);const F=await x;return F.config=w,F}function W(A){return A instanceof y.Tensor}const U=y==null?void 0:y.env;U!=null&&U.wasm&&(U.wasm.wasmPaths=`https://cdn.jsdelivr.net/npm/@huggingface/transformers@${I.env.version}/dist/`,U.wasm.proxy=!1,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(U.wasm.numThreads=1)),U!=null&&U.webgpu&&(U.webgpu.powerPreference="high-performance");function q(){var A;return(A=U==null?void 0:U.wasm)==null?void 0:A.proxy}I.env.backends.onnx=U},"./src/base/feature_extraction_utils.js":(Ee,$,r)=>{r.r($),r.d($,{FeatureExtractor:()=>X,validate_audio_inputs:()=>R});var f=r("./src/utils/constants.js"),I=r("./src/utils/generic.js"),N=r("./src/utils/hub.js");class X extends I.Callable{constructor(v){super(),this.config=v}static async from_pretrained(v,M){const y=await(0,N.getModelJSON)(v,f.FEATURE_EXTRACTOR_NAME,!0,M);return new this(y)}}function R(g,v){var M;if(!(g instanceof Float32Array||g instanceof Float64Array))throw new Error(`${v} expects input to be a Float32Array or a Float64Array, but got ${((M=g==null?void 0:g.constructor)==null?void 0:M.name)??typeof g} instead. 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No instance will be fused."),ae=new Set);const xe=S.class_queries_logits??S.logits,re=(S.masks_queries_logits??S.pred_masks).sigmoid();let[Te,ce,$e]=xe.dims;if($e-=1,oe!==null&&oe.length!==Te)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Oe=[];for(let Ce=0;Ce<Te;++Ce){let tt=oe!==null?oe[Ce]:null,Ge=xe[Ce],ye=re[Ce],[J,de,ke]=H(Ge,ye,w,$e);if(ke.length===0){let[te,Ke]=tt??ye.dims.slice(-2),Ue=new I.Tensor("int32",new Int32Array(te*Ke).fill(-1),[te,Ke]);Oe.push({segmentation:Ue,segments_info:[]});continue}let[Be,Je]=ne(J,de,ke,x,F,ae,tt);Oe.push({segmentation:Be,segments_info:Je})}return Oe}function q(S,w=.5,x=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class A extends f.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,x,F=2){const ae=w.height,oe=w.width,xe=x.height,we=x.width;let re=Math.min(ae,xe),Te=Math.min(oe,we);return re===ae&&Te===oe?w:(ae>oe?Te=Math.floor(oe*re/ae):oe>ae&&(re=Math.floor(ae*Te/oe)),await w.resize(Te,re,{resample:F}))}async crop_margin(w,x=200){const F=w.clone().grayscale(),ae=(0,N.min)(F.data)[0],xe=(0,N.max)(F.data)[0]-ae;if(xe===0)return w;const we=x/255;let re=F.width,Te=F.height,ce=0,$e=0;const Oe=F.data;for(let Ce=0;Ce<F.height;++Ce){const tt=Ce*F.width;for(let Ge=0;Ge<F.width;++Ge)(Oe[tt+Ge]-ae)/xe<we&&(re=Math.min(re,Ge),Te=Math.min(Te,Ce),ce=Math.max(ce,Ge),$e=Math.max($e,Ce))}return w=await w.crop([re,Te,ce,$e]),w}pad_image(w,x,F,{mode:ae="constant",center:oe=!1,constant_values:xe=0}={}){const[we,re,Te]=x;let ce,$e;if(typeof F=="number"?(ce=F,$e=F):F==="square"?ce=$e=Math.max(we,re):(ce=F.width,$e=F.height),ce!==re||$e!==we){const Oe=new Float32Array(ce*$e*Te);if(Array.isArray(xe))for(let Ge=0;Ge<Oe.length;++Ge)Oe[Ge]=xe[Ge%Te];else xe!==0&&Oe.fill(xe);const[Ce,tt]=oe?[Math.floor((ce-re)/2),Math.floor(($e-we)/2)]:[0,0];for(let Ge=0;Ge<we;++Ge){const ye=(Ge+tt)*ce,J=Ge*re;for(let de=0;de<re;++de){const ke=(ye+de+Ce)*Te,Be=(J+de)*Te;for(let Je=0;Je<Te;++Je)Oe[ke+Je]=w[Be+Je]}}if(ae==="symmetric"){if(oe)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const Ge=we-1,ye=re-1;for(let J=0;J<$e;++J){const de=J*ce,ke=(0,X.calculateReflectOffset)(J,Ge)*re;for(let Be=0;Be<ce;++Be){if(J<we&&Be<re)continue;const Je=(de+Be)*Te,te=(ke+(0,X.calculateReflectOffset)(Be,ye))*Te;for(let Ke=0;Ke<Te;++Ke)Oe[Je+Ke]=w[te+Ke]}}}w=Oe,x=[$e,ce,Te]}return[w,x]}rescale(w){for(let x=0;x<w.length;++x)w[x]=this.rescale_factor*w[x]}get_resize_output_image_size(w,x){const[F,ae]=w.size;let oe,xe;if(this.do_thumbnail){const{height:we,width:re}=x;oe=Math.min(we,re)}else Number.isInteger(x)?(oe=x,xe=this.config.max_size??oe):x!==void 0&&(oe=x.shortest_edge,xe=x.longest_edge);if(oe!==void 0||xe!==void 0){const we=oe===void 0?1:Math.max(oe/F,oe/ae),re=F*we,Te=ae*we,ce=xe===void 0?1:Math.min(xe/re,xe/Te);let $e=Math.floor(Number((re*ce).toFixed(2))),Oe=Math.floor(Number((Te*ce).toFixed(2)));return this.size_divisibility!==void 0&&([$e,Oe]=M([$e,Oe],this.size_divisibility)),[$e,Oe]}else if(x!==void 0&&x.width!==void 0&&x.height!==void 0){let we=x.width,re=x.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Te=re/ae,ce=we/F;Math.abs(1-ce)<Math.abs(1-Te)?Te=ce:ce=Te,re=v(Te*ae,this.config.ensure_multiple_of),we=v(ce*F,this.config.ensure_multiple_of)}return[we,re]}else{if(this.size_divisibility!==void 0)return M([F,ae],this.size_divisibility);if(x.min_pixels!==void 0&&x.max_pixels!==void 0){const{min_pixels:we,max_pixels:re}=x,Te=this.config.patch_size*this.config.merge_size;return W(ae,F,Te,we,re)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(x)}`)}}async resize(w){const[x,F]=this.get_resize_output_image_size(w,this.size);return await w.resize(x,F,{resample:this.resample})}async preprocess(w,{do_normalize:x=null,do_pad:F=null,do_convert_rgb:ae=null,do_convert_grayscale:oe=null,do_flip_channel_order:xe=null}={}){this.do_crop_margin&&(w=await this.crop_margin(w));const[we,re]=w.size;if(ae??this.do_convert_rgb?w=w.rgb():oe&&(w=w.grayscale()),this.do_resize&&(w=await this.resize(w)),this.do_thumbnail&&(w=await this.thumbnail(w,this.size,this.resample)),this.do_center_crop){let Ce,tt;Number.isInteger(this.crop_size)?(Ce=this.crop_size,tt=this.crop_size):(Ce=this.crop_size.width,tt=this.crop_size.height),w=await w.center_crop(Ce,tt)}const Te=[w.height,w.width];let ce=Float32Array.from(w.data),$e=[w.height,w.width,w.channels];if(this.do_rescale&&this.rescale(ce),x??this.do_normalize){let Ce=this.image_mean;Array.isArray(this.image_mean)||(Ce=new Array(w.channels).fill(Ce));let tt=this.image_std;if(Array.isArray(this.image_std)||(tt=new Array(w.channels).fill(Ce)),Ce.length!==w.channels||tt.length!==w.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Ce.length}) and \`image_std\` (${tt.length}) must match the number of channels in the image (${w.channels}).`);for(let Ge=0;Ge<ce.length;Ge+=w.channels)for(let ye=0;ye<w.channels;++ye)ce[Ge+ye]=(ce[Ge+ye]-Ce[ye])/tt[ye]}if(F??this.do_pad){if(this.pad_size)[ce,$e]=this.pad_image(ce,[w.height,w.width,w.channels],this.pad_size);else if(this.size_divisibility){const[Ce,tt]=M([$e[1],$e[0]],this.size_divisibility);[ce,$e]=this.pad_image(ce,$e,{width:Ce,height:tt})}}if(xe??this.do_flip_channel_order){if($e[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Ce=0;Ce<ce.length;Ce+=3){const tt=ce[Ce];ce[Ce]=ce[Ce+2],ce[Ce+2]=tt}}const Oe=new I.Tensor("float32",ce,$e).permute(2,0,1);return{original_size:[re,we],reshaped_input_size:Te,pixel_values:Oe}}async _call(w,...x){Array.isArray(w)||(w=[w]);const F=await Promise.all(w.map(oe=>this.preprocess(oe)));return{pixel_values:(0,I.stack)(F.map(oe=>oe.pixel_values),0),original_sizes:F.map(oe=>oe.original_size),reshaped_input_sizes:F.map(oe=>oe.reshaped_input_size)}}static async from_pretrained(w,x){const F=await(0,R.getModelJSON)(w,g.IMAGE_PROCESSOR_NAME,!0,x);return new this(F)}}},"./src/base/processing_utils.js":(Ee,$,r)=>{r.r($),r.d($,{Processor:()=>X});var f=r("./src/utils/constants.js"),I=r("./src/utils/generic.js"),N=r("./src/utils/hub.js");class X extends I.Callable{constructor(g,v){super(),this.config=g,this.components=v}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(g,v={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(g,{tokenize:!1,...v})}batch_decode(...g){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...g)}decode(...g){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...g)}async _call(g,...v){for(const M of[this.image_processor,this.feature_extractor,this.tokenizer])if(M)return M(g,...v);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(g,v){const[M,y]=await Promise.all([this.uses_processor_config?(0,N.getModelJSON)(g,f.PROCESSOR_NAME,!0,v):{},Promise.all(this.classes.filter(b=>b in this).map(async b=>{const O=await this[b].from_pretrained(g,v);return[b.replace(/_class$/,""),O]})).then(Object.fromEntries)]);return new this(M,y)}}_e(X,"classes",["image_processor_class","tokenizer_class","feature_extractor_class"]),_e(X,"uses_processor_config",!1)},"./src/configs.js":(Ee,$,r)=>{r.r($),r.d($,{AutoConfig:()=>v,PretrainedConfig:()=>g,getKeyValueShapes:()=>R});var 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f=r("./src/utils/generic.js");r("./src/utils/tensor.js");var I=r("./src/utils/maths.js");class N extends f.Callable{_call(w,x){throw Error("`_call` should be implemented in a subclass")}}class X extends f.Callable{_call(w,x){throw Error("`_call` should be implemented in a subclass")}}class R extends f.Callable{constructor(){super(),this.processors=[]}push(w){this.processors.push(w)}extend(w){this.processors.push(...w)}_call(w,x){let F=x;for(const ae of this.processors)F=ae(w,F);return F}[Symbol.iterator](){return this.processors.values()}}class g extends N{constructor(w){super(),this.bos_token_id=w}_call(w,x){for(let F=0;F<w.length;++F)if(w[F].length===1){const ae=x[F].data;ae.fill(-1/0),ae[this.bos_token_id]=0}return x}}class v extends N{constructor(w,x){super(),this.max_length=w,this.eos_token_id=Array.isArray(x)?x:[x]}_call(w,x){for(let F=0;F<w.length;++F)if(w[F].length===this.max_length-1){const ae=x[F].data;ae.fill(-1/0);for(const oe of this.eos_token_id)ae[oe]=0}return x}}class M extends N{constructor(w,x){super(),this.begin_suppress_tokens=w,this.begin_index=x}_call(w,x){for(let F=0;F<w.length;++F)if(w[F].length===this.begin_index){const ae=x[F].data;for(const oe of this.begin_suppress_tokens)ae[oe]=-1/0}return x}}class y extends N{constructor(w,x){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=x.length,x.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=w.max_initial_timestamp_index}_call(w,x){for(let F=0;F<w.length;++F){const ae=x[F].data;if(ae[this.no_timestamps_token_id]=-1/0,w[F].length===this.begin_index-1){ae.fill(-1/0),ae[this.timestamp_begin]=0;continue}const oe=w[F].slice(this.begin_index),xe=oe.length>=1&&oe[oe.length-1]>=this.timestamp_begin,we=oe.length<2||oe[oe.length-2]>=this.timestamp_begin;if(xe&&(we?ae.subarray(this.timestamp_begin).fill(-1/0):ae.subarray(0,this.eos_token_id).fill(-1/0)),w[F].length===this.begin_index&&this.max_initial_timestamp_index!==null){const $e=this.timestamp_begin+this.max_initial_timestamp_index;ae.subarray($e+1).fill(-1/0)}const re=(0,I.log_softmax)(ae),Te=Math.log(re.subarray(this.timestamp_begin).map(Math.exp).reduce(($e,Oe)=>$e+Oe)),ce=(0,I.max)(re.subarray(0,this.timestamp_begin))[0];Te>ce&&ae.subarray(0,this.timestamp_begin).fill(-1/0)}return x}}class b extends N{constructor(w){super(),this.no_repeat_ngram_size=w}getNgrams(w){const x=w.length,F=[];for(let oe=0;oe<x+1-this.no_repeat_ngram_size;++oe){const xe=[];for(let we=0;we<this.no_repeat_ngram_size;++we)xe.push(w[oe+we]);F.push(xe.map(Number))}const ae=new Map;for(const oe of F){const 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Got batch size ${x.dims[0]} for the logits and ${w.length} for the input ids.`);const F=w.length,ae=x.slice([0,F],null),oe=x.slice([F,x.dims[0]],null);for(let xe=0;xe<oe.data.length;++xe)oe.data[xe]+=(ae.data[xe]-oe.data[xe])*this.guidance_scale;return oe}}class U extends X{constructor(w){super(),this.temperature=w}_call(w,x){const F=x.data;for(let ae=0;ae<F.length;++ae)F[ae]/=this.temperature;return x}}class q extends X{constructor(w,{filter_value:x=-1/0,min_tokens_to_keep:F=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(F)||F<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${F}`);this.top_p=w,this.filter_value=x,this.min_tokens_to_keep=F}}class A extends X{constructor(w,{filter_value:x=-1/0,min_tokens_to_keep:F=1}={}){if(super(),!Number.isInteger(w)||w<0)throw new Error(`\`top_k\` must be a positive integer, but is ${w}`);this.top_k=Math.max(w,F),this.filter_value=x}}},"./src/generation/logits_sampler.js":(Ee,$,r)=>{r.r($),r.d($,{LogitsSampler:()=>X});var f=r("./src/utils/generic.js"),I=r("./src/utils/tensor.js"),N=r("./src/utils/maths.js");r("./src/generation/configuration_utils.js");class X extends f.Callable{constructor(y){super(),this.generation_config=y}async _call(y){return this.sample(y)}async sample(y){throw Error("sample should be implemented in subclasses.")}getLogits(y,b){let O=y.dims.at(-1),H=y.data;if(b===-1)H=H.slice(-O);else{let se=b*O;H=H.slice(se,se+O)}return H}randomSelect(y){let b=0;for(let H=0;H<y.length;++H)b+=y[H];let O=Math.random()*b;for(let H=0;H<y.length;++H)if(O-=y[H],O<=0)return H;return 0}static getSampler(y){if(y.do_sample)return new g(y);if(y.num_beams>1)return new v(y);if(y.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${y.num_return_sequences}.`);return new R(y)}}class R extends X{async sample(y){const b=(0,N.max)(y.data)[1];return[[BigInt(b),0]]}}class g extends X{async sample(y){let b=y.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[O,H]=await(0,I.topk)(y,b),se=(0,N.softmax)(O.data);return Array.from({length:this.generation_config.num_beams},()=>{const ne=this.randomSelect(se);return[H.data[ne],Math.log(se[ne])]})}}class v extends X{async sample(y){let b=y.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[O,H]=await(0,I.topk)(y,b),se=(0,N.softmax)(O.data);return Array.from({length:this.generation_config.num_beams},(ne,W)=>[H.data[W],Math.log(se[W])])}}},"./src/generation/stopping_criteria.js":(Ee,$,r)=>{r.r($),r.d($,{EosTokenCriteria:()=>R,InterruptableStoppingCriteria:()=>g,MaxLengthCriteria:()=>X,StoppingCriteria:()=>I,StoppingCriteriaList:()=>N});var f=r("./src/utils/generic.js");class I extends f.Callable{_call(M,y){throw Error("StoppingCriteria needs to be subclassed")}}class N extends f.Callable{constructor(){super(),this.criteria=[]}push(M){this.criteria.push(M)}extend(M){M instanceof N?M=M.criteria:M instanceof I&&(M=[M]),this.criteria.push(...M)}_call(M,y){const b=new Array(M.length).fill(!1);for(const O of this.criteria){const H=O(M,y);for(let se=0;se<b.length;++se)b[se]||(b[se]=H[se])}return b}[Symbol.iterator](){return this.criteria.values()}}class X extends I{constructor(M,y=null){super(),this.max_length=M,this.max_position_embeddings=y}_call(M){return M.map(y=>y.length>=this.max_length)}}class R extends I{constructor(M){super(),Array.isArray(M)||(M=[M]),this.eos_token_id=M}_call(M,y){return M.map(b=>{const O=b.at(-1);return this.eos_token_id.some(H=>O==H)})}}class g extends I{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(M,y){return new Array(M.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(Ee,$,r)=>{r.r($),r.d($,{BaseStreamer:()=>X,TextStreamer:()=>g,WhisperTextStreamer:()=>v});var f=r("./src/utils/core.js"),I=r("./src/tokenizers.js"),N=r("./src/env.js");class X{put(y){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const R=N.apis.IS_PROCESS_AVAILABLE?M=>process.stdout.write(M):M=>console.log(M);class g extends X{constructor(y,{skip_prompt:b=!1,callback_function:O=null,token_callback_function:H=null,skip_special_tokens:se=!0,decode_kwargs:ne={},...W}={}){super(),this.tokenizer=y,this.skip_prompt=b,this.callback_function=O??R,this.token_callback_function=H,this.decode_kwargs={skip_special_tokens:se,...ne,...W},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(y){var se;if(y.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 b=y[0];(se=this.token_callback_function)==null||se.call(this,b),this.token_cache=(0,f.mergeArrays)(this.token_cache,b);const O=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let H;O.endsWith(`
`)?(H=O.slice(this.print_len),this.token_cache=[],this.print_len=0):O.length>0&&(0,I.is_chinese_char)(O.charCodeAt(O.length-1))?(H=O.slice(this.print_len),this.print_len+=H.length):(H=O.slice(this.print_len,O.lastIndexOf(" ")+1),this.print_len+=H.length),this.on_finalized_text(H,!1)}end(){let y;this.token_cache.length>0?(y=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):y="",this.next_tokens_are_prompt=!0,this.on_finalized_text(y,!0)}on_finalized_text(y,b){var O,H;y.length>0&&((O=this.callback_function)==null||O.call(this,y)),b&&this.callback_function===R&&N.apis.IS_PROCESS_AVAILABLE&&((H=this.callback_function)==null||H.call(this,`
`))}}class v extends g{constructor(y,{skip_prompt:b=!1,callback_function:O=null,token_callback_function:H=null,on_chunk_start:se=null,on_chunk_end:ne=null,on_finalize:W=null,time_precision:U=.02,skip_special_tokens:q=!0,decode_kwargs:A={}}={}){super(y,{skip_prompt:b,skip_special_tokens:q,callback_function:O,token_callback_function:H,decode_kwargs:A}),this.timestamp_begin=y.timestamp_begin,this.on_chunk_start=se,this.on_chunk_end=ne,this.on_finalize=W,this.time_precision=U,this.waiting_for_timestamp=!1}put(y){var O,H;if(y.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const b=y[0];if(b.length===1){const se=Number(b[0])-this.timestamp_begin;if(se>=0){const ne=se*this.time_precision;this.waiting_for_timestamp?(O=this.on_chunk_end)==null||O.call(this,ne):(H=this.on_chunk_start)==null||H.call(this,ne),this.waiting_for_timestamp=!this.waiting_for_timestamp,y=[[]]}}return super.put(y)}end(){var 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Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");fe.token_type_ids=(0,b.zeros_like)(fe.input_ids)}if(j.inputNames.includes("pixel_mask")&&!fe.pixel_mask){if(!fe.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const Fe=fe.pixel_values.dims;fe.pixel_mask=(0,b.ones)([Fe[0],Fe[2],Fe[3]])}return await we(j,fe)}async function Ce(_,T,j=!1){const fe=_.sessions[j?"decoder_model_merged":"model"],{past_key_values:Fe,...De}=T;if(fe.inputNames.includes("use_cache_branch")&&(De.use_cache_branch=ce(!!Fe)),fe.inputNames.includes("position_ids")&&De.attention_mask&&!De.position_ids){const rt=_.config.model_type==="paligemma"?1:0;De.position_ids=J(De,Fe,rt)}_.addPastKeyValues(De,Fe);const Ze=(0,R.pick)(De,fe.inputNames);return await we(fe,Ze)}function tt({image_token_id:_,inputs_embeds:T,image_features:j,input_ids:fe,attention_mask:Fe}){const De=fe.tolist().map(bt=>bt.reduce((Rt,Wt,Dt)=>(Wt==_&&Rt.push(Dt),Rt),[])),Ze=De.reduce((bt,Rt)=>bt+Rt.length,0),rt=j.dims[0];if(Ze!==rt)throw new Error(`Image features and image tokens do not match: tokens: ${Ze}, features ${rt}`);let ft=0;for(let bt=0;bt<De.length;++bt){const Rt=De[bt],Wt=T[bt];for(let Dt=0;Dt<Rt.length;++Dt)Wt[Rt[Dt]].data.set(j[ft++].data)}return{inputs_embeds:T,attention_mask:Fe}}async function Ge(_,{input_ids:T=null,attention_mask:j=null,pixel_values:fe=null,position_ids:Fe=null,inputs_embeds:De=null,past_key_values:Ze=null,generation_config:rt=null,logits_processor:ft=null,...bt}){if(!De){if(De=await _.encode_text({input_ids:T,...bt}),fe&&T.dims[1]!==1){const Wt=await _.encode_image({pixel_values:fe,...bt});({inputs_embeds:De,attention_mask:j}=_._merge_input_ids_with_image_features({image_features:Wt,inputs_embeds:De,input_ids:T,attention_mask:j}))}else if(Ze&&fe&&T.dims[1]===1){const Wt=T.dims[1],Dt=Object.values(Ze)[0].dims.at(-2);j=(0,b.cat)([(0,b.ones)([T.dims[0],Dt]),j.slice(null,[j.dims[1]-Wt,j.dims[1]])],1)}}if(!Fe&&_.config.model_type==="qwen2_vl"){const{image_grid_thw:Wt,video_grid_thw:Dt}=bt;[Fe]=_.get_rope_index(T,Wt,Dt,j)}return await Ce(_,{inputs_embeds:De,past_key_values:Ze,attention_mask:j,position_ids:Fe,generation_config:rt,logits_processor:ft},!0)}function ye(_,T=0){const[j,fe]=_.dims,Fe=_.data,De=new BigInt64Array(Fe.length);for(let Ze=0;Ze<j;++Ze){const rt=Ze*fe;let ft=BigInt(T);for(let bt=0;bt<fe;++bt){const Rt=rt+bt;Fe[Rt]===0n?De[Rt]=BigInt(1):(De[Rt]=ft,ft+=Fe[Rt])}}return{data:De,dims:_.dims}}function J(_,T=null,j=0){const{input_ids:fe,inputs_embeds:Fe,attention_mask:De}=_,{data:Ze,dims:rt}=ye(De,j);let ft=new b.Tensor("int64",Ze,rt);if(T){const bt=-(fe??Fe).dims.at(1);ft=ft.slice(null,[bt,null])}return ft}function de(_,T,j,fe){if(j.past_key_values){const Fe=Object.values(j.past_key_values)[0].dims.at(-2),{input_ids:De,attention_mask:Ze}=j;if(!(Ze&&Ze.dims[1]>De.dims[1])){if(Fe<De.dims[1])j.input_ids=De.slice(null,[Fe,null]);else if(_.config.image_token_index!=null&&De.data.some(rt=>rt==_.config.image_token_index)){const rt=_.config.num_image_tokens;if(!rt)throw new Error("`num_image_tokens` is missing in the model configuration.");const ft=De.dims[1]-(Fe-rt);j.input_ids=De.slice(null,[-ft,null]),j.attention_mask=(0,b.ones)([1,Fe+ft])}}}return j}function ke(_,T,j,fe){return j.past_key_values&&(T=T.map(Fe=>[Fe.at(-1)])),{...j,decoder_input_ids:Te(T)}}function Be(_,...T){return _.config.is_encoder_decoder?ke(_,...T):de(_,...T)}function Je(_,T,j,fe){const Fe=!!j.past_key_values;return fe.guidance_scale!==null&&fe.guidance_scale>1&&(Fe?j.input_ids=(0,b.cat)([j.input_ids,j.input_ids],0):(j.input_ids=(0,b.cat)([j.input_ids,(0,b.full_like)(j.input_ids,BigInt(fe.pad_token_id))],0),j.attention_mask=(0,b.cat)([j.attention_mask,(0,b.full_like)(j.attention_mask,0n)],0))),(Fe||!j.pixel_values)&&(j.pixel_values=(0,b.full)([0,0,3,384,384],1)),Fe&&(j.images_seq_mask=new b.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),j.images_emb_mask=new b.Tensor("bool",new Array(0).fill(!1),[1,1,0])),j}class te extends X.Callable{constructor(j,fe,Fe){super();_e(this,"main_input_name","input_ids");_e(this,"forward_params",["input_ids","attention_mask"]);this.config=j,this.sessions=fe,this.configs=Fe;const De=x.get(this.constructor),Ze=S.get(De);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ze){case A.DecoderOnly:this.can_generate=!0,this._forward=Ce,this._prepare_inputs_for_generation=de;break;case A.Seq2Seq:case A.Vision2Seq:case A.Musicgen:this.can_generate=!0,this._forward=$e,this._prepare_inputs_for_generation=ke;break;case A.EncoderDecoder:this._forward=$e;break;case A.ImageTextToText:this.can_generate=!0,this._forward=Ge,this._prepare_inputs_for_generation=Be;break;case A.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=Be;break;case A.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=Je;break;default:this._forward=Oe;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var fe;const j=[];for(const Fe of Object.values(this.sessions))(fe=Fe==null?void 0:Fe.handler)!=null&&fe.dispose&&j.push(Fe.handler.dispose());return await Promise.all(j)}static async from_pretrained(j,{progress_callback:fe=null,config:Fe=null,cache_dir:De=null,local_files_only:Ze=!1,revision:rt="main",model_file_name:ft=null,subfolder:bt="onnx",device:Rt=null,dtype:Wt=null,use_external_data_format:Dt=null,session_options:Gt={}}={}){let es={progress_callback:fe,config:Fe,cache_dir:De,local_files_only:Ze,revision:rt,model_file_name:ft,subfolder:bt,device:Rt,dtype:Wt,use_external_data_format:Dt,session_options:Gt};const ns=x.get(this),Yt=S.get(ns);Fe=es.config=await f.AutoConfig.from_pretrained(j,es);let as;if(Yt===A.DecoderOnly)as=await Promise.all([ae(j,{model:es.model_file_name??"model"},es),oe(j,{generation_config:"generation_config.json"},es)]);else if(Yt===A.Seq2Seq||Yt===A.Vision2Seq)as=await Promise.all([ae(j,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},es),oe(j,{generation_config:"generation_config.json"},es)]);else if(Yt===A.MaskGeneration)as=await Promise.all([ae(j,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},es)]);else if(Yt===A.EncoderDecoder)as=await Promise.all([ae(j,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},es)]);else if(Yt===A.ImageTextToText){const Es={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Fe.is_encoder_decoder&&(Es.model="encoder_model"),as=await Promise.all([ae(j,Es,es),oe(j,{generation_config:"generation_config.json"},es)])}else if(Yt===A.Musicgen)as=await Promise.all([ae(j,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},es),oe(j,{generation_config:"generation_config.json"},es)]);else if(Yt===A.MultiModality)as=await Promise.all([ae(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"},es),oe(j,{generation_config:"generation_config.json"},es)]);else if(Yt===A.Phi3V)as=await Promise.all([ae(j,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},es),oe(j,{generation_config:"generation_config.json"},es)]);else{if(Yt!==A.EncoderOnly){const Es=ns??(Fe==null?void 0:Fe.model_type);Es!=="custom"&&console.warn(`Model type for '${Es}' not found, assuming encoder-only architecture. Please report this at ${v.GITHUB_ISSUE_URL}.`)}as=await Promise.all([ae(j,{model:es.model_file_name??"model"},es)])}return new this(Fe,...as)}async _call(j){return await this.forward(j)}async forward(j){return await this._forward(this,j)}get generation_config(){var j;return((j=this.configs)==null?void 0:j.generation_config)??null}_get_logits_warper(j){const fe=new M.LogitsProcessorList;return j.temperature!==null&&j.temperature!==1&&fe.push(new M.TemperatureLogitsWarper(j.temperature)),j.top_k!==null&&j.top_k!==0&&fe.push(new M.TopKLogitsWarper(j.top_k)),j.top_p!==null&&j.top_p<1&&fe.push(new M.TopPLogitsWarper(j.top_p)),fe}_get_logits_processor(j,fe,Fe=null){const De=new M.LogitsProcessorList;if(j.repetition_penalty!==null&&j.repetition_penalty!==1&&De.push(new M.RepetitionPenaltyLogitsProcessor(j.repetition_penalty)),j.no_repeat_ngram_size!==null&&j.no_repeat_ngram_size>0&&De.push(new M.NoRepeatNGramLogitsProcessor(j.no_repeat_ngram_size)),j.bad_words_ids!==null&&De.push(new M.NoBadWordsLogitsProcessor(j.bad_words_ids,j.eos_token_id)),j.min_length!==null&&j.eos_token_id!==null&&j.min_length>0&&De.push(new M.MinLengthLogitsProcessor(j.min_length,j.eos_token_id)),j.min_new_tokens!==null&&j.eos_token_id!==null&&j.min_new_tokens>0&&De.push(new M.MinNewTokensLengthLogitsProcessor(fe,j.min_new_tokens,j.eos_token_id)),j.forced_bos_token_id!==null&&De.push(new M.ForcedBOSTokenLogitsProcessor(j.forced_bos_token_id)),j.forced_eos_token_id!==null&&De.push(new M.ForcedEOSTokenLogitsProcessor(j.max_length,j.forced_eos_token_id)),j.begin_suppress_tokens!==null){const Ze=fe>1||j.forced_bos_token_id===null?fe:fe+1;De.push(new M.SuppressTokensAtBeginLogitsProcessor(j.begin_suppress_tokens,Ze))}return j.guidance_scale!==null&&j.guidance_scale>1&&De.push(new M.ClassifierFreeGuidanceLogitsProcessor(j.guidance_scale)),Fe!==null&&De.extend(Fe),De}_prepare_generation_config(j,fe,Fe=y.GenerationConfig){const De={...this.config};for(const rt of["decoder","generator","text_config"])rt in De&&Object.assign(De,De[rt]);const Ze=new Fe(De);return Object.assign(Ze,this.generation_config??{}),j&&Object.assign(Ze,j),fe&&Object.assign(Ze,(0,R.pick)(fe,Object.getOwnPropertyNames(Ze))),Ze}_get_stopping_criteria(j,fe=null){const Fe=new se.StoppingCriteriaList;return j.max_length!==null&&Fe.push(new se.MaxLengthCriteria(j.max_length,this.config.max_position_embeddings??null)),j.eos_token_id!==null&&Fe.push(new se.EosTokenCriteria(j.eos_token_id)),fe&&Fe.extend(fe),Fe}_validate_model_class(){if(!this.can_generate){const j=[xa,Pa,Ao,Vd],fe=x.get(this.constructor),Fe=new Set,De=this.config.model_type;for(const rt of j){const ft=rt.get(De);ft&&Fe.add(ft[0])}let Ze=`The current model class (${fe}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Fe.size>0&&(Ze+=` Please use the following class instead: ${[...Fe].join(", ")}`),Error(Ze)}}prepare_inputs_for_generation(...j){return this._prepare_inputs_for_generation(this,...j)}_update_model_kwargs_for_generation({generated_input_ids:j,outputs:fe,model_inputs:Fe,is_encoder_decoder:De}){return Fe.past_key_values=this.getPastKeyValues(fe,Fe.past_key_values),Fe.input_ids=new b.Tensor("int64",j.flat(),[j.length,1]),De||(Fe.attention_mask=(0,b.cat)([Fe.attention_mask,(0,b.ones)([Fe.attention_mask.dims[0],1])],1)),Fe.position_ids=null,Fe}_prepare_model_inputs({inputs:j,bos_token_id:fe,model_kwargs:Fe}){const De=(0,R.pick)(Fe,this.forward_params),Ze=this.main_input_name;if(Ze in De){if(j)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else De[Ze]=j;return{inputs_tensor:De[Ze],model_inputs:De,model_input_name:Ze}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:j,model_inputs:fe,model_input_name:Fe,generation_config:De}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!fe.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:rt,pixel_values:ft,attention_mask:bt,...Rt}=fe,Wt=await this._prepare_inputs_embeds(fe);fe={...Rt,...(0,R.pick)(Wt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ze}=await Oe(this,fe);if(De.guidance_scale!==null&&De.guidance_scale>1)Ze=(0,b.cat)([Ze,(0,b.full_like)(Ze,0)],0),"attention_mask"in fe&&(fe.attention_mask=(0,b.cat)([fe.attention_mask,(0,b.zeros_like)(fe.attention_mask)],0));else if(fe.decoder_input_ids){const rt=Te(fe.decoder_input_ids).dims[0];if(rt!==Ze.dims[0]){if(Ze.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ze.dims[0]}) than the decoder inputs (${rt}).`);Ze=(0,b.cat)(Array.from({length:rt},()=>Ze),0)}}return fe.encoder_outputs=Ze,fe}_prepare_decoder_input_ids_for_generation({batch_size:j,model_input_name:fe,model_kwargs:Fe,decoder_start_token_id:De,bos_token_id:Ze,generation_config:rt}){let{decoder_input_ids:ft,...bt}=Fe;if(!(ft instanceof b.Tensor)){if(ft)Array.isArray(ft[0])||(ft=Array.from({length:j},()=>ft));else if(De??(De=Ze),this.config.model_type==="musicgen")ft=Array.from({length:j*this.config.decoder.num_codebooks},()=>[De]);else if(Array.isArray(De)){if(De.length!==j)throw new Error(`\`decoder_start_token_id\` expcted to have length ${j} but got ${De.length}`);ft=De}else ft=Array.from({length:j},()=>[De]);ft=Te(ft)}return Fe.decoder_attention_mask=(0,b.ones_like)(ft),{input_ids:ft,model_inputs:bt}}async generate({inputs:j=null,generation_config:fe=null,logits_processor:Fe=null,stopping_criteria:De=null,streamer:Ze=null,...rt}){this._validate_model_class(),fe=this._prepare_generation_config(fe,rt);let{inputs_tensor:ft,model_inputs:bt,model_input_name:Rt}=this._prepare_model_inputs({inputs:j,model_kwargs:rt});const Wt=this.config.is_encoder_decoder;Wt&&("encoder_outputs"in bt||(bt=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:ft,model_inputs:bt,model_input_name:Rt,generation_config:fe})));let Dt;Wt?{input_ids:Dt,model_inputs:bt}=this._prepare_decoder_input_ids_for_generation({batch_size:bt[Rt].dims.at(0),model_input_name:Rt,model_kwargs:bt,decoder_start_token_id:fe.decoder_start_token_id,bos_token_id:fe.bos_token_id,generation_config:fe}):Dt=bt[Rt];let Gt=Dt.dims.at(-1);fe.max_new_tokens!==null&&(fe.max_length=Gt+fe.max_new_tokens);const es=this._get_logits_processor(fe,Gt,Fe),ns=this._get_stopping_criteria(fe,De),Yt=bt[Rt].dims.at(0),as=ne.LogitsSampler.getSampler(fe),Es=new Array(Yt).fill(0),xs=Dt.tolist();Ze&&Ze.put(xs);let cs,Ts={};for(;;){if(bt=this.prepare_inputs_for_generation(xs,bt,fe),cs=await this.forward(bt),fe.output_attentions&&fe.return_dict_in_generate){const ur=this.getAttentions(cs);for(const Mr in ur)Mr in Ts||(Ts[Mr]=[]),Ts[Mr].push(ur[Mr])}const Hs=cs.logits.slice(null,-1,null),Er=es(xs,Hs),En=[];for(let ur=0;ur<Er.dims.at(0);++ur){const Mr=Er[ur],Oo=await as(Mr);for(const[Qn,Fo]of Oo){const Do=BigInt(Qn);Es[ur]+=Fo,xs[ur].push(Do),En.push([Do]);break}}if(Ze&&Ze.put(En),ns(xs).every(ur=>ur))break;bt=this._update_model_kwargs_for_generation({generated_input_ids:En,outputs:cs,model_inputs:bt,is_encoder_decoder:Wt})}Ze&&Ze.end();const Is=this.getPastKeyValues(cs,bt.past_key_values,!0),tr=new b.Tensor("int64",xs.flat(),[xs.length,xs[0].length]);if(fe.return_dict_in_generate)return{sequences:tr,past_key_values:Is,...Ts};for(const Hs of Object.values(cs))Hs.location==="gpu-buffer"&&Hs.dispose();return tr}getPastKeyValues(j,fe,Fe=!1){const De=Object.create(null);for(const Ze in j)if(Ze.startsWith("present")){const rt=Ze.replace("present","past_key_values"),ft=Ze.includes("encoder");if(ft&&fe?De[rt]=fe[rt]:De[rt]=j[Ze],fe&&(!ft||Fe)){const bt=fe[rt];bt.location==="gpu-buffer"&&bt.dispose()}}return De}getAttentions(j){const fe={};for(const Fe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const De in j)De.startsWith(Fe)&&(Fe in fe||(fe[Fe]=[]),fe[Fe].push(j[De]));return fe}addPastKeyValues(j,fe){var Fe,De,Ze;if(fe)Object.assign(j,fe);else{const rt=this.sessions.decoder_model_merged??this.sessions.model,ft=((Fe=rt==null?void 0:rt.config)==null?void 0:Fe.kv_cache_dtype)??"float32",bt=ft==="float16"?new Uint16Array:[],Rt=((Ze=(De=j[this.main_input_name]??j.attention_mask)==null?void 0:De.dims)==null?void 0:Ze[0])??1,Wt=(0,f.getKeyValueShapes)(this.config,{batch_size:Rt});for(const Dt in Wt)j[Dt]=new b.Tensor(ft,bt,Wt[Dt])}}async encode_image({pixel_values:j}){const fe=(await we(this.sessions.vision_encoder,{pixel_values:j})).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 (${fe.dims[1]}).`),this.config.num_image_tokens=fe.dims[1]),fe}async encode_text({input_ids:j}){return(await we(this.sessions.embed_tokens,{input_ids:j})).inputs_embeds}}class Ke{}class Ue extends Ke{constructor({last_hidden_state:T,hidden_states:j=null,attentions:fe=null}){super(),this.last_hidden_state=T,this.hidden_states=j,this.attentions=fe}}class le extends te{}class be extends le{}class Ve extends le{async _call(T){return new Ys(await super._call(T))}}class We extends le{async _call(T){return new Zt(await super._call(T))}}class Ne extends le{async _call(T){return new Ds(await super._call(T))}}class je extends le{async _call(T){return new er(await super._call(T))}}class st extends te{}class ut extends st{}class pt extends st{async _call(T){return new Ys(await super._call(T))}}class lt extends st{async _call(T){return new Zt(await super._call(T))}}class mt extends st{async _call(T){return new Ds(await super._call(T))}}class L extends te{}class ie extends L{}class G extends te{}class he extends G{}class Se extends G{async _call(T){return new Ys(await super._call(T))}}class Re extends G{async _call(T){return new Zt(await super._call(T))}}class qe extends G{async _call(T){return new Ds(await super._call(T))}}class at extends G{async _call(T){return new er(await super._call(T))}}class ct extends te{}class vt extends ct{}class kt extends ct{async _call(T){return new Ys(await super._call(T))}}class $t extends ct{async _call(T){return new Zt(await super._call(T))}}class os extends ct{async _call(T){return new Ds(await super._call(T))}}class Ms extends ct{async _call(T){return new er(await super._call(T))}}class ks extends te{}class Ls extends ks{}class sr extends ks{async _call(T){return new Ys(await super._call(T))}}class Cr extends ks{async _call(T){return new Zt(await super._call(T))}}class Yr extends ks{async _call(T){return new Ds(await super._call(T))}}class Us extends ks{async _call(T){return new er(await super._call(T))}}class vr extends te{}class Nt extends vr{}class Jr extends vr{async _call(T){return new Ys(await super._call(T))}}class kr extends vr{async _call(T){return new Zt(await super._call(T))}}class Sr extends vr{async _call(T){return new Ds(await super._call(T))}}class Zr extends vr{async _call(T){return new er(await super._call(T))}}class dr extends te{}class Br extends dr{}class $r extends dr{async _call(T){return new Ys(await super._call(T))}}class Rr extends dr{async _call(T){return new Zt(await super._call(T))}}class Nr extends dr{async _call(T){return new Ds(await super._call(T))}}class ir extends dr{async _call(T){return new er(await super._call(T))}}class ot extends te{}class xt extends ot{}class Ft extends ot{async _call(T){return new Ys(await super._call(T))}}class Vs extends ot{async _call(T){return new Zt(await super._call(T))}}class jr extends ot{async _call(T){return new Ds(await super._call(T))}}class Ar extends ot{async _call(T){return new er(await super._call(T))}}class bs extends te{}class ar extends bs{}class Os extends bs{async _call(T){return new Zt(await super._call(T))}}class xr extends bs{async _call(T){return new Ds(await super._call(T))}}class ss extends bs{async _call(T){return new er(await super._call(T))}}class gn extends bs{async _call(T){return new Ys(await super._call(T))}}class Ur extends te{}class oo extends Ur{}class On extends Ur{async _call(T){return new Ys(await super._call(T))}}class Fn extends Ur{async _call(T){return new Zt(await super._call(T))}}class Dn extends Ur{async _call(T){return new Ds(await super._call(T))}}class Vr extends te{}class Ln extends Vr{}class io extends Vr{async _call(T){return new Ys(await super._call(T))}}class Wr extends Vr{async _call(T){return new Zt(await super._call(T))}}class mr extends Vr{async _call(T){return new er(await super._call(T))}}class lr extends te{}class wn extends lr{}class en extends lr{async _call(T){return new Ys(await super._call(T))}}class yn extends lr{async _call(T){return new Zt(await super._call(T))}}class Mn extends lr{async _call(T){return new Ds(await super._call(T))}}class bn extends lr{async _call(T){return new er(await super._call(T))}}class Lt extends te{}class vn extends Lt{}class zn extends Lt{async _call(T){return new Ys(await super._call(T))}}class Bn extends Lt{async _call(T){return new Zt(await super._call(T))}}class Rn extends Lt{async _call(T){return new er(await super._call(T))}}class Gr extends te{}class Nn extends Gr{}class xn extends Gr{async _call(T){return new Zt(await super._call(T))}}class jn extends Gr{async _call(T){return new er(await super._call(T))}}class is extends Gr{async _call(T){return new Ys(await super._call(T))}}class Pe extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class E extends Pe{}class Q extends Pe{}class ue extends te{}class ve extends ue{}class Ae extends ue{}class Xe extends te{}class ht extends Xe{}class gt extends Xe{}class _t extends te{}class Tt extends _t{}class Kt extends _t{}class _s extends _t{async _call(T){return new Zt(await super._call(T))}}class us extends te{}class Fs extends us{}class zt extends us{}class rs extends us{async _call(T){return new Zt(await super._call(T))}}class rr extends us{}class Ws extends te{}class ze extends Ws{}class Zs extends Ws{}class Ir extends te{}class Ss extends Ir{}class Xs extends Ir{}class Ot extends te{}class or extends Ot{}class _r extends Ot{async _call(T){return new Ys(await super._call(T))}}class fs extends Ot{async _call(T){return new Zt(await super._call(T))}}class $s extends Ot{async _call(T){return new Ds(await super._call(T))}}class Mt extends Ot{async _call(T){return new er(await super._call(T))}}class Xt extends te{}class zs extends Xt{}class As extends Xt{async _call(T){return new Ys(await super._call(T))}}class Gs extends Xt{async _call(T){return new Zt(await super._call(T))}}class St extends Xt{async _call(T){return new Ds(await super._call(T))}}class tn extends Xt{async _call(T){return new er(await super._call(T))}}class Qe extends te{}class At extends Qe{}class ja extends Qe{async _call(T){return new Ys(await super._call(T))}}class Wo extends Qe{async _call(T){return new Zt(await super._call(T))}}class Ua extends Qe{async _call(T){return new Ds(await super._call(T))}}class Va extends Qe{async _call(T){return new er(await super._call(T))}}class Jt extends te{}class Wa extends Jt{}class Go extends Jt{}class Ko extends te{constructor(){super(...arguments);_e(this,"requires_attention_mask",!1);_e(this,"main_input_name","input_features");_e(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Ga extends Ko{}class Ka extends Ko{_prepare_generation_config(T,j){return super._prepare_generation_config(T,j,U.WhisperGenerationConfig)}_retrieve_init_tokens(T){const j=[T.decoder_start_token_id];let fe=T.language;const Fe=T.task;if(T.is_multilingual){fe||(console.warn("No language specified - defaulting to English (en)."),fe="en");const Ze=`<|${(0,q.whisper_language_to_code)(fe)}|>`;j.push(T.lang_to_id[Ze]),j.push(T.task_to_id[Fe??"transcribe"])}else if(fe||Fe)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!T.return_timestamps&&T.no_timestamps_token_id&&j.at(-1)!==T.no_timestamps_token_id?j.push(T.no_timestamps_token_id):T.return_timestamps&&j.at(-1)===T.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),j.pop()),j.filter(De=>De!=null)}async generate({inputs:T=null,generation_config:j=null,logits_processor:fe=null,stopping_criteria:Fe=null,...De}){j=this._prepare_generation_config(j,De);const Ze=De.decoder_input_ids??this._retrieve_init_tokens(j);if(j.return_timestamps&&(fe??(fe=new M.LogitsProcessorList),fe.push(new M.WhisperTimeStampLogitsProcessor(j,Ze))),j.begin_suppress_tokens&&(fe??(fe=new M.LogitsProcessorList),fe.push(new M.SuppressTokensAtBeginLogitsProcessor(j.begin_suppress_tokens,Ze.length))),j.return_token_timestamps){if(!j.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.");j.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),j.output_attentions=!0,j.return_dict_in_generate=!0}const rt=await super.generate({inputs:T,generation_config:j,logits_processor:fe,decoder_input_ids:Ze,...De});return j.return_token_timestamps&&(rt.token_timestamps=this._extract_token_timestamps(rt,j.alignment_heads,j.num_frames)),rt}_extract_token_timestamps(T,j,fe=null,Fe=.02){if(!T.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`.");fe==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 De=this.config.median_filter_width;De===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),De=7);const Ze=T.cross_attentions,rt=Array.from({length:this.config.decoder_layers},(ns,Yt)=>(0,b.cat)(Ze.map(as=>as[Yt]),2)),ft=(0,b.stack)(j.map(([ns,Yt])=>{if(ns>=rt.length)throw new Error(`Layer index ${ns} is out of bounds for cross attentions (length ${rt.length}).`);return fe?rt[ns].slice(null,Yt,null,[0,fe]):rt[ns].slice(null,Yt)})).transpose(1,0,2,3),[bt,Rt]=(0,b.std_mean)(ft,-2,0,!0),Wt=ft.clone();for(let ns=0;ns<Wt.dims[0];++ns){const Yt=Wt[ns];for(let as=0;as<Yt.dims[0];++as){const Es=Yt[as],xs=bt[ns][as][0].data,cs=Rt[ns][as][0].data;for(let Ts=0;Ts<Es.dims[0];++Ts){let Is=Es[Ts].data;for(let tr=0;tr<Is.length;++tr)Is[tr]=(Is[tr]-cs[tr])/xs[tr];Is.set((0,H.medianFilter)(Is,De))}}}const Dt=[(0,b.mean)(Wt,1)],Gt=T.sequences.dims,es=new b.Tensor("float32",new Float32Array(Gt[0]*Gt[1]),Gt);for(let ns=0;ns<Gt[0];++ns){const Yt=Dt[ns].neg().squeeze_(0),[as,Es]=(0,H.dynamic_time_warping)(Yt.tolist()),xs=Array.from({length:as.length-1},(Is,tr)=>as[tr+1]-as[tr]),cs=(0,R.mergeArrays)([1],xs).map(Is=>!!Is),Ts=[];for(let Is=0;Is<cs.length;++Is)cs[Is]&&Ts.push(Es[Is]*Fe);es[ns].data.set(Ts,1)}return es}}class Ho extends te{constructor(){super(...arguments);_e(this,"requires_attention_mask",!1);_e(this,"main_input_name","input_values");_e(this,"forward_params",["input_values","decoder_input_ids","past_key_values"])}}class cr extends Ho{}class Ha extends Ho{}class qo extends te{constructor(){super(...arguments);_e(this,"main_input_name","pixel_values");_e(this,"forward_params",["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"])}}class Kr extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class ao extends Kr{_merge_input_ids_with_image_features({inputs_embeds:T,image_features:j,input_ids:fe,attention_mask:Fe}){const De=this.config.image_token_index,rt=fe.tolist().map(Dt=>Dt.findIndex(Gt=>Gt==De)),ft=rt.every(Dt=>Dt===-1),bt=rt.every(Dt=>Dt!==-1);if(!ft&&!bt)throw new Error("Every input should contain either 0 or 1 image token.");if(ft)return{inputs_embeds:T,attention_mask:Fe};const Rt=[],Wt=[];for(let Dt=0;Dt<rt.length;++Dt){const Gt=rt[Dt],es=T[Dt],ns=j[Dt],Yt=Fe[Dt];Rt.push((0,b.cat)([es.slice([0,Gt]),ns,es.slice([Gt+1,es.dims[0]])],0)),Wt.push((0,b.cat)([Yt.slice([0,Gt]),(0,b.ones)([ns.dims[0]]),Yt.slice([Gt+1,Yt.dims[0]])],0))}return{inputs_embeds:(0,b.stack)(Rt,0),attention_mask:(0,b.stack)(Wt,0)}}}class qa extends ao{}class Qa extends ao{}class Xa extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);_e(this,"main_input_name","inputs_embeds")}}class Ya extends Xa{_merge_input_ids_with_image_features({inputs_embeds:T,image_features:j,input_ids:fe,attention_mask:Fe}){return{inputs_embeds:(0,b.cat)([j,T],1),attention_mask:(0,b.cat)([(0,b.ones)(j.dims.slice(0,2)),Fe],1)}}async _prepare_inputs_embeds({input_ids:T,pixel_values:j,inputs_embeds:fe,attention_mask:Fe}){if(!T&&!j)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let De,Ze;return T&&(De=await this.encode_text({input_ids:T})),j&&(Ze=await this.encode_image({pixel_values:j})),De&&Ze?{inputs_embeds:fe,attention_mask:Fe}=this._merge_input_ids_with_image_features({inputs_embeds:De,image_features:Ze,input_ids:T,attention_mask:Fe}):fe=De||Ze,{inputs_embeds:fe,attention_mask:Fe}}async forward({input_ids:T,pixel_values:j,attention_mask:fe,decoder_input_ids:Fe,decoder_attention_mask:De,encoder_outputs:Ze,past_key_values:rt,inputs_embeds:ft,decoder_inputs_embeds:bt}){if(ft||({inputs_embeds:ft,attention_mask:fe}=await this._prepare_inputs_embeds({input_ids:T,pixel_values:j,inputs_embeds:ft,attention_mask:fe})),!Ze){let{last_hidden_state:Dt}=await Oe(this,{inputs_embeds:ft,attention_mask:fe});Ze=Dt}if(!bt){if(!Fe)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");bt=await this.encode_text({input_ids:Fe})}return await Ce(this,{inputs_embeds:bt,attention_mask:De,encoder_attention_mask:fe,encoder_hidden_states:Ze,past_key_values:rt},!0)}}class Ja extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class Za extends Ja{_merge_input_ids_with_image_features(T){const j=T.image_features.dims.at(-1),fe=T.image_features.view(-1,j);return tt({image_token_id:this.config.image_token_index,...T,image_features:fe})}}class el extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"])}}class Qo extends el{async encode_image({pixel_values:T,pixel_attention_mask:j}){return(await we(this.sessions.vision_encoder,{pixel_values:T,pixel_attention_mask:j})).image_features}_merge_input_ids_with_image_features(T){const j=T.image_features.dims.at(-1),fe=T.image_features.view(-1,j);return tt({image_token_id:this.config.image_token_id,...T,image_features:fe})}}class tl extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"])}}class pr extends tl{async forward({input_ids:T=null,attention_mask:j=null,pixel_values:fe=null,image_sizes:Fe=null,position_ids:De=null,inputs_embeds:Ze=null,past_key_values:rt=null,generation_config:ft=null,logits_processor:bt=null,...Rt}){if(!Ze){let Dt;if(fe&&T.dims[1]!==1){if(!Fe)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:Dt}=await we(this.sessions.vision_encoder,{pixel_values:fe,image_sizes:Fe}))}else{const Gt=this.config.normalized_config.hidden_size;Dt=new b.Tensor("float32",[],[0,Gt])}({inputs_embeds:Ze}=await we(this.sessions.prepare_inputs_embeds,{input_ids:T,image_features:Dt}))}return await Ce(this,{inputs_embeds:Ze,past_key_values:rt,attention_mask:j,position_ids:De,generation_config:ft,logits_processor:bt},!1)}}class sn extends te{}class sl extends sn{}class Vc extends sn{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"text_model"})}}class rl extends sn{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"text_model"})}}class Wc extends sn{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"vision_model"})}}class nl extends sn{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"vision_model"})}}class Xo extends te{}class ol extends Xo{}class il extends Xo{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"text_model"})}}class al extends sn{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"vision_model"})}}class Gc extends te{}class fr extends Gc{}class lo extends te{}class uo extends lo{async forward(T){const j=!T.input_ids,fe=!T.pixel_values;if(j&&fe)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(j&&(T.input_ids=(0,b.ones)([T.pixel_values.dims[0],1])),fe){const{image_size:bt}=this.config.vision_config;T.pixel_values=(0,b.full)([0,3,bt,bt],0)}const{text_embeddings:Fe,image_embeddings:De,l2norm_text_embeddings:Ze,l2norm_image_embeddings:rt}=await super.forward(T),ft={};return j||(ft.text_embeddings=Fe,ft.l2norm_text_embeddings=Ze),fe||(ft.image_embeddings=De,ft.l2norm_image_embeddings=rt),ft}}class Yo extends lo{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"text_model"})}}class gr extends lo{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"vision_model"})}}class Jo extends te{}class ll extends Jo{}class ul extends Jo{}class Zo extends te{}class dl extends Zo{}class cl extends Zo{}class ei extends te{}class pl extends ei{}class hl extends ei{}class ti extends te{}class wr extends ti{}class ml extends ti{}class si extends te{}class _l extends si{}class fl extends si{}class ri extends te{}class gl extends ri{}class wl extends ri{}class ni extends te{}class yl extends ni{}class Ml extends ni{}class co extends te{}class oi extends co{}class bl extends co{}class ii extends te{}class ai extends ii{}class Kc extends ii{}class li extends te{}class po extends li{}class vl extends li{}class ui extends te{}class xl extends ui{}class Un extends ui{}class di extends te{}class Tl extends di{}class ci extends di{}class pi extends te{}class El extends pi{}class Pl extends pi{}class hi extends te{}class Hc extends hi{}class Cl extends hi{}class mi extends te{}class kl extends mi{}class qc extends mi{}class _i extends te{}class ds extends _i{}class Sl extends _i{}class fi extends te{}class $l extends fi{}class Al extends fi{}class gi extends te{}class Il extends gi{}class Ol extends gi{}class wi extends te{}class Fl extends wi{}class Dl extends wi{}class yi extends te{}class Ll extends yi{}class zl extends yi{}class Mi extends te{}class Vn extends Mi{}class Bl extends Mi{}class ho extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"])}}class Rl extends ho{get_rope_index(T,j,fe,Fe){const{vision_config:De,image_token_id:Ze,video_token_id:rt,vision_start_token_id:ft}=this.config,bt=De.spatial_merge_size??2,Rt=[];if(j||fe){let Wt=T.tolist();Fe||(Fe=(0,b.ones_like)(T));const Dt=Fe.tolist(),Gt=Array.from({length:3},Es=>Array.from({length:T.dims[0]},xs=>Array.from({length:T.dims[1]},cs=>1))),es=j?j.tolist():[],ns=fe?fe.tolist():[];let Yt=0,as=0;for(let Es=0;Es<Wt.length;++Es){const xs=Wt[Es].filter((Ps,Bs)=>Dt[Es][Bs]==1),Ts=xs.reduce((Ps,Bs,qr)=>(Bs==ft&&Ps.push(qr),Ps),[]).map(Ps=>xs[Ps+1]),Is=Ts.filter(Ps=>Ps==Ze).length,tr=Ts.filter(Ps=>Ps==rt).length;let Hs=[],Er=0,En=Is,Io=tr;for(let Ps=0;Ps<Ts.length;++Ps){const Bs=xs.findIndex((Xr,hr)=>hr>Er&&Xr==Ze),qr=xs.findIndex((Xr,hr)=>hr>Er&&Xr==rt),cn=En>0&&Bs!==-1?Bs:xs.length+1,Xn=Io>0&&qr!==-1?qr:xs.length+1;let Lo,Yn,Aa,Ia;cn<Xn?([Yn,Aa,Ia]=es[Yt],++Yt,--En,Lo=cn):([Yn,Aa,Ia]=ns[as],++as,--Io,Lo=Xn);const[Zp,Oa,Jn]=[Number(Yn),Math.floor(Number(Aa)/bt),Math.floor(Number(Ia)/bt)],zo=Lo-Er,Qr=Hs.length>0?(0,H.max)(Hs.at(-1))[0]+1:0;Hs.push(Array.from({length:3*zo},(Xr,hr)=>Qr+hr%zo));const Fa=zo+Qr,Pn=Zp*Oa*Jn,Ic=Array.from({length:Pn},(Xr,hr)=>Fa+Math.floor(hr/(Oa*Jn))),Oc=Array.from({length:Pn},(Xr,hr)=>Fa+Math.floor(hr/Jn)%Oa),Fc=Array.from({length:Pn},(Xr,hr)=>Fa+hr%Jn);Hs.push([Ic,Oc,Fc].flat()),Er=Lo+Pn}if(Er<xs.length){const Ps=Hs.length>0?(0,H.max)(Hs.at(-1))[0]+1:0,Bs=xs.length-Er;Hs.push(Array.from({length:3*Bs},(qr,cn)=>Ps+cn%Bs))}const ur=Hs.reduce((Ps,Bs)=>Ps+Bs.length,0),Mr=new Array(ur);let Oo=0;for(let Ps=0;Ps<3;++Ps)for(let Bs=0;Bs<Hs.length;++Bs){const qr=Hs[Bs],cn=qr.length/3;for(let Xn=Ps*cn;Xn<(Ps+1)*cn;++Xn)Mr[Oo++]=qr[Xn]}let Qn=0;const Fo=Dt[Es];for(let Ps=0;Ps<Fo.length;++Ps)if(Fo[Ps]==1){for(let Bs=0;Bs<3;++Bs)Gt[Bs][Es][Ps]=Mr[Bs*ur/3+Qn];++Qn}const Do=(0,H.max)(Mr)[0];Rt.push(Do+1-Wt[Es].length)}return[new b.Tensor("int64",Gt.flat(1/0),[3,T.dims[0],T.dims[1]]),new b.Tensor("int64",Rt,[Rt.length,1])]}else if(Fe){const{data:Wt,dims:Dt}=ye(Fe),Gt=BigInt64Array.from({length:3*Wt.length},(ns,Yt)=>Wt[Yt%Wt.length]),es=Array.from({length:Dt[0]},(ns,Yt)=>(0,H.max)(Wt.subarray(Dt[1]*Yt,Dt[1]*(Yt+1)))[0]+1n+BigInt(Dt[1]));return[new b.Tensor("int64",Gt,[3,...Dt]),new b.Tensor("int64",es,[es.length,1])]}else{const[Wt,Dt]=T.dims,Gt=BigInt64Array.from({length:3*Wt*Dt},(es,ns)=>BigInt(Math.floor(ns%Dt/Wt)));return[new b.Tensor("int64",Gt,[3,...T.dims]),(0,b.zeros)([Wt,1])]}}async encode_image({pixel_values:T,image_grid_thw:j}){return(await we(this.sessions.vision_encoder,{pixel_values:T,grid_thw:j})).image_features}_merge_input_ids_with_image_features(T){return tt({image_token_id:this.config.image_token_id,...T})}prepare_inputs_for_generation(T,j,fe){if(j.attention_mask&&!j.position_ids)if(!j.past_key_values)[j.position_ids,j.rope_deltas]=this.get_rope_index(j.input_ids,j.image_grid_thw,j.video_grid_thw,j.attention_mask);else{j.pixel_values=null;const Fe=BigInt(Object.values(j.past_key_values)[0].dims.at(-2)),De=j.rope_deltas.map(Ze=>Fe+Ze);j.position_ids=(0,b.stack)([De,De,De],0)}return j}}class bi extends te{}class Nl extends bi{}class jl extends bi{}class vi extends te{}class Ul extends vi{}class Vl extends vi{}class xi extends te{}class Wl extends xi{}class Gl extends xi{}class Ti extends te{}class Kl extends Ti{}class Hl extends Ti{}class Ei extends te{}class ql extends Ei{}class Ql extends Ei{}class mo extends te{}class Xl extends mo{}class Pi extends mo{async _call(T){return new Zt(await super._call(T))}}class _o extends te{}class Yl extends _o{}class Jl extends _o{async _call(T){return new Zt(await super._call(T))}}class Zl extends te{}class eu extends Zl{}class Ci extends te{}class tu extends Ci{}class ki extends Ci{async _call(T){return new Zt(await super._call(T))}}class su extends te{}class ru extends su{}class Si extends te{}class Qc extends Si{}class nu extends Si{async _call(T){return new Zt(await super._call(T))}}class ou extends te{}class yr extends ou{}class $i extends te{}class iu extends $i{}class au extends $i{async _call(T){return new Zt(await super._call(T))}}class lu extends te{}class uu extends lu{async _call(T){return new $c(await super._call(T))}}class Ai extends te{}class du extends Ai{}class cu extends Ai{async _call(T){return new Zt(await super._call(T))}}class Ii extends te{}class pu extends Ii{}class Xc extends Ii{async _call(T){return new Zt(await super._call(T))}}class Oi extends te{}class hu extends Oi{}class mu extends Oi{}class Fi extends te{}class _u extends Fi{}class fu extends Fi{}class gu extends te{}class rn extends gu{}class nn extends gu{async _call(T){return new Zt(await super._call(T))}}class Or extends te{}class Di extends Or{}class on extends Or{async _call(T){return new Li(await super._call(T))}}class Ks extends Or{async _call(T){return new zi(await super._call(T))}}class Li extends Ke{constructor({logits:T,pred_boxes:j}){super(),this.logits=T,this.pred_boxes=j}}class zi extends Ke{constructor({logits:T,pred_boxes:j,pred_masks:fe}){super(),this.logits=T,this.pred_boxes=j,this.pred_masks=fe}}class Bi extends te{}class Yc extends Bi{}class Wn extends Bi{async _call(T){return new Ri(await super._call(T))}}class Ri extends Ke{constructor({logits:T,pred_boxes:j}){super(),this.logits=T,this.pred_boxes=j}}class fo extends te{}class wu extends fo{}class yu extends fo{async _call(T){return new Ni(await super._call(T))}}class Ni extends Li{}class go extends te{}class Mu extends go{}class ji extends go{async _call(T){return new Zt(await super._call(T))}}class Ui extends te{}class wo extends Ui{}class Vi extends Ui{async _call(T){return new Zt(await super._call(T))}}class Wi extends te{}class bu extends Wi{}class Jc extends Wi{async _call(T){return new Zt(await super._call(T))}}class Gi extends te{}class Ki extends Gi{}class Gn extends Gi{async _call(T){return new Zt(await super._call(T))}}class Hi extends te{}class qi extends Hi{}class vu extends Hi{}class Qi extends te{}class xu extends Qi{}class Zc extends Qi{}class Tu extends te{}class Eu extends Tu{}class Xi extends te{}class Pu extends Xi{}class yo extends Xi{}class Cu extends Xi{}class Mo extends te{}class Yi extends Mo{}class bo extends te{}class ku extends bo{}class Su extends bo{}class vo extends te{}class ep extends vo{}class $u extends vo{}class tp extends te{}class Au extends tp{}class Ji extends te{}class Iu extends Ji{}class Zi extends Ji{async _call(T){return new Zt(await super._call(T))}}class ea extends te{}class Ou extends ea{}class ta extends ea{async _call(T){return new Zt(await super._call(T))}}class sa extends te{}class Fu extends sa{}class sp extends sa{async _call(T){return new Zt(await super._call(T))}}class ra extends te{}class Du extends ra{}class Lu extends ra{async _call(T){return new Zt(await super._call(T))}}class rp extends te{}class zu extends rp{}class na extends te{}class Bu extends na{}class Ru extends na{async _call(T){return new Nu(await super._call(T))}}class Nu extends Ke{constructor({logits:T,pred_boxes:j}){super(),this.logits=T,this.pred_boxes=j}}class np extends te{}class xo extends np{async get_image_embeddings({pixel_values:T}){return await Oe(this,{pixel_values:T})}async forward(T){if((!T.image_embeddings||!T.image_positional_embeddings)&&(T={...T,...await this.get_image_embeddings(T)}),!T.input_labels&&T.input_points){const fe=T.input_points.dims.slice(0,-1),Fe=fe.reduce((De,Ze)=>De*Ze,1);T.input_labels=new b.Tensor("int64",new BigInt64Array(Fe).fill(1n),fe)}const j={image_embeddings:T.image_embeddings,image_positional_embeddings:T.image_positional_embeddings};return T.input_points&&(j.input_points=T.input_points),T.input_labels&&(j.input_labels=T.input_labels),T.input_boxes&&(j.input_boxes=T.input_boxes),await we(this.sessions.prompt_encoder_mask_decoder,j)}async _call(T){return new Kn(await super._call(T))}}class Kn extends Ke{constructor({iou_scores:T,pred_masks:j}){super(),this.iou_scores=T,this.pred_masks=j}}class To extends te{}class ju extends To{}class Uu extends To{}class oa extends te{}class Vu extends oa{}class ia extends oa{}class Hr extends te{}class Wu extends Hr{}class Gu extends Hr{async _call(T){return new dn(await super._call(T))}}class op extends Hr{async _call(T){return new Zt(await super._call(T))}}class Ku extends Hr{async _call(T){return new Ds(await super._call(T))}}class Eo extends te{}class Hu extends Eo{}class qu extends Eo{async _call(T){return new Ds(await super._call(T))}}class Qu extends te{}class ip extends Qu{}class Po extends te{}class Xu extends Po{}class ap extends Po{async _call(T){return new dn(await super._call(T))}}class Yu extends Po{async _call(T){return new Zt(await super._call(T))}}class Hn extends te{}class Ju extends Hn{}class Zu extends Hn{async _call(T){return new dn(await super._call(T))}}class lp extends Hn{async _call(T){return new Zt(await super._call(T))}}class ed extends Hn{async _call(T){return new Ds(await super._call(T))}}class Co extends te{}class td extends Co{}class up extends Co{async _call(T){return new dn(await super._call(T))}}class sd extends Co{async _call(T){return new Zt(await super._call(T))}}class dp extends te{}class rd extends Hr{}class nd extends Hr{async _call(T){return new dn(await super._call(T))}}class cp extends Hr{async _call(T){return new Zt(await super._call(T))}}class Tn extends te{}class od extends Tn{}class id extends Tn{async _call(T){return new dn(await super._call(T))}}class ad extends Tn{async _call(T){return new Zt(await super._call(T))}}class pp extends Tn{async _call(T){return new Sc(await super._call(T))}}class ld extends Tn{async _call(T){return new Ds(await super._call(T))}}class ud extends te{}class dd extends ud{}class ko extends te{}class Yp extends ko{}class Tr extends ko{}class Fr extends ko{async generate_speech(T,j,{threshold:fe=.5,minlenratio:Fe=0,maxlenratio:De=20,vocoder:Ze=null}={}){const rt={input_ids:T},{encoder_outputs:ft,encoder_attention_mask:bt}=await Oe(this,rt),Rt=ft.dims[1]/this.config.reduction_factor,Wt=Math.floor(Rt*De),Dt=Math.floor(Rt*Fe),Gt=this.config.num_mel_bins;let es=[],ns=null,Yt=null,as=0;for(;;){++as;const cs=ce(!!Yt);let Ts;Yt?Ts=Yt.output_sequence_out:Ts=new b.Tensor("float32",new Float32Array(Gt),[1,1,Gt]);let Is={use_cache_branch:cs,output_sequence:Ts,encoder_attention_mask:bt,speaker_embeddings:j,encoder_hidden_states:ft};this.addPastKeyValues(Is,ns),Yt=await we(this.sessions.decoder_model_merged,Is),ns=this.getPastKeyValues(Yt,ns);const{prob:tr,spectrum:Hs}=Yt;if(es.push(Hs),as>=Dt&&(Array.from(tr.data).filter(Er=>Er>=fe).length>0||as>=Wt))break}const Es=(0,b.cat)(es),{waveform:xs}=await we(Ze.sessions.model,{spectrogram:Es});return{spectrogram:Es,waveform:xs}}}class an extends te{constructor(){super(...arguments);_e(this,"main_input_name","spectrogram")}}class ln extends te{}class cd extends ln{}class aa extends te{}class pd extends aa{}class hd extends aa{}class la extends te{}class md extends la{}class _d extends la{}class ua extends te{}class fd extends ua{}class gd extends ua{}class da extends te{}class nr extends da{}class wd extends da{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"text_model"})}}class yd extends da{static async from_pretrained(T,j={}){return super.from_pretrained(T,{...j,model_file_name:j.model_file_name??"audio_model"})}}class ca extends te{}class pa extends ca{async _call(T){return new Ac(await super._call(T))}}class un extends te{}class hp extends un{}class Md extends un{}class bd extends un{}class ha extends te{}class vd extends ha{}class xd extends ha{}class So extends te{}class Td extends So{}class Ed extends So{async _call(T){return new Zt(await super._call(T))}}class ma extends te{}class mp extends ma{}class _p extends ma{}class $o extends te{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(j){const[fe,Fe]=j.dims,De=this.config.decoder.num_codebooks,Ze=Fe-De;let rt=0;for(let Rt=0;Rt<j.size;++Rt){if(j.data[Rt]===this.config.decoder.pad_token_id)continue;const Wt=Rt%Fe,Dt=Math.floor(Rt/Fe)%De,Gt=Wt-Dt;Gt>0&&Gt<=Ze&&(j.data[rt++]=j.data[Rt])}const ft=Math.floor(fe/De),bt=rt/(ft*De);return new b.Tensor(j.type,j.data.slice(0,rt),[ft,De,bt])}prepare_inputs_for_generation(j,fe,Fe){let De=structuredClone(j);for(let rt=0;rt<De.length;++rt)for(let ft=0;ft<De[rt].length;++ft)rt%this.config.decoder.num_codebooks>=ft&&(De[rt][ft]=BigInt(this.config.decoder.pad_token_id));return Fe.guidance_scale!==null&&Fe.guidance_scale>1&&(De=De.concat(De)),super.prepare_inputs_for_generation(De,fe,Fe)}async generate(j){const fe=await super.generate(j),Fe=this._apply_and_filter_by_delay_pattern_mask(fe).unsqueeze_(0),{audio_values:De}=await we(this.sessions.encodec_decode,{audio_codes:Fe});return De}}class _a extends te{}class fp extends _a{}class fa extends _a{async _call(T){return new Zt(await super._call(T))}}class ga extends te{}class Pd extends ga{}class Cd extends ga{async _call(T){return new Zt(await super._call(T))}}class kd extends te{}class Sd extends kd{}class $d extends kd{async _call(T){return new Zt(await super._call(T))}}class wa extends te{}class gp extends wa{}class Ad extends wa{async _call(T){return new Zt(await super._call(T))}}class Id extends te{}class wp extends Id{}class Od extends te{}class Fd extends Od{constructor(...j){super(...j);_e(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(j){const fe=this._generation_mode??"text";let Fe;if(fe==="text"||!j.past_key_values){const bt=this.sessions.prepare_inputs_embeds,Rt=(0,R.pick)(j,bt.inputNames);Fe=await we(bt,Rt)}else{const bt=this.sessions.gen_img_embeds,Rt=(0,R.pick)({image_ids:j.input_ids},bt.inputNames);Fe=await we(bt,Rt)}const De={...j,...Fe},Ze=await Ce(this,De),rt=this.sessions[fe==="text"?"lm_head":"gen_head"];if(!rt)throw new Error(`Unable to find "${rt}" generation head`);const ft=await we(rt,(0,R.pick)(Ze,rt.inputNames));return{...Fe,...Ze,...ft}}async generate(j){return this._generation_mode="text",super.generate(j)}async generate_images(j){this._generation_mode="image";const fe=(j.inputs??j[this.main_input_name]).dims[1],De=(await super.generate(j)).slice(null,[fe,null]),Ze=this.sessions.image_decode,{decoded_image:rt}=await we(Ze,{generated_tokens:De}),ft=rt.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),bt=[];for(const Rt of ft){const Wt=O.RawImage.fromTensor(Rt);bt.push(Wt)}return bt}}class Dd extends Ke{constructor({char_logits:T,bpe_logits:j,wp_logits:fe}){super(),this.char_logits=T,this.bpe_logits=j,this.wp_logits=fe}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class Ld extends te{}class zd extends Ld{async _call(T){return new Dd(await super._call(T))}}class Bd extends te{}class Rd extends Bd{}class Nd extends Bd{}class ya extends te{}class jd extends ya{}class Ud extends ya{}class ws{static async from_pretrained(T,{progress_callback:j=null,config:fe=null,cache_dir:Fe=null,local_files_only:De=!1,revision:Ze="main",model_file_name:rt=null,subfolder:ft="onnx",device:bt=null,dtype:Rt=null,use_external_data_format:Wt=null,session_options:Dt={}}={}){const Gt={progress_callback:j,config:fe,cache_dir:Fe,local_files_only:De,revision:Ze,model_file_name:rt,subfolder:ft,device:bt,dtype:Rt,use_external_data_format:Wt,session_options:Dt};if(Gt.config=await f.AutoConfig.from_pretrained(T,Gt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const es of this.MODEL_CLASS_MAPPINGS){const ns=es.get(Gt.config.model_type);if(ns)return await ns[1].from_pretrained(T,Gt)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${Gt.config.model_type}", attempting to construct from base class.`),await te.from_pretrained(T,Gt);throw Error(`Unsupported model type: ${Gt.config.model_type}`)}}_e(ws,"MODEL_CLASS_MAPPINGS",null),_e(ws,"BASE_IF_FAIL",!1);const yp=new Map([["bert",["BertModel",be]],["modernbert",["ModernBertModel",ut]],["nomic_bert",["NomicBertModel",ie]],["roformer",["RoFormerModel",he]],["electra",["ElectraModel",Ls]],["esm",["EsmModel",oo]],["convbert",["ConvBertModel",vt]],["camembert",["CamembertModel",Nt]],["deberta",["DebertaModel",Br]],["deberta-v2",["DebertaV2Model",xt]],["mpnet",["MPNetModel",wn]],["albert",["AlbertModel",Nn]],["distilbert",["DistilBertModel",ar]],["roberta",["RobertaModel",or]],["xlm",["XLMModel",zs]],["xlm-roberta",["XLMRobertaModel",At]],["clap",["ClapModel",nr]],["clip",["CLIPModel",sl]],["clipseg",["CLIPSegModel",ll]],["chinese_clip",["ChineseCLIPModel",fr]],["siglip",["SiglipModel",ol]],["jina_clip",["JinaCLIPModel",uo]],["mobilebert",["MobileBertModel",Ln]],["squeezebert",["SqueezeBertModel",vn]],["wav2vec2",["Wav2Vec2Model",Wu]],["wav2vec2-bert",["Wav2Vec2BertModel",td]],["unispeech",["UniSpeechModel",Xu]],["unispeech-sat",["UniSpeechSatModel",Ju]],["hubert",["HubertModel",rd]],["wavlm",["WavLMModel",od]],["audio-spectrogram-transformer",["ASTModel",Wa]],["vits",["VitsModel",pa]],["pyannote",["PyAnnoteModel",Hu]],["wespeaker-resnet",["WeSpeakerResNetModel",ip]],["detr",["DetrModel",Di]],["rt_detr",["RTDetrModel",Yc]],["table-transformer",["TableTransformerModel",wu]],["vit",["ViTModel",Xl]],["ijepa",["IJepaModel",Yl]],["pvt",["PvtModel",tu]],["vit_msn",["ViTMSNModel",Qc]],["vit_mae",["ViTMAEModel",ru]],["groupvit",["GroupViTModel",yr]],["fastvit",["FastViTModel",iu]],["mobilevit",["MobileViTModel",du]],["mobilevitv2",["MobileViTV2Model",pu]],["owlvit",["OwlViTModel",hu]],["owlv2",["Owlv2Model",_u]],["beit",["BeitModel",rn]],["deit",["DeiTModel",Mu]],["hiera",["HieraModel",wo]],["convnext",["ConvNextModel",Iu]],["convnextv2",["ConvNextV2Model",Ou]],["dinov2",["Dinov2Model",Fu]],["dinov2_with_registers",["Dinov2WithRegistersModel",Du]],["resnet",["ResNetModel",bu]],["swin",["SwinModel",Ki]],["swin2sr",["Swin2SRModel",qi]],["donut-swin",["DonutSwinModel",Au]],["yolos",["YolosModel",Bu]],["dpt",["DPTModel",xu]],["glpn",["GLPNModel",ep]],["hifigan",["SpeechT5HifiGan",an]],["efficientnet",["EfficientNetModel",Td]],["decision_transformer",["DecisionTransformerModel",wp]],["patchtst",["PatchTSTForPrediction",Rd]],["patchtsmixer",["PatchTSMixerForPrediction",jd]],["mobilenet_v1",["MobileNetV1Model",fp]],["mobilenet_v2",["MobileNetV2Model",Pd]],["mobilenet_v3",["MobileNetV3Model",Sd]],["mobilenet_v4",["MobileNetV4Model",gp]],["maskformer",["MaskFormerModel",ku]],["mgp-str",["MgpstrForSceneTextRecognition",zd]],["style_text_to_speech_2",["StyleTextToSpeech2Model",dd]]]),Mp=new Map([["t5",["T5Model",E]],["longt5",["LongT5Model",ve]],["mt5",["MT5Model",ht]],["bart",["BartModel",Tt]],["mbart",["MBartModel",Fs]],["marian",["MarianModel",ju]],["whisper",["WhisperModel",Ga]],["m2m_100",["M2M100Model",Vu]],["blenderbot",["BlenderbotModel",ze]],["blenderbot-small",["BlenderbotSmallModel",Ss]]]),bp=new Map([["bloom",["BloomModel",Wl]],["jais",["JAISModel",pl]],["gpt2",["GPT2Model",dl]],["gptj",["GPTJModel",gl]],["gpt_bigcode",["GPTBigCodeModel",yl]],["gpt_neo",["GPTNeoModel",wr]],["gpt_neox",["GPTNeoXModel",_l]],["codegen",["CodeGenModel",oi]],["llama",["LlamaModel",ai]],["exaone",["ExaoneModel",Tl]],["olmo",["OlmoModel",Hc]],["olmo2",["Olmo2Model",kl]],["mobilellm",["MobileLLMModel",El]],["granite",["GraniteModel",ds]],["cohere",["CohereModel",$l]],["gemma",["GemmaModel",Il]],["gemma2",["Gemma2Model",Fl]],["helium",["HeliumModel",po]],["glm",["GlmModel",xl]],["openelm",["OpenELMModel",Ll]],["qwen2",["Qwen2Model",Vn]],["phi",["PhiModel",Nl]],["phi3",["Phi3Model",Ul]],["mpt",["MptModel",Kl]],["opt",["OPTModel",ql]],["mistral",["MistralModel",pd]],["starcoder2",["Starcoder2Model",md]],["falcon",["FalconModel",fd]],["stablelm",["StableLmModel",vd]]]),Vd=new Map([["speecht5",["SpeechT5ForSpeechToText",Tr]],["whisper",["WhisperForConditionalGeneration",Ka]],["moonshine",["MoonshineForConditionalGeneration",Ha]]]),qn=new Map([["speecht5",["SpeechT5ForTextToSpeech",Fr]]]),Ma=new Map([["vits",["VitsModel",pa]],["musicgen",["MusicgenForConditionalGeneration",$o]]]),ba=new Map([["bert",["BertForSequenceClassification",We]],["modernbert",["ModernBertForSequenceClassification",lt]],["roformer",["RoFormerForSequenceClassification",Re]],["electra",["ElectraForSequenceClassification",Cr]],["esm",["EsmForSequenceClassification",Fn]],["convbert",["ConvBertForSequenceClassification",$t]],["camembert",["CamembertForSequenceClassification",kr]],["deberta",["DebertaForSequenceClassification",Rr]],["deberta-v2",["DebertaV2ForSequenceClassification",Vs]],["mpnet",["MPNetForSequenceClassification",yn]],["albert",["AlbertForSequenceClassification",xn]],["distilbert",["DistilBertForSequenceClassification",Os]],["roberta",["RobertaForSequenceClassification",fs]],["xlm",["XLMForSequenceClassification",Gs]],["xlm-roberta",["XLMRobertaForSequenceClassification",Wo]],["bart",["BartForSequenceClassification",_s]],["mbart",["MBartForSequenceClassification",rs]],["mobilebert",["MobileBertForSequenceClassification",Wr]],["squeezebert",["SqueezeBertForSequenceClassification",Bn]]]),va=new Map([["bert",["BertForTokenClassification",Ne]],["modernbert",["ModernBertForTokenClassification",mt]],["roformer",["RoFormerForTokenClassification",qe]],["electra",["ElectraForTokenClassification",Yr]],["esm",["EsmForTokenClassification",Dn]],["convbert",["ConvBertForTokenClassification",os]],["camembert",["CamembertForTokenClassification",Sr]],["deberta",["DebertaForTokenClassification",Nr]],["deberta-v2",["DebertaV2ForTokenClassification",jr]],["mpnet",["MPNetForTokenClassification",Mn]],["distilbert",["DistilBertForTokenClassification",xr]],["roberta",["RobertaForTokenClassification",$s]],["xlm",["XLMForTokenClassification",St]],["xlm-roberta",["XLMRobertaForTokenClassification",Ua]]]),Ao=new Map([["t5",["T5ForConditionalGeneration",Q]],["longt5",["LongT5ForConditionalGeneration",Ae]],["mt5",["MT5ForConditionalGeneration",gt]],["bart",["BartForConditionalGeneration",Kt]],["mbart",["MBartForConditionalGeneration",zt]],["marian",["MarianMTModel",Uu]],["m2m_100",["M2M100ForConditionalGeneration",ia]],["blenderbot",["BlenderbotForConditionalGeneration",Zs]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Xs]]]),xa=new Map([["bloom",["BloomForCausalLM",Gl]],["gpt2",["GPT2LMHeadModel",cl]],["jais",["JAISLMHeadModel",hl]],["gptj",["GPTJForCausalLM",wl]],["gpt_bigcode",["GPTBigCodeForCausalLM",Ml]],["gpt_neo",["GPTNeoForCausalLM",ml]],["gpt_neox",["GPTNeoXForCausalLM",fl]],["codegen",["CodeGenForCausalLM",bl]],["llama",["LlamaForCausalLM",Kc]],["exaone",["ExaoneForCausalLM",ci]],["olmo",["OlmoForCausalLM",Cl]],["olmo2",["Olmo2ForCausalLM",qc]],["mobilellm",["MobileLLMForCausalLM",Pl]],["granite",["GraniteForCausalLM",Sl]],["cohere",["CohereForCausalLM",Al]],["gemma",["GemmaForCausalLM",Ol]],["gemma2",["Gemma2ForCausalLM",Dl]],["helium",["HeliumForCausalLM",vl]],["glm",["GlmForCausalLM",Un]],["openelm",["OpenELMForCausalLM",zl]],["qwen2",["Qwen2ForCausalLM",Bl]],["phi",["PhiForCausalLM",jl]],["phi3",["Phi3ForCausalLM",Vl]],["mpt",["MptForCausalLM",Hl]],["opt",["OPTForCausalLM",Ql]],["mbart",["MBartForCausalLM",rr]],["mistral",["MistralForCausalLM",hd]],["starcoder2",["Starcoder2ForCausalLM",_d]],["falcon",["FalconForCausalLM",gd]],["trocr",["TrOCRForCausalLM",cd]],["stablelm",["StableLmForCausalLM",xd]],["phi3_v",["Phi3VForCausalLM",pr]]]),vp=new Map([["multi_modality",["MultiModalityCausalLM",Fd]]]),Ta=new Map([["bert",["BertForMaskedLM",Ve]],["modernbert",["ModernBertForMaskedLM",pt]],["roformer",["RoFormerForMaskedLM",Se]],["electra",["ElectraForMaskedLM",sr]],["esm",["EsmForMaskedLM",On]],["convbert",["ConvBertForMaskedLM",kt]],["camembert",["CamembertForMaskedLM",Jr]],["deberta",["DebertaForMaskedLM",$r]],["deberta-v2",["DebertaV2ForMaskedLM",Ft]],["mpnet",["MPNetForMaskedLM",en]],["albert",["AlbertForMaskedLM",is]],["distilbert",["DistilBertForMaskedLM",gn]],["roberta",["RobertaForMaskedLM",_r]],["xlm",["XLMWithLMHeadModel",As]],["xlm-roberta",["XLMRobertaForMaskedLM",ja]],["mobilebert",["MobileBertForMaskedLM",io]],["squeezebert",["SqueezeBertForMaskedLM",zn]]]),Ea=new Map([["bert",["BertForQuestionAnswering",je]],["roformer",["RoFormerForQuestionAnswering",at]],["electra",["ElectraForQuestionAnswering",Us]],["convbert",["ConvBertForQuestionAnswering",Ms]],["camembert",["CamembertForQuestionAnswering",Zr]],["deberta",["DebertaForQuestionAnswering",ir]],["deberta-v2",["DebertaV2ForQuestionAnswering",Ar]],["mpnet",["MPNetForQuestionAnswering",bn]],["albert",["AlbertForQuestionAnswering",jn]],["distilbert",["DistilBertForQuestionAnswering",ss]],["roberta",["RobertaForQuestionAnswering",Mt]],["xlm",["XLMForQuestionAnswering",tn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Va]],["mobilebert",["MobileBertForQuestionAnswering",mr]],["squeezebert",["SqueezeBertForQuestionAnswering",Rn]]]),Pa=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",qo]],["idefics3",["Idefics3ForConditionalGeneration",Qo]]]),xp=new Map([["llava",["LlavaForConditionalGeneration",ao]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",qa]],["moondream1",["Moondream1ForConditionalGeneration",Qa]],["florence2",["Florence2ForConditionalGeneration",Ya]],["qwen2-vl",["Qwen2VLForConditionalGeneration",Rl]],["idefics3",["Idefics3ForConditionalGeneration",Qo]],["paligemma",["PaliGemmaForConditionalGeneration",Za]]]),Wd=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",qo]]]),Gd=new Map([["vit",["ViTForImageClassification",Pi]],["ijepa",["IJepaForImageClassification",Jl]],["pvt",["PvtForImageClassification",ki]],["vit_msn",["ViTMSNForImageClassification",nu]],["fastvit",["FastViTForImageClassification",au]],["mobilevit",["MobileViTForImageClassification",cu]],["mobilevitv2",["MobileViTV2ForImageClassification",Xc]],["beit",["BeitForImageClassification",nn]],["deit",["DeiTForImageClassification",ji]],["hiera",["HieraForImageClassification",Vi]],["convnext",["ConvNextForImageClassification",Zi]],["convnextv2",["ConvNextV2ForImageClassification",ta]],["dinov2",["Dinov2ForImageClassification",sp]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Lu]],["resnet",["ResNetForImageClassification",Jc]],["swin",["SwinForImageClassification",Gn]],["segformer",["SegformerForImageClassification",Md]],["efficientnet",["EfficientNetForImageClassification",Ed]],["mobilenet_v1",["MobileNetV1ForImageClassification",fa]],["mobilenet_v2",["MobileNetV2ForImageClassification",Cd]],["mobilenet_v3",["MobileNetV3ForImageClassification",$d]],["mobilenet_v4",["MobileNetV4ForImageClassification",Ad]]]),Kd=new Map([["detr",["DetrForObjectDetection",on]],["rt_detr",["RTDetrForObjectDetection",Wn]],["table-transformer",["TableTransformerForObjectDetection",yu]],["yolos",["YolosForObjectDetection",Ru]]]),Ca=new Map([["owlvit",["OwlViTForObjectDetection",mu]],["owlv2",["Owlv2ForObjectDetection",fu]],["grounding-dino",["GroundingDinoForObjectDetection",zu]]]),Hd=new Map([["detr",["DetrForSegmentation",Ks]],["clipseg",["CLIPSegForImageSegmentation",ul]]]),qd=new Map([["segformer",["SegformerForSemanticSegmentation",bd]],["sapiens",["SapiensForSemanticSegmentation",Pu]]]),Qd=new Map([["detr",["DetrForSegmentation",Ks]],["maskformer",["MaskFormerForInstanceSegmentation",Su]]]),Xd=new Map([["sam",["SamModel",xo]]]),Tp=new Map([["wav2vec2",["Wav2Vec2ForCTC",Gu]],["wav2vec2-bert",["Wav2Vec2BertForCTC",up]],["unispeech",["UniSpeechForCTC",ap]],["unispeech-sat",["UniSpeechSatForCTC",Zu]],["wavlm",["WavLMForCTC",id]],["hubert",["HubertForCTC",nd]]]),Yd=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",op]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",sd]],["unispeech",["UniSpeechForSequenceClassification",Yu]],["unispeech-sat",["UniSpeechSatForSequenceClassification",lp]],["wavlm",["WavLMForSequenceClassification",ad]],["hubert",["HubertForSequenceClassification",cp]],["audio-spectrogram-transformer",["ASTForAudioClassification",Go]]]),Jd=new Map([["wavlm",["WavLMForXVector",pp]]]),Zd=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",ed]],["wavlm",["WavLMForAudioFrameClassification",ld]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Ku]],["pyannote",["PyAnnoteForAudioFrameClassification",qu]]]),ec=new Map([["vitmatte",["VitMatteForImageMatting",uu]]]),Jp=new Map([["patchtst",["PatchTSTForPrediction",Nd]],["patchtsmixer",["PatchTSMixerForPrediction",Ud]]]),tc=new Map([["swin2sr",["Swin2SRForImageSuperResolution",vu]]]),sc=new Map([["dpt",["DPTForDepthEstimation",Zc]],["depth_anything",["DepthAnythingForDepthEstimation",Eu]],["glpn",["GLPNForDepthEstimation",$u]],["sapiens",["SapiensForDepthEstimation",yo]],["depth_pro",["DepthProForDepthEstimation",Yi]]]),rc=new Map([["sapiens",["SapiensForNormalEstimation",Cu]]]),Ep=new Map([["vitpose",["VitPoseForPoseEstimation",eu]]]),nc=new Map([["clip",["CLIPVisionModelWithProjection",nl]],["siglip",["SiglipVisionModel",al]],["jina_clip",["JinaCLIPVisionModel",gr]]]),oc=[[yp,A.EncoderOnly],[Mp,A.EncoderDecoder],[bp,A.DecoderOnly],[ba,A.EncoderOnly],[va,A.EncoderOnly],[Ao,A.Seq2Seq],[Vd,A.Seq2Seq],[xa,A.DecoderOnly],[vp,A.MultiModality],[Ta,A.EncoderOnly],[Ea,A.EncoderOnly],[Pa,A.Vision2Seq],[xp,A.ImageTextToText],[Gd,A.EncoderOnly],[Hd,A.EncoderOnly],[Qd,A.EncoderOnly],[qd,A.EncoderOnly],[ec,A.EncoderOnly],[Jp,A.EncoderOnly],[tc,A.EncoderOnly],[sc,A.EncoderOnly],[rc,A.EncoderOnly],[Ep,A.EncoderOnly],[Kd,A.EncoderOnly],[Ca,A.EncoderOnly],[Xd,A.MaskGeneration],[Tp,A.EncoderOnly],[Yd,A.EncoderOnly],[qn,A.Seq2Seq],[Ma,A.EncoderOnly],[Jd,A.EncoderOnly],[Zd,A.EncoderOnly],[nc,A.EncoderOnly]];for(const[_,T]of oc)for(const[j,fe]of _.values())S.set(j,T),x.set(fe,j),w.set(j,fe);const Pp=[["MusicgenForConditionalGeneration",$o,A.Musicgen],["Phi3VForCausalLM",pr,A.Phi3V],["CLIPTextModelWithProjection",rl,A.EncoderOnly],["SiglipTextModel",il,A.EncoderOnly],["JinaCLIPTextModel",Yo,A.EncoderOnly],["ClapTextModelWithProjection",wd,A.EncoderOnly],["ClapAudioModelWithProjection",yd,A.EncoderOnly]];for(const[_,T,j]of Pp)S.set(_,j),x.set(T,_),w.set(_,T);class ka extends ws{}_e(ka,"MODEL_CLASS_MAPPINGS",oc.map(T=>T[0])),_e(ka,"BASE_IF_FAIL",!0);class Cp extends ws{}_e(Cp,"MODEL_CLASS_MAPPINGS",[ba]);class ic extends ws{}_e(ic,"MODEL_CLASS_MAPPINGS",[va]);class ac extends ws{}_e(ac,"MODEL_CLASS_MAPPINGS",[Ao]);class lc extends ws{}_e(lc,"MODEL_CLASS_MAPPINGS",[Vd]);class Sa extends ws{}_e(Sa,"MODEL_CLASS_MAPPINGS",[qn]);class uc extends ws{}_e(uc,"MODEL_CLASS_MAPPINGS",[Ma]);class dc extends ws{}_e(dc,"MODEL_CLASS_MAPPINGS",[xa]);class cc extends ws{}_e(cc,"MODEL_CLASS_MAPPINGS",[Ta]);class pc extends ws{}_e(pc,"MODEL_CLASS_MAPPINGS",[Ea]);class hc extends ws{}_e(hc,"MODEL_CLASS_MAPPINGS",[Pa]);class mc extends ws{}_e(mc,"MODEL_CLASS_MAPPINGS",[Gd]);class _c extends ws{}_e(_c,"MODEL_CLASS_MAPPINGS",[Hd]);class fc extends ws{}_e(fc,"MODEL_CLASS_MAPPINGS",[qd]);class $a extends ws{}_e($a,"MODEL_CLASS_MAPPINGS",[Qd]);class gc extends ws{}_e(gc,"MODEL_CLASS_MAPPINGS",[Kd]);class wc extends ws{}_e(wc,"MODEL_CLASS_MAPPINGS",[Ca]);class yc extends ws{}_e(yc,"MODEL_CLASS_MAPPINGS",[Xd]);class Mc extends ws{}_e(Mc,"MODEL_CLASS_MAPPINGS",[Tp]);class bc extends ws{}_e(bc,"MODEL_CLASS_MAPPINGS",[Yd]);class vc extends ws{}_e(vc,"MODEL_CLASS_MAPPINGS",[Jd]);class xc extends ws{}_e(xc,"MODEL_CLASS_MAPPINGS",[Zd]);class kp extends ws{}_e(kp,"MODEL_CLASS_MAPPINGS",[Wd]);class Tc extends ws{}_e(Tc,"MODEL_CLASS_MAPPINGS",[ec]);class Ec extends ws{}_e(Ec,"MODEL_CLASS_MAPPINGS",[tc]);class Pc extends ws{}_e(Pc,"MODEL_CLASS_MAPPINGS",[sc]);class Sp extends ws{}_e(Sp,"MODEL_CLASS_MAPPINGS",[rc]);class Cc extends ws{}_e(Cc,"MODEL_CLASS_MAPPINGS",[Ep]);class kc extends ws{}_e(kc,"MODEL_CLASS_MAPPINGS",[nc]);class $p extends Ke{constructor({logits:T,past_key_values:j,encoder_outputs:fe,decoder_attentions:Fe=null,cross_attentions:De=null}){super(),this.logits=T,this.past_key_values=j,this.encoder_outputs=fe,this.decoder_attentions=Fe,this.cross_attentions=De}}class Zt extends Ke{constructor({logits:T,...j}){super(),this.logits=T;const fe=Object.values(j);fe.length>0&&(this.attentions=fe)}}class Sc extends Ke{constructor({logits:T,embeddings:j}){super(),this.logits=T,this.embeddings=j}}class Ds extends Ke{constructor({logits:T}){super(),this.logits=T}}class Ys extends Ke{constructor({logits:T}){super(),this.logits=T}}class er extends Ke{constructor({start_logits:T,end_logits:j}){super(),this.start_logits=T,this.end_logits=j}}class dn extends Ke{constructor({logits:T}){super(),this.logits=T}}class Ap extends Ke{constructor({logits:T,past_key_values:j}){super(),this.logits=T,this.past_key_values=j}}class $c extends Ke{constructor({alphas:T}){super(),this.alphas=T}}class Ac extends Ke{constructor({waveform:T,spectrogram:j}){super(),this.waveform=T,this.spectrogram=j}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(Ee,$,r)=>{r.r($),r.d($,{ASTFeatureExtractor:()=>N});var f=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var I=r("./src/utils/audio.js");class N extends f.FeatureExtractor{constructor(R){super(R);const g=this.config.sampling_rate,v=(0,I.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(g/2),g,null,"kaldi",!0);for(let M=0;M<v.length;++M)v[M].push(0);this.mel_filters=v,this.window=(0,I.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(R,g){return(0,I.spectrogram)(R,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:g,transpose:!0})}async _call(R){(0,f.validate_audio_inputs)(R,"ASTFeatureExtractor");const g=await this._extract_fbank_features(R,this.config.max_length);if(this.config.do_normalize){const v=this.std*2,M=g.data;for(let y=0;y<M.length;++y)M[y]=(M[y]-this.mean)/v}return{input_values:g.unsqueeze_(0)}}}},"./src/models/auto/feature_extraction_auto.js":(Ee,$,r)=>{r.r($),r.d($,{AutoFeatureExtractor:()=>X});var f=r("./src/utils/constants.js"),I=r("./src/utils/hub.js");r("./src/base/feature_extraction_utils.js");var N=r("./src/models/feature_extractors.js");class X{static async from_pretrained(g,v={}){const M=await(0,I.getModelJSON)(g,f.FEATURE_EXTRACTOR_NAME,!0,v),y=M.feature_extractor_type,b=N[y];if(!b)throw new Error(`Unknown feature_extractor_type: '${y}'. 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Please report this at ${f.GITHUB_ISSUE_URL}.`),O=N.ImageProcessor),new O(y)}}},"./src/models/auto/processing_auto.js":(Ee,$,r)=>{r.r($),r.d($,{AutoProcessor:()=>v});var f=r("./src/utils/constants.js"),I=r("./src/utils/hub.js"),N=r("./src/base/processing_utils.js"),X=r("./src/models/processors.js"),R=r("./src/models/image_processors.js"),g=r("./src/models/feature_extractors.js");class v{static async from_pretrained(y,b={}){const O=await(0,I.getModelJSON)(y,f.IMAGE_PROCESSOR_NAME,!0,b),{image_processor_type:H,feature_extractor_type:se,processor_class:ne}=O;if(ne&&X[ne])return X[ne].from_pretrained(y,b);if(!H&&!se)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const W={};if(H){const q=R[H];if(!q)throw new Error(`Unknown image_processor_type: '${H}'.`);W.image_processor=new q(O)}if(se){const q=R[se];if(q)W.image_processor=new q(O);else{const A=g[se];if(!A)throw new Error(`Unknown feature_extractor_type: '${se}'.`);W.feature_extractor=new 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f.FeatureExtractor{constructor(R){super(R),this.mel_filters=(0,I.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,I.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,I.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(R,g,v,M){let y;const b=R.length-g;if(b>0)if(v==="rand_trunc"){const O=Math.floor(Math.random()*(b+1));R=R.subarray(O,O+g),y=await this._extract_fbank_features(R,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${v}" not implemented`);else{if(b<0){let O=new Float64Array(g);if(O.set(R),M==="repeat")for(let H=R.length;H<g;H+=R.length)O.set(R.subarray(0,Math.min(R.length,g-H)),H);else if(M==="repeatpad")for(let H=R.length;H<-b;H+=R.length)O.set(R,H);R=O}if(v==="fusion")throw new Error(`Truncation strategy "${v}" not implemented`);y=await this._extract_fbank_features(R,this.mel_filters_slaney,this.config.nb_max_samples)}return y.unsqueeze_(0)}async _extract_fbank_features(R,g,v=null){return(0,I.spectrogram)(R,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:g,log_mel:"dB",max_num_frames:v,do_pad:!1,transpose:!0})}async _call(R,{max_length:g=null}={}){return(0,f.validate_audio_inputs)(R,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(R,g??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}},"./src/models/clip/image_processing_clip.js":(Ee,$,r)=>{r.r($),r.d($,{CLIPFeatureExtractor:()=>N,CLIPImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends f.ImageProcessor{}class N extends I{}},"./src/models/convnext/image_processing_convnext.js":(Ee,$,r)=>{r.r($),r.d($,{ConvNextFeatureExtractor:()=>N,ConvNextImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends f.ImageProcessor{constructor(R){super(R),this.crop_pct=this.config.crop_pct??.875}async resize(R){var v;const g=(v=this.size)==null?void 0:v.shortest_edge;if(g===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(g<384){const M=Math.floor(g/this.crop_pct),[y,b]=this.get_resize_output_image_size(R,{shortest_edge:M});R=await R.resize(y,b,{resample:this.resample}),R=await R.center_crop(g,g)}else R=await R.resize(g,g,{resample:this.resample});return R}}class N extends I{}},"./src/models/deit/image_processing_deit.js":(Ee,$,r)=>{r.r($),r.d($,{DeiTFeatureExtractor:()=>N,DeiTImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends f.ImageProcessor{}class N extends I{}},"./src/models/detr/image_processing_detr.js":(Ee,$,r)=>{r.r($),r.d($,{DetrFeatureExtractor:()=>X,DetrImageProcessor:()=>N});var f=r("./src/base/image_processors_utils.js"),I=r("./src/utils/tensor.js");class N extends f.ImageProcessor{async _call(g){const v=await super._call(g),M=[v.pixel_values.dims[0],64,64],y=(0,I.full)(M,1n);return{...v,pixel_mask:y}}post_process_object_detection(...g){return(0,f.post_process_object_detection)(...g)}post_process_panoptic_segmentation(...g){return(0,f.post_process_panoptic_segmentation)(...g)}post_process_instance_segmentation(...g){return(0,f.post_process_instance_segmentation)(...g)}}class X extends N{}},"./src/models/donut/image_processing_donut.js":(Ee,$,r)=>{r.r($),r.d($,{DonutFeatureExtractor:()=>N,DonutImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends f.ImageProcessor{pad_image(R,g,v,M={}){const[y,b,O]=g;let H=this.image_mean;Array.isArray(this.image_mean)||(H=new Array(O).fill(H));let se=this.image_std;Array.isArray(se)||(se=new Array(O).fill(H));const ne=H.map((W,U)=>-W/se[U]);return super.pad_image(R,g,v,{center:!0,constant_values:ne,...M})}}class N extends I{}},"./src/models/dpt/image_processing_dpt.js":(Ee,$,r)=>{r.r($),r.d($,{DPTFeatureExtractor:()=>N,DPTImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends f.ImageProcessor{}class N extends I{}},"./src/models/efficientnet/image_processing_efficientnet.js":(Ee,$,r)=>{r.r($),r.d($,{EfficientNetImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends 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f=r("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),I=r("./src/models/clap/feature_extraction_clap.js"),N=r("./src/models/moonshine/feature_extraction_moonshine.js"),X=r("./src/models/pyannote/feature_extraction_pyannote.js"),R=r("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),g=r("./src/models/speecht5/feature_extraction_speecht5.js"),v=r("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),M=r("./src/models/wespeaker/feature_extraction_wespeaker.js"),y=r("./src/models/whisper/feature_extraction_whisper.js"),b=r("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(Ee,$,r)=>{r.r($),r.d($,{Florence2Processor:()=>X});var f=r("./src/base/processing_utils.js"),I=r("./src/models/auto/image_processing_auto.js"),N=r("./src/tokenizers.js");class X extends f.Processor{constructor(g,v){super(g,v);const{tasks_answer_post_processing_type:M,task_prompts_without_inputs:y,task_prompts_with_input:b}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(M??{})),this.task_prompts_without_inputs=new Map(Object.entries(y??{})),this.task_prompts_with_input=new Map(Object.entries(b??{})),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(g){typeof g=="string"&&(g=[g]);const v=[];for(const M of g)if(this.task_prompts_without_inputs.has(M))v.push(this.task_prompts_without_inputs.get(M));else{for(const[y,b]of this.task_prompts_with_input)if(M.includes(y)){v.push(b.replaceAll("{input}",M).replaceAll(y,""));break}v.length!==g.length&&v.push(M)}return v}post_process_generation(g,v,M){const y=this.tasks_answer_post_processing_type.get(v)??"pure_text";g=g.replaceAll("<s>","").replaceAll("</s>","");let b;switch(y){case"pure_text":b=g;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const O=y==="ocr"?"quad_boxes":"bboxes",H=g.matchAll(this.regexes[O]),se=[],ne=[];for(const[W,U,...q]of H)se.push(U?U.trim():se.at(-1)??""),ne.push(q.map((A,S)=>(Number(A)+.5)/this.size_per_bin*M[S%2]));b={labels:se,[O]:ne};break;default:throw new Error(`Task "${v}" (of type "${y}") not yet implemented.`)}return{[v]:b}}async _call(g,v=null,M={}){if(!g&&!v)throw new Error("Either text or images must be provided");const y=await this.image_processor(g,M),b=v?this.tokenizer(v,M):{};return{...y,...b}}}_e(X,"tokenizer_class",N.AutoTokenizer),_e(X,"image_processor_class",I.AutoImageProcessor)},"./src/models/glpn/image_processing_glpn.js":(Ee,$,r)=>{r.r($),r.d($,{GLPNFeatureExtractor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends f.ImageProcessor{}},"./src/models/grounding_dino/image_processing_grounding_dino.js":(Ee,$,r)=>{r.r($),r.d($,{GroundingDinoImageProcessor:()=>N});var f=r("./src/base/image_processors_utils.js"),I=r("./src/utils/tensor.js");class N extends f.ImageProcessor{async _call(R){const g=await super._call(R),v=g.pixel_values.dims,M=(0,I.ones)([v[0],v[2],v[3]]);return{...g,pixel_mask:M}}}},"./src/models/grounding_dino/processing_grounding_dino.js":(Ee,$,r)=>{r.r($),r.d($,{GroundingDinoProcessor:()=>g});var f=r("./src/base/processing_utils.js"),I=r("./src/models/auto/image_processing_auto.js"),N=r("./src/tokenizers.js"),X=r("./src/base/image_processors_utils.js");function R(v,M){const b=v.dims.at(-1)-1,O=v.tolist();O.fill(!1,0,1),O.fill(!1,b);const H=M.tolist();return O.map((se,ne)=>se?ne:null).filter(se=>se!==null).map(se=>H[se])}class g extends f.Processor{async _call(M,y,b={}){const O=M?await this.image_processor(M,b):{};return{...y?this.tokenizer(y,b):{},...O}}post_process_grounded_object_detection(M,y,{box_threshold:b=.25,text_threshold:O=.25,target_sizes:H=null}={}){const{logits:se,pred_boxes:ne}=M,W=se.dims[0];if(H!==null&&H.length!==W)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const U=se.dims.at(1),q=se.sigmoid(),A=q.max(-1).tolist(),S=ne.tolist().map(x=>x.map(F=>(0,X.center_to_corners_format)(F))),w=[];for(let x=0;x<W;++x){const F=H!==null?H[x]:null;F!==null&&(S[x]=S[x].map(re=>re.map((Te,ce)=>Te*F[(ce+1)%2])));const ae=A[x],oe=[],xe=[],we=[];for(let re=0;re<U;++re){const Te=ae[re];if(Te<=b)continue;const ce=S[x][re],$e=q[x][re];oe.push(Te),we.push(ce);const Oe=R($e.gt(O),y[x]);xe.push(Oe)}w.push({scores:oe,boxes:we,labels:this.batch_decode(xe)})}return w}}_e(g,"tokenizer_class",N.AutoTokenizer),_e(g,"image_processor_class",I.AutoImageProcessor)},"./src/models/idefics3/image_processing_idefics3.js":(Ee,$,r)=>{r.r($),r.d($,{Idefics3ImageProcessor:()=>N});var f=r("./src/base/image_processors_utils.js"),I=r("./src/utils/tensor.js");class N extends f.ImageProcessor{constructor(R){super(R),this.do_image_splitting=R.do_image_splitting??!0,this.max_image_size=R.max_image_size}get_resize_for_vision_encoder(R,g){let[v,M]=R.dims.slice(-2);const y=M/v;return M>=v?(M=Math.ceil(M/g)*g,v=Math.floor(M/y),v=Math.ceil(v/g)*g):(v=Math.ceil(v/g)*g,M=Math.floor(v*y),M=Math.ceil(M/g)*g),{height:v,width:M}}async _call(R,{do_image_splitting:g=null,return_row_col_info:v=!1}={}){let M;if(!Array.isArray(R))M=[[R]];else{if(R.length===0||!R[0])throw new Error("No images provided.");Array.isArray(R[0])?M=R:M=[R]}let y=[],b=[],O=[];const H=[],se=[];for(const x of M){let F=await Promise.all(x.map(xe=>this.preprocess(xe)));H.push(...F.map(xe=>xe.original_size)),se.push(...F.map(xe=>xe.reshaped_input_size)),F.forEach(xe=>xe.pixel_values.unsqueeze_(0));const{longest_edge:ae}=this.max_image_size;let oe;if(g??this.do_image_splitting){let xe=new Array(F.length),we=new Array(F.length);oe=await Promise.all(F.map(async(re,Te)=>{const ce=this.get_resize_for_vision_encoder(re.pixel_values,ae),$e=await(0,I.interpolate_4d)(re.pixel_values,{size:[ce.height,ce.width]}),{frames:Oe,num_splits_h:Ce,num_splits_w:tt}=await this.split_image($e,this.max_image_size);return xe[Te]=Ce,we[Te]=tt,(0,I.cat)(Oe,0)})),b.push(xe),O.push(we)}else{const xe=[ae,ae];oe=await Promise.all(F.map(we=>(0,I.interpolate_4d)(we.pixel_values,{size:xe}))),b.push(new Array(F.length).fill(0)),O.push(new Array(F.length).fill(0))}y.push((0,I.cat)(oe,0))}const ne=y.length,[W,U,q,A]=y[0].dims;let S,w;if(ne===1)S=y[0].unsqueeze_(0),w=(0,I.full)([ne,W,q,A],!0);else{const x=Math.max(...y.map(oe=>oe.dims.at(0)));w=(0,I.full)([ne,x,q,A],!0);const F=w.data,ae=x*q*A;for(let oe=0;oe<ne;++oe){const xe=y[oe].dims[0];if(xe<x){y[oe]=(0,I.cat)([y[oe],(0,I.full)([x-xe,U,q,A],0)],0);const we=oe*ae+xe*q*A,re=(oe+1)*ae;F.fill(!1,we,re)}}S=(0,I.stack)(y,0)}return{pixel_values:S,pixel_attention_mask:w,original_sizes:H,reshaped_input_sizes:se,...v?{rows:b,cols:O}:{}}}async split_image(R,{longest_edge:g}){const v=g,M=g,y=[],[b,O]=R.dims.slice(-2);let H=0,se=0;if(b>v||O>M){H=Math.ceil(b/v),se=Math.ceil(O/M);const ne=Math.ceil(b/H),W=Math.ceil(O/se);for(let A=0;A<H;++A)for(let S=0;S<se;++S){let w,x,F,ae;A===H-1?(x=b-ne,ae=b):(x=A*ne,ae=(A+1)*ne),S===se-1?(w=O-W,F=O):(w=S*W,F=(S+1)*W);const oe=[x,w],xe=[ae,F],we=await(0,I.slice)(R,oe,xe,[2,3]);y.push(we)}const U=v,q=M;(b!==U||O!==q)&&(R=await(0,I.interpolate_4d)(R,{size:[U,q]}))}return y.push(R),{frames:y,num_splits_h:H,num_splits_w:se}}}},"./src/models/idefics3/processing_idefics3.js":(Ee,$,r)=>{r.r($),r.d($,{Idefics3Processor:()=>M});var f=r("./src/base/processing_utils.js"),I=r("./src/models/auto/image_processing_auto.js"),N=r("./src/tokenizers.js");r("./src/utils/image.js");var X=r("./src/utils/core.js");function R(y,b,O,H,se,ne){let W="";for(let U=0;U<b;++U){for(let q=0;q<O;++q)W+=H+`<row_${U+1}_col_${q+1}>`+se.repeat(y);W+=`
`}return W+=`
${H}${ne}`+se.repeat(y)+`${H}`,W}function g(y,b,O,H){return`${b}${H}`+O.repeat(y)+`${b}`}function v(y,b,O,H,se,ne){return y===0&&b===0?g(O,H,se,ne):R(O,y,b,H,se,ne)}class M extends f.Processor{constructor(){super(...arguments);_e(this,"fake_image_token","<fake_token_around_image>");_e(this,"image_token","<image>");_e(this,"global_img_token","<global-img>")}async _call(O,H=null,se={}){se.return_row_col_info??(se.return_row_col_info=!0);let ne;H&&(ne=await this.image_processor(H,se)),Array.isArray(O)||(O=[O]);const W=ne.rows??[new Array(O.length).fill(0)],U=ne.cols??[new Array(O.length).fill(0)],q=this.config.image_seq_len,A=[],S=[];for(let x=0;x<O.length;++x){const F=O[x],ae=W[x],oe=U[x];A.push((0,X.count)(F,this.image_token));const xe=ae.map((Te,ce)=>v(Te,oe[ce],q,this.fake_image_token,this.image_token,this.global_img_token)),we=F.split(this.image_token);if(we.length===0)throw new Error("The image token should be present in the text.");let re=we[0];for(let Te=0;Te<xe.length;++Te)re+=xe[Te]+we[Te+1];S.push(re)}return{...this.tokenizer(S),...ne}}}_e(M,"image_processor_class",I.AutoImageProcessor),_e(M,"tokenizer_class",N.AutoTokenizer),_e(M,"uses_processor_config",!0)},"./src/models/image_processors.js":(Ee,$,r)=>{r.r($),r.d($,{BeitFeatureExtractor:()=>f.BeitFeatureExtractor,BitImageProcessor:()=>I.BitImageProcessor,CLIPFeatureExtractor:()=>X.CLIPFeatureExtractor,CLIPImageProcessor:()=>X.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>N.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>R.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>R.ConvNextImageProcessor,DPTFeatureExtractor:()=>y.DPTFeatureExtractor,DPTImageProcessor:()=>y.DPTImageProcessor,DeiTFeatureExtractor:()=>g.DeiTFeatureExtractor,DeiTImageProcessor:()=>g.DeiTImageProcessor,DetrFeatureExtractor:()=>v.DetrFeatureExtractor,DetrImageProcessor:()=>v.DetrImageProcessor,DonutFeatureExtractor:()=>M.DonutFeatureExtractor,DonutImageProcessor:()=>M.DonutImageProcessor,EfficientNetImageProcessor:()=>b.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>O.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>H.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>se.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>W.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>U.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>q.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>A.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>A.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>S.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>S.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>w.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>w.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>x.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>x.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>F.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>F.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>ae.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>ae.MobileViTImageProcessor,NougatImageProcessor:()=>oe.NougatImageProcessor,OwlViTFeatureExtractor:()=>we.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>we.OwlViTImageProcessor,Owlv2ImageProcessor:()=>xe.Owlv2ImageProcessor,Phi3VImageProcessor:()=>re.Phi3VImageProcessor,PvtImageProcessor:()=>Te.PvtImageProcessor,Qwen2VLImageProcessor:()=>ce.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>$e.RTDetrImageProcessor,SamImageProcessor:()=>Oe.SamImageProcessor,SegformerFeatureExtractor:()=>Ce.SegformerFeatureExtractor,SegformerImageProcessor:()=>Ce.SegformerImageProcessor,SiglipImageProcessor:()=>tt.SiglipImageProcessor,Swin2SRImageProcessor:()=>Ge.Swin2SRImageProcessor,VLMImageProcessor:()=>ne.VLMImageProcessor,ViTFeatureExtractor:()=>ye.ViTFeatureExtractor,ViTImageProcessor:()=>ye.ViTImageProcessor,VitMatteImageProcessor:()=>J.VitMatteImageProcessor,VitPoseImageProcessor:()=>de.VitPoseImageProcessor,YolosFeatureExtractor:()=>ke.YolosFeatureExtractor,YolosImageProcessor:()=>ke.YolosImageProcessor});var f=r("./src/models/beit/image_processing_beit.js"),I=r("./src/models/bit/image_processing_bit.js"),N=r("./src/models/chinese_clip/image_processing_chinese_clip.js"),X=r("./src/models/clip/image_processing_clip.js"),R=r("./src/models/convnext/image_processing_convnext.js"),g=r("./src/models/deit/image_processing_deit.js"),v=r("./src/models/detr/image_processing_detr.js"),M=r("./src/models/donut/image_processing_donut.js"),y=r("./src/models/dpt/image_processing_dpt.js"),b=r("./src/models/efficientnet/image_processing_efficientnet.js"),O=r("./src/models/glpn/image_processing_glpn.js"),H=r("./src/models/grounding_dino/image_processing_grounding_dino.js"),se=r("./src/models/idefics3/image_processing_idefics3.js"),ne=r("./src/models/janus/image_processing_janus.js"),W=r("./src/models/jina_clip/image_processing_jina_clip.js"),U=r("./src/models/llava_onevision/image_processing_llava_onevision.js"),q=r("./src/models/mask2former/image_processing_mask2former.js"),A=r("./src/models/maskformer/image_processing_maskformer.js"),S=r("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),w=r("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),x=r("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),F=r("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),ae=r("./src/models/mobilevit/image_processing_mobilevit.js"),oe=r("./src/models/nougat/image_processing_nougat.js"),xe=r("./src/models/owlv2/image_processing_owlv2.js"),we=r("./src/models/owlvit/image_processing_owlvit.js"),re=r("./src/models/phi3_v/image_processing_phi3_v.js"),Te=r("./src/models/pvt/image_processing_pvt.js"),ce=r("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),$e=r("./src/models/rt_detr/image_processing_rt_detr.js"),Oe=r("./src/models/sam/image_processing_sam.js"),Ce=r("./src/models/segformer/image_processing_segformer.js"),tt=r("./src/models/siglip/image_processing_siglip.js"),Ge=r("./src/models/swin2sr/image_processing_swin2sr.js"),ye=r("./src/models/vit/image_processing_vit.js"),J=r("./src/models/vitmatte/image_processing_vitmatte.js"),de=r("./src/models/vitpose/image_processing_vitpose.js"),ke=r("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(Ee,$,r)=>{r.r($),r.d($,{VLMImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends f.ImageProcessor{constructor(X){super({do_pad:!0,pad_size:{width:X.image_size,height:X.image_size},...X}),this.constant_values=this.config.background_color.map(R=>R*this.rescale_factor)}pad_image(X,R,g,v){return super.pad_image(X,R,g,{constant_values:this.constant_values,center:!0,...v})}}},"./src/models/janus/processing_janus.js":(Ee,$,r)=>{r.r($),r.d($,{VLChatProcessor:()=>v});var f=r("./src/base/processing_utils.js"),I=r("./src/models/auto/image_processing_auto.js"),N=r("./src/tokenizers.js"),X=r("./src/utils/core.js"),R=r("./src/utils/tensor.js"),g=r("./src/utils/image.js");class v extends f.Processor{constructor(y,b){super(y,b),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(y,{images:b=null,chat_template:O="default"}={}){b?Array.isArray(b)||(b=[b]):b=await Promise.all(y.filter(oe=>oe.images).flatMap(oe=>oe.images).map(oe=>g.RawImage.read(oe)));const H=this.tokenizer,se=H.apply_chat_template(y,{tokenize:!1,add_generation_prompt:!0,chat_template:O}),ne=oe=>H.encode(oe,{add_special_tokens:!1}),W=se.split(this.image_tag),U=W.length-1;if(b.length!==U)throw new Error(`Number of images provided (${b.length}) does not match number of "${this.image_tag}" image tags (${U})`);const[q,A,S]=H.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let w=ne(W[0]),x=new Array(w.length).fill(!1);for(let oe=1;oe<W.length;++oe){const xe=new Array(this.num_image_tokens).fill(q),we=ne(W[oe]);w=(0,X.mergeArrays)(w,[A],xe,[S],we);const re=new Array(this.num_image_tokens).fill(!0);x=(0,X.mergeArrays)(x,[!1],re,[!1],new Array(we.length).fill(!1))}const F=[1,w.length],ae={input_ids:new R.Tensor("int64",w,F),attention_mask:new R.Tensor("int64",new Array(w.length).fill(1),F),images_seq_mask:new R.Tensor("bool",x,F),images_emb_mask:new 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H}generate_crop_boxes(g,v,{crop_n_layers:M=0,overlap_ratio:y=.3413333333333333,points_per_crop:b=32,crop_n_points_downscale_factor:O=1}={}){}}},"./src/models/sam/processing_sam.js":(Ee,$,r)=>{r.r($),r.d($,{SamProcessor:()=>N});var f=r("./src/base/processing_utils.js"),I=r("./src/models/auto/image_processing_auto.js");class N extends f.Processor{async _call(...R){return await this.image_processor(...R)}post_process_masks(...R){return this.image_processor.post_process_masks(...R)}reshape_input_points(...R){return this.image_processor.reshape_input_points(...R)}}_e(N,"image_processor_class",I.AutoImageProcessor)},"./src/models/seamless_m4t/feature_extraction_seamless_m4t.js":(Ee,$,r)=>{r.r($),r.d($,{SeamlessM4TFeatureExtractor:()=>X});var f=r("./src/base/feature_extraction_utils.js"),I=r("./src/utils/tensor.js"),N=r("./src/utils/audio.js");class X extends f.FeatureExtractor{constructor(g){super(g);const 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Te=re*w+F;x[Te]=(x[Te]-oe)/we}}}let H;if(v){const[S,w]=O.dims,x=O.data,F=S%M;if(F>0){const ae=new Float32Array(w*(S+F));ae.set(x),ae.fill(this.config.padding_value,x.length);const oe=S+F;O=new I.Tensor(O.type,ae,[oe,w]),b&&(H=new I.Tensor("int64",new BigInt64Array(oe),[1,oe]),H.data.fill(1n,0,S))}}const[se,ne]=O.dims,W=this.config.stride;if(se%W!==0)throw new Error(`The number of frames (${se}) must be a multiple of the stride (${W}).`);const q=O.view(1,Math.floor(se/W),ne*W),A={input_features:q};if(b){const S=q.dims[1],w=new BigInt64Array(S);if(H){const x=H.data;for(let F=1,ae=0;F<se;F+=W,++ae)w[ae]=x[F]}else w.fill(1n);A.attention_mask=new I.Tensor("int64",w,[1,S])}return A}}},"./src/models/segformer/image_processing_segformer.js":(Ee,$,r)=>{r.r($),r.d($,{SegformerFeatureExtractor:()=>N,SegformerImageProcessor:()=>I});var f=r("./src/base/image_processors_utils.js");class I extends 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Ge=Ce[tt];re+=Ge,Te=Math.max(Te,Ge),xe+=(tt+.5)*Ge,we+=Oe*Ge}}if(g!=null&&Te<g)continue;const $e=[F*xe/re,ae*we/re];S.push($e),x.push(oe),w.push(Te)}U.push({bbox:A,scores:w,labels:x,keypoints:S})}H.push(U)}return H}}},"./src/models/wav2vec2/feature_extraction_wav2vec2.js":(Ee,$,r)=>{r.r($),r.d($,{Wav2Vec2FeatureExtractor:()=>N});var f=r("./src/base/feature_extraction_utils.js"),I=r("./src/utils/tensor.js");class N extends f.FeatureExtractor{_zero_mean_unit_var_norm(R){const v=R.reduce((y,b)=>y+b,0)/R.length,M=R.reduce((y,b)=>y+(b-v)**2,0)/R.length;return R.map(y=>(y-v)/Math.sqrt(M+1e-7))}async _call(R){(0,f.validate_audio_inputs)(R,"Wav2Vec2FeatureExtractor"),R instanceof Float64Array&&(R=new Float32Array(R));let g=R;this.config.do_normalize&&(g=this._zero_mean_unit_var_norm(g));const v=[1,g.length];return{input_values:new I.Tensor("float32",g,v),attention_mask:new I.Tensor("int64",new 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f=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var I=r("./src/utils/audio.js");class N extends f.FeatureExtractor{constructor(R){super(R);const g=this.config.sampling_rate,v=(0,I.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(g/2),g,null,"kaldi",!0);for(let M=0;M<v.length;++M)v[M].push(0);this.mel_filters=v,this.window=(0,I.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(R){return R=R.map(g=>g*32768),(0,I.spectrogram)(R,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(R){(0,f.validate_audio_inputs)(R,"WeSpeakerFeatureExtractor");const g=(await this._extract_fbank_features(R)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const v=g.mean(1).data,M=g.data,[y,b,O]=g.dims;for(let 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If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),v=g.slice(0,this.config.n_samples)):(v=new Float32Array(this.config.n_samples),v.set(g)),{input_features:(await this._extract_fbank_features(v)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":(Ee,$,r)=>{r.r($),r.d($,{WhisperGenerationConfig:()=>I});var f=r("./src/generation/configuration_utils.js");class I extends f.GenerationConfig{constructor(){super(...arguments);_e(this,"return_timestamps",null);_e(this,"return_token_timestamps",null);_e(this,"num_frames",null);_e(this,"alignment_heads",null);_e(this,"task",null);_e(this,"language",null);_e(this,"no_timestamps_token_id",null);_e(this,"prompt_ids",null);_e(this,"is_multilingual",null);_e(this,"lang_to_id",null);_e(this,"task_to_id",null);_e(this,"max_initial_timestamp_index",1)}}},"./src/models/whisper/processing_whisper.js":(Ee,$,r)=>{r.r($),r.d($,{WhisperProcessor:()=>X});var 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se=(0,f.isONNXProxy)(),ne=Object.fromEntries(Object.entries(H).map(([U,q])=>[U,(se?q.clone():q).ort_tensor])),W=await(O=X?O.then(()=>b.run(ne)):b.run(ne));return Array.isArray(y)?y.map(U=>new I.Tensor(W[U])):new I.Tensor(W[y])}};class g{static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=R([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=R([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=R([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=R([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=R([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=R([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=R([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=R([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}}_e(g,"session_options",{})},"./src/pipelines.js":(Ee,$,r)=>{r.r($),r.d($,{AudioClassificationPipeline:()=>we,AutomaticSpeechRecognitionPipeline:()=>Te,DepthEstimationPipeline:()=>ke,DocumentQuestionAnsweringPipeline:()=>ye,FeatureExtractionPipeline:()=>oe,FillMaskPipeline:()=>q,ImageClassificationPipeline:()=>$e,ImageFeatureExtractionPipeline:()=>xe,ImageSegmentationPipeline:()=>Oe,ImageToImagePipeline:()=>de,ImageToTextPipeline:()=>ce,ObjectDetectionPipeline:()=>tt,Pipeline:()=>se,QuestionAnsweringPipeline:()=>U,SummarizationPipeline:()=>S,Text2TextGenerationPipeline:()=>A,TextClassificationPipeline:()=>ne,TextGenerationPipeline:()=>F,TextToAudioPipeline:()=>J,TokenClassificationPipeline:()=>W,TranslationPipeline:()=>w,ZeroShotAudioClassificationPipeline:()=>re,ZeroShotClassificationPipeline:()=>ae,ZeroShotImageClassificationPipeline:()=>Ce,ZeroShotObjectDetectionPipeline:()=>Ge,pipeline:()=>te});var f=r("./src/tokenizers.js"),I=r("./src/models.js"),N=r("./src/models/auto/processing_auto.js");r("./src/base/processing_utils.js");var X=r("./src/utils/generic.js"),R=r("./src/utils/core.js"),g=r("./src/utils/maths.js"),v=r("./src/utils/audio.js"),M=r("./src/utils/tensor.js"),y=r("./src/utils/image.js");async function b(Ue){return Array.isArray(Ue)||(Ue=[Ue]),await Promise.all(Ue.map(le=>y.RawImage.read(le)))}async function O(Ue,le){return Array.isArray(Ue)||(Ue=[Ue]),await Promise.all(Ue.map(be=>typeof be=="string"||be instanceof URL?(0,v.read_audio)(be,le):be instanceof Float64Array?new Float32Array(be):be))}function H(Ue,le){le&&(Ue=Ue.map(je=>je|0));const[be,Ve,We,Ne]=Ue;return{xmin:be,ymin:Ve,xmax:We,ymax:Ne}}class se extends X.Callable{constructor({task:le,model:be,tokenizer:Ve=null,processor:We=null}){super(),this.task=le,this.model=be,this.tokenizer=Ve,this.processor=We}async dispose(){await this.model.dispose()}}class ne extends se{constructor(le){super(le)}async _call(le,{top_k:be=1}={}){const Ve=this.tokenizer(le,{padding:!0,truncation:!0}),We=await this.model(Ve),Ne=this.model.config.problem_type==="multi_label_classification"?ut=>ut.sigmoid():ut=>new M.Tensor("float32",(0,g.softmax)(ut.data),ut.dims),je=this.model.config.id2label,st=[];for(const ut of We.logits){const pt=Ne(ut),lt=await(0,M.topk)(pt,be),mt=lt[0].tolist(),ie=lt[1].tolist().map((G,he)=>({label:je?je[G]:`LABEL_${G}`,score:mt[he]}));be===1?st.push(...ie):st.push(ie)}return Array.isArray(le)||be===1?st:st[0]}}class W extends se{constructor(le){super(le)}async _call(le,{ignore_labels:be=["O"]}={}){const Ve=Array.isArray(le),We=this.tokenizer(Ve?le:[le],{padding:!0,truncation:!0}),je=(await this.model(We)).logits,st=this.model.config.id2label,ut=[];for(let pt=0;pt<je.dims[0];++pt){const lt=We.input_ids[pt],mt=je[pt],L=[];for(let ie=0;ie<mt.dims[0];++ie){const G=mt[ie],he=(0,g.max)(G.data)[1],Se=st?st[he]:`LABEL_${he}`;if(be.includes(Se))continue;const Re=this.tokenizer.decode([lt[ie].item()],{skip_special_tokens:!0});if(Re==="")continue;const qe=(0,g.softmax)(G.data);L.push({entity:Se,score:qe[he],index:ie,word:Re})}ut.push(L)}return Ve?ut:ut[0]}}class U extends se{constructor(le){super(le)}async _call(le,be,{top_k:Ve=1}={}){const We=this.tokenizer(le,{text_pair:be,padding:!0,truncation:!0}),{start_logits:Ne,end_logits:je}=await this.model(We),st=We.input_ids.tolist(),ut=We.attention_mask.tolist(),pt=this.tokenizer.all_special_ids,lt=[];for(let mt=0;mt<Ne.dims[0];++mt){const L=st[mt],ie=L.findIndex(at=>at==this.tokenizer.sep_token_id);ut[mt].map((at,ct)=>at==1&&(ct===0||ct>ie&&pt.findIndex(vt=>vt==L[ct])===-1));const G=Ne[mt].tolist(),he=je[mt].tolist();for(let at=1;at<G.length;++at)(ut[mt]==0||at<=ie||pt.findIndex(ct=>ct==L[at])!==-1)&&(G[at]=-1/0,he[at]=-1/0);const Se=(0,g.softmax)(G).map((at,ct)=>[at,ct]),Re=(0,g.softmax)(he).map((at,ct)=>[at,ct]);Se[0][0]=0,Re[0][0]=0;const qe=(0,R.product)(Se,Re).filter(at=>at[0][1]<=at[1][1]).map(at=>[at[0][1],at[1][1],at[0][0]*at[1][0]]).sort((at,ct)=>ct[2]-at[2]);for(let at=0;at<Math.min(qe.length,Ve);++at){const[ct,vt,kt]=qe[at],$t=L.slice(ct,vt+1),os=this.tokenizer.decode($t,{skip_special_tokens:!0});lt.push({answer:os,score:kt})}}return Ve===1?lt[0]:lt}}class q extends se{constructor(le){super(le)}async _call(le,{top_k:be=5}={}){const Ve=this.tokenizer(le,{padding:!0,truncation:!0}),{logits:We}=await this.model(Ve),Ne=[],je=Ve.input_ids.tolist();for(let st=0;st<je.length;++st){const ut=je[st],pt=ut.findIndex(G=>G==this.tokenizer.mask_token_id);if(pt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const lt=We[st][pt],mt=await(0,M.topk)(new M.Tensor("float32",(0,g.softmax)(lt.data),lt.dims),be),L=mt[0].tolist(),ie=mt[1].tolist();Ne.push(ie.map((G,he)=>{const Se=ut.slice();return Se[pt]=G,{score:L[he],token:Number(G),token_str:this.tokenizer.decode([G]),sequence:this.tokenizer.decode(Se,{skip_special_tokens:!0})}}))}return Array.isArray(le)?Ne:Ne[0]}}class A extends se{constructor(be){super(be);_e(this,"_key","generated_text")}async _call(be,Ve={}){Array.isArray(be)||(be=[be]),this.model.config.prefix&&(be=be.map(pt=>this.model.config.prefix+pt));const We=this.model.config.task_specific_params;We&&We[this.task]&&We[this.task].prefix&&(be=be.map(pt=>We[this.task].prefix+pt));const Ne=this.tokenizer,je={padding:!0,truncation:!0};let st;this instanceof w&&"_build_translation_inputs"in Ne?st=Ne._build_translation_inputs(be,je,Ve):st=Ne(be,je);const ut=await this.model.generate({...st,...Ve});return Ne.batch_decode(ut,{skip_special_tokens:!0}).map(pt=>({[this._key]:pt}))}}class S extends A{constructor(be){super(be);_e(this,"_key","summary_text")}}class w extends A{constructor(be){super(be);_e(this,"_key","translation_text")}}function x(Ue){return Array.isArray(Ue)&&Ue.every(le=>"role"in le&&"content"in le)}class F extends se{constructor(le){super(le)}async _call(le,be={}){let Ve=!1,We=!1,Ne;if(typeof le=="string")Ne=le=[le];else if(Array.isArray(le)&&le.every(ie=>typeof ie=="string"))Ve=!0,Ne=le;else{if(x(le))le=[le];else if(Array.isArray(le)&&le.every(x))Ve=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");We=!0,Ne=le.map(ie=>this.tokenizer.apply_chat_template(ie,{tokenize:!1,add_generation_prompt:!0}))}const je=be.add_special_tokens??!1,st=We?!1:be.return_full_text??!0;this.tokenizer.padding_side="left";const ut=this.tokenizer(Ne,{add_special_tokens:je,padding:!0,truncation:!0}),pt=await this.model.generate({...ut,...be}),lt=this.tokenizer.batch_decode(pt,{skip_special_tokens:!0});let mt;!st&&ut.input_ids.dims.at(-1)>0&&(mt=this.tokenizer.batch_decode(ut.input_ids,{skip_special_tokens:!0}).map(ie=>ie.length));const L=Array.from({length:le.length},ie=>[]);for(let ie=0;ie<lt.length;++ie){const G=Math.floor(ie/pt.dims[0]*le.length);mt&&(lt[ie]=lt[ie].slice(mt[G])),L[G].push({generated_text:We?[...le[G],{role:"assistant",content:lt[ie]}]:lt[ie]})}return!Ve&&L.length===1?L[0]:L}}class ae extends se{constructor(le){super(le),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([be,Ve])=>[be.toLowerCase(),Ve])),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(le,be,{hypothesis_template:Ve="This example is {}.",multi_label:We=!1}={}){const Ne=Array.isArray(le);Ne||(le=[le]),Array.isArray(be)||(be=[be]);const je=be.map(pt=>Ve.replace("{}",pt)),st=We||be.length===1,ut=[];for(const pt of le){const lt=[];for(const ie of je){const G=this.tokenizer(pt,{text_pair:ie,padding:!0,truncation:!0}),he=await this.model(G);st?lt.push([he.logits.data[this.contradiction_id],he.logits.data[this.entailment_id]]):lt.push(he.logits.data[this.entailment_id])}const L=(st?lt.map(ie=>(0,g.softmax)(ie)[1]):(0,g.softmax)(lt)).map((ie,G)=>[ie,G]).sort((ie,G)=>G[0]-ie[0]);ut.push({sequence:pt,labels:L.map(ie=>be[ie[1]]),scores:L.map(ie=>ie[0])})}return Ne?ut:ut[0]}}class oe extends se{constructor(le){super(le)}async _call(le,{pooling:be="none",normalize:Ve=!1,quantize:We=!1,precision:Ne="binary"}={}){const je=this.tokenizer(le,{padding:!0,truncation:!0}),st=await this.model(je);let ut=st.last_hidden_state??st.logits??st.token_embeddings;if(be!=="none")if(be==="mean")ut=(0,M.mean_pooling)(ut,je.attention_mask);else if(be==="cls")ut=ut.slice(null,0);else throw Error(`Pooling method '${be}' not supported.`);return Ve&&(ut=ut.normalize(2,-1)),We&&(ut=(0,M.quantize_embeddings)(ut,Ne)),ut}}class xe extends se{constructor(le){super(le)}async _call(le,{pool:be=null}={}){const Ve=await b(le),{pixel_values:We}=await this.processor(Ve),Ne=await this.model({pixel_values:We});let je;if(be){if(!("pooler_output"in Ne))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");je=Ne.pooler_output}else je=Ne.last_hidden_state??Ne.logits??Ne.image_embeds;return je}}class we extends se{constructor(le){super(le)}async _call(le,{top_k:be=5}={}){const Ve=this.processor.feature_extractor.config.sampling_rate,We=await O(le,Ve),Ne=this.model.config.id2label,je=[];for(const st of We){const ut=await this.processor(st),lt=(await this.model(ut)).logits[0],mt=await(0,M.topk)(new M.Tensor("float32",(0,g.softmax)(lt.data),lt.dims),be),L=mt[0].tolist(),G=mt[1].tolist().map((he,Se)=>({label:Ne?Ne[he]:`LABEL_${he}`,score:L[Se]}));je.push(G)}return Array.isArray(le)?je:je[0]}}class re extends se{constructor(le){super(le)}async _call(le,be,{hypothesis_template:Ve="This is a sound of {}."}={}){const We=!Array.isArray(le);We&&(le=[le]);const Ne=be.map(lt=>Ve.replace("{}",lt)),je=this.tokenizer(Ne,{padding:!0,truncation:!0}),st=this.processor.feature_extractor.config.sampling_rate,ut=await O(le,st),pt=[];for(const lt of ut){const mt=await this.processor(lt),L=await this.model({...je,...mt}),ie=(0,g.softmax)(L.logits_per_audio.data);pt.push([...ie].map((G,he)=>({score:G,label:be[he]})))}return We?pt[0]:pt}}class Te extends se{constructor(le){super(le)}async _call(le,be={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(le,be);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(le,be);case"moonshine":return this._call_moonshine(le,be);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(le,be){be.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),be.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Ve=!Array.isArray(le);Ve&&(le=[le]);const We=this.processor.feature_extractor.config.sampling_rate,Ne=await O(le,We),je=[];for(const st of Ne){const ut=await this.processor(st),lt=(await this.model(ut)).logits[0],mt=[];for(const ie of lt)mt.push((0,g.max)(ie.data)[1]);const L=this.tokenizer.decode(mt);je.push({text:L})}return Ve?je[0]:je}async _call_whisper(le,be){const Ve=be.return_timestamps??!1,We=be.chunk_length_s??0,Ne=be.force_full_sequences??!1;let je=be.stride_length_s??null;const st={...be};Ve==="word"&&(st.return_token_timestamps=!0,st.return_timestamps=!1);const ut=!Array.isArray(le);ut&&(le=[le]);const pt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,lt=this.processor.feature_extractor.config.hop_length,mt=this.processor.feature_extractor.config.sampling_rate,L=await O(le,mt),ie=[];for(const G of L){let he=[];if(We>0){if(je===null)je=We/6;else if(We<=je)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const qe=mt*We,at=mt*je,ct=qe-2*at;let vt=0;for(;;){const kt=vt+qe,$t=G.subarray(vt,kt),os=await this.processor($t),Ms=vt===0,ks=kt>=G.length;if(he.push({stride:[$t.length,Ms?0:at,ks?0:at],input_features:os.input_features,is_last:ks}),ks)break;vt+=ct}}else he=[{stride:[G.length,0,0],input_features:(await this.processor(G)).input_features,is_last:!0}];for(const qe of he){st.num_frames=Math.floor(qe.stride[0]/lt);const at=await this.model.generate({inputs:qe.input_features,...st});Ve==="word"?(qe.tokens=at.sequences.tolist()[0],qe.token_timestamps=at.token_timestamps.tolist()[0].map(ct=>(0,g.round)(ct,2))):qe.tokens=at[0].tolist(),qe.stride=qe.stride.map(ct=>ct/mt)}const[Se,Re]=this.tokenizer._decode_asr(he,{time_precision:pt,return_timestamps:Ve,force_full_sequences:Ne});ie.push({text:Se,...Re})}return ut?ie[0]:ie}async _call_moonshine(le,be){const Ve=!Array.isArray(le);Ve&&(le=[le]);const We=this.processor.feature_extractor.config.sampling_rate,Ne=await O(le,We),je=[];for(const st of Ne){const ut=await this.processor(st),pt=Math.floor(st.length/We)*6,lt=await this.model.generate({max_new_tokens:pt,...be,...ut}),mt=this.processor.batch_decode(lt,{skip_special_tokens:!0})[0];je.push({text:mt})}return Ve?je[0]:je}}class ce extends se{constructor(le){super(le)}async _call(le,be={}){const Ve=Array.isArray(le),We=await b(le),{pixel_values:Ne}=await this.processor(We),je=[];for(const st of Ne){st.dims=[1,...st.dims];const ut=await this.model.generate({inputs:st,...be}),pt=this.tokenizer.batch_decode(ut,{skip_special_tokens:!0}).map(lt=>({generated_text:lt.trim()}));je.push(pt)}return Ve?je:je[0]}}class $e extends se{constructor(le){super(le)}async _call(le,{top_k:be=5}={}){const Ve=await b(le),{pixel_values:We}=await this.processor(Ve),Ne=await this.model({pixel_values:We}),je=this.model.config.id2label,st=[];for(const ut of Ne.logits){const pt=await(0,M.topk)(new M.Tensor("float32",(0,g.softmax)(ut.data),ut.dims),be),lt=pt[0].tolist(),L=pt[1].tolist().map((ie,G)=>({label:je?je[ie]:`LABEL_${ie}`,score:lt[G]}));st.push(L)}return Array.isArray(le)?st:st[0]}}class Oe extends se{constructor(le){super(le),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(le,{threshold:be=.5,mask_threshold:Ve=.5,overlap_mask_area_threshold:We=.8,label_ids_to_fuse:Ne=null,target_sizes:je=null,subtask:st=null}={}){if(Array.isArray(le)&&le.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const pt=await b(le),lt=pt.map(Re=>[Re.height,Re.width]),{pixel_values:mt,pixel_mask:L}=await this.processor(pt),ie=await this.model({pixel_values:mt,pixel_mask:L});let G=null;if(st!==null)G=this.subtasks_mapping[st];else for(let[Re,qe]of Object.entries(this.subtasks_mapping))if(qe in this.processor.image_processor){G=this.processor.image_processor[qe].bind(this.processor.image_processor),st=Re;break}const he=this.model.config.id2label,Se=[];if(st==="panoptic"||st==="instance"){const Re=G(ie,be,Ve,We,Ne,je??lt)[0],qe=Re.segmentation;for(const at of Re.segments_info){const ct=new Uint8ClampedArray(qe.data.length);for(let kt=0;kt<qe.data.length;++kt)qe.data[kt]===at.id&&(ct[kt]=255);const vt=new y.RawImage(ct,qe.dims[1],qe.dims[0],1);Se.push({score:at.score,label:he[at.label_id],mask:vt})}}else if(st==="semantic"){const{segmentation:Re,labels:qe}=G(ie,je??lt)[0];for(const at of qe){const ct=new Uint8ClampedArray(Re.data.length);for(let kt=0;kt<Re.data.length;++kt)Re.data[kt]===at&&(ct[kt]=255);const vt=new y.RawImage(ct,Re.dims[1],Re.dims[0],1);Se.push({score:null,label:he[at],mask:vt})}}else throw Error(`Subtask ${st} not supported.`);return Se}}class Ce extends se{constructor(le){super(le)}async _call(le,be,{hypothesis_template:Ve="This is a photo of {}"}={}){const We=Array.isArray(le),Ne=await b(le),je=be.map(L=>Ve.replace("{}",L)),st=this.tokenizer(je,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:ut}=await this.processor(Ne),pt=await this.model({...st,pixel_values:ut}),lt=this.model.config.model_type==="siglip"?L=>L.sigmoid().data:L=>(0,g.softmax)(L.data),mt=[];for(const L of pt.logits_per_image){const G=[...lt(L)].map((he,Se)=>({score:he,label:be[Se]}));G.sort((he,Se)=>Se.score-he.score),mt.push(G)}return We?mt:mt[0]}}class tt extends se{constructor(le){super(le)}async _call(le,{threshold:be=.9,percentage:Ve=!1}={}){const We=Array.isArray(le);if(We&&le.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ne=await b(le),je=Ve?null:Ne.map(ie=>[ie.height,ie.width]),{pixel_values:st,pixel_mask:ut}=await this.processor(Ne),pt=await this.model({pixel_values:st,pixel_mask:ut}),lt=this.processor.image_processor.post_process_object_detection(pt,be,je),mt=this.model.config.id2label,L=lt.map(ie=>ie.boxes.map((G,he)=>({score:ie.scores[he],label:mt[ie.classes[he]],box:H(G,!Ve)})));return We?L:L[0]}}class Ge extends se{constructor(le){super(le)}async _call(le,be,{threshold:Ve=.1,top_k:We=null,percentage:Ne=!1}={}){const je=Array.isArray(le),st=await b(le),ut=this.tokenizer(be,{padding:!0,truncation:!0}),pt=await this.processor(st),lt=[];for(let mt=0;mt<st.length;++mt){const L=st[mt],ie=Ne?null:[[L.height,L.width]],G=pt.pixel_values[mt].unsqueeze_(0),he=await this.model({...ut,pixel_values:G});let Se;if("post_process_grounded_object_detection"in this.processor){const Re=this.processor.post_process_grounded_object_detection(he,ut.input_ids,{box_threshold:Ve,text_threshold:Ve,target_sizes:ie})[0];Se=Re.boxes.map((qe,at)=>({score:Re.scores[at],label:Re.labels[at],box:H(qe,!Ne)}))}else{const Re=this.processor.image_processor.post_process_object_detection(he,Ve,ie,!0)[0];Se=Re.boxes.map((qe,at)=>({score:Re.scores[at],label:be[Re.classes[at]],box:H(qe,!Ne)}))}Se.sort((Re,qe)=>qe.score-Re.score),We!==null&&(Se=Se.slice(0,We)),lt.push(Se)}return je?lt:lt[0]}}class ye extends se{constructor(le){super(le)}async _call(le,be,Ve={}){const We=(await b(le))[0],{pixel_values:Ne}=await this.processor(We),je=`<s_docvqa><s_question>${be}</s_question><s_answer>`,st=this.tokenizer(je,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,ut=await this.model.generate({inputs:Ne,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:st,...Ve}),lt=this.tokenizer.batch_decode(ut)[0].match(/<s_answer>(.*?)<\/s_answer>/);let mt=null;return lt&&lt.length>=2&&(mt=lt[1].trim()),[{answer:mt}]}}class J extends se{constructor(be){super(be);_e(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=be.vocoder??null}async _call(be,{speaker_embeddings:Ve=null}={}){return this.processor?this._call_text_to_spectrogram(be,{speaker_embeddings:Ve}):this._call_text_to_waveform(be)}async _call_text_to_waveform(be){const Ve=this.tokenizer(be,{padding:!0,truncation:!0}),{waveform:We}=await this.model(Ve),Ne=this.model.config.sampling_rate;return new v.RawAudio(We.data,Ne)}async _call_text_to_spectrogram(be,{speaker_embeddings:Ve}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await I.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ve=="string"||Ve instanceof URL)&&(Ve=new Float32Array(await(await fetch(Ve)).arrayBuffer())),Ve instanceof Float32Array)Ve=new M.Tensor("float32",Ve,[1,Ve.length]);else if(!(Ve instanceof M.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:We}=this.tokenizer(be,{padding:!0,truncation:!0}),{waveform:Ne}=await this.model.generate_speech(We,Ve,{vocoder:this.vocoder}),je=this.processor.feature_extractor.config.sampling_rate;return new v.RawAudio(Ne.data,je)}}class de extends se{constructor(le){super(le)}async _call(le){const be=await b(le),Ve=await this.processor(be),We=await this.model(Ve),Ne=[];for(const je of We.reconstruction){const st=je.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ne.push(y.RawImage.fromTensor(st))}return Ne.length>1?Ne:Ne[0]}}class ke extends se{constructor(le){super(le)}async _call(le){const be=await b(le),Ve=await this.processor(be),{predicted_depth:We}=await this.model(Ve),Ne=[];for(let je=0;je<be.length;++je){const st=We[je],[ut,pt]=st.dims.slice(-2),[lt,mt]=be[je].size,L=(await(0,M.interpolate_4d)(st.view(1,1,ut,pt),{size:[mt,lt],mode:"bilinear"})).view(mt,lt),ie=L.min().item(),G=L.max().item(),he=L.sub(ie).div_(G-ie).mul_(255).to("uint8").unsqueeze(0),Se=y.RawImage.fromTensor(he);Ne.push({predicted_depth:L,depth:Se})}return Ne.length>1?Ne:Ne[0]}}const Be=Object.freeze({"text-classification":{tokenizer:f.AutoTokenizer,pipeline:ne,model:I.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:f.AutoTokenizer,pipeline:W,model:I.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:f.AutoTokenizer,pipeline:U,model:I.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:f.AutoTokenizer,pipeline:q,model:I.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:f.AutoTokenizer,pipeline:S,model:I.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:f.AutoTokenizer,pipeline:w,model:I.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:f.AutoTokenizer,pipeline:A,model:I.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:f.AutoTokenizer,pipeline:F,model:I.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:f.AutoTokenizer,pipeline:ae,model:I.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:we,model:I.AutoModelForAudioClassification,processor:N.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:f.AutoTokenizer,pipeline:re,model:I.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:f.AutoTokenizer,pipeline:Te,model:[I.AutoModelForSpeechSeq2Seq,I.AutoModelForCTC],processor:N.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:f.AutoTokenizer,pipeline:J,model:[I.AutoModelForTextToWaveform,I.AutoModelForTextToSpectrogram],processor:[N.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:f.AutoTokenizer,pipeline:ce,model:I.AutoModelForVision2Seq,processor:N.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:$e,model:I.AutoModelForImageClassification,processor:N.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Oe,model:[I.AutoModelForImageSegmentation,I.AutoModelForSemanticSegmentation,I.AutoModelForUniversalSegmentation],processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:f.AutoTokenizer,pipeline:Ce,model:I.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:tt,model:I.AutoModelForObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:f.AutoTokenizer,pipeline:Ge,model:I.AutoModelForZeroShotObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:f.AutoTokenizer,pipeline:ye,model:I.AutoModelForDocumentQuestionAnswering,processor:N.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:de,model:I.AutoModelForImageToImage,processor:N.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:ke,model:I.AutoModelForDepthEstimation,processor:N.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:f.AutoTokenizer,pipeline:oe,model:I.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:N.AutoProcessor,pipeline:xe,model:[I.AutoModelForImageFeatureExtraction,I.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Je=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function te(Ue,le=null,{progress_callback:be=null,config:Ve=null,cache_dir:We=null,local_files_only:Ne=!1,revision:je="main",device:st=null,dtype:ut=null,model_file_name:pt=null,session_options:lt={}}={}){Ue=Je[Ue]??Ue;const mt=Be[Ue.split("_",1)[0]];if(!mt)throw Error(`Unsupported pipeline: ${Ue}. Must be one of [${Object.keys(Be)}]`);le||(le=mt.default.model,console.log(`No model specified. Using default model: "${le}".`));const L={progress_callback:be,config:Ve,cache_dir:We,local_files_only:Ne,revision:je,device:st,dtype:ut,model_file_name:pt,session_options:lt},ie=new Map([["tokenizer",mt.tokenizer],["model",mt.model],["processor",mt.processor]]),G=await Ke(ie,le,L);G.task=Ue,(0,R.dispatchCallback)(be,{status:"ready",task:Ue,model:le});const he=mt.pipeline;return new he(G)}async function Ke(Ue,le,be){const Ve=Object.create(null),We=[];for(const[Ne,je]of Ue.entries()){if(!je)continue;let st;Array.isArray(je)?st=new Promise(async(ut,pt)=>{var mt,L;let lt;for(const ie of je){if(ie===null){ut(null);return}try{ut(await ie.from_pretrained(le,be));return}catch(G){if((mt=G.message)!=null&&mt.includes("Unsupported model type"))lt=G;else if((L=G.message)!=null&&L.includes("Could not locate file"))lt=G;else{pt(G);return}}}pt(lt)}):st=je.from_pretrained(le,be),Ve[Ne]=st,We.push(st)}await Promise.all(We);for(const[Ne,je]of Object.entries(Ve))Ve[Ne]=await je;return Ve}},"./src/tokenizers.js":(Ee,$,r)=>{r.r($),r.d($,{AlbertTokenizer:()=>kr,AutoTokenizer:()=>is,BartTokenizer:()=>Ar,BertTokenizer:()=>Jr,BlenderbotSmallTokenizer:()=>Bn,BlenderbotTokenizer:()=>zn,BloomTokenizer:()=>xr,CLIPTokenizer:()=>Mn,CamembertTokenizer:()=>ot,CodeGenTokenizer:()=>yn,CodeLlamaTokenizer:()=>Ur,CohereTokenizer:()=>xn,ConvBertTokenizer:()=>Rr,DebertaTokenizer:()=>dr,DebertaV2Tokenizer:()=>Br,DistilBertTokenizer:()=>ir,ElectraTokenizer:()=>Ft,EsmTokenizer:()=>Vr,FalconTokenizer:()=>Fn,GPT2Tokenizer:()=>jr,GPTNeoXTokenizer:()=>Dn,GemmaTokenizer:()=>io,Grok1Tokenizer:()=>Wr,HerbertTokenizer:()=>$r,LlamaTokenizer:()=>gn,M2M100Tokenizer:()=>wn,MBart50Tokenizer:()=>ar,MBartTokenizer:()=>bs,MPNetTokenizer:()=>On,MarianTokenizer:()=>Lt,MgpstrTokenizer:()=>jn,MobileBertTokenizer:()=>Sr,NllbTokenizer:()=>lr,NougatTokenizer:()=>Gr,PreTrainedTokenizer:()=>Nt,Qwen2Tokenizer:()=>Ln,RoFormerTokenizer:()=>Nr,RobertaTokenizer:()=>Os,SiglipTokenizer:()=>bn,SpeechT5Tokenizer:()=>Rn,SqueezeBertTokenizer:()=>Zr,T5Tokenizer:()=>Vs,TokenizerModel:()=>xe,VitsTokenizer:()=>Nn,Wav2Vec2CTCTokenizer:()=>vn,WhisperTokenizer:()=>en,XLMRobertaTokenizer:()=>oo,XLMTokenizer:()=>xt,is_chinese_char:()=>q});var f=r("./src/utils/generic.js"),I=r("./src/utils/core.js"),N=r("./src/utils/hub.js"),X=r("./src/utils/maths.js"),R=r("./src/utils/tensor.js"),g=r("./src/utils/data-structures.js"),v=r("./node_modules/@huggingface/jinja/dist/index.js"),M=r("./src/models/whisper/common_whisper.js");async function y(Pe,E){const Q=await Promise.all([(0,N.getModelJSON)(Pe,"tokenizer.json",!0,E),(0,N.getModelJSON)(Pe,"tokenizer_config.json",!0,E)]);return E.legacy!==null&&(Q[1].legacy=E.legacy),Q}function b(Pe,E){const Q=[];let ue=0;for(const ve of Pe.matchAll(E)){const Ae=ve[0];ue<ve.index&&Q.push(Pe.slice(ue,ve.index)),Ae.length>0&&Q.push(Ae),ue=ve.index+Ae.length}return ue<Pe.length&&Q.push(Pe.slice(ue)),Q}function O(Pe,E=!0){if(Pe.Regex!==void 0){let Q=Pe.Regex.replace(/\\([#&~])/g,"$1");for(const[ue,ve]of ae)Q=Q.replaceAll(ue,ve);return new RegExp(Q,"gu")}else if(Pe.String!==void 0){const Q=(0,I.escapeRegExp)(Pe.String);return new RegExp(E?Q:`(${Q})`,"gu")}else return console.warn("Unknown pattern type:",Pe),null}function H(Pe){return new Map(Object.entries(Pe))}function se(Pe){const E=Pe.dims;switch(E.length){case 1:return Pe.tolist();case 2:if(E[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return Pe.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${E.length}.`)}}function ne(Pe){return Pe.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function W(Pe){return Pe.replace(new RegExp("\\p{M}","gu"),"")}function U(Pe){return W(Pe.toLowerCase())}function q(Pe){return Pe>=19968&&Pe<=40959||Pe>=13312&&Pe<=19903||Pe>=131072&&Pe<=173791||Pe>=173824&&Pe<=177983||Pe>=177984&&Pe<=178207||Pe>=178208&&Pe<=183983||Pe>=63744&&Pe<=64255||Pe>=194560&&Pe<=195103}function A(Pe,E,Q){const ue=[];let ve=0;for(;ve<Pe.length;){if(ue.push(Pe[ve]),(E.get(Pe[ve])??Q)!==Q){++ve;continue}for(;++ve<Pe.length&&(E.get(Pe[ve])??Q)===Q;)E.get(ue.at(-1))!==Q&&(ue[ue.length-1]+=Pe[ve])}return ue}function S(Pe){return Pe.match(/\S+/g)||[]}const w="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",x=new RegExp(`^[${w}]+$`,"gu"),F=".,!?…。，、।۔،",ae=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${F}])]+`,` ?[^\\s${F}]+`]]);class oe{constructor(E){this.content=E.content,this.id=E.id,this.single_word=E.single_word??!1,this.lstrip=E.lstrip??!1,this.rstrip=E.rstrip??!1,this.special=E.special??!1,this.normalized=E.normalized??null}}class xe extends f.Callable{constructor(E){super(),this.config=E,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(E,...Q){switch(E.type){case"WordPiece":return new we(E);case"Unigram":return new re(E,...Q);case"BPE":return new $e(E);default:if(E.vocab)return Array.isArray(E.vocab)?new re(E,...Q):typeof E.vocab=="object"&&E.continuing_subword_prefix&&E.unk_token?new we(E):new Oe(E,...Q);throw new Error(`Unknown TokenizerModel type: ${E.type}`)}}_call(E){return E=this.encode(E),this.fuse_unk&&(E=A(E,this.tokens_to_ids,this.unk_token_id)),E}encode(E){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(E){return E.map(Q=>this.tokens_to_ids.get(Q)??this.unk_token_id)}convert_ids_to_tokens(E){return E.map(Q=>this.vocab[Q]??this.unk_token)}}class we extends xe{constructor(E){super(E),this.tokens_to_ids=H(E.vocab),this.unk_token_id=this.tokens_to_ids.get(E.unk_token),this.unk_token=E.unk_token,this.max_input_chars_per_word=E.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,ue]of this.tokens_to_ids)this.vocab[ue]=Q}encode(E){const Q=[];for(const ue of E){const ve=[...ue];if(ve.length>this.max_input_chars_per_word){Q.push(this.unk_token);continue}let Ae=!1,Xe=0;const ht=[];for(;Xe<ve.length;){let gt=ve.length,_t=null;for(;Xe<gt;){let Tt=ve.slice(Xe,gt).join("");if(Xe>0&&(Tt=this.config.continuing_subword_prefix+Tt),this.tokens_to_ids.has(Tt)){_t=Tt;break}--gt}if(_t===null){Ae=!0;break}ht.push(_t),Xe=gt}Ae?Q.push(this.unk_token):Q.push(...ht)}return Q}}class re extends xe{constructor(E,Q){super(E);const ue=E.vocab.length;this.vocab=new Array(ue),this.scores=new Array(ue);for(let ve=0;ve<ue;++ve)[this.vocab[ve],this.scores[ve]]=E.vocab[ve];this.unk_token_id=E.unk_id,this.unk_token=this.vocab[E.unk_id],this.tokens_to_ids=new Map(this.vocab.map((ve,Ae)=>[ve,Ae])),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,X.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new g.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(E){const Q=E.chars,ue=1;let ve=0;for(;ve<Q.length;){let Ae=!1;const Xe=Q.slice(ve).join(""),ht=this.trie.commonPrefixSearch(Xe);for(const gt of ht){const _t=this.tokens_to_ids.get(gt),Tt=this.scores[_t],Kt=(0,I.len)(gt);E.insert(ve,Kt,Tt,_t),!Ae&&Kt===ue&&(Ae=!0)}Ae||E.insert(ve,ue,this.unk_score,this.unk_token_id),ve+=ue}}tokenize(E){const Q=new g.TokenLattice(E,this.bos_token_id,this.eos_token_id);return this.populateNodes(Q),Q.tokens()}encode(E){const Q=[];for(const ue of E){const ve=this.tokenize(ue);Q.push(...ve)}return Q}}const Te=(()=>{const Pe=[...Array.from({length:94},(ve,Ae)=>Ae+33),...Array.from({length:12},(ve,Ae)=>Ae+161),...Array.from({length:82},(ve,Ae)=>Ae+174)],E=Pe.slice();let Q=0;for(let ve=0;ve<256;++ve)Pe.includes(ve)||(Pe.push(ve),E.push(256+Q),Q+=1);const ue=E.map(ve=>String.fromCharCode(ve));return Object.fromEntries(Pe.map((ve,Ae)=>[ve,ue[Ae]]))})(),ce=(0,I.reverseDictionary)(Te);class $e extends xe{constructor(E){super(E),this.tokens_to_ids=H(E.vocab),this.unk_token_id=this.tokens_to_ids.get(E.unk_token),this.unk_token=E.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ue,ve]of this.tokens_to_ids)this.vocab[ve]=ue;const Q=Array.isArray(E.merges[0]);this.merges=Q?E.merges:E.merges.map(ue=>ue.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ue,ve)=>[JSON.stringify(ue),ve])),this.end_of_word_suffix=E.end_of_word_suffix,this.continuing_subword_suffix=E.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(E){if(E.length===0)return[];const Q=this.cache.get(E);if(Q!==void 0)return Q;const ue=Array.from(E);this.end_of_word_suffix&&(ue[ue.length-1]+=this.end_of_word_suffix);let ve=[];if(ue.length>1){const Ae=new g.PriorityQueue((gt,_t)=>gt.score<_t.score);let Xe={token:ue[0],bias:0,prev:null,next:null},ht=Xe;for(let gt=1;gt<ue.length;++gt){const _t={bias:gt/ue.length,token:ue[gt],prev:ht,next:null};ht.next=_t,this._add_node(Ae,ht),ht=_t}for(;!Ae.isEmpty();){const gt=Ae.pop();if(gt.deleted||!gt.next||gt.next.deleted)continue;if(gt.deleted=!0,gt.next.deleted=!0,gt.prev){const Tt={...gt.prev};gt.prev.deleted=!0,gt.prev=Tt,Tt.prev?Tt.prev.next=Tt:Xe=Tt}const _t={token:gt.token+gt.next.token,bias:gt.bias,prev:gt.prev,next:gt.next.next};_t.prev?(_t.prev.next=_t,this._add_node(Ae,_t.prev)):Xe=_t,_t.next&&(_t.next.prev=_t,this._add_node(Ae,_t))}for(let gt=Xe;gt!==null;gt=gt.next)ve.push(gt.token)}else ve=ue;if(this.continuing_subword_suffix)for(let Ae=0;Ae<ve.length-1;++Ae)ve[Ae]+=this.continuing_subword_suffix;return this.cache.set(E,ve),ve}_add_node(E,Q){const ue=this.bpe_ranks.get(JSON.stringify([Q.token,Q.next.token]));ue!==void 0&&(Q.score=ue+Q.bias,E.push(Q))}encode(E){const Q=[];for(const ue of E){if(this.ignore_merges&&this.tokens_to_ids.has(ue)){Q.push(ue);continue}const ve=this.bpe(ue);for(const Ae of ve)if(this.tokens_to_ids.has(Ae))Q.push(Ae);else if(this.byte_fallback){const Xe=Array.from(this.text_encoder.encode(Ae)).map(ht=>`<0x${ht.toString(16).toUpperCase().padStart(2,"0")}>`);Xe.every(ht=>this.tokens_to_ids.has(ht))?Q.push(...Xe):Q.push(this.unk_token)}else Q.push(this.unk_token)}return Q}}class Oe extends xe{constructor(E,Q){super(E),this.tokens_to_ids=H(Q.target_lang?E.vocab[Q.target_lang]:E.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[ue,ve]of this.tokens_to_ids)this.vocab[ve]=ue}encode(E){return E}}class Ce extends f.Callable{constructor(E){super(),this.config=E}static fromConfig(E){if(E===null)return null;switch(E.type){case"BertNormalizer":return new Ke(E);case"Precompiled":return new Ms(E);case"Sequence":return new te(E);case"Replace":return new tt(E);case"NFC":return new Ge(E);case"NFKC":return new ye(E);case"NFKD":return new J(E);case"Strip":return new de(E);case"StripAccents":return new ke(E);case"Lowercase":return new Be(E);case"Prepend":return new Je(E);default:throw new Error(`Unknown Normalizer type: ${E.type}`)}}normalize(E){throw Error("normalize should be implemented in subclass.")}_call(E){return this.normalize(E)}}class tt extends Ce{normalize(E){const Q=O(this.config.pattern);return Q===null?E:E.replaceAll(Q,this.config.content)}}class Ge extends Ce{normalize(E){return E=E.normalize("NFC"),E}}class ye extends Ce{normalize(E){return E=E.normalize("NFKC"),E}}class J extends Ce{normalize(E){return E=E.normalize("NFKD"),E}}class de extends Ce{normalize(E){return this.config.strip_left&&this.config.strip_right?E=E.trim():(this.config.strip_left&&(E=E.trimStart()),this.config.strip_right&&(E=E.trimEnd())),E}}class ke extends Ce{normalize(E){return E=W(E),E}}class Be extends Ce{normalize(E){return E=E.toLowerCase(),E}}class Je extends Ce{normalize(E){return E=this.config.prepend+E,E}}class te extends Ce{constructor(E){super(E),this.normalizers=E.normalizers.map(Q=>Ce.fromConfig(Q))}normalize(E){return this.normalizers.reduce((Q,ue)=>ue.normalize(Q),E)}}class Ke extends Ce{_tokenize_chinese_chars(E){const Q=[];for(let ue=0;ue<E.length;++ue){const ve=E[ue],Ae=ve.charCodeAt(0);q(Ae)?(Q.push(" "),Q.push(ve),Q.push(" ")):Q.push(ve)}return Q.join("")}stripAccents(E){return E.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(E){switch(E){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(E)}}_clean_text(E){const Q=[];for(const ue of E){const ve=ue.charCodeAt(0);ve===0||ve===65533||this._is_control(ue)||(/^\s$/.test(ue)?Q.push(" "):Q.push(ue))}return Q.join("")}normalize(E){return this.config.clean_text&&(E=this._clean_text(E)),this.config.handle_chinese_chars&&(E=this._tokenize_chinese_chars(E)),this.config.lowercase?(E=E.toLowerCase(),this.config.strip_accents!==!1&&(E=this.stripAccents(E))):this.config.strip_accents&&(E=this.stripAccents(E)),E}}class Ue extends f.Callable{static fromConfig(E){if(E===null)return null;switch(E.type){case"BertPreTokenizer":return new le(E);case"Sequence":return new ks(E);case"Whitespace":return new Ls(E);case"WhitespaceSplit":return new sr(E);case"Metaspace":return new $t(E);case"ByteLevel":return new be(E);case"Split":return new Ve(E);case"Punctuation":return new We(E);case"Digits":return new Ne(E);case"Replace":return new Cr(E);default:throw new Error(`Unknown PreTokenizer type: ${E.type}`)}}pre_tokenize_text(E,Q){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(E,Q){return(Array.isArray(E)?E.map(ue=>this.pre_tokenize_text(ue,Q)):this.pre_tokenize_text(E,Q)).flat()}_call(E,Q){return this.pre_tokenize(E,Q)}}class le extends Ue{constructor(E){super(),this.pattern=new RegExp(`[^\\s${w}]+|[${w}]`,"gu")}pre_tokenize_text(E,Q){return E.trim().match(this.pattern)||[]}}class be extends Ue{constructor(E){super(),this.config=E,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Te,this.text_encoder=new TextEncoder}pre_tokenize_text(E,Q){return this.add_prefix_space&&!E.startsWith(" ")&&(E=" "+E),(this.use_regex?E.match(this.pattern)||[]:[E]).map(ve=>Array.from(this.text_encoder.encode(ve),Ae=>this.byte_encoder[Ae]).join(""))}}class Ve extends Ue{constructor(E){super(),this.config=E,this.pattern=O(this.config.pattern,this.config.invert)}pre_tokenize_text(E,Q){var ue;return this.pattern===null?[]:this.config.invert?E.match(this.pattern)||[]:((ue=this.config.behavior)==null?void 0:ue.toLowerCase())==="removed"?E.split(this.pattern).filter(ve=>ve):b(E,this.pattern)}}class We extends Ue{constructor(E){super(),this.config=E,this.pattern=new RegExp(`[^${w}]+|[${w}]+`,"gu")}pre_tokenize_text(E,Q){return E.match(this.pattern)||[]}}class Ne extends Ue{constructor(E){super(),this.config=E;const Q=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(Q,"gu")}pre_tokenize_text(E,Q){return E.match(this.pattern)||[]}}class je extends f.Callable{constructor(E){super(),this.config=E}static fromConfig(E){if(E===null)return null;switch(E.type){case"TemplateProcessing":return new pt(E);case"ByteLevel":return new lt(E);case"RobertaProcessing":return new ut(E);case"BertProcessing":return new st(E);case"Sequence":return new mt(E);default:throw new Error(`Unknown PostProcessor type: ${E.type}`)}}post_process(E,...Q){throw Error("post_process should be implemented in subclass.")}_call(E,...Q){return this.post_process(E,...Q)}}class st extends je{constructor(E){super(E),this.cls=E.cls[0],this.sep=E.sep[0]}post_process(E,Q=null,{add_special_tokens:ue=!0}={}){ue&&(E=(0,I.mergeArrays)([this.cls],E,[this.sep]));let ve=new Array(E.length).fill(0);if(Q!==null){const Ae=ue&&this instanceof ut?[this.sep]:[],Xe=ue?[this.sep]:[];E=(0,I.mergeArrays)(E,Ae,Q,Xe),ve=(0,I.mergeArrays)(ve,new Array(Q.length+Ae.length+Xe.length).fill(1))}return{tokens:E,token_type_ids:ve}}}class ut extends st{}class pt extends je{constructor(E){super(E),this.single=E.single,this.pair=E.pair}post_process(E,Q=null,{add_special_tokens:ue=!0}={}){const ve=Q===null?this.single:this.pair;let Ae=[],Xe=[];for(const ht of ve)"SpecialToken"in ht?ue&&(Ae.push(ht.SpecialToken.id),Xe.push(ht.SpecialToken.type_id)):"Sequence"in ht&&(ht.Sequence.id==="A"?(Ae=(0,I.mergeArrays)(Ae,E),Xe=(0,I.mergeArrays)(Xe,new Array(E.length).fill(ht.Sequence.type_id))):ht.Sequence.id==="B"&&(Ae=(0,I.mergeArrays)(Ae,Q),Xe=(0,I.mergeArrays)(Xe,new Array(Q.length).fill(ht.Sequence.type_id))));return{tokens:Ae,token_type_ids:Xe}}}class lt extends je{post_process(E,Q=null){return Q&&(E=(0,I.mergeArrays)(E,Q)),{tokens:E}}}class mt extends je{constructor(E){super(E),this.processors=E.processors.map(Q=>je.fromConfig(Q))}post_process(E,Q=null,ue={}){let ve;for(const Ae of this.processors)if(Ae instanceof lt)E=Ae.post_process(E).tokens,Q&&(Q=Ae.post_process(Q).tokens);else{const Xe=Ae.post_process(E,Q,ue);E=Xe.tokens,ve=Xe.token_type_ids}return{tokens:E,token_type_ids:ve}}}class L extends f.Callable{constructor(E){super(),this.config=E,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=E.trim_offsets}static fromConfig(E){if(E===null)return null;switch(E.type){case"WordPiece":return new Re(E);case"Metaspace":return new os(E);case"ByteLevel":return new qe(E);case"Replace":return new ie(E);case"ByteFallback":return new G(E);case"Fuse":return new he(E);case"Strip":return new Se(E);case"Sequence":return new ct(E);case"CTC":return new at(E);case"BPEDecoder":return new vt(E);default:throw new Error(`Unknown Decoder type: ${E.type}`)}}_call(E){return this.decode(E)}decode(E){return this.decode_chain(E).join("")}decode_chain(E){throw Error("`decode_chain` should be implemented in subclass.")}}class ie extends L{decode_chain(E){const Q=O(this.config.pattern);return Q===null?E:E.map(ue=>ue.replaceAll(Q,this.config.content))}}class G extends L{constructor(E){super(E),this.text_decoder=new TextDecoder}decode_chain(E){const Q=[];let ue=[];for(const ve of E){let Ae=null;if(ve.length===6&&ve.startsWith("<0x")&&ve.endsWith(">")){const Xe=parseInt(ve.slice(3,5),16);isNaN(Xe)||(Ae=Xe)}if(Ae!==null)ue.push(Ae);else{if(ue.length>0){const Xe=this.text_decoder.decode(Uint8Array.from(ue));Q.push(Xe),ue=[]}Q.push(ve)}}if(ue.length>0){const ve=this.text_decoder.decode(Uint8Array.from(ue));Q.push(ve),ue=[]}return Q}}class he extends L{decode_chain(E){return[E.join("")]}}class Se extends L{constructor(E){super(E),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(E){return E.map(Q=>{let ue=0;for(let Ae=0;Ae<this.start&&Q[Ae]===this.content;++Ae){ue=Ae+1;continue}let ve=Q.length;for(let Ae=0;Ae<this.stop;++Ae){const Xe=Q.length-Ae-1;if(Q[Xe]===this.content){ve=Xe;continue}else break}return Q.slice(ue,ve)})}}class Re extends L{constructor(E){super(E),this.cleanup=E.cleanup}decode_chain(E){return E.map((Q,ue)=>(ue!==0&&(Q.startsWith(this.config.prefix)?Q=Q.replace(this.config.prefix,""):Q=" "+Q),this.cleanup&&(Q=ne(Q)),Q))}}class qe extends L{constructor(E){super(E),this.byte_decoder=ce,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(E){const Q=E.join(""),ue=new Uint8Array([...Q].map(Ae=>this.byte_decoder[Ae]));return this.text_decoder.decode(ue)}decode_chain(E){const Q=[];let ue=[];for(const ve of E)this.added_tokens.find(Ae=>Ae.content===ve)!==void 0?(ue.length>0&&(Q.push(this.convert_tokens_to_string(ue)),ue=[]),Q.push(ve)):ue.push(ve);return ue.length>0&&Q.push(this.convert_tokens_to_string(ue)),Q}}class at extends L{constructor(E){super(E),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(E){if(E.length===0)return"";const Q=[E[0]];for(let Ae=1;Ae<E.length;++Ae)E[Ae]!==Q.at(-1)&&Q.push(E[Ae]);let ve=Q.filter(Ae=>Ae!==this.pad_token).join("");return this.cleanup&&(ve=ne(ve).replaceAll(this.word_delimiter_token," ").trim()),ve}decode_chain(E){return[this.convert_tokens_to_string(E)]}}class ct extends L{constructor(E){super(E),this.decoders=E.decoders.map(Q=>L.fromConfig(Q))}decode_chain(E){return this.decoders.reduce((Q,ue)=>ue.decode_chain(Q),E)}}class vt extends L{constructor(E){super(E),this.suffix=this.config.suffix}decode_chain(E){return E.map((Q,ue)=>Q.replaceAll(this.suffix,ue===E.length-1?"":" "))}}class kt extends L{decode_chain(E){let Q="";for(let ue=1;ue<E.length;ue+=2)Q+=E[ue];return[Q]}}class $t extends Ue{constructor(E){super(),this.addPrefixSpace=E.add_prefix_space,this.replacement=E.replacement,this.strRep=E.str_rep||this.replacement,this.prepend_scheme=E.prepend_scheme??"always"}pre_tokenize_text(E,{section_index:Q=void 0}={}){let ue=E.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!ue.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&Q===0)&&(ue=this.strRep+ue),[ue]}}class os extends L{constructor(E){super(E),this.addPrefixSpace=E.add_prefix_space,this.replacement=E.replacement}decode_chain(E){const Q=[];for(let ue=0;ue<E.length;++ue){let ve=E[ue].replaceAll(this.replacement," ");this.addPrefixSpace&&ue==0&&ve.startsWith(" ")&&(ve=ve.substring(1)),Q.push(ve)}return Q}}class Ms extends Ce{constructor(E){super(E),this.charsmap=E.precompiled_charsmap}normalize(E){return E=E.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),E=E.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),E.includes("～")?E=E.split("～").map(ue=>ue.normalize("NFKC")).join("～"):E=E.normalize("NFKC"),E}}class ks extends Ue{constructor(E){super(),this.tokenizers=E.pretokenizers.map(Q=>Ue.fromConfig(Q))}pre_tokenize_text(E,Q){return this.tokenizers.reduce((ue,ve)=>ve.pre_tokenize(ue,Q),[E])}}class Ls extends Ue{constructor(E){super()}pre_tokenize_text(E,Q){return E.match(/\w+|[^\w\s]+/g)||[]}}class sr extends Ue{constructor(E){super()}pre_tokenize_text(E,Q){return S(E)}}class Cr extends Ue{constructor(E){super(),this.config=E,this.pattern=O(this.config.pattern),this.content=this.config.content}pre_tokenize_text(E,Q){return this.pattern===null?[E]:[E.replaceAll(this.pattern,this.config.content)]}}const Yr=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Us(Pe,E,Q,ue){for(const ve of Object.keys(Pe)){const Ae=E-Pe[ve].length,Xe=Q(ve),ht=new Array(Ae).fill(Xe);Pe[ve]=ue==="right"?(0,I.mergeArrays)(Pe[ve],ht):(0,I.mergeArrays)(ht,Pe[ve])}}function vr(Pe,E){for(const Q of Object.keys(Pe))Pe[Q].length=E}class Nt extends f.Callable{constructor(Q,ue){super();_e(this,"return_token_type_ids",!1);_e(this,"padding_side","right");this._tokenizer_config=ue,this.normalizer=Ce.fromConfig(Q.normalizer),this.pre_tokenizer=Ue.fromConfig(Q.pre_tokenizer),this.model=xe.fromConfig(Q.model,ue),this.post_processor=je.fromConfig(Q.post_processor),this.decoder=L.fromConfig(Q.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ve of Q.added_tokens){const Ae=new oe(ve);this.added_tokens.push(Ae),this.model.tokens_to_ids.set(Ae.content,Ae.id),this.model.vocab[Ae.id]=Ae.content,Ae.special&&(this.special_tokens.push(Ae.content),this.all_special_ids.push(Ae.id))}if(this.additional_special_tokens=ue.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((ve,Ae)=>Ae.content.length-ve.content.length).map(ve=>`${ve.lstrip?"\\s*":""}(${(0,I.escapeRegExp)(ve.content)})${ve.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=ue.model_max_length,this.remove_space=ue.remove_space,this.clean_up_tokenization_spaces=ue.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ue.do_lowercase_and_remove_accent??!1,ue.padding_side&&(this.padding_side=ue.padding_side),this.legacy=!1,this.chat_template=ue.chat_template??null,Array.isArray(this.chat_template)){const ve=Object.create(null);for(const{name:Ae,template:Xe}of this.chat_template){if(typeof Ae!="string"||typeof Xe!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ve[Ae]=Xe}this.chat_template=ve}this._compiled_template_cache=new Map}getToken(...Q){for(const ue of Q){const ve=this._tokenizer_config[ue];if(ve)if(typeof ve=="object"){if(ve.__type==="AddedToken")return ve.content;throw Error(`Unknown token: ${ve}`)}else return ve}return null}static async from_pretrained(Q,{progress_callback:ue=null,config:ve=null,cache_dir:Ae=null,local_files_only:Xe=!1,revision:ht="main",legacy:gt=null}={}){const _t=await y(Q,{progress_callback:ue,config:ve,cache_dir:Ae,local_files_only:Xe,revision:ht,legacy:gt});return new this(..._t)}_call(Q,{text_pair:ue=null,add_special_tokens:ve=!0,padding:Ae=!1,truncation:Xe=null,max_length:ht=null,return_tensor:gt=!0,return_token_type_ids:_t=null}={}){const Tt=Array.isArray(Q);let Kt;if(Tt){if(Q.length===0)throw Error("text array must be non-empty");if(ue!==null){if(Array.isArray(ue)){if(Q.length!==ue.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Kt=Q.map((us,Fs)=>this._encode_plus(us,{text_pair:ue[Fs],add_special_tokens:ve,return_token_type_ids:_t}))}else Kt=Q.map(us=>this._encode_plus(us,{add_special_tokens:ve,return_token_type_ids:_t}))}else{if(Q==null)throw Error("text may not be null or undefined");if(Array.isArray(ue))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Kt=[this._encode_plus(Q,{text_pair:ue,add_special_tokens:ve,return_token_type_ids:_t})]}if(ht===null?Ae==="max_length"?ht=this.model_max_length:ht=(0,X.max)(Kt.map(us=>us.input_ids.length))[0]:Xe||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."),ht=Math.min(ht,this.model_max_length??1/0),Ae||Xe)for(let us=0;us<Kt.length;++us)Kt[us].input_ids.length!==ht&&(Kt[us].input_ids.length>ht?Xe&&vr(Kt[us],ht):Ae&&Us(Kt[us],ht,Fs=>Fs==="input_ids"?this.pad_token_id:0,this.padding_side));const _s={};if(gt){if(!(Ae&&Xe)&&Kt.some(Fs=>{var zt;for(const rs of Object.keys(Fs))if(Fs[rs].length!==((zt=Kt[0][rs])==null?void 0:zt.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 us=[Kt.length,Kt[0].input_ids.length];for(const Fs of Object.keys(Kt[0]))_s[Fs]=new R.Tensor("int64",BigInt64Array.from(Kt.flatMap(zt=>zt[Fs]).map(BigInt)),us)}else{for(const us of Object.keys(Kt[0]))_s[us]=Kt.map(Fs=>Fs[us]);if(!Tt)for(const us of Object.keys(_s))_s[us]=_s[us][0]}return _s}_encode_text(Q){return Q===null?null:(this.added_tokens_regex?Q.split(this.added_tokens_regex).filter(Ae=>Ae):[Q]).map((Ae,Xe)=>{if(this.added_tokens.find(gt=>gt.content===Ae)!==void 0)return Ae;{if(this.remove_space===!0&&(Ae=Ae.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(Ae=U(Ae)),this.normalizer!==null&&(Ae=this.normalizer(Ae)),Ae.length===0)return[];const gt=this.pre_tokenizer!==null?this.pre_tokenizer(Ae,{section_index:Xe}):[Ae];return this.model(gt)}}).flat()}_encode_plus(Q,{text_pair:ue=null,add_special_tokens:ve=!0,return_token_type_ids:Ae=null}={}){const{tokens:Xe,token_type_ids:ht}=this._tokenize_helper(Q,{pair:ue,add_special_tokens:ve}),gt=this.model.convert_tokens_to_ids(Xe),_t={input_ids:gt,attention_mask:new Array(gt.length).fill(1)};return(Ae??this.return_token_type_ids)&&ht&&(_t.token_type_ids=ht),_t}_tokenize_helper(Q,{pair:ue=null,add_special_tokens:ve=!1}={}){const Ae=this._encode_text(Q),Xe=this._encode_text(ue);return this.post_processor?this.post_processor(Ae,Xe,{add_special_tokens:ve}):{tokens:(0,I.mergeArrays)(Ae??[],Xe??[])}}tokenize(Q,{pair:ue=null,add_special_tokens:ve=!1}={}){return this._tokenize_helper(Q,{pair:ue,add_special_tokens:ve}).tokens}encode(Q,{text_pair:ue=null,add_special_tokens:ve=!0,return_token_type_ids:Ae=null}={}){return this._encode_plus(Q,{text_pair:ue,add_special_tokens:ve,return_token_type_ids:Ae}).input_ids}batch_decode(Q,ue={}){return Q instanceof R.Tensor&&(Q=Q.tolist()),Q.map(ve=>this.decode(ve,ue))}decode(Q,ue={}){if(Q instanceof R.Tensor&&(Q=se(Q)),!Array.isArray(Q)||Q.length===0||!(0,I.isIntegralNumber)(Q[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(Q,ue)}decode_single(Q,{skip_special_tokens:ue=!1,clean_up_tokenization_spaces:ve=null}){let Ae=this.model.convert_ids_to_tokens(Q);ue&&(Ae=Ae.filter(ht=>!this.special_tokens.includes(ht)));let Xe=this.decoder?this.decoder(Ae):Ae.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Xe=Xe.replaceAll(this.decoder.end_of_word_suffix," "),ue&&(Xe=Xe.trim())),(ve??this.clean_up_tokenization_spaces)&&(Xe=ne(Xe)),Xe}get_chat_template({chat_template:Q=null,tools:ue=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ve=this.chat_template;if(Q!==null&&Object.hasOwn(ve,Q))Q=ve[Q];else if(Q===null)if(ue!==null&&"tool_use"in ve)Q=ve.tool_use;else if("default"in ve)Q=ve.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(ve).sort()}.`)}else if(Q===null)if(this.chat_template)Q=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 Q}apply_chat_template(Q,{tools:ue=null,documents:ve=null,chat_template:Ae=null,add_generation_prompt:Xe=!1,tokenize:ht=!0,padding:gt=!1,truncation:_t=!1,max_length:Tt=null,return_tensor:Kt=!0,return_dict:_s=!1,tokenizer_kwargs:us={},...Fs}={}){if(Ae=this.get_chat_template({chat_template:Ae,tools:ue}),typeof Ae!="string")throw Error(`chat_template must be a string, but got ${typeof Ae}`);let zt=this._compiled_template_cache.get(Ae);zt===void 0&&(zt=new v.Template(Ae),this._compiled_template_cache.set(Ae,zt));const rs=Object.create(null);for(const Ws of Yr){const ze=this.getToken(Ws);ze&&(rs[Ws]=ze)}const rr=zt.render({messages:Q,add_generation_prompt:Xe,tools:ue,documents:ve,...rs,...Fs});if(ht){const Ws=this._call(rr,{add_special_tokens:!1,padding:gt,truncation:_t,max_length:Tt,return_tensor:Kt,...us});return _s?Ws:Ws.input_ids}return rr}}class Jr extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class kr extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class Sr extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class Zr extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class dr extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class Br extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class $r extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class Rr extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class Nr extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class ir extends Nt{}class ot extends Nt{}class xt extends Nt{constructor(Q,ue){super(Q,ue);_e(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ft extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class Vs extends Nt{}class jr extends Nt{}class Ar extends Nt{}class bs extends Nt{constructor(E,Q){super(E,Q),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ue=>this.languageRegex.test(ue)),this.lang_to_token=ue=>ue}_build_translation_inputs(E,Q,ue){return mr(this,E,Q,ue)}}class ar extends bs{}class Os extends Nt{}class xr extends Nt{}const ss="▁";class gn extends Nt{constructor(Q,ue){super(Q,ue);_e(this,"padding_side","left");this.legacy=ue.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new $t({replacement:ss,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(Q){if(Q===null)return null;if(this.legacy||Q.length===0)return super._encode_text(Q);let ue=super._encode_text(ss+Q.replaceAll(ss," "));return ue.length>1&&ue[0]===ss&&this.special_tokens.includes(ue[1])&&(ue=ue.slice(1)),ue}}class Ur extends Nt{}class oo extends Nt{}class On extends Nt{}class Fn extends Nt{}class Dn extends Nt{}class Vr extends Nt{}class Ln extends Nt{}class io extends Nt{}class Wr extends Nt{}function mr(Pe,E,Q,ue){if(!("language_codes"in Pe)||!Array.isArray(Pe.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Pe)||!(Pe.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Pe)||typeof Pe.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ve=ue.src_lang,Ae=ue.tgt_lang;if(!Pe.language_codes.includes(Ae))throw new Error(`Target language code "${Ae}" is not valid. Must be one of: {${Pe.language_codes.join(", ")}}`);if(ve!==void 0){if(!Pe.language_codes.includes(ve))throw new Error(`Source language code "${ve}" is not valid. Must be one of: {${Pe.language_codes.join(", ")}}`);for(const Xe of Pe.post_processor.config.single)if("SpecialToken"in Xe&&Pe.languageRegex.test(Xe.SpecialToken.id)){Xe.SpecialToken.id=Pe.lang_to_token(ve);break}}return ue.forced_bos_token_id=Pe.model.convert_tokens_to_ids([Pe.lang_to_token(Ae)])[0],Pe._call(E,Q)}class lr extends Nt{constructor(E,Q){super(E,Q),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(ue=>this.languageRegex.test(ue)),this.lang_to_token=ue=>ue}_build_translation_inputs(E,Q,ue){return mr(this,E,Q,ue)}}class wn extends Nt{constructor(E,Q){super(E,Q),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(ue=>this.languageRegex.test(ue)).map(ue=>ue.slice(2,-2)),this.lang_to_token=ue=>`__${ue}__`}_build_translation_inputs(E,Q,ue){return mr(this,E,Q,ue)}}class en extends Nt{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(E,{return_timestamps:Q=!1,return_language:ue=!1,time_precision:ve=null,force_full_sequences:Ae=!0}={}){if(ve===null)throw Error("Must specify time_precision");let Xe=null;const ht=Q==="word";function gt(){return{language:Xe,timestamp:[null,null],text:""}}const _t=[];let Tt=gt(),Kt=0;const _s=this.timestamp_begin,Fs=_s+1500;let zt=[],rs=[],rr=!1,Ws=null;const ze=new Set(this.all_special_ids);for(const Ss of E){const Xs=Ss.tokens,Ot=ht?Ss.token_timestamps:null;let or=null,_r=_s;if("stride"in Ss){const[Mt,Xt,zs]=Ss.stride;if(Kt-=Xt,Ws=Mt-zs,Xt&&(_r=Xt/ve+_s),zs)for(let As=Xs.length-1;As>=0;--As){const Gs=Number(Xs[As]);if(Gs>=_s){if(or!==null&&(Gs-_s)*ve<Ws)break;or=Gs}}}let fs=[],$s=[];for(let Mt=0;Mt<Xs.length;++Mt){const Xt=Number(Xs[Mt]);if(ze.has(Xt)){const zs=this.decode([Xt]),As=M.WHISPER_LANGUAGE_MAPPING.get(zs.slice(2,-2));if(As!==void 0){if(Xe!==null&&As!==Xe&&!Q){zt.push(fs);const Gs=this.findLongestCommonSequence(zt)[0],St=this.decode(Gs);Tt.text=St,_t.push(Tt),zt=[],fs=[],Tt=gt()}Xe=Tt.language=As}}else if(Xt>=_s&&Xt<=Fs){const zs=(Xt-_s)*ve+Kt,As=(0,X.round)(zs,2);if(or!==null&&Xt>=or)rr=!0;else if(rr||zt.length>0&&Xt<_r)rr=!1;else if(Tt.timestamp[0]===null)Tt.timestamp[0]=As;else if(As!==Tt.timestamp[0]){Tt.timestamp[1]=As,zt.push(fs),ht&&rs.push($s);const[Gs,St]=this.findLongestCommonSequence(zt,rs),tn=this.decode(Gs);Tt.text=tn,ht&&(Tt.words=this.collateWordTimestamps(Gs,St,Xe)),_t.push(Tt),zt=[],fs=[],rs=[],$s=[],Tt=gt()}}else if(fs.push(Xt),ht){let zs=(0,X.round)(Ot[Mt]+Kt,2),As;if(Mt+1<Ot.length){As=(0,X.round)(Ot[Mt+1]+Kt,2);const Gs=this.decode([Xt]);x.test(Gs)&&(As=(0,X.round)(Math.min(zs+ve,As),2))}else As=null;$s.push([zs,As])}}if("stride"in Ss){const[Mt,Xt,zs]=Ss.stride;Kt+=Mt-zs}fs.length>0?(zt.push(fs),ht&&rs.push($s)):zt.every(Mt=>Mt.length===0)&&(Tt=gt(),zt=[],fs=[],rs=[],$s=[])}if(zt.length>0){if(Ae&&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[Ss,Xs]=this.findLongestCommonSequence(zt,rs),Ot=this.decode(Ss);Tt.text=Ot,ht&&(Tt.words=this.collateWordTimestamps(Ss,Xs,Xe)),_t.push(Tt)}let Zs=Object.create(null);const Ir=_t.map(Ss=>Ss.text).join("");if(Q||ue){for(let Ss=0;Ss<_t.length;++Ss){const Xs=_t[Ss];Q||delete Xs.timestamp,ue||delete Xs.language}if(ht){const Ss=[];for(const Xs of _t)for(const Ot of Xs.words)Ss.push(Ot);Zs={chunks:Ss}}else Zs={chunks:_t}}return[Ir,Zs]}findLongestCommonSequence(E,Q=null){let ue=E[0],ve=ue.length,Ae=[];const Xe=Array.isArray(Q)&&Q.length>0;let ht=Xe?[]:null,gt=Xe?Q[0]:null;for(let _t=1;_t<E.length;++_t){const Tt=E[_t];let Kt=0,_s=[ve,ve,0,0];const us=Tt.length;for(let Zs=1;Zs<ve+us;++Zs){const Ir=Math.max(0,ve-Zs),Ss=Math.min(ve,ve+us-Zs),Xs=ue.slice(Ir,Ss),Ot=Math.max(0,Zs-ve),or=Math.min(us,Zs),_r=Tt.slice(Ot,or);if(Xs.length!==_r.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let fs;Xe?fs=Xs.filter((Xt,zs)=>Xt===_r[zs]&&gt[Ir+zs]<=Q[_t][Ot+zs]).length:fs=Xs.filter((Xt,zs)=>Xt===_r[zs]).length;const $s=Zs/1e4,Mt=fs/Zs+$s;fs>1&&Mt>Kt&&(Kt=Mt,_s=[Ir,Ss,Ot,or])}const[Fs,zt,rs,rr]=_s,Ws=Math.floor((zt+Fs)/2),ze=Math.floor((rr+rs)/2);Ae.push(...ue.slice(0,Ws)),ue=Tt.slice(ze),ve=ue.length,Xe&&(ht.push(...gt.slice(0,Ws)),gt=Q[_t].slice(ze))}return Ae.push(...ue),Xe?(ht.push(...gt),[Ae,ht]):[Ae,[]]}collateWordTimestamps(E,Q,ue){const[ve,Ae,Xe]=this.combineTokensIntoWords(E,ue),ht=[];for(let gt=0;gt<ve.length;++gt){const _t=Xe[gt];ht.push({text:ve[gt],timestamp:[Q[_t.at(0)][0],Q[_t.at(-1)][1]]})}return ht}combineTokensIntoWords(E,Q,ue=`"'“¡¿([{-`,ve=`"'.。,，!！?？:：”)]}、`){Q=Q??"english";let Ae,Xe,ht;return["chinese","japanese","thai","lao","myanmar"].includes(Q)?[Ae,Xe,ht]=this.splitTokensOnUnicode(E):[Ae,Xe,ht]=this.splitTokensOnSpaces(E),this.mergePunctuations(Ae,Xe,ht,ue,ve)}decode(E,Q){let ue;return Q!=null&&Q.decode_with_timestamps?(E instanceof R.Tensor&&(E=se(E)),ue=this.decodeWithTimestamps(E,Q)):ue=super.decode(E,Q),ue}decodeWithTimestamps(E,Q){const ue=(Q==null?void 0:Q.time_precision)??.02,ve=Array.from(this.all_special_ids).at(-1)+1;let Ae=[[]];for(let Xe of E)if(Xe=Number(Xe),Xe>=ve){const ht=((Xe-ve)*ue).toFixed(2);Ae.push(`<|${ht}|>`),Ae.push([])}else Ae[Ae.length-1].push(Xe);return Ae=Ae.map(Xe=>typeof Xe=="string"?Xe:super.decode(Xe,Q)),Ae.join("")}splitTokensOnUnicode(E){const Q=this.decode(E,{decode_with_timestamps:!0}),ue="�",ve=[],Ae=[],Xe=[];let ht=[],gt=[],_t=0;for(let Tt=0;Tt<E.length;++Tt){const Kt=E[Tt];ht.push(Kt),gt.push(Tt);const _s=this.decode(ht,{decode_with_timestamps:!0});(!_s.includes(ue)||Q[_t+_s.indexOf(ue)]===ue)&&(ve.push(_s),Ae.push(ht),Xe.push(gt),ht=[],gt=[],_t+=_s.length)}return[ve,Ae,Xe]}splitTokensOnSpaces(E){const[Q,ue,ve]=this.splitTokensOnUnicode(E),Ae=[],Xe=[],ht=[],gt=new RegExp(`^[${w}]$`,"gu");for(let _t=0;_t<Q.length;++_t){const Tt=Q[_t],Kt=ue[_t],_s=ve[_t],us=Kt[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),Fs=Tt.startsWith(" "),zt=Tt.trim(),rs=gt.test(zt);if(us||Fs||rs||Ae.length===0)Ae.push(Tt),Xe.push(Kt),ht.push(_s);else{const rr=Ae.length-1;Ae[rr]+=Tt,Xe[rr].push(...Kt),ht[rr].push(..._s)}}return[Ae,Xe,ht]}mergePunctuations(E,Q,ue,ve,Ae){const Xe=structuredClone(E),ht=structuredClone(Q),gt=structuredClone(ue);let _t=Xe.length-2,Tt=Xe.length-1;for(;_t>=0;)Xe[_t].startsWith(" ")&&ve.includes(Xe[_t].trim())?(Xe[Tt]=Xe[_t]+Xe[Tt],ht[Tt]=(0,I.mergeArrays)(ht[_t],ht[Tt]),gt[Tt]=(0,I.mergeArrays)(gt[_t],gt[Tt]),Xe[_t]="",ht[_t]=[],gt[_t]=[]):Tt=_t,--_t;for(_t=0,Tt=1;Tt<Xe.length;)!Xe[_t].endsWith(" ")&&Ae.includes(Xe[Tt])?(Xe[_t]+=Xe[Tt],ht[_t]=(0,I.mergeArrays)(ht[_t],ht[Tt]),gt[_t]=(0,I.mergeArrays)(gt[_t],gt[Tt]),Xe[Tt]="",ht[Tt]=[],gt[Tt]=[]):_t=Tt,++Tt;return[Xe.filter(Kt=>Kt),ht.filter(Kt=>Kt.length>0),gt.filter(Kt=>Kt.length>0)]}}class yn extends Nt{}class Mn extends Nt{}class bn extends Nt{}class Lt extends Nt{constructor(E,Q){super(E,Q),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ue=>this.languageRegex.test(ue)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(E){if(E===null)return null;const[Q,...ue]=E.trim().split(this.languageRegex);if(ue.length===0)return super._encode_text(Q);if(ue.length===2){const[ve,Ae]=ue;return this.supported_language_codes.includes(ve)||console.warn(`Unsupported language code "${ve}" detected, which may lead to unexpected behavior. 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S=0;S<A;++S)U[4*S+3]=q[S];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(W,U,{resample:q=2}={}){if(this.width===W&&this.height===U)return this;let A=b[q]??q;const S=(0,f.isNullishDimension)(W),w=(0,f.isNullishDimension)(U);if(S&&w)return this;if(S?W=U/this.height*this.width:w&&(U=W/this.width*this.height),y){const x=this.channels,F=this.toCanvas(),ae=g(W,U).getContext("2d");return ae.drawImage(F,0,0,W,U),new H(ae.getImageData(0,0,W,U).data,W,U,4).convert(x)}else{let x=this.toSharp();switch(A){case"box":case"hamming":(A==="box"||A==="hamming")&&(console.warn(`Resampling method ${A} is not yet supported. Using bilinear instead.`),A="bilinear");case"nearest":case"bilinear":case"bicubic":x=x.affine([W/this.width,0,0,U/this.height],{interpolator:A});break;case"lanczos":x=x.resize({width:W,height:U,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${A} is not supported.`)}return await M(x)}}async pad([W,U,q,A]){if(W=Math.max(W,0),U=Math.max(U,0),q=Math.max(q,0),A=Math.max(A,0),W===0&&U===0&&q===0&&A===0)return this;if(y){const S=this.channels,w=this.toCanvas(),x=this.width+W+U,F=this.height+q+A,ae=g(x,F).getContext("2d");return ae.drawImage(w,0,0,this.width,this.height,W,q,this.width,this.height),new H(ae.getImageData(0,0,x,F).data,x,F,4).convert(S)}else{const S=this.toSharp().extend({left:W,right:U,top:q,bottom:A});return await M(S)}}async crop([W,U,q,A]){if(W=Math.max(W,0),U=Math.max(U,0),q=Math.min(q,this.width-1),A=Math.min(A,this.height-1),W===0&&U===0&&q===this.width-1&&A===this.height-1)return this;const S=q-W+1,w=A-U+1;if(y){const x=this.channels,F=this.toCanvas(),ae=g(S,w).getContext("2d");return ae.drawImage(F,W,U,S,w,0,0,S,w),new H(ae.getImageData(0,0,S,w).data,S,w,4).convert(x)}else{const x=this.toSharp().extract({left:W,top:U,width:S,height:w});return await M(x)}}async center_crop(W,U){if(this.width===W&&this.height===U)return this;const q=(this.width-W)/2,A=(this.height-U)/2;if(y){const S=this.channels,w=this.toCanvas(),x=g(W,U).getContext("2d");let F=0,ae=0,oe=0,xe=0;return q>=0?F=q:oe=-q,A>=0?ae=A:xe=-A,x.drawImage(w,F,ae,W,U,oe,xe,W,U),new H(x.getImageData(0,0,W,U).data,W,U,4).convert(S)}else{let S=this.toSharp();if(q>=0&&A>=0)S=S.extract({left:Math.floor(q),top:Math.floor(A),width:W,height:U});else if(q<=0&&A<=0){const w=Math.floor(-A),x=Math.floor(-q);S=S.extend({top:w,left:x,right:W-this.width-x,bottom:U-this.height-w})}else{let w=[0,0],x=0;A<0?(w[0]=Math.floor(-A),w[1]=U-this.height-w[0]):x=Math.floor(A);let F=[0,0],ae=0;q<0?(F[0]=Math.floor(-q),F[1]=W-this.width-F[0]):ae=Math.floor(q),S=S.extend({top:w[0],bottom:w[1],left:F[0],right:F[1]}).extract({left:ae,top:x,width:W,height:U})}return await M(S)}}async toBlob(W="image/png",U=1){if(!y)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:W,quality:U})}toTensor(W="CHW"){let U=new X.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(W!=="HWC")if(W==="CHW")U=U.permute(2,0,1);else throw new Error(`Unsupported channel format: ${W}`);return U}toCanvas(){if(!y)throw new Error("toCanvas() is only supported in browser environments.");const W=this.clone().rgba(),U=g(W.width,W.height),q=new v(W.data,W.width,W.height);return U.getContext("2d").putImageData(q,0,0),U}split(){const{data:W,width:U,height:q,channels:A}=this,S=W.constructor,w=W.length/A,x=Array.from({length:A},()=>new S(w));for(let F=0;F<w;++F){const ae=A*F;for(let oe=0;oe<A;++oe)x[oe][F]=W[ae+oe]}return x.map(F=>new H(F,U,q,1))}_update(W,U,q,A=null){return this.data=W,this.width=U,this.height=q,A!==null&&(this.channels=A),this}clone(){return new H(this.data.slice(),this.width,this.height,this.channels)}convert(W){if(this.channels===W)return this;switch(W){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: ${this.channels}`)}return this}async save(W){if(y){if(N.apis.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const U=W.split(".").pop().toLowerCase(),q=O.get(U)??"image/png",A=await this.toBlob(q);(0,f.saveBlob)(W,A)}else{if(N.apis.IS_FS_AVAILABLE)return await this.toSharp().toFile(W);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(y)throw new Error("toSharp() is only supported in server-side environments.");return R(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}const se=H.read.bind(H)},"./src/utils/maths.js":(Ee,$,r)=>{r.r($),r.d($,{FFT:()=>se,bankers_round:()=>U,cos_sim:()=>g,dot:()=>R,dynamic_time_warping:()=>q,interpolate_data:()=>f,log_softmax:()=>X,magnitude:()=>v,max:()=>y,medianFilter:()=>ne,min:()=>M,permute_data:()=>I,round:()=>W,softmax:()=>N});function f(A,[S,w,x],[F,ae],oe="bilinear",xe=!1){const we=ae/x,re=F/w,Te=new A.constructor(F*ae*S),ce=w*x,$e=F*ae;for(let Oe=0;Oe<F;++Oe)for(let Ce=0;Ce<ae;++Ce){const tt=Oe*ae+Ce,Ge=(Ce+.5)/we-.5,ye=(Oe+.5)/re-.5;let J=Math.floor(Ge),de=Math.floor(ye);const ke=Math.min(J+1,x-1),Be=Math.min(de+1,w-1);J=Math.max(J,0),de=Math.max(de,0);const Je=Ge-J,te=ye-de,Ke=(1-Je)*(1-te),Ue=Je*(1-te),le=(1-Je)*te,be=Je*te,Ve=de*x,We=Be*x,Ne=Ve+J,je=Ve+ke,st=We+J,ut=We+ke;for(let pt=0;pt<S;++pt){const lt=pt*ce;Te[pt*$e+tt]=Ke*A[lt+Ne]+Ue*A[lt+je]+le*A[lt+st]+be*A[lt+ut]}}return Te}function I(A,S,w){const x=new Array(w.length),F=new Array(w.length);for(let xe=w.length-1,we=1;xe>=0;--xe)F[xe]=we,x[xe]=S[w[xe]],we*=x[xe];const ae=w.map((xe,we)=>F[w.indexOf(we)]),oe=new A.constructor(A.length);for(let xe=0;xe<A.length;++xe){let we=0;for(let re=S.length-1,Te=xe;re>=0;--re)we+=Te%S[re]*ae[re],Te=Math.floor(Te/S[re]);oe[we]=A[xe]}return[oe,x]}function N(A){const S=y(A)[0],w=A.map(ae=>Math.exp(ae-S)),x=w.reduce((ae,oe)=>ae+oe,0);return w.map(ae=>ae/x)}function X(A){const S=y(A)[0];let w=0;for(let ae=0;ae<A.length;++ae)w+=Math.exp(A[ae]-S);const x=Math.log(w);return A.map(ae=>ae-S-x)}function R(A,S){let w=0;for(let x=0;x<A.length;++x)w+=A[x]*S[x];return w}function g(A,S){const w=R(A,S),x=v(A),F=v(S);return w/(x*F)}function v(A){return Math.sqrt(A.reduce((S,w)=>S+w*w,0))}function M(A){if(A.length===0)throw Error("Array must not be empty");let S=A[0],w=0;for(let x=1;x<A.length;++x)A[x]<S&&(S=A[x],w=x);return[S,w]}function y(A){if(A.length===0)throw Error("Array must not be empty");let S=A[0],w=0;for(let x=1;x<A.length;++x)A[x]>S&&(S=A[x],w=x);return[S,w]}function b(A){return A>0&&(A&A-1)===0}class O{constructor(S){if(this.size=S|0,this.size<=1||!b(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=S<<1,this.table=new Float64Array(this.size*2);for(let x=0;x<this.table.length;x+=2){const F=Math.PI*x/this.size;this.table[x]=Math.cos(F),this.table[x+1]=-Math.sin(F)}let w=0;for(let x=1;this.size>x;x<<=1)++w;this._width=w%2===0?w-1:w,this._bitrev=new Int32Array(1<<this._width);for(let x=0;x<this._bitrev.length;++x){this._bitrev[x]=0;for(let F=0;F<this._width;F+=2){const ae=this._width-F-2;this._bitrev[x]|=(x>>>F&3)<<ae}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(S,w){const x=w||new Array(S.length>>>1);for(let F=0;F<S.length;F+=2)x[F>>>1]=S[F];return x}toComplexArray(S,w){const x=w||this.createComplexArray();for(let F=0;F<x.length;F+=2)x[F]=S[F>>>1],x[F+1]=0;return x}transform(S,w){if(S===w)throw new Error("Input and output buffers must be different");this._transform4(S,w,1)}realTransform(S,w){if(S===w)throw new Error("Input and output buffers must be different");this._realTransform4(S,w,1)}inverseTransform(S,w){if(S===w)throw new Error("Input and output buffers must be different");this._transform4(S,w,-1);for(let x=0;x<S.length;++x)S[x]/=this.size}_transform4(S,w,x){const F=this._csize;let oe=1<<this._width,xe=F/oe<<1,we,re;const Te=this._bitrev;if(xe===4)for(we=0,re=0;we<F;we+=xe,++re){const $e=Te[re];this._singleTransform2(w,S,we,$e,oe)}else for(we=0,re=0;we<F;we+=xe,++re){const $e=Te[re];this._singleTransform4(w,S,we,$e,oe,x)}const ce=this.table;for(oe>>=2;oe>=2;oe>>=2){xe=F/oe<<1;const $e=xe>>>2;for(we=0;we<F;we+=xe){const Oe=we+$e-1;for(let Ce=we,tt=0;Ce<Oe;Ce+=2,tt+=oe){const Ge=Ce,ye=Ge+$e,J=ye+$e,de=J+$e,ke=S[Ge],Be=S[Ge+1],Je=S[ye],te=S[ye+1],Ke=S[J],Ue=S[J+1],le=S[de],be=S[de+1],Ve=ce[tt],We=x*ce[tt+1],Ne=Je*Ve-te*We,je=Je*We+te*Ve,st=ce[2*tt],ut=x*ce[2*tt+1],pt=Ke*st-Ue*ut,lt=Ke*ut+Ue*st,mt=ce[3*tt],L=x*ce[3*tt+1],ie=le*mt-be*L,G=le*L+be*mt,he=ke+pt,Se=Be+lt,Re=ke-pt,qe=Be-lt,at=Ne+ie,ct=je+G,vt=x*(Ne-ie),kt=x*(je-G);S[Ge]=he+at,S[Ge+1]=Se+ct,S[ye]=Re+kt,S[ye+1]=qe-vt,S[J]=he-at,S[J+1]=Se-ct,S[de]=Re-kt,S[de+1]=qe+vt}}}}_singleTransform2(S,w,x,F,ae){const oe=S[F],xe=S[F+1],we=S[F+ae],re=S[F+ae+1];w[x]=oe+we,w[x+1]=xe+re,w[x+2]=oe-we,w[x+3]=xe-re}_singleTransform4(S,w,x,F,ae,oe){const xe=ae*2,we=ae*3,re=S[F],Te=S[F+1],ce=S[F+ae],$e=S[F+ae+1],Oe=S[F+xe],Ce=S[F+xe+1],tt=S[F+we],Ge=S[F+we+1],ye=re+Oe,J=Te+Ce,de=re-Oe,ke=Te-Ce,Be=ce+tt,Je=$e+Ge,te=oe*(ce-tt),Ke=oe*($e-Ge);w[x]=ye+Be,w[x+1]=J+Je,w[x+2]=de+Ke,w[x+3]=ke-te,w[x+4]=ye-Be,w[x+5]=J-Je,w[x+6]=de-Ke,w[x+7]=ke+te}_realTransform4(S,w,x){const F=this._csize;let oe=1<<this._width,xe=F/oe<<1,we,re;const Te=this._bitrev;if(xe===4)for(we=0,re=0;we<F;we+=xe,++re){const Oe=Te[re];this._singleRealTransform2(w,S,we,Oe>>>1,oe>>>1)}else for(we=0,re=0;we<F;we+=xe,++re){const Oe=Te[re];this._singleRealTransform4(w,S,we,Oe>>>1,oe>>>1,x)}const ce=this.table;for(oe>>=2;oe>=2;oe>>=2){xe=F/oe<<1;const Oe=xe>>>1,Ce=Oe>>>1,tt=Ce>>>1;for(we=0;we<F;we+=xe)for(let Ge=0,ye=0;Ge<=tt;Ge+=2,ye+=oe){const J=we+Ge,de=J+Ce,ke=de+Ce,Be=ke+Ce,Je=S[J],te=S[J+1],Ke=S[de],Ue=S[de+1],le=S[ke],be=S[ke+1],Ve=S[Be],We=S[Be+1],Ne=Je,je=te,st=ce[ye],ut=x*ce[ye+1],pt=Ke*st-Ue*ut,lt=Ke*ut+Ue*st,mt=ce[2*ye],L=x*ce[2*ye+1],ie=le*mt-be*L,G=le*L+be*mt,he=ce[3*ye],Se=x*ce[3*ye+1],Re=Ve*he-We*Se,qe=Ve*Se+We*he,at=Ne+ie,ct=je+G,vt=Ne-ie,kt=je-G,$t=pt+Re,os=lt+qe,Ms=x*(pt-Re),ks=x*(lt-qe);if(S[J]=at+$t,S[J+1]=ct+os,S[de]=vt+ks,S[de+1]=kt-Ms,Ge===0){S[ke]=at-$t,S[ke+1]=ct-os;continue}if(Ge===tt)continue;const Ls=we+Ce-Ge,sr=we+Oe-Ge;S[Ls]=vt-x*ks,S[Ls+1]=-kt-x*Ms,S[sr]=at-x*$t,S[sr+1]=-ct+x*os}}const $e=F>>>1;for(let Oe=2;Oe<$e;Oe+=2)S[F-Oe]=S[Oe],S[F-Oe+1]=-S[Oe+1]}_singleRealTransform2(S,w,x,F,ae){const oe=S[F],xe=S[F+ae];w[x]=oe+xe,w[x+1]=0,w[x+2]=oe-xe,w[x+3]=0}_singleRealTransform4(S,w,x,F,ae,oe){const xe=ae*2,we=ae*3,re=S[F],Te=S[F+ae],ce=S[F+xe],$e=S[F+we],Oe=re+ce,Ce=re-ce,tt=Te+$e,Ge=oe*(Te-$e);w[x]=Oe+tt,w[x+1]=0,w[x+2]=Ce,w[x+3]=-Ge,w[x+4]=Oe-tt,w[x+5]=0,w[x+6]=Ce,w[x+7]=Ge}}class H{constructor(S){const w=2*(S-1),x=2*(2*S-1),F=2**Math.ceil(Math.log2(x));this.bufferSize=F,this._a=w;const ae=new Float64Array(x),oe=new Float64Array(F);this._chirpBuffer=new Float64Array(F),this._buffer1=new Float64Array(F),this._buffer2=new Float64Array(F),this._outBuffer1=new Float64Array(F),this._outBuffer2=new Float64Array(F);const xe=-2*Math.PI/S,we=Math.cos(xe),re=Math.sin(xe);for(let Te=0;Te<x>>1;++Te){const ce=(Te+1-S)**2/2,$e=Math.sqrt(we**2+re**2)**ce,Oe=ce*Math.atan2(re,we),Ce=2*Te;ae[Ce]=$e*Math.cos(Oe),ae[Ce+1]=$e*Math.sin(Oe),oe[Ce]=ae[Ce],oe[Ce+1]=-ae[Ce+1]}this._slicedChirpBuffer=ae.subarray(w,x),this._f=new O(F>>1),this._f.transform(this._chirpBuffer,oe)}_transform(S,w,x){const F=this._buffer1,ae=this._buffer2,oe=this._outBuffer1,xe=this._outBuffer2,we=this._chirpBuffer,re=this._slicedChirpBuffer,Te=this._a;if(x)for(let ce=0;ce<re.length;ce+=2){const $e=ce+1,Oe=ce>>1,Ce=w[Oe];F[ce]=Ce*re[ce],F[$e]=Ce*re[$e]}else for(let ce=0;ce<re.length;ce+=2){const $e=ce+1;F[ce]=w[ce]*re[ce]-w[$e]*re[$e],F[$e]=w[ce]*re[$e]+w[$e]*re[ce]}this._f.transform(oe,F);for(let ce=0;ce<we.length;ce+=2){const $e=ce+1;ae[ce]=oe[ce]*we[ce]-oe[$e]*we[$e],ae[$e]=oe[ce]*we[$e]+oe[$e]*we[ce]}this._f.inverseTransform(xe,ae);for(let ce=0;ce<xe.length;ce+=2){const $e=xe[ce+Te],Oe=xe[ce+Te+1],Ce=re[ce],tt=re[ce+1];S[ce]=$e*Ce-Oe*tt,S[ce+1]=$e*tt+Oe*Ce}}transform(S,w){this._transform(S,w,!1)}realTransform(S,w){this._transform(S,w,!0)}}class se{constructor(S){this.fft_length=S,this.isPowerOfTwo=b(S),this.isPowerOfTwo?(this.fft=new O(S),this.outputBufferSize=2*S):(this.fft=new H(S),this.outputBufferSize=this.fft.bufferSize)}realTransform(S,w){this.fft.realTransform(S,w)}transform(S,w){this.fft.transform(S,w)}}function ne(A,S){if(S%2===0||S<=0)throw new Error("Window size must be a positive odd number");const w=new A.constructor(A.length),x=new A.constructor(S),F=Math.floor(S/2);for(let ae=0;ae<A.length;++ae){let oe=0;for(let xe=-F;xe<=F;++xe){let we=ae+xe;we<0?we=Math.abs(we):we>=A.length&&(we=2*(A.length-1)-we),x[oe++]=A[we]}x.sort(),w[ae]=x[F]}return w}function W(A,S){const w=Math.pow(10,S);return Math.round(A*w)/w}function U(A){const S=Math.round(A);return Math.abs(A)%1===.5?S%2===0?S:S-1:S}function q(A){const S=A.length,w=A[0].length,x=[S+1,w+1],F=Array.from({length:x[0]},()=>Array(x[1]).fill(1/0));F[0][0]=0;const ae=Array.from({length:x[0]},()=>Array(x[1]).fill(-1));for(let Te=1;Te<x[1];++Te)for(let ce=1;ce<x[0];++ce){const $e=F[ce-1][Te-1],Oe=F[ce-1][Te],Ce=F[ce][Te-1];let tt,Ge;$e<Oe&&$e<Ce?(tt=$e,Ge=0):Oe<$e&&Oe<Ce?(tt=Oe,Ge=1):(tt=Ce,Ge=2),F[ce][Te]=A[ce-1][Te-1]+tt,ae[ce][Te]=Ge}for(let Te=0;Te<x[1];++Te)ae[0][Te]=2;for(let Te=0;Te<x[0];++Te)ae[Te][0]=1;let oe=S,xe=w,we=[],re=[];for(;oe>0||xe>0;)switch(we.push(oe-1),re.push(xe-1),ae[oe][xe]){case 0:--oe,--xe;break;case 1:--oe;break;case 2:--xe;break;default:throw new Error(`Internal error in dynamic time warping. 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Proxy(this,{get:(de,ke)=>{if(typeof ke=="string"){let Be=Number(ke);if(Number.isInteger(Be))return de._getitem(Be)}return de[ke]},set:(de,ke,Be)=>de[ke]=Be})}get dims(){return this.ort_tensor.dims}set dims(J){this.ort_tensor.dims=J}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[J,...de]=this.dims;if(de.length>0){const ke=de.reduce((Be,Je)=>Be*Je);for(let Be=0;Be<J;++Be)yield this._subarray(Be,ke,de)}else yield*this.data}_getitem(J){const[de,...ke]=this.dims;if(J=S(J,de),ke.length>0){const Be=ke.reduce((Je,te)=>Je*te);return this._subarray(J,Be,ke)}else return new R(this.type,[this.data[J]],ke)}indexOf(J){const de=this.data;for(let ke=0;ke<de.length;++ke)if(de[ke]==J)return ke;return-1}_subarray(J,de,ke){const Be=J*de,Je=(J+1)*de,te="subarray"in this.data?this.data.subarray(Be,Je):this.data.slice(Be,Je);return new R(this.type,te,ke)}item(){const J=this.data;if(J.length!==1)throw new Error(`a Tensor with ${J.length} elements cannot be converted to Scalar`);return J[0]}tolist(){return g(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const J=this.data;for(let de=0;de<J.length;++de)J[de]=1/(1+Math.exp(-J[de]));return this}map(J){return this.clone().map_(J)}map_(J){const de=this.data;for(let ke=0;ke<de.length;++ke)de[ke]=J(de[ke],ke,de);return this}mul(J){return this.clone().mul_(J)}mul_(J){const de=this.data;for(let ke=0;ke<de.length;++ke)de[ke]*=J;return this}div(J){return this.clone().div_(J)}div_(J){const de=this.data;for(let ke=0;ke<de.length;++ke)de[ke]/=J;return this}add(J){return this.clone().add_(J)}add_(J){const de=this.data;for(let ke=0;ke<de.length;++ke)de[ke]+=J;return this}sub(J){return this.clone().sub_(J)}sub_(J){const de=this.data;for(let ke=0;ke<de.length;++ke)de[ke]-=J;return this}clone(){return new 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ForCausalLM;c.Olmo2Model;c.Olmo2PreTrainedModel;c.OlmoForCausalLM;c.OlmoModel;c.OlmoPreTrainedModel;c.OpenELMForCausalLM;c.OpenELMModel;c.OpenELMPreTrainedModel;c.OwlViTFeatureExtractor;c.OwlViTForObjectDetection;c.OwlViTImageProcessor;c.OwlViTModel;c.OwlViTPreTrainedModel;c.OwlViTProcessor;c.Owlv2ForObjectDetection;c.Owlv2ImageProcessor;c.Owlv2Model;c.Owlv2PreTrainedModel;c.PaliGemmaForConditionalGeneration;c.PaliGemmaPreTrainedModel;c.PaliGemmaProcessor;c.PatchTSMixerForPrediction;c.PatchTSMixerModel;c.PatchTSMixerPreTrainedModel;c.PatchTSTForPrediction;c.PatchTSTModel;c.PatchTSTPreTrainedModel;c.Phi3ForCausalLM;c.Phi3Model;c.Phi3PreTrainedModel;c.Phi3VForCausalLM;c.Phi3VImageProcessor;c.Phi3VPreTrainedModel;c.Phi3VProcessor;c.PhiForCausalLM;c.PhiModel;c.PhiPreTrainedModel;c.Pipeline;c.PreTrainedModel;c.PreTrainedTokenizer;c.PretrainedConfig;c.PretrainedMixin;c.Processor;c.PvtForImageClassification;c.PvtImageProcessor;c.PvtModel;c.PvtPreTrainedModel;c.PyAnnoteFeatureExtractor;c.PyAnnoteForAudioFrameClassification;c.PyAnnoteModel;c.PyAnnotePreTrainedModel;c.PyAnnoteProcessor;c.QuestionAnsweringModelOutput;c.QuestionAnsweringPipeline;c.Qwen2ForCausalLM;c.Qwen2Model;c.Qwen2PreTrainedModel;c.Qwen2Tokenizer;c.Qwen2VLForConditionalGeneration;c.Qwen2VLImageProcessor;c.Qwen2VLPreTrainedModel;c.Qwen2VLProcessor;c.RTDetrForObjectDetection;c.RTDetrImageProcessor;c.RTDetrModel;c.RTDetrObjectDetectionOutput;c.RTDetrPreTrainedModel;c.RawAudio;c.RawImage;c.RepetitionPenaltyLogitsProcessor;c.ResNetForImageClassification;c.ResNetModel;c.ResNetPreTrainedModel;c.RoFormerForMaskedLM;c.RoFormerForQuestionAnswering;c.RoFormerForSequenceClassification;c.RoFormerForTokenClassification;c.RoFormerModel;c.RoFormerPreTrainedModel;c.RoFormerTokenizer;c.RobertaForMaskedLM;c.RobertaForQuestionAnswering;c.RobertaForSequenceClassification;c.RobertaForTokenClassification;c.RobertaModel;c.RobertaPreTrainedModel;c.RobertaTokenizer;c.SamImageProcessor;c.SamImageSegmentationOutput;c.SamModel;c.SamPreTrainedModel;c.SamProcessor;c.SapiensForDepthEstimation;c.SapiensForNormalEstimation;c.SapiensForSemanticSegmentation;c.SapiensPreTrainedModel;c.SeamlessM4TFeatureExtractor;c.SegformerFeatureExtractor;c.SegformerForImageClassification;c.SegformerForSemanticSegmentation;c.SegformerImageProcessor;c.SegformerModel;c.SegformerPreTrainedModel;c.Seq2SeqLMOutput;c.SequenceClassifierOutput;c.SiglipImageProcessor;c.SiglipModel;c.SiglipPreTrainedModel;c.SiglipTextModel;c.SiglipTokenizer;c.SiglipVisionModel;c.SpeechT5FeatureExtractor;c.SpeechT5ForSpeechToText;c.SpeechT5ForTextToSpeech;c.SpeechT5HifiGan;c.SpeechT5Model;c.SpeechT5PreTrainedModel;c.SpeechT5Processor;c.SpeechT5Tokenizer;c.SqueezeBertForMaskedLM;c.SqueezeBertForQuestionAnswering;c.SqueezeBertForSequenceClassification;c.SqueezeBertModel;c.SqueezeBertPreTrainedModel;c.SqueezeBertTokenizer;c.StableLmForCausalLM;c.StableLmModel;c.StableLmPreTrainedModel;c.Starcoder2ForCausalLM;c.Starcoder2Model;c.Starcoder2PreTrainedModel;c.StoppingCriteria;c.StoppingCriteriaList;c.StyleTextToSpeech2Model;c.StyleTextToSpeech2PreTrainedModel;c.SummarizationPipeline;c.SuppressTokensAtBeginLogitsProcessor;c.Swin2SRForImageSuperResolution;c.Swin2SRImageProcessor;c.Swin2SRModel;c.Swin2SRPreTrainedModel;c.SwinForImageClassification;c.SwinModel;c.SwinPreTrainedModel;c.T5ForConditionalGeneration;c.T5Model;c.T5PreTrainedModel;c.T5Tokenizer;c.TableTransformerForObjectDetection;c.TableTransformerModel;c.TableTransformerObjectDetectionOutput;c.TableTransformerPreTrainedModel;c.TemperatureLogitsWarper;var $h=c.Tensor;c.Text2TextGenerationPipeline;c.TextClassificationPipeline;c.TextGenerationPipeline;c.TextStreamer;c.TextToAudioPipeline;c.TokenClassificationPipeline;c.TokenClassifierOutput;c.TokenizerModel;c.TopKLogitsWarper;c.TopPLogitsWarper;c.TrOCRForCausalLM;c.TrOCRPreTrainedModel;c.TranslationPipeline;c.UniSpeechForCTC;c.UniSpeechForSequenceClassification;c.UniSpeechModel;c.UniSpeechPreTrainedModel;c.UniSpeechSatForAudioFrameClassification;c.UniSpeechSatForCTC;c.UniSpeechSatForSequenceClassification;c.UniSpeechSatModel;c.UniSpeechSatPreTrainedModel;c.VLChatProcessor;c.VLMImageProcessor;c.ViTFeatureExtractor;c.ViTForImageClassification;c.ViTImageProcessor;c.ViTMAEModel;c.ViTMAEPreTrainedModel;c.ViTMSNForImageClassification;c.ViTMSNModel;c.ViTMSNPreTrainedModel;c.ViTModel;c.ViTPreTrainedModel;c.VisionEncoderDecoderModel;c.VitMatteForImageMatting;c.VitMatteImageProcessor;c.VitMattePreTrainedModel;c.VitPoseForPoseEstimation;c.VitPoseImageProcessor;c.VitPosePreTrainedModel;c.VitsModel;c.VitsModelOutput;c.VitsPreTrainedModel;c.VitsTokenizer;c.Wav2Vec2BertForCTC;c.Wav2Vec2BertForSequenceClassification;c.Wav2Vec2BertModel;c.Wav2Vec2BertPreTrainedModel;c.Wav2Vec2CTCTokenizer;c.Wav2Vec2FeatureExtractor;c.Wav2Vec2ForAudioFrameClassification;c.Wav2Vec2ForCTC;c.Wav2Vec2ForSequenceClassification;c.Wav2Vec2Model;c.Wav2Vec2PreTrainedModel;c.Wav2Vec2Processor;c.Wav2Vec2ProcessorWithLM;c.WavLMForAudioFrameClassification;c.WavLMForCTC;c.WavLMForSequenceClassification;c.WavLMForXVector;c.WavLMModel;c.WavLMPreTrainedModel;c.WeSpeakerFeatureExtractor;c.WeSpeakerResNetModel;c.WeSpeakerResNetPreTrainedModel;c.WhisperFeatureExtractor;c.WhisperForConditionalGeneration;c.WhisperModel;c.WhisperPreTrainedModel;c.WhisperProcessor;c.WhisperTextStreamer;c.WhisperTimeStampLogitsProcessor;c.WhisperTokenizer;c.XLMForQuestionAnswering;c.XLMForSequenceClassification;c.XLMForTokenClassification;c.XLMModel;c.XLMPreTrainedModel;c.XLMRobertaForMaskedLM;c.XLMRobertaForQuestionAnswering;c.XLMRobertaForSequenceClassification;c.XLMRobertaForTokenClassification;c.XLMRobertaModel;c.XLMRobertaPreTrainedModel;c.XLMRobertaTokenizer;c.XLMTokenizer;c.XLMWithLMHeadModel;c.XVectorOutput;c.YolosFeatureExtractor;c.YolosForObjectDetection;c.YolosImageProcessor;c.YolosModel;c.YolosObjectDetectionOutput;c.YolosPreTrainedModel;c.ZeroShotAudioClassificationPipeline;c.ZeroShotClassificationPipeline;c.ZeroShotImageClassificationPipeline;c.ZeroShotObjectDetectionPipeline;c.bankers_round;c.cat;c.cos_sim;c.dot;c.dynamic_time_warping;c.env;c.full;c.full_like;c.getKeyValueShapes;c.hamming;c.hanning;c.interpolate;c.interpolate_4d;c.interpolate_data;c.is_chinese_char;c.layer_norm;c.load_image;c.log_softmax;c.magnitude;c.matmul;c.max;c.mean;c.mean_pooling;c.medianFilter;c.mel_filter_bank;c.min;c.ones;c.ones_like;c.permute;c.permute_data;var Gf=c.pipeline;c.quantize_embeddings;c.rand;c.read_audio;c.rfft;c.round;c.slice;c.softmax;c.spectrogram;c.stack;c.std_mean;c.topk;c.window_function;c.zeros;c.zeros_like;const Na=16e3,Ah=Na/1e3,Kf=.3,Hf=.1,qf=400,Qf=qf*Ah,Xf=80,kh=Xf*Ah,Yf=250*Ah,Jf=30,Zf=512,eg=Math.ceil(kh/Zf);async function tg(){try{return!("gpu"in navigator)||!navigator.gpu?!1:(await navigator.gpu.requestAdapter(),!0)}catch(Ee){return console.error(Ee),!1}}var In=(Ee=>(Ee.Status="status",Ee.Output="output",Ee.Info="info",Ee.Request="request",Ee.Error="error",Ee.Load="load",Ee))(In||{}),Ih=(Ee=>(Ee.RecordingStart="recording_start",Ee.RecordingEnd="recording_end",Ee.Ready="ready",Ee))(Ih||{}),qp=(Ee=>(Ee.UntilNext="until_next",Ee))(qp||{});let Sh,Qp,Xp=Promise.resolve();const Ra=new Float32Array(Jf*Na);let An=0;const sg=new $h("int64",[Na],[]);let h_=new $h("float32",new Float32Array(2*1*128),[2,1,128]),Nc=!1,jc=0;const rg={webgpu:{encoder_model:"fp32",decoder_model_merged:"q4"},wasm:{encoder_model:"fp32",decoder_model_merged:"q8"}};async function ng(){return await Wf.from_pretrained("onnx-community/silero-vad",{config:{model_type:"custom"},dtype:"fp32"}).catch(Ee=>{throw self.postMessage({type:In.Error,error:Ee}),Ee})}async function og(Ee){return await Gf("automatic-speech-recognition","onnx-community/moonshine-base-ONNX",{device:Ee,dtype:rg[Ee]}).catch($=>{throw self.postMessage({type:In.Error,error:$}),$})}async function ig(Ee){if(Sh===void 0)return console.warn("VAD model not loaded yet"),!1;const $=new $h("float32",Ee,[1,Ee.length]),{stateN:r,output:f}=await(Xp=Xp.then(N=>Sh({input:$,sr:sg,state:h_})));h_=r;const I=f.data[0];return I>Kf||Nc&&I>=Hf}async function ag(Ee,$){if(Qp===void 0){console.warn("Transcriber model not loaded yet");return}const{text:r}=await(Xp=Xp.then(f=>Qp(Ee)));self.postMessage({type:In.Output,buffer:Ee,message:r,...$})}function w_(Ee=0){self.postMessage({type:In.Status,status:Ih.RecordingEnd,message:"Transcribing...",duration:qp.UntilNext}),Ra.fill(0,Ee),An=Ee,Nc=!1,jc=0}const Uc=[];function m_(Ee){const r=Date.now()-(jc+kh)/Na*1e3,f=r-An/Na*1e3,I=r-f,N=(Ee==null?void 0:Ee.length)??0,X=Ra.slice(0,An+kh),R=Uc.reduce((M,y)=>M+y.length,0),g=new Float32Array(R+X.length);let v=0;for(const M of Uc)g.set(M,v),v+=M.length;g.set(X,v),ag(g,{start:f,end:r,duration:I}),Ee&&Ra.set(Ee,0),w_(N)}async function lg(){const Ee=await tg()?"webgpu":"wasm";self.postMessage({type:In.Info,message:`Using device: "${Ee}"`}),self.postMessage({type:In.Info,message:"Loading models...",duration:qp.UntilNext}),Sh=await ng(),Qp=await og(Ee),await Qp(new Float32Array(Na)),self.postMessage({type:"status",status:"ready",message:"Ready!"}),self.onmessage=async $=>{const{buffer:r}=$.data,f=Nc,I=await ig(r);if(!f&&!I){Uc.length>=eg&&Uc.shift(),Uc.push(r);return}const N=Ra.length-An;if(r.length>=N){Ra.set(r.subarray(0,N),An),An+=N;const X=r.subarray(N);m_(X);return}else Ra.set(r,An),An+=r.length;if(I){Nc||self.postMessage({type:In.Status,status:Ih.RecordingStart,message:"Listening...",duration:qp.UntilNext}),Nc=!0,jc=0;return}if(jc+=r.length,!(jc<Qf)){if(An<Yf){w_();return}m_()}}}self.addEventListener("message",Ee=>{const{type:$}=Ee.data;switch($){case In.Load:lg();break}});