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             Please use the previous external buffer!`)}else n=Me();return this.storageCache.set(n,{gpuData:{id:n,type:0,buffer:e},originalSize:t}),ir("verbose",()=>`[WebGPU] GpuDataManager.registerExternalBuffer(size=${t}) => id=${n}, registered.`),n}unregisterExternalBuffer(e){e!==void 0&&(this.storageCache.delete(e),ir("verbose",()=>`[WebGPU] GpuDataManager.unregisterExternalBuffer() => id=${e}`))}create(e,t=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST){let r=H(e),n,o=(t&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE,a=(t&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM;if(o||a){let d=(o?this.freeBuffers:this.freeUniformBuffers).get(r);d?d.length>0?n=d.pop():n=this.backend.device.createBuffer({size:r,usage:t}):n=this.backend.device.createBuffer({size:r,usage:t})}else n=this.backend.device.createBuffer({size:r,usage:t});let i={id:Me(),type:0,buffer:n};return this.storageCache.set(i.id,{gpuData:i,originalSize:Number(e)}),ir("verbose",()=>`[WebGPU] GpuDataManager.create(size=${e}) => id=${i.id}`),i}get(e){var t;return(t=this.storageCache.get(e))==null?void 0:t.gpuData}release(e){let t=typeof e=="bigint"?Number(e):e,r=this.storageCache.get(t);if(!r){if(this.storageCache.size===0)return 0;throw new Error("releasing data does not exist")}return ir("verbose",()=>`[WebGPU] GpuDataManager.release(id=${t}), gpuDataId=${r.gpuData.id}`),this.storageCache.delete(t),this.buffersPending.push(r.gpuData.buffer),r.originalSize}async download(e,t){let r=this.storageCache.get(Number(e));if(!r)throw new Error("data does not exist");await Ce(this.backend,r.gpuData.buffer,r.originalSize,t)}refreshPendingBuffers(){if(this.buffersPending.length!==0)if(this.backend.sessionStatus==="default"){for(let e of this.buffersPending){let t=Mn.get(e.size);if((e.usage&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE){let r=this.freeBuffers.get(e.size)||[];t===void 0||r.length>=t?e.destroy():r.push(e)}else if((e.usage&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM){let r=this.freeUniformBuffers.get(e.size)||[];t===void 0||r.length>=t?e.destroy():r.push(e)}else e.destroy()}this.buffersPending=[]}else{let e=this.capturedPendingBuffers.get(this.backend.currentSessionId);e||(e=[],this.capturedPendingBuffers.set(this.backend.currentSessionId,e));for(let t of this.buffersPending)e.push(t);this.buffersPending=[]}}dispose(){this.freeBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.freeUniformBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache.forEach(e=>{e.gpuData.buffer.destroy()}),this.capturedPendingBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.capturedPendingBuffers=new Map}onCreateSession(){this.sessionCount+=1}onReleaseSession(e){let t=this.capturedPendingBuffers.get(e);t&&(t.forEach(r=>{r.destroy()}),this.capturedPendingBuffers.delete(e)),this.sessionCount-=1,this.sessionCount===0&&(ir("warning",()=>"[WebGPU] Clearing webgpu buffer cache"),this.storageCache.forEach(r=>{r.gpuData.buffer.destroy()}),this.storageCache=new Map)}},ct=(...e)=>new He(...e)}),ht,it,Pt=w(()=>{ht=class{constructor(e){Object.assign(this,e)}get cacheKey(){return this.key||(this.key=Object.getOwnPropertyNames(this).sort().map(e=>`${this[e]}`).join(";")),this.key}},it=e=>new ht(e)}),hr,rr,$e,wr,Rr,Yr,Jr,Bt=w(()=>{hr=class{static calcMatMulShape(e,t){return e[1]!==t[0]?void 0:[e[0],t[1]]}},rr=class{static calcShape(e,t,r=!1){let n=e.length,o=t.length;if(n===0)return t;if(o===0)return e;let a=Math.max(e.length,t.length),i=new Array(a);if(r){if(n<2||o<2)return;let d=hr.calcMatMulShape([e[n-2],e[n-1]],[t[o-2],t[o-1]]);if(d===void 0)return;[i[a-2],i[a-1]]=d}for(let d=r?3:1;d<=a;d++){let p=n-d<0?1:e[n-d],h=o-d<0?1:t[o-d];if(p!==h&&p>1&&h>1)return;let k=Math.max(p,h);if(p&&h)i[a-d]=Math.max(p,h);else{if(k>1)return;i[a-d]=0}}return i}static isValidBroadcast(e,t){let r=e.length,n=t.length;if(r>n)return!1;for(let o=1;o<=r;o++)if(e[r-o]!==1&&e[r-o]!==t[n-o])return!1;return!0}},$e=class Rp{static size(t){return Rp.getSizeFromDimensionRange(t,0,t.length)}static convertShape(t,r=4){let n=t.length;if(n===0)return[];let o=new Array(n),a=n-1;for(;a>=0;){if(t[a]%r===0){o[a]=t[a]/r;break}if(r%t[a]!==0)throw new Error("cannot convert shape");o[a]=1,r/=t[a],a--}for(a--;a>=0;a--)o[a]=t[a];return o}static sizeFromDimension(t,r){if(r<0||r>t.length)throw new Error(`invalid dimension of ${r} for sizeFromDimension as Tensor has ${t.length} dimensions.`);return Rp.getSizeFromDimensionRange(t,r,t.length)}static sizeToDimension(t,r){if(r<0||r>t.length)throw new Error(`invalid dimension of ${r} for sizeToDimension as Tensor has ${t.length} dimensions.`);return Rp.getSizeFromDimensionRange(t,0,r)}static getSizeFromDimensionRange(t,r,n){let o=1;for(let a=r;a<n;a++){if(t[a]<0)throw new Error("cannot get valid size from specified dimension range. 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computePoolOutputShape(t,r,n,o,a,i,d){if(r.length<=0)throw new Error("input shape must be of size greater than 0");let p=[r[0],r[1]];return Oc.computeShapeHelper(t,r,p,n,o,a,i,d),p}static computeConvOutputShape(t,r,n,o,a,i,d){if(t.length<=0||r.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let p=[t[0],r[0]];return Oc.computeShapeHelper(!1,t,p,n,o,a,i,d),p}static computeShapeHelper(t,r,n,o,a,i,d,p){if(t)for(let h=0;h<r.length-2;h++)n.push(1);else for(let h=0;h<r.length-2;h++)n.push(Oc.adjustPadAndReturnShape(r[h+2],o[h],a[h],i[h],d,h,h+r.length-2,p))}static adjustPadAndReturnShape(t,r,n,o,a,i,d,p){let h=n*(o-1)+1;if(p&&p!=="NOTSET")switch(p){case"VALID":return a[i]=0,a[d]=0,Math.floor((t-h)/r+1);case"SAME_LOWER":case"SAME_UPPER":if(n!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let k=((t+r-1)/r-1)*r+o-t;return a[i]=Math.floor(p==="SAME_LOWER"?(k+1)/2:k/2),a[d]=k-a[i],Math.floor((t+k-o)/r+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((t+a[i]+a[d]-h)/r+1)}},Rr=class{static getShapeOfGemmResult(e,t,r,n,o){if(e.length!==2||r.length!==2)throw new Error("shape need to be of size 2");let a,i,d;t?(a=e[1],i=e[0]):(a=e[0],i=e[1]);let p=-1;if(n?(d=r[0],p=1):(d=r[1],p=0),r[p]!==i)throw new Error("dimension mismatch");if(a<=0||d<=0||i<=0)throw new Error("invalid shape specified");if(o&&!rr.isValidBroadcast(o,[a,d]))throw new Error("gemm: invalid bias shape for broadcast");return[a,d,i]}},Yr=-34028234663852886e22,Jr=34028234663852886e22}),Nr,ps,er,mr,vt,yr,Es,Dr,Hr,Mt,br,ze,wt,ts,en,oo,bn,Qt=w(()=>{zt(),Bt(),Nr=64,ps=(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${t}<u32>`:"u32";case 7:if(t>1)throw new Error("currently not supported vecX of uint64 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4]`:n==="f16"?`${e}[${Math.floor(t/8)}][${Math.floor(t%8/4)}][${t%8%4}]`:`${e}[${Math.floor(t/4)}][${t%4}]`:r>1?`${e}[${t}]`:e,br=(e,t,r,n,o)=>{let a=typeof r=="number",i=a?r:r.length,d=[...new Array(i).keys()],p=i<2?"u32":i<=4?`vec${i}<u32>`:`array<u32, ${i}>`,h=ps(t,o),k=typeof h=="string"?h:h[1],S=typeof h=="string"?h:h[0],u={indices:p,value:k,storage:S,tensor:t},B=ot=>typeof ot=="string"?ot:`${ot}u`,R={offsetToIndices:!1,indicesToOffset:!1,broadcastedIndicesToOffset:!1,set:!1,setByIndices:!1,get:!1,getByIndices:!1},U=a?"uniforms.":"",Z=`${U}${e}_shape`,te=`${U}${e}_strides`,X="";for(let ot=0;ot<i-1;ot++)X+=`
    let dim${ot} = current / ${Mt(te,ot,i)};
    let rest${ot} = current % ${Mt(te,ot,i)};
    indices[${ot}] = dim${ot};
    current = rest${ot};
    `;X+=`indices[${i-1}] = current;`;let _e=i<2?"":`
  fn o2i_${e}(offset: u32) -> ${u.indices} {
    var indices: ${u.indices};
    var current = offset;
    ${X}
    return indices;
  }`,me=ot=>(R.offsetToIndices=!0,i<2?ot:`o2i_${e}(${ot})`),ye=[];if(i>=2)for(let ot=i-1;ot>=0;ot--)ye.push(`${Mt(te,ot,i)} * (indices[${ot}])`);let Ae=i<2?"":`
  fn i2o_${e}(indices: ${u.indices}) -> u32 {
    return ${ye.join("+")};
  }`,Ie=ot=>(R.indicesToOffset=!0,i<2?ot:`i2o_${e}(${ot})`),Ge=(...ot)=>i===0?"0u":`${u.indices}(${ot.map(B).join(",")})`,lt=(ot,Et)=>i<2?`${ot}`:`${Mt(ot,Et,i)}`,xt=(ot,Et,cr)=>i<2?`${ot}=${cr};`:`${Mt(ot,Et,i)}=${cr};`,Kt={},Yt=(ot,Et)=>{R.broadcastedIndicesToOffset=!0;let cr=`${Et.name}broadcastedIndicesTo${e}Offset`;if(cr in Kt)return`${cr}(${ot})`;let Lr=[];for(let ys=i-1;ys>=0;ys--){let Kr=Et.indicesGet("outputIndices",ys+Et.rank-i);Lr.push(`${lt(te,ys)} * (${Kr} % ${lt(Z,ys)})`)}return Kt[cr]=`fn ${cr}(outputIndices: ${Et.type.indices}) -> u32 {
             return ${Lr.length>0?Lr.join("+"):"0u"};
           }`,`${cr}(${ot})`},Ct=(ot,Et)=>(()=>{if(u.storage===u.value)return`${e}[${ot}]=${Et};`;if(u.storage==="vec2<u32>"&&u.value==="i32")return`${e}[${ot}]=vec2<u32>(u32(${Et}), select(0u, 0xFFFFFFFFu, ${Et} < 0));`;if(u.storage==="vec2<u32>"&&u.value==="u32")return`${e}[${ot}]=vec2<u32>(u32(${Et}), 0u);`;if(u.storage==="u32"&&u.value==="vec4<bool>")return`${e}[${ot}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${Et}));`;throw new Error(`not supported combination of storage type ${u.storage} and value type ${u.value} yet`)})(),Jt=ot=>(()=>{if(u.storage===u.value)return`${e}[${ot}]`;if(u.storage==="vec2<u32>"&&u.value==="i32")return`i32(${e}[${ot}].x)`;if(u.storage==="vec2<u32>"&&u.value==="u32")return`u32(${e}[${ot}].x)`;if(u.storage==="u32"&&u.value==="vec4<bool>")return`vec4<bool>(bool(${e}[${ot}] & 0xFFu), bool(${e}[${ot}] & 0xFF00u), bool(${e}[${ot}] & 0xFF0000u), bool(${e}[${ot}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${u.storage} and value type ${u.value} yet`)})(),$t=i<2?"":`
  fn get_${e}ByIndices(indices: ${u.indices}) -> ${k} {
    return ${Jt(`i2o_${e}(indices)`)};
  }`,jt=i<2?"":(()=>{let ot=d.map(cr=>`d${cr}: u32`).join(", "),Et=d.map(cr=>`d${cr}`).join(", ");return`
  fn get_${e}(${ot}) -> ${k} {
    return get_${e}ByIndices(${Ge(Et)});
  }`})(),vr=(...ot)=>{if(ot.length!==i)throw new Error(`indices length must be ${i}`);let Et=ot.map(B).join(",");return i===0?Jt("0u"):i===1?Jt(Et[0]):(R.get=!0,R.getByIndices=!0,R.indicesToOffset=!0,`get_${e}(${Et})`)},Ht=ot=>i<2?Jt(ot):(R.getByIndices=!0,R.indicesToOffset=!0,`get_${e}ByIndices(${ot})`),Gt=i<2?"":`
  fn set_${e}ByIndices(indices: ${u.indices}, value: ${k}) {
    ${Ct(`i2o_${e}(indices)`,"value")}
  }`,Pr=i<2?"":(()=>{let ot=d.map(cr=>`d${cr}: u32`).join(", "),Et=d.map(cr=>`d${cr}`).join(", ");return`
  fn set_${e}(${ot}, value: ${k}) {
    set_${e}ByIndices(${Ge(Et)}, value);
  }`})();return{impl:()=>{let ot=[],Et=!1;return R.offsetToIndices&&(ot.push(_e),Et=!0),R.indicesToOffset&&(ot.push(Ae),Et=!0),R.broadcastedIndicesToOffset&&(Object.values(Kt).forEach(cr=>ot.push(cr)),Et=!0),R.set&&(ot.push(Pr),Et=!0),R.setByIndices&&(ot.push(Gt),Et=!0),R.get&&(ot.push(jt),Et=!0),R.getByIndices&&(ot.push($t),Et=!0),!a&&Et&&ot.unshift(`const ${Z} = ${u.indices}(${r.join(",")});`,`const ${te} = ${u.indices}(${$e.computeStrides(r).join(",")});`),ot.join(`
`)},type:u,offsetToIndices:me,indicesToOffset:Ie,broadcastedIndicesToOffset:Yt,indices:Ge,indicesGet:lt,indicesSet:xt,set:(...ot)=>{if(ot.length!==i+1)throw new Error(`indices length must be ${i}`);let Et=ot[i];if(typeof Et!="string")throw new Error("value must be string");let cr=ot.slice(0,i).map(B).join(",");return i===0?Ct("0u",Et):i===1?Ct(cr[0],Et):(R.set=!0,R.setByIndices=!0,R.indicesToOffset=!0,`set_${e}(${cr}, ${Et})`)},setByOffset:Ct,setByIndices:(ot,Et)=>i<2?Ct(ot,Et):(R.setByIndices=!0,R.indicesToOffset=!0,`set_${e}ByIndices(${ot}, ${Et});`),get:vr,getByOffset:Jt,getByIndices:Ht,usage:n,name:e,strides:te,shape:Z,rank:i}},ze=(e,t,r,n=1)=>br(e,t,r,"input",n),wt=(e,t,r,n=1)=>br(e,t,r,"output",n),ts=(e,t,r)=>br(e,t,r,"atomicOutput",1),en=(e,t,r,n=1)=>br(e,t,r,"internal",n),oo=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=Nr){let t=typeof e=="number"?e:e[0],r=typeof e=="number"?1:e[1],n=typeof e=="number"?1:e[2];if(t>this.limits.maxComputeWorkgroupSizeX||r>this.limits.maxComputeWorkgroupSizeY||n>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${t}, ${r}, ${n}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(t*r*n>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${t}, ${r}, ${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 * ${t*r*n}u + local_idx;`;return`@compute @workgroup_size(${t}, ${r}, ${n})
  fn main(${a}) {
    ${i}
  `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let r=e.usage==="input"?"read":"read_write",n=e.usage==="atomicOutput"?"atomic<i32>":e.type.storage;return`@group(0) @binding(${t}) var<storage, ${r}> ${e.name}: array<${n}>;`}declareVariables(...e){return e.map(t=>this.declareVariable(t,this.variableIndex++)).join(`
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  ${R.registerUniform("output_size","u32").declareVariables(U,Z)}
  ${R.mainStart()}
    ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
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    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${R}u + local_id.x;
    let input_row = workgroup_id_x * ${R}u + local_id.y;
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    let output_col = workgroup_id_x * ${R}u + local_id.x;
    let output_row = workgroup_id_y * ${R}u + local_id.y;
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    }
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  ${R.registerUniform("output_size","u32").declareVariables(U,Z)}

  ${Pa(o,n,U,Z)}

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

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           bestValue = ${Go[n]};
          }
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         var reduceSize = min(Length, ${u}u);
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            let candidate = aBestValues[local_idx + interval];
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            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
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         if (local_idx == 0u) {
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        ${U.registerUniform("output_size","u32").declareVariables(te,X)}

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

          ${Z.join(`
`)}
          ${_e[0]}       // init ops for reduce max/min
          ${_e[1]}
          ${me}
          ${_e[3]}
          ${_e.length===4?X.setByOffset("global_idx","value"):_e.slice(4).join(`
`)}
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`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {
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         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};e.compute(ao("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},oi=e=>it(e)}),rl,uo,ii,sl,nl,vn,ol,il,co=w(()=>{zt(),Bt(),yn(),Qt(),rl=(e,t)=>{let r=e[0],n=e[1],o=e[2],a=e[3],i=e[4],d=e[5];if(i&&d)throw new Error("Attention cannot have both past and attention_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let p=r.dims[0],h=r.dims[1],k=r.dims[2];if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==k)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let S=o.dims[0]/3,u=S,B=u;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let _e of t.qkvHiddenSizes)if(_e%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");S=t.qkvHiddenSizes[0],u=t.qkvHiddenSizes[1],B=t.qkvHiddenSizes[2]}let R=h;if(S!==u)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==S+u+B)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let U=0;if(i){if(u!==B)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]!==u/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(U=i.dims[3])}let Z=R+U,te=-1,X=0;if(a)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(d){if(d.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(d.dims[0]!==p||d.dims[1]!==t.numHeads||d.dims[2]!==h||d.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:U,kvSequenceLength:R,totalSequenceLength:Z,maxSequenceLength:te,inputHiddenSize:k,hiddenSize:S,vHiddenSize:B,headSize:Math.floor(S/t.numHeads),vHeadSize:Math.floor(B/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:X,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},uo=(e,t,r)=>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;
      }
       `:`
    ${r?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,ii=(e,t,r,n,o,a,i,d)=>{let p=yr(i?1:a),h=64,k=a/p;k<h&&(h=32);let S=Math.ceil(a/p/h),u=[{type:12,data:t},{type:12,data:r},{type:12,data:n},{type:12,data:o},{type:12,data:k},{type:12,data:S}],B=er(e.dataType,p),R=mr(1,p),U=["type"];i&&U.push("type"),d&&U.push("type");let Z=te=>{let X=wt("x",e.dataType,e.dims,p),_e=[X],me=i?ze("seq_lens",i.dataType,i.dims):void 0;me&&_e.push(me);let ye=d?ze("total_sequence_length_input",d.dataType,d.dims):void 0;ye&&_e.push(ye);let Ae=mr(e.dataType),Ie=[{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}>;
  ${te.registerUniforms(Ie).declareVariables(..._e)}
  ${te.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;
    ${uo(me,ye,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${h}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${i?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${R}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${R}(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 = ${R}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      sum_vector += exp(${R}(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] = ${X.type.value}(${Ae}(1.0) / ${Ae}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${R}(x[offset + i]);
        x[offset + i] = ${X.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] = ${X.type.value}(${Ae}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${B};${p}`,inputDependencies:U},getShaderSource:Z,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(a/h),y:o,z:t*r},programUniforms:u})}},sl=(e,t,r,n,o,a,i,d,p)=>{let h=i+a.kvSequenceLength,k=[a.batchSize,a.numHeads,a.sequenceLength,h],S=e>1&&n,u=a.kvNumHeads?a.kvNumHeads:a.numHeads,B=S?[a.batchSize,u,h,a.headSize]:void 0,R=a.nReps?a.nReps:1,U=a.scale===0?1/Math.sqrt(a.headSize):a.scale,Z=yr(a.headSize),te=a.headSize/Z,X=12,_e={x:Math.ceil(h/X),y:Math.ceil(a.sequenceLength/X),z:a.batchSize*a.numHeads},me=[{type:12,data:a.sequenceLength},{type:12,data:te},{type:12,data:h},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:U},{type:12,data:i},{type:12,data:a.kvSequenceLength},{type:12,data:R}],ye=S&&n&&$e.size(n.dims)>0,Ae=["type","type"];ye&&Ae.push("type"),o&&Ae.push("type"),d&&Ae.push("type"),p&&Ae.push("type");let Ie=[{dims:k,dataType:t.dataType,gpuDataType:0}];S&&Ie.push({dims:B,dataType:t.dataType,gpuDataType:0});let Ge=lt=>{let xt=ze("q",t.dataType,t.dims,Z),Kt=ze("key",r.dataType,r.dims,Z),Yt=[xt,Kt];if(ye){let Gt=ze("past_key",n.dataType,n.dims,Z);Yt.push(Gt)}o&&Yt.push(ze("attention_bias",o.dataType,o.dims));let Ct=d?ze("seq_lens",d.dataType,d.dims):void 0;Ct&&Yt.push(Ct);let Jt=p?ze("total_sequence_length_input",p.dataType,p.dims):void 0;Jt&&Yt.push(Jt);let $t=wt("output",t.dataType,k),jt=[$t];S&&jt.push(wt("present_key",t.dataType,B,Z));let vr=mr(1,Z),Ht=[{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 = ${X}u;

  var<workgroup> tileQ: array<${xt.type.storage}, ${X*X}>;
  var<workgroup> tileK: array<${xt.type.storage}, ${X*X}>;
  ${lt.registerUniforms(Ht).declareVariables(...Yt,...jt)}
  ${lt.mainStart([X,X,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${R===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${R===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;
    ${uo(Ct,Jt,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${ye&&S?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${S?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${vr}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
      ${ye&&S?`
              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];
          }`}
      ${S?`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 += ${vr}(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] = ${$t.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${Z};${o!==void 0};${n!==void 0};${e}`,inputDependencies:Ae},getRunData:()=>({outputs:Ie,dispatchGroup:_e,programUniforms:me}),getShaderSource:Ge}},nl=(e,t,r,n,o,a,i=void 0,d=void 0)=>{let p=a+o.kvSequenceLength,h=o.nReps?o.nReps:1,k=o.vHiddenSize*h,S=e>1&&n,u=o.kvNumHeads?o.kvNumHeads:o.numHeads,B=S?[o.batchSize,u,p,o.headSize]:void 0,R=[o.batchSize,o.sequenceLength,k],U=12,Z={x:Math.ceil(o.vHeadSize/U),y:Math.ceil(o.sequenceLength/U),z:o.batchSize*o.numHeads},te=[{type:12,data:o.sequenceLength},{type:12,data:p},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:k},{type:12,data:a},{type:12,data:o.kvSequenceLength},{type:12,data:h}],X=S&&n&&$e.size(n.dims)>0,_e=["type","type"];X&&_e.push("type"),i&&_e.push("type"),d&&_e.push("type");let me=[{dims:R,dataType:t.dataType,gpuDataType:0}];S&&me.push({dims:B,dataType:t.dataType,gpuDataType:0});let ye=Ae=>{let Ie=ze("probs",t.dataType,t.dims),Ge=ze("v",r.dataType,r.dims),lt=[Ie,Ge];X&&lt.push(ze("past_value",n.dataType,n.dims));let xt=i?ze("seq_lens",i.dataType,i.dims):void 0;i&&lt.push(xt);let Kt=d?ze("total_sequence_length_input",d.dataType,d.dims):void 0;d&&lt.push(Kt);let Yt=[wt("output",t.dataType,R)];S&&Yt.push(wt("present_value",t.dataType,B));let Ct=[{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 = ${U}u;
  var<workgroup> tileQ: array<${Ie.type.value}, ${U*U}>;
  var<workgroup> tileV: array<${Ie.type.value}, ${U*U}>;
  ${Ae.registerUniforms(Ct).declareVariables(...lt,...Yt)}
  ${Ae.mainStart([U,U,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;
   ${uo(xt,Kt,!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
   ${X&&S?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${S?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${Ie.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;
        ${X&&S?`
        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];
            }`}
        ${S?`
            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:_e},getRunData:()=>({outputs:me,dispatchGroup:Z,programUniforms:te}),getShaderSource:ye}},vn=(e,t,r,n,o,a,i,d,p,h,k=void 0,S=void 0)=>{let u=Math.min(e.outputCount,1+(i?1:0)+(d?1:0)),B=u>1?h.pastSequenceLength:0,R=B+h.kvSequenceLength,U=p&&$e.size(p.dims)>0?p:void 0,Z=[t,r];u>1&&i&&$e.size(i.dims)>0&&Z.push(i),U&&Z.push(U),k&&Z.push(k),S&&Z.push(S);let te=e.compute(sl(u,t,r,i,U,h,B,k,S),{inputs:Z,outputs:u>1?[-1,1]:[-1]})[0];e.compute(ii(te,h.batchSize,h.numHeads,B,h.sequenceLength,R,k,S),{inputs:k&&S?[te,k,S]:[te],outputs:[]});let X=[te,n];u>1&&d&&$e.size(d.dims)>0&&X.push(d),k&&X.push(k),S&&X.push(S),e.compute(nl(u,te,n,d,h,B,k,S),{inputs:X,outputs:u>1?[0,2]:[0]})},ol=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,o=t.inputHiddenSize,a=t.headSize,i=12,d={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}],k=S=>{let u=wt("output_q",p[0].dataType,r),B=wt("output_k",p[0].dataType,r),R=wt("output_v",p[0].dataType,r),U=ze("input",p[0].dataType,p[0].dims),Z=ze("weight",p[1].dataType,p[1].dims),te=ze("bias",p[2].dataType,p[2].dims),X=U.type.storage,_e=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${i}u;
  var<workgroup> tileInput: array<${X}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${X}, ${i*i}>;
  var<workgroup> tileWeightK: array<${X}, ${i*i}>;
  var<workgroup> tileWeightV: array<${X}, ${i*i}>;
  ${S.registerUniforms(_e).declareVariables(U,Z,te,u,B,R)}
  ${S.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 = ${X}(0);
    var valueK = ${X}(0);
    var valueV = ${X}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:d,programUniforms:h}),getShaderSource:k},{inputs:p,outputs:[-1,-1,-1]})},il=(e,t)=>{let r=rl(e.inputs,t),[n,o,a]=ol(e,r);return vn(e,n,o,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r)}}),al,ll,ai,ul,jc=w(()=>{Qe(),zt(),Bt(),Pt(),Qt(),al=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,o,a)=>{let i=o.length;if(i!==n.length)throw new Error(`${a}: num dimensions != ${i}`);o.forEach((d,p)=>{if(d!==n[p])throw new Error(`${a}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},ll=(e,t)=>{let{epsilon:r,spatial:n,format:o}=t,a=e[0].dims,i=n?yr(a[a.length-1]):1,d=o==="NHWC"&&a.length>1?i:1,p=$e.size(a)/i,h=n,k=h?a.length:a,S=ze("x",e[0].dataType,e[0].dims,i),u=ze("scale",e[1].dataType,e[1].dims,d),B=ze("bias",e[2].dataType,e[2].dims,d),R=ze("inputMean",e[3].dataType,e[3].dims,d),U=ze("inputVar",e[4].dataType,e[4].dims,d),Z=wt("y",e[0].dataType,k,i),te=()=>{let _e="";if(n)_e=`let cOffset = ${a.length===1?"0u":o==="NHWC"?`outputIndices[${a.length-1}] / ${i}`:"outputIndices[1]"};`;else if(o==="NCHW")_e=`
            ${Z.indicesSet("outputIndices","0","0")}
            let cOffset = ${Z.indicesToOffset("outputIndices")};`;else{_e=`var cIndices = ${u.type.indices}(0);
                       cIndices[0] = outputIndices[${a.length-1}];`;for(let me=1;me<u.rank;me++)_e+=`cIndices[${me}] = outputIndices[${me}];`;_e+=`let cOffset = ${u.indicesToOffset("cIndices")};`}return _e},X=_e=>`
  const epsilon = ${r};
  ${_e.registerUniform("outputSize","u32").declareVariables(S,u,B,R,U,Z)}
  ${_e.mainStart()}
  ${_e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${Z.offsetToIndices(`global_idx * ${i}`)};
    ${te()}
    let scale = ${u.getByOffset("cOffset")};
    let bias = ${B.getByOffset("cOffset")};
    let inputMean = ${R.getByOffset("cOffset")};
    let inputVar = ${U.getByOffset("cOffset")};
    let x = ${S.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}_${i}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:X,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h?[{type:12,data:p},...vt(a)]:[{type:12,data:p}]})}},ai=e=>it(e),ul=(e,t)=>{let{inputs:r,outputCount:n}=e,o=ai({...t,outputCount:n});if(T.webgpu.validateInputContent&&al(r,o),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(ll(r,o))}}),li,dl,cl,pl=w(()=>{Bt(),Qt(),li=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},dl=e=>{let t=e[0].dims,r=e[0].dims[2],n=$e.size(t)/4,o=e[0].dataType,a=ze("input",o,t,4),i=ze("bias",o,[r],4),d=ze("residual",o,t,4),p=wt("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 = ${r}u / 4;
  ${h.declareVariables(a,i,d,p)}

  ${h.mainStart()}
    ${h.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${a.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${d.getByOffset("global_idx")};
    ${p.setByOffset("global_idx","value")}
  }`}},cl=e=>{li(e.inputs),e.compute(dl(e.inputs))}}),hl,ur,ui,ml,_l,di,fl,gl,ci,wl,yl,pi,Ml,bl,hi,vl,Nn,Tl,po,mi,xl,El,_i,Pl,Cl,fi,kl,Sl,gi,$l,Al,wi,Il,Ol,yi,Mi,Fl,ho,bi,Dl,Ll,zl,vi,Bl,Rl,Ti=w(()=>{zt(),Bt(),Pt(),Qt(),hl=(e,t,r,n,o,a,i)=>{let d=Math.ceil(t/4),p="";typeof o=="string"?p=`${o}(a)`:p=o("a");let h=ze("inputData",r,[d],4),k=wt("outputData",n,[d],4),S=[{name:"vec_size",type:"u32"}];return i&&S.push(...i),`
      ${e.registerUniforms(S).declareVariables(h,k)}

  ${a??""}

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

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

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

  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,t.cacheKey))},po=(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));
}`,mi=e=>{let t=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,po(t)))},xl=e=>{e.compute(ur(e.inputs[0],"Exp","exp"))},El=e=>{e.compute(ur(e.inputs[0],"Floor","floor"))},_i=e=>{let t=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,po(t)))},Pl=(e,t)=>{let r=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},Cl=e=>{e.compute(ur(e.inputs[0],"Not",t=>`!${t}`))},fi=e=>{e.compute(ur(e.inputs[0],"Neg",t=>`-${t}`))},kl=e=>{e.compute(ur(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Sl=e=>{let t=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},gi=e=>{e.compute(ur(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},$l=e=>it(e),Al=(e,t)=>{let r=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},wi=e=>{e.compute(ur(e.inputs[0],"Sin","sin"))},Il=e=>{e.compute(ur(e.inputs[0],"Sinh","sinh"))},Ol=e=>{e.compute(ur(e.inputs[0],"Sqrt","sqrt"))},yi=e=>{e.compute(ur(e.inputs[0],"Tan","tan"))},Mi=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Fl=e=>{e.compute(ur(e.inputs[0],"Tanh",Mi))},ho=(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 ${Mi("v")};
}
`,bi=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Dl=e=>{let t=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"FastGelu",bi,ho(t),void 0,e.inputs[0].dataType))},Ll=(e,t)=>{let r=mr(e.inputs[0].dataType);return e.compute(ur(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},zl=e=>{e.compute(ur(e.inputs[0],"Log","log"))},vi=(e,t)=>`
const alpha = vec4<${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<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Bl=e=>`quick_gelu_impl(${e})`,Rl=(e,t)=>{let r=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"QuickGelu",Bl,vi(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),Nl,xi,jl,Uc=w(()=>{Bt(),Qt(),Ti(),Nl=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")},xi=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=ze("input",e[0].dataType,e[0].dims,4),n=ze("bias",e[0].dataType,[e[0].dims[2]],4),o=wt("output",e[0].dataType,t,4),a=$e.size(t)/4,i=er(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:d=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

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

  ${po(i)}

  ${d.mainStart()}
    ${d.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);

    ${o.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},jl=e=>{Nl(e.inputs),e.compute(xi(e.inputs))}}),Ul,Wl,us,Vl,Gl,Kl,Hl,Ei,ql,Xl,Pi,Ql,Yl,Jl=w(()=>{zt(),Bt(),Qt(),Ul=(e,t,r,n,o,a,i,d,p,h,k,S)=>{let u,B;typeof d=="string"?u=B=(X,_e)=>`${d}((${X}),(${_e}))`:typeof d=="function"?u=B=d:(u=d.scalar,B=d.vector);let R=wt("outputData",k,n.length,4),U=ze("aData",p,t.length,4),Z=ze("bData",h,r.length,4),te;if(o)if(a){let X=$e.size(t)===1,_e=$e.size(r)===1,me=t.length>0&&t[t.length-1]%4===0,ye=r.length>0&&r[r.length-1]%4===0;X||_e?te=R.setByOffset("global_idx",B(X?`${U.type.value}(${U.getByOffset("0")}.x)`:U.getByOffset("global_idx"),_e?`${Z.type.value}(${Z.getByOffset("0")}.x)`:Z.getByOffset("global_idx"))):te=`
            let outputIndices = ${R.offsetToIndices("global_idx * 4u")};
            let offsetA = ${U.broadcastedIndicesToOffset("outputIndices",R)};
            let offsetB = ${Z.broadcastedIndicesToOffset("outputIndices",R)};
            ${R.setByOffset("global_idx",B(i||me?U.getByOffset("offsetA / 4u"):`${U.type.value}(${U.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||ye?Z.getByOffset("offsetB / 4u"):`${Z.type.value}(${Z.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else te=R.setByOffset("global_idx",B(U.getByOffset("global_idx"),Z.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let X=(_e,me,ye="")=>{let Ae=`aData[indexA${me}][componentA${me}]`,Ie=`bData[indexB${me}][componentB${me}]`;return`
            let outputIndices${me} = ${R.offsetToIndices(`global_idx * 4u + ${me}u`)};
            let offsetA${me} = ${U.broadcastedIndicesToOffset(`outputIndices${me}`,R)};
            let offsetB${me} = ${Z.broadcastedIndicesToOffset(`outputIndices${me}`,R)};
            let indexA${me} = offsetA${me} / 4u;
            let indexB${me} = offsetB${me} / 4u;
            let componentA${me} = offsetA${me} % 4u;
            let componentB${me} = offsetB${me} % 4u;
            ${_e}[${me}] = ${ye}(${u(Ae,Ie)});
          `};k===9?te=`
            var data = vec4<u32>(0);
            ${X("data",0,"u32")}
            ${X("data",1,"u32")}
            ${X("data",2,"u32")}
            ${X("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:te=`
            ${X("outputData[global_idx]",0)}
            ${X("outputData[global_idx]",1)}
            ${X("outputData[global_idx]",2)}
            ${X("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(U,Z,R)}

        ${S??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${te}
      }`},Wl=(e,t,r,n,o,a,i=r.dataType)=>{let d=r.dims.map(U=>Number(U)??1),p=n.dims.map(U=>Number(U)??1),h=!$e.areEqual(d,p),k=d,S=$e.size(d),u=!1,B=!1,R=[h];if(h){let U=rr.calcShape(d,p,!1);if(!U)throw new Error("Can't perform binary op on the given tensors");k=U.slice(),S=$e.size(k);let Z=$e.size(d)===1,te=$e.size(p)===1,X=d.length>0&&d[d.length-1]%4===0,_e=p.length>0&&p[p.length-1]%4===0;R.push(Z),R.push(te),R.push(X),R.push(_e);let me=1;for(let ye=1;ye<k.length;ye++){let Ae=d[d.length-ye],Ie=p[p.length-ye];if(Ae===Ie)me*=Ae;else break}me%4===0?(B=!0,u=!0):(Z||te||X||_e)&&(u=!0)}else u=!0;return R.push(u),{name:e,shaderCache:{hint:t+R.map(U=>U.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:U=>Ul(U,d,p,k,u,h,B,o,r.dataType,n.dataType,i,a),getRunData:()=>({outputs:[{dims:k,dataType:i}],dispatchGroup:{x:Math.ceil(S/64/4)},programUniforms:[{type:12,data:Math.ceil($e.size(k)/4)},...vt(d,p,k)]})}},us=(e,t,r,n,o,a)=>{e.compute(Wl(t,o??"",e.inputs[0],e.inputs[1],r,n,a))},Vl=e=>{us(e,"Add",(t,r)=>`${t}+${r}`)},Gl=e=>{us(e,"Div",(t,r)=>`${t}/${r}`)},Kl=e=>{us(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Hl=e=>{us(e,"Mul",(t,r)=>`${t}*${r}`)},Ei=e=>{let t=ze("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;us(e,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
      if (b == ${t}(0.0)) {
        return ${t}(1.0);
      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
        return ${t}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
      // TODO: implement vectorized pow
      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},ql=e=>{us(e,"Sub",(t,r)=>`${t}-${r}`)},Xl=e=>{us(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},Pi=e=>{us(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Ql=e=>{us(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Yl=e=>{us(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}}),Zl,eu,Ci,tu,ru,ki,Wc=w(()=>{zt(),Bt(),Pt(),Qt(),Zl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],o=n.dataType,a=n.dims.length;e.forEach((i,d)=>{if(d!==r){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")})}})},eu=(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;
  }`,Ci=(e,t)=>{let r=e.length,n=[];for(let o=0;o<r;++o){let a=t.setByOffset("global_idx",e[o].getByIndices("indices"));r===1?n.push(a):o===0?n.push(`if (inputIndex == ${o}u) { ${a} }`):o===r-1?n.push(`else { ${a} }`):n.push(`else if (inputIndex == ${o}) { ${a} }`)}return n.join(`
`)},tu=(e,t,r,n)=>{let o=$e.size(r),a=new Array(e.length),i=new Array(e.length),d=0,p=[],h=[],k=[{type:12,data:o}];for(let U=0;U<e.length;++U)d+=e[U].dims[t],a[U]=d,h.push(e[U].dims.length),i[U]=ze(`input${U}`,n,h[U]),p.push("rank"),k.push({type:12,data:a[U]});for(let U=0;U<e.length;++U)k.push(...vt(e[U].dims));k.push(...vt(r));let S=wt("output",n,r.length),u=S.indicesGet("indices",t),B=Array.from(Array(a.length).keys()).map(U=>`uniforms.sizeInConcatAxis${U}`).join(","),R=U=>`

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

  ${eu(a.length,B)}

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

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

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

    ${Ci(i,S)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:k}),getShaderSource:R}},ru=(e,t)=>{let r=e.inputs,n=r[0].dims,o=$e.normalizeAxis(t.axis,n.length);Zl(r,o);let a=n.slice();a[o]=r.reduce((d,p)=>d+(p.dims.length>o?p.dims[o]:0),0);let i=r.filter(d=>$e.size(d.dims)>0);e.compute(tu(i,o,a,r[0].dataType),{inputs:i})},ki=e=>it({axis:e.axis})}),rn,As,sn,Si,Vs=w(()=>{zt(),Bt(),rn=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},As=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},sn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Si=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[Yr,Jr];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),Vr,su,mo=w(()=>{Vr=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},su=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),nu,Vc=w(()=>{nu=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),jn,_o,$i=w(()=>{zt(),Bt(),Qt(),Vs(),jn=(e,t,r,n,o)=>{let a=n-r;return`
      ${Array.from({length:r}).map((i,d)=>`
      if (${Mt(t.shape,d,t.rank)} != 1) {
        ${t.indicesSet(e,d,Mt(o,d+a,n))}
      } else {
        ${t.indicesSet(e,d,0)}
      }`).join("")}
`},_o=(e,t,r,n,o=!1,a)=>{let i=e[0].dims,d=e[1].dims,p=i[i.length-2],h=d[d.length-1],k=i[i.length-1],S=yr(h),u=yr(k),B=yr(p),R=$e.size(r)/S/B,U=e.length>2,Z=n?n.slice(0,-2):r.slice(0,-2),te=[$e.size(Z),p,h],X=[{type:12,data:R},{type:12,data:p},{type:12,data:h},{type:12,data:k}];As(t,X),X.push(...vt(Z,i,d)),U&&X.push(...vt(e[2].dims)),X.push(...vt(te));let _e=me=>{let ye=en("batch_dims",e[0].dataType,Z.length),Ae=ze("a",e[0].dataType,i.length,u),Ie=ze("b",e[1].dataType,d.length,S),Ge=wt("output",e[0].dataType,te.length,S),lt=er(Ge.type.tensor),xt=rn(t,Ge.type.value,lt),Kt=[Ae,Ie],Yt="";if(U){let $t=o?S:1;Kt.push(ze("bias",e[2].dataType,e[2].dims.length,$t)),Yt=`${o?`value += bias[col / ${$t}];`:`value += ${Ge.type.value}(bias[row + i]);`}`}let Ct=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];sn(t,Ct);let Jt=()=>{let $t=`var a_data: ${Ae.type.value};`;for(let jt=0;jt<u;jt++)$t+=`
              let b_data${jt} = b[(b_offset + (k + ${jt}) * uniforms.N + col) / ${S}];`;for(let jt=0;jt<B;jt++){$t+=`a_data = a[(a_offset + (row + ${jt}) * uniforms.K + k) / ${u}];`;for(let vr=0;vr<u;vr++)$t+=`
            values[${jt}] = fma(${Ie.type.value}(a_data${u===1?"":`[${vr}]`}), b_data${vr}, values[${jt}]);
`}return $t};return`
  ${me.registerUniforms(Ct).registerInternalVariables(ye).declareVariables(...Kt,Ge)}
  ${me.mainStart()}
    ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${S})) * ${S};
    var index1 = global_idx / (uniforms.N / ${S});
    let stride1 = uniforms.M / ${B};
    let row = (index1 % stride1) * ${B};
    let batch = index1 / stride1;

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

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

    var b_indices: ${Ie.type.indices};
    ${jn("b_indices",Ie,Ie.rank-2,ye.rank,"batch_indices")}
    ${Ie.indicesSet("b_indices",Ie.rank-2,0)}
    ${Ie.indicesSet("b_indices",Ie.rank-1,0)}
    let b_offset = ${Ie.indicesToOffset("b_indices")};
    var values: array<${Ge.type.value}, ${B}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) {
      ${Jt()}
    }
    for (var i = 0u; i < ${B}u; i++) {
      var value = values[i];
      ${Yt}
      ${xt}
      let cur_indices = ${Ge.type.indices}(batch, row + i, col);
      let offset = ${Ge.indicesToOffset("cur_indices")};
      ${Ge.setByOffset(`offset / ${S}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${S};${u};${B};${o}`,inputDependencies:U?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(R/64)},programUniforms:X}),getShaderSource:_e}}}),ou,iu,fo,Ai,au,go,lu,wo,yo=w(()=>{zt(),Bt(),Qt(),Vs(),$i(),mo(),ou=(e,t)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${t?", batchIndices":""});
        `,iu=(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];"}
        }`,fo=(e,t,r="f32",n,o=!1,a=32,i=!1,d=32)=>{let p=t[1]*e[1],h=t[0]*e[0],k=o?p:a,S=o?a:p,u=k/t[0],B=a/t[1];if(!((o&&u===4&&e[1]===4||!o&&(u===3||u===4))&&k%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${u} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${u} must be 3 or 4.
  tileAWidth ${k} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${u}<${r}>, ${k/u}>, ${S}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${h/e[0]}>, ${a}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${u};
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(d/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
  var kStart = ${i?`i32(globalId.z) * ${d}`:"0"};

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${B};
  for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let inputRow = tileRow + innerRow;
          let inputCol = tileCol;
          ${ou(o,n)}
      }

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

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

          ${iu(o,u)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},Ai=(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":""});
            `,au=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",go=(e,t,r="f32",n,o=!1,a=32,i=!1,d=32,p=!1)=>{let h=e[1]*t[1],k=e[0]*t[0],S=o?h:a,u=o?a:h;if(!(u%t[1]===0&&S%t[0]===0&&a%t[1]===0))throw new Error(`tileAHight ${u} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${S} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let B=u/t[1],R=S/t[0],U=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) * ${k};

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

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

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

let tileRowA = i32(localId.y) * ${B};
let tileColA = i32(localId.x) * ${R};
let tileRowB = i32(localId.y) * ${U};
// Loop over shared dimension.
for (var t = 0; t < num_tiles; t = t + 1) {
  // Load one tile of A into local memory.
  for (var innerRow = 0; innerRow < ${B}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${R}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${Ai(o,n)}
    }
  }

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

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

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${au(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<${r}, ${S}>, ${u}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${k}>, ${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(d/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${i?`i32(globalId.z) * ${d}`:"0"};

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Vr(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 + ${o?"bias[colIn]":`${Vr(e,h)}(bias[row])`};`:""}
        ${r}
        ${p.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},wo=(e,t,r,n,o=!1,a)=>{let i=e[0].dims,d=e[1].dims,p=i.slice(0,-2),h=d.slice(0,-2),k=n?n.slice(0,-2):r.slice(0,-2),S=$e.size(k),u=i[i.length-2],B=i[i.length-1],R=d[d.length-1],U=B%4===0&&R%4===0,Z=u<=8?[4,1,1]:[4,4,1],te=[8,8,1],X=[Math.ceil(R/te[0]/Z[0]),Math.ceil(u/te[1]/Z[1]),Math.ceil(S/te[2]/Z[2])],_e=U?4:1,me=[...p,u,B/_e],ye=me.length,Ae=[...h,B,R/_e],Ie=Ae.length,Ge=[S,u,R/_e],lt=[{type:6,data:u},{type:6,data:R},{type:6,data:B}];As(t,lt),lt.push(...vt(k,me,Ae));let xt=["rank","rank"],Kt=e.length>2;Kt&&(lt.push(...vt(e[2].dims)),xt.push("rank")),lt.push(...vt(Ge));let Yt=Ct=>{let Jt=k.length,$t=en("batchDims",e[0].dataType,Jt,1),jt=er(e[0].dataType),vr=ze("a",e[0].dataType,ye,_e),Ht=ze("b",e[1].dataType,Ie,_e),Gt=wt("result",e[0].dataType,Ge.length,_e),Pr=[vr,Ht];if(Kt){let ys=o?_e:1;Pr.push(ze("bias",e[2].dataType,e[2].dims.length,ys))}let ot=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];sn(t,ot);let Et=er(Gt.type.tensor),cr=rn(t,Gt.type.value,Et),Lr=lu(_e,Kt,cr,[$t,vr,Ht,Gt],o);return`
  ${Ct.registerUniforms(ot).registerInternalVariables($t).declareVariables(...Pr,Gt)}
  ${Lr}
  ${U?fo(Z,te,jt,$t):go(Z,te,jt,$t)}
                   `};return{name:"MatMul",shaderCache:{hint:`${Z};${t.activation};${U};${o}`,inputDependencies:xt},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:X[0],y:X[1],z:X[2]},programUniforms:lt}),getShaderSource:Yt}}}),uu,du,cu=w(()=>{zt(),Qr(),Qt(),Vs(),mo(),Vc(),yo(),uu=(e,t,r,n,o=!1,a,i=4,d=4,p=4,h="f32")=>{let k=lt=>{switch(lt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${lt} is not supported.`)}},S=lt=>{switch(lt){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 ${lt} is not supported.`)}},u=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,B=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,R=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",U=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",Z=e?"row":"col",te=e?"col":"row",X=`
    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 = ${te} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${te} / 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 = ${te} % inChannels;
    var resData = ${Vr(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 < ${R} && xCol >= 0 && xCol < ${U}) {
      ${u}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${k(i)}
    }
    return resData;`,_e=e?t&&n?`
    let col = colIn * ${i};
    ${X}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${X}
    }
    return ${Vr(i,h)}(0.0);`:n&&r?`
    let col = colIn * ${i};
    ${X}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${X}
    }
    return ${Vr(i,h)}(0.0);`,me=`${S(d)}`,ye=Vr(p,h),Ae=Vr(e?i:d,h),Ie=Vr(e?d:i,h),Ge=rn(a,ye,h);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ae} {
      ${e?_e:me}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${ye}) {
      let col = colIn * ${p};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${B}
      ${su(o)}
      ${Ge}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},du=(e,t,r,n,o,a,i,d,p)=>{let h=t.format==="NHWC",k=h?e[0].dims[3]:e[0].dims[1],S=r[0],u=h?r[2]:r[3],B=h?r[1]:r[2],R=h?r[3]:r[1],U=h&&(k%4===0||k%3===0)&&R%4===0,Z=h?R:u*B,te=h?u*B:R,X=[8,8,1],_e=n<=8?[4,1,1]:[4,4,1],me=[Math.ceil(Z/X[0]/_e[0]),Math.ceil(te/X[1]/_e[1]),Math.ceil(S/X[2]/_e[2])];ir("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${me}`);let ye=U?h&&k%4!==0?3:4:1,Ae=X[1]*_e[1],Ie=X[0]*_e[0],Ge=Math.max(X[0]*ye,X[1]),lt=n%Ae===0,xt=o%Ie===0,Kt=a%Ge===0,Yt=U?[ye,4,4]:[1,1,1],Ct=[{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}];As(t,Ct),Ct.push(...vt(e[0].dims,e[1].dims));let Jt=["rank","rank"];i&&(Ct.push(...vt(e[2].dims)),Jt.push("rank")),Ct.push(...vt(r));let $t=jt=>{let vr=[{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}];sn(t,vr);let Ht=U?4:1,Gt=er(e[0].dataType),Pr=`
      fn setOutputAtIndex(flatIndex : i32, value : ${U?`vec4<${Gt}>`:Gt}) {
        result[flatIndex] = ${U?`vec4<${Gt}>`:Gt}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${U?`vec4<${Gt}>`:Gt}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${U?"/ 4":""}, value);
      }`,ot=ze("x",e[0].dataType,e[0].dims.length,ye===3?1:ye),Et=ze("w",e[1].dataType,e[1].dims.length,Ht),cr=[ot,Et],Lr=wt("result",e[0].dataType,r.length,Ht);if(i){let ys=ze("bias",e[2].dataType,e[2].dims.length,Ht);cr.push(ys),Pr+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${U?`vec4<${Gt}>`:Gt} {
          return bias[coords.${h?"w":"y"}${U?"/ 4":""}];
        }`}return`
        ${nu("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 };
        ${jt.registerUniforms(vr).declareVariables(...cr,Lr)}
        ${Pr}
        ${uu(h,lt,xt,Kt,i,t,Yt[0],Yt[1],Yt[2],Gt)}
        ${U?fo(_e,X,Gt,void 0,!h,Ge):go(_e,X,Gt,void 0,!h,Ge,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${ye};${U};${lt};${xt};${Kt};${Ae};${Ie};${Ge}`,inputDependencies:Jt},getRunData:()=>({outputs:[{dims:p?p(r):r,dataType:e[0].dataType}],dispatchGroup:{x:me[0],y:me[1],z:me[2]},programUniforms:Ct}),getShaderSource:$t}}}),pu,Ii,Tn,hu,Oi,Fi,mu,_u,Gc=w(()=>{zt(),Qr(),Bt(),Qt(),Vs(),mo(),pu=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t},Ii=e=>typeof e=="number"?[e,e,e]:e,Tn=(e,t)=>t<=1?e:e+(e-1)*(t-1),hu=(e,t,r,n=1)=>{let o=Tn(t,n);return Math.floor((e[0]*(r-1)-r+o)/2)},Oi=(e,t,r,n,o)=>{o==null&&(o=hu(e,t[0],n[0]));let a=[0,0,0,r];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},Fi=(e,t,r,n,o,a,i,d,p,h)=>{let k,S,u,B;if(e==="VALID"&&(e=0),typeof e=="number"){k={top:e,bottom:e,left:e,right:e,front:e,back:e};let R=Oi([t,r,n,1],[d,p,h],1,[o,a,i],e);S=R[0],u=R[1],B=R[2]}else if(Array.isArray(e)){if(!e.every((U,Z,te)=>U===te[0]))throw Error(`Unsupported padding parameter: ${e}`);k={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let R=Oi([t,r,n,1],[d,p,h],1,[o,a,i],e[0]);S=R[0],u=R[1],B=R[2]}else if(e==="SAME_UPPER"){S=Math.ceil(t/o),u=Math.ceil(r/a),B=Math.ceil(n/i);let R=(S-1)*o+d-t,U=(u-1)*a+p-r,Z=(B-1)*i+h-n,te=Math.floor(R/2),X=R-te,_e=Math.floor(U/2),me=U-_e,ye=Math.floor(Z/2),Ae=Z-ye;k={top:_e,bottom:me,left:ye,right:Ae,front:te,back:X}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:k,outDepth:S,outHeight:u,outWidth:B}},mu=(e,t,r,n,o,a=!1,i="channelsLast")=>{let d,p,h,k,S;if(i==="channelsLast")[d,p,h,k,S]=e;else if(i==="channelsFirst")[d,S,p,h,k]=e;else throw new Error(`Unknown dataFormat ${i}`);let[u,,B,R,U]=t,[Z,te,X]=Ii(r),[_e,me,ye]=Ii(n),Ae=Tn(B,_e),Ie=Tn(R,me),Ge=Tn(U,ye),{padInfo:lt,outDepth:xt,outHeight:Kt,outWidth:Yt}=Fi(o,p,h,k,Z,te,X,Ae,Ie,Ge),Ct=a?u*S:u,Jt=[0,0,0,0,0];return i==="channelsFirst"?Jt=[d,Ct,xt,Kt,Yt]:i==="channelsLast"&&(Jt=[d,xt,Kt,Yt,Ct]),{batchSize:d,dataFormat:i,inDepth:p,inHeight:h,inWidth:k,inChannels:S,outDepth:xt,outHeight:Kt,outWidth:Yt,outChannels:Ct,padInfo:lt,strideDepth:Z,strideHeight:te,strideWidth:X,filterDepth:B,filterHeight:R,filterWidth:U,effectiveFilterDepth:Ae,effectiveFilterHeight:Ie,effectiveFilterWidth:Ge,dilationDepth:_e,dilationHeight:me,dilationWidth:ye,inShape:e,outShape:Jt,filterShape:t}},_u=(e,t,r,n,o,a)=>{let i=a==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let d=[64,1,1],p={x:r.map((Z,te)=>te)},h=[Math.ceil(pu(p.x.map(Z=>r[Z]))/d[0]),1,1];ir("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let k=1,S=$e.size(r),u=[{type:12,data:S},{type:12,data:n},{type:12,data:o},{type:12,data:t.strides},{type:12,data:t.dilations}];As(t,u),u.push(...vt(e[0].dims,e[1].dims));let B=["rank","rank"],R=e.length===3;R&&(u.push(...vt(e[2].dims)),B.push("rank")),u.push(...vt(r));let U=Z=>{let te=[{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}];sn(t,te);let X=1,_e=er(e[0].dataType),me=ze("x",e[0].dataType,e[0].dims.length,k),ye=ze("W",e[1].dataType,e[1].dims.length,X),Ae=[me,ye],Ie=wt("result",e[0].dataType,r.length,X),Ge="";if(R){let Kt=ze("bias",e[2].dataType,e[2].dims.length,X);Ae.push(Kt),Ge+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${_e} {
          return bias[${i?Mt("coords",4,5):Mt("coords",1,5)}];
        }`}let lt=Vr(k,_e),xt=rn(t,lt,_e);return`
            ${Ge}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${me.getByIndices("aIndices")};
            }
            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${ye.getByIndices("aIndices")};
            }
          ${Z.registerUniforms(te).declareVariables(...Ae,Ie)}
          ${Z.mainStart()}
          ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${Ie.offsetToIndices("global_idx")};
              let batch = ${Mt("coords",0,me.rank)};
              let d2 = ${i?Mt("coords",me.rank-1,me.rank):Mt("coords",1,me.rank)};
              let xFRCCorner = vec3<u32>(${i?Mt("coords",1,me.rank):Mt("coords",2,me.rank)},
              ${i?Mt("coords",2,me.rank):Mt("coords",3,me.rank)},
              ${i?Mt("coords",3,me.rank):Mt("coords",4,me.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${i?Mt("uniforms.x_shape",1,me.rank):Mt("uniforms.x_shape",2,me.rank)};
              let xShapeZ = ${i?Mt("uniforms.x_shape",2,me.rank):Mt("uniforms.x_shape",3,me.rank)};
              let xShapeW = ${i?Mt("uniforms.x_shape",3,me.rank):Mt("uniforms.x_shape",4,me.rank)};
              let xShapeU = ${i?Mt("uniforms.x_shape",4,me.rank):Mt("uniforms.x_shape",1,me.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

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

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

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

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${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);
                    }
                  }
                }
              }
              ${R?"value = value + getBiasByOutputCoords(coords)":""};
              ${xt}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${i};${k};${R}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:h[0],y:h[1],z:h[2]},programUniforms:u}),getShaderSource:U}}}),fu,Mo,Kc=w(()=>{zt(),Bt(),Qt(),Vs(),fu=(e,t,r,n)=>{let o=e.length>2,a=o?"value += b[output_channel];":"",i=e[0].dims,d=e[1].dims,p=t.format==="NHWC",h=p?r[3]:r[1],k=h/t.group,S=p&&k>=4?yr(h):1,u=$e.size(r)/S,B=[{type:12,data:u},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:k}];As(t,B),B.push(...vt(i,[d[0],d[1],d[2],d[3]/S]));let R=o?["rank","rank","rank"]:["rank","rank"];B.push(...vt([r[0],r[1],r[2],r[3]/S]));let U=Z=>{let te=wt("output",e[0].dataType,r.length,S),X=er(te.type.tensor),_e=rn(t,te.type.value,X),me=ze("x",e[0].dataType,i.length),ye=ze("w",e[1].dataType,d.length,S),Ae=[me,ye];o&&Ae.push(ze("b",e[2].dataType,e[2].dims,S));let Ie=[{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"}];sn(t,Ie);let Ge=p?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

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

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

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

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

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

            let xVal = ${me.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${ye.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${Z.registerUniforms(Ie).declareVariables(...Ae,te)}

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

    let outputIndices = ${te.offsetToIndices("global_idx")};
    let batch: u32 = outputIndices[0];
    let output_channel: u32 = outputIndices[${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 * ${S} / uniforms.output_channels_per_group;
    var in_channel_offset = group_id * uniforms.w_shape[${p?2:1}];

    var value: ${te.type.value} = ${te.type.value}(0);
    ${Ge}
    ${a}
    ${_e}
    ${te.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${S}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:B}),getShaderSource:U}},Mo=(e,t,r,n)=>{let o=e.length>2,a=yr(r[3]),i=yr(r[2]),d=$e.size(r)/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],k=[r[0],r[1],r[2],r[3]/a],S=[{type:12,data:d},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];As(t,S),S.push(...vt(p,h,k));let u=(i-1)*t.strides[1]+h[1],B=R=>{let U=wt("output",e[0].dataType,k.length,a),Z=er(U.type.tensor),te=rn(t,U.type.value,Z),X=ze("x",e[0].dataType,p.length,a),_e=ze("w",e[1].dataType,h.length,a),me=[X,_e];o&&me.push(ze("b",e[2].dataType,e[2].dims,a));let ye=o?"value += b[output_channel];":"",Ae=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return sn(t,Ae),`
  ${R.registerUniforms(Ae).declareVariables(...me,U)}
  ${R.mainStart()}
    ${R.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<${X.type.value}, ${u}>;
    var values: array<${U.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 < ${u}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${X.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${X.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${h[1]}; w_width++) {
          let w_val = ${_e.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];
      ${ye}
      ${te}
      ${U.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${i};${u};${h[0]};${h[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:S}),getShaderSource:B}}}),gu,bo,wu,vo,To,Di,yu,Li,zi,Hc=w(()=>{Bt(),cu(),Gc(),yo(),Kc(),Vs(),$i(),Ws(),gu=(e,t,r,n,o,a)=>{let i=e[0],d=e.slice(a?1:2,a?3:4),p=d.length,h=t[0],k=t.slice(2).map((u,B)=>u+(u-1)*(r[B]-1)),S=d.map((u,B)=>u+n[B]+n[B+p]).map((u,B)=>Math.floor((u-k[B]+o[B])/o[B]));return S.splice(0,0,i),S.splice(a?3:1,0,h),S},bo=[2,3,1,0],wu=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let 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 ${o*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},vo=(e,t)=>{let r=e.kernelShape.slice();r.length<t[1].dims.length-2&&r.push(...Array(t[1].dims.length-2-r.length).fill(0));for(let a=2;a<t[1].dims.length;++a)r[a-2]===0&&(r[a-2]=t[1].dims[a]);let n=e.pads.slice();wr.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,n,e.format==="NHWC",e.autoPad);let o=Object.assign({},e);return Object.assign(o,{kernelShape:r,pads:n}),o},To=e=>{let t=Si(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,a=e.group,i=e.kernel_shape,d=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:n,format:r,dilations:o,group:a,kernelShape:i,pads:d,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},Di=(e,t,r,n)=>{let o=r.format==="NHWC",a=gu(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,o);if(r.group!==1){let Ae=[t[0]];if(o){let Ie=e.kernelCustomData.wT??e.compute(rs(t[1],bo),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ie),Ae.push(Ie)}else Ae.push(t[1]);t.length===3&&Ae.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1?e.compute(Mo(Ae,r,a,n),{inputs:Ae}):e.compute(fu(Ae,r,a,n),{inputs:Ae});return}let i=t.length===3,d=t[0].dims[o?1:2],p=t[0].dims[o?2:3],h=t[0].dims[o?3:1],k=t[1].dims[2],S=t[1].dims[3],u=a[o?1:2],B=a[o?2:3],R=a[o?3:1],U=o&&k===d&&S===p&&r.pads[0]===0&&r.pads[1]===0;if(U||k===1&&S===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let Ae=a[0],Ie,Ge,lt,xt=[];if(o){let Ct=e.kernelCustomData.wT??e.compute(rs(t[1],bo),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ct),U){let Jt=d*p*h;Ie=t[0].reshape([1,Ae,Jt]),Ge=Ct.reshape([1,Jt,R]),lt=[1,Ae,R]}else Ie=t[0].reshape([Ae,d*p,h]),Ge=Ct.reshape([1,h,R]),lt=[Ae,u*B,R];xt.push(Ie),xt.push(Ge)}else Ie=t[0].reshape([Ae,h,d*p]),Ge=t[1].reshape([1,R,h]),lt=[Ae,R,u*B],xt.push(Ge),xt.push(Ie);i&&xt.push(t[2]);let Kt=lt[2],Yt=xt[0].dims[xt[0].dims.length-1];Kt<8&&Yt<8?e.compute(_o(xt,r,a,lt,o,n),{inputs:xt}):e.compute(wo(xt,r,a,lt,o,n),{inputs:xt});return}let Z=!0,te=e.kernelCustomData.wT??e.compute(rs(t[1],bo),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=te);let X=[t[0],te];i&&X.push(t[2]);let _e=o?u*B:R,me=o?R:u*B,ye=k*S*h;e.compute(du(X,r,a,_e,me,ye,i,Z,n),{inputs:X})},yu=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let o=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),i=[1].concat(t.dilations),d=[1].concat(t.kernelShape),p=vo({...t,pads:o,strides:a,dilations:i,kernelShape:d},n);Di(e,n,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]])},Li=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",o=vo(r,t),a=r.autoPad==="NOTSET"?r.pads:r.autoPad,i=mu(t[0].dims,t[1].dims,r.strides,r.dilations,a,!1,n);e.compute(_u(t,o,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],n))},zi=(e,t)=>{if(wu(e.inputs,t),e.inputs[0].dims.length===3)yu(e,t);else if(e.inputs[0].dims.length===5)Li(e,e.inputs,t);else{let r=vo(t,e.inputs);Di(e,e.inputs,r)}}}),Mu,qc=w(()=>{zt(),Qr(),Bt(),Qt(),Mu=(e,t,r)=>{let n=e.length>2,o=t.outputShape,a=t.format==="NHWC",i=t.group,d=e[1].dims,p=d[2]/i,h=d[3],k=a?yr(h):1,S=$e.size(o)/k,u=[Math.ceil(S/64),1,1];ir("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${u}`);let B=["rank","rank"],R=[t.strides[0],t.strides[1]],U=[t.kernelShape[a?1:2],t.kernelShape[a?2:3]],Z=[t.dilations[0],t.dilations[1]],te=[U[0]+(t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)),U[1]+(t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1))],X=[te[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),te[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],_e=[{type:12,data:S},{type:12,data:R},{type:12,data:U},{type:12,data:Z},{type:12,data:te},{type:6,data:X},{type:12,data:p},{type:12,data:h},...vt(e[0].dims,e[1].dims)];n&&(_e.push(...vt(e[2].dims)),B.push("rank")),_e.push(...vt(o));let me=ye=>{let Ae=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:R.length},{name:"filter_dims",type:"u32",length:U.length},{name:"dilations",type:"u32",length:U.length},{name:"effective_filter_dims",type:"u32",length:te.length},{name:"pads",type:"i32",length:X.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],Ie=er(e[0].dataType),Ge=a?1:2,lt=a?2:3,xt=a?3:1,Kt=ze("W",e[1].dataType,e[1].dims.length,k),Yt=ze("Dy",e[0].dataType,e[0].dims.length),Ct=[Yt,Kt];n&&Ct.push(ze("bias",e[2].dataType,[o[xt]].length,k));let Jt=wt("result",e[0].dataType,o.length,k),$t=`
            let outputIndices = ${Jt.offsetToIndices(`global_idx * ${k}`)};
            let batch = ${Jt.indicesGet("outputIndices",0)};
            let d1 = ${Jt.indicesGet("outputIndices",xt)};
            let r = ${Jt.indicesGet("outputIndices",Ge)};
            let c = ${Jt.indicesGet("outputIndices",lt)};
            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 = ${Jt.type.value}(0.0);
            for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${Ie}(dyRCorner) + ${Ie}(wR)) / ${Ie}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${Ie}(uniforms.Dy_shape[${Ge}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);

              for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${Ie}(dyCCorner) + ${Ie}(wC)) / ${Ie}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${Ie}(uniforms.Dy_shape[${lt}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {
                  let xValue = ${a?Yt.get("batch","idyR","idyC","inputChannel"):Yt.get("batch","inputChannel","idyR","idyC")};
                  let w_offset = ${Kt.indicesToOffset(`${Kt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
                  let wValue = ${Kt.getByOffset(`w_offset / ${k}`)};
                  dotProd = dotProd + xValue * wValue;
                  inputChannel = inputChannel + 1;
                }
              }
            }
            let value = dotProd${n?` + bias[d1 / ${k}]`:""};
            ${Jt.setByOffset("global_idx","value")};
          `;return`
    ${ye.registerUniforms(Ae).declareVariables(...Ct,Jt)}
      ${ye.mainStart()}
      ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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        ${U("data",1,"u32")}
        ${U("data",2,"u32")}
        ${U("data",3,"u32")}
        ${B.setByOffset("global_idx","data")}
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      `}else te=`
      let outputIndices = ${U.offsetToIndices("global_idx")};
      ${Z("")};
      let value = ${B.getByIndices("dataIndices")};
      ${U.setByOffset("global_idx","value")};
      `;return`
      ${u.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(B,R,U)}
      ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${te}
      }`};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:k}),getShaderSource:S}},Hu=e=>it({axis:e.axis}),So=(e,t)=>{let r=e.inputs;Gu(r),e.compute(Ku(e.inputs,t))}}),qu,Xu,ep,tp=w(()=>{zt(),Bt(),Qt(),qu=(e,t,r,n,o,a,i,d,p)=>{let h=[{type:12,data:a},{type:12,data:n},{type:12,data:o},{type:12,data:r},{type:12,data:i},{type:12,data:d},{type:12,data:p}],k=[a];h.push(...vt(t.dims,k));let S=u=>{let B=ze("indices_data",t.dataType,t.dims.length),R=wt("input_slice_offsets_data",12,1,1),U=[B,R],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:r.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
  ${u.registerUniforms(Z).declareVariables(...U)}
  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
    var relative_slice_offset = 0;
    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
      let input_dim_idx = uniforms.batch_dims + dim_idx;
      if (index < 0) {
        ${o.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
      }
      ${r.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:`${o.length}_${r.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:k,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:S},{inputs:[t],outputs:[-1]})[0]},Xu=(e,t)=>{let r=e.inputs,n=r[0].dims,o=r[0].dataType,a=r[1].dims,i=a[a.length-1],d=$e.sizeToDimension(a,a.length-1),p=$e.sizeFromDimension(n,t.batchDims+i),h=$e.sizeToDimension(n,t.batchDims),k=$e.sizeFromDimension(n,t.batchDims),S=d/h,u=new Array(i),B=p;for(let me=0;me<i;++me)u[i-1-me]=B,B*=n[t.batchDims+i-1-me];let R=qu(e,r[1],u,t.batchDims,n,d,S,k,i),U=t.batchDims+i;if(U>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(U)),te=$e.size(Z),X=[{type:12,data:te},{type:12,data:p},...vt(r[0].dims,R.dims,Z)],_e=me=>{let ye=ze("data",r[0].dataType,r[0].dims.length),Ae=ze("slice_offsets",12,R.dims.length),Ie=wt("output",r[0].dataType,Z.length);return`
          ${me.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(ye,Ae,Ie)}
            ${me.mainStart()}
            ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`};e.compute({name:"GatherND",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:Z,dataType:o}],dispatchGroup:{x:Math.ceil(te/64)},programUniforms:X}),getShaderSource:_e},{inputs:[r[0],R]})},ep=e=>({batchDims:e.batch_dims,cacheKey:""})}),Qu,Yu,xn,Ju,rp=w(()=>{zt(),Bt(),Pt(),Qt(),Qu=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=$e.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((d,p)=>p===r?Math.ceil(d/n)===a.dims[p]:d===a.dims[p]).reduce((d,p)=>d&&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((d,p)=>d===a.dims[p]).reduce((d,p)=>d&&p,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Yu=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r.length,a=$e.normalizeAxis(t.gatherAxis,o),i=$e.normalizeAxis(t.quantizeAxis,o),d=r.slice(0);d.splice(a,1,...n);let p=$e.size(d),h=e[2].dataType,k=e[0].dataType===22,S=[{type:12,data:p},{type:12,data:i},{type:12,data:a},{type:12,data:t.blockSize},...vt(...e.map((B,R)=>B.dims),d)],u=B=>{let R=ze("data",e[0].dataType,e[0].dims.length),U=ze("inputIndices",e[1].dataType,e[1].dims.length),Z=ze("scales",e[2].dataType,e[2].dims.length),te=e.length>3?ze("zeroPoint",e[3].dataType,e[3].dims.length):void 0,X=wt("output",h,d.length),_e=[R,U,Z];te&&_e.push(te);let me=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${B.registerUniforms(me).declareVariables(..._e,X)}
        ${B.mainStart()}
        let output_indices = ${X.offsetToIndices("global_idx")};
        var indices_indices = ${U.type.indices}(0);
        ${n.length>1?`
          for (var i: u32 = 0; i < ${n.length}; i++) {
            let index = ${X.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${U.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${X.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${R.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${X.indicesGet("output_indices","i")};
          ${R.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${U.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${r[a]};
        }
        ${R.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${d.length}; i++) {
          let index = ${X.indicesGet("output_indices",`i + ${n.length} - 1`)};
          ${R.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${R.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${R.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${k?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${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")};
        ${te?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${te.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${te.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${k?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${mr(h)}(quantized_data - zero_point) * scale;
        ${X.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((B,R)=>R!==1).map(B=>B.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(B,R)=>"rank")},getRunData:()=>({outputs:[{dims:d,dataType:h}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:S}),getShaderSource:u}},xn=(e,t)=>{let r=e.inputs;Qu(r,t),e.compute(Yu(e.inputs,t))},Ju=e=>it({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Zu,ed,td,$o,Wp=w(()=>{zt(),Bt(),Pt(),Qt(),Zu=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.`)},ed=(e,t)=>{let r=e[0].dims,n=e[0].dataType,o=r.length,a=e[1].dims,i=e[1].dataType,d=$e.normalizeAxis(t.axis,o),p=r[d],h=a.slice(0),k=$e.size(h),S=ze("input",n,o),u=ze("indicesInput",i,a.length),B=wt("output",n,h.length),R=[{type:12,data:k},{type:6,data:p},{type:12,data:d}];return R.push(...vt(r,a,h)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:R}),getShaderSource:U=>`
      ${U.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(S,u,B)}
      ${U.mainStart()}
      ${U.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${B.setByOffset("global_idx","value")};
  }`}},td=e=>it({axis:e.axis}),$o=(e,t)=>{let r=e.inputs;Zu(r),e.compute(ed(e.inputs,t))}}),rd,sd,nd,od,sp=w(()=>{zt(),Bt(),Qt(),rd=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")},sd=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[o,a,i]=Rr.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),d=[o,a];if(!d)throw new Error("Can't use gemm on the given tensors");let p=16,h=Math.ceil(a/p),k=Math.ceil(o/p),S=!0,u=$e.size(d),B=[{type:12,data:S?h:u},{type:12,data:o},{type:12,data:a},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],R=["type","type"];e.length===3&&(B.push(...vt(e[2].dims)),R.push("rank")),B.push(...vt(d));let U=te=>{let X="";t.transA&&t.transB?X="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?X="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?X="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(X="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let _e=t.alpha===1?"":"value *= uniforms.alpha;",me=ze("a",e[0].dataType,e[0].dims),ye=ze("b",e[1].dataType,e[1].dims),Ae=me.type.value,Ie=null,Ge=[me,ye];e.length===3&&(Ie=ze("c",e[2].dataType,e[2].dims.length),Ge.push(Ie));let lt=wt("output",e[0].dataType,d.length);Ge.push(lt);let xt=[{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`
  ${te.registerUniforms(xt).declareVariables(...Ge)}

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

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

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

    ${_e}
    ${Ie!=null?`let cOffset = ${Ie.broadcastedIndicesToOffset("vec2(m, n)",lt)}; value += ${Ae}(uniforms.beta) * ${Ie.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`},Z=te=>{let X=ze("a",e[0].dataType,e[0].dims),_e=ze("b",e[1].dataType,e[1].dims),me=null,ye=[X,_e];e.length===3&&(me=ze("c",e[2].dataType,e[2].dims.length),ye.push(me));let Ae=wt("output",e[0].dataType,d.length);ye.push(Ae);let Ie=[{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"}],Ge="",lt="";t.transA&&t.transB?(lt=`
      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] = ${X.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] = ${_e.type.value}(0);
      }
      `,Ge="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):t.transA&&!t.transB?(lt=`
      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] = ${X.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] = ${_e.type.value}(0);
      }
      `,Ge="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!t.transA&&t.transB?(lt=`
      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] = ${X.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] = ${_e.type.value}(0);
      }
      `,Ge="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!t.transA&&!t.transB&&(lt=`
      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] = ${X.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] = ${_e.type.value}(0);
      }
      `,Ge="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let xt=t.alpha===1?"":"value *= uniforms.alpha;";return`
  ${te.registerUniforms(Ie).declareVariables(...ye)}
  var<workgroup> tile_a: array<array<${X.type.storage}, ${p}>, ${p}>;
  var<workgroup> tile_b: array<array<${_e.type.storage}, ${p}>, ${p}>;
  ${te.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 = ${Ae.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${lt}
      k_start = k_start + ${p};
      workgroupBarrier();

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

    ${xt}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${me!=null?`let cOffset = ${me.broadcastedIndicesToOffset("vec2(m, n)",Ae)}; value += ${Ae.type.value}(uniforms.beta) * ${me.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`};return S?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:h*k},programUniforms:B}),getShaderSource:Z}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:B}),getShaderSource:U}},nd=e=>{let t=e.transA,r=e.transB,n=e.alpha,o=e.beta;return{transA:t,transB:r,alpha:n,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},od=(e,t)=>{rd(e.inputs),e.compute(sd(e.inputs,t))}}),fs,Is,nn,Ks,id,ad,Wi,ld,ud,Ao,dd,cd,pd,hd,md=w(()=>{zt(),Bt(),Pt(),Qt(),[fs,Is,nn,Ks]=[0,1,2,3],id=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 ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},ad=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,Wi=e=>`
  fn gs_bicubic_interpolate(p: mat4x4<${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;
  }
`,ld=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));
    `}
  }
`,ud=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);
      }`:""}
`,Ao=(e,t,r)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${t} {
     var pixel = ${t}(0);
     var indices = vec4<u32>(0);
     indices[${fs}] = batch;
     indices[${Is}] = channel;`+(()=>{switch(r.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${nn}] = u32(r);
            indices[${Ks}] = u32(c);
          }
        `;case"border":return`
          indices[${nn}] = u32(clamp(r, 0, H - 1));
          indices[${Ks}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${nn}] = gs_reflect(r, border[1], border[3]);
          indices[${Ks}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${r.paddingMode} is not supported`)}})()+`
    return ${e.getByIndices("indices")};
  }
`,dd=(e,t,r)=>(()=>{switch(r.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${fs}], indices[${Is}], 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[${fs}], indices[${Is}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${fs}], indices[${Is}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${fs}], indices[${Is}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${fs}], indices[${Is}], 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[${fs}], indices[${Is}], 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 ${r.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,cd=(e,t)=>{let r=ze("x",e[0].dataType,e[0].dims.length),n=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=ze("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]],[fs,Is,nn,Ks]=[0,3,1,2]);let i=wt("output",e[0].dataType,a.length),d=r.type.value,p=$e.size(a),h=[{type:12,data:p},...vt(e[0].dims,n,a)],k=S=>`
  ${S.registerUniform("output_size","u32").declareVariables(r,o,i)}
  ${ad}
  ${Wi(d)}
  ${ld(t)}
  ${ud(t)}
  ${Ao(r,d,t)}

  ${S.mainStart()}
    ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${nn}]);
      let W_in = i32(uniforms.x_shape[${Ks}]);

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

      ${dd(i,d,t)}
  }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:S=>{let u=$e.size(a);return{outputs:[{dims:a,dataType:S[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:h}},getShaderSource:k}},pd=(e,t)=>{id(e.inputs),e.compute(cd(e.inputs,t))},hd=e=>it({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Gr,np,_d,Vi,Gi,Vn,fd,Ki=w(()=>{zt(),Bt(),Pt(),yn(),co(),Qt(),Ws(),Gr=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,np=(e,t)=>{let r=e[0],n=Gr(e,1),o=Gr(e,2),a=Gr(e,3),i=Gr(e,4),d=Gr(e,5),p=Gr(e,6),h=Gr(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let k=r.dims[0],S=r.dims[1],u=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],B=S,R=0,U=0,Z=Math.floor(u/t.numHeads);if(p&&h&&$e.size(p.dims)&&$e.size(h.dims)){if(p.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(p.dims[0]!==k||p.dims[1]!==t.numHeads||p.dims[3]!==Z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[0]!==k||h.dims[1]!==t.numHeads||h.dims[3]!==Z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[2]!==h.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(h.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');R=p.dims[2],U=p.dims[2]}else if(p&&$e.size(p.dims)||h&&$e.size(h.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let te;if(n&&$e.size(n.dims)>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');te=2,B=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.');te=5,B=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');te=0,B=n.dims[2]}}else{if(r.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(r.dims[2]!==t.numHeads||r.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');te=3}if(a&&$e.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let X=R+B,_e=0;if(i&&$e.size(i.dims)>0){_e=8;let Ie=i.dims;throw Ie.length===1?Ie[0]===k?_e=1:Ie[0]===3*k+2&&(_e=3):Ie.length===2&&Ie[0]===k&&Ie[1]===X&&(_e=5),_e===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let me=!1,ye=u;if(o&&$e.size(o.dims)>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(B!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');ye=o.dims[2]}else{if(B!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');ye=o.dims[1]*o.dims[3],me=!0}}let Ae=!1;if(i&&$e.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(d&&$e.size(d.dims)>0){if(d.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(d.dims[0]!==k||d.dims[1]!==t.numHeads||d.dims[2]!==S||d.dims[3]!==X)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:k,sequenceLength:S,pastSequenceLength:R,kvSequenceLength:B,totalSequenceLength:X,maxSequenceLength:U,inputHiddenSize:0,hiddenSize:u,vHiddenSize:ye,headSize:Z,vHeadSize:Math.floor(ye/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:_e,scale:t.scale,broadcastResPosBias:Ae,passPastInKv:me,qkvFormat:te}},_d=e=>it({...e}),Vi=it({perm:[0,2,1,3]}),Gi=(e,t,r,n,o,a,i)=>{let d=[n,o,a],p=$e.size(d),h=[{type:12,data:p},{type:12,data:i},{type:12,data:a}],k=S=>{let u=wt("qkv_with_bias",t.dataType,d),B=ze("qkv",t.dataType,d),R=ze("bias",r.dataType,d),U=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${S.registerUniforms(U).declareVariables(B,R,u)}
  ${S.mainStart()}
    ${S.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:d,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:k},{inputs:[t,r],outputs:[-1]})[0]},Vn=(e,t,r,n,o,a,i,d)=>{let p=a;if(i&&$e.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=Gi(e,a,i,t,n,r*o,d),p=p.reshape([t,n,r,o]),r===1||n===1?p:e.compute(rs(p,Vi.perm),{inputs:[p],outputs:[-1]})[0]}else return a.dims.length===3&&(p=a.reshape([t,n,r,o])),r===1||n===1?p:e.compute(rs(p,Vi.perm),{inputs:[p],outputs:[-1]})[0]},fd=(e,t)=>{let r=np(e.inputs,t),n=e.inputs[0],o=Gr(e.inputs,1),a=Gr(e.inputs,2),i=Gr(e.inputs,3),d=Gr(e.inputs,4),p=Gr(e.inputs,5),h=Gr(e.inputs,6),k=Gr(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 S=o&&a&&o.dims.length===4&&a.dims.length===4,u=Vn(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(S)return vn(e,u,o,a,d,void 0,h,k,p,r);if(!o||!a)throw new Error("key and value must be provided");let B=Vn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,o,i,r.hiddenSize),R=Vn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,i,2*r.hiddenSize);vn(e,u,B,R,d,void 0,h,k,p,r)}}),gd,wd,Hi,op,yd,qi,Xi,Md=w(()=>{zt(),Bt(),Pt(),Qt(),gd=e=>{if(!e||e.length<1)throw new Error("too few inputs")},wd=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>r.push(Number(o))),n=r.length),it({numOutputs:n,axis:t.axis,splitSizes:r})},Hi=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${Mt("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,op=e=>{let t=e.length,r=[];for(let n=0;n<t;++n){let o=e[n].setByIndices("indices","input[global_idx]");t===1?r.push(o):n===0?r.push(`if (output_number == ${n}u) { ${o} }`):n===t-1?r.push(`else { ${o} }`):r.push(`else if (output_number == ${n}) { ${o} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},yd=(e,t)=>{let r=e[0].dims,n=$e.size(r),o=e[0].dataType,a=$e.normalizeAxis(t.axis,r.length),i=new Array(t.numOutputs),d=ze("input",o,r.length),p=new Array(t.numOutputs),h=[],k=[],S=0,u=[{type:12,data:n}];for(let R=0;R<t.numOutputs;R++){S+=t.splitSizes[R],p[R]=S;let U=r.slice();U[a]=t.splitSizes[R],k.push(U),i[R]=wt(`output${R}`,o,U.length),h.push({dims:k[R],dataType:e[0].dataType})}u.push({type:12,data:p},...vt(r,...k));let B=R=>`
  ${R.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(d,...i)}
  ${Hi(p.length)}
  ${op(i)}

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

    var indices = ${d.offsetToIndices("global_idx")};
    var index = ${d.indicesGet("indices",a)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Mt("uniforms.size_in_split_axis","output_number - 1u",p.length)};
      ${d.indicesSet("indices",a,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:B,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:u})}},qi=(e,t)=>{gd(e.inputs);let r=e.inputs.length===1?t:wd(e.inputs,t);e.compute(yd(e.inputs,r),{inputs:[0]})},Xi=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return it({axis:t,numOutputs:n,splitSizes:r})}}),bd,Qi,Yi,vd,Td=w(()=>{Pt(),co(),Ki(),Md(),Ws(),bd=(e,t)=>{if(t.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let r=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(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let d=!1,p=r.dims[0],h=r.dims[1],k=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],S=h,u=0,B=!n||n.dims.length===0,R=Math.floor(B?k/(t.numHeads+2*t.kvNumHeads):k/t.numHeads);B&&(k=R*t.numHeads);let U=a&&a.dims.length!==0,Z=i&&i.dims.length!==0;if(U&&a.dims.length===4&&a.dims[0]===p&&a.dims[1]!==t.kvNumHeads&&a.dims[2]===t.kvNumHeads&&a.dims[3]===R)throw new Error("BSNH pastKey/pastValue is not supported");if(U&&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');u=a.dims[2]}else if(U||Z)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let te=1;if(n&&n.dims.length>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(r.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');S=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==R)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.');S=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==R)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');S=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');te=3}let X=0,_e=!1,me=t.kvNumHeads?R*t.kvNumHeads:k;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.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(S!==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(S!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');me=o.dims[1]*o.dims[3],_e=!0}}let ye=e.length>4?e[5]:void 0;if(ye&&ye.dims.length!==1&&ye.dims[0]!==p)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:p,sequenceLength:h,pastSequenceLength:u,kvSequenceLength:S,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:k,vHiddenSize:me,headSize:R,vHeadSize:Math.floor(me/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:X,scale:t.scale,broadcastResPosBias:!1,passPastInKv:_e,qkvFormat:te}},Qi=it({perm:[0,2,1,3]}),Yi=(e,t,r)=>{let n=t,o=r.kvNumHeads;return t.dims.length===3&&r.kvSequenceLength!==0&&(n=t.reshape([r.batchSize,r.kvSequenceLength,o,r.headSize]),n=e.compute(rs(n,Qi.perm),{inputs:[n],outputs:[-1]})[0]),n},vd=(e,t)=>{var Z;let r=bd(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,d=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,p=e.inputs.length>4?e.inputs[5]:void 0,h=e.inputs.length>5?e.inputs[6]:void 0,k=r.kvNumHeads?r.kvNumHeads:r.numHeads,S=it({axis:2,numOutputs:3,splitSizes:[r.numHeads*r.headSize,k*r.headSize,k*r.headSize]}),[u,B,R]=!o&&!a?e.compute(yd([n],S),{inputs:[n],outputs:[-1,-1,-1]}):[n,o,a],U=Vn(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,u,void 0,0);vn(e,U,Yi(e,B,r),Yi(e,R,r),void 0,void 0,i,d,void 0,r,p,h)}}),Ji,xd,Zi,Ed,ip=w(()=>{zt(),Bt(),Ws(),Qt(),Ji=(e,t,r,n,o,a,i,d)=>{let p=yr(a),h=p===1?"f32":`vec${p}f`,k=p===1?"vec2f":`mat2x${p}f`,S=o*i,u=64;S===1&&(u=256);let B=[o,i,a/p],R=[o,i,2],U=["rank","type","type"],Z=[];Z.push(...vt(B,R));let te=X=>{let _e=ze("x",t.dataType,3,p),me=ze("scale",r.dataType,r.dims),ye=ze("bias",n.dataType,n.dims),Ae=wt("output",1,3,2),Ie=[_e,me,ye,Ae];return`
  var<workgroup> workgroup_shared : array<${k}, ${u}>;
  const workgroup_size = ${u}u;
  ${X.declareVariables(...Ie)}
  ${X.mainStart(u)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${h}(0);
    var squared_sum = ${h}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${h}(${_e.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${k}(sum, squared_sum);
    workgroupBarrier();

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

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${d}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${d};${u}`,inputDependencies:U},getRunData:()=>({outputs:[{dims:R,dataType:1}],dispatchGroup:{x:S},programUniforms:Z}),getShaderSource:te},{inputs:[t,r,n],outputs:[-1]})[0]},xd=(e,t,r)=>{let n=t[0].dims,o=n,a=2,i=n[0],d=n[1],p=$e.sizeFromDimension(n,a),h=yr(p),k=$e.size(o)/h,S=Ji(e,t[0],t[1],t[2],i,p,d,r.epsilon),u=[i,d,p/h],B=[i,d],R=["type","none"],U=Z=>{let te=ze("x",t[0].dataType,u.length,h),X=ze("scale_shift",1,B.length,2),_e=wt("output",t[0].dataType,u.length,h),me=[te,X,_e];return`
  ${Z.registerUniform("output_size","u32").declareVariables(...me)}
  ${Z.mainStart()}
  ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${_e.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${X.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${te.getByOffset("global_idx")} * ${_e.type.value}(scale_shift.x) + ${_e.type.value}(scale_shift.y);
      ${_e.setByOffset("global_idx","value")};
  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${h}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:[{type:12,data:k},...vt(u,B,u)]}),getShaderSource:U},{inputs:[t[0],S]})},Zi=(e,t,r)=>{let n=t[0].dims,o=n,a=n[0],i=n[n.length-1],d=$e.sizeFromDimension(n,1)/i,p=yr(i),h=$e.size(o)/p,k=[{type:12,data:d},{type:12,data:Math.floor(i/p)}],S=["type","type"],u=!1,B=[0,n.length-1];for(let te=0;te<n.length-2;te++)u=u||n[te+1]!==1,B.push(te+1);u=u&&n[n.length-1]!==1;let R=u?e.compute(rs(e.inputs[0],B),{inputs:[e.inputs[0]],outputs:[-1]})[0]:e.inputs[0].reshape(Array.from({length:n.length},(te,X)=>n[B[X]])),U=Ji(e,R,t[1],t[2],a,d,i,r.epsilon),Z=te=>{let X=er(t[0].dataType),_e=p===1?"vec2f":`mat${p}x2f`,me=Ie=>{let Ge=Ie===0?"x":"y",lt=p===1?"f32":`vec${p}f`;switch(p){case 1:return`${X}(${lt}(scale.${Ge}))`;case 2:return`vec2<${X}>(${lt}(scale[0].${Ge}, scale[1].${Ge}))`;case 4:return`vec4<${X}>(${lt}(scale[0].${Ge}, scale[1].${Ge}, scale[2].${Ge}, scale[3].${Ge}))`;default:throw new Error(`Not supported compoents ${p}`)}},ye=ze("input",t[0].dataType,t[0].dims,p),Ae=wt("output",t[0].dataType,o,p);return`
  @group(0) @binding(0) var<storage, read> input : array<${ye.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${_e}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${Ae.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

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

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${me(0)}, ${me(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:Z},{inputs:[t[0],U]})},Ed=(e,t)=>{t.format==="NHWC"?Zi(e,e.inputs,t):xd(e,e.inputs,t)}}),Pd,Cd,kd,ap=w(()=>{zt(),Bt(),Qt(),Pd=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Cd=(e,t,r)=>{let n=t.simplified,o=e[0].dims,a=e[1],i=!n&&e[2],d=o,p=$e.normalizeAxis(t.axis,o.length),h=$e.sizeToDimension(o,p),k=$e.sizeFromDimension(o,p),S=$e.size(a.dims),u=i?$e.size(i.dims):0;if(S!==k||i&&u!==k)throw new Error(`Size of X.shape()[axis:] == ${k}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${S} and bias size of ${u}`);let B=[];for(let ye=0;ye<o.length;++ye)ye<p?B.push(o[ye]):B.push(1);let R=yr(k),U=["type","type"],Z=[{type:12,data:h},{type:1,data:k},{type:12,data:Math.floor(k/R)},{type:1,data:t.epsilon}];i&&U.push("type");let te=r>1,X=r>2,_e=ye=>{let Ae=er(e[0].dataType),Ie=[ze("x",e[0].dataType,e[0].dims,R),ze("scale",a.dataType,a.dims,R)];i&&Ie.push(ze("bias",i.dataType,i.dims,R)),Ie.push(wt("output",e[0].dataType,d,R)),te&&Ie.push(wt("mean_data_output",1,B)),X&&Ie.push(wt("inv_std_output",1,B));let Ge=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${ye.registerUniforms(Ge).declareVariables(...Ie)}
  ${ye.mainStart()}
    ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Es("f32",R)};
    var mean_square_vector = ${Es("f32",R)};

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

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

    ${te?"mean_data_output[global_idx] = mean":""};
    ${X?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},me=[{dims:d,dataType:e[0].dataType}];return te&&me.push({dims:B,dataType:1}),X&&me.push({dims:B,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${R};${r};${n}`,inputDependencies:U},getRunData:()=>({outputs:me,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:Z}),getShaderSource:_e}},kd=(e,t)=>{Pd(e.inputs),e.compute(Cd(e.inputs,t,e.outputCount))}}),Sd,$d,lp=w(()=>{Bt(),$i(),yo(),Sd=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.")},$d=e=>{Sd(e.inputs);let t=rr.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let r=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];if(r<8&&n<8)e.compute(_o(e.inputs,{activation:""},t));else{let o=t[t.length-2],a=$e.size(e.inputs[0].dims.slice(0,-2)),i=$e.size(e.inputs[1].dims.slice(0,-2));if(a!==1&&o===1&&i===1){let d=e.inputs[0].reshape([1,a,n]),p=e.inputs[1].reshape([1,n,r]),h=[1,a,r],k=[d,p];e.compute(wo(k,{activation:""},t,h),{inputs:k})}else e.compute(wo(e.inputs,{activation:""},t))}}}),ea,Ad,Id,ta,Od,up=w(()=>{zt(),Bt(),Pt(),Qt(),ea=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,i=e[1];if(!$e.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 d=e[2].dims;if($e.size(d)!==t.n*o)throw new Error("scales input size error.");if(e.length===4){let p=e[3].dims,h=t.bits>4?t.n*o:t.n*Math.floor((o+1)/2);if($e.size(p)!==h)throw new Error("zeroPoints input size error.")}},Ad=(e,t)=>{let r=e[0].dims,n=r.length,o=r[n-2],a=t.k,i=t.n,d=r.slice(0,n-2),p=$e.size(d),h=e[1].dims[2]/4,k=e[0].dataType,S=yr(t.k),u=yr(h),B=yr(i),R=d.concat([o,i]),U=o>1&&i/B%2===0?2:1,Z=$e.size(R)/B/U,te=64,X=[],_e=[p,o,a/S],me=$e.convertShape(e[1].dims).slice();me.splice(-1,1,h/u),X.push(...vt(_e)),X.push(...vt(me)),X.push(...vt(e[2].dims)),e.length===4&&X.push(...vt($e.convertShape(e[3].dims)));let ye=[p,o,i/B];X.push(...vt(ye));let Ae=Ie=>{let Ge=_e.length,lt=ze("a",e[0].dataType,Ge,S),xt=ze("b",12,me.length,u),Kt=ze("scales",e[2].dataType,e[2].dims.length),Yt=[lt,xt,Kt],Ct=e.length===4?ze("zero_points",12,e[3].dims.length):void 0;Ct&&Yt.push(Ct);let Jt=ye.length,$t=wt("output",e[0].dataType,Jt,B),jt=er(e[0].dataType),vr=(()=>{switch(S){case 1:return`array<${jt}, 8>`;case 2:return`mat4x2<${jt}>`;case 4:return`mat2x4<${jt}>`;default:throw new Error(`${S}-component is not supported.`)}})(),Ht=()=>{let ot=`
          // reuse a data
            var input_offset = ${lt.indicesToOffset(`${lt.type.indices}(batch, row, word_offset)`)};
            var a_data: ${vr};
            for (var j: u32 = 0; j < ${8/S}; j++) {
              a_data[j] = ${lt.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let Et=0;Et<B*U;Et++)ot+=`
            b_value = ${u===1?`b${Et}_data`:`b${Et}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${vr}(${Array.from({length:4},(cr,Lr)=>`${jt}(b_value_lower[${Lr}]), ${jt}(b_value_upper[${Lr}])`).join(", ")});
            b_dequantized_values = ${S===1?`${vr}(${Array.from({length:8},(cr,Lr)=>`(b_quantized_values[${Lr}] - ${Ct?`zero_point${Et}`:"zero_point"}) * scale${Et}`).join(", ")});`:`(b_quantized_values - ${vr}(${Array(8).fill(`${Ct?`zero_point${Et}`:"zero_point"}`).join(",")})) * scale${Et};`};
            workgroup_shared[local_id.x * ${U} + ${Math.floor(Et/B)}]${B>1?`[${Et%B}]`:""} += ${Array.from({length:8/S},(cr,Lr)=>`${S===1?`a_data[${Lr}] * b_dequantized_values[${Lr}]`:`dot(a_data[${Lr}], b_dequantized_values[${Lr}])`}`).join(" + ")};
          `;return ot},Gt=()=>{let ot=`
            var col_index = col * ${B};
            ${Ct?`
            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 = ${jt}(8);`}
            `;for(let Et=0;Et<B*U;Et++)ot+=`
            let scale${Et} = ${Kt.getByOffset("col_index * nBlocksPerCol + block")};
            ${Ct?`
            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 = ${Ct.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${Et} = ${jt}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return ot},Pr=()=>{let ot=`col_index = col * ${B};`;for(let Et=0;Et<B*U;Et++)ot+=`
            let b${Et}_data = ${xt.getByIndices(`${xt.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return ot+=`
            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: ${vr};
            var b_dequantized_values: ${vr};`,ot};return`
        var<workgroup> workgroup_shared: array<${$t.type.value}, ${U*te}>;
        ${Ie.declareVariables(...Yt,$t)}
        ${Ie.mainStart([te,1,1])}
          let output_indices = ${$t.offsetToIndices(`(global_idx / ${te}) * ${U}`)};
          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 += ${te}) {
            //process one block
            var word_offset: u32 = block * ${t.blockSize/S};
            ${Gt()}
            for (var word: u32 = 0; word < ${h}; word += ${u}) {
              ${Pr()}
              for (var i: u32 = 0; i < ${u}; i++) {
                ${Ht()}
                word_offset += ${8/S};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${U}) {
            var output_value: ${$t.type.value} = ${$t.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${te}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${U};
            }
            ${$t.setByIndices(`${$t.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${S};${u};${B};${U};${te}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:R,dataType:k}],dispatchGroup:{x:Z},programUniforms:X}),getShaderSource:Ae}},Id=(e,t)=>{let r=e[0].dims,n=r.length,o=r[n-2],a=t.k,i=t.n,d=r.slice(0,n-2),p=$e.size(d),h=e[1].dims[2]/4,k=e[0].dataType,S=yr(t.k),u=yr(h),B=d.concat([o,i]),R=128,U=i%8===0?8:i%4===0?4:1,Z=R/U,te=Z*u*8,X=te/S,_e=te/t.blockSize,me=$e.size(B)/U,ye=[],Ae=[p,o,a/S],Ie=$e.convertShape(e[1].dims).slice();Ie.splice(-1,1,h/u),ye.push(...vt(Ae)),ye.push(...vt(Ie)),ye.push(...vt(e[2].dims)),e.length===4&&ye.push(...vt($e.convertShape(e[3].dims)));let Ge=[p,o,i];ye.push(...vt(Ge));let lt=xt=>{let Kt=Ae.length,Yt=ze("a",e[0].dataType,Kt,S),Ct=ze("b",12,Ie.length,u),Jt=ze("scales",e[2].dataType,e[2].dims.length),$t=[Yt,Ct,Jt],jt=e.length===4?ze("zero_points",12,e[3].dims.length):void 0;jt&&$t.push(jt);let vr=Ge.length,Ht=wt("output",e[0].dataType,vr),Gt=er(e[0].dataType),Pr=()=>{switch(S){case 1:return`
          let a_data0 = vec4<${Gt}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${Gt}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${Gt}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${Gt}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${S}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${Yt.type.value}, ${X}>;
        var<workgroup> inter_results: array<array<${Ht.type.value}, ${Z}>, ${U}>;
        ${xt.declareVariables(...$t,Ht)}
        ${xt.mainStart([Z,U,1])}
          let output_indices = ${Ht.offsetToIndices(`workgroup_index * ${U}`)};
          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) / ${_e} + 1;

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

            // each thread process one block
            let b_row = col + local_id.y;
            let block = tile * ${_e} + local_id.x;
            ${jt?`
            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 = ${jt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point = ${Gt}((zero_point_word) & 0xFu);`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${Gt}(8);`}
            let scale = ${Jt.getByOffset("b_row * n_blocks_per_col + block")};
            let b_data = ${Ct.getByIndices(`${Ct.type.indices}(b_row, block, 0)`)};
            var word_offset = local_id.x * ${t.blockSize/S};
            for (var i: u32 = 0; i < ${u}; i++) {
              ${Pr()}
              let b_value = ${u===1?"b_data":"b_data[i]"};
              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
              let b_quantized_values = mat2x4<${Gt}>(${Array.from({length:4},(ot,Et)=>`${Gt}(b_value_lower[${Et}]), ${Gt}(b_value_upper[${Et}])`).join(", ")});
              let b_dequantized_values = (b_quantized_values - mat2x4<${Gt}>(${Array(8).fill("zero_point").join(",")})) * scale;
              inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(ot,Et)=>`${`dot(a_data${Et}, b_dequantized_values[${Et}])`}`).join(" + ")};
              word_offset += ${8/S};
            }
            workgroupBarrier();
          }

          if (local_idx < ${U}) {
            var output_value: ${Ht.type.value} = ${Ht.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])
            {
              ${Ht.setByIndices(`${Ht.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${S};${u};${Z};${U}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:k}],dispatchGroup:{x:me},programUniforms:ye}),getShaderSource:lt}},ta=(e,t)=>{ea(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(Id(e.inputs,t)):e.compute(Ad(e.inputs,t))},Od=e=>it(e)}),_r,dp,cp,pp,ra,Fd,Dd,Ld,zd,Bd=w(()=>{zt(),Bt(),Qt(),_r=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},dp=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
            k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Mt("uniforms.x_shape",o,t)})) {
              break;
            }
            offset += k * i32(${Mt("uniforms.x_strides",o,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},cp=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Mt("uniforms.x_shape",o,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Mt("uniforms.x_shape",o,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Mt("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},pp=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Mt("uniforms.x_shape",o,t)})) {
                  k = i32(${Mt("uniforms.x_shape",o,t)}) - 1;
                }
                offset += k * i32(${Mt("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},ra=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=`
                k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)};
                if (k < 0)  {
                  k += i32(${Mt("uniforms.x_shape",o,t)}]);
                }
                if (k >= i32(${Mt("uniforms.x_shape",o,t)})) {
                  k -= i32(${Mt("uniforms.x_shape",o,t)});
                }
                offset += k * i32(${Mt("uniforms.x_strides",o,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Fd=(e,t,r)=>{switch(r.mode){case 0:return dp(e,t,r.pads.length);case 1:return cp(e,t,r.pads.length);case 2:return pp(e,t,r.pads.length);case 3:return ra(e,t,r.pads.length);default:throw new Error("Invalid mode")}},Dd=(e,t)=>{let r=$e.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,o=$e.size(r),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(...vt(e[0].dims,r));let d=["rank"],p=h=>{let k=wt("output",e[0].dataType,r.length),S=ze("x",e[0].dataType,n.length),u=S.type.value,B=Fd(k,n.length,t),R=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&R.push({name:"constant_value",type:i?u:"f32"}),`
            ${h.registerUniforms(R).declareVariables(S,k)}
            ${h.mainStart()}
            ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${u}(0);
            ${B}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${i}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil($e.size(r)/64)},programUniforms:a}),getShaderSource:p}},Ld=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,o=e[0].dims.length,a=new Int32Array(2*o).fill(0);if(e.length>=4){let d=e[3].getBigInt64Array();for(let p=0;p<d.length;p++)a[Number(d[p])]=Number(r[p]),a[Number(d[p])+o]=Number(r[p+d.length])}else r.forEach((d,p)=>a[Number(p)]=Number(d));let i=[];return a.forEach(d=>i.push(d)),{mode:t.mode,value:n,pads:i}}else return t},zd=(e,t)=>{_r(e.inputs);let r=Ld(e.inputs,t);e.compute(Dd(e.inputs,r),{inputs:[0]})}}),En,Rd,sa,na,Io,hp,mp,oa,ia,Nd,jd,aa,Ud,Wd,la,Vd,Gd,Kd,Hd,Vp=w(()=>{Qe(),zt(),Bt(),Qt(),En=e=>{if(T.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Rd=(e,t,r)=>{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(),d=t.strides.slice(),p=a?t.dilations.slice():[],h=t.pads.slice();wr.adjustPoolAttributes(r,o,i,d,p,h);let k=wr.computePoolOutputShape(r,o,d,p,i,h,t.autoPad),S=Object.assign({},t);a?Object.assign(S,{kernelShape:i,strides:d,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(S,{kernelShape:i,strides:d,pads:h,cacheKey:t.cacheKey});let u=k.slice();return u.push(u.splice(1,1)[0]),[S,n?u:k]},sa=(e,t)=>{let r=t.format==="NHWC",n=$e.size(e),o=$e.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 d=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],k=t.pads[t.pads.length-1],S=!!(h+k);a.push({type:12,data:d},{type:12,data:p},{type:12,data:h},{type:12,data:k}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let u=!1;if(t.kernelShape.length===2){let B=t.kernelShape[t.kernelShape.length-2],R=t.strides[t.strides.length-2],U=t.pads[t.pads.length/2-2],Z=t.pads[t.pads.length-2];u=!!(U+Z),a.push({type:12,data:B},{type:12,data:R},{type:12,data:U},{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,S,u]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=$e.computeStrides(t.kernelShape);a.push({type:12,data:d},{type:12,data:t.pads},{type:12,data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:d.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,k)=>h+k);return[a,i,!!p,!1,!1]}},na=(e,t,r,n,o,a,i,d,p,h,k,S)=>{let u=o.format==="NHWC",B=t.type.value,R=wt("output",t.type.tensor,n);if(o.kernelShape.length<=2){let U="",Z="",te="",X=r-(u?2:1);if(k?U=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${X}] = indices[${X}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${X}] < 0 || xIndices[${X}]
                      >= uniforms.x_shape[${X}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`:U=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${X}] = indices[${X}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`,o.kernelShape.length===2){let _e=r-(u?3:2);S?Z=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${_e}] = indices[${_e}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${_e}] < 0 || xIndices[${_e}] >= uniforms.x_shape[${_e}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:Z=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${_e}] = indices[${_e}] * uniforms.sh - uniforms.phStart + j;
                `,te=`
              }
            `}return`
            ${e.registerUniforms(p).declareVariables(t,R)}

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

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

              var value = ${B}(${d});
              var pad = 0;
              ${Z}
              ${U}
              ${te}
              ${i}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},Io=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,hp=e=>`${Io(e)};${e.countIncludePad}`,mp=e=>`${Io(e)};${e.storageOrder};${e.dilations}`,oa=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}),ia=(e,t,r,n)=>{let[o,a]=Rd(t,n,r),i=ze("x",t.dataType,t.dims.length),d=i.type.value,p="value += x_val;",h="";o.countIncludePad?h+=`value /= ${d}(uniforms.kernelSize);`:h+=`value /= ${d}(i32(uniforms.kernelSize) - pad);`;let[k,S,u,B,R]=sa(a,o);k.push(...vt(t.dims,a));let U=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${u};${B};${R}`,inputDependencies:U},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil($e.size(a)/64)},programUniforms:k}),getShaderSource:Z=>na(Z,i,t.dims.length,a.length,o,p,h,0,S,u,B,R)}},Nd=e=>{let t=e.count_include_pad!==0,r=oa(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:hp(n)}},jd=(e,t)=>{En(e.inputs),e.compute(ia("AveragePool",e.inputs[0],!1,t))},aa={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Ud=e=>{let t=e.format;return{format:t,...aa,cacheKey:t}},Wd=(e,t)=>{En(e.inputs),e.compute(ia("GlobalAveragePool",e.inputs[0],!0,t))},la=(e,t,r,n)=>{let[o,a]=Rd(t,n,r),i=`
      value = max(x_val, value);
    `,d="",p=ze("x",t.dataType,t.dims.length),h=["rank"],[k,S,u,B,R]=sa(a,o);return k.push(...vt(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${u};${B};${R}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil($e.size(a)/64)},programUniforms:k}),getShaderSource:U=>na(U,p,t.dims.length,a.length,o,i,d,t.dataType===10?-65504:-1e5,S,u,B,R)}},Vd=(e,t)=>{En(e.inputs),e.compute(la("MaxPool",e.inputs[0],!1,t))},Gd=e=>{let t=e.storage_order,r=e.dilations,n=oa(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:r,...n,cacheKey:""};return{...o,cacheKey:mp(o)}},Kd=e=>{let t=e.format;return{format:t,...aa,cacheKey:t}},Hd=(e,t)=>{En(e.inputs),e.compute(la("GlobalMaxPool",e.inputs[0],!0,t))}}),qd,Xd,Qd,Yd,_p=w(()=>{zt(),Bt(),Pt(),Qt(),qd=(e,t)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((r,n)=>r===e[2].dims[n]).reduce((r,n)=>r&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((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 r=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSize<Math.ceil(r/n)||t.blockSize>Math.ceil(r/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Xd=(e,t)=>{let r=$e.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,o=n===3,a=e[0].dims,i=e[1].dataType,d=$e.size(a),p=n===3||n===2,h=p?[Math.ceil($e.size(e[0].dims)/4)]:e[0].dims,k=e[1].dims,S=e.length>2?e[2]:void 0,u=S?p?[Math.ceil($e.size(S.dims)/4)]:S.dims:void 0,B=k.length===0||k.length===1&&k[0]===1,R=B===!1&&k.length===1,U=yr(d),Z=B&&(!p||U===4),te=Z?U:1,X=Z&&!p?U:1,_e=ze("input",p?12:n,h.length,X),me=ze("scale",i,k.length),ye=S?ze("zero_point",p?12:n,u.length):void 0,Ae=wt("output",i,a.length,te),Ie=[_e,me];ye&&Ie.push(ye);let Ge=[h,k];S&&Ge.push(u);let lt=[{type:12,data:d/te},{type:12,data:r},{type:12,data:t.blockSize},...vt(...Ge,a)],xt=Kt=>{let Yt=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${Kt.registerUniforms(Yt).declareVariables(...Ie,Ae)}
      ${Kt.mainStart()}
          ${Kt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${Ae.offsetToIndices("global_idx")};

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

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

          // Set zero-point input
          ${ye?B?p?`
                let zero_point_input = ${ye.getByOffset("0")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${ye.getByOffset("0")}`:R?p?`
                let zero_point_index = ${Ae.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${ye.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${Ae.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${ye.getByOffset("zero_point_index")};`:p?`
                let zero_point_offset = ${me.indicesToOffset("scale_indices")};
                let zero_point_input = ${ye.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${ye.getByIndices("scale_indices")};`:`let zero_point_value = ${p?o?"i32":"u32":_e.type.value}(0);`};
      // Compute and write output
      ${Ae.setByOffset("global_idx",`${Ae.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:ye?["rank","rank","rank"]:["rank","rank"]},getShaderSource:xt,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(d/te/64),y:1,z:1},programUniforms:lt})}},Qd=(e,t)=>{qd(e.inputs,t),e.compute(Xd(e.inputs,t))},Yd=e=>it({axis:e.axis,blockSize:e.blockSize})}),Jd,fp,ua,gp=w(()=>{Qe(),zt(),Qt(),Jd=(e,t,r)=>{let n=e===t,o=e<t&&r<0,a=e>t&&r>0;if(n||o||a)throw new Error("Range these inputs' contents are invalid.")},fp=(e,t,r,n)=>{let o=Math.abs(Math.ceil((t-e)/r)),a=[o],i=o,d=[{type:12,data:i},{type:n,data:e},{type:n,data:r},...vt(a)],p=h=>{let k=wt("output",n,a.length),S=k.type.value,u=[{name:"outputSize",type:"u32"},{name:"start",type:S},{name:"delta",type:S}];return`
        ${h.registerUniforms(u).declareVariables(k)}
        ${h.mainStart()}
        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${S}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d})}},ua=e=>{let t=0,r=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),T.webgpu.validateInputContent&&Jd(t,r,n),e.compute(fp(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),Zd,ec,tc,rc,wp=w(()=>{zt(),Bt(),Pt(),Qt(),Zd=(e,t,r,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`${t}=${r};`;case"add":return n==="i32"||n==="u32"?`atomicAdd(&${t}, bitcast<${n}>(${r}));`:`
              ${o}bitcast<${n}>(oldValue) + (${r})${a}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${r}));`:`
                ${o}max(bitcast<f32>(oldValue), (${r}))${a}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${r}));`:`${o}min(bitcast<${n}>(oldValue), (${r}))${a}`;case"mul":return`${o}(bitcast<${n}>(oldValue) * (${r}))${a}`;default:throw new Error(`Reduction ${e} is not supported.`)}},ec=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r,a=1,i=Math.ceil($e.size(n)/a),d=n[n.length-1],p=$e.sizeFromDimension(r,d),h=[{type:12,data:i},{type:12,data:d},{type:12,data:p},...vt(e[1].dims,e[2].dims,o)],k=S=>{let u=ze("indices",e[1].dataType,e[1].dims.length),B=ze("updates",e[2].dataType,e[2].dims.length,a),R=t.reduction!=="none"&&t.reduction!==""?ts("output",e[0].dataType,o.length):wt("output",e[0].dataType,o.length,a);return`
      ${S.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(u,B,R)}
      ${S.mainStart()}
        ${S.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];
    ${Zd(t.reduction,"output[data_offset + i]","value",R.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:k}},tc=e=>it({reduction:e.reduction}),rc=(e,t)=>{e.compute(ec(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),sc,nc,oc,ic,ac,lc,uc,dc,cc,pc,hc,da,mc,_c,fc,gc,wc,yc,Mc,yp=w(()=>{zt(),Bt(),Pt(),Qt(),sc=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},nc=(e,t,r)=>{t.every(o=>o>=0&&o<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return t.forEach((o,a)=>n[o]=e[a]),n},oc=(e,t,r,n,o,a)=>{let[i,d,p]=r>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(k=>a.push(k));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(d>0&&e.length>d&&e[d].dims.length===1&&e[d].dims[0]>0){if(e[d].getFloat32Array().forEach(k=>n.push(k)),n.length!==0&&n.length!==h&&r>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");sc(n,t),t.axes.length>0&&nc(n,t.axes,h).forEach((k,S)=>n[S]=k)}if(p>0&&e.length>p&&e[p].dims.length===1&&e[p].dims[0]>0&&(e[p].getBigInt64Array().forEach(k=>o.push(Number(k))),o.length!==0&&o.length!==h&&r>=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")},ic=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating
                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
                    // offset-by-one error later in floor().
                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +
                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /
                        ${t}(lengthResized - 1);
                  } else {
                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);
                  const adjustment = ${t}(lengthResized) / outputWidth;
                  const center = ${t}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",ac=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",lc=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),o=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,i)=>{n[a]=o[i],n[i+r]=o[t.length+i]}),n):o},uc=(e,t,r,n)=>{let o=[];if(r.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]=r[i])}else r.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},dc=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(a=>t[a]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let o=e.slice();return r.axes.length>0?(r.axes.forEach(a=>t[a]=n),r.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},cc=(e,t,r,n,o)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {
      var original_indices: array<${e.type.value}, ${r.length}>;
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${Mt("uniforms.scales","i",n)};
        var roi_low = ${Mt("uniforms.roi","i",o)};
        var roi_hi = ${Mt("uniforms.roi",`i + ${t.length}`,o)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${Mt("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${Mt("uniforms.output_shape","i",r.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,pc=(e,t,r,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 = ${Mt("uniforms.scales","i",o)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Mt("uniforms.roi","i",a)};
          var roi_hi = ${Mt("uniforms.roi",`i + ${r.length}`,a)};
          var input_shape_i = ${Mt("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${Mt("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${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;
    }`,hc=(e,t)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${Mt("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,da=(e,t,r,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",r,"batch")};
`:"",mc=(e,t,r,n,o)=>{let[a,i,d,p]=r.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, ${r[i]} - 1))`)};
      ${e.indicesSet("input_indices",d,`max(0, min(col, ${r[d]} - 1))`)};
      ${da(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[${d}];
      ${n?`if (row < 0 || row > (${r[i]} - 1) || col < 0 || col > (${r[d]} - 1)) {
        return ${o};
      }`:""};
      row = max(0, min(row, ${r[i]} - 1));
      col = max(0, min(col, ${r[d]} - 1));
      var row1: u32 = u32(row);
      var col1: u32 = u32(col);
      var row2: u32 = u32(row + 1);
      var col2: u32 = u32(col + 1);
      var channel: u32 = ${r.length>2?`u32(originalIndices[${p}])`:"0"};
      var batch: u32 =  ${r.length>2?`u32(originalIndices[${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);
    }`},_c=(e,t,r,n,o,a,i,d,p,h)=>{let k=r.length===2,[S,u]=k?[0,1]:[2,3],B=e.type.value,R=U=>{let Z=U===S?"row":"col";return`
      fn ${Z}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${B} {
        var output_index = ${t.indicesGet("output_indices",U)};
        var originalIdx: ${B} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[U]},
        ${n[U]}, ${r[U]}, ${a[U]}, ${a[U]} + ${r.length});
        var fractOriginalIdx: ${B} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

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

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

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

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

      var x111: ${k} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${k} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${k} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${k} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${k} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${k} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${k} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${k} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${k} = abs(depth - ${k}(depth1));
      var dx2: ${k} = abs(${k}(depth2) - depth);
      var dy1: ${k} = abs(height - ${k}(height1));
      var dy2: ${k} = abs(${k}(height2) - height);
      var dz1: ${k} = abs(width - ${k}(width1));
      var dz2: ${k} = abs(${k}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},gc=(e,t,r,n,o,a)=>{let i=e.dims,d=lc(a,t.axes,i.length),p=uc(i,n,o,t.axes),h=n.slice();n.length===0&&(h=i.map((X,_e)=>X===0?1:p[_e]/X),t.keepAspectRatioPolicy!=="stretch"&&(p=dc(i,h,t)));let k=wt("output",e.dataType,p.length),S=ze("input",e.dataType,i.length),u=$e.size(p),B=i.length===p.length&&i.every((X,_e)=>X===p[_e]),R=t.coordinateTransformMode==="tf_crop_and_resize",U=t.extrapolationValue,Z=S.type.value,te=X=>`
      ${B?"":`
      ${ic(t.coordinateTransformMode,Z)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${hc(S,i)};
              ${ac(t.nearestMode,r,Z)};
              ${pc(S,k,i,p,h.length,d.length,R)};
              `;case"linear":return`
              ${cc(k,i,p,h.length,d.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${mc(S,k,i,R,U)}`;if(i.length===3||i.length===5)return`${fc(S,k,i,R,U)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${_c(S,k,i,p,h,d,t.cubicCoeffA,R,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")}})()};
      `}
      ${X.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",d.length).declareVariables(S,k)}
      ${X.mainStart()}
        ${X.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${B?"output[global_idx] = input[global_idx];":`
        let output_indices = ${k.offsetToIndices("global_idx")};
        var input_indices: ${S.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${S.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${h.length>0?h:""}|${o.length>0?o:""}|${d.length>0?d:""}|${B}|${i}`,inputDependencies:["rank"]},getShaderSource:te,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:[{type:12,data:u},{type:1,data:h},{type:1,data:d},...vt(i,p)]})}},wc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},yc=(e,t)=>{let r=[],n=[],o=[],a=wc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");oc(e.inputs,t,a,r,n,o),e.compute(gc(e.inputs[0],t,a,r,n,o),{inputs:[0]})},Mc=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,o=e.cubicCoeffA,a=e.excludeOutside!==0,i=e.extrapolationValue,d=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return it({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:o,excludeOutside:a,extrapolationValue:i,keepAspectRatioPolicy:d,mode:p,nearestMode:h})}}),bc,vc,Mp,Xt=w(()=>{zt(),Bt(),Pt(),Qt(),bc=(e,t)=>{let[r,n,o,a]=e,{numHeads:i,rotaryEmbeddingDim:d}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!$e.areEqual(n.dims,[])&&!$e.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 ${a.dims.length}`);if(!$e.areEqual(o.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(d>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=r.dims[0],h=r.dims[r.dims.length-2],k=o.dims[0],S=$e.sizeFromDimension(r.dims,1)/h,u=d===0?o.dims[1]*2:S/i;if(d>u)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(u/2!==o.dims[1]&&d/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(h>k)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},vc=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:o,scale:a}=t,i=e[0].dims[0],d=$e.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=d/p,k=e[2].dims[1],S=o===0?k*2:h/n,u=new Array(i,p,h/S,S-k),B=$e.computeStrides(u),R=[{type:1,data:a},{type:12,data:u},{type:12,data:B},...e[0].dims.length===3?new Array({type:12,data:[d,h,S,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[d,S,p*S,1]}):[],...vt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],U=Z=>{let te=ze("input",e[0].dataType,e[0].dims.length),X=ze("position_ids",e[1].dataType,e[1].dims.length),_e=ze("cos_cache",e[2].dataType,e[2].dims.length),me=ze("sin_cache",e[3].dataType,e[3].dims.length),ye=wt("output",e[0].dataType,e[0].dims.length);return Z.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:u.length},{name:"global_strides",type:"u32",length:B.length},{name:"input_output_strides",type:"u32",length:B.length}]),`
        ${Z.declareVariables(te,X,_e,me,ye)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${X.broadcastedIndicesToOffset("bsnh.xy",wt("",X.type.tensor,2))};
            let position_id =
                u32(${X.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});
            let j = i + select(half_rotary_emb_dim, 1, ${r});
            let re = ${te.getByOffset("i")} * ${_e.get("position_id","bsnh[3]")} -
                ${te.getByOffset("j")} * ${me.get("position_id","bsnh[3]")};
            ${ye.setByOffset("i","re")}
            let im = ${te.getByOffset("i")} * ${me.get("position_id","bsnh[3]")} +
                ${te.getByOffset("j")} * ${_e.get("position_id","bsnh[3]")};
            ${ye.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${ye.setByOffset("k",te.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:it({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:U,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil($e.size(u)/Nr)},programUniforms:R})}},Mp=(e,t)=>{bc(e.inputs,t),e.compute(vc(e.inputs,t))}}),Tc,jr,Wr,Zr=w(()=>{zt(),Bt(),Qt(),Tc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],n=e[2];if(t.dataType!==r.dataType||t.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==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")}},jr=(e,t,r,n)=>{let o=t.simplified,a=e[0].dims,i=$e.size(a),d=a,p=i,h=a.slice(-1)[0],k=n?a.slice(0,-1).concat(1):[],S=!o&&e.length>3,u=e.length>4,B=n&&r>1,R=n&&r>2,U=r>3,Z=64,te=yr(h),X=[{type:12,data:p},{type:12,data:te},{type:12,data:h},{type:1,data:t.epsilon}],_e=ye=>{let Ae=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Ie=[ze("x",e[0].dataType,e[0].dims,te),ze("skip",e[1].dataType,e[1].dims,te),ze("gamma",e[2].dataType,e[2].dims,te)];S&&Ie.push(ze("beta",e[3].dataType,e[3].dims,te)),u&&Ie.push(ze("bias",e[4].dataType,e[4].dims,te)),Ie.push(wt("output",e[0].dataType,d,te)),B&&Ie.push(wt("mean_output",1,k)),R&&Ie.push(wt("inv_std_output",1,k)),U&&Ie.push(wt("input_skip_bias_sum",e[0].dataType,d,te));let Ge=er(e[0].dataType),lt=er(1,te);return`

      ${ye.registerUniforms(Ae).declareVariables(...Ie)}
      var<workgroup> sum_shared : array<${lt}, ${Z}>;
      var<workgroup> sum_squared_shared : array<${lt}, ${Z}>;

      ${ye.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 = ${u?"bias[offset1d + i]":Ge+"(0.0)"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${U?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${Dr(Ge,te,"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 = ${Hr("sum",te)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Hr("square_sum",te)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon);
        ${B?"mean_output[global_idx] = mean;":""}
        ${R?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${o?"":`- ${Ge}(mean)`}) *
            ${Ge}(inv_std_dev) * gamma[offset1d + i]
            ${S?"+ beta[offset1d + i]":""};
        }
      }`},me=[{dims:d,dataType:e[0].dataType}];return r>1&&me.push({dims:k,dataType:1}),r>2&&me.push({dims:k,dataType:1}),r>3&&me.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${te};${B};${R};${U}`,inputDependencies:e.map((ye,Ae)=>"type")},getShaderSource:_e,getRunData:()=>({outputs:me,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:X})}},Wr=(e,t)=>{Tc(e.inputs);let r=[0];e.outputCount>1&&r.push(-3),e.outputCount>2&&r.push(-3),e.outputCount>3&&r.push(3),e.compute(jr(e.inputs,t,e.outputCount,!1),{outputs:r})}}),on,Oo,xc,ca,_,x,j,be,Oe=w(()=>{zt(),Bt(),Pt(),Qt(),on=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},Oo=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},xc=(e,t)=>{if(e.length>1){let r=Oo(e,1),n=Oo(e,2),o=Oo(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),it({starts:r,ends:n,axes:o})}else return t},ca=(e,t,r,n,o)=>{let a=e;return e<0&&(a+=r[n[t]]),o[t]<0?Math.max(0,Math.min(a,r[n[t]]-1)):Math.max(0,Math.min(a,r[n[t]]))},_=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${r.length}; i >= 0; i--) {
            let input_shape_i = ${Mt("uniforms.input_shape","i",r.length)};
            let steps_i = ${Mt("uniforms.steps","i",r.length)};
            let signs_i = ${Mt("uniforms.signs","i",r.length)};
            let starts_i = ${Mt("uniforms.starts","i",r.length)};
            var output_index = ${t.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,x=(e,t)=>{let r=e[0].dims,n=$e.size(r),o=t.axes.length>0?$e.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],a=Oo(e,4);a.forEach(te=>te!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(o.length).fill(1));let i=t.starts.map((te,X)=>ca(te,X,r,o,a)),d=t.ends.map((te,X)=>ca(te,X,r,o,a));if(o.length!==i.length||o.length!==d.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==r.length)for(let te=0;te<r.length;++te)o.includes(te)||(i.splice(te,0,0),d.splice(te,0,r[te]),a.splice(te,0,1));let p=a.map(te=>Math.sign(te));a.forEach((te,X,_e)=>{if(te<0){let me=(d[X]-i[X])/te,ye=i[X],Ae=ye+me*a[X];i[X]=Ae,d[X]=ye,_e[X]=-te}});let h=r.slice(0);o.forEach((te,X)=>{h[te]=Math.ceil((d[te]-i[te])/a[te])});let k={dims:h,dataType:e[0].dataType},S=wt("output",e[0].dataType,h.length),u=ze("input",e[0].dataType,e[0].dims.length),B=$e.size(h),R=[{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}],U=[{type:12,data:B},{type:12,data:i},{type:6,data:p},{type:12,data:a},...vt(e[0].dims,h)],Z=te=>`
      ${te.registerUniforms(R).declareVariables(u,S)}
        ${_(u,S,r)}
        ${te.mainStart()}
          ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${S.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${S.setByOffset("global_idx",u.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${i.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:Z,getRunData:()=>({outputs:[k],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:U})}},j=(e,t)=>{on(e.inputs,t);let r=xc(e.inputs,t);e.compute(x(e.inputs,r),{inputs:[0]})},be=e=>{let t=e.starts,r=e.ends,n=e.axes;return it({starts:t,ends:r,axes:n})}}),Se,Ye,tt,mt,Tt=w(()=>{zt(),Bt(),Pt(),Ws(),Qt(),Se=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Ye=(e,t)=>{let r=e.inputs[0],n=r.dims,o=$e.size(n),a=n.length,i=$e.normalizeAxis(t.axis,a),d=i<n.length-1,p,h=[];d?(h=Array.from({length:a},(Ie,Ge)=>Ge),h[i]=a-1,h[a-1]=i,p=e.compute(rs(r,h),{inputs:[r],outputs:[-1]})[0]):p=r;let k=p.dims,S=k[a-1],u=o/S,B=yr(S),R=S/B,U=64;u===1&&(U=256);let Z=(Ie,Ge)=>Ge===4?`max(max(${Ie}.x, ${Ie}.y), max(${Ie}.z, ${Ie}.w))`:Ge===2?`max(${Ie}.x, ${Ie}.y)`:Ge===3?`max(max(${Ie}.x, ${Ie}.y), ${Ie}.z)`:Ie,te=ze("x",p.dataType,p.dims,B),X=wt("result",p.dataType,p.dims,B),_e=te.type.value,me=er(p.dataType)==="f32"?`var threadMax = ${_e}(-3.402823e+38f);`:`var threadMax = ${_e}(-65504.0h);`,ye=Ie=>`
      var<workgroup> rowMaxShared : ${_e};
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        }
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          workgroupBarrier();
        }
        if (lindex == 0) {
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        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${_e}(0.0);
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          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) {
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            let offset_c${B} = ${p.broadcastedIndicesToOffset(`output_indices${B}`,a)};
            let index_a${B} = offset_a${B} / 4u;
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            let component_a${B} = offset_a${B} % 4u;
            let component_b${B} = offset_b${B} % 4u;
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            ${S("data",0,"u32")}
            ${S("data",1,"u32")}
            ${S("data",2,"u32")}
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            ${S("output_data[global_idx]",1)}
            ${S("output_data[global_idx]",2)}
            ${S("output_data[global_idx]",3)}
          `}return`
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U=Number(B-this.queryTimeBase),Z=Number(R-this.queryTimeBase);if(!Number.isSafeInteger(U)||!Number.isSafeInteger(Z))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:S.map(te=>({dims:te.dims,dataType:Ts(te.dataType)})),outputsMetadata:u.map(te=>({dims:te.dims,dataType:Ts(te.dataType)})),kernelId:i,kernelType:p,kernelName:h,programName:k,startTime:U,endTime:Z});else{let te="";S.forEach((_e,me)=>{te+=`input[${me}]: [${_e.dims}] | ${Ts(_e.dataType)}, `});let X="";u.forEach((_e,me)=>{X+=`output[${me}]: [${_e.dims}] | ${Ts(_e.dataType)}, `}),console.log(`[profiling] kernel "${i}|${p}|${h}|${k}" ${te}${X}execution time: ${Z-U} ns`)}Re("GPU",`${k}::${B}::${R}`)}e.unmap(),this.pendingQueries.delete(e)}),Ne()}run(e,t,r,n,o,a){Ve(e.name);let i=[];for(let X=0;X<t.length;++X){let _e=t[X].data;if(_e===0)continue;let me=this.gpuDataManager.get(_e);if(!me)throw new Error(`no GPU data for input: ${_e}`);i.push(me)}let{outputs:d,dispatchGroup:p,programUniforms:h}=e.getRunData(t),k=r.length===0?d.map((X,_e)=>_e):r;if(k.length!==d.length)throw new Error(`Output size ${k.length} must be equal to ${d.length}.`);let S=[],u=[];for(let X=0;X<d.length;++X){if(!Number.isInteger(k[X])||k[X]<-3||k[X]>=a)throw new Error(`Invalid output index: ${k[X]}`);if(k[X]===-3)continue;let _e=k[X]===-1,me=k[X]===-2,ye=_e||me?o(d[X].dataType,d[X].dims):n(k[X],d[X].dataType,d[X].dims);if(S.push(ye),ye.data===0)continue;let Ae=this.gpuDataManager.get(ye.data);if(!Ae)throw new Error(`no GPU data for output: ${ye.data}`);if(_e&&this.temporaryData.push(Ae),me){let Ie=this.kernelPersistentData.get(this.currentKernelId);Ie||(Ie=[],this.kernelPersistentData.set(this.currentKernelId,Ie)),Ie.push(Ae)}u.push(Ae)}if(i.length!==t.length||u.length!==S.length){if(u.length===0)return Ne(e.name),S;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let B;if(h){let X=0,_e=[];h.forEach(Ie=>{let Ge=typeof Ie.data=="number"?[Ie.data]:Ie.data;if(Ge.length===0)return;let lt=Ie.type===10?2:4,xt,Kt;Ie.type===10?(Kt=Ge.length>4?16:Ge.length>2?8:Ge.length*lt,xt=Ge.length>4?16:lt*Ge.length):(Kt=Ge.length<=2?Ge.length*lt:16,xt=16),X=Math.ceil(X/Kt)*Kt,_e.push(X);let Yt=Ie.type===10?8:4;X+=Ge.length>4?Math.ceil(Ge.length/Yt)*xt:Ge.length*lt});let me=16;X=Math.ceil(X/me)*me;let ye=new ArrayBuffer(X);h.forEach((Ie,Ge)=>{let lt=_e[Ge],xt=typeof Ie.data=="number"?[Ie.data]:Ie.data;if(Ie.type===6)new Int32Array(ye,lt,xt.length).set(xt);else if(Ie.type===12)new Uint32Array(ye,lt,xt.length).set(xt);else if(Ie.type===10)new Uint16Array(ye,lt,xt.length).set(xt);else if(Ie.type===1)new Float32Array(ye,lt,xt.length).set(xt);else throw new Error(`Unsupported uniform type: ${Ts(Ie.type)}`)});let Ae=this.gpuDataManager.create(X,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Ae.buffer,0,ye,0,X),this.gpuDataManager.release(Ae.id),B={offset:0,size:X,buffer:Ae.buffer}}let R=this.programManager.normalizeDispatchGroupSize(p),U=R[1]===1&&R[2]===1,Z=gs(e,t,U),te=this.programManager.getArtifact(Z);if(te||(te=this.programManager.build(e,R),this.programManager.setArtifact(Z,te),ir("info",()=>`[artifact] key: ${Z}, programName: ${e.name}`)),h&&te.uniformVariablesInfo){if(h.length!==te.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${te.uniformVariablesInfo.length}, got ${h.length} in program "${te.programInfo.name}".`);for(let X=0;X<h.length;X++){let _e=h[X],me=_e.type,ye=typeof _e.data=="number"?1:_e.data.length,[Ae,Ie]=te.uniformVariablesInfo[X];if(me!==Ae||ye!==Ie)throw new Error(`Uniform variable ${X} mismatch: expect type ${Ae} with size ${Ie}, got type ${me} with size ${ye} in program "${te.programInfo.name}".`)}}if(ir("info",()=>`[ProgramManager] run "${e.name}" (key=${Z}) with ${R[0]}x${R[1]}x${R[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let X={kernelId:this.currentKernelId,programName:te.programInfo.name,inputTensorViews:t,outputTensorViews:S};this.pendingKernels.push(X),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(X)}return this.programManager.run(te,i,u,R,B),Ne(e.name),S}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,r,n){let o=dr.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:o[0],attributes:[o[1],r]};this.kernels.set(t,a)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let r of t)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,r){let n=this.kernels.get(e);if(!n)throw new Error(`kernel not created: ${e}`);let o=n.kernelType,a=n.kernelName,i=n.kernelEntry,d=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,d[0]&&(d[1]=d[0](d[1]),d[0]=void 0),ir("info",()=>`[WebGPU] Start to run kernel "[${o}] ${a}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),i(t,d[1]),0}catch(h){return r.push(Promise.resolve(`[WebGPU] Kernel "[${o}] ${a}" failed. ${h}`)),1}finally{p&&r.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${o}] ${a}": ${h.message}`:null));for(let h of this.temporaryData)this.gpuDataManager.release(h.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,r,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(r,n,a);return o.set(t,[i,r]),i}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[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,r){return async()=>{let n=await Ce(this,e,t);return wn(n.buffer,r)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){ir("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(){ir("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){ir("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),r=e.length;this.pendingKernels=[];for(let n=0;n<r;n++){let 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()}}}),ha,Gn,Fo,Kn,Tr,Ir,Hs,an,Hn=w(()=>{Qr(),ha=1,Gn=()=>ha++,Fo=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),Kn=(e,t)=>{let r=Fo.get(e);if(!r)throw new Error("Unsupported data type.");return t.length>0?Math.ceil(t.reduce((n,o)=>n*o)*r/8):0},Tr=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 Kn(this.dataType,this.tensorShape)}destroy(){ir("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(e){this.mlContext.writeTensor(this.mlTensor,e)}async read(e){return e?this.mlContext.readTensor(this.mlTensor,e):this.mlContext.readTensor(this.mlTensor)}sameTypeAndShape(e,t){return this.dataType===e&&this.tensorShape.length===t.length&&this.tensorShape.every((r,n)=>r===t[n])}},Ir=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,r){if(this.wrapper){if(this.wrapper.sameTypeAndShape(e,t))return this.wrapper.tensor;if(r){if(this.wrapper.byteLength!==Kn(e,t))throw new Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let n=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(e,t,n,!0,!0),r&&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 ir("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()}},Hs=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=Gn();return this.tensorTrackersById.set(e,new Ir(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,r,n){ir("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${t}, shape: ${r}, copyOld: ${n}}`);let o=this.tensorTrackersById.get(e);if(!o)throw new Error("Tensor not found.");return o.ensureTensor(t,r,n)}upload(e,t){let r=this.tensorTrackersById.get(e);if(!r)throw new Error("Tensor not found.");r.upload(t)}async download(e,t){ir("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${t==null?void 0:t.byteLength}}`);let r=this.tensorTrackersById.get(e);if(!r)throw new Error("Tensor not found.");return r.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,r,n){let o=Gn(),a=new Tr({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:r,shape:n});return this.tensorTrackersById.set(o,new Ir(this,a)),this.externalTensors.add(a),o}async getCachedTensor(e,t,r,n,o){let a=this.backend.currentSessionId;for(let[p,h]of this.freeTensors.entries())if(h.sameTypeAndShape(e,t)){ir("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, shape: ${t}}`);let k=this.freeTensors.splice(p,1)[0];return k.sessionId=a,k}let i=this.backend.currentContext;ir("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let d=await i.createTensor({dataType:e,shape:t,dimensions:t,usage:r,writable:n,readable:o});return new Tr({sessionId:a,context:i,tensor:d,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},an=(...e)=>new Hs(...e)}),qn,ma,_a,Ec=w(()=>{zt(),Ot(),Rn(),Hn(),Qr(),qn=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),ma=(e,t)=>{if(e===t)return!0;if(e===void 0||t===void 0)return!1;let r=Object.keys(e).sort(),n=Object.keys(t).sort();return r.length===n.length&&r.every((o,a)=>o===n[a]&&e[o]===t[o])},_a=class{constructor(e){this.tensorManager=an(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new 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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 r=this.sessionIdsByMLContext.get(t);r||(r=new Set,this.sessionIdsByMLContext.set(t,r)),r.add(e)}onReleaseSession(e){let t=this.mlContextBySessionId.get(e);if(!t)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let r=this.sessionIdsByMLContext.get(t);if(r.delete(e),r.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){ir("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,r,n){let o=qn.get(t);if(!o)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,o,r,n)}uploadTensor(e,t){if(!_t().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");ir("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 r=await this.tensorManager.download(e);return wn(r,t)}}registerMLTensor(e,t,r){let n=qn.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let o=this.tensorManager.registerTensor(this.currentContext,e,n,r);return ir("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${r}} -> {tensorId: ${o}}`),o}registerMLConstant(e,t,r,n,o,a){if(!a)throw new Error("External mounted files are not available.");let i=e;e.startsWith("./")&&(i=e.substring(2));let d=a.get(i);if(!d)throw new Error(`File with name ${i} not found in preloaded 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t=$e.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=$e.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=$e.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=$e.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if($e.size(t)!==$e.size(this.dims))throw new Error("Invalid new shape");return new p_(this.module,this.dataType,this.data,t)}},Do=class{constructor(e,t,r){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=r/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 d=[];for(let p=0;p<i;p++){let h=Number(e.getValue(n*o++,a)),k=Number(e.getValue(n*o++,"*")),S=Number(e.getValue(n*o++,a)),u=[];for(let B=0;B<S;B++)u.push(Number(e.getValue(n*o++,a)));d.push(new Xn(e,h,k,u))}this.inputs=d}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 r=((i=t==null?void 0:t.inputs)==null?void 0:i.map(d=>typeof d=="number"?this.inputs[d]:d))??this.inputs,n=(t==null?void 0:t.outputs)??[],o=(d,p,h)=>new Xn(this.module,p,this.output(d,h),h),a=(d,p)=>{let h=as(d,p);if(!h)throw new Error(`Unsupported data type: ${d}`);let 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Dh(this.sessionId,o,d,i,p,r),k={};for(let S=0;S<h.length;S++)k[this.outputNames[i[S]]]=a[S]??Bh(h[S]);return Ne(),k}startProfiling(){}endProfiling(){Lh(this.sessionId)}}}),Nh={};v(Nh,{OnnxruntimeWebAssemblyBackend:()=>Zp,initializeFlags:()=>Jp,wasmBackend:()=>jh});var Jp,Zp,jh,__=w(()=>{Qe(),zh(),m_(),ks(),Jp=()=>{if((typeof T.wasm.initTimeout!="number"||T.wasm.initTimeout<0)&&(T.wasm.initTimeout=0),T.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 T.wasm.proxy!="boolean"&&(T.wasm.proxy=!1),typeof T.wasm.trace!="boolean"&&(T.wasm.trace=!1),typeof T.wasm.numThreads!="number"||!Number.isInteger(T.wasm.numThreads)||T.wasm.numThreads<=0)if(typeof self<"u"&&!self.crossOriginIsolated)T.wasm.numThreads=1;else{let e=typeof navigator>"u"?W("node:os").cpus().length:navigator.hardwareConcurrency;T.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},Zp=class{async init(e){Jp(),await $h(),await Ah(e)}async createInferenceSessionHandler(e,t){let r=new Rh;return await r.loadModel(e,t),Promise.resolve(r)}},jh=new Zp});Qe(),Qe(),Qe();var f_="1.21.0-dev.20241205-d27fecd3d3",g_=Fe;{let e=(__(),M(Nh)).wasmBackend;q("webgpu",e,5),q("webnn",e,5),q("cpu",e,10),q("wasm",e,10)}Object.defineProperty(T.versions,"web",{value:f_,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":(Te,A,s)=>{var f;s.r(A),s.d(A,{Tensor:()=>W.Tensor,createInferenceSession:()=>ie,deviceToExecutionProviders:()=>q,isONNXProxy:()=>Y,isONNXTensor:()=>z});var O=s("./src/env.js"),N=s("?2ce3"),Q=s("./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs"),W=s("./node_modules/onnxruntime-common/dist/esm/index.js");const w=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 y,M;const b=Symbol.for("onnxruntime");if(b in globalThis)M=globalThis[b];else if(O.apis.IS_NODE_ENV){switch(M=N??(f||(f=s.t(N,2))),process.platform){case"win32":v.push("dml");break;case"linux":process.arch==="x64"&&v.push("cuda");break}v.push("cpu"),y=["cpu"]}else M=Q,O.apis.IS_WEBNN_AVAILABLE&&v.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),O.apis.IS_WEBGPU_AVAILABLE&&v.push("webgpu"),v.push("wasm"),y=["wasm"];const L=M.InferenceSession;function q(D=null){if(!D)return y;switch(D){case"auto":return v;case"gpu":return v.filter($=>["webgpu","cuda","dml","webnn-gpu"].includes($))}if(v.includes(D))return[w[D]??D];throw new Error(`Unsupported device: "${D}". 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No instance will be fused."),ee=new Set);const le=$.class_queries_logits??$.logits,fe=($.masks_queries_logits??$.pred_masks).sigmoid();let[ke,xe,Le]=le.dims;if(Le-=1,J!==null&&J.length!==ke)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let qe=[];for(let Ue=0;Ue<ke;++Ue){let ut=J!==null?J[Ue]:null,ue=le[Ue],re=fe[Ue],[he,Pe,Be]=q(ue,re,g,Le);if(Be.length===0){let[oe,Je]=ut??re.dims.slice(-2),De=new O.Tensor("int32",new Int32Array(oe*Je).fill(-1),[oe,Je]);qe.push({segmentation:De,segments_info:[]});continue}let[et,Xe]=ie(he,Pe,Be,C,T,ee,ut);qe.push({segmentation:et,segments_info:Xe})}return qe}function Y($,g=.5,C=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class D extends f.Callable{constructor(g){super(),this.image_mean=g.image_mean??g.mean,this.image_std=g.image_std??g.std,this.resample=g.resample??2,this.do_rescale=g.do_rescale??!0,this.rescale_factor=g.rescale_factor??.00392156862745098,this.do_normalize=g.do_normalize,this.do_thumbnail=g.do_thumbnail,this.size=g.size??g.image_size,this.do_resize=g.do_resize??this.size!==void 0,this.size_divisibility=g.size_divisibility??g.size_divisor,this.do_center_crop=g.do_center_crop,this.crop_size=g.crop_size,this.do_convert_rgb=g.do_convert_rgb??!0,this.do_crop_margin=g.do_crop_margin,this.pad_size=g.pad_size,this.do_pad=g.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=g.do_flip_channel_order??!1,this.config=g}async thumbnail(g,C,T=2){const ee=g.height,J=g.width,le=C.height,de=C.width;let fe=Math.min(ee,le),ke=Math.min(J,de);return fe===ee&&ke===J?g:(ee>J?ke=Math.floor(J*fe/ee):J>ee&&(fe=Math.floor(ee*ke/J)),await g.resize(ke,fe,{resample:T}))}async crop_margin(g,C=200){const T=g.clone().grayscale(),ee=(0,N.min)(T.data)[0],le=(0,N.max)(T.data)[0]-ee;if(le===0)return g;const de=C/255;let fe=T.width,ke=T.height,xe=0,Le=0;const qe=T.data;for(let Ue=0;Ue<T.height;++Ue){const ut=Ue*T.width;for(let ue=0;ue<T.width;++ue)(qe[ut+ue]-ee)/le<de&&(fe=Math.min(fe,ue),ke=Math.min(ke,Ue),xe=Math.max(xe,ue),Le=Math.max(Le,Ue))}return g=await g.crop([fe,ke,xe,Le]),g}pad_image(g,C,T,{mode:ee="constant",center:J=!1,constant_values:le=0}={}){const[de,fe,ke]=C;let xe,Le;if(typeof T=="number"?(xe=T,Le=T):T==="square"?xe=Le=Math.max(de,fe):(xe=T.width,Le=T.height),xe!==fe||Le!==de){const qe=new Float32Array(xe*Le*ke);if(Array.isArray(le))for(let ue=0;ue<qe.length;++ue)qe[ue]=le[ue%ke];else le!==0&&qe.fill(le);const[Ue,ut]=J?[Math.floor((xe-fe)/2),Math.floor((Le-de)/2)]:[0,0];for(let ue=0;ue<de;++ue){const re=(ue+ut)*xe,he=ue*fe;for(let Pe=0;Pe<fe;++Pe){const Be=(re+Pe+Ue)*ke,et=(he+Pe)*ke;for(let Xe=0;Xe<ke;++Xe)qe[Be+Xe]=g[et+Xe]}}if(ee==="symmetric"){if(J)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const ue=de-1,re=fe-1;for(let he=0;he<Le;++he){const Pe=he*xe,Be=(0,Q.calculateReflectOffset)(he,ue)*fe;for(let et=0;et<xe;++et){if(he<de&&et<fe)continue;const Xe=(Pe+et)*ke,oe=(Be+(0,Q.calculateReflectOffset)(et,re))*ke;for(let Je=0;Je<ke;++Je)qe[Xe+Je]=g[oe+Je]}}}g=qe,C=[Le,xe,ke]}return[g,C]}rescale(g){for(let C=0;C<g.length;++C)g[C]=this.rescale_factor*g[C]}get_resize_output_image_size(g,C){const[T,ee]=g.size;let J,le;if(this.do_thumbnail){const{height:de,width:fe}=C;J=Math.min(de,fe)}else Number.isInteger(C)?(J=C,le=this.config.max_size??J):C!==void 0&&(J=C.shortest_edge,le=C.longest_edge);if(J!==void 0||le!==void 0){const de=J===void 0?1:Math.max(J/T,J/ee),fe=T*de,ke=ee*de,xe=le===void 0?1:Math.min(le/fe,le/ke);let Le=Math.floor(Number((fe*xe).toFixed(2))),qe=Math.floor(Number((ke*xe).toFixed(2)));return this.size_divisibility!==void 0&&([Le,qe]=y([Le,qe],this.size_divisibility)),[Le,qe]}else if(C!==void 0&&C.width!==void 0&&C.height!==void 0){let de=C.width,fe=C.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let ke=fe/ee,xe=de/T;Math.abs(1-xe)<Math.abs(1-ke)?ke=xe:xe=ke,fe=v(ke*ee,this.config.ensure_multiple_of),de=v(xe*T,this.config.ensure_multiple_of)}return[de,fe]}else{if(this.size_divisibility!==void 0)return y([T,ee],this.size_divisibility);if(C.min_pixels!==void 0&&C.max_pixels!==void 0){const{min_pixels:de,max_pixels:fe}=C,ke=this.config.patch_size*this.config.merge_size;return z(ee,T,ke,de,fe)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(C)}`)}}async resize(g){const[C,T]=this.get_resize_output_image_size(g,this.size);return await g.resize(C,T,{resample:this.resample})}async preprocess(g,{do_normalize:C=null,do_pad:T=null,do_convert_rgb:ee=null,do_convert_grayscale:J=null,do_flip_channel_order:le=null}={}){this.do_crop_margin&&(g=await this.crop_margin(g));const[de,fe]=g.size;if(ee??this.do_convert_rgb?g=g.rgb():J&&(g=g.grayscale()),this.do_resize&&(g=await this.resize(g)),this.do_thumbnail&&(g=await this.thumbnail(g,this.size,this.resample)),this.do_center_crop){let Ue,ut;Number.isInteger(this.crop_size)?(Ue=this.crop_size,ut=this.crop_size):(Ue=this.crop_size.width,ut=this.crop_size.height),g=await g.center_crop(Ue,ut)}const ke=[g.height,g.width];let xe=Float32Array.from(g.data),Le=[g.height,g.width,g.channels];if(this.do_rescale&&this.rescale(xe),C??this.do_normalize){let Ue=this.image_mean;Array.isArray(this.image_mean)||(Ue=new Array(g.channels).fill(Ue));let ut=this.image_std;if(Array.isArray(this.image_std)||(ut=new Array(g.channels).fill(Ue)),Ue.length!==g.channels||ut.length!==g.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Ue.length}) and \`image_std\` (${ut.length}) must match the number of channels in the image (${g.channels}).`);for(let ue=0;ue<xe.length;ue+=g.channels)for(let re=0;re<g.channels;++re)xe[ue+re]=(xe[ue+re]-Ue[re])/ut[re]}if(T??this.do_pad){if(this.pad_size)[xe,Le]=this.pad_image(xe,[g.height,g.width,g.channels],this.pad_size);else if(this.size_divisibility){const[Ue,ut]=y([Le[1],Le[0]],this.size_divisibility);[xe,Le]=this.pad_image(xe,Le,{width:Ue,height:ut})}}if(le??this.do_flip_channel_order){if(Le[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Ue=0;Ue<xe.length;Ue+=3){const ut=xe[Ue];xe[Ue]=xe[Ue+2],xe[Ue+2]=ut}}const qe=new O.Tensor("float32",xe,Le).permute(2,0,1);return{original_size:[fe,de],reshaped_input_size:ke,pixel_values:qe}}async _call(g,...C){Array.isArray(g)||(g=[g]);const T=await Promise.all(g.map(J=>this.preprocess(J)));return{pixel_values:(0,O.stack)(T.map(J=>J.pixel_values),0),original_sizes:T.map(J=>J.original_size),reshaped_input_sizes:T.map(J=>J.reshaped_input_size)}}static async from_pretrained(g,C){const T=await(0,W.getModelJSON)(g,w.IMAGE_PROCESSOR_NAME,!0,C);return new this(T)}}},"./src/base/processing_utils.js":(Te,A,s)=>{s.r(A),s.d(A,{Processor:()=>Q});var f=s("./src/utils/constants.js"),O=s("./src/utils/generic.js"),N=s("./src/utils/hub.js");class Q extends O.Callable{constructor(w,v){super(),this.config=w,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(w,v={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(w,{tokenize:!1,...v})}batch_decode(...w){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...w)}async _call(w,...v){for(const y of[this.image_processor,this.feature_extractor,this.tokenizer])if(y)return y(w,...v);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(w,v){const[y,M]=await Promise.all([this.uses_processor_config?(0,N.getModelJSON)(w,f.PROCESSOR_NAME,!0,v):{},Promise.all(this.classes.filter(b=>b in this).map(async b=>{const L=await this[b].from_pretrained(w,v);return[b.replace(/_class$/,""),L]})).then(Object.fromEntries)]);return new this(y,M)}}ge(Q,"classes",["image_processor_class","tokenizer_class","feature_extractor_class"]),ge(Q,"uses_processor_config",!1)},"./src/configs.js":(Te,A,s)=>{s.r(A),s.d(A,{AutoConfig:()=>v,PretrainedConfig:()=>w,getKeyValueShapes:()=>W});var f=s("./src/utils/core.js"),O=s("./src/utils/hub.js");async function N(y,M){return await(0,O.getModelJSON)(y,"config.json",!0,M)}function Q(y){const M={};let b={};switch(y.model_type){case"llava":case"paligemma":case"florence2":case"llava_onevision":case"idefics3":b=Q(y.text_config);break;case"moondream1":b=Q(y.phi_config);break;case"musicgen":b=Q(y.decoder);break;case"multi_modality":b=Q(y.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":M.num_heads="n_head",M.num_layers="n_layer",M.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":M.num_heads="num_attention_heads",M.num_layers="num_hidden_layers",M.hidden_size="hidden_size";break;case"llama":case"olmo":case"olmo2":case"mobilellm":case"granite":case"cohere":case"mistral":case"starcoder2":case"qwen2":case"qwen2_vl":case"phi":case"phi3":case"phi3_v":M.num_heads="num_key_value_heads",M.num_layers="num_hidden_layers",M.hidden_size="hidden_size",M.num_attention_heads="num_attention_heads";break;case"gemma":case"gemma2":M.num_heads="num_key_value_heads",M.num_layers="num_hidden_layers",M.dim_kv="head_dim";break;case"openelm":M.num_heads="num_kv_heads",M.num_layers="num_transformer_layers",M.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":M.num_heads="num_heads",M.num_layers="num_layers",M.hidden_size="hidden_size";break;case"bloom":M.num_heads="n_head",M.num_layers="n_layer",M.hidden_size="hidden_size";break;case"mpt":M.num_heads="n_heads",M.num_layers="n_layers",M.hidden_size="d_model";break;case"exaone":M.num_heads="num_key_value_heads",M.num_layers="num_layers",M.dim_kv="head_dim",M.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":M.num_decoder_layers="num_decoder_layers",M.num_decoder_heads="num_heads",M.decoder_dim_kv="d_kv",M.num_encoder_layers="num_layers",M.num_encoder_heads="num_heads",M.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":M.num_decoder_layers="decoder_layers",M.num_decoder_heads="decoder_attention_heads",M.decoder_hidden_size="d_model",M.num_encoder_layers="encoder_layers",M.num_encoder_heads="encoder_attention_heads",M.encoder_hidden_size="d_model";break;case"speecht5":M.num_decoder_layers="decoder_layers",M.num_decoder_heads="decoder_attention_heads",M.decoder_hidden_size="hidden_size",M.num_encoder_layers="encoder_layers",M.num_encoder_heads="encoder_attention_heads",M.encoder_hidden_size="hidden_size";break;case"trocr":M.num_encoder_layers=M.num_decoder_layers="decoder_layers",M.num_encoder_heads=M.num_decoder_heads="decoder_attention_heads",M.encoder_hidden_size=M.decoder_hidden_size="d_model";break;case"musicgen_decoder":case"moonshine":M.num_encoder_layers=M.num_decoder_layers="num_hidden_layers",M.num_encoder_heads=M.num_decoder_heads="num_attention_heads",M.encoder_hidden_size=M.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const q=Q(y.decoder),se="num_decoder_layers"in q,ie=(0,f.pick)(y,["model_type","is_encoder_decoder"]);return se?(ie.num_decoder_layers=q.num_decoder_layers,ie.num_decoder_heads=q.num_decoder_heads,ie.decoder_hidden_size=q.decoder_hidden_size,ie.num_encoder_layers=q.num_encoder_layers,ie.num_encoder_heads=q.num_encoder_heads,ie.encoder_hidden_size=q.encoder_hidden_size):(ie.num_layers=q.num_layers,ie.num_heads=q.num_heads,ie.hidden_size=q.hidden_size),ie}const L={...b,...(0,f.pick)(y,["model_type","multi_query","is_encoder_decoder"])};for(const q in M)L[q]=y[M[q]];return L}function W(y,{prefix:M="past_key_values",batch_size:b=1}={}){const L={},q=y.normalized_config;if(q.is_encoder_decoder&&"num_encoder_heads"in q&&"num_decoder_heads"in q){const se=q.encoder_dim_kv??q.encoder_hidden_size/q.num_encoder_heads,ie=q.decoder_dim_kv??q.decoder_hidden_size/q.num_decoder_heads,z=[b,q.num_encoder_heads,0,se],V=[b,q.num_decoder_heads,0,ie];for(let Y=0;Y<q.num_decoder_layers;++Y)L[`${M}.${Y}.encoder.key`]=z,L[`${M}.${Y}.encoder.value`]=z,L[`${M}.${Y}.decoder.key`]=V,L[`${M}.${Y}.decoder.value`]=V}else{const se=q.num_heads,ie=q.num_layers,z=q.dim_kv??q.hidden_size/(q.num_attention_heads??se);if(q.model_type==="falcon"){const V=[b*se,0,z];for(let Y=0;Y<ie;++Y)L[`${M}.${Y}.key`]=V,L[`${M}.${Y}.value`]=V}else if(q.multi_query){const V=[b*se,0,2*z];for(let Y=0;Y<ie;++Y)L[`${M}.${Y}.key_value`]=V}else if(q.model_type==="bloom"){const V=[b*se,z,0],Y=[b*se,0,z];for(let D=0;D<ie;++D)L[`${M}.${D}.key`]=V,L[`${M}.${D}.value`]=Y}else if(q.model_type==="openelm")for(let V=0;V<ie;++V){const Y=[b,se[V],0,z];L[`${M}.${V}.key`]=Y,L[`${M}.${V}.value`]=Y}else{const V=[b,se,0,z];for(let Y=0;Y<ie;++Y)L[`${M}.${Y}.key`]=V,L[`${M}.${Y}.value`]=V}}return L}class w{constructor(M){ge(this,"model_type",null);ge(this,"is_encoder_decoder",!1);ge(this,"max_position_embeddings");ge(this,"transformers.js_config");Object.assign(this,M),this.normalized_config=Q(this)}static async from_pretrained(M,{progress_callback:b=null,config:L=null,cache_dir:q=null,local_files_only:se=!1,revision:ie="main"}={}){L&&!(L instanceof w)&&(L=new w(L));const z=L??await N(M,{progress_callback:b,config:L,cache_dir:q,local_files_only:se,revision:ie});return new this(z)}}class v{static async from_pretrained(...M){return w.from_pretrained(...M)}}},"./src/env.js":(Te,A,s)=>{var T,ee;s.r(A),s.d(A,{apis:()=>ie,env:()=>g});var f=s("?569f"),O=s("?3f59"),N=s("?154a");const Q="3.2.3",W=typeof window<"u"&&typeof window.document<"u",w=typeof self<"u"&&((T=self.constructor)==null?void 0:T.name)==="DedicatedWorkerGlobalScope",v=typeof self<"u"&&"caches"in self,y=typeof navigator<"u"&&"gpu"in navigator,M=typeof navigator<"u"&&"ml"in navigator,b=typeof process<"u",L=b&&((ee=process==null?void 0:process.release)==null?void 0:ee.name)==="node",q=!C(f),se=!C(O),ie=Object.freeze({IS_BROWSER_ENV:W,IS_WEBWORKER_ENV:w,IS_WEB_CACHE_AVAILABLE:v,IS_WEBGPU_AVAILABLE:y,IS_WEBNN_AVAILABLE:M,IS_PROCESS_AVAILABLE:b,IS_NODE_ENV:L,IS_FS_AVAILABLE:q,IS_PATH_AVAILABLE:se}),z=q&&se;let V="./";if(z){const J=Object(import.meta).url;J?V=O.dirname(O.dirname(N.fileURLToPath(J))):typeof __dirname<"u"&&(V=O.dirname(__dirname))}const Y=z?O.join(V,"/.cache/"):null,D="/models/",$=z?O.join(V,D):D,g={version:Q,backends:{onnx:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!(W||w),localModelPath:$,useFS:q,useBrowserCache:v,useFSCache:q,cacheDir:Y,useCustomCache:!1,customCache:null};function C(J){return Object.keys(J).length===0}},"./src/generation/configuration_utils.js":(Te,A,s)=>{s.r(A),s.d(A,{GenerationConfig:()=>O});var f=s("./src/utils/core.js");class O{constructor(Q){ge(this,"max_length",20);ge(this,"max_new_tokens",null);ge(this,"min_length",0);ge(this,"min_new_tokens",null);ge(this,"early_stopping",!1);ge(this,"max_time",null);ge(this,"do_sample",!1);ge(this,"num_beams",1);ge(this,"num_beam_groups",1);ge(this,"penalty_alpha",null);ge(this,"use_cache",!0);ge(this,"temperature",1);ge(this,"top_k",50);ge(this,"top_p",1);ge(this,"typical_p",1);ge(this,"epsilon_cutoff",0);ge(this,"eta_cutoff",0);ge(this,"diversity_penalty",0);ge(this,"repetition_penalty",1);ge(this,"encoder_repetition_penalty",1);ge(this,"length_penalty",1);ge(this,"no_repeat_ngram_size",0);ge(this,"bad_words_ids",null);ge(this,"force_words_ids",null);ge(this,"renormalize_logits",!1);ge(this,"constraints",null);ge(this,"forced_bos_token_id",null);ge(this,"forced_eos_token_id",null);ge(this,"remove_invalid_values",!1);ge(this,"exponential_decay_length_penalty",null);ge(this,"suppress_tokens",null);ge(this,"streamer",null);ge(this,"begin_suppress_tokens",null);ge(this,"forced_decoder_ids",null);ge(this,"guidance_scale",null);ge(this,"num_return_sequences",1);ge(this,"output_attentions",!1);ge(this,"output_hidden_states",!1);ge(this,"output_scores",!1);ge(this,"return_dict_in_generate",!1);ge(this,"pad_token_id",null);ge(this,"bos_token_id",null);ge(this,"eos_token_id",null);ge(this,"encoder_no_repeat_ngram_size",0);ge(this,"decoder_start_token_id",null);ge(this,"generation_kwargs",{});Object.assign(this,(0,f.pick)(Q,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(Te,A,s)=>{s.r(A),s.d(A,{ClassifierFreeGuidanceLogitsProcessor:()=>z,ForcedBOSTokenLogitsProcessor:()=>w,ForcedEOSTokenLogitsProcessor:()=>v,LogitsProcessor:()=>N,LogitsProcessorList:()=>W,LogitsWarper:()=>Q,MinLengthLogitsProcessor:()=>q,MinNewTokensLengthLogitsProcessor:()=>se,NoBadWordsLogitsProcessor:()=>ie,NoRepeatNGramLogitsProcessor:()=>b,RepetitionPenaltyLogitsProcessor:()=>L,SuppressTokensAtBeginLogitsProcessor:()=>y,TemperatureLogitsWarper:()=>V,TopKLogitsWarper:()=>D,TopPLogitsWarper:()=>Y,WhisperTimeStampLogitsProcessor:()=>M});var f=s("./src/utils/generic.js");s("./src/utils/tensor.js");var O=s("./src/utils/maths.js");class N extends f.Callable{_call(g,C){throw Error("`_call` should be implemented in a subclass")}}class Q extends f.Callable{_call(g,C){throw Error("`_call` should be implemented in a subclass")}}class W extends f.Callable{constructor(){super(),this.processors=[]}push(g){this.processors.push(g)}extend(g){this.processors.push(...g)}_call(g,C){let T=C;for(const ee of this.processors)T=ee(g,T);return T}[Symbol.iterator](){return this.processors.values()}}class w extends N{constructor(g){super(),this.bos_token_id=g}_call(g,C){for(let T=0;T<g.length;++T)if(g[T].length===1){const ee=C[T].data;ee.fill(-1/0),ee[this.bos_token_id]=0}return C}}class v extends N{constructor(g,C){super(),this.max_length=g,this.eos_token_id=Array.isArray(C)?C:[C]}_call(g,C){for(let T=0;T<g.length;++T)if(g[T].length===this.max_length-1){const ee=C[T].data;ee.fill(-1/0);for(const J of this.eos_token_id)ee[J]=0}return C}}class y extends N{constructor(g,C){super(),this.begin_suppress_tokens=g,this.begin_index=C}_call(g,C){for(let T=0;T<g.length;++T)if(g[T].length===this.begin_index){const ee=C[T].data;for(const J of this.begin_suppress_tokens)ee[J]=-1/0}return C}}class M extends N{constructor(g,C){super(),this.eos_token_id=Array.isArray(g.eos_token_id)?g.eos_token_id[0]:g.eos_token_id,this.no_timestamps_token_id=g.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=C.length,C.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=g.max_initial_timestamp_index}_call(g,C){for(let T=0;T<g.length;++T){const ee=C[T].data;if(ee[this.no_timestamps_token_id]=-1/0,g[T].length===this.begin_index-1){ee.fill(-1/0),ee[this.timestamp_begin]=0;continue}const J=g[T].slice(this.begin_index),le=J.length>=1&&J[J.length-1]>=this.timestamp_begin,de=J.length<2||J[J.length-2]>=this.timestamp_begin;if(le&&(de?ee.subarray(this.timestamp_begin).fill(-1/0):ee.subarray(0,this.eos_token_id).fill(-1/0)),g[T].length===this.begin_index&&this.max_initial_timestamp_index!==null){const Le=this.timestamp_begin+this.max_initial_timestamp_index;ee.subarray(Le+1).fill(-1/0)}const fe=(0,O.log_softmax)(ee),ke=Math.log(fe.subarray(this.timestamp_begin).map(Math.exp).reduce((Le,qe)=>Le+qe)),xe=(0,O.max)(fe.subarray(0,this.timestamp_begin))[0];ke>xe&&ee.subarray(0,this.timestamp_begin).fill(-1/0)}return C}}class b extends N{constructor(g){super(),this.no_repeat_ngram_size=g}getNgrams(g){const C=g.length,T=[];for(let J=0;J<C+1-this.no_repeat_ngram_size;++J){const le=[];for(let de=0;de<this.no_repeat_ngram_size;++de)le.push(g[J+de]);T.push(le.map(Number))}const ee=new Map;for(const J of T){const le=J.slice(0,J.length-1),de=JSON.stringify(le),fe=ee.get(de)??[];fe.push(J[J.length-1]),ee.set(de,fe)}return ee}getGeneratedNgrams(g,C){const T=C.slice(C.length+1-this.no_repeat_ngram_size,C.length);return g.get(JSON.stringify(T.map(Number)))??[]}calcBannedNgramTokens(g){const C=[];if(g.length+1<this.no_repeat_ngram_size)return C;{const T=this.getNgrams(g);return this.getGeneratedNgrams(T,g)}}_call(g,C){for(let T=0;T<g.length;++T){const ee=C[T].data,J=this.calcBannedNgramTokens(g[T]);for(const le of J)ee[le]=-1/0}return C}}class L extends N{constructor(g){super(),this.penalty=g}_call(g,C){for(let T=0;T<g.length;++T){const ee=C[T].data;for(const J of new Set(g[T])){const le=Number(J);ee[le]<0?ee[le]*=this.penalty:ee[le]/=this.penalty}}return C}}class q extends N{constructor(g,C){super(),this.min_length=g,this.eos_token_id=Array.isArray(C)?C:[C]}_call(g,C){for(let T=0;T<g.length;++T)if(g[T].length<this.min_length){const ee=C[T].data;for(const J of this.eos_token_id)ee[J]=-1/0}return C}}class se extends N{constructor(g,C,T){super(),this.prompt_length_to_skip=g,this.min_new_tokens=C,this.eos_token_id=Array.isArray(T)?T:[T]}_call(g,C){for(let T=0;T<g.length;++T)if(g[T].length-this.prompt_length_to_skip<this.min_new_tokens){const J=C[T].data;for(const le of this.eos_token_id)J[le]=-1/0}return C}}class ie extends N{constructor(g,C){super(),this.bad_words_ids=g,this.eos_token_id=Array.isArray(C)?C:[C]}_call(g,C){for(let T=0;T<g.length;++T){const ee=C[T].data,J=g[T];for(const le of this.bad_words_ids){let de=!0;for(let fe=1;fe<=le.length-1&&le.length<J.length;++fe)if(le.at(-fe-1)!=J.at(-fe)){de=!1;break}de&&(ee[le.at(-1)]=-1/0)}}return C}}class z extends N{constructor(g){if(super(),g<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${g}.`);this.guidance_scale=g}_call(g,C){if(C.dims[0]!==2*g.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${C.dims[0]} for the logits and ${g.length} for the input ids.`);const T=g.length,ee=C.slice([0,T],null),J=C.slice([T,C.dims[0]],null);for(let le=0;le<J.data.length;++le)J.data[le]+=(ee.data[le]-J.data[le])*this.guidance_scale;return J}}class V extends Q{constructor(g){super(),this.temperature=g}_call(g,C){const T=C.data;for(let ee=0;ee<T.length;++ee)T[ee]/=this.temperature;return C}}class Y extends Q{constructor(g,{filter_value:C=-1/0,min_tokens_to_keep:T=1}={}){if(super(),g<0||g>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${g}`);if(!Number.isInteger(T)||T<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${T}`);this.top_p=g,this.filter_value=C,this.min_tokens_to_keep=T}}class D extends Q{constructor(g,{filter_value:C=-1/0,min_tokens_to_keep:T=1}={}){if(super(),!Number.isInteger(g)||g<0)throw new Error(`\`top_k\` must be a positive integer, but is ${g}`);this.top_k=Math.max(g,T),this.filter_value=C}}},"./src/generation/logits_sampler.js":(Te,A,s)=>{s.r(A),s.d(A,{LogitsSampler:()=>Q});var f=s("./src/utils/generic.js"),O=s("./src/utils/tensor.js"),N=s("./src/utils/maths.js");s("./src/generation/configuration_utils.js");class Q extends f.Callable{constructor(M){super(),this.generation_config=M}async _call(M){return this.sample(M)}async sample(M){throw Error("sample should be implemented in subclasses.")}getLogits(M,b){let L=M.dims.at(-1),q=M.data;if(b===-1)q=q.slice(-L);else{let se=b*L;q=q.slice(se,se+L)}return q}randomSelect(M){let b=0;for(let q=0;q<M.length;++q)b+=M[q];let L=Math.random()*b;for(let q=0;q<M.length;++q)if(L-=M[q],L<=0)return q;return 0}static getSampler(M){if(M.do_sample)return new w(M);if(M.num_beams>1)return new v(M);if(M.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${M.num_return_sequences}.`);return new W(M)}}class W extends Q{async sample(M){const b=(0,N.max)(M.data)[1];return[[BigInt(b),0]]}}class w extends Q{async sample(M){let b=M.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[L,q]=await(0,O.topk)(M,b),se=(0,N.softmax)(L.data);return Array.from({length:this.generation_config.num_beams},()=>{const ie=this.randomSelect(se);return[q.data[ie],Math.log(se[ie])]})}}class v extends Q{async sample(M){let b=M.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[L,q]=await(0,O.topk)(M,b),se=(0,N.softmax)(L.data);return Array.from({length:this.generation_config.num_beams},(ie,z)=>[q.data[z],Math.log(se[z])])}}},"./src/generation/stopping_criteria.js":(Te,A,s)=>{s.r(A),s.d(A,{EosTokenCriteria:()=>W,InterruptableStoppingCriteria:()=>w,MaxLengthCriteria:()=>Q,StoppingCriteria:()=>O,StoppingCriteriaList:()=>N});var f=s("./src/utils/generic.js");class O extends f.Callable{_call(y,M){throw Error("StoppingCriteria needs to be subclassed")}}class N extends f.Callable{constructor(){super(),this.criteria=[]}push(y){this.criteria.push(y)}extend(y){y instanceof N?y=y.criteria:y instanceof O&&(y=[y]),this.criteria.push(...y)}_call(y,M){const b=new Array(y.length).fill(!1);for(const L of this.criteria){const q=L(y,M);for(let se=0;se<b.length;++se)b[se]||(b[se]=q[se])}return b}[Symbol.iterator](){return this.criteria.values()}}class Q extends O{constructor(y,M=null){super(),this.max_length=y,this.max_position_embeddings=M}_call(y){return y.map(M=>M.length>=this.max_length)}}class W extends O{constructor(y){super(),Array.isArray(y)||(y=[y]),this.eos_token_id=y}_call(y,M){return y.map(b=>{const L=b.at(-1);return this.eos_token_id.some(q=>L==q)})}}class w extends O{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(y,M){return new Array(y.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(Te,A,s)=>{s.r(A),s.d(A,{BaseStreamer:()=>Q,TextStreamer:()=>w,WhisperTextStreamer:()=>v});var f=s("./src/utils/core.js"),O=s("./src/tokenizers.js"),N=s("./src/env.js");class Q{put(M){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const W=N.apis.IS_PROCESS_AVAILABLE?y=>process.stdout.write(y):y=>console.log(y);class w extends Q{constructor(M,{skip_prompt:b=!1,callback_function:L=null,token_callback_function:q=null,decode_kwargs:se={},...ie}={}){super(),this.tokenizer=M,this.skip_prompt=b,this.callback_function=L??W,this.token_callback_function=q,this.decode_kwargs={...se,...ie},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(M){var se;if(M.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=M[0];(se=this.token_callback_function)==null||se.call(this,b),this.token_cache=(0,f.mergeArrays)(this.token_cache,b);const L=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let q;L.endsWith(`
`)?(q=L.slice(this.print_len),this.token_cache=[],this.print_len=0):L.length>0&&(0,O.is_chinese_char)(L.charCodeAt(L.length-1))?(q=L.slice(this.print_len),this.print_len+=q.length):(q=L.slice(this.print_len,L.lastIndexOf(" ")+1),this.print_len+=q.length),this.on_finalized_text(q,!1)}end(){let M;this.token_cache.length>0?(M=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):M="",this.next_tokens_are_prompt=!0,this.on_finalized_text(M,!0)}on_finalized_text(M,b){var L,q;M.length>0&&((L=this.callback_function)==null||L.call(this,M)),b&&this.callback_function===W&&N.apis.IS_PROCESS_AVAILABLE&&((q=this.callback_function)==null||q.call(this,`
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Ue(_,{inputs_embeds:Se,past_key_values:Ye,attention_mask:j,position_ids:Oe,generation_config:tt,logits_processor:mt},!0)}function re(_,x=0){const[j,be]=_.dims,Oe=_.data,Se=new BigInt64Array(Oe.length);for(let Ye=0;Ye<j;++Ye){const tt=Ye*be;let mt=BigInt(x);for(let Tt=0;Tt<be;++Tt){const Lt=tt+Tt;Oe[Lt]===0n?Se[Lt]=BigInt(1):(Se[Lt]=mt,mt+=Oe[Lt])}}return{data:Se,dims:_.dims}}function he(_,x=null,j=0){const{input_ids:be,inputs_embeds:Oe,attention_mask:Se}=_,{data:Ye,dims:tt}=re(Se,j);let mt=new b.Tensor("int64",Ye,tt);if(x){const Tt=-(be??Oe).dims.at(1);mt=mt.slice(null,[Tt,null])}return mt}function Pe(_,x,j,be){if(j.past_key_values){const Oe=Object.values(j.past_key_values)[0].dims.at(-2),{input_ids:Se,attention_mask:Ye}=j;if(!(Ye&&Ye.dims[1]>Se.dims[1])){if(Oe<Se.dims[1])j.input_ids=Se.slice(null,[Oe,null]);else if(_.config.image_token_index!=null&&Se.data.some(tt=>tt==_.config.image_token_index)){const tt=_.config.num_image_tokens;if(!tt)throw new Error("`num_image_tokens` is missing in the model configuration.");const mt=Se.dims[1]-(Oe-tt);j.input_ids=Se.slice(null,[-mt,null]),j.attention_mask=(0,b.ones)([1,Oe+mt])}}}return j}function Be(_,x,j,be){return j.past_key_values&&(x=x.map(Oe=>[Oe.at(-1)])),{...j,decoder_input_ids:ke(x)}}function et(_,...x){return _.config.is_encoder_decoder?Be(_,...x):Pe(_,...x)}function Xe(_,x,j,be){const Oe=!!j.past_key_values;return be.guidance_scale!==null&&be.guidance_scale>1&&(Oe?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(be.pad_token_id))],0),j.attention_mask=(0,b.cat)([j.attention_mask,(0,b.full_like)(j.attention_mask,0n)],0))),(Oe||!j.pixel_values)&&(j.pixel_values=(0,b.full)([0,0,3,384,384],1)),Oe&&(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 oe extends Q.Callable{constructor(j,be,Oe){super();ge(this,"main_input_name","input_ids");ge(this,"forward_params",["input_ids","attention_mask"]);this.config=j,this.sessions=be,this.configs=Oe;const Se=C.get(this.constructor),Ye=$.get(Se);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ye){case D.DecoderOnly:this.can_generate=!0,this._forward=Ue,this._prepare_inputs_for_generation=Pe;break;case D.Seq2Seq:case D.Vision2Seq:case D.Musicgen:this.can_generate=!0,this._forward=Le,this._prepare_inputs_for_generation=Be;break;case D.EncoderDecoder:this._forward=Le;break;case D.ImageTextToText:this.can_generate=!0,this._forward=ue,this._prepare_inputs_for_generation=et;break;case D.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=et;break;case D.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=Xe;break;default:this._forward=qe;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var be;const j=[];for(const Oe of Object.values(this.sessions))(be=Oe==null?void 0:Oe.handler)!=null&&be.dispose&&j.push(Oe.handler.dispose());return await Promise.all(j)}static async from_pretrained(j,{progress_callback:be=null,config:Oe=null,cache_dir:Se=null,local_files_only:Ye=!1,revision:tt="main",model_file_name:mt=null,subfolder:Tt="onnx",device:Lt=null,dtype:Ut=null,use_external_data_format:Dt=null,session_options:Vt={}}={}){let Zt={progress_callback:be,config:Oe,cache_dir:Se,local_files_only:Ye,revision:tt,model_file_name:mt,subfolder:Tt,device:Lt,dtype:Ut,use_external_data_format:Dt,session_options:Vt};const sr=C.get(this),qt=$.get(sr);Oe=Zt.config=await f.AutoConfig.from_pretrained(j,Zt);let or;if(qt===D.DecoderOnly)or=await Promise.all([ee(j,{model:Zt.model_file_name??"model"},Zt),J(j,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.Seq2Seq||qt===D.Vision2Seq)or=await Promise.all([ee(j,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Zt),J(j,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.MaskGeneration)or=await Promise.all([ee(j,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},Zt)]);else if(qt===D.EncoderDecoder)or=await Promise.all([ee(j,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Zt)]);else if(qt===D.ImageTextToText){const Cr={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Oe.is_encoder_decoder&&(Cr.model="encoder_model"),or=await Promise.all([ee(j,Cr,Zt),J(j,{generation_config:"generation_config.json"},Zt)])}else if(qt===D.Musicgen)or=await Promise.all([ee(j,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},Zt),J(j,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.MultiModality)or=await Promise.all([ee(j,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},Zt),J(j,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.Phi3V)or=await Promise.all([ee(j,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},Zt),J(j,{generation_config:"generation_config.json"},Zt)]);else{if(qt!==D.EncoderOnly){const Cr=sr??(Oe==null?void 0:Oe.model_type);Cr!=="custom"&&console.warn(`Model type for '${Cr}' not found, assuming encoder-only architecture. Please report this at ${v.GITHUB_ISSUE_URL}.`)}or=await Promise.all([ee(j,{model:Zt.model_file_name??"model"},Zt)])}return new this(Oe,...or)}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 be=new y.LogitsProcessorList;return j.temperature!==null&&j.temperature!==1&&be.push(new y.TemperatureLogitsWarper(j.temperature)),j.top_k!==null&&j.top_k!==0&&be.push(new y.TopKLogitsWarper(j.top_k)),j.top_p!==null&&j.top_p<1&&be.push(new y.TopPLogitsWarper(j.top_p)),be}_get_logits_processor(j,be,Oe=null){const Se=new y.LogitsProcessorList;if(j.repetition_penalty!==null&&j.repetition_penalty!==1&&Se.push(new y.RepetitionPenaltyLogitsProcessor(j.repetition_penalty)),j.no_repeat_ngram_size!==null&&j.no_repeat_ngram_size>0&&Se.push(new y.NoRepeatNGramLogitsProcessor(j.no_repeat_ngram_size)),j.bad_words_ids!==null&&Se.push(new y.NoBadWordsLogitsProcessor(j.bad_words_ids,j.eos_token_id)),j.min_length!==null&&j.eos_token_id!==null&&j.min_length>0&&Se.push(new y.MinLengthLogitsProcessor(j.min_length,j.eos_token_id)),j.min_new_tokens!==null&&j.eos_token_id!==null&&j.min_new_tokens>0&&Se.push(new y.MinNewTokensLengthLogitsProcessor(be,j.min_new_tokens,j.eos_token_id)),j.forced_bos_token_id!==null&&Se.push(new y.ForcedBOSTokenLogitsProcessor(j.forced_bos_token_id)),j.forced_eos_token_id!==null&&Se.push(new y.ForcedEOSTokenLogitsProcessor(j.max_length,j.forced_eos_token_id)),j.begin_suppress_tokens!==null){const Ye=be>1||j.forced_bos_token_id===null?be:be+1;Se.push(new y.SuppressTokensAtBeginLogitsProcessor(j.begin_suppress_tokens,Ye))}return j.guidance_scale!==null&&j.guidance_scale>1&&Se.push(new y.ClassifierFreeGuidanceLogitsProcessor(j.guidance_scale)),Oe!==null&&Se.extend(Oe),Se}_prepare_generation_config(j,be,Oe=M.GenerationConfig){const Se={...this.config};for(const tt of["decoder","generator","text_config"])tt in Se&&Object.assign(Se,Se[tt]);const Ye=new Oe(Se);return Object.assign(Ye,this.generation_config??{}),j&&Object.assign(Ye,j),be&&Object.assign(Ye,(0,W.pick)(be,Object.getOwnPropertyNames(Ye))),Ye}_get_stopping_criteria(j,be=null){const Oe=new se.StoppingCriteriaList;return j.max_length!==null&&Oe.push(new se.MaxLengthCriteria(j.max_length,this.config.max_position_embeddings??null)),j.eos_token_id!==null&&Oe.push(new se.EosTokenCriteria(j.eos_token_id)),be&&Oe.extend(be),Oe}_validate_model_class(){if(!this.can_generate){const j=[En,Io,Bd,ra],be=C.get(this.constructor),Oe=new Set,Se=this.config.model_type;for(const tt of j){const mt=tt.get(Se);mt&&Oe.add(mt[0])}let Ye=`The current model class (${be}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Oe.size>0&&(Ye+=` Please use the following class instead: ${[...Oe].join(", ")}`),Error(Ye)}}prepare_inputs_for_generation(...j){return this._prepare_inputs_for_generation(this,...j)}_update_model_kwargs_for_generation({generated_input_ids:j,outputs:be,model_inputs:Oe,is_encoder_decoder:Se}){return Oe.past_key_values=this.getPastKeyValues(be,Oe.past_key_values),Oe.input_ids=new b.Tensor("int64",j.flat(),[j.length,1]),Se||(Oe.attention_mask=(0,b.cat)([Oe.attention_mask,(0,b.ones)([Oe.attention_mask.dims[0],1])],1)),Oe.position_ids=null,Oe}_prepare_model_inputs({inputs:j,bos_token_id:be,model_kwargs:Oe}){const Se=(0,W.pick)(Oe,this.forward_params),Ye=this.main_input_name;if(Ye in Se){if(j)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Se[Ye]=j;return{inputs_tensor:Se[Ye],model_inputs:Se,model_input_name:Ye}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:j,model_inputs:be,model_input_name:Oe,generation_config:Se}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!be.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:tt,pixel_values:mt,attention_mask:Tt,...Lt}=be,Ut=await this._prepare_inputs_embeds(be);be={...Lt,...(0,W.pick)(Ut,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ye}=await qe(this,be);if(Se.guidance_scale!==null&&Se.guidance_scale>1)Ye=(0,b.cat)([Ye,(0,b.full_like)(Ye,0)],0),"attention_mask"in be&&(be.attention_mask=(0,b.cat)([be.attention_mask,(0,b.zeros_like)(be.attention_mask)],0));else if(be.decoder_input_ids){const tt=ke(be.decoder_input_ids).dims[0];if(tt!==Ye.dims[0]){if(Ye.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ye.dims[0]}) than the decoder inputs (${tt}).`);Ye=(0,b.cat)(Array.from({length:tt},()=>Ye),0)}}return be.encoder_outputs=Ye,be}_prepare_decoder_input_ids_for_generation({batch_size:j,model_input_name:be,model_kwargs:Oe,decoder_start_token_id:Se,bos_token_id:Ye,generation_config:tt}){let{decoder_input_ids:mt,...Tt}=Oe;if(!(mt instanceof b.Tensor)){if(mt)Array.isArray(mt[0])||(mt=Array.from({length:j},()=>mt));else if(Se??(Se=Ye),this.config.model_type==="musicgen")mt=Array.from({length:j*this.config.decoder.num_codebooks},()=>[Se]);else if(Array.isArray(Se)){if(Se.length!==j)throw new Error(`\`decoder_start_token_id\` expcted to have length ${j} but got ${Se.length}`);mt=Se}else mt=Array.from({length:j},()=>[Se]);mt=ke(mt)}return Oe.decoder_attention_mask=(0,b.ones_like)(mt),{input_ids:mt,model_inputs:Tt}}async generate({inputs:j=null,generation_config:be=null,logits_processor:Oe=null,stopping_criteria:Se=null,streamer:Ye=null,...tt}){this._validate_model_class(),be=this._prepare_generation_config(be,tt);let{inputs_tensor:mt,model_inputs:Tt,model_input_name:Lt}=this._prepare_model_inputs({inputs:j,model_kwargs:tt});const Ut=this.config.is_encoder_decoder;Ut&&("encoder_outputs"in Tt||(Tt=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:mt,model_inputs:Tt,model_input_name:Lt,generation_config:be})));let Dt;Ut?{input_ids:Dt,model_inputs:Tt}=this._prepare_decoder_input_ids_for_generation({batch_size:Tt[Lt].dims.at(0),model_input_name:Lt,model_kwargs:Tt,decoder_start_token_id:be.decoder_start_token_id,bos_token_id:be.bos_token_id,generation_config:be}):Dt=Tt[Lt];let Vt=Dt.dims.at(-1);be.max_new_tokens!==null&&(be.max_length=Vt+be.max_new_tokens);const Zt=this._get_logits_processor(be,Vt,Oe),sr=this._get_stopping_criteria(be,Se),qt=Tt[Lt].dims.at(0),or=ie.LogitsSampler.getSampler(be),Cr=new Array(qt).fill(0),Er=Dt.tolist();Ye&&Ye.put(Er);let dr,kr={};for(;;){if(Tt=this.prepare_inputs_for_generation(Er,Tt,be),dr=await this.forward(Tt),be.output_attentions&&be.return_dict_in_generate){const ss=this.getAttentions(dr);for(const Os in ss)Os in kr||(kr[Os]=[]),kr[Os].push(ss[Os])}const Ur=dr.logits.slice(null,-1,null),gs=Zt(Er,Ur),Pn=[];for(let ss=0;ss<gs.dims.at(0);++ss){const Os=gs[ss],ha=await or(Os);for(const[Gn,Fo]of ha){const Kn=BigInt(Gn);Cr[ss]+=Fo,Er[ss].push(Kn),Pn.push([Kn]);break}}if(Ye&&Ye.put(Pn),sr(Er).every(ss=>ss))break;Tt=this._update_model_kwargs_for_generation({generated_input_ids:Pn,outputs:dr,model_inputs:Tt,is_encoder_decoder:Ut})}Ye&&Ye.end();const $r=this.getPastKeyValues(dr,Tt.past_key_values,!0),qr=new b.Tensor("int64",Er.flat(),[Er.length,Er[0].length]);if(be.return_dict_in_generate)return{sequences:qr,past_key_values:$r,...kr};for(const Ur of Object.values(dr))Ur.location==="gpu-buffer"&&Ur.dispose();return qr}getPastKeyValues(j,be,Oe=!1){const Se=Object.create(null);for(const Ye in j)if(Ye.startsWith("present")){const tt=Ye.replace("present","past_key_values"),mt=Ye.includes("encoder");if(mt&&be?Se[tt]=be[tt]:Se[tt]=j[Ye],be&&(!mt||Oe)){const Tt=be[tt];Tt.location==="gpu-buffer"&&Tt.dispose()}}return Se}getAttentions(j){const be={};for(const Oe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Se in j)Se.startsWith(Oe)&&(Oe in be||(be[Oe]=[]),be[Oe].push(j[Se]));return be}addPastKeyValues(j,be){var Oe,Se,Ye;if(be)Object.assign(j,be);else{const tt=this.sessions.decoder_model_merged??this.sessions.model,mt=((Oe=tt==null?void 0:tt.config)==null?void 0:Oe.kv_cache_dtype)??"float32",Tt=mt==="float16"?new Uint16Array:[],Lt=((Ye=(Se=j[this.main_input_name]??j.attention_mask)==null?void 0:Se.dims)==null?void 0:Ye[0])??1,Ut=(0,f.getKeyValueShapes)(this.config,{batch_size:Lt});for(const Dt in Ut)j[Dt]=new b.Tensor(mt,Tt,Ut[Dt])}}async encode_image({pixel_values:j}){const be=(await de(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 (${be.dims[1]}).`),this.config.num_image_tokens=be.dims[1]),be}async encode_text({input_ids:j}){return(await de(this.sessions.embed_tokens,{input_ids:j})).inputs_embeds}}class Je{}class De extends Je{constructor({last_hidden_state:x,hidden_states:j=null,attentions:be=null}){super(),this.last_hidden_state=x,this.hidden_states=j,this.attentions=be}}class ce extends oe{}class ve extends ce{}class Re extends ce{async _call(x){return new Wr(await super._call(x))}}class je extends ce{async _call(x){return new Xt(await super._call(x))}}class Ve extends ce{async _call(x){return new jr(await super._call(x))}}class Ne extends ce{async _call(x){return new Zr(await super._call(x))}}class Ze extends oe{}class at extends Ze{}class ft extends Ze{async _call(x){return new Wr(await super._call(x))}}class dt extends Ze{async _call(x){return new Xt(await super._call(x))}}class gt extends Ze{async _call(x){return new jr(await super._call(x))}}class F extends oe{}class ne extends F{}class K extends oe{}class pe extends K{}class Fe extends K{async _call(x){return new Wr(await super._call(x))}}class Qe extends K{async _call(x){return new Xt(await super._call(x))}}class st extends K{async _call(x){return new jr(await super._call(x))}}class pt extends K{async _call(x){return new Zr(await super._call(x))}}class It extends oe{}class St extends It{}class Ft extends It{async _call(x){return new Wr(await super._call(x))}}class At extends It{async _call(x){return new Xt(await super._call(x))}}class nr extends It{async _call(x){return new jr(await super._call(x))}}class gr extends It{async _call(x){return new Zr(await super._call(x))}}class Sr extends oe{}class Ar extends Sr{}class Xr extends Sr{async _call(x){return new Wr(await super._call(x))}}class ns extends Sr{async _call(x){return new Xt(await super._call(x))}}class qs extends Sr{async _call(x){return new jr(await super._call(x))}}class Ls extends Sr{async _call(x){return new Zr(await super._call(x))}}class Ms extends oe{}class Nt extends Ms{}class Xs extends Ms{async _call(x){return new Wr(await super._call(x))}}class Cs extends Ms{async _call(x){return new Xt(await super._call(x))}}class zs extends Ms{async _call(x){return new jr(await super._call(x))}}class ks extends Ms{async _call(x){return new Zr(await super._call(x))}}class os extends oe{}class Ss extends os{}class cs extends os{async _call(x){return new Wr(await super._call(x))}}class $s extends os{async _call(x){return new Xt(await super._call(x))}}class Qs extends os{async _call(x){return new jr(await super._call(x))}}class is extends os{async _call(x){return new Zr(await super._call(x))}}class nt extends oe{}class _t extends nt{}class Ot extends nt{async _call(x){return new Wr(await super._call(x))}}class lr extends nt{async _call(x){return new Xt(await super._call(x))}}class bs extends nt{async _call(x){return new jr(await super._call(x))}}class tr extends nt{async _call(x){return new Zr(await super._call(x))}}class es extends oe{}class Bs extends es{}class Ys extends es{async _call(x){return new Xt(await super._call(x))}}class Rs extends es{async _call(x){return new jr(await super._call(x))}}class vs extends es{async _call(x){return new Zr(await super._call(x))}}class In extends es{async _call(x){return new Wr(await super._call(x))}}class Ns extends oe{}class On extends Ns{}class no extends Ns{async _call(x){return new Wr(await super._call(x))}}class js extends Ns{async _call(x){return new Xt(await super._call(x))}}class Ts extends Ns{async _call(x){return new jr(await super._call(x))}}class as extends oe{}class mn extends as{}class Js extends as{async _call(x){return new Wr(await super._call(x))}}class _n extends as{async _call(x){return new Xt(await super._call(x))}}class Zs extends as{async _call(x){return new Zr(await super._call(x))}}class xs extends oe{}class zt extends xs{}class fn extends xs{async _call(x){return new Wr(await super._call(x))}}class Fn extends xs{async _call(x){return new Xt(await super._call(x))}}class Dn extends xs{async _call(x){return new jr(await super._call(x))}}class Ln extends xs{async _call(x){return new Zr(await super._call(x))}}class Us extends oe{}class zn extends Us{}class gn extends Us{async _call(x){return new Wr(await super._call(x))}}class Bn extends Us{async _call(x){return new Xt(await super._call(x))}}class ir extends Us{async _call(x){return new Zr(await super._call(x))}}class Qr extends oe{}class wn extends Qr{}class Rn extends Qr{async _call(x){return new Xt(await super._call(x))}}class yn extends Qr{async _call(x){return new Zr(await super._call(x))}}class Mn extends Qr{async _call(x){return new Wr(await super._call(x))}}class Ee extends oe{constructor(){super(...arguments);ge(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class P extends Ee{}class H extends Ee{}class ae extends oe{}class Me extends ae{}class Ce extends ae{}class He extends oe{}class ct extends He{}class yt extends He{}class ht extends oe{}class it extends ht{}class Pt extends ht{}class hr extends ht{async _call(x){return new Xt(await super._call(x))}}class rr extends oe{}class $e extends rr{}class wr extends rr{}class Rr extends rr{async _call(x){return new Xt(await super._call(x))}}class Yr extends rr{}class Jr extends oe{}class Bt extends Jr{}class Nr extends Jr{}class ps extends oe{}class er extends ps{}class mr extends ps{}class vt extends oe{}class yr extends vt{}class Es extends vt{async _call(x){return new Wr(await super._call(x))}}class Dr extends vt{async _call(x){return new Xt(await super._call(x))}}class Hr extends vt{async _call(x){return new jr(await super._call(x))}}class Mt extends vt{async _call(x){return new Zr(await super._call(x))}}class br extends oe{}class ze extends br{}class wt extends br{async _call(x){return new Wr(await super._call(x))}}class ts extends br{async _call(x){return new Xt(await super._call(x))}}class en extends br{async _call(x){return new jr(await super._call(x))}}class oo extends br{async _call(x){return new Zr(await super._call(x))}}class bn extends oe{}class Qt extends bn{}class xa extends bn{async _call(x){return new Wr(await super._call(x))}}class Uo extends bn{async _call(x){return new Xt(await super._call(x))}}class Ea extends bn{async _call(x){return new jr(await super._call(x))}}class Pa extends bn{async _call(x){return new Zr(await super._call(x))}}class Wo extends oe{}class Ca extends Wo{}class rs extends Wo{}class Vo extends oe{constructor(){super(...arguments);ge(this,"requires_attention_mask",!1);ge(this,"main_input_name","input_features");ge(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class ka extends Vo{}class Ws extends Vo{_prepare_generation_config(x,j){return super._prepare_generation_config(x,j,V.WhisperGenerationConfig)}_retrieve_init_tokens(x){const j=[x.decoder_start_token_id];let be=x.language;const Oe=x.task;if(x.is_multilingual){be||(console.warn("No language specified - defaulting to English (en)."),be="en");const Ye=`<|${(0,Y.whisper_language_to_code)(be)}|>`;j.push(x.lang_to_id[Ye]),j.push(x.task_to_id[Oe??"transcribe"])}else if(be||Oe)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!x.return_timestamps&&x.no_timestamps_token_id&&j.at(-1)!==x.no_timestamps_token_id?j.push(x.no_timestamps_token_id):x.return_timestamps&&j.at(-1)===x.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(Se=>Se!=null)}async generate({inputs:x=null,generation_config:j=null,logits_processor:be=null,stopping_criteria:Oe=null,...Se}){j=this._prepare_generation_config(j,Se);const Ye=Se.decoder_input_ids??this._retrieve_init_tokens(j);if(j.return_timestamps&&(be??(be=new y.LogitsProcessorList),be.push(new y.WhisperTimeStampLogitsProcessor(j,Ye))),j.begin_suppress_tokens&&(be??(be=new y.LogitsProcessorList),be.push(new y.SuppressTokensAtBeginLogitsProcessor(j.begin_suppress_tokens,Ye.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 tt=await super.generate({inputs:x,generation_config:j,logits_processor:be,decoder_input_ids:Ye,...Se});return j.return_token_timestamps&&(tt.token_timestamps=this._extract_token_timestamps(tt,j.alignment_heads,j.num_frames)),tt}_extract_token_timestamps(x,j,be=null,Oe=.02){if(!x.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`.");be==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 Se=this.config.median_filter_width;Se===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Se=7);const Ye=x.cross_attentions,tt=Array.from({length:this.config.decoder_layers},(sr,qt)=>(0,b.cat)(Ye.map(or=>or[qt]),2)),mt=(0,b.stack)(j.map(([sr,qt])=>{if(sr>=tt.length)throw new Error(`Layer index ${sr} is out of bounds for cross attentions (length ${tt.length}).`);return be?tt[sr].slice(null,qt,null,[0,be]):tt[sr].slice(null,qt)})).transpose(1,0,2,3),[Tt,Lt]=(0,b.std_mean)(mt,-2,0,!0),Ut=mt.clone();for(let sr=0;sr<Ut.dims[0];++sr){const qt=Ut[sr];for(let or=0;or<qt.dims[0];++or){const Cr=qt[or],Er=Tt[sr][or][0].data,dr=Lt[sr][or][0].data;for(let kr=0;kr<Cr.dims[0];++kr){let $r=Cr[kr].data;for(let qr=0;qr<$r.length;++qr)$r[qr]=($r[qr]-dr[qr])/Er[qr];$r.set((0,q.medianFilter)($r,Se))}}}const Dt=[(0,b.mean)(Ut,1)],Vt=x.sequences.dims,Zt=new b.Tensor("float32",new Float32Array(Vt[0]*Vt[1]),Vt);for(let sr=0;sr<Vt[0];++sr){const qt=Dt[sr].neg().squeeze_(0),[or,Cr]=(0,q.dynamic_time_warping)(qt.tolist()),Er=Array.from({length:or.length-1},($r,qr)=>or[qr+1]-or[qr]),dr=(0,W.mergeArrays)([1],Er).map($r=>!!$r),kr=[];for(let $r=0;$r<dr.length;++$r)dr[$r]&&kr.push(Cr[$r]*Oe);Zt[sr].data.set(kr,1)}return Zt}}class Go extends oe{constructor(){super(...arguments);ge(this,"requires_attention_mask",!1);ge(this,"main_input_name","input_values");ge(this,"forward_params",["input_values","decoder_input_ids","past_key_values"])}}class zc extends Go{}class Sa extends Go{}class Ko extends oe{constructor(){super(...arguments);ge(this,"main_input_name","pixel_values");ge(this,"forward_params",["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"])}}class $a extends oe{constructor(){super(...arguments);ge(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class io extends $a{_merge_input_ids_with_image_features({inputs_embeds:x,image_features:j,input_ids:be,attention_mask:Oe}){const Se=this.config.image_token_index,tt=be.tolist().map(Dt=>Dt.findIndex(Vt=>Vt==Se)),mt=tt.every(Dt=>Dt===-1),Tt=tt.every(Dt=>Dt!==-1);if(!mt&&!Tt)throw new Error("Every input should contain either 0 or 1 image token.");if(mt)return{inputs_embeds:x,attention_mask:Oe};const Lt=[],Ut=[];for(let Dt=0;Dt<tt.length;++Dt){const Vt=tt[Dt],Zt=x[Dt],sr=j[Dt],qt=Oe[Dt];Lt.push((0,b.cat)([Zt.slice([0,Vt]),sr,Zt.slice([Vt+1,Zt.dims[0]])],0)),Ut.push((0,b.cat)([qt.slice([0,Vt]),(0,b.ones)([sr.dims[0]]),qt.slice([Vt+1,qt.dims[0]])],0))}return{inputs_embeds:(0,b.stack)(Lt,0),attention_mask:(0,b.stack)(Ut,0)}}}class Aa extends io{}class Ia extends io{}class Oa extends oe{constructor(){super(...arguments);ge(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"]);ge(this,"main_input_name","inputs_embeds")}}class Fa extends Oa{_merge_input_ids_with_image_features({inputs_embeds:x,image_features:j,input_ids:be,attention_mask:Oe}){return{inputs_embeds:(0,b.cat)([j,x],1),attention_mask:(0,b.cat)([(0,b.ones)(j.dims.slice(0,2)),Oe],1)}}async _prepare_inputs_embeds({input_ids:x,pixel_values:j,inputs_embeds:be,attention_mask:Oe}){if(!x&&!j)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let Se,Ye;return x&&(Se=await this.encode_text({input_ids:x})),j&&(Ye=await this.encode_image({pixel_values:j})),Se&&Ye?{inputs_embeds:be,attention_mask:Oe}=this._merge_input_ids_with_image_features({inputs_embeds:Se,image_features:Ye,input_ids:x,attention_mask:Oe}):be=Se||Ye,{inputs_embeds:be,attention_mask:Oe}}async forward({input_ids:x,pixel_values:j,attention_mask:be,decoder_input_ids:Oe,decoder_attention_mask:Se,encoder_outputs:Ye,past_key_values:tt,inputs_embeds:mt,decoder_inputs_embeds:Tt}){if(mt||({inputs_embeds:mt,attention_mask:be}=await this._prepare_inputs_embeds({input_ids:x,pixel_values:j,inputs_embeds:mt,attention_mask:be})),!Ye){let{last_hidden_state:Dt}=await qe(this,{inputs_embeds:mt,attention_mask:be});Ye=Dt}if(!Tt){if(!Oe)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");Tt=await this.encode_text({input_ids:Oe})}return await Ue(this,{inputs_embeds:Tt,attention_mask:Se,encoder_attention_mask:be,encoder_hidden_states:Ye,past_key_values:tt},!0)}}class hs extends oe{constructor(){super(...arguments);ge(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class Da extends hs{_merge_input_ids_with_image_features(x){const j=x.image_features.dims.at(-1),be=x.image_features.view(-1,j);return ut({image_token_id:this.config.image_token_index,...x,image_features:be})}}class La extends oe{constructor(){super(...arguments);ge(this,"forward_params",["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"])}}class Ho extends La{async encode_image({pixel_values:x,pixel_attention_mask:j}){return(await de(this.sessions.vision_encoder,{pixel_values:x,pixel_attention_mask:j})).image_features}_merge_input_ids_with_image_features(x){const j=x.image_features.dims.at(-1),be=x.image_features.view(-1,j);return ut({image_token_id:this.config.image_token_id,...x,image_features:be})}}class za extends oe{constructor(){super(...arguments);ge(this,"forward_params",["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"])}}class qo extends za{async forward({input_ids:x=null,attention_mask:j=null,pixel_values:be=null,image_sizes:Oe=null,position_ids:Se=null,inputs_embeds:Ye=null,past_key_values:tt=null,generation_config:mt=null,logits_processor:Tt=null,...Lt}){if(!Ye){let Dt;if(be&&x.dims[1]!==1){if(!Oe)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:Dt}=await de(this.sessions.vision_encoder,{pixel_values:be,image_sizes:Oe}))}else{const Vt=this.config.normalized_config.hidden_size;Dt=new b.Tensor("float32",[],[0,Vt])}({inputs_embeds:Ye}=await de(this.sessions.prepare_inputs_embeds,{input_ids:x,image_features:Dt}))}return await Ue(this,{inputs_embeds:Ye,past_key_values:tt,attention_mask:j,position_ids:Se,generation_config:mt,logits_processor:Tt},!1)}}class tn extends oe{}class Ba extends tn{}class Bc extends tn{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"text_model"})}}class Ra extends tn{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"text_model"})}}class Rc extends tn{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"vision_model"})}}class Nc extends tn{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"vision_model"})}}class ls extends oe{}class Na extends ls{}class ao extends ls{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"text_model"})}}class Xo extends tn{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"vision_model"})}}class ms extends oe{}class ja extends ms{}class lo extends oe{}class Ua extends lo{async forward(x){const j=!x.input_ids,be=!x.pixel_values;if(j&&be)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(j&&(x.input_ids=(0,b.ones)([x.pixel_values.dims[0],1])),be){const{image_size:Tt}=this.config.vision_config;x.pixel_values=(0,b.full)([0,3,Tt,Tt],0)}const{text_embeddings:Oe,image_embeddings:Se,l2norm_text_embeddings:Ye,l2norm_image_embeddings:tt}=await super.forward(x),mt={};return j||(mt.text_embeddings=Oe,mt.l2norm_text_embeddings=Ye),be||(mt.image_embeddings=Se,mt.l2norm_image_embeddings=tt),mt}}class Wa extends lo{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"text_model"})}}class Va extends lo{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"vision_model"})}}class Qo extends oe{}class Ga extends Qo{}class Ka extends Qo{}class Yo extends oe{}class Ha extends Yo{}class _s extends Yo{}class Jo extends oe{}class qa extends Jo{}class Xa extends Jo{}class Zo extends oe{}class Qa extends Zo{}class Ya extends Zo{}class ei extends oe{}class Ja extends ei{}class Za extends ei{}class ti extends oe{}class ri extends ti{}class si extends ti{}class ni extends oe{}class el extends ni{}class oi extends ni{}class tl extends oe{}class rl extends tl{}class uo extends tl{}class ii extends oe{}class sl extends ii{}class nl extends ii{}class vn extends oe{}class ol extends vn{}class il extends vn{}class co extends oe{}class al extends co{}class ll extends co{}class ai extends oe{}class ul extends ai{}class jc extends ai{}class li extends oe{}class dl extends li{}class cl extends li{}class pl extends oe{}class hl extends pl{}class ur extends pl{}class ui extends oe{}class ml extends ui{}class _l extends ui{}class di extends oe{}class fl extends di{}class gl extends di{}class ci extends oe{}class wl extends ci{}class yl extends ci{}class pi extends oe{}class Ml extends pi{}class bl extends pi{}class hi extends oe{}class vl extends hi{}class Nn extends hi{}class Tl extends oe{constructor(){super(...arguments);ge(this,"forward_params",["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"])}}class po extends Tl{get_rope_index(x,j,be,Oe){const{vision_config:Se,image_token_id:Ye,video_token_id:tt,vision_start_token_id:mt}=this.config,Tt=Se.spatial_merge_size??2,Lt=[];if(j||be){let Ut=x.tolist();Oe||(Oe=(0,b.ones_like)(x));const Dt=Oe.tolist(),Vt=Array.from({length:3},Cr=>Array.from({length:x.dims[0]},Er=>Array.from({length:x.dims[1]},dr=>1))),Zt=j?j.tolist():[],sr=be?be.tolist():[];let qt=0,or=0;for(let Cr=0;Cr<Ut.length;++Cr){const Er=Ut[Cr].filter((Tr,Ir)=>Dt[Cr][Ir]==1),kr=Er.reduce((Tr,Ir,Hs)=>(Ir==mt&&Tr.push(Hs),Tr),[]).map(Tr=>Er[Tr+1]),$r=kr.filter(Tr=>Tr==Ye).length,qr=kr.filter(Tr=>Tr==tt).length;let Ur=[],gs=0,Pn=$r,pa=qr;for(let Tr=0;Tr<kr.length;++Tr){const Ir=Er.findIndex((Fs,ds)=>ds>gs&&Fs==Ye),Hs=Er.findIndex((Fs,ds)=>ds>gs&&Fs==tt),an=Pn>0&&Ir!==-1?Ir:Er.length+1,Hn=pa>0&&Hs!==-1?Hs:Er.length+1;let qn,ma,_a,Ec;an<Hn?([ma,_a,Ec]=Zt[qt],++qt,--Pn,qn=an):([ma,_a,Ec]=sr[or],++or,--pa,qn=Hn);const[bp,Xn,Do]=[Number(ma),Math.floor(Number(_a)/Tt),Math.floor(Number(Ec)/Tt)],fa=qn-gs,vp=Ur.length>0?(0,q.max)(Ur.at(-1))[0]+1:0;Ur.push(Array.from({length:3*fa},(Fs,ds)=>vp+ds%fa));const ga=fa+vp,Qn=bp*Xn*Do,Pc=Array.from({length:Qn},(Fs,ds)=>ga+Math.floor(ds/(Xn*Do))),ln=Array.from({length:Qn},(Fs,ds)=>ga+Math.floor(ds/Do)%Xn),Tp=Array.from({length:Qn},(Fs,ds)=>ga+ds%Do);Ur.push([Pc,ln,Tp].flat()),gs=qn+Qn}if(gs<Er.length){const Tr=Ur.length>0?(0,q.max)(Ur.at(-1))[0]+1:0,Ir=Er.length-gs;Ur.push(Array.from({length:3*Ir},(Hs,an)=>Tr+an%Ir))}const ss=Ur.reduce((Tr,Ir)=>Tr+Ir.length,0),Os=new Array(ss);let ha=0;for(let Tr=0;Tr<3;++Tr)for(let Ir=0;Ir<Ur.length;++Ir){const Hs=Ur[Ir],an=Hs.length/3;for(let Hn=Tr*an;Hn<(Tr+1)*an;++Hn)Os[ha++]=Hs[Hn]}let Gn=0;const Fo=Dt[Cr];for(let Tr=0;Tr<Fo.length;++Tr)if(Fo[Tr]==1){for(let Ir=0;Ir<3;++Ir)Vt[Ir][Cr][Tr]=Os[Ir*ss/3+Gn];++Gn}const Kn=(0,q.max)(Os)[0];Lt.push(Kn+1-Ut[Cr].length)}return[new b.Tensor("int64",Vt.flat(1/0),[3,x.dims[0],x.dims[1]]),new b.Tensor("int64",Lt,[Lt.length,1])]}else if(Oe){const{data:Ut,dims:Dt}=re(Oe),Vt=BigInt64Array.from({length:3*Ut.length},(sr,qt)=>Ut[qt%Ut.length]),Zt=Array.from({length:Dt[0]},(sr,qt)=>(0,q.max)(Ut.subarray(Dt[1]*qt,Dt[1]*(qt+1)))[0]+1+Dt[1]);return[new b.Tensor("int64",Vt,[3,...Dt]),new b.Tensor("int64",Zt,[Zt.length,1])]}else{const[Ut,Dt]=x.dims,Vt=BigInt64Array.from({length:3*Ut*Dt},(Zt,sr)=>BigInt(Math.floor(sr%Dt/Ut)));return[new b.Tensor("int64",Vt,[3,...x.dims]),(0,b.zeros)([Ut,1])]}}async encode_image({pixel_values:x,image_grid_thw:j}){return(await de(this.sessions.vision_encoder,{pixel_values:x,grid_thw:j})).image_features}_merge_input_ids_with_image_features(x){return ut({image_token_id:this.config.image_token_id,...x})}prepare_inputs_for_generation(x,j,be){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 Oe=BigInt(Object.values(j.past_key_values)[0].dims.at(-2)),Se=j.rope_deltas.map(Ye=>Oe+Ye);j.position_ids=(0,b.stack)([Se,Se,Se],0)}return j}}class mi extends oe{}class xl extends mi{}class El extends mi{}class _i extends oe{}class Pl extends _i{}class Cl extends _i{}class fi extends oe{}class kl extends fi{}class Sl extends fi{}class gi extends oe{}class $l extends gi{}class Al extends gi{}class wi extends oe{}class Il extends wi{}class Ol extends wi{}class yi extends oe{}class Mi extends yi{}class Fl extends yi{async _call(x){return new Xt(await super._call(x))}}class ho extends oe{}class bi extends ho{}class Dl extends ho{async _call(x){return new Xt(await super._call(x))}}class Ll extends oe{}class zl extends Ll{}class vi extends oe{}class Bl extends vi{}class Rl extends vi{async _call(x){return new Xt(await super._call(x))}}class Ti extends oe{}class Nl extends Ti{}class xi extends oe{}class jl extends xi{}class Uc extends xi{async _call(x){return new Xt(await super._call(x))}}class Ul extends oe{}class Wl extends Ul{}class us extends oe{}class Vl extends us{}class Gl extends us{async _call(x){return new Xt(await super._call(x))}}class Kl extends oe{}class Hl extends Kl{async _call(x){return new xc(await super._call(x))}}class Ei extends oe{}class ql extends Ei{}class Xl extends Ei{async _call(x){return new Xt(await super._call(x))}}class Pi extends oe{}class Ql extends Pi{}class Yl extends Pi{async _call(x){return new Xt(await super._call(x))}}class Jl extends oe{}class Zl extends Jl{}class eu extends Jl{}class Ci extends oe{}class tu extends Ci{}class ru extends Ci{}class ki extends oe{}class Wc extends ki{}class rn extends ki{async _call(x){return new Xt(await super._call(x))}}class As extends oe{}class sn extends As{}class Si extends As{async _call(x){return new Vr(await super._call(x))}}class Vs extends As{async _call(x){return new su(await super._call(x))}}class Vr extends Je{constructor({logits:x,pred_boxes:j}){super(),this.logits=x,this.pred_boxes=j}}class su extends Je{constructor({logits:x,pred_boxes:j,pred_masks:be}){super(),this.logits=x,this.pred_boxes=j,this.pred_masks=be}}class mo extends oe{}class nu extends mo{}class Vc extends mo{async _call(x){return new jn(await super._call(x))}}class jn extends Je{constructor({logits:x,pred_boxes:j}){super(),this.logits=x,this.pred_boxes=j}}class _o extends oe{}class $i extends _o{}class ou extends _o{async _call(x){return new iu(await super._call(x))}}class iu extends Vr{}class fo extends oe{}class Ai extends fo{}class au extends fo{async _call(x){return new Xt(await super._call(x))}}class go extends oe{}class lu extends go{}class wo extends go{async _call(x){return new Xt(await super._call(x))}}class yo extends oe{}class uu extends yo{}class du extends yo{async _call(x){return new Xt(await super._call(x))}}class cu extends oe{}class pu extends cu{}class Ii extends cu{async _call(x){return new Xt(await super._call(x))}}class Tn extends oe{}class hu extends Tn{}class Oi extends Tn{}class Fi extends oe{}class mu extends Fi{}class _u extends Fi{}class Gc extends oe{}class fu extends Gc{}class Mo extends oe{}class Kc extends Mo{}class gu extends Mo{}class bo extends Mo{}class wu extends oe{}class vo extends wu{}class To extends oe{}class Di extends To{}class yu extends To{}class Li extends oe{}class zi extends Li{}class Hc extends Li{}class Mu extends oe{}class qc extends Mu{}class Bi extends oe{}class bu extends Bi{}class vu extends Bi{async _call(x){return new Xt(await super._call(x))}}class xo extends oe{}class Tu extends xo{}class xu extends xo{async _call(x){return new Xt(await super._call(x))}}class Eo extends oe{}class Eu extends Eo{}class Pu extends Eo{async _call(x){return new Xt(await super._call(x))}}class Cu extends oe{}class ku extends Cu{}class Su extends Cu{async _call(x){return new Xt(await super._call(x))}}class Ri extends oe{}class Xc extends Ri{}class $u extends Ri{async _call(x){return new Au(await super._call(x))}}class Au extends Je{constructor({logits:x,pred_boxes:j}){super(),this.logits=x,this.pred_boxes=j}}class Iu extends oe{}class Ou extends Iu{async get_image_embeddings({pixel_values:x}){return await qe(this,{pixel_values:x})}async forward(x){if((!x.image_embeddings||!x.image_positional_embeddings)&&(x={...x,...await this.get_image_embeddings(x)}),!x.input_labels&&x.input_points){const be=x.input_points.dims.slice(0,-1),Oe=be.reduce((Se,Ye)=>Se*Ye,1);x.input_labels=new b.Tensor("int64",new BigInt64Array(Oe).fill(1n),be)}const j={image_embeddings:x.image_embeddings,image_positional_embeddings:x.image_positional_embeddings};return x.input_points&&(j.input_points=x.input_points),x.input_labels&&(j.input_labels=x.input_labels),x.input_boxes&&(j.input_boxes=x.input_boxes),await de(this.sessions.prompt_encoder_mask_decoder,j)}async _call(x){return new Fu(await super._call(x))}}class Fu extends Je{constructor({iou_scores:x,pred_masks:j}){super(),this.iou_scores=x,this.pred_masks=j}}class Du extends oe{}class Po extends Du{}class Un extends Du{}class Co extends oe{}class Lu extends Co{}class zu extends Co{}class Gs extends oe{}class Bu extends Gs{}class Ni extends Gs{async _call(x){return new on(await super._call(x))}}class Ru extends Gs{async _call(x){return new Xt(await super._call(x))}}class Nu extends Gs{async _call(x){return new jr(await super._call(x))}}class ji extends oe{}class Qc extends ji{}class ju extends ji{async _call(x){return new jr(await super._call(x))}}class Ui extends oe{}class Uu extends Ui{}class ko extends oe{}class Wu extends ko{}class Yc extends ko{async _call(x){return new on(await super._call(x))}}class Vu extends ko{async _call(x){return new Xt(await super._call(x))}}class Wn extends oe{}class Jc extends Wn{}class Gu extends Wn{async _call(x){return new on(await super._call(x))}}class Ku extends Wn{async _call(x){return new Xt(await super._call(x))}}class Hu extends Wn{async _call(x){return new jr(await super._call(x))}}class So extends oe{}class Zc extends So{}class qu extends So{async _call(x){return new on(await super._call(x))}}class Xu extends So{async _call(x){return new Xt(await super._call(x))}}class ep extends oe{}class tp extends Gs{}class Qu extends Gs{async _call(x){return new on(await super._call(x))}}class Yu extends Gs{async _call(x){return new Xt(await super._call(x))}}class xn extends oe{}class Ju extends xn{}class rp extends xn{async _call(x){return new on(await super._call(x))}}class Zu extends xn{async _call(x){return new Xt(await super._call(x))}}class ed extends xn{async _call(x){return new Tc(await super._call(x))}}class td extends xn{async _call(x){return new jr(await super._call(x))}}class $o extends oe{}class Wp extends $o{}class rd extends $o{}class sd extends $o{async generate_speech(x,j,{threshold:be=.5,minlenratio:Oe=0,maxlenratio:Se=20,vocoder:Ye=null}={}){const tt={input_ids:x},{encoder_outputs:mt,encoder_attention_mask:Tt}=await qe(this,tt),Lt=mt.dims[1]/this.config.reduction_factor,Ut=Math.floor(Lt*Se),Dt=Math.floor(Lt*Oe),Vt=this.config.num_mel_bins;let Zt=[],sr=null,qt=null,or=0;for(;;){++or;const dr=xe(!!qt);let kr;qt?kr=qt.output_sequence_out:kr=new b.Tensor("float32",new Float32Array(Vt),[1,1,Vt]);let $r={use_cache_branch:dr,output_sequence:kr,encoder_attention_mask:Tt,speaker_embeddings:j,encoder_hidden_states:mt};this.addPastKeyValues($r,sr),qt=await de(this.sessions.decoder_model_merged,$r),sr=this.getPastKeyValues(qt,sr);const{prob:qr,spectrum:Ur}=qt;if(Zt.push(Ur),or>=Dt&&(Array.from(qr.data).filter(gs=>gs>=be).length>0||or>=Ut))break}const Cr=(0,b.cat)(Zt),{waveform:Er}=await de(Ye.sessions.model,{spectrogram:Cr});return{spectrogram:Cr,waveform:Er}}}class nd extends oe{constructor(){super(...arguments);ge(this,"main_input_name","spectrogram")}}class od extends oe{}class sp extends od{}class fs extends oe{}class Is extends fs{}class nn extends fs{}class Ks extends oe{}class id extends Ks{}class ad extends Ks{}class Wi extends oe{}class ld extends Wi{}class ud extends Wi{}class Ao extends oe{}class dd extends Ao{}class cd extends Ao{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"text_model"})}}class pd extends Ao{static async from_pretrained(x,j={}){return super.from_pretrained(x,{...j,model_file_name:j.model_file_name??"audio_model"})}}class hd extends oe{}class md extends hd{async _call(x){return new ca(await super._call(x))}}class Gr extends oe{}class np extends Gr{}class _d extends Gr{}class Vi extends Gr{}class Gi extends oe{}class Vn extends Gi{}class fd extends Gi{}class Ki extends oe{}class gd extends Ki{}class wd extends Ki{async _call(x){return new Xt(await super._call(x))}}class Hi extends oe{}class op extends Hi{}class yd extends Hi{}class qi extends oe{constructor(){super(...arguments);ge(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[be,Oe]=j.dims,Se=this.config.decoder.num_codebooks,Ye=Oe-Se;let tt=0;for(let Lt=0;Lt<j.size;++Lt){if(j.data[Lt]===this.config.decoder.pad_token_id)continue;const Ut=Lt%Oe,Dt=Math.floor(Lt/Oe)%Se,Vt=Ut-Dt;Vt>0&&Vt<=Ye&&(j.data[tt++]=j.data[Lt])}const mt=Math.floor(be/Se),Tt=tt/(mt*Se);return new b.Tensor(j.type,j.data.slice(0,tt),[mt,Se,Tt])}prepare_inputs_for_generation(j,be,Oe){let Se=structuredClone(j);for(let tt=0;tt<Se.length;++tt)for(let mt=0;mt<Se[tt].length;++mt)tt%this.config.decoder.num_codebooks>=mt&&(Se[tt][mt]=BigInt(this.config.decoder.pad_token_id));return Oe.guidance_scale!==null&&Oe.guidance_scale>1&&(Se=Se.concat(Se)),super.prepare_inputs_for_generation(Se,be,Oe)}async generate(j){const be=await super.generate(j),Oe=this._apply_and_filter_by_delay_pattern_mask(be).unsqueeze_(0),{audio_values:Se}=await de(this.sessions.encodec_decode,{audio_codes:Oe});return Se}}class Xi extends oe{}class Md extends Xi{}class bd extends Xi{async _call(x){return new Xt(await super._call(x))}}class Qi extends oe{}class Yi extends Qi{}class vd extends Qi{async _call(x){return new Xt(await super._call(x))}}class Td extends oe{}class Ji extends Td{}class xd extends Td{async _call(x){return new Xt(await super._call(x))}}class Zi extends oe{}class Ed extends Zi{}class ip extends Zi{async _call(x){return new Xt(await super._call(x))}}class Pd extends oe{}class Cd extends Pd{}class kd extends oe{}class ap extends kd{constructor(...j){super(...j);ge(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 be=this._generation_mode??"text";let Oe;if(be==="text"||!j.past_key_values){const Tt=this.sessions.prepare_inputs_embeds,Lt=(0,W.pick)(j,Tt.inputNames);Oe=await de(Tt,Lt)}else{const Tt=this.sessions.gen_img_embeds,Lt=(0,W.pick)({image_ids:j.input_ids},Tt.inputNames);Oe=await de(Tt,Lt)}const Se={...j,...Oe},Ye=await Ue(this,Se),tt=this.sessions[be==="text"?"lm_head":"gen_head"];if(!tt)throw new Error(`Unable to find "${tt}" generation head`);const mt=await de(tt,(0,W.pick)(Ye,tt.inputNames));return{...Oe,...Ye,...mt}}async generate(j){return this._generation_mode="text",super.generate(j)}async generate_images(j){this._generation_mode="image";const be=(j.inputs??j[this.main_input_name]).dims[1],Se=(await super.generate(j)).slice(null,[be,null]),Ye=this.sessions.image_decode,{decoded_image:tt}=await de(Ye,{generated_tokens:Se}),mt=tt.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),Tt=[];for(const Lt of mt){const Ut=L.RawImage.fromTensor(Lt);Tt.push(Ut)}return Tt}}class Sd extends Je{constructor({char_logits:x,bpe_logits:j,wp_logits:be}){super(),this.char_logits=x,this.bpe_logits=j,this.wp_logits=be}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class $d extends oe{}class lp extends $d{async _call(x){return new Sd(await super._call(x))}}class ea extends oe{}class Ad extends ea{}class Id extends ea{}class ta extends oe{}class Od extends ta{}class up extends ta{}class _r{static async from_pretrained(x,{progress_callback:j=null,config:be=null,cache_dir:Oe=null,local_files_only:Se=!1,revision:Ye="main",model_file_name:tt=null,subfolder:mt="onnx",device:Tt=null,dtype:Lt=null,use_external_data_format:Ut=null,session_options:Dt={}}={}){const Vt={progress_callback:j,config:be,cache_dir:Oe,local_files_only:Se,revision:Ye,model_file_name:tt,subfolder:mt,device:Tt,dtype:Lt,use_external_data_format:Ut,session_options:Dt};if(Vt.config=await f.AutoConfig.from_pretrained(x,Vt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const Zt of this.MODEL_CLASS_MAPPINGS){const sr=Zt.get(Vt.config.model_type);if(sr)return await sr[1].from_pretrained(x,Vt)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${Vt.config.model_type}", attempting to construct from base class.`),await oe.from_pretrained(x,Vt);throw Error(`Unsupported model type: ${Vt.config.model_type}`)}}ge(_r,"MODEL_CLASS_MAPPINGS",null),ge(_r,"BASE_IF_FAIL",!1);const dp=new Map([["bert",["BertModel",ve]],["modernbert",["ModernBertModel",at]],["nomic_bert",["NomicBertModel",ne]],["roformer",["RoFormerModel",pe]],["electra",["ElectraModel",Ar]],["esm",["EsmModel",On]],["convbert",["ConvBertModel",St]],["camembert",["CamembertModel",Nt]],["deberta",["DebertaModel",Ss]],["deberta-v2",["DebertaV2Model",_t]],["mpnet",["MPNetModel",zt]],["albert",["AlbertModel",wn]],["distilbert",["DistilBertModel",Bs]],["roberta",["RobertaModel",yr]],["xlm",["XLMModel",ze]],["xlm-roberta",["XLMRobertaModel",Qt]],["clap",["ClapModel",dd]],["clip",["CLIPModel",Ba]],["clipseg",["CLIPSegModel",Ga]],["chinese_clip",["ChineseCLIPModel",ja]],["siglip",["SiglipModel",Na]],["jina_clip",["JinaCLIPModel",Ua]],["mobilebert",["MobileBertModel",mn]],["squeezebert",["SqueezeBertModel",zn]],["wav2vec2",["Wav2Vec2Model",Bu]],["wav2vec2-bert",["Wav2Vec2BertModel",Zc]],["unispeech",["UniSpeechModel",Wu]],["unispeech-sat",["UniSpeechSatModel",Jc]],["hubert",["HubertModel",tp]],["wavlm",["WavLMModel",Ju]],["audio-spectrogram-transformer",["ASTModel",Ca]],["vits",["VitsModel",md]],["pyannote",["PyAnnoteModel",Qc]],["wespeaker-resnet",["WeSpeakerResNetModel",Uu]],["detr",["DetrModel",sn]],["rt_detr",["RTDetrModel",nu]],["table-transformer",["TableTransformerModel",$i]],["vit",["ViTModel",Mi]],["ijepa",["IJepaModel",bi]],["pvt",["PvtModel",Bl]],["vit_msn",["ViTMSNModel",jl]],["vit_mae",["ViTMAEModel",Nl]],["groupvit",["GroupViTModel",Wl]],["fastvit",["FastViTModel",Vl]],["mobilevit",["MobileViTModel",ql]],["mobilevitv2",["MobileViTV2Model",Ql]],["owlvit",["OwlViTModel",Zl]],["owlv2",["Owlv2Model",tu]],["beit",["BeitModel",Wc]],["deit",["DeiTModel",Ai]],["hiera",["HieraModel",lu]],["convnext",["ConvNextModel",bu]],["convnextv2",["ConvNextV2Model",Tu]],["dinov2",["Dinov2Model",Eu]],["dinov2_with_registers",["Dinov2WithRegistersModel",ku]],["resnet",["ResNetModel",uu]],["swin",["SwinModel",pu]],["swin2sr",["Swin2SRModel",hu]],["donut-swin",["DonutSwinModel",qc]],["yolos",["YolosModel",Xc]],["dpt",["DPTModel",mu]],["glpn",["GLPNModel",zi]],["hifigan",["SpeechT5HifiGan",nd]],["efficientnet",["EfficientNetModel",gd]],["decision_transformer",["DecisionTransformerModel",Cd]],["patchtst",["PatchTSTForPrediction",Ad]],["patchtsmixer",["PatchTSMixerForPrediction",Od]],["mobilenet_v1",["MobileNetV1Model",Md]],["mobilenet_v2",["MobileNetV2Model",Yi]],["mobilenet_v3",["MobileNetV3Model",Ji]],["mobilenet_v4",["MobileNetV4Model",Ed]],["maskformer",["MaskFormerModel",Di]],["mgp-str",["MgpstrForSceneTextRecognition",lp]]]),cp=new Map([["t5",["T5Model",P]],["longt5",["LongT5Model",Me]],["mt5",["MT5Model",ct]],["bart",["BartModel",it]],["mbart",["MBartModel",$e]],["marian",["MarianModel",Po]],["whisper",["WhisperModel",ka]],["m2m_100",["M2M100Model",Lu]],["blenderbot",["BlenderbotModel",Bt]],["blenderbot-small",["BlenderbotSmallModel",er]]]),pp=new Map([["bloom",["BloomModel",kl]],["jais",["JAISModel",qa]],["gpt2",["GPT2Model",Ha]],["gptj",["GPTJModel",ri]],["gpt_bigcode",["GPTBigCodeModel",el]],["gpt_neo",["GPTNeoModel",Qa]],["gpt_neox",["GPTNeoXModel",Ja]],["codegen",["CodeGenModel",rl]],["llama",["LlamaModel",sl]],["exaone",["ExaoneModel",ol]],["olmo",["OlmoModel",ul]],["olmo2",["Olmo2Model",dl]],["mobilellm",["MobileLLMModel",al]],["granite",["GraniteModel",hl]],["cohere",["CohereModel",ml]],["gemma",["GemmaModel",fl]],["gemma2",["Gemma2Model",wl]],["openelm",["OpenELMModel",Ml]],["qwen2",["Qwen2Model",vl]],["phi",["PhiModel",xl]],["phi3",["Phi3Model",Pl]],["mpt",["MptModel",$l]],["opt",["OPTModel",Il]],["mistral",["MistralModel",Is]],["starcoder2",["Starcoder2Model",id]],["falcon",["FalconModel",ld]],["stablelm",["StableLmModel",Vn]]]),ra=new Map([["speecht5",["SpeechT5ForSpeechToText",rd]],["whisper",["WhisperForConditionalGeneration",Ws]],["moonshine",["MoonshineForConditionalGeneration",Sa]]]),Fd=new Map([["speecht5",["SpeechT5ForTextToSpeech",sd]]]),Dd=new Map([["vits",["VitsModel",md]],["musicgen",["MusicgenForConditionalGeneration",qi]]]),Ld=new Map([["bert",["BertForSequenceClassification",je]],["modernbert",["ModernBertForSequenceClassification",dt]],["roformer",["RoFormerForSequenceClassification",Qe]],["electra",["ElectraForSequenceClassification",ns]],["esm",["EsmForSequenceClassification",js]],["convbert",["ConvBertForSequenceClassification",At]],["camembert",["CamembertForSequenceClassification",Cs]],["deberta",["DebertaForSequenceClassification",$s]],["deberta-v2",["DebertaV2ForSequenceClassification",lr]],["mpnet",["MPNetForSequenceClassification",Fn]],["albert",["AlbertForSequenceClassification",Rn]],["distilbert",["DistilBertForSequenceClassification",Ys]],["roberta",["RobertaForSequenceClassification",Dr]],["xlm",["XLMForSequenceClassification",ts]],["xlm-roberta",["XLMRobertaForSequenceClassification",Uo]],["bart",["BartForSequenceClassification",hr]],["mbart",["MBartForSequenceClassification",Rr]],["mobilebert",["MobileBertForSequenceClassification",_n]],["squeezebert",["SqueezeBertForSequenceClassification",Bn]]]),zd=new Map([["bert",["BertForTokenClassification",Ve]],["modernbert",["ModernBertForTokenClassification",gt]],["roformer",["RoFormerForTokenClassification",st]],["electra",["ElectraForTokenClassification",qs]],["esm",["EsmForTokenClassification",Ts]],["convbert",["ConvBertForTokenClassification",nr]],["camembert",["CamembertForTokenClassification",zs]],["deberta",["DebertaForTokenClassification",Qs]],["deberta-v2",["DebertaV2ForTokenClassification",bs]],["mpnet",["MPNetForTokenClassification",Dn]],["distilbert",["DistilBertForTokenClassification",Rs]],["roberta",["RobertaForTokenClassification",Hr]],["xlm",["XLMForTokenClassification",en]],["xlm-roberta",["XLMRobertaForTokenClassification",Ea]]]),Bd=new Map([["t5",["T5ForConditionalGeneration",H]],["longt5",["LongT5ForConditionalGeneration",Ce]],["mt5",["MT5ForConditionalGeneration",yt]],["bart",["BartForConditionalGeneration",Pt]],["mbart",["MBartForConditionalGeneration",wr]],["marian",["MarianMTModel",Un]],["m2m_100",["M2M100ForConditionalGeneration",zu]],["blenderbot",["BlenderbotForConditionalGeneration",Nr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",mr]]]),En=new Map([["bloom",["BloomForCausalLM",Sl]],["gpt2",["GPT2LMHeadModel",_s]],["jais",["JAISLMHeadModel",Xa]],["gptj",["GPTJForCausalLM",si]],["gpt_bigcode",["GPTBigCodeForCausalLM",oi]],["gpt_neo",["GPTNeoForCausalLM",Ya]],["gpt_neox",["GPTNeoXForCausalLM",Za]],["codegen",["CodeGenForCausalLM",uo]],["llama",["LlamaForCausalLM",nl]],["exaone",["ExaoneForCausalLM",il]],["olmo",["OlmoForCausalLM",jc]],["olmo2",["Olmo2ForCausalLM",cl]],["mobilellm",["MobileLLMForCausalLM",ll]],["granite",["GraniteForCausalLM",ur]],["cohere",["CohereForCausalLM",_l]],["gemma",["GemmaForCausalLM",gl]],["gemma2",["Gemma2ForCausalLM",yl]],["openelm",["OpenELMForCausalLM",bl]],["qwen2",["Qwen2ForCausalLM",Nn]],["phi",["PhiForCausalLM",El]],["phi3",["Phi3ForCausalLM",Cl]],["mpt",["MptForCausalLM",Al]],["opt",["OPTForCausalLM",Ol]],["mbart",["MBartForCausalLM",Yr]],["mistral",["MistralForCausalLM",nn]],["starcoder2",["Starcoder2ForCausalLM",ad]],["falcon",["FalconForCausalLM",ud]],["trocr",["TrOCRForCausalLM",sp]],["stablelm",["StableLmForCausalLM",fd]],["phi3_v",["Phi3VForCausalLM",qo]]]),Rd=new Map([["multi_modality",["MultiModalityCausalLM",ap]]]),sa=new Map([["bert",["BertForMaskedLM",Re]],["modernbert",["ModernBertForMaskedLM",ft]],["roformer",["RoFormerForMaskedLM",Fe]],["electra",["ElectraForMaskedLM",Xr]],["esm",["EsmForMaskedLM",no]],["convbert",["ConvBertForMaskedLM",Ft]],["camembert",["CamembertForMaskedLM",Xs]],["deberta",["DebertaForMaskedLM",cs]],["deberta-v2",["DebertaV2ForMaskedLM",Ot]],["mpnet",["MPNetForMaskedLM",fn]],["albert",["AlbertForMaskedLM",Mn]],["distilbert",["DistilBertForMaskedLM",In]],["roberta",["RobertaForMaskedLM",Es]],["xlm",["XLMWithLMHeadModel",wt]],["xlm-roberta",["XLMRobertaForMaskedLM",xa]],["mobilebert",["MobileBertForMaskedLM",Js]],["squeezebert",["SqueezeBertForMaskedLM",gn]]]),na=new Map([["bert",["BertForQuestionAnswering",Ne]],["roformer",["RoFormerForQuestionAnswering",pt]],["electra",["ElectraForQuestionAnswering",Ls]],["convbert",["ConvBertForQuestionAnswering",gr]],["camembert",["CamembertForQuestionAnswering",ks]],["deberta",["DebertaForQuestionAnswering",is]],["deberta-v2",["DebertaV2ForQuestionAnswering",tr]],["mpnet",["MPNetForQuestionAnswering",Ln]],["albert",["AlbertForQuestionAnswering",yn]],["distilbert",["DistilBertForQuestionAnswering",vs]],["roberta",["RobertaForQuestionAnswering",Mt]],["xlm",["XLMForQuestionAnswering",oo]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Pa]],["mobilebert",["MobileBertForQuestionAnswering",Zs]],["squeezebert",["SqueezeBertForQuestionAnswering",ir]]]),Io=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ko]],["idefics3",["Idefics3ForConditionalGeneration",Ho]]]),hp=new Map([["llava",["LlavaForConditionalGeneration",io]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Aa]],["moondream1",["Moondream1ForConditionalGeneration",Ia]],["florence2",["Florence2ForConditionalGeneration",Fa]],["qwen2-vl",["Qwen2VLForConditionalGeneration",po]],["idefics3",["Idefics3ForConditionalGeneration",Ho]],["paligemma",["PaliGemmaForConditionalGeneration",Da]]]),mp=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ko]]]),oa=new Map([["vit",["ViTForImageClassification",Fl]],["ijepa",["IJepaForImageClassification",Dl]],["pvt",["PvtForImageClassification",Rl]],["vit_msn",["ViTMSNForImageClassification",Uc]],["fastvit",["FastViTForImageClassification",Gl]],["mobilevit",["MobileViTForImageClassification",Xl]],["mobilevitv2",["MobileViTV2ForImageClassification",Yl]],["beit",["BeitForImageClassification",rn]],["deit",["DeiTForImageClassification",au]],["hiera",["HieraForImageClassification",wo]],["convnext",["ConvNextForImageClassification",vu]],["convnextv2",["ConvNextV2ForImageClassification",xu]],["dinov2",["Dinov2ForImageClassification",Pu]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Su]],["resnet",["ResNetForImageClassification",du]],["swin",["SwinForImageClassification",Ii]],["segformer",["SegformerForImageClassification",_d]],["efficientnet",["EfficientNetForImageClassification",wd]],["mobilenet_v1",["MobileNetV1ForImageClassification",bd]],["mobilenet_v2",["MobileNetV2ForImageClassification",vd]],["mobilenet_v3",["MobileNetV3ForImageClassification",xd]],["mobilenet_v4",["MobileNetV4ForImageClassification",ip]]]),ia=new Map([["detr",["DetrForObjectDetection",Si]],["rt_detr",["RTDetrForObjectDetection",Vc]],["table-transformer",["TableTransformerForObjectDetection",ou]],["yolos",["YolosForObjectDetection",$u]]]),Nd=new Map([["owlvit",["OwlViTForObjectDetection",eu]],["owlv2",["Owlv2ForObjectDetection",ru]]]),jd=new Map([["detr",["DetrForSegmentation",Vs]],["clipseg",["CLIPSegForImageSegmentation",Ka]]]),aa=new Map([["segformer",["SegformerForSemanticSegmentation",Vi]],["sapiens",["SapiensForSemanticSegmentation",Kc]]]),Ud=new Map([["detr",["DetrForSegmentation",Vs]],["maskformer",["MaskFormerForInstanceSegmentation",yu]]]),Wd=new Map([["sam",["SamModel",Ou]]]),la=new Map([["wav2vec2",["Wav2Vec2ForCTC",Ni]],["wav2vec2-bert",["Wav2Vec2BertForCTC",qu]],["unispeech",["UniSpeechForCTC",Yc]],["unispeech-sat",["UniSpeechSatForCTC",Gu]],["wavlm",["WavLMForCTC",rp]],["hubert",["HubertForCTC",Qu]]]),Vd=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Ru]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Xu]],["unispeech",["UniSpeechForSequenceClassification",Vu]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Ku]],["wavlm",["WavLMForSequenceClassification",Zu]],["hubert",["HubertForSequenceClassification",Yu]],["audio-spectrogram-transformer",["ASTForAudioClassification",rs]]]),Gd=new Map([["wavlm",["WavLMForXVector",ed]]]),Kd=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Hu]],["wavlm",["WavLMForAudioFrameClassification",td]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Nu]],["pyannote",["PyAnnoteForAudioFrameClassification",ju]]]),Hd=new Map([["vitmatte",["VitMatteForImageMatting",Hl]]]),Vp=new Map([["patchtst",["PatchTSTForPrediction",Id]],["patchtsmixer",["PatchTSMixerForPrediction",up]]]),qd=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Oi]]]),Xd=new Map([["dpt",["DPTForDepthEstimation",_u]],["depth_anything",["DepthAnythingForDepthEstimation",fu]],["glpn",["GLPNForDepthEstimation",Hc]],["sapiens",["SapiensForDepthEstimation",gu]],["depth_pro",["DepthProForDepthEstimation",vo]]]),Qd=new Map([["sapiens",["SapiensForNormalEstimation",bo]]]),Yd=new Map([["vitpose",["VitPoseForPoseEstimation",zl]]]),_p=new Map([["clip",["CLIPVisionModelWithProjection",Nc]],["siglip",["SiglipVisionModel",Xo]],["jina_clip",["JinaCLIPVisionModel",Va]]]),Jd=[[dp,D.EncoderOnly],[cp,D.EncoderDecoder],[pp,D.DecoderOnly],[Ld,D.EncoderOnly],[zd,D.EncoderOnly],[Bd,D.Seq2Seq],[ra,D.Seq2Seq],[En,D.DecoderOnly],[Rd,D.MultiModality],[sa,D.EncoderOnly],[na,D.EncoderOnly],[Io,D.Vision2Seq],[hp,D.ImageTextToText],[oa,D.EncoderOnly],[jd,D.EncoderOnly],[Ud,D.EncoderOnly],[aa,D.EncoderOnly],[Hd,D.EncoderOnly],[Vp,D.EncoderOnly],[qd,D.EncoderOnly],[Xd,D.EncoderOnly],[Qd,D.EncoderOnly],[Yd,D.EncoderOnly],[ia,D.EncoderOnly],[Nd,D.EncoderOnly],[Wd,D.MaskGeneration],[la,D.EncoderOnly],[Vd,D.EncoderOnly],[Fd,D.Seq2Seq],[Dd,D.EncoderOnly],[Gd,D.EncoderOnly],[Kd,D.EncoderOnly],[_p,D.EncoderOnly]];for(const[_,x]of Jd)for(const[j,be]of _.values())$.set(j,x),C.set(be,j),g.set(j,be);const fp=[["MusicgenForConditionalGeneration",qi,D.Musicgen],["Phi3VForCausalLM",qo,D.Phi3V],["CLIPTextModelWithProjection",Ra,D.EncoderOnly],["SiglipTextModel",ao,D.EncoderOnly],["JinaCLIPTextModel",Wa,D.EncoderOnly],["ClapTextModelWithProjection",cd,D.EncoderOnly],["ClapAudioModelWithProjection",pd,D.EncoderOnly]];for(const[_,x,j]of fp)$.set(_,j),C.set(x,_),g.set(_,x);class ua extends _r{}ge(ua,"MODEL_CLASS_MAPPINGS",Jd.map(x=>x[0])),ge(ua,"BASE_IF_FAIL",!0);class gp extends _r{}ge(gp,"MODEL_CLASS_MAPPINGS",[Ld]);class Zd extends _r{}ge(Zd,"MODEL_CLASS_MAPPINGS",[zd]);class ec extends _r{}ge(ec,"MODEL_CLASS_MAPPINGS",[Bd]);class tc extends _r{}ge(tc,"MODEL_CLASS_MAPPINGS",[ra]);class rc extends _r{}ge(rc,"MODEL_CLASS_MAPPINGS",[Fd]);class wp extends _r{}ge(wp,"MODEL_CLASS_MAPPINGS",[Dd]);class sc extends _r{}ge(sc,"MODEL_CLASS_MAPPINGS",[En]);class nc extends _r{}ge(nc,"MODEL_CLASS_MAPPINGS",[sa]);class oc extends _r{}ge(oc,"MODEL_CLASS_MAPPINGS",[na]);class ic extends _r{}ge(ic,"MODEL_CLASS_MAPPINGS",[Io]);class ac extends _r{}ge(ac,"MODEL_CLASS_MAPPINGS",[oa]);class lc extends _r{}ge(lc,"MODEL_CLASS_MAPPINGS",[jd]);class uc extends _r{}ge(uc,"MODEL_CLASS_MAPPINGS",[aa]);class dc extends _r{}ge(dc,"MODEL_CLASS_MAPPINGS",[Ud]);class cc extends _r{}ge(cc,"MODEL_CLASS_MAPPINGS",[ia]);class pc extends _r{}ge(pc,"MODEL_CLASS_MAPPINGS",[Nd]);class hc extends _r{}ge(hc,"MODEL_CLASS_MAPPINGS",[Wd]);class da extends _r{}ge(da,"MODEL_CLASS_MAPPINGS",[la]);class mc extends _r{}ge(mc,"MODEL_CLASS_MAPPINGS",[Vd]);class _c extends _r{}ge(_c,"MODEL_CLASS_MAPPINGS",[Gd]);class fc extends _r{}ge(fc,"MODEL_CLASS_MAPPINGS",[Kd]);class gc extends _r{}ge(gc,"MODEL_CLASS_MAPPINGS",[mp]);class wc extends _r{}ge(wc,"MODEL_CLASS_MAPPINGS",[Hd]);class yc extends _r{}ge(yc,"MODEL_CLASS_MAPPINGS",[qd]);class Mc extends _r{}ge(Mc,"MODEL_CLASS_MAPPINGS",[Xd]);class yp extends _r{}ge(yp,"MODEL_CLASS_MAPPINGS",[Qd]);class bc extends _r{}ge(bc,"MODEL_CLASS_MAPPINGS",[Yd]);class vc extends _r{}ge(vc,"MODEL_CLASS_MAPPINGS",[_p]);class Mp extends Je{constructor({logits:x,past_key_values:j,encoder_outputs:be,decoder_attentions:Oe=null,cross_attentions:Se=null}){super(),this.logits=x,this.past_key_values=j,this.encoder_outputs=be,this.decoder_attentions=Oe,this.cross_attentions=Se}}class Xt extends Je{constructor({logits:x}){super(),this.logits=x}}class Tc extends Je{constructor({logits:x,embeddings:j}){super(),this.logits=x,this.embeddings=j}}class jr extends Je{constructor({logits:x}){super(),this.logits=x}}class Wr extends Je{constructor({logits:x}){super(),this.logits=x}}class Zr extends Je{constructor({start_logits:x,end_logits:j}){super(),this.start_logits=x,this.end_logits=j}}class on extends Je{constructor({logits:x}){super(),this.logits=x}}class Oo extends Je{constructor({logits:x,past_key_values:j}){super(),this.logits=x,this.past_key_values=j}}class xc extends Je{constructor({alphas:x}){super(),this.alphas=x}}class ca extends Je{constructor({waveform:x,spectrogram:j}){super(),this.waveform=x,this.spectrogram=j}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(Te,A,s)=>{s.r(A),s.d(A,{ASTFeatureExtractor:()=>N});var f=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var O=s("./src/utils/audio.js");class N extends f.FeatureExtractor{constructor(W){super(W);const w=this.config.sampling_rate,v=(0,O.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(w/2),w,null,"kaldi",!0);for(let y=0;y<v.length;++y)v[y].push(0);this.mel_filters=v,this.window=(0,O.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(W,w){return(0,O.spectrogram)(W,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:w,transpose:!0})}async _call(W){(0,f.validate_audio_inputs)(W,"ASTFeatureExtractor");const w=await this._extract_fbank_features(W,this.config.max_length);if(this.config.do_normalize){const v=this.std*2,y=w.data;for(let M=0;M<y.length;++M)y[M]=(y[M]-this.mean)/v}return{input_values:w.unsqueeze_(0)}}}},"./src/models/auto/feature_extraction_auto.js":(Te,A,s)=>{s.r(A),s.d(A,{AutoFeatureExtractor:()=>Q});var f=s("./src/utils/constants.js"),O=s("./src/utils/hub.js");s("./src/base/feature_extraction_utils.js");var N=s("./src/models/feature_extractors.js");class Q{static async from_pretrained(w,v={}){const y=await(0,O.getModelJSON)(w,f.FEATURE_EXTRACTOR_NAME,!0,v),M=y.feature_extractor_type,b=N[M];if(!b)throw new Error(`Unknown feature_extractor_type: '${M}'. Please report this at ${f.GITHUB_ISSUE_URL}.`);return new b(y)}}},"./src/models/auto/image_processing_auto.js":(Te,A,s)=>{s.r(A),s.d(A,{AutoImageProcessor:()=>W});var f=s("./src/utils/constants.js"),O=s("./src/utils/hub.js"),N=s("./src/base/image_processors_utils.js"),Q=s("./src/models/image_processors.js");class W{static async from_pretrained(v,y={}){const M=await(0,O.getModelJSON)(v,f.IMAGE_PROCESSOR_NAME,!0,y),b=M.image_processor_type??M.feature_extractor_type;let L=Q[b];return L||(b!==void 0&&console.warn(`Image processor type '${b}' not found, assuming base ImageProcessor. Please report this at ${f.GITHUB_ISSUE_URL}.`),L=N.ImageProcessor),new L(M)}}},"./src/models/auto/processing_auto.js":(Te,A,s)=>{s.r(A),s.d(A,{AutoProcessor:()=>v});var f=s("./src/utils/constants.js"),O=s("./src/utils/hub.js"),N=s("./src/base/processing_utils.js"),Q=s("./src/models/processors.js"),W=s("./src/models/image_processors.js"),w=s("./src/models/feature_extractors.js");class v{static async from_pretrained(M,b={}){const L=await(0,O.getModelJSON)(M,f.IMAGE_PROCESSOR_NAME,!0,b),{image_processor_type:q,feature_extractor_type:se,processor_class:ie}=L;if(ie&&Q[ie])return Q[ie].from_pretrained(M,b);if(!q&&!se)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const z={};if(q){const Y=W[q];if(!Y)throw new Error(`Unknown image_processor_type: '${q}'.`);z.image_processor=new Y(L)}if(se){const Y=W[se];if(Y)z.image_processor=new Y(L);else{const D=w[se];if(!D)throw new Error(`Unknown feature_extractor_type: '${se}'.`);z.feature_extractor=new 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f.FeatureExtractor{constructor(W){super(W),this.mel_filters=(0,O.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,O.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,O.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(W,w,v,y){let M;const b=W.length-w;if(b>0)if(v==="rand_trunc"){const L=Math.floor(Math.random()*(b+1));W=W.subarray(L,L+w),M=await this._extract_fbank_features(W,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${v}" not implemented`);else{if(b<0){let L=new Float64Array(w);if(L.set(W),y==="repeat")for(let q=W.length;q<w;q+=W.length)L.set(W.subarray(0,Math.min(W.length,w-q)),q);else if(y==="repeatpad")for(let q=W.length;q<-b;q+=W.length)L.set(W,q);W=L}if(v==="fusion")throw new Error(`Truncation strategy "${v}" not implemented`);M=await this._extract_fbank_features(W,this.mel_filters_slaney,this.config.nb_max_samples)}return M.unsqueeze_(0)}async _extract_fbank_features(W,w,v=null){return(0,O.spectrogram)(W,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:w,log_mel:"dB",max_num_frames:v,do_pad:!1,transpose:!0})}async _call(W,{max_length:w=null}={}){return(0,f.validate_audio_inputs)(W,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(W,w??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}},"./src/models/clip/image_processing_clip.js":(Te,A,s)=>{s.r(A),s.d(A,{CLIPFeatureExtractor:()=>N,CLIPImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/convnext/image_processing_convnext.js":(Te,A,s)=>{s.r(A),s.d(A,{ConvNextFeatureExtractor:()=>N,ConvNextImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{constructor(W){super(W),this.crop_pct=this.config.crop_pct??.875}async resize(W){var v;const w=(v=this.size)==null?void 0:v.shortest_edge;if(w===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(w<384){const y=Math.floor(w/this.crop_pct),[M,b]=this.get_resize_output_image_size(W,{shortest_edge:y});W=await W.resize(M,b,{resample:this.resample}),W=await W.center_crop(w,w)}else W=await W.resize(w,w,{resample:this.resample});return W}}class N extends O{}},"./src/models/deit/image_processing_deit.js":(Te,A,s)=>{s.r(A),s.d(A,{DeiTFeatureExtractor:()=>N,DeiTImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/detr/image_processing_detr.js":(Te,A,s)=>{s.r(A),s.d(A,{DetrFeatureExtractor:()=>Q,DetrImageProcessor:()=>N});var f=s("./src/base/image_processors_utils.js"),O=s("./src/utils/tensor.js");class N extends f.ImageProcessor{async _call(w){const v=await super._call(w),y=[v.pixel_values.dims[0],64,64],M=(0,O.full)(y,1n);return{...v,pixel_mask:M}}post_process_object_detection(...w){return(0,f.post_process_object_detection)(...w)}post_process_panoptic_segmentation(...w){return(0,f.post_process_panoptic_segmentation)(...w)}post_process_instance_segmentation(...w){return(0,f.post_process_instance_segmentation)(...w)}}class Q extends N{}},"./src/models/donut/image_processing_donut.js":(Te,A,s)=>{s.r(A),s.d(A,{DonutFeatureExtractor:()=>N,DonutImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{pad_image(W,w,v,y={}){const[M,b,L]=w;let q=this.image_mean;Array.isArray(this.image_mean)||(q=new Array(L).fill(q));let se=this.image_std;Array.isArray(se)||(se=new Array(L).fill(q));const ie=q.map((z,V)=>-z/se[V]);return super.pad_image(W,w,v,{center:!0,constant_values:ie,...y})}}class N extends O{}},"./src/models/dpt/image_processing_dpt.js":(Te,A,s)=>{s.r(A),s.d(A,{DPTFeatureExtractor:()=>N,DPTImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/efficientnet/image_processing_efficientnet.js":(Te,A,s)=>{s.r(A),s.d(A,{EfficientNetImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{constructor(Q){super(Q),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(W=>W*W))}}},"./src/models/feature_extractors.js":(Te,A,s)=>{s.r(A),s.d(A,{ASTFeatureExtractor:()=>f.ASTFeatureExtractor,ClapFeatureExtractor:()=>O.ClapFeatureExtractor,ImageFeatureExtractor:()=>b.ImageProcessor,MoonshineFeatureExtractor:()=>N.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>Q.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>W.SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor:()=>w.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>v.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>y.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>M.WhisperFeatureExtractor});var f=s("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),O=s("./src/models/clap/feature_extraction_clap.js"),N=s("./src/models/moonshine/feature_extraction_moonshine.js"),Q=s("./src/models/pyannote/feature_extraction_pyannote.js"),W=s("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),w=s("./src/models/speecht5/feature_extraction_speecht5.js"),v=s("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),y=s("./src/models/wespeaker/feature_extraction_wespeaker.js"),M=s("./src/models/whisper/feature_extraction_whisper.js"),b=s("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(Te,A,s)=>{s.r(A),s.d(A,{Florence2Processor:()=>Q});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js");class Q extends f.Processor{constructor(w,v){super(w,v);const{tasks_answer_post_processing_type:y,task_prompts_without_inputs:M,task_prompts_with_input:b}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(y??{})),this.task_prompts_without_inputs=new Map(Object.entries(M??{})),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(w){typeof w=="string"&&(w=[w]);const v=[];for(const y of w)if(this.task_prompts_without_inputs.has(y))v.push(this.task_prompts_without_inputs.get(y));else{for(const[M,b]of this.task_prompts_with_input)if(y.includes(M)){v.push(b.replaceAll("{input}",y).replaceAll(M,""));break}v.length!==w.length&&v.push(y)}return v}post_process_generation(w,v,y){const M=this.tasks_answer_post_processing_type.get(v)??"pure_text";w=w.replaceAll("<s>","").replaceAll("</s>","");let b;switch(M){case"pure_text":b=w;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const L=M==="ocr"?"quad_boxes":"bboxes",q=w.matchAll(this.regexes[L]),se=[],ie=[];for(const[z,V,...Y]of q)se.push(V?V.trim():se.at(-1)??""),ie.push(Y.map((D,$)=>(Number(D)+.5)/this.size_per_bin*y[$%2]));b={labels:se,[L]:ie};break;default:throw new Error(`Task "${v}" (of type "${M}") not yet implemented.`)}return{[v]:b}}async _call(w,v=null,y={}){if(!w&&!v)throw new Error("Either text or images must be provided");const M=await this.image_processor(w,y),b=v?this.tokenizer(v,y):{};return{...M,...b}}}ge(Q,"tokenizer_class",N.AutoTokenizer),ge(Q,"image_processor_class",O.AutoImageProcessor)},"./src/models/glpn/image_processing_glpn.js":(Te,A,s)=>{s.r(A),s.d(A,{GLPNFeatureExtractor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}},"./src/models/idefics3/image_processing_idefics3.js":(Te,A,s)=>{s.r(A),s.d(A,{Idefics3ImageProcessor:()=>N});var f=s("./src/base/image_processors_utils.js"),O=s("./src/utils/tensor.js");class N extends f.ImageProcessor{constructor(W){super(W),this.do_image_splitting=W.do_image_splitting??!0,this.max_image_size=W.max_image_size}get_resize_for_vision_encoder(W,w){let[v,y]=W.dims.slice(-2);const M=y/v;return y>=v?(y=Math.ceil(y/w)*w,v=Math.floor(y/M),v=Math.ceil(v/w)*w):(v=Math.ceil(v/w)*w,y=Math.floor(v*M),y=Math.ceil(y/w)*w),{height:v,width:y}}async _call(W,{do_image_splitting:w=null,return_row_col_info:v=!1}={}){let y;if(!Array.isArray(W))y=[[W]];else{if(W.length===0||!W[0])throw new Error("No images provided.");Array.isArray(W[0])?y=W:y=[W]}let M=[],b=[],L=[];const q=[],se=[];for(const C of y){let T=await Promise.all(C.map(le=>this.preprocess(le)));q.push(...T.map(le=>le.original_size)),se.push(...T.map(le=>le.reshaped_input_size)),T.forEach(le=>le.pixel_values.unsqueeze_(0));const{longest_edge:ee}=this.max_image_size;let J;if(w??this.do_image_splitting){let le=new Array(T.length),de=new Array(T.length);J=await Promise.all(T.map(async(fe,ke)=>{const xe=this.get_resize_for_vision_encoder(fe.pixel_values,ee),Le=await(0,O.interpolate_4d)(fe.pixel_values,{size:[xe.height,xe.width]}),{frames:qe,num_splits_h:Ue,num_splits_w:ut}=await this.split_image(Le,this.max_image_size);return le[ke]=Ue,de[ke]=ut,(0,O.cat)(qe,0)})),b.push(le),L.push(de)}else{const le=[ee,ee];J=await Promise.all(T.map(de=>(0,O.interpolate_4d)(de.pixel_values,{size:le}))),b.push(new Array(T.length).fill(0)),L.push(new Array(T.length).fill(0))}M.push((0,O.cat)(J,0))}const ie=M.length,[z,V,Y,D]=M[0].dims;let $,g;if(ie===1)$=M[0].unsqueeze_(0),g=(0,O.full)([ie,z,Y,D],!0);else{const C=Math.max(...M.map(J=>J.dims.at(0)));g=(0,O.full)([ie,C,Y,D],!0);const T=g.data,ee=C*Y*D;for(let J=0;J<ie;++J){const le=M[J].dims[0];if(le<C){M[J]=(0,O.cat)([M[J],(0,O.full)([C-le,V,Y,D],0)],0);const de=J*ee+le*Y*D,fe=(J+1)*ee;T.fill(!1,de,fe)}}$=(0,O.stack)(M,0)}return{pixel_values:$,pixel_attention_mask:g,original_sizes:q,reshaped_input_sizes:se,...v?{rows:b,cols:L}:{}}}async split_image(W,{longest_edge:w}){const v=w,y=w,M=[],[b,L]=W.dims.slice(-2);let q=0,se=0;if(b>v||L>y){q=Math.ceil(b/v),se=Math.ceil(L/y);const ie=Math.ceil(b/q),z=Math.ceil(L/se);for(let D=0;D<q;++D)for(let $=0;$<se;++$){let g,C,T,ee;D===q-1?(C=b-ie,ee=b):(C=D*ie,ee=(D+1)*ie),$===se-1?(g=L-z,T=L):(g=$*z,T=($+1)*z);const J=[C,g],le=[ee,T],de=await(0,O.slice)(W,J,le,[2,3]);M.push(de)}const V=v,Y=y;(b!==V||L!==Y)&&(W=await(0,O.interpolate_4d)(W,{size:[V,Y]}))}return M.push(W),{frames:M,num_splits_h:q,num_splits_w:se}}}},"./src/models/idefics3/processing_idefics3.js":(Te,A,s)=>{s.r(A),s.d(A,{Idefics3Processor:()=>y});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js");s("./src/utils/image.js");var Q=s("./src/utils/core.js");function W(M,b,L,q,se,ie){let z="";for(let V=0;V<b;++V){for(let Y=0;Y<L;++Y)z+=q+`<row_${V+1}_col_${Y+1}>`+se.repeat(M);z+=`
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ke=0;ke<le.length;++ke)fe+=le[ke]+de[ke+1];$.push(fe)}return{...this.tokenizer($),...ie}}}ge(y,"image_processor_class",O.AutoImageProcessor),ge(y,"tokenizer_class",N.AutoTokenizer),ge(y,"uses_processor_config",!0)},"./src/models/image_processors.js":(Te,A,s)=>{s.r(A),s.d(A,{BeitFeatureExtractor:()=>f.BeitFeatureExtractor,BitImageProcessor:()=>O.BitImageProcessor,CLIPFeatureExtractor:()=>Q.CLIPFeatureExtractor,CLIPImageProcessor:()=>Q.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>N.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>W.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>W.ConvNextImageProcessor,DPTFeatureExtractor:()=>M.DPTFeatureExtractor,DPTImageProcessor:()=>M.DPTImageProcessor,DeiTFeatureExtractor:()=>w.DeiTFeatureExtractor,DeiTImageProcessor:()=>w.DeiTImageProcessor,DetrFeatureExtractor:()=>v.DetrFeatureExtractor,DetrImageProcessor:()=>v.DetrImageProcessor,DonutFeatureExtractor:()=>y.DonutFeatureExtractor,DonutImageProcessor:()=>y.DonutImageProcessor,EfficientNetImageProcessor:()=>b.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>L.GLPNFeatureExtractor,Idefics3ImageProcessor:()=>q.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>ie.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>z.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>V.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>Y.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>Y.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>D.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>D.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>$.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>$.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>g.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>g.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>C.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>C.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>T.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>T.MobileViTImageProcessor,NougatImageProcessor:()=>ee.NougatImageProcessor,OwlViTFeatureExtractor:()=>le.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>le.OwlViTImageProcessor,Owlv2ImageProcessor:()=>J.Owlv2ImageProcessor,Phi3VImageProcessor:()=>de.Phi3VImageProcessor,PvtImageProcessor:()=>fe.PvtImageProcessor,Qwen2VLImageProcessor:()=>ke.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>xe.RTDetrImageProcessor,SamImageProcessor:()=>Le.SamImageProcessor,SegformerFeatureExtractor:()=>qe.SegformerFeatureExtractor,SegformerImageProcessor:()=>qe.SegformerImageProcessor,SiglipImageProcessor:()=>Ue.SiglipImageProcessor,Swin2SRImageProcessor:()=>ut.Swin2SRImageProcessor,VLMImageProcessor:()=>se.VLMImageProcessor,ViTFeatureExtractor:()=>ue.ViTFeatureExtractor,ViTImageProcessor:()=>ue.ViTImageProcessor,VitMatteImageProcessor:()=>re.VitMatteImageProcessor,VitPoseImageProcessor:()=>he.VitPoseImageProcessor,YolosFeatureExtractor:()=>Pe.YolosFeatureExtractor,YolosImageProcessor:()=>Pe.YolosImageProcessor});var f=s("./src/models/beit/image_processing_beit.js"),O=s("./src/models/bit/image_processing_bit.js"),N=s("./src/models/chinese_clip/image_processing_chinese_clip.js"),Q=s("./src/models/clip/image_processing_clip.js"),W=s("./src/models/convnext/image_processing_convnext.js"),w=s("./src/models/deit/image_processing_deit.js"),v=s("./src/models/detr/image_processing_detr.js"),y=s("./src/models/donut/image_processing_donut.js"),M=s("./src/models/dpt/image_processing_dpt.js"),b=s("./src/models/efficientnet/image_processing_efficientnet.js"),L=s("./src/models/glpn/image_processing_glpn.js"),q=s("./src/models/idefics3/image_processing_idefics3.js"),se=s("./src/models/janus/image_processing_janus.js"),ie=s("./src/models/jina_clip/image_processing_jina_clip.js"),z=s("./src/models/llava_onevision/image_processing_llava_onevision.js"),V=s("./src/models/mask2former/image_processing_mask2former.js"),Y=s("./src/models/maskformer/image_processing_maskformer.js"),D=s("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),$=s("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),g=s("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),C=s("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),T=s("./src/models/mobilevit/image_processing_mobilevit.js"),ee=s("./src/models/nougat/image_processing_nougat.js"),J=s("./src/models/owlv2/image_processing_owlv2.js"),le=s("./src/models/owlvit/image_processing_owlvit.js"),de=s("./src/models/phi3_v/image_processing_phi3_v.js"),fe=s("./src/models/pvt/image_processing_pvt.js"),ke=s("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),xe=s("./src/models/rt_detr/image_processing_rt_detr.js"),Le=s("./src/models/sam/image_processing_sam.js"),qe=s("./src/models/segformer/image_processing_segformer.js"),Ue=s("./src/models/siglip/image_processing_siglip.js"),ut=s("./src/models/swin2sr/image_processing_swin2sr.js"),ue=s("./src/models/vit/image_processing_vit.js"),re=s("./src/models/vitmatte/image_processing_vitmatte.js"),he=s("./src/models/vitpose/image_processing_vitpose.js"),Pe=s("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(Te,A,s)=>{s.r(A),s.d(A,{VLMImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{constructor(Q){super({do_pad:!0,pad_size:{width:Q.image_size,height:Q.image_size},...Q}),this.constant_values=this.config.background_color.map(W=>W*this.rescale_factor)}pad_image(Q,W,w,v){return super.pad_image(Q,W,w,{constant_values:this.constant_values,center:!0,...v})}}},"./src/models/janus/processing_janus.js":(Te,A,s)=>{s.r(A),s.d(A,{VLChatProcessor:()=>v});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js"),Q=s("./src/utils/core.js"),W=s("./src/utils/tensor.js"),w=s("./src/utils/image.js");class v extends f.Processor{constructor(M,b){super(M,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(M,{images:b=null,chat_template:L="default"}={}){b?Array.isArray(b)||(b=[b]):b=await Promise.all(M.filter(J=>J.images).flatMap(J=>J.images).map(J=>w.RawImage.read(J)));const q=this.tokenizer,se=q.apply_chat_template(M,{tokenize:!1,add_generation_prompt:!0,chat_template:L}),ie=J=>q.encode(J,{add_special_tokens:!1}),z=se.split(this.image_tag),V=z.length-1;if(b.length!==V)throw new Error(`Number of images provided (${b.length}) does not match number of "${this.image_tag}" image tags (${V})`);const[Y,D,$]=q.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let g=ie(z[0]),C=new Array(g.length).fill(!1);for(let J=1;J<z.length;++J){const le=new Array(this.num_image_tokens).fill(Y),de=ie(z[J]);g=(0,Q.mergeArrays)(g,[D],le,[$],de);const fe=new Array(this.num_image_tokens).fill(!0);C=(0,Q.mergeArrays)(C,[!1],fe,[!1],new Array(de.length).fill(!1))}const T=[1,g.length],ee={input_ids:new W.Tensor("int64",g,T),attention_mask:new W.Tensor("int64",new Array(g.length).fill(1),T),images_seq_mask:new W.Tensor("bool",C,T),images_emb_mask:new W.Tensor("bool",new Array(V*this.num_image_tokens).fill(!0),[1,V,this.num_image_tokens])};if(b&&b.length>0){const J=await this.image_processor(b);return J.pixel_values.unsqueeze_(0),{...ee,...J}}return ee}}ge(v,"image_processor_class",O.AutoImageProcessor),ge(v,"tokenizer_class",N.AutoTokenizer),ge(v,"uses_processor_config",!0)},"./src/models/jina_clip/image_processing_jina_clip.js":(Te,A,s)=>{s.r(A),s.d(A,{JinaCLIPImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{constructor(Q){const{resize_mode:W,fill_color:w,interpolation:v,size:y,...M}=Q,b=W==="squash"?{width:y,height:y}:W==="shortest"?{shortest_edge:y}:{longest_edge:y},L=v==="bicubic"?3:2;super({...M,size:b,resample:L,do_center_crop:!0,crop_size:y,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(Te,A,s)=>{s.r(A),s.d(A,{JinaCLIPProcessor:()=>Q});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js");class Q extends f.Processor{async _call(w=null,v=null,y={}){if(!w&&!v)throw new Error("Either text or images must be provided");const M=w?this.tokenizer(w,y):{},b=v?await this.image_processor(v,y):{};return{...M,...b}}}ge(Q,"tokenizer_class",N.AutoTokenizer),ge(Q,"image_processor_class",O.AutoImageProcessor)},"./src/models/llava_onevision/image_processing_llava_onevision.js":(Te,A,s)=>{s.r(A),s.d(A,{LlavaOnevisionImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}},"./src/models/mask2former/image_processing_mask2former.js":(Te,A,s)=>{s.r(A),s.d(A,{Mask2FormerImageProcessor:()=>O});var f=s("./src/models/maskformer/image_processing_maskformer.js");class O extends f.MaskFormerImageProcessor{}},"./src/models/maskformer/image_processing_maskformer.js":(Te,A,s)=>{s.r(A),s.d(A,{MaskFormerFeatureExtractor:()=>N,MaskFormerImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{post_process_panoptic_segmentation(...W){return(0,f.post_process_panoptic_segmentation)(...W)}post_process_instance_segmentation(...W){return(0,f.post_process_instance_segmentation)(...W)}}class N extends O{}},"./src/models/mgp_str/processing_mgp_str.js":(Te,A,s)=>{s.r(A),s.d(A,{MgpstrProcessor:()=>w});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js"),Q=s("./src/utils/maths.js");const W={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class w extends f.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(y,M){if(!W.hasOwnProperty(M))throw new Error(`Format ${M} is not supported.`);const[b,L]=W[M],q=this[b].bind(this),[se,ie]=y.dims,z=[],V=[],Y=y.tolist();for(let $=0;$<se;++$){const g=Y[$],C=[],T=[];for(let J=1;J<ie;++J){const[le,de]=(0,Q.max)((0,Q.softmax)(g[J]));if(T.push(le),de==L)break;C.push(de)}const ee=T.length>0?T.reduce((J,le)=>J*le,1):0;V.push(C),z.push(ee)}return[q(V),z]}char_decode(y){return this.char_tokenizer.batch_decode(y).map(M=>M.replaceAll(" ",""))}bpe_decode(y){return this.bpe_tokenizer.batch_decode(y)}wp_decode(y){return this.wp_tokenizer.batch_decode(y).map(M=>M.replaceAll(" ",""))}batch_decode([y,M,b]){const[L,q]=this._decode_helper(y,"char"),[se,ie]=this._decode_helper(M,"bpe"),[z,V]=this._decode_helper(b,"wp"),Y=[],D=[];for(let $=0;$<L.length;++$){const[g,C]=(0,Q.max)([q[$],ie[$],V[$]]);Y.push([L[$],se[$],z[$]][C]),D.push(g)}return{generated_text:Y,scores:D,char_preds:L,bpe_preds:se,wp_preds:z}}static async from_pretrained(...y){const M=await super.from_pretrained(...y),b=await N.AutoTokenizer.from_pretrained("Xenova/gpt2"),L=await N.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return M.components={image_processor:M.image_processor,char_tokenizer:M.tokenizer,bpe_tokenizer:b,wp_tokenizer:L},M}async _call(y,M=null){const b=await this.image_processor(y);return M&&(b.labels=this.tokenizer(M).input_ids),b}}ge(w,"tokenizer_class",N.AutoTokenizer),ge(w,"image_processor_class",O.AutoImageProcessor)},"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileNetV1FeatureExtractor:()=>N,MobileNetV1ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileNetV2FeatureExtractor:()=>N,MobileNetV2ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileNetV3FeatureExtractor:()=>N,MobileNetV3ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileNetV4FeatureExtractor:()=>N,MobileNetV4ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/mobilevit/image_processing_mobilevit.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileViTFeatureExtractor:()=>N,MobileViTImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/moonshine/feature_extraction_moonshine.js":(Te,A,s)=>{s.r(A),s.d(A,{MoonshineFeatureExtractor:()=>N});var f=s("./src/base/feature_extraction_utils.js"),O=s("./src/utils/tensor.js");class N extends f.FeatureExtractor{async _call(W){(0,f.validate_audio_inputs)(W,"MoonshineFeatureExtractor"),W instanceof Float64Array&&(W=new Float32Array(W));const w=[1,W.length];return{input_values:new O.Tensor("float32",W,w)}}}},"./src/models/moonshine/processing_moonshine.js":(Te,A,s)=>{s.r(A),s.d(A,{MoonshineProcessor:()=>Q});var f=s("./src/models/auto/feature_extraction_auto.js"),O=s("./src/tokenizers.js"),N=s("./src/base/processing_utils.js");class Q extends N.Processor{async _call(w){return await this.feature_extractor(w)}}ge(Q,"tokenizer_class",O.AutoTokenizer),ge(Q,"feature_extractor_class",f.AutoFeatureExtractor)},"./src/models/nougat/image_processing_nougat.js":(Te,A,s)=>{s.r(A),s.d(A,{NougatImageProcessor:()=>O});var f=s("./src/models/donut/image_processing_donut.js");class O extends f.DonutImageProcessor{}},"./src/models/owlv2/image_processing_owlv2.js":(Te,A,s)=>{s.r(A),s.d(A,{Owlv2ImageProcessor:()=>O});var f=s("./src/models/owlvit/image_processing_owlvit.js");class O extends f.OwlViTImageProcessor{}},"./src/models/owlvit/image_processing_owlvit.js":(Te,A,s)=>{s.r(A),s.d(A,{OwlViTFeatureExtractor:()=>N,OwlViTImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{post_process_object_detection(...W){return(0,f.post_process_object_detection)(...W)}}class N extends O{}},"./src/models/owlvit/processing_owlvit.js":(Te,A,s)=>{s.r(A),s.d(A,{OwlViTProcessor:()=>Q});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js");class Q extends f.Processor{}ge(Q,"tokenizer_class",N.AutoTokenizer),ge(Q,"image_processor_class",O.AutoImageProcessor)},"./src/models/paligemma/processing_paligemma.js":(Te,A,s)=>{s.r(A),s.d(A,{PaliGemmaProcessor:()=>w});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js");const Q="<image>";function W(v,y,M,b,L){return`${b.repeat(M*L)}${y}${v}
`}class w extends f.Processor{async _call(y,M=null,b={}){M||(console.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),M=""),Array.isArray(y)||(y=[y]),Array.isArray(M)||(M=[M]);const L=this.tokenizer.bos_token,q=this.image_processor.config.image_seq_length;let se;M.some(V=>V.includes(Q))?se=M.map(V=>{const Y=V.replaceAll(Q,Q.repeat(q)),D=Y.lastIndexOf(Q),$=D===-1?0:D+Q.length;return Y.slice(0,$)+L+Y.slice($)+`
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f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/feature_extraction_auto.js");class N extends f.Processor{async _call(W){return await this.feature_extractor(W)}}ge(N,"feature_extractor_class",O.AutoFeatureExtractor)},"./src/models/wespeaker/feature_extraction_wespeaker.js":(Te,A,s)=>{s.r(A),s.d(A,{WeSpeakerFeatureExtractor:()=>N});var f=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var O=s("./src/utils/audio.js");class N extends f.FeatureExtractor{constructor(W){super(W);const w=this.config.sampling_rate,v=(0,O.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(w/2),w,null,"kaldi",!0);for(let y=0;y<v.length;++y)v[y].push(0);this.mel_filters=v,this.window=(0,O.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(W){return W=W.map(w=>w*32768),(0,O.spectrogram)(W,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(W){(0,f.validate_audio_inputs)(W,"WeSpeakerFeatureExtractor");const w=(await this._extract_fbank_features(W)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const v=w.mean(1).data,y=w.data,[M,b,L]=w.dims;for(let q=0;q<M;++q){const se=q*b*L,ie=q*L;for(let z=0;z<b;++z){const V=se+z*L;for(let Y=0;Y<L;++Y)y[V+Y]-=v[ie+Y]}}}return{input_features:w}}}},"./src/models/whisper/common_whisper.js":(Te,A,s)=>{s.r(A),s.d(A,{WHISPER_LANGUAGE_MAPPING:()=>O,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>N,whisper_language_to_code:()=>Q});const f=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],O=new Map(f),N=new Map([...f.map(([W,w])=>[w,W]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function Q(W){W=W.toLowerCase();let w=N.get(W);if(w===void 0)if(O.has(W))w=W;else{const y=W.length===2?O.keys():O.values();throw new Error(`Language "${W}" is not supported. Must be one of: ${JSON.stringify(y)}`)}return w}},"./src/models/whisper/feature_extraction_whisper.js":(Te,A,s)=>{s.r(A),s.d(A,{WhisperFeatureExtractor:()=>Q});var f=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var O=s("./src/utils/audio.js"),N=s("./src/utils/maths.js");class Q extends f.FeatureExtractor{constructor(w){var v;super(w),(v=this.config).mel_filters??(v.mel_filters=(0,O.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=(0,O.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(w){const v=await(0,O.spectrogram)(w,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),y=v.data,M=(0,N.max)(y)[0];for(let b=0;b<y.length;++b)y[b]=(Math.max(y[b],M-8)+4)/4;return v}async _call(w){(0,f.validate_audio_inputs)(w,"WhisperFeatureExtractor");let v;return w.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),v=w.slice(0,this.config.n_samples)):(v=new Float32Array(this.config.n_samples),v.set(w)),{input_features:(await this._extract_fbank_features(v)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":(Te,A,s)=>{s.r(A),s.d(A,{WhisperGenerationConfig:()=>O});var f=s("./src/generation/configuration_utils.js");class O extends f.GenerationConfig{constructor(){super(...arguments);ge(this,"return_timestamps",null);ge(this,"return_token_timestamps",null);ge(this,"num_frames",null);ge(this,"alignment_heads",null);ge(this,"task",null);ge(this,"language",null);ge(this,"no_timestamps_token_id",null);ge(this,"prompt_ids",null);ge(this,"is_multilingual",null);ge(this,"lang_to_id",null);ge(this,"task_to_id",null);ge(this,"max_initial_timestamp_index",1)}}},"./src/models/whisper/processing_whisper.js":(Te,A,s)=>{s.r(A),s.d(A,{WhisperProcessor:()=>Q});var f=s("./src/models/auto/feature_extraction_auto.js"),O=s("./src/tokenizers.js"),N=s("./src/base/processing_utils.js");class Q extends N.Processor{async _call(w){return await this.feature_extractor(w)}}ge(Q,"tokenizer_class",O.AutoTokenizer),ge(Q,"feature_extractor_class",f.AutoFeatureExtractor)},"./src/models/yolos/image_processing_yolos.js":(Te,A,s)=>{s.r(A),s.d(A,{YolosFeatureExtractor:()=>N,YolosImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{post_process_object_detection(...W){return(0,f.post_process_object_detection)(...W)}}class N extends O{}},"./src/ops/registry.js":(Te,A,s)=>{s.r(A),s.d(A,{TensorOpRegistry:()=>Q});var f=s("./src/backends/onnx.js"),O=s("./src/utils/tensor.js");const N=async(W,w,v)=>{const y=await(0,f.createInferenceSession)(new Uint8Array(W),w);return async M=>{const b=(0,f.isONNXProxy)(),L=Object.fromEntries(Object.entries(M).map(([se,ie])=>[se,(b?ie.clone():ie).ort_tensor])),q=await y.run(L);return Array.isArray(v)?v.map(se=>new O.Tensor(q[se])):new O.Tensor(q[v])}};class Q{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=N([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=N([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=N([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=N([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=N([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=N([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=N([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}}ge(Q,"session_options",{})},"./src/pipelines.js":(Te,A,s)=>{s.r(A),s.d(A,{AudioClassificationPipeline:()=>de,AutomaticSpeechRecognitionPipeline:()=>ke,DepthEstimationPipeline:()=>Be,DocumentQuestionAnsweringPipeline:()=>re,FeatureExtractionPipeline:()=>J,FillMaskPipeline:()=>Y,ImageClassificationPipeline:()=>Le,ImageFeatureExtractionPipeline:()=>le,ImageSegmentationPipeline:()=>qe,ImageToImagePipeline:()=>Pe,ImageToTextPipeline:()=>xe,ObjectDetectionPipeline:()=>ut,Pipeline:()=>se,QuestionAnsweringPipeline:()=>V,SummarizationPipeline:()=>$,Text2TextGenerationPipeline:()=>D,TextClassificationPipeline:()=>ie,TextGenerationPipeline:()=>T,TextToAudioPipeline:()=>he,TokenClassificationPipeline:()=>z,TranslationPipeline:()=>g,ZeroShotAudioClassificationPipeline:()=>fe,ZeroShotClassificationPipeline:()=>ee,ZeroShotImageClassificationPipeline:()=>Ue,ZeroShotObjectDetectionPipeline:()=>ue,pipeline:()=>oe});var f=s("./src/tokenizers.js"),O=s("./src/models.js"),N=s("./src/models/auto/processing_auto.js");s("./src/base/processing_utils.js");var Q=s("./src/utils/generic.js"),W=s("./src/utils/core.js"),w=s("./src/utils/maths.js"),v=s("./src/utils/audio.js"),y=s("./src/utils/tensor.js"),M=s("./src/utils/image.js");async function b(De){return Array.isArray(De)||(De=[De]),await Promise.all(De.map(ce=>M.RawImage.read(ce)))}async function L(De,ce){return Array.isArray(De)||(De=[De]),await Promise.all(De.map(ve=>typeof ve=="string"||ve instanceof URL?(0,v.read_audio)(ve,ce):ve instanceof Float64Array?new Float32Array(ve):ve))}function q(De,ce){ce&&(De=De.map(Ne=>Ne|0));const[ve,Re,je,Ve]=De;return{xmin:ve,ymin:Re,xmax:je,ymax:Ve}}class se extends Q.Callable{constructor({task:ce,model:ve,tokenizer:Re=null,processor:je=null}){super(),this.task=ce,this.model=ve,this.tokenizer=Re,this.processor=je}async dispose(){await this.model.dispose()}}class ie extends se{constructor(ce){super(ce)}async _call(ce,{top_k:ve=1}={}){const Re=this.tokenizer(ce,{padding:!0,truncation:!0}),je=await this.model(Re),Ve=this.model.config.problem_type==="multi_label_classification"?at=>at.sigmoid():at=>new y.Tensor("float32",(0,w.softmax)(at.data),at.dims),Ne=this.model.config.id2label,Ze=[];for(const at of je.logits){const ft=Ve(at),dt=await(0,y.topk)(ft,ve),gt=dt[0].tolist(),ne=dt[1].tolist().map((K,pe)=>({label:Ne?Ne[K]:`LABEL_${K}`,score:gt[pe]}));ve===1?Ze.push(...ne):Ze.push(ne)}return Array.isArray(ce)||ve===1?Ze:Ze[0]}}class z extends se{constructor(ce){super(ce)}async _call(ce,{ignore_labels:ve=["O"]}={}){const Re=Array.isArray(ce),je=this.tokenizer(Re?ce:[ce],{padding:!0,truncation:!0}),Ne=(await this.model(je)).logits,Ze=this.model.config.id2label,at=[];for(let ft=0;ft<Ne.dims[0];++ft){const dt=je.input_ids[ft],gt=Ne[ft],F=[];for(let ne=0;ne<gt.dims[0];++ne){const K=gt[ne],pe=(0,w.max)(K.data)[1],Fe=Ze?Ze[pe]:`LABEL_${pe}`;if(ve.includes(Fe))continue;const Qe=this.tokenizer.decode([dt[ne].item()],{skip_special_tokens:!0});if(Qe==="")continue;const st=(0,w.softmax)(K.data);F.push({entity:Fe,score:st[pe],index:ne,word:Qe})}at.push(F)}return Re?at:at[0]}}class V extends se{constructor(ce){super(ce)}async _call(ce,ve,{top_k:Re=1}={}){const je=this.tokenizer(ce,{text_pair:ve,padding:!0,truncation:!0}),{start_logits:Ve,end_logits:Ne}=await this.model(je),Ze=je.input_ids.tolist(),at=je.attention_mask.tolist(),ft=this.tokenizer.all_special_ids,dt=[];for(let gt=0;gt<Ve.dims[0];++gt){const F=Ze[gt],ne=F.findIndex(pt=>pt==this.tokenizer.sep_token_id);at[gt].map((pt,It)=>pt==1&&(It===0||It>ne&&ft.findIndex(St=>St==F[It])===-1));const K=Ve[gt].tolist(),pe=Ne[gt].tolist();for(let pt=1;pt<K.length;++pt)(at[gt]==0||pt<=ne||ft.findIndex(It=>It==F[pt])!==-1)&&(K[pt]=-1/0,pe[pt]=-1/0);const Fe=(0,w.softmax)(K).map((pt,It)=>[pt,It]),Qe=(0,w.softmax)(pe).map((pt,It)=>[pt,It]);Fe[0][0]=0,Qe[0][0]=0;const st=(0,W.product)(Fe,Qe).filter(pt=>pt[0][1]<=pt[1][1]).map(pt=>[pt[0][1],pt[1][1],pt[0][0]*pt[1][0]]).sort((pt,It)=>It[2]-pt[2]);for(let pt=0;pt<Math.min(st.length,Re);++pt){const[It,St,Ft]=st[pt],At=F.slice(It,St+1),nr=this.tokenizer.decode(At,{skip_special_tokens:!0});dt.push({answer:nr,score:Ft})}}return Re===1?dt[0]:dt}}class Y extends se{constructor(ce){super(ce)}async _call(ce,{top_k:ve=5}={}){const Re=this.tokenizer(ce,{padding:!0,truncation:!0}),{logits:je}=await this.model(Re),Ve=[],Ne=Re.input_ids.tolist();for(let Ze=0;Ze<Ne.length;++Ze){const at=Ne[Ze],ft=at.findIndex(K=>K==this.tokenizer.mask_token_id);if(ft===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const dt=je[Ze][ft],gt=await(0,y.topk)(new y.Tensor("float32",(0,w.softmax)(dt.data),dt.dims),ve),F=gt[0].tolist(),ne=gt[1].tolist();Ve.push(ne.map((K,pe)=>{const Fe=at.slice();return Fe[ft]=K,{score:F[pe],token:Number(K),token_str:this.tokenizer.decode([K]),sequence:this.tokenizer.decode(Fe,{skip_special_tokens:!0})}}))}return Array.isArray(ce)?Ve:Ve[0]}}class D extends se{constructor(ve){super(ve);ge(this,"_key","generated_text")}async _call(ve,Re={}){Array.isArray(ve)||(ve=[ve]),this.model.config.prefix&&(ve=ve.map(ft=>this.model.config.prefix+ft));const je=this.model.config.task_specific_params;je&&je[this.task]&&je[this.task].prefix&&(ve=ve.map(ft=>je[this.task].prefix+ft));const Ve=this.tokenizer,Ne={padding:!0,truncation:!0};let Ze;this instanceof g&&"_build_translation_inputs"in Ve?Ze=Ve._build_translation_inputs(ve,Ne,Re):Ze=Ve(ve,Ne);const at=await this.model.generate({...Ze,...Re});return Ve.batch_decode(at,{skip_special_tokens:!0}).map(ft=>({[this._key]:ft}))}}class $ extends D{constructor(ve){super(ve);ge(this,"_key","summary_text")}}class g extends D{constructor(ve){super(ve);ge(this,"_key","translation_text")}}function C(De){return Array.isArray(De)&&De.every(ce=>"role"in ce&&"content"in ce)}class T extends se{constructor(ce){super(ce)}async _call(ce,ve={}){let Re=!1,je=!1,Ve;if(typeof ce=="string")Ve=ce=[ce];else if(Array.isArray(ce)&&ce.every(ne=>typeof ne=="string"))Re=!0,Ve=ce;else{if(C(ce))ce=[ce];else if(Array.isArray(ce)&&ce.every(C))Re=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");je=!0,Ve=ce.map(ne=>this.tokenizer.apply_chat_template(ne,{tokenize:!1,add_generation_prompt:!0}))}const Ne=ve.add_special_tokens??!1,Ze=je?!1:ve.return_full_text??!0;this.tokenizer.padding_side="left";const at=this.tokenizer(Ve,{add_special_tokens:Ne,padding:!0,truncation:!0}),ft=await this.model.generate({...at,...ve}),dt=this.tokenizer.batch_decode(ft,{skip_special_tokens:!0});let gt;!Ze&&at.input_ids.dims.at(-1)>0&&(gt=this.tokenizer.batch_decode(at.input_ids,{skip_special_tokens:!0}).map(ne=>ne.length));const F=Array.from({length:ce.length},ne=>[]);for(let ne=0;ne<dt.length;++ne){const K=Math.floor(ne/ft.dims[0]*ce.length);gt&&(dt[ne]=dt[ne].slice(gt[K])),F[K].push({generated_text:je?[...ce[K],{role:"assistant",content:dt[ne]}]:dt[ne]})}return!Re&&F.length===1?F[0]:F}}class ee extends se{constructor(ce){super(ce),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([ve,Re])=>[ve.toLowerCase(),Re])),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(ce,ve,{hypothesis_template:Re="This example is {}.",multi_label:je=!1}={}){const Ve=Array.isArray(ce);Ve||(ce=[ce]),Array.isArray(ve)||(ve=[ve]);const Ne=ve.map(ft=>Re.replace("{}",ft)),Ze=je||ve.length===1,at=[];for(const ft of ce){const dt=[];for(const ne of Ne){const K=this.tokenizer(ft,{text_pair:ne,padding:!0,truncation:!0}),pe=await this.model(K);Ze?dt.push([pe.logits.data[this.contradiction_id],pe.logits.data[this.entailment_id]]):dt.push(pe.logits.data[this.entailment_id])}const F=(Ze?dt.map(ne=>(0,w.softmax)(ne)[1]):(0,w.softmax)(dt)).map((ne,K)=>[ne,K]).sort((ne,K)=>K[0]-ne[0]);at.push({sequence:ft,labels:F.map(ne=>ve[ne[1]]),scores:F.map(ne=>ne[0])})}return Ve?at:at[0]}}class J extends se{constructor(ce){super(ce)}async _call(ce,{pooling:ve="none",normalize:Re=!1,quantize:je=!1,precision:Ve="binary"}={}){const Ne=this.tokenizer(ce,{padding:!0,truncation:!0}),Ze=await this.model(Ne);let at=Ze.last_hidden_state??Ze.logits??Ze.token_embeddings;if(ve!=="none")if(ve==="mean")at=(0,y.mean_pooling)(at,Ne.attention_mask);else if(ve==="cls")at=at.slice(null,0);else throw Error(`Pooling method '${ve}' not supported.`);return Re&&(at=at.normalize(2,-1)),je&&(at=(0,y.quantize_embeddings)(at,Ve)),at}}class le extends se{constructor(ce){super(ce)}async _call(ce,{pool:ve=null}={}){const Re=await b(ce),{pixel_values:je}=await this.processor(Re),Ve=await this.model({pixel_values:je});let Ne;if(ve){if(!("pooler_output"in Ve))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Ne=Ve.pooler_output}else Ne=Ve.last_hidden_state??Ve.logits??Ve.image_embeds;return Ne}}class de extends se{constructor(ce){super(ce)}async _call(ce,{top_k:ve=5}={}){const Re=this.processor.feature_extractor.config.sampling_rate,je=await L(ce,Re),Ve=this.model.config.id2label,Ne=[];for(const Ze of je){const at=await this.processor(Ze),dt=(await this.model(at)).logits[0],gt=await(0,y.topk)(new y.Tensor("float32",(0,w.softmax)(dt.data),dt.dims),ve),F=gt[0].tolist(),K=gt[1].tolist().map((pe,Fe)=>({label:Ve?Ve[pe]:`LABEL_${pe}`,score:F[Fe]}));Ne.push(K)}return Array.isArray(ce)?Ne:Ne[0]}}class fe extends se{constructor(ce){super(ce)}async _call(ce,ve,{hypothesis_template:Re="This is a sound of {}."}={}){const je=!Array.isArray(ce);je&&(ce=[ce]);const Ve=ve.map(dt=>Re.replace("{}",dt)),Ne=this.tokenizer(Ve,{padding:!0,truncation:!0}),Ze=this.processor.feature_extractor.config.sampling_rate,at=await L(ce,Ze),ft=[];for(const dt of at){const gt=await this.processor(dt),F=await this.model({...Ne,...gt}),ne=(0,w.softmax)(F.logits_per_audio.data);ft.push([...ne].map((K,pe)=>({score:K,label:ve[pe]})))}return je?ft[0]:ft}}class ke extends se{constructor(ce){super(ce)}async _call(ce,ve={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(ce,ve);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(ce,ve);case"moonshine":return this._call_moonshine(ce,ve);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(ce,ve){ve.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),ve.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Re=!Array.isArray(ce);Re&&(ce=[ce]);const je=this.processor.feature_extractor.config.sampling_rate,Ve=await L(ce,je),Ne=[];for(const Ze of Ve){const at=await this.processor(Ze),dt=(await this.model(at)).logits[0],gt=[];for(const ne of dt)gt.push((0,w.max)(ne.data)[1]);const F=this.tokenizer.decode(gt);Ne.push({text:F})}return Re?Ne[0]:Ne}async _call_whisper(ce,ve){const Re=ve.return_timestamps??!1,je=ve.chunk_length_s??0,Ve=ve.force_full_sequences??!1;let Ne=ve.stride_length_s??null;const Ze={...ve};Re==="word"&&(Ze.return_token_timestamps=!0,Ze.return_timestamps=!1);const at=!Array.isArray(ce);at&&(ce=[ce]);const ft=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,dt=this.processor.feature_extractor.config.hop_length,gt=this.processor.feature_extractor.config.sampling_rate,F=await L(ce,gt),ne=[];for(const K of F){let pe=[];if(je>0){if(Ne===null)Ne=je/6;else if(je<=Ne)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const st=gt*je,pt=gt*Ne,It=st-2*pt;let St=0;for(;;){const Ft=St+st,At=K.subarray(St,Ft),nr=await this.processor(At),gr=St===0,Sr=Ft>=K.length;if(pe.push({stride:[At.length,gr?0:pt,Sr?0:pt],input_features:nr.input_features,is_last:Sr}),Sr)break;St+=It}}else pe=[{stride:[K.length,0,0],input_features:(await this.processor(K)).input_features,is_last:!0}];for(const st of pe){Ze.num_frames=Math.floor(st.stride[0]/dt);const pt=await this.model.generate({inputs:st.input_features,...Ze});Re==="word"?(st.tokens=pt.sequences.tolist()[0],st.token_timestamps=pt.token_timestamps.tolist()[0].map(It=>(0,w.round)(It,2))):st.tokens=pt[0].tolist(),st.stride=st.stride.map(It=>It/gt)}const[Fe,Qe]=this.tokenizer._decode_asr(pe,{time_precision:ft,return_timestamps:Re,force_full_sequences:Ve});ne.push({text:Fe,...Qe})}return at?ne[0]:ne}async _call_moonshine(ce,ve){const Re=!Array.isArray(ce);Re&&(ce=[ce]);const je=this.processor.feature_extractor.config.sampling_rate,Ve=await L(ce,je),Ne=[];for(const Ze of Ve){const at=await this.processor(Ze),ft=Math.floor(Ze.length/je)*6,dt=await this.model.generate({max_new_tokens:ft,...ve,...at}),gt=this.processor.batch_decode(dt,{skip_special_tokens:!0})[0];Ne.push({text:gt})}return Re?Ne[0]:Ne}}class xe extends se{constructor(ce){super(ce)}async _call(ce,ve={}){const Re=Array.isArray(ce),je=await b(ce),{pixel_values:Ve}=await this.processor(je),Ne=[];for(const Ze of Ve){Ze.dims=[1,...Ze.dims];const at=await this.model.generate({inputs:Ze,...ve}),ft=this.tokenizer.batch_decode(at,{skip_special_tokens:!0}).map(dt=>({generated_text:dt.trim()}));Ne.push(ft)}return Re?Ne:Ne[0]}}class Le extends se{constructor(ce){super(ce)}async _call(ce,{top_k:ve=5}={}){const Re=await b(ce),{pixel_values:je}=await this.processor(Re),Ve=await this.model({pixel_values:je}),Ne=this.model.config.id2label,Ze=[];for(const at of Ve.logits){const ft=await(0,y.topk)(new y.Tensor("float32",(0,w.softmax)(at.data),at.dims),ve),dt=ft[0].tolist(),F=ft[1].tolist().map((ne,K)=>({label:Ne?Ne[ne]:`LABEL_${ne}`,score:dt[K]}));Ze.push(F)}return Array.isArray(ce)?Ze:Ze[0]}}class qe extends se{constructor(ce){super(ce),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(ce,{threshold:ve=.5,mask_threshold:Re=.5,overlap_mask_area_threshold:je=.8,label_ids_to_fuse:Ve=null,target_sizes:Ne=null,subtask:Ze=null}={}){if(Array.isArray(ce)&&ce.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const ft=await b(ce),dt=ft.map(Qe=>[Qe.height,Qe.width]),{pixel_values:gt,pixel_mask:F}=await this.processor(ft),ne=await this.model({pixel_values:gt,pixel_mask:F});let K=null;if(Ze!==null)K=this.subtasks_mapping[Ze];else for(let[Qe,st]of Object.entries(this.subtasks_mapping))if(st in this.processor.image_processor){K=this.processor.image_processor[st].bind(this.processor.image_processor),Ze=Qe;break}const pe=this.model.config.id2label,Fe=[];if(Ze==="panoptic"||Ze==="instance"){const Qe=K(ne,ve,Re,je,Ve,Ne??dt)[0],st=Qe.segmentation;for(const pt of Qe.segments_info){const It=new Uint8ClampedArray(st.data.length);for(let Ft=0;Ft<st.data.length;++Ft)st.data[Ft]===pt.id&&(It[Ft]=255);const St=new M.RawImage(It,st.dims[1],st.dims[0],1);Fe.push({score:pt.score,label:pe[pt.label_id],mask:St})}}else if(Ze==="semantic"){const{segmentation:Qe,labels:st}=K(ne,Ne??dt)[0];for(const pt of st){const It=new Uint8ClampedArray(Qe.data.length);for(let Ft=0;Ft<Qe.data.length;++Ft)Qe.data[Ft]===pt&&(It[Ft]=255);const St=new M.RawImage(It,Qe.dims[1],Qe.dims[0],1);Fe.push({score:null,label:pe[pt],mask:St})}}else throw Error(`Subtask ${Ze} not supported.`);return Fe}}class Ue extends se{constructor(ce){super(ce)}async _call(ce,ve,{hypothesis_template:Re="This is a photo of {}"}={}){const je=Array.isArray(ce),Ve=await b(ce),Ne=ve.map(F=>Re.replace("{}",F)),Ze=this.tokenizer(Ne,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:at}=await this.processor(Ve),ft=await this.model({...Ze,pixel_values:at}),dt=this.model.config.model_type==="siglip"?F=>F.sigmoid().data:F=>(0,w.softmax)(F.data),gt=[];for(const F of ft.logits_per_image){const K=[...dt(F)].map((pe,Fe)=>({score:pe,label:ve[Fe]}));K.sort((pe,Fe)=>Fe.score-pe.score),gt.push(K)}return je?gt:gt[0]}}class ut extends se{constructor(ce){super(ce)}async _call(ce,{threshold:ve=.9,percentage:Re=!1}={}){const je=Array.isArray(ce);if(je&&ce.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ve=await b(ce),Ne=Re?null:Ve.map(ne=>[ne.height,ne.width]),{pixel_values:Ze,pixel_mask:at}=await this.processor(Ve),ft=await this.model({pixel_values:Ze,pixel_mask:at}),dt=this.processor.image_processor.post_process_object_detection(ft,ve,Ne),gt=this.model.config.id2label,F=dt.map(ne=>ne.boxes.map((K,pe)=>({score:ne.scores[pe],label:gt[ne.classes[pe]],box:q(K,!Re)})));return je?F:F[0]}}class ue extends se{constructor(ce){super(ce)}async _call(ce,ve,{threshold:Re=.1,top_k:je=null,percentage:Ve=!1}={}){const Ne=Array.isArray(ce),Ze=await b(ce),at=this.tokenizer(ve,{padding:!0,truncation:!0}),ft=await this.processor(Ze),dt=[];for(let gt=0;gt<Ze.length;++gt){const F=Ze[gt],ne=Ve?null:[[F.height,F.width]],K=ft.pixel_values[gt].unsqueeze_(0),pe=await this.model({...at,pixel_values:K}),Fe=this.processor.image_processor.post_process_object_detection(pe,Re,ne,!0)[0];let Qe=Fe.boxes.map((st,pt)=>({score:Fe.scores[pt],label:ve[Fe.classes[pt]],box:q(st,!Ve)})).sort((st,pt)=>pt.score-st.score);je!==null&&(Qe=Qe.slice(0,je)),dt.push(Qe)}return Ne?dt:dt[0]}}class re extends se{constructor(ce){super(ce)}async _call(ce,ve,Re={}){const je=(await b(ce))[0],{pixel_values:Ve}=await this.processor(je),Ne=`<s_docvqa><s_question>${ve}</s_question><s_answer>`,Ze=this.tokenizer(Ne,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,at=await this.model.generate({inputs:Ve,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ze,...Re}),dt=this.tokenizer.batch_decode(at)[0].match(/<s_answer>(.*?)<\/s_answer>/);let gt=null;return dt&&dt.length>=2&&(gt=dt[1].trim()),[{answer:gt}]}}class he extends se{constructor(ve){super(ve);ge(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=ve.vocoder??null}async _call(ve,{speaker_embeddings:Re=null}={}){return this.processor?this._call_text_to_spectrogram(ve,{speaker_embeddings:Re}):this._call_text_to_waveform(ve)}async _call_text_to_waveform(ve){const Re=this.tokenizer(ve,{padding:!0,truncation:!0}),{waveform:je}=await this.model(Re),Ve=this.model.config.sampling_rate;return{audio:je.data,sampling_rate:Ve}}async _call_text_to_spectrogram(ve,{speaker_embeddings:Re}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await O.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Re=="string"||Re instanceof URL)&&(Re=new Float32Array(await(await fetch(Re)).arrayBuffer())),Re instanceof Float32Array)Re=new y.Tensor("float32",Re,[1,Re.length]);else if(!(Re instanceof y.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:je}=this.tokenizer(ve,{padding:!0,truncation:!0}),{waveform:Ve}=await this.model.generate_speech(je,Re,{vocoder:this.vocoder}),Ne=this.processor.feature_extractor.config.sampling_rate;return{audio:Ve.data,sampling_rate:Ne}}}class Pe extends se{constructor(ce){super(ce)}async _call(ce){const ve=await b(ce),Re=await this.processor(ve),je=await this.model(Re),Ve=[];for(const Ne of je.reconstruction){const Ze=Ne.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ve.push(M.RawImage.fromTensor(Ze))}return Ve.length>1?Ve:Ve[0]}}class Be extends se{constructor(ce){super(ce)}async _call(ce){const ve=await b(ce),Re=await this.processor(ve),{predicted_depth:je}=await this.model(Re),Ve=[];for(let Ne=0;Ne<ve.length;++Ne){const Ze=(0,y.interpolate)(je[Ne],ve[Ne].size.reverse(),"bilinear",!1),at=Ze.mul_(255/(0,w.max)(Ze.data)[0]).to("uint8");Ve.push({predicted_depth:je[Ne],depth:M.RawImage.fromTensor(at)})}return Ve.length>1?Ve:Ve[0]}}const et=Object.freeze({"text-classification":{tokenizer:f.AutoTokenizer,pipeline:ie,model:O.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:f.AutoTokenizer,pipeline:z,model:O.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:f.AutoTokenizer,pipeline:V,model:O.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:f.AutoTokenizer,pipeline:Y,model:O.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:f.AutoTokenizer,pipeline:$,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:f.AutoTokenizer,pipeline:g,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:f.AutoTokenizer,pipeline:D,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:f.AutoTokenizer,pipeline:T,model:O.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:f.AutoTokenizer,pipeline:ee,model:O.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:de,model:O.AutoModelForAudioClassification,processor:N.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:f.AutoTokenizer,pipeline:fe,model:O.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:f.AutoTokenizer,pipeline:ke,model:[O.AutoModelForSpeechSeq2Seq,O.AutoModelForCTC],processor:N.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:f.AutoTokenizer,pipeline:he,model:[O.AutoModelForTextToWaveform,O.AutoModelForTextToSpectrogram],processor:[N.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:f.AutoTokenizer,pipeline:xe,model:O.AutoModelForVision2Seq,processor:N.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Le,model:O.AutoModelForImageClassification,processor:N.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:qe,model:[O.AutoModelForImageSegmentation,O.AutoModelForSemanticSegmentation,O.AutoModelForUniversalSegmentation],processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:f.AutoTokenizer,pipeline:Ue,model:O.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ut,model:O.AutoModelForObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:f.AutoTokenizer,pipeline:ue,model:O.AutoModelForZeroShotObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:f.AutoTokenizer,pipeline:re,model:O.AutoModelForDocumentQuestionAnswering,processor:N.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:Pe,model:O.AutoModelForImageToImage,processor:N.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Be,model:O.AutoModelForDepthEstimation,processor:N.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:f.AutoTokenizer,pipeline:J,model:O.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:N.AutoProcessor,pipeline:le,model:[O.AutoModelForImageFeatureExtraction,O.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Xe=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function oe(De,ce=null,{progress_callback:ve=null,config:Re=null,cache_dir:je=null,local_files_only:Ve=!1,revision:Ne="main",device:Ze=null,dtype:at=null,model_file_name:ft=null,session_options:dt={}}={}){De=Xe[De]??De;const gt=et[De.split("_",1)[0]];if(!gt)throw Error(`Unsupported pipeline: ${De}. Must be one of [${Object.keys(et)}]`);ce||(ce=gt.default.model,console.log(`No model specified. Using default model: "${ce}".`));const F={progress_callback:ve,config:Re,cache_dir:je,local_files_only:Ve,revision:Ne,device:Ze,dtype:at,model_file_name:ft,session_options:dt},ne=new Map([["tokenizer",gt.tokenizer],["model",gt.model],["processor",gt.processor]]),K=await Je(ne,ce,F);K.task=De,(0,W.dispatchCallback)(ve,{status:"ready",task:De,model:ce});const pe=gt.pipeline;return new pe(K)}async function Je(De,ce,ve){const Re=Object.create(null),je=[];for(const[Ve,Ne]of De.entries()){if(!Ne)continue;let Ze;Array.isArray(Ne)?Ze=new Promise(async(at,ft)=>{var gt,F;let dt;for(const ne of Ne){if(ne===null){at(null);return}try{at(await ne.from_pretrained(ce,ve));return}catch(K){if((gt=K.message)!=null&&gt.includes("Unsupported model type"))dt=K;else if((F=K.message)!=null&&F.includes("Could not locate file"))dt=K;else{ft(K);return}}}ft(dt)}):Ze=Ne.from_pretrained(ce,ve),Re[Ve]=Ze,je.push(Ze)}await Promise.all(je);for(const[Ve,Ne]of Object.entries(Re))Re[Ve]=await Ne;return Re}},"./src/tokenizers.js":(Te,A,s)=>{s.r(A),s.d(A,{AlbertTokenizer:()=>Cs,AutoTokenizer:()=>Mn,BartTokenizer:()=>tr,BertTokenizer:()=>Xs,BlenderbotSmallTokenizer:()=>Bn,BlenderbotTokenizer:()=>gn,BloomTokenizer:()=>Rs,CLIPTokenizer:()=>Dn,CamembertTokenizer:()=>nt,CodeGenTokenizer:()=>Fn,CodeLlamaTokenizer:()=>Ns,CohereTokenizer:()=>Rn,ConvBertTokenizer:()=>$s,DebertaTokenizer:()=>os,DebertaV2Tokenizer:()=>Ss,DistilBertTokenizer:()=>is,ElectraTokenizer:()=>Ot,EsmTokenizer:()=>as,FalconTokenizer:()=>js,GPT2Tokenizer:()=>bs,GPTNeoXTokenizer:()=>Ts,GemmaTokenizer:()=>Js,Grok1Tokenizer:()=>_n,HerbertTokenizer:()=>cs,LlamaTokenizer:()=>In,M2M100Tokenizer:()=>zt,MBart50Tokenizer:()=>Bs,MBartTokenizer:()=>es,MPNetTokenizer:()=>no,MarianTokenizer:()=>Us,MgpstrTokenizer:()=>yn,MobileBertTokenizer:()=>zs,NllbTokenizer:()=>xs,NougatTokenizer:()=>Qr,PreTrainedTokenizer:()=>Nt,Qwen2Tokenizer:()=>mn,RoFormerTokenizer:()=>Qs,RobertaTokenizer:()=>Ys,SiglipTokenizer:()=>Ln,SpeechT5Tokenizer:()=>ir,SqueezeBertTokenizer:()=>ks,T5Tokenizer:()=>lr,TokenizerModel:()=>le,VitsTokenizer:()=>wn,Wav2Vec2CTCTokenizer:()=>zn,WhisperTokenizer:()=>fn,XLMRobertaTokenizer:()=>On,XLMTokenizer:()=>_t,is_chinese_char:()=>Y});var f=s("./src/utils/generic.js"),O=s("./src/utils/core.js"),N=s("./src/utils/hub.js"),Q=s("./src/utils/maths.js"),W=s("./src/utils/tensor.js"),w=s("./src/utils/data-structures.js"),v=s("./node_modules/@huggingface/jinja/dist/index.js"),y=s("./src/models/whisper/common_whisper.js");s("./src/utils/constants.js");async function M(Ee,P){const H=await Promise.all([(0,N.getModelJSON)(Ee,"tokenizer.json",!0,P),(0,N.getModelJSON)(Ee,"tokenizer_config.json",!0,P)]);return P.legacy!==null&&(H[1].legacy=P.legacy),H}function b(Ee,P){const H=[];let ae=0;for(const Me of Ee.matchAll(P)){const Ce=Me[0];ae<Me.index&&H.push(Ee.slice(ae,Me.index)),Ce.length>0&&H.push(Ce),ae=Me.index+Ce.length}return ae<Ee.length&&H.push(Ee.slice(ae)),H}function L(Ee,P=!0){if(Ee.Regex!==void 0){let H=Ee.Regex.replace(/\\([#&~])/g,"$1");for(const[ae,Me]of ee)H=H.replaceAll(ae,Me);return new RegExp(H,"gu")}else if(Ee.String!==void 0){const H=(0,O.escapeRegExp)(Ee.String);return new RegExp(P?H:`(${H})`,"gu")}else return console.warn("Unknown pattern type:",Ee),null}function q(Ee){return new Map(Object.entries(Ee))}function se(Ee){const P=Ee.dims;switch(P.length){case 1:return Ee.tolist();case 2:if(P[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return Ee.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${P.length}.`)}}function ie(Ee){return Ee.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 z(Ee){return Ee.replace(new RegExp("\\p{M}","gu"),"")}function V(Ee){return z(Ee.toLowerCase())}function Y(Ee){return Ee>=19968&&Ee<=40959||Ee>=13312&&Ee<=19903||Ee>=131072&&Ee<=173791||Ee>=173824&&Ee<=177983||Ee>=177984&&Ee<=178207||Ee>=178208&&Ee<=183983||Ee>=63744&&Ee<=64255||Ee>=194560&&Ee<=195103}function D(Ee,P,H){const ae=[];let Me=0;for(;Me<Ee.length;){if(ae.push(Ee[Me]),(P.get(Ee[Me])??H)!==H){++Me;continue}for(;++Me<Ee.length&&(P.get(Ee[Me])??H)===H;)P.get(ae.at(-1))!==H&&(ae[ae.length-1]+=Ee[Me])}return ae}function $(Ee){return Ee.match(/\S+/g)||[]}const g="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",C=new RegExp(`^[${g}]+$`,"gu"),T=".,!?…。，、।۔،",ee=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${T}])]+`,` ?[^\\s${T}]+`]]);class J{constructor(P){this.content=P.content,this.id=P.id,this.single_word=P.single_word??!1,this.lstrip=P.lstrip??!1,this.rstrip=P.rstrip??!1,this.special=P.special??!1,this.normalized=P.normalized??null}}class le extends f.Callable{constructor(P){super(),this.config=P,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(P,...H){switch(P.type){case"WordPiece":return new de(P);case"Unigram":return new fe(P,...H);case"BPE":return new Le(P);default:if(P.vocab)return Array.isArray(P.vocab)?new fe(P,...H):new qe(P,...H);throw new Error(`Unknown TokenizerModel type: ${P.type}`)}}_call(P){return P=this.encode(P),this.fuse_unk&&(P=D(P,this.tokens_to_ids,this.unk_token_id)),P}encode(P){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(P){return P.map(H=>this.tokens_to_ids.get(H)??this.unk_token_id)}convert_ids_to_tokens(P){return P.map(H=>this.vocab[H]??this.unk_token)}}class de extends le{constructor(P){super(P),this.tokens_to_ids=q(P.vocab),this.unk_token_id=this.tokens_to_ids.get(P.unk_token),this.unk_token=P.unk_token,this.max_input_chars_per_word=P.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[H,ae]of this.tokens_to_ids)this.vocab[ae]=H}encode(P){const H=[];for(const ae of P){const Me=[...ae];if(Me.length>this.max_input_chars_per_word){H.push(this.unk_token);continue}let Ce=!1,He=0;const ct=[];for(;He<Me.length;){let yt=Me.length,ht=null;for(;He<yt;){let it=Me.slice(He,yt).join("");if(He>0&&(it=this.config.continuing_subword_prefix+it),this.tokens_to_ids.has(it)){ht=it;break}--yt}if(ht===null){Ce=!0;break}ct.push(ht),He=yt}Ce?H.push(this.unk_token):H.push(...ct)}return H}}class fe extends le{constructor(P,H){super(P);const ae=P.vocab.length;this.vocab=new Array(ae),this.scores=new Array(ae);for(let Me=0;Me<ae;++Me){const Ce=P.vocab[Me];this.vocab[Me]=Ce[0],this.scores[Me]=Ce[1]}this.unk_token_id=P.unk_id,this.unk_token=this.vocab[P.unk_id],this.tokens_to_ids=new Map(this.vocab.map((Me,Ce)=>[Me,Ce])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=H.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,Q.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new w.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(P){const H=P.chars,ae=1;let Me=0;for(;Me<H.length;){let Ce=!1;const He=H.slice(Me).join(""),ct=this.trie.commonPrefixSearch(He);for(const yt of ct){const ht=this.tokens_to_ids.get(yt),it=this.scores[ht],Pt=(0,O.len)(yt);P.insert(Me,Pt,it,ht),!Ce&&Pt===ae&&(Ce=!0)}Ce||P.insert(Me,ae,this.unk_score,this.unk_token_id),Me+=ae}}tokenize(P){const H=new w.TokenLattice(P,this.bos_token_id,this.eos_token_id);return this.populateNodes(H),H.tokens()}encode(P){const H=[];for(const ae of P){const Me=this.tokenize(ae);H.push(...Me)}return H}}const ke=(()=>{const Ee=[...Array.from({length:94},(Me,Ce)=>Ce+33),...Array.from({length:12},(Me,Ce)=>Ce+161),...Array.from({length:82},(Me,Ce)=>Ce+174)],P=Ee.slice();let H=0;for(let Me=0;Me<256;++Me)Ee.includes(Me)||(Ee.push(Me),P.push(256+H),H+=1);const ae=P.map(Me=>String.fromCharCode(Me));return Object.fromEntries(Ee.map((Me,Ce)=>[Me,ae[Ce]]))})(),xe=(0,O.reverseDictionary)(ke);class Le extends le{constructor(P){super(P),this.tokens_to_ids=q(P.vocab),this.unk_token_id=this.tokens_to_ids.get(P.unk_token),this.unk_token=P.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ae,Me]of this.tokens_to_ids)this.vocab[Me]=ae;const H=Array.isArray(P.merges[0]);this.merges=H?P.merges:P.merges.map(ae=>ae.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ae,Me)=>[JSON.stringify(ae),Me])),this.end_of_word_suffix=P.end_of_word_suffix,this.continuing_subword_suffix=P.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(P){if(P.length===0)return[];const H=this.cache.get(P);if(H!==void 0)return H;const ae=Array.from(P);this.end_of_word_suffix&&(ae[ae.length-1]+=this.end_of_word_suffix);let Me=[];if(ae.length>1){const Ce=new w.PriorityQueue((yt,ht)=>yt.score<ht.score);let He={token:ae[0],bias:0,prev:null,next:null},ct=He;for(let yt=1;yt<ae.length;++yt){const ht={bias:yt/ae.length,token:ae[yt],prev:ct,next:null};ct.next=ht,this._add_node(Ce,ct),ct=ht}for(;!Ce.isEmpty();){const yt=Ce.pop();if(yt.deleted||!yt.next||yt.next.deleted)continue;if(yt.deleted=!0,yt.next.deleted=!0,yt.prev){const it={...yt.prev};yt.prev.deleted=!0,yt.prev=it,it.prev?it.prev.next=it:He=it}const ht={token:yt.token+yt.next.token,bias:yt.bias,prev:yt.prev,next:yt.next.next};ht.prev?(ht.prev.next=ht,this._add_node(Ce,ht.prev)):He=ht,ht.next&&(ht.next.prev=ht,this._add_node(Ce,ht))}for(let yt=He;yt!==null;yt=yt.next)Me.push(yt.token)}else Me=ae;if(this.continuing_subword_suffix)for(let Ce=0;Ce<Me.length-1;++Ce)Me[Ce]+=this.continuing_subword_suffix;return this.cache.set(P,Me),Me}_add_node(P,H){const ae=this.bpe_ranks.get(JSON.stringify([H.token,H.next.token]));ae!==void 0&&(H.score=ae+H.bias,P.push(H))}encode(P){const H=[];for(const ae of P){if(this.ignore_merges&&this.tokens_to_ids.has(ae)){H.push(ae);continue}const Me=this.bpe(ae);for(const Ce of Me)if(this.tokens_to_ids.has(Ce))H.push(Ce);else if(this.byte_fallback){const He=Array.from(this.text_encoder.encode(Ce)).map(ct=>`<0x${ct.toString(16).toUpperCase().padStart(2,"0")}>`);He.every(ct=>this.tokens_to_ids.has(ct))?H.push(...He):H.push(this.unk_token)}else H.push(this.unk_token)}return H}}class qe extends le{constructor(P,H){super(P),this.tokens_to_ids=q(H.target_lang?P.vocab[H.target_lang]:P.vocab),this.bos_token=H.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=H.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=H.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=H.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[ae,Me]of this.tokens_to_ids)this.vocab[Me]=ae}encode(P){return P}}class Ue extends f.Callable{constructor(P){super(),this.config=P}static fromConfig(P){if(P===null)return null;switch(P.type){case"BertNormalizer":return new Je(P);case"Precompiled":return new gr(P);case"Sequence":return new oe(P);case"Replace":return new ut(P);case"NFC":return new ue(P);case"NFKC":return new re(P);case"NFKD":return new he(P);case"Strip":return new Pe(P);case"StripAccents":return new Be(P);case"Lowercase":return new et(P);case"Prepend":return new Xe(P);default:throw new Error(`Unknown Normalizer type: ${P.type}`)}}normalize(P){throw Error("normalize should be implemented in subclass.")}_call(P){return this.normalize(P)}}class ut extends Ue{normalize(P){const H=L(this.config.pattern);return H===null?P:P.replaceAll(H,this.config.content)}}class ue extends Ue{normalize(P){return P=P.normalize("NFC"),P}}class re extends Ue{normalize(P){return P=P.normalize("NFKC"),P}}class he extends Ue{normalize(P){return P=P.normalize("NFKD"),P}}class Pe extends Ue{normalize(P){return this.config.strip_left&&this.config.strip_right?P=P.trim():(this.config.strip_left&&(P=P.trimStart()),this.config.strip_right&&(P=P.trimEnd())),P}}class Be extends Ue{normalize(P){return P=z(P),P}}class et extends Ue{normalize(P){return P=P.toLowerCase(),P}}class Xe extends Ue{normalize(P){return P=this.config.prepend+P,P}}class oe extends Ue{constructor(P){super(P),this.normalizers=P.normalizers.map(H=>Ue.fromConfig(H))}normalize(P){return this.normalizers.reduce((H,ae)=>ae.normalize(H),P)}}class Je extends Ue{_tokenize_chinese_chars(P){const H=[];for(let ae=0;ae<P.length;++ae){const Me=P[ae],Ce=Me.charCodeAt(0);Y(Ce)?(H.push(" "),H.push(Me),H.push(" ")):H.push(Me)}return H.join("")}stripAccents(P){return P.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(P){switch(P){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(P)}}_clean_text(P){const H=[];for(const ae of P){const Me=ae.charCodeAt(0);Me===0||Me===65533||this._is_control(ae)||(/^\s$/.test(ae)?H.push(" "):H.push(ae))}return H.join("")}normalize(P){return this.config.clean_text&&(P=this._clean_text(P)),this.config.handle_chinese_chars&&(P=this._tokenize_chinese_chars(P)),this.config.lowercase?(P=P.toLowerCase(),this.config.strip_accents!==!1&&(P=this.stripAccents(P))):this.config.strip_accents&&(P=this.stripAccents(P)),P}}class De extends f.Callable{static fromConfig(P){if(P===null)return null;switch(P.type){case"BertPreTokenizer":return new ce(P);case"Sequence":return new Sr(P);case"Whitespace":return new Ar(P);case"WhitespaceSplit":return new Xr(P);case"Metaspace":return new At(P);case"ByteLevel":return new ve(P);case"Split":return new Re(P);case"Punctuation":return new je(P);case"Digits":return new Ve(P);case"Replace":return new ns(P);default:throw new Error(`Unknown PreTokenizer type: ${P.type}`)}}pre_tokenize_text(P,H){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(P,H){return(Array.isArray(P)?P.map(ae=>this.pre_tokenize_text(ae,H)):this.pre_tokenize_text(P,H)).flat()}_call(P,H){return this.pre_tokenize(P,H)}}class ce extends De{constructor(P){super(),this.pattern=new RegExp(`[^\\s${g}]+|[${g}]`,"gu")}pre_tokenize_text(P,H){return P.trim().match(this.pattern)||[]}}class ve extends De{constructor(P){super(),this.config=P,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=ke,this.text_encoder=new TextEncoder}pre_tokenize_text(P,H){return this.add_prefix_space&&!P.startsWith(" ")&&(P=" "+P),(this.use_regex?P.match(this.pattern)||[]:[P]).map(Me=>Array.from(this.text_encoder.encode(Me),Ce=>this.byte_encoder[Ce]).join(""))}}class Re extends De{constructor(P){super(),this.config=P,this.pattern=L(this.config.pattern,this.config.invert)}pre_tokenize_text(P,H){var ae;return this.pattern===null?[]:this.config.invert?P.match(this.pattern)||[]:((ae=this.config.behavior)==null?void 0:ae.toLowerCase())==="removed"?P.split(this.pattern).filter(Me=>Me):b(P,this.pattern)}}class je extends De{constructor(P){super(),this.config=P,this.pattern=new RegExp(`[^${g}]+|[${g}]+`,"gu")}pre_tokenize_text(P,H){return P.match(this.pattern)||[]}}class Ve extends De{constructor(P){super(),this.config=P;const H=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(H,"gu")}pre_tokenize_text(P,H){return P.match(this.pattern)||[]}}class Ne extends f.Callable{constructor(P){super(),this.config=P}static fromConfig(P){if(P===null)return null;switch(P.type){case"TemplateProcessing":return new ft(P);case"ByteLevel":return new dt(P);case"RobertaProcessing":return new at(P);case"BertProcessing":return new Ze(P);case"Sequence":return new gt(P);default:throw new Error(`Unknown PostProcessor type: ${P.type}`)}}post_process(P,...H){throw Error("post_process should be implemented in subclass.")}_call(P,...H){return this.post_process(P,...H)}}class Ze extends Ne{constructor(P){super(P),this.cls=P.cls[0],this.sep=P.sep[0]}post_process(P,H=null,{add_special_tokens:ae=!0}={}){ae&&(P=(0,O.mergeArrays)([this.cls],P,[this.sep]));let Me=new Array(P.length).fill(0);if(H!==null){const Ce=ae&&this instanceof at?[this.sep]:[],He=ae?[this.sep]:[];P=(0,O.mergeArrays)(P,Ce,H,He),Me=(0,O.mergeArrays)(Me,new Array(H.length+Ce.length+He.length).fill(1))}return{tokens:P,token_type_ids:Me}}}class at extends Ze{}class ft extends Ne{constructor(P){super(P),this.single=P.single,this.pair=P.pair}post_process(P,H=null,{add_special_tokens:ae=!0}={}){const Me=H===null?this.single:this.pair;let Ce=[],He=[];for(const ct of Me)"SpecialToken"in ct?ae&&(Ce.push(ct.SpecialToken.id),He.push(ct.SpecialToken.type_id)):"Sequence"in ct&&(ct.Sequence.id==="A"?(Ce=(0,O.mergeArrays)(Ce,P),He=(0,O.mergeArrays)(He,new Array(P.length).fill(ct.Sequence.type_id))):ct.Sequence.id==="B"&&(Ce=(0,O.mergeArrays)(Ce,H),He=(0,O.mergeArrays)(He,new Array(H.length).fill(ct.Sequence.type_id))));return{tokens:Ce,token_type_ids:He}}}class dt extends Ne{post_process(P,H=null){return H&&(P=(0,O.mergeArrays)(P,H)),{tokens:P}}}class gt extends Ne{constructor(P){super(P),this.processors=P.processors.map(H=>Ne.fromConfig(H))}post_process(P,H=null,ae={}){let Me;for(const Ce of this.processors)if(Ce instanceof dt)P=Ce.post_process(P).tokens,H&&(H=Ce.post_process(H).tokens);else{const He=Ce.post_process(P,H,ae);P=He.tokens,Me=He.token_type_ids}return{tokens:P,token_type_ids:Me}}}class F extends f.Callable{constructor(P){super(),this.config=P,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=P.trim_offsets}static fromConfig(P){if(P===null)return null;switch(P.type){case"WordPiece":return new Qe(P);case"Metaspace":return new nr(P);case"ByteLevel":return new st(P);case"Replace":return new ne(P);case"ByteFallback":return new K(P);case"Fuse":return new pe(P);case"Strip":return new Fe(P);case"Sequence":return new It(P);case"CTC":return new pt(P);case"BPEDecoder":return new St(P);default:throw new Error(`Unknown Decoder type: ${P.type}`)}}_call(P){return this.decode(P)}decode(P){return this.decode_chain(P).join("")}decode_chain(P){throw Error("`decode_chain` should be implemented in subclass.")}}class ne extends F{decode_chain(P){const H=L(this.config.pattern);return H===null?P:P.map(ae=>ae.replaceAll(H,this.config.content))}}class K extends F{constructor(P){super(P),this.text_decoder=new TextDecoder}decode_chain(P){const H=[];let ae=[];for(const Me of P){let Ce=null;if(Me.length===6&&Me.startsWith("<0x")&&Me.endsWith(">")){const He=parseInt(Me.slice(3,5),16);isNaN(He)||(Ce=He)}if(Ce!==null)ae.push(Ce);else{if(ae.length>0){const He=this.text_decoder.decode(Uint8Array.from(ae));H.push(He),ae=[]}H.push(Me)}}if(ae.length>0){const Me=this.text_decoder.decode(Uint8Array.from(ae));H.push(Me),ae=[]}return H}}class pe extends F{decode_chain(P){return[P.join("")]}}class Fe extends F{constructor(P){super(P),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(P){return P.map(H=>{let ae=0;for(let Ce=0;Ce<this.start&&H[Ce]===this.content;++Ce){ae=Ce+1;continue}let Me=H.length;for(let Ce=0;Ce<this.stop;++Ce){const He=H.length-Ce-1;if(H[He]===this.content){Me=He;continue}else break}return H.slice(ae,Me)})}}class Qe extends F{constructor(P){super(P),this.cleanup=P.cleanup}decode_chain(P){return P.map((H,ae)=>(ae!==0&&(H.startsWith(this.config.prefix)?H=H.replace(this.config.prefix,""):H=" "+H),this.cleanup&&(H=ie(H)),H))}}class st extends F{constructor(P){super(P),this.byte_decoder=xe,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(P){const H=P.join(""),ae=new Uint8Array([...H].map(Ce=>this.byte_decoder[Ce]));return this.text_decoder.decode(ae)}decode_chain(P){const H=[];let ae=[];for(const Me of P)this.added_tokens.find(Ce=>Ce.content===Me)!==void 0?(ae.length>0&&(H.push(this.convert_tokens_to_string(ae)),ae=[]),H.push(Me)):ae.push(Me);return ae.length>0&&H.push(this.convert_tokens_to_string(ae)),H}}class pt extends F{constructor(P){super(P),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(P){if(P.length===0)return"";const H=[P[0]];for(let Ce=1;Ce<P.length;++Ce)P[Ce]!==H.at(-1)&&H.push(P[Ce]);let Me=H.filter(Ce=>Ce!==this.pad_token).join("");return this.cleanup&&(Me=ie(Me).replaceAll(this.word_delimiter_token," ").trim()),Me}decode_chain(P){return[this.convert_tokens_to_string(P)]}}class It extends F{constructor(P){super(P),this.decoders=P.decoders.map(H=>F.fromConfig(H))}decode_chain(P){return this.decoders.reduce((H,ae)=>ae.decode_chain(H),P)}}class St extends F{constructor(P){super(P),this.suffix=this.config.suffix}decode_chain(P){return P.map((H,ae)=>H.replaceAll(this.suffix,ae===P.length-1?"":" "))}}class Ft extends F{decode_chain(P){let H="";for(let ae=1;ae<P.length;ae+=2)H+=P[ae];return[H]}}class At extends De{constructor(P){super(),this.addPrefixSpace=P.add_prefix_space,this.replacement=P.replacement,this.strRep=P.str_rep||this.replacement,this.prepend_scheme=P.prepend_scheme??"always"}pre_tokenize_text(P,{section_index:H=void 0}={}){let ae=P.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!ae.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&H===0)&&(ae=this.strRep+ae),[ae]}}class nr extends F{constructor(P){super(P),this.addPrefixSpace=P.add_prefix_space,this.replacement=P.replacement}decode_chain(P){const H=[];for(let ae=0;ae<P.length;++ae){let Me=P[ae].replaceAll(this.replacement," ");this.addPrefixSpace&&ae==0&&Me.startsWith(" ")&&(Me=Me.substring(1)),H.push(Me)}return H}}class gr extends Ue{constructor(P){super(P),this.charsmap=P.precompiled_charsmap}normalize(P){return P=P.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),P=P.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),P.includes("～")?P=P.split("～").map(ae=>ae.normalize("NFKC")).join("～"):P=P.normalize("NFKC"),P}}class Sr extends De{constructor(P){super(),this.tokenizers=P.pretokenizers.map(H=>De.fromConfig(H))}pre_tokenize_text(P,H){return this.tokenizers.reduce((ae,Me)=>Me.pre_tokenize(ae,H),[P])}}class Ar extends De{constructor(P){super()}pre_tokenize_text(P,H){return P.match(/\w+|[^\w\s]+/g)||[]}}class Xr extends De{constructor(P){super()}pre_tokenize_text(P,H){return $(P)}}class ns extends De{constructor(P){super(),this.config=P,this.pattern=L(this.config.pattern),this.content=this.config.content}pre_tokenize_text(P,H){return this.pattern===null?[P]:[P.replaceAll(this.pattern,this.config.content)]}}const qs=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ls(Ee,P,H,ae){for(const Me of Object.keys(Ee)){const Ce=P-Ee[Me].length,He=H(Me),ct=new Array(Ce).fill(He);Ee[Me]=ae==="right"?(0,O.mergeArrays)(Ee[Me],ct):(0,O.mergeArrays)(ct,Ee[Me])}}function Ms(Ee,P){for(const H of Object.keys(Ee))Ee[H].length=P}class Nt extends f.Callable{constructor(H,ae){super();ge(this,"return_token_type_ids",!1);ge(this,"padding_side","right");this._tokenizer_config=ae,this.normalizer=Ue.fromConfig(H.normalizer),this.pre_tokenizer=De.fromConfig(H.pre_tokenizer),this.model=le.fromConfig(H.model,ae),this.post_processor=Ne.fromConfig(H.post_processor),this.decoder=F.fromConfig(H.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const Me of H.added_tokens){const Ce=new J(Me);this.added_tokens.push(Ce),this.model.tokens_to_ids.set(Ce.content,Ce.id),this.model.vocab[Ce.id]=Ce.content,Ce.special&&(this.special_tokens.push(Ce.content),this.all_special_ids.push(Ce.id))}if(this.additional_special_tokens=ae.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((Me,Ce)=>Ce.content.length-Me.content.length).map(Me=>`${Me.lstrip?"\\s*":""}(${(0,O.escapeRegExp)(Me.content)})${Me.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=ae.model_max_length,this.remove_space=ae.remove_space,this.clean_up_tokenization_spaces=ae.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ae.do_lowercase_and_remove_accent??!1,ae.padding_side&&(this.padding_side=ae.padding_side),this.legacy=!1,this.chat_template=ae.chat_template??null,Array.isArray(this.chat_template)){const Me=Object.create(null);for(const{name:Ce,template:He}of this.chat_template){if(typeof Ce!="string"||typeof He!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');Me[Ce]=He}this.chat_template=Me}this._compiled_template_cache=new Map}getToken(...H){for(const ae of H){const Me=this._tokenizer_config[ae];if(Me)if(typeof Me=="object"){if(Me.__type==="AddedToken")return Me.content;throw Error(`Unknown token: ${Me}`)}else return Me}return null}static async from_pretrained(H,{progress_callback:ae=null,config:Me=null,cache_dir:Ce=null,local_files_only:He=!1,revision:ct="main",legacy:yt=null}={}){const ht=await M(H,{progress_callback:ae,config:Me,cache_dir:Ce,local_files_only:He,revision:ct,legacy:yt});return new this(...ht)}_call(H,{text_pair:ae=null,add_special_tokens:Me=!0,padding:Ce=!1,truncation:He=null,max_length:ct=null,return_tensor:yt=!0,return_token_type_ids:ht=null}={}){const it=Array.isArray(H);let Pt;if(it){if(H.length===0)throw Error("text array must be non-empty");if(ae!==null){if(Array.isArray(ae)){if(H.length!==ae.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Pt=H.map((rr,$e)=>this._encode_plus(rr,{text_pair:ae[$e],add_special_tokens:Me,return_token_type_ids:ht}))}else Pt=H.map(rr=>this._encode_plus(rr,{add_special_tokens:Me,return_token_type_ids:ht}))}else{if(H==null)throw Error("text may not be null or undefined");if(Array.isArray(ae))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Pt=[this._encode_plus(H,{text_pair:ae,add_special_tokens:Me,return_token_type_ids:ht})]}if(ct===null?Ce==="max_length"?ct=this.model_max_length:ct=(0,Q.max)(Pt.map(rr=>rr.input_ids.length))[0]:He||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),ct=Math.min(ct,this.model_max_length??1/0),Ce||He)for(let rr=0;rr<Pt.length;++rr)Pt[rr].input_ids.length!==ct&&(Pt[rr].input_ids.length>ct?He&&Ms(Pt[rr],ct):Ce&&Ls(Pt[rr],ct,$e=>$e==="input_ids"?this.pad_token_id:0,this.padding_side));const hr={};if(yt){if(!(Ce&&He)&&Pt.some($e=>{var wr;for(const Rr of Object.keys($e))if($e[Rr].length!==((wr=Pt[0][Rr])==null?void 0:wr.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 rr=[Pt.length,Pt[0].input_ids.length];for(const $e of Object.keys(Pt[0]))hr[$e]=new W.Tensor("int64",BigInt64Array.from(Pt.flatMap(wr=>wr[$e]).map(BigInt)),rr)}else{for(const rr of Object.keys(Pt[0]))hr[rr]=Pt.map($e=>$e[rr]);if(!it)for(const rr of Object.keys(hr))hr[rr]=hr[rr][0]}return hr}_encode_text(H){return H===null?null:(this.added_tokens_regex?H.split(this.added_tokens_regex).filter(Ce=>Ce):[H]).map((Ce,He)=>{if(this.added_tokens.find(yt=>yt.content===Ce)!==void 0)return Ce;{if(this.remove_space===!0&&(Ce=Ce.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(Ce=V(Ce)),this.normalizer!==null&&(Ce=this.normalizer(Ce)),Ce.length===0)return[];const yt=this.pre_tokenizer!==null?this.pre_tokenizer(Ce,{section_index:He}):[Ce];return this.model(yt)}}).flat()}_encode_plus(H,{text_pair:ae=null,add_special_tokens:Me=!0,return_token_type_ids:Ce=null}={}){const{tokens:He,token_type_ids:ct}=this._tokenize_helper(H,{pair:ae,add_special_tokens:Me}),yt=this.model.convert_tokens_to_ids(He),ht={input_ids:yt,attention_mask:new Array(yt.length).fill(1)};return(Ce??this.return_token_type_ids)&&ct&&(ht.token_type_ids=ct),ht}_tokenize_helper(H,{pair:ae=null,add_special_tokens:Me=!1}={}){const Ce=this._encode_text(H),He=this._encode_text(ae);return this.post_processor?this.post_processor(Ce,He,{add_special_tokens:Me}):{tokens:(0,O.mergeArrays)(Ce??[],He??[])}}tokenize(H,{pair:ae=null,add_special_tokens:Me=!1}={}){return this._tokenize_helper(H,{pair:ae,add_special_tokens:Me}).tokens}encode(H,{text_pair:ae=null,add_special_tokens:Me=!0,return_token_type_ids:Ce=null}={}){return this._encode_plus(H,{text_pair:ae,add_special_tokens:Me,return_token_type_ids:Ce}).input_ids}batch_decode(H,ae={}){return H instanceof W.Tensor&&(H=H.tolist()),H.map(Me=>this.decode(Me,ae))}decode(H,ae={}){if(H instanceof W.Tensor&&(H=se(H)),!Array.isArray(H)||H.length===0||!(0,O.isIntegralNumber)(H[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(H,ae)}decode_single(H,{skip_special_tokens:ae=!1,clean_up_tokenization_spaces:Me=null}){let Ce=this.model.convert_ids_to_tokens(H);ae&&(Ce=Ce.filter(ct=>!this.special_tokens.includes(ct)));let He=this.decoder?this.decoder(Ce):Ce.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(He=He.replaceAll(this.decoder.end_of_word_suffix," "),ae&&(He=He.trim())),(Me??this.clean_up_tokenization_spaces)&&(He=ie(He)),He}get_chat_template({chat_template:H=null,tools:ae=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const Me=this.chat_template;if(H!==null&&Object.hasOwn(Me,H))H=Me[H];else if(H===null)if(ae!==null&&"tool_use"in Me)H=Me.tool_use;else if("default"in Me)H=Me.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 ${Object.keys(Me).sort()}.`)}else if(H===null)if(this.chat_template)H=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return H}apply_chat_template(H,{tools:ae=null,documents:Me=null,chat_template:Ce=null,add_generation_prompt:He=!1,tokenize:ct=!0,padding:yt=!1,truncation:ht=!1,max_length:it=null,return_tensor:Pt=!0,return_dict:hr=!1,tokenizer_kwargs:rr={},...$e}={}){if(Ce=this.get_chat_template({chat_template:Ce,tools:ae}),typeof Ce!="string")throw Error(`chat_template must be a string, but got ${typeof Ce}`);let wr=this._compiled_template_cache.get(Ce);wr===void 0&&(wr=new v.Template(Ce),this._compiled_template_cache.set(Ce,wr));const Rr=Object.create(null);for(const Jr of qs){const Bt=this.getToken(Jr);Bt&&(Rr[Jr]=Bt)}const Yr=wr.render({messages:H,add_generation_prompt:He,tools:ae,documents:Me,...Rr,...$e});if(ct){const Jr=this._call(Yr,{add_special_tokens:!1,padding:yt,truncation:ht,max_length:it,return_tensor:Pt,...rr});return hr?Jr:Jr.input_ids}return Yr}}class Xs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class Cs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class zs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class ks extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class os extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class Ss extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class cs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class $s extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class Qs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class is extends Nt{}class nt extends Nt{}class _t extends Nt{constructor(H,ae){super(H,ae);ge(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ot extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class lr extends Nt{}class bs extends Nt{}class tr extends Nt{}class es extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ae=>this.languageRegex.test(ae)),this.lang_to_token=ae=>ae}_build_translation_inputs(P,H,ae){return Zs(this,P,H,ae)}}class Bs extends es{}class Ys extends Nt{}class Rs extends Nt{}const vs="▁";class In extends Nt{constructor(H,ae){super(H,ae);ge(this,"padding_side","left");this.legacy=ae.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new At({replacement:vs,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(H){if(H===null)return null;if(this.legacy||H.length===0)return super._encode_text(H);let ae=super._encode_text(vs+H.replaceAll(vs," "));return ae.length>1&&ae[0]===vs&&this.special_tokens.includes(ae[1])&&(ae=ae.slice(1)),ae}}class Ns extends Nt{}class On extends Nt{}class no extends Nt{}class js extends Nt{}class Ts extends Nt{}class as extends Nt{}class mn extends Nt{}class Js extends Nt{}class _n extends Nt{}function Zs(Ee,P,H,ae){if(!("language_codes"in Ee)||!Array.isArray(Ee.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Ee)||!(Ee.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Ee)||typeof Ee.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const Me=ae.src_lang,Ce=ae.tgt_lang;if(!Ee.language_codes.includes(Ce))throw new Error(`Target language code "${Ce}" is not valid. Must be one of: {${Ee.language_codes.join(", ")}}`);if(Me!==void 0){if(!Ee.language_codes.includes(Me))throw new Error(`Source language code "${Me}" is not valid. Must be one of: {${Ee.language_codes.join(", ")}}`);for(const He of Ee.post_processor.config.single)if("SpecialToken"in He&&Ee.languageRegex.test(He.SpecialToken.id)){He.SpecialToken.id=Ee.lang_to_token(Me);break}}return ae.forced_bos_token_id=Ee.model.convert_tokens_to_ids([Ee.lang_to_token(Ce)])[0],Ee._call(P,H)}class xs extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(ae=>this.languageRegex.test(ae)),this.lang_to_token=ae=>ae}_build_translation_inputs(P,H,ae){return Zs(this,P,H,ae)}}class zt extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(ae=>this.languageRegex.test(ae)).map(ae=>ae.slice(2,-2)),this.lang_to_token=ae=>`__${ae}__`}_build_translation_inputs(P,H,ae){return Zs(this,P,H,ae)}}class fn extends Nt{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(P,{return_timestamps:H=!1,return_language:ae=!1,time_precision:Me=null,force_full_sequences:Ce=!0}={}){if(Me===null)throw Error("Must specify time_precision");let He=null;const ct=H==="word";function yt(){return{language:He,timestamp:[null,null],text:""}}const ht=[];let it=yt(),Pt=0;const hr=this.timestamp_begin,$e=hr+1500;let wr=[],Rr=[],Yr=!1,Jr=null;const Bt=new Set(this.all_special_ids);for(const er of P){const mr=er.tokens,vt=ct?er.token_timestamps:null;let yr=null,Es=hr;if("stride"in er){const[Mt,br,ze]=er.stride;if(Pt-=br,Jr=Mt-ze,br&&(Es=br/Me+hr),ze)for(let wt=mr.length-1;wt>=0;--wt){const ts=Number(mr[wt]);if(ts>=hr){if(yr!==null&&(ts-hr)*Me<Jr)break;yr=ts}}}let Dr=[],Hr=[];for(let Mt=0;Mt<mr.length;++Mt){const br=Number(mr[Mt]);if(Bt.has(br)){const ze=this.decode([br]),wt=y.WHISPER_LANGUAGE_MAPPING.get(ze.slice(2,-2));if(wt!==void 0){if(He!==null&&wt!==He&&!H){wr.push(Dr);const ts=this.findLongestCommonSequence(wr)[0],en=this.decode(ts);it.text=en,ht.push(it),wr=[],Dr=[],it=yt()}He=it.language=wt}}else if(br>=hr&&br<=$e){const ze=(br-hr)*Me+Pt,wt=(0,Q.round)(ze,2);if(yr!==null&&br>=yr)Yr=!0;else if(Yr||wr.length>0&&br<Es)Yr=!1;else if(it.timestamp[0]===null)it.timestamp[0]=wt;else if(wt!==it.timestamp[0]){it.timestamp[1]=wt,wr.push(Dr),ct&&Rr.push(Hr);const[ts,en]=this.findLongestCommonSequence(wr,Rr),oo=this.decode(ts);it.text=oo,ct&&(it.words=this.collateWordTimestamps(ts,en,He)),ht.push(it),wr=[],Dr=[],Rr=[],Hr=[],it=yt()}}else if(Dr.push(br),ct){let ze=(0,Q.round)(vt[Mt]+Pt,2),wt;if(Mt+1<vt.length){wt=(0,Q.round)(vt[Mt+1]+Pt,2);const ts=this.decode([br]);C.test(ts)&&(wt=(0,Q.round)(Math.min(ze+Me,wt),2))}else wt=null;Hr.push([ze,wt])}}if("stride"in er){const[Mt,br,ze]=er.stride;Pt+=Mt-ze}Dr.length>0?(wr.push(Dr),ct&&Rr.push(Hr)):wr.every(Mt=>Mt.length===0)&&(it=yt(),wr=[],Dr=[],Rr=[],Hr=[])}if(wr.length>0){if(Ce&&H)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[er,mr]=this.findLongestCommonSequence(wr,Rr),vt=this.decode(er);it.text=vt,ct&&(it.words=this.collateWordTimestamps(er,mr,He)),ht.push(it)}let Nr=Object.create(null);const ps=ht.map(er=>er.text).join("");if(H||ae){for(let er=0;er<ht.length;++er){const mr=ht[er];H||delete mr.timestamp,ae||delete mr.language}if(ct){const er=[];for(const mr of ht)for(const vt of mr.words)er.push(vt);Nr={chunks:er}}else Nr={chunks:ht}}return[ps,Nr]}findLongestCommonSequence(P,H=null){let ae=P[0],Me=ae.length,Ce=[];const He=Array.isArray(H)&&H.length>0;let ct=He?[]:null,yt=He?H[0]:null;for(let ht=1;ht<P.length;++ht){const it=P[ht];let Pt=0,hr=[Me,Me,0,0];const rr=it.length;for(let Nr=1;Nr<Me+rr;++Nr){const ps=Math.max(0,Me-Nr),er=Math.min(Me,Me+rr-Nr),mr=ae.slice(ps,er),vt=Math.max(0,Nr-Me),yr=Math.min(rr,Nr),Es=it.slice(vt,yr);if(mr.length!==Es.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let Dr;He?Dr=mr.filter((br,ze)=>br===Es[ze]&&yt[ps+ze]<=H[ht][vt+ze]).length:Dr=mr.filter((br,ze)=>br===Es[ze]).length;const Hr=Nr/1e4,Mt=Dr/Nr+Hr;Dr>1&&Mt>Pt&&(Pt=Mt,hr=[ps,er,vt,yr])}const[$e,wr,Rr,Yr]=hr,Jr=Math.floor((wr+$e)/2),Bt=Math.floor((Yr+Rr)/2);Ce.push(...ae.slice(0,Jr)),ae=it.slice(Bt),Me=ae.length,He&&(ct.push(...yt.slice(0,Jr)),yt=H[ht].slice(Bt))}return Ce.push(...ae),He?(ct.push(...yt),[Ce,ct]):[Ce,[]]}collateWordTimestamps(P,H,ae){const[Me,Ce,He]=this.combineTokensIntoWords(P,ae),ct=[];for(let yt=0;yt<Me.length;++yt){const ht=He[yt];ct.push({text:Me[yt],timestamp:[H[ht.at(0)][0],H[ht.at(-1)][1]]})}return ct}combineTokensIntoWords(P,H,ae=`"'“¡¿([{-`,Me=`"'.。,，!！?？:：”)]}、`){H=H??"english";let Ce,He,ct;return["chinese","japanese","thai","lao","myanmar"].includes(H)?[Ce,He,ct]=this.splitTokensOnUnicode(P):[Ce,He,ct]=this.splitTokensOnSpaces(P),this.mergePunctuations(Ce,He,ct,ae,Me)}decode(P,H){let ae;return H!=null&&H.decode_with_timestamps?(P instanceof W.Tensor&&(P=se(P)),ae=this.decodeWithTimestamps(P,H)):ae=super.decode(P,H),ae}decodeWithTimestamps(P,H){const ae=(H==null?void 0:H.time_precision)??.02,Me=Array.from(this.all_special_ids).at(-1)+1;let Ce=[[]];for(let He of P)if(He=Number(He),He>=Me){const ct=((He-Me)*ae).toFixed(2);Ce.push(`<|${ct}|>`),Ce.push([])}else Ce[Ce.length-1].push(He);return Ce=Ce.map(He=>typeof He=="string"?He:super.decode(He,H)),Ce.join("")}splitTokensOnUnicode(P){const H=this.decode(P,{decode_with_timestamps:!0}),ae="�",Me=[],Ce=[],He=[];let ct=[],yt=[],ht=0;for(let it=0;it<P.length;++it){const Pt=P[it];ct.push(Pt),yt.push(it);const hr=this.decode(ct,{decode_with_timestamps:!0});(!hr.includes(ae)||H[ht+hr.indexOf(ae)]===ae)&&(Me.push(hr),Ce.push(ct),He.push(yt),ct=[],yt=[],ht+=hr.length)}return[Me,Ce,He]}splitTokensOnSpaces(P){const[H,ae,Me]=this.splitTokensOnUnicode(P),Ce=[],He=[],ct=[],yt=new RegExp(`^[${g}]$`,"gu");for(let ht=0;ht<H.length;++ht){const it=H[ht],Pt=ae[ht],hr=Me[ht],rr=Pt[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),$e=it.startsWith(" "),wr=it.trim(),Rr=yt.test(wr);if(rr||$e||Rr||Ce.length===0)Ce.push(it),He.push(Pt),ct.push(hr);else{const Yr=Ce.length-1;Ce[Yr]+=it,He[Yr].push(...Pt),ct[Yr].push(...hr)}}return[Ce,He,ct]}mergePunctuations(P,H,ae,Me,Ce){const He=structuredClone(P),ct=structuredClone(H),yt=structuredClone(ae);let ht=He.length-2,it=He.length-1;for(;ht>=0;)He[ht].startsWith(" ")&&Me.includes(He[ht].trim())?(He[it]=He[ht]+He[it],ct[it]=(0,O.mergeArrays)(ct[ht],ct[it]),yt[it]=(0,O.mergeArrays)(yt[ht],yt[it]),He[ht]="",ct[ht]=[],yt[ht]=[]):it=ht,--ht;for(ht=0,it=1;it<He.length;)!He[ht].endsWith(" ")&&Ce.includes(He[it])?(He[ht]+=He[it],ct[ht]=(0,O.mergeArrays)(ct[ht],ct[it]),yt[ht]=(0,O.mergeArrays)(yt[ht],yt[it]),He[it]="",ct[it]=[],yt[it]=[]):ht=it,++it;return[He.filter(Pt=>Pt),ct.filter(Pt=>Pt.length>0),yt.filter(Pt=>Pt.length>0)]}}class Fn extends Nt{}class Dn extends Nt{}class Ln extends Nt{}class Us extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ae=>this.languageRegex.test(ae)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(P){if(P===null)return null;const[H,...ae]=P.trim().split(this.languageRegex);if(ae.length===0)return super._encode_text(H);if(ae.length===2){const[Me,Ce]=ae;return this.supported_language_codes.includes(Me)||console.warn(`Unsupported language code "${Me}" detected, which may lead to unexpected behavior. 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this;const z=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let V=0,Y=0;V<this.data.length;V+=this.channels){const D=this.data[V],$=this.data[V+1],g=this.data[V+2];z[Y++]=Math.round(.2989*D+.587*$+.114*g)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(z,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const z=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let V=0,Y=0;V<this.data.length;++V)z[Y++]=this.data[V],z[Y++]=this.data[V],z[Y++]=this.data[V];break;case 4:for(let V=0,Y=0;V<this.data.length;V+=4)z[Y++]=this.data[V],z[Y++]=this.data[V+1],z[Y++]=this.data[V+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(z,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const z=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let V=0,Y=0;V<this.data.length;++V)z[Y++]=this.data[V],z[Y++]=this.data[V],z[Y++]=this.data[V],z[Y++]=255;break;case 3:for(let V=0,Y=0;V<this.data.length;V+=3)z[Y++]=this.data[V],z[Y++]=this.data[V+1],z[Y++]=this.data[V+2],z[Y++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(z,this.width,this.height,4)}putAlpha(z){if(z.width!==this.width||z.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${z.width}x${z.height}`);if(z.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${z.channels}`);const V=this.data,Y=z.data,D=this.width*this.height;if(this.channels===3){const $=new Uint8ClampedArray(D*4);for(let g=0,C=0,T=0;g<D;++g)$[T++]=V[C++],$[T++]=V[C++],$[T++]=V[C++],$[T++]=Y[g];return this._update($,this.width,this.height,4)}else if(this.channels===4){for(let $=0;$<D;++$)V[4*$+3]=Y[$];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(z,V,{resample:Y=2}={}){if(this.width===z&&this.height===V)return this;let D=b[Y]??Y;const $=(0,f.isNullishDimension)(z),g=(0,f.isNullishDimension)(V);if($&&g)return this;if($?z=V/this.height*this.width:g&&(V=z/this.width*this.height),M){const C=this.channels,T=this.toCanvas(),ee=w(z,V).getContext("2d");return ee.drawImage(T,0,0,z,V),new q(ee.getImageData(0,0,z,V).data,z,V,4).convert(C)}else{let C=this.toSharp();switch(D){case"box":case"hamming":(D==="box"||D==="hamming")&&(console.warn(`Resampling method ${D} is not yet supported. Using bilinear instead.`),D="bilinear");case"nearest":case"bilinear":case"bicubic":C=C.affine([z/this.width,0,0,V/this.height],{interpolator:D});break;case"lanczos":C=C.resize({width:z,height:V,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${D} is not supported.`)}return await y(C)}}async pad([z,V,Y,D]){if(z=Math.max(z,0),V=Math.max(V,0),Y=Math.max(Y,0),D=Math.max(D,0),z===0&&V===0&&Y===0&&D===0)return this;if(M){const $=this.channels,g=this.toCanvas(),C=this.width+z+V,T=this.height+Y+D,ee=w(C,T).getContext("2d");return ee.drawImage(g,0,0,this.width,this.height,z,Y,this.width,this.height),new q(ee.getImageData(0,0,C,T).data,C,T,4).convert($)}else{const $=this.toSharp().extend({left:z,right:V,top:Y,bottom:D});return await y($)}}async crop([z,V,Y,D]){if(z=Math.max(z,0),V=Math.max(V,0),Y=Math.min(Y,this.width-1),D=Math.min(D,this.height-1),z===0&&V===0&&Y===this.width-1&&D===this.height-1)return this;const $=Y-z+1,g=D-V+1;if(M){const C=this.channels,T=this.toCanvas(),ee=w($,g).getContext("2d");return ee.drawImage(T,z,V,$,g,0,0,$,g),new q(ee.getImageData(0,0,$,g).data,$,g,4).convert(C)}else{const C=this.toSharp().extract({left:z,top:V,width:$,height:g});return await y(C)}}async center_crop(z,V){if(this.width===z&&this.height===V)return this;const Y=(this.width-z)/2,D=(this.height-V)/2;if(M){const $=this.channels,g=this.toCanvas(),C=w(z,V).getContext("2d");let T=0,ee=0,J=0,le=0;return Y>=0?T=Y:J=-Y,D>=0?ee=D:le=-D,C.drawImage(g,T,ee,z,V,J,le,z,V),new q(C.getImageData(0,0,z,V).data,z,V,4).convert($)}else{let $=this.toSharp();if(Y>=0&&D>=0)$=$.extract({left:Math.floor(Y),top:Math.floor(D),width:z,height:V});else if(Y<=0&&D<=0){const g=Math.floor(-D),C=Math.floor(-Y);$=$.extend({top:g,left:C,right:z-this.width-C,bottom:V-this.height-g})}else{let g=[0,0],C=0;D<0?(g[0]=Math.floor(-D),g[1]=V-this.height-g[0]):C=Math.floor(D);let T=[0,0],ee=0;Y<0?(T[0]=Math.floor(-Y),T[1]=z-this.width-T[0]):ee=Math.floor(Y),$=$.extend({top:g[0],bottom:g[1],left:T[0],right:T[1]}).extract({left:ee,top:C,width:z,height:V})}return await y($)}}async toBlob(z="image/png",V=1){if(!M)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:z,quality:V})}toTensor(z="CHW"){let V=new Q.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(z!=="HWC")if(z==="CHW")V=V.permute(2,0,1);else throw new Error(`Unsupported channel format: ${z}`);return V}toCanvas(){if(!M)throw new Error("toCanvas() is only supported in browser environments.");const z=this.clone().rgba(),V=w(z.width,z.height),Y=new v(z.data,z.width,z.height);return V.getContext("2d").putImageData(Y,0,0),V}split(){const{data:z,width:V,height:Y,channels:D}=this,$=z.constructor,g=z.length/D,C=Array.from({length:D},()=>new $(g));for(let T=0;T<g;++T){const ee=D*T;for(let J=0;J<D;++J)C[J][T]=z[ee+J]}return C.map(T=>new q(T,V,Y,1))}_update(z,V,Y,D=null){return this.data=z,this.width=V,this.height=Y,D!==null&&(this.channels=D),this}clone(){return new q(this.data.slice(),this.width,this.height,this.channels)}convert(z){if(this.channels===z)return this;switch(z){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: ${this.channels}`)}return this}async save(z){if(M){if(N.apis.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const V=z.split(".").pop().toLowerCase(),Y=L.get(V)??"image/png",D=await this.toBlob(Y),$=URL.createObjectURL(D),g=document.createElement("a");g.href=$,g.download=z,g.click(),g.remove()}else{if(N.env.useFS)return await this.toSharp().toFile(z);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(M)throw new Error("toSharp() is only supported in server-side environments.");return W(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}const se=q.read.bind(q)},"./src/utils/maths.js":(Te,A,s)=>{s.r(A),s.d(A,{FFT:()=>se,bankers_round:()=>V,cos_sim:()=>w,dot:()=>W,dynamic_time_warping:()=>Y,interpolate_data:()=>f,log_softmax:()=>Q,magnitude:()=>v,max:()=>M,medianFilter:()=>ie,min:()=>y,permute_data:()=>O,round:()=>z,softmax:()=>N});function f(D,[$,g,C],[T,ee],J="bilinear",le=!1){const de=ee/C,fe=T/g,ke=new D.constructor(T*ee*$),xe=g*C,Le=T*ee;for(let qe=0;qe<T;++qe)for(let Ue=0;Ue<ee;++Ue){const ut=qe*ee+Ue,ue=(Ue+.5)/de-.5,re=(qe+.5)/fe-.5;let he=Math.floor(ue),Pe=Math.floor(re);const Be=Math.min(he+1,C-1),et=Math.min(Pe+1,g-1);he=Math.max(he,0),Pe=Math.max(Pe,0);const Xe=ue-he,oe=re-Pe,Je=(1-Xe)*(1-oe),De=Xe*(1-oe),ce=(1-Xe)*oe,ve=Xe*oe,Re=Pe*C,je=et*C,Ve=Re+he,Ne=Re+Be,Ze=je+he,at=je+Be;for(let ft=0;ft<$;++ft){const dt=ft*xe;ke[ft*Le+ut]=Je*D[dt+Ve]+De*D[dt+Ne]+ce*D[dt+Ze]+ve*D[dt+at]}}return ke}function O(D,$,g){const C=new Array(g.length),T=new Array(g.length);for(let le=g.length-1,de=1;le>=0;--le)T[le]=de,C[le]=$[g[le]],de*=C[le];const ee=g.map((le,de)=>T[g.indexOf(de)]),J=new D.constructor(D.length);for(let le=0;le<D.length;++le){let de=0;for(let fe=$.length-1,ke=le;fe>=0;--fe)de+=ke%$[fe]*ee[fe],ke=Math.floor(ke/$[fe]);J[de]=D[le]}return[J,C]}function N(D){const $=M(D)[0],g=D.map(ee=>Math.exp(ee-$)),C=g.reduce((ee,J)=>ee+J,0);return g.map(ee=>ee/C)}function Q(D){const $=M(D)[0];let g=0;for(let ee=0;ee<D.length;++ee)g+=Math.exp(D[ee]-$);const C=Math.log(g);return D.map(ee=>ee-$-C)}function W(D,$){let g=0;for(let C=0;C<D.length;++C)g+=D[C]*$[C];return g}function w(D,$){const g=W(D,$),C=v(D),T=v($);return g/(C*T)}function v(D){return Math.sqrt(D.reduce(($,g)=>$+g*g,0))}function y(D){if(D.length===0)throw Error("Array must not be empty");let $=D[0],g=0;for(let C=1;C<D.length;++C)D[C]<$&&($=D[C],g=C);return[$,g]}function M(D){if(D.length===0)throw Error("Array must not be empty");let $=D[0],g=0;for(let C=1;C<D.length;++C)D[C]>$&&($=D[C],g=C);return[Number($),g]}function b(D){return D>0&&(D&D-1)===0}class L{constructor($){if(this.size=$|0,this.size<=1||!b(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=$<<1,this.table=new Float64Array(this.size*2);for(let C=0;C<this.table.length;C+=2){const T=Math.PI*C/this.size;this.table[C]=Math.cos(T),this.table[C+1]=-Math.sin(T)}let g=0;for(let C=1;this.size>C;C<<=1)++g;this._width=g%2===0?g-1:g,this._bitrev=new Int32Array(1<<this._width);for(let C=0;C<this._bitrev.length;++C){this._bitrev[C]=0;for(let T=0;T<this._width;T+=2){const ee=this._width-T-2;this._bitrev[C]|=(C>>>T&3)<<ee}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray($,g){const C=g||new Array($.length>>>1);for(let T=0;T<$.length;T+=2)C[T>>>1]=$[T];return C}toComplexArray($,g){const C=g||this.createComplexArray();for(let T=0;T<C.length;T+=2)C[T]=$[T>>>1],C[T+1]=0;return C}transform($,g){if($===g)throw new Error("Input and output buffers must be different");this._transform4($,g,1)}realTransform($,g){if($===g)throw new Error("Input and output buffers must be different");this._realTransform4($,g,1)}inverseTransform($,g){if($===g)throw new Error("Input and output buffers must be different");this._transform4($,g,-1);for(let C=0;C<$.length;++C)$[C]/=this.size}_transform4($,g,C){const T=this._csize;let J=1<<this._width,le=T/J<<1,de,fe;const ke=this._bitrev;if(le===4)for(de=0,fe=0;de<T;de+=le,++fe){const Le=ke[fe];this._singleTransform2(g,$,de,Le,J)}else for(de=0,fe=0;de<T;de+=le,++fe){const Le=ke[fe];this._singleTransform4(g,$,de,Le,J,C)}const xe=this.table;for(J>>=2;J>=2;J>>=2){le=T/J<<1;const Le=le>>>2;for(de=0;de<T;de+=le){const qe=de+Le-1;for(let Ue=de,ut=0;Ue<qe;Ue+=2,ut+=J){const ue=Ue,re=ue+Le,he=re+Le,Pe=he+Le,Be=$[ue],et=$[ue+1],Xe=$[re],oe=$[re+1],Je=$[he],De=$[he+1],ce=$[Pe],ve=$[Pe+1],Re=xe[ut],je=C*xe[ut+1],Ve=Xe*Re-oe*je,Ne=Xe*je+oe*Re,Ze=xe[2*ut],at=C*xe[2*ut+1],ft=Je*Ze-De*at,dt=Je*at+De*Ze,gt=xe[3*ut],F=C*xe[3*ut+1],ne=ce*gt-ve*F,K=ce*F+ve*gt,pe=Be+ft,Fe=et+dt,Qe=Be-ft,st=et-dt,pt=Ve+ne,It=Ne+K,St=C*(Ve-ne),Ft=C*(Ne-K);$[ue]=pe+pt,$[ue+1]=Fe+It,$[re]=Qe+Ft,$[re+1]=st-St,$[he]=pe-pt,$[he+1]=Fe-It,$[Pe]=Qe-Ft,$[Pe+1]=st+St}}}}_singleTransform2($,g,C,T,ee){const J=$[T],le=$[T+1],de=$[T+ee],fe=$[T+ee+1];g[C]=J+de,g[C+1]=le+fe,g[C+2]=J-de,g[C+3]=le-fe}_singleTransform4($,g,C,T,ee,J){const le=ee*2,de=ee*3,fe=$[T],ke=$[T+1],xe=$[T+ee],Le=$[T+ee+1],qe=$[T+le],Ue=$[T+le+1],ut=$[T+de],ue=$[T+de+1],re=fe+qe,he=ke+Ue,Pe=fe-qe,Be=ke-Ue,et=xe+ut,Xe=Le+ue,oe=J*(xe-ut),Je=J*(Le-ue);g[C]=re+et,g[C+1]=he+Xe,g[C+2]=Pe+Je,g[C+3]=Be-oe,g[C+4]=re-et,g[C+5]=he-Xe,g[C+6]=Pe-Je,g[C+7]=Be+oe}_realTransform4($,g,C){const T=this._csize;let J=1<<this._width,le=T/J<<1,de,fe;const ke=this._bitrev;if(le===4)for(de=0,fe=0;de<T;de+=le,++fe){const qe=ke[fe];this._singleRealTransform2(g,$,de,qe>>>1,J>>>1)}else for(de=0,fe=0;de<T;de+=le,++fe){const qe=ke[fe];this._singleRealTransform4(g,$,de,qe>>>1,J>>>1,C)}const xe=this.table;for(J>>=2;J>=2;J>>=2){le=T/J<<1;const qe=le>>>1,Ue=qe>>>1,ut=Ue>>>1;for(de=0;de<T;de+=le)for(let ue=0,re=0;ue<=ut;ue+=2,re+=J){const he=de+ue,Pe=he+Ue,Be=Pe+Ue,et=Be+Ue,Xe=$[he],oe=$[he+1],Je=$[Pe],De=$[Pe+1],ce=$[Be],ve=$[Be+1],Re=$[et],je=$[et+1],Ve=Xe,Ne=oe,Ze=xe[re],at=C*xe[re+1],ft=Je*Ze-De*at,dt=Je*at+De*Ze,gt=xe[2*re],F=C*xe[2*re+1],ne=ce*gt-ve*F,K=ce*F+ve*gt,pe=xe[3*re],Fe=C*xe[3*re+1],Qe=Re*pe-je*Fe,st=Re*Fe+je*pe,pt=Ve+ne,It=Ne+K,St=Ve-ne,Ft=Ne-K,At=ft+Qe,nr=dt+st,gr=C*(ft-Qe),Sr=C*(dt-st);if($[he]=pt+At,$[he+1]=It+nr,$[Pe]=St+Sr,$[Pe+1]=Ft-gr,ue===0){$[Be]=pt-At,$[Be+1]=It-nr;continue}if(ue===ut)continue;const Ar=de+Ue-ue,Xr=de+qe-ue;$[Ar]=St-C*Sr,$[Ar+1]=-Ft-C*gr,$[Xr]=pt-C*At,$[Xr+1]=-It+C*nr}}const Le=T>>>1;for(let qe=2;qe<Le;qe+=2)$[T-qe]=$[qe],$[T-qe+1]=-$[qe+1]}_singleRealTransform2($,g,C,T,ee){const J=$[T],le=$[T+ee];g[C]=J+le,g[C+1]=0,g[C+2]=J-le,g[C+3]=0}_singleRealTransform4($,g,C,T,ee,J){const le=ee*2,de=ee*3,fe=$[T],ke=$[T+ee],xe=$[T+le],Le=$[T+de],qe=fe+xe,Ue=fe-xe,ut=ke+Le,ue=J*(ke-Le);g[C]=qe+ut,g[C+1]=0,g[C+2]=Ue,g[C+3]=-ue,g[C+4]=qe-ut,g[C+5]=0,g[C+6]=Ue,g[C+7]=ue}}class q{constructor($){const g=2*($-1),C=2*(2*$-1),T=2**Math.ceil(Math.log2(C));this.bufferSize=T,this._a=g;const ee=new Float64Array(C),J=new Float64Array(T);this._chirpBuffer=new Float64Array(T),this._buffer1=new Float64Array(T),this._buffer2=new Float64Array(T),this._outBuffer1=new Float64Array(T),this._outBuffer2=new Float64Array(T);const le=-2*Math.PI/$,de=Math.cos(le),fe=Math.sin(le);for(let ke=0;ke<C>>1;++ke){const xe=(ke+1-$)**2/2,Le=Math.sqrt(de**2+fe**2)**xe,qe=xe*Math.atan2(fe,de),Ue=2*ke;ee[Ue]=Le*Math.cos(qe),ee[Ue+1]=Le*Math.sin(qe),J[Ue]=ee[Ue],J[Ue+1]=-ee[Ue+1]}this._slicedChirpBuffer=ee.subarray(g,C),this._f=new L(T>>1),this._f.transform(this._chirpBuffer,J)}_transform($,g,C){const T=this._buffer1,ee=this._buffer2,J=this._outBuffer1,le=this._outBuffer2,de=this._chirpBuffer,fe=this._slicedChirpBuffer,ke=this._a;if(C)for(let xe=0;xe<fe.length;xe+=2){const Le=xe+1,qe=xe>>1,Ue=g[qe];T[xe]=Ue*fe[xe],T[Le]=Ue*fe[Le]}else for(let xe=0;xe<fe.length;xe+=2){const Le=xe+1;T[xe]=g[xe]*fe[xe]-g[Le]*fe[Le],T[Le]=g[xe]*fe[Le]+g[Le]*fe[xe]}this._f.transform(J,T);for(let xe=0;xe<de.length;xe+=2){const Le=xe+1;ee[xe]=J[xe]*de[xe]-J[Le]*de[Le],ee[Le]=J[xe]*de[Le]+J[Le]*de[xe]}this._f.inverseTransform(le,ee);for(let xe=0;xe<le.length;xe+=2){const Le=le[xe+ke],qe=le[xe+ke+1],Ue=fe[xe],ut=fe[xe+1];$[xe]=Le*Ue-qe*ut,$[xe+1]=Le*ut+qe*Ue}}transform($,g){this._transform($,g,!1)}realTransform($,g){this._transform($,g,!0)}}class se{constructor($){this.fft_length=$,this.isPowerOfTwo=b($),this.isPowerOfTwo?(this.fft=new L($),this.outputBufferSize=2*$):(this.fft=new q($),this.outputBufferSize=this.fft.bufferSize)}realTransform($,g){this.fft.realTransform($,g)}transform($,g){this.fft.transform($,g)}}function ie(D,$){if($%2===0||$<=0)throw new Error("Window size must be a positive odd number");const g=new D.constructor(D.length),C=new D.constructor($),T=Math.floor($/2);for(let ee=0;ee<D.length;++ee){let J=0;for(let le=-T;le<=T;++le){let de=ee+le;de<0?de=Math.abs(de):de>=D.length&&(de=2*(D.length-1)-de),C[J++]=D[de]}C.sort(),g[ee]=C[T]}return g}function z(D,$){const g=Math.pow(10,$);return Math.round(D*g)/g}function V(D){const $=Math.round(D);return Math.abs(D)%1===.5?$%2===0?$:$-1:$}function Y(D){const $=D.length,g=D[0].length,C=[$+1,g+1],T=Array.from({length:C[0]},()=>Array(C[1]).fill(1/0));T[0][0]=0;const ee=Array.from({length:C[0]},()=>Array(C[1]).fill(-1));for(let ke=1;ke<C[1];++ke)for(let xe=1;xe<C[0];++xe){const Le=T[xe-1][ke-1],qe=T[xe-1][ke],Ue=T[xe][ke-1];let ut,ue;Le<qe&&Le<Ue?(ut=Le,ue=0):qe<Le&&qe<Ue?(ut=qe,ue=1):(ut=Ue,ue=2),T[xe][ke]=D[xe-1][ke-1]+ut,ee[xe][ke]=ue}for(let ke=0;ke<C[1];++ke)ee[0][ke]=2;for(let ke=0;ke<C[0];++ke)ee[ke][0]=1;let J=$,le=g,de=[],fe=[];for(;J>0||le>0;)switch(de.push(J-1),fe.push(le-1),ee[J][le]){case 0:--J,--le;break;case 1:--J;break;case 2:--le;break;default:throw new Error(`Internal error in dynamic time warping. 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Eh=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.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 Nf=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 Ta=16e3,Ph=Ta/1e3,jf=.3,Uf=.1,Wf=400,Vf=Wf*Ph,Gf=80,Th=Gf*Ph,Kf=250*Ph,Hf=30,qf=512,Xf=Math.ceil(Th/qf);async function Qf(){try{return!("gpu"in navigator)||!navigator.gpu?!1:(await navigator.gpu.requestAdapter(),!0)}catch(Te){return console.error(Te),!1}}var An=(Te=>(Te.Status="status",Te.Output="output",Te.Info="info",Te.Request="request",Te.Error="error",Te.Load="load",Te))(An||{}),Ch=(Te=>(Te.RecordingStart="recording_start",Te.RecordingEnd="recording_end",Te.Ready="ready",Te))(Ch||{}),Np=(Te=>(Te.UntilNext="until_next",Te))(Np||{});let xh,jp,Up=Promise.resolve();const va=new Float32Array(Hf*Ta);let $n=0;const Yf=new Eh("int64",[Ta],[]);let l_=new Eh("float32",new Float32Array(2*1*128),[2,1,128]),Fc=!1,Dc=0;const 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