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 * ONNX Runtime Web v1.22.0-dev.20250409-89f8206ba4
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}get mlTensor(){if(this.ensureValid(),!this.mlTensorData)throw new Error("The data is not stored as a WebNN MLTensor.");return this.mlTensorData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":case"ml-tensor":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;let r=await 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${e}`)}},Va=e=>e==="float32"||e==="float16"||e==="int32"||e==="int64"||e==="uint32"||e==="uint8"||e==="bool"||e==="uint4"||e==="int4",Ua=e=>e==="float32"||e==="float16"||e==="int32"||e==="int64"||e==="uint32"||e==="uint64"||e==="int8"||e==="uint8"||e==="bool"||e==="uint4"||e==="int4",Wa=e=>{switch(e){case"none":return 0;case"cpu":return 1;case"cpu-pinned":return 2;case"texture":return 3;case"gpu-buffer":return 4;case"ml-tensor":return 5;default:throw new Error(`unsupported data location: ${e}`)}}}),Ga,fd=je(()=>{Pa(),Ga=async e=>{if(typeof e=="string"){let r=await fetch(e);if(!r.ok)throw new Error(`failed to load external data file: ${e}`);let t=r.headers.get("Content-Length"),s=t?parseInt(t,10):0;if(s<1073741824)return new Uint8Array(await r.arrayBuffer());{if(!r.body)throw new Error(`failed to load external data file: ${e}, no response body.`);let o=r.body.getReader(),n;try{n=new ArrayBuffer(s)}catch(a){if(a instanceof RangeError){let l=Math.ceil(s/65536);n=new WebAssembly.Memory({initial:l,maximum:l}).buffer}else throw a}let i=0;for(;;){let{done:a,value:l}=await o.read();if(a)break;let u=l.byteLength;new Uint8Array(n,i,u).set(l),i+=u}return new Uint8Array(n,0,s)}}else return e instanceof Blob?new Uint8Array(await e.arrayBuffer()):e instanceof Uint8Array?e:new Uint8Array(e)}}),_d,gd,wd,Md,Ka,bd,It,Bs=je(()=>{ft(),_d=["V","I","W","E","F"],gd=(e,r)=>{console.log(`[${_d[e]},${new Date().toISOString()}]${r}`)},Ka=(e,r)=>{wd=e,Md=r},bd=(e,r)=>{let t=ii(e),s=ii(wd);t>=s&&gd(t,typeof r=="function"?r():r)},It=(...e)=>{Md&&bd(...e)}}),yd,Nn,Me,ai,vd,xd,Td,yt=je(()=>{yd=class{static calcMatMulShape(e,r){return e[1]!==r[0]?void 0:[e[0],r[1]]}},Nn=class{static calcShape(e,r,t=!1){let s=e.length,o=r.length;if(s===0)return r;if(o===0)return e;let n=Math.max(e.length,r.length),i=new Array(n);if(t){if(s<2||o<2)return;let a=yd.calcMatMulShape([e[s-2],e[s-1]],[r[o-2],r[o-1]]);if(a===void 0)return;[i[n-2],i[n-1]]=a}for(let a=t?3:1;a<=n;a++){let l=s-a<0?1:e[s-a],u=o-a<0?1:r[o-a];if(l!==u&&l>1&&u>1)return;let p=Math.max(l,u);if(l&&u)i[n-a]=Math.max(l,u);else{if(p>1)return;i[n-a]=0}}return i}static isValidBroadcast(e,r){let t=e.length,s=r.length;if(t>s)return!1;for(let o=1;o<=t;o++)if(e[t-o]!==1&&e[t-o]!==r[s-o])return!1;return!0}},Me=class _a{static size(r){return _a.getSizeFromDimensionRange(r,0,r.length)}static convertShape(r,t=4){let s=r.length;if(s===0)return[];let o=new Array(s),n=s-1;for(;n>=0;){if(r[n]%t===0){o[n]=r[n]/t;break}if(t%r[n]!==0)throw new Error("cannot convert shape");o[n]=1,t/=r[n],n--}for(n--;n>=0;n--)o[n]=r[n];return o}static sizeFromDimension(r,t){if(t<0||t>r.length)throw new Error(`invalid dimension of ${t} for sizeFromDimension as Tensor has ${r.length} dimensions.`);return _a.getSizeFromDimensionRange(r,t,r.length)}static sizeToDimension(r,t){if(t<0||t>r.length)throw new Error(`invalid dimension of ${t} for sizeToDimension as Tensor has ${r.length} dimensions.`);return _a.getSizeFromDimensionRange(r,0,t)}static getSizeFromDimensionRange(r,t,s){let o=1;for(let n=t;n<s;n++){if(r[n]<0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains negative values in them.");o*=Number(r[n])}return o}static computeStrides(r){let t=r.length;if(t===0)return[];if(t===1)return[1];let s=new Array(t);s[t-1]=1,s[t-2]=r[t-1];for(let o=t-3;o>=0;--o)s[o]=s[o+1]*r[o+1];return s}static normalizeAxis(r,t){if(r<-t&&r>=t)throw new Error("unsupported axis for this operation.");return r<0?r+t:r}static normalizeAxes(r,t){return r.map(s=>this.normalizeAxis(s,t??r.length))}static sortBasedOnPerm(r,t){return t?t.map(s=>r[s]):r.slice().reverse()}static padShape(r,t){let s=r.length;return r.map((o,n)=>o+t[n]+t[n+s])}static areEqual(r,t){return r.length!==t.length?!1:r.every((s,o)=>s===t[o])}},ai=class ei{static adjustPoolAttributes(r,t,s,o,n,i){if(!r&&s.length!==t.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(r)for(let a=0;a<t.length-2;a++)a>=s.length?s.push(t[a+2]):s[a]=t[a+2];for(let a=0;a<s.length;a++)if(a<o.length){if(o[a]<0)throw new Error("strides should be greater than or equal to 1")}else o.push(1);for(let a=0;a<s.length;a++)if(a<n.length){if(n[a]<0)throw new Error("dilations should be greater than or equal to 1")}else n.push(1);for(let a=0;a<s.length*2;a++)if(a<i.length){if(i[a]<0)throw new Error("pad should be greater than or equal to 1")}else i.push(0);for(let a=0;a<s.length;a++){if(s[a]<=0)throw new Error("kernel shapes need to be greater than 0");if(i[a]>=s[a]||i[a+s.length]>=s[a])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(r,t,s,o,n,i,a){if(a){if(n.length!==2*(r.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(t.length!==r.length-2)throw new Error("length of strides should be the length of data dimensions");if(o.length!==r.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let l=0;l<r.length-2;l++)ei.adjustPadAndReturnShape(r[l+(i?1:2)],t[l],s[l],o[l],n,l,l+r.length-2,a)}}static computePoolOutputShape(r,t,s,o,n,i,a){if(t.length<=0)throw new Error("input shape must be of size greater than 0");let l=[t[0],t[1]];return ei.computeShapeHelper(r,t,l,s,o,n,i,a),l}static computeConvOutputShape(r,t,s,o,n,i,a){if(r.length<=0||t.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let l=[r[0],t[0]];return ei.computeShapeHelper(!1,r,l,s,o,n,i,a),l}static computeShapeHelper(r,t,s,o,n,i,a,l){if(r)for(let u=0;u<t.length-2;u++)s.push(1);else for(let u=0;u<t.length-2;u++)s.push(ei.adjustPadAndReturnShape(t[u+2],o[u],n[u],i[u],a,u,u+t.length-2,l))}static adjustPadAndReturnShape(r,t,s,o,n,i,a,l){let u=s*(o-1)+1;if(l&&l!=="NOTSET")switch(l){case"VALID":return n[i]=0,n[a]=0,Math.floor((r-u)/t+1);case"SAME_LOWER":case"SAME_UPPER":if(s!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let p=((r+t-1)/t-1)*t+o-r;return n[i]=Math.floor(l==="SAME_LOWER"?(p+1)/2:p/2),n[a]=p-n[i],Math.floor((r+p-o)/t+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((r+n[i]+n[a]-u)/t+1)}},vd=class{static getShapeOfGemmResult(e,r,t,s,o){if(e.length!==2||t.length!==2)throw new Error("shape need to be of size 2");let n,i,a;r?(n=e[1],i=e[0]):(n=e[0],i=e[1]);let l=-1;if(s?(a=t[0],l=1):(a=t[1],l=0),t[l]!==i)throw new Error("dimension mismatch");if(n<=0||a<=0||i<=0)throw new Error("invalid shape specified");if(o&&!Nn.isValidBroadcast(o,[n,a]))throw new Error("gemm: invalid bias shape for broadcast");return[n,a,i]}},xd=-34028234663852886e22,Td=34028234663852886e22}),Ha,Ed=je(()=>{ft(),Ha=(e,r)=>new(Na(r))(e)}),qa,Qa,Pd,Xa,Cd,Ja,Ya,Za,Sd,$d,Sv=je(()=>{Bs(),qa=(e,r=!0)=>{if(e.byteLength%8!==0)throw new Error("Invalid Uint8Array length - must be a multiple of 8 (BigInt).");let t=e.byteLength/8,s=new BigInt64Array(e.buffer,e.byteOffset,t),o=new Int32Array(t);for(let n=0;n<t;n++){let i=s[n];if(i>2147483647n||i<-2147483648n)throw new Error(`Overflow occurred when converting BigInt to Int32 at index ${n}: ${i}`);o[n]=Number(i)}return r?new Uint8Array(o.buffer):o},Qa=(e,r=!0)=>{if(e.byteLength%4!==0)throw new Error("Invalid Uint8Array length - must be a multiple of 4 (Int32).");let t=e.byteLength/4,s=new Int32Array(e.buffer,e.byteOffset,t),o=BigInt64Array.from(s,BigInt);return r?new Uint8Array(o.buffer):o},Pd=1,Xa=()=>Pd++,Cd=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),Ja=(e,r)=>{let t=Cd.get(e);if(!t)throw new Error("Unsupported data type.");return r.length>0?Math.ceil(r.reduce((s,o)=>s*o)*t/8):0},Ya=class{constructor(e){this.shouldConvertInt64toInt32=!1,this.isInt64ToInt32Converted=!1;let{sessionId:r,context:t,tensor:s,dataType:o,shape:n,shouldConvertInt64toInt32:i=!1}=e;this.sessionId=r,this.mlContext=t,this.mlTensor=s,this.dataType=o,this.tensorShape=n,this.shouldConvertInt64toInt32=i}get tensor(){return this.mlTensor}get type(){return this.dataType}get shape(){return this.tensorShape}get byteLength(){return Ja(this.dataType,this.tensorShape)}destroy(){It("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(e){this.mlContext.writeTensor(this.mlTensor,e)}async read(e,r){if(e){let t=await this.mlContext.readTensor(this.mlTensor),s=Qa(new Uint8Array(t));if(r){(r instanceof ArrayBuffer?new Uint8Array(r):new Uint8Array(r.buffer,r.byteOffset,r.byteLength)).set(s);return}else return s.buffer}else return r?this.mlContext.readTensor(this.mlTensor,r):this.mlContext.readTensor(this.mlTensor)}canReuseTensor(e,r,t){return this.mlContext===e&&this.dataType===r&&this.tensorShape.length===t.length&&this.tensorShape.every((s,o)=>s===t[o])}setIsInt64ToInt32Converted(e){this.isInt64ToInt32Converted=e}},Za=class{constructor(e,r){this.tensorManager=e,this.wrapper=r}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(e,r,t,s){let o=r,n=this.tensorManager.getMLContext(e),i=o==="int64"&&!n.opSupportLimits().input.dataTypes.includes("int64");if(i&&(o="int32",It("verbose",()=>"[WebNN] TensorIdTracker.ensureTensor: convert dataType from int64 to int32")),this.wrapper){if(this.wrapper.canReuseTensor(n,o,t))return this.wrapper.tensor;if(s){if(this.wrapper.byteLength!==Ja(o,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 a=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(e,o,t,a,!0,!0,i),s&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(e){let r=e;if(this.wrapper)if(this.wrapper.shouldConvertInt64toInt32&&(r=qa(e,!0),this.wrapper.setIsInt64ToInt32Converted(!0)),r.byteLength===this.wrapper.byteLength){this.wrapper.write(r);return}else It("verbose",()=>"Data size does not match tensor size. Releasing tensor."),this.releaseTensor();this.activeUpload?this.activeUpload.set(r):this.activeUpload=new Uint8Array(r)}async download(e){var r,t,s;if(this.activeUpload){let o=(r=this.wrapper)!=null&&r.isInt64ToInt32Converted?Qa(this.activeUpload):this.activeUpload;if(e){e instanceof ArrayBuffer?new Uint8Array(e).set(o):new Uint8Array(e.buffer,e.byteOffset,e.byteLength).set(o);return}else return o.buffer}if(!this.wrapper)throw new Error("Tensor has not been created.");return e?this.wrapper.read((t=this.wrapper)==null?void 0:t.shouldConvertInt64toInt32,e):this.wrapper.read((s=this.wrapper)==null?void 0:s.shouldConvertInt64toInt32)}},Sd=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}getMLContext(e){let r=this.backend.getMLContext(e);if(!r)throw new Error("MLContext not found for session.");return r}reserveTensorId(){let e=Xa();return this.tensorTrackersById.set(e,new Za(this)),e}releaseTensorId(e){let r=this.tensorTrackersById.get(e);r&&(this.tensorTrackersById.delete(e),r.tensorWrapper&&this.releaseTensor(r.tensorWrapper))}async ensureTensor(e,r,t,s,o){It("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${r}, dataType: ${t}, shape: ${s}, copyOld: ${o}}`);let n=this.tensorTrackersById.get(r);if(!n)throw new Error("Tensor not found.");return n.ensureTensor(e,t,s,o)}upload(e,r){let t=this.tensorTrackersById.get(e);if(!t)throw new Error("Tensor not found.");t.upload(r)}async download(e,r){It("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${r==null?void 0:r.byteLength}}`);let t=this.tensorTrackersById.get(e);if(!t)throw new Error("Tensor not found.");return t.download(r)}releaseTensorsForSession(e){for(let r of this.freeTensors)r.sessionId===e&&r.destroy();this.freeTensors=this.freeTensors.filter(r=>r.sessionId!==e)}registerTensor(e,r,t,s){let o=this.getMLContext(e),n=Xa(),i=new Ya({sessionId:e,context:o,tensor:r,dataType:t,shape:s});return this.tensorTrackersById.set(n,new Za(this,i)),this.externalTensors.add(i),n}async getCachedTensor(e,r,t,s,o,n,i=!1){let a=this.getMLContext(e);for(let[u,p]of this.freeTensors.entries())if(p.canReuseTensor(a,r,t)){It("verbose",()=>`[WebNN] Reusing tensor {dataType: ${r}, shape: ${t}}`);let c=this.freeTensors.splice(u,1)[0];return c.sessionId=e,c}It("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${r}, shape: ${t}}`);let l=await a.createTensor({dataType:r,shape:t,dimensions:t,usage:s,writable:o,readable:n});return new Ya({sessionId:e,context:a,tensor:l,dataType:r,shape:t,shouldConvertInt64toInt32:i})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},$d=(...e)=>new Sd(...e)}),li,kd,Id,$v=je(()=>{ft(),nn(),Ed(),Sv(),Bs(),li=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),kd=(e,r)=>{if(e===r)return!0;if(e===void 0||r===void 0)return!1;let t=Object.keys(e).sort(),s=Object.keys(r).sort();return t.length===s.length&&t.every((o,n)=>o===s[n]&&e[o]===r[o])},Id=class{constructor(e){this.tensorManager=$d(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],this.sessionGraphInputs=new Map,this.temporaryGraphInputs=[],this.temporarySessionTensorIds=new Map,Ka(e.logLevel,!!e.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(e){It("verbose",()=>`[WebNN] onRunStart {sessionId: ${e}}`),this.activeSessionId=e}onRunEnd(e){It("verbose",()=>`[WebNN] onRunEnd {sessionId: ${e}}`);let r=this.temporarySessionTensorIds.get(e);if(r){for(let t of r)It("verbose",()=>`[WebNN] releasing temporary tensor {tensorId: ${t}}`),this.tensorManager.releaseTensorId(t);this.temporarySessionTensorIds.delete(e),this.activeSessionId=void 0}}async createMLContext(e){if(e instanceof GPUDevice){let t=this.mlContextCache.findIndex(s=>s.gpuDevice===e);if(t!==-1)return this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:s}),s}}else if(e===void 0){let t=this.mlContextCache.findIndex(s=>s.options===void 0&&s.gpuDevice===void 0);if(t!==-1)return this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:s}),s}}let r=this.mlContextCache.findIndex(t=>kd(t.options,e));if(r!==-1)return this.mlContextCache[r].mlContext;{let t=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:t}),t}}registerMLContext(e,r){this.mlContextBySessionId.set(e,r);let t=this.sessionIdsByMLContext.get(r);t||(t=new Set,this.sessionIdsByMLContext.set(r,t)),t.add(e),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(e,this.temporaryGraphInputs),this.temporaryGraphInputs=[])}onReleaseSession(e){this.sessionGraphInputs.delete(e);let r=this.mlContextBySessionId.get(e);if(!r)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let t=this.sessionIdsByMLContext.get(r);if(t.delete(e),t.size===0){this.sessionIdsByMLContext.delete(r);let s=this.mlContextCache.findIndex(o=>o.mlContext===r);s!==-1&&this.mlContextCache.splice(s,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){It("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,r,t,s,o){let n=li.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e??this.currentSessionId,r,n,s,o)}async createTemporaryTensor(e,r,t){It("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${r}, shape: ${t}}`);let s=li.get(r);if(!s)throw new Error(`Unsupported ONNX data type: ${r}`);let o=this.tensorManager.reserveTensorId();await this.tensorManager.ensureTensor(e,o,s,t,!1);let n=this.temporarySessionTensorIds.get(e);return n?n.push(o):this.temporarySessionTensorIds.set(e,[o]),o}uploadTensor(e,r){if(!Qt().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");It("verbose",()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${r.byteLength}}`),this.tensorManager.upload(e,r)}async downloadTensor(e,r){return this.tensorManager.download(e,r)}createMLTensorDownloader(e,r){return async()=>{let t=await this.tensorManager.download(e);return Ha(t,r)}}registerMLTensor(e,r,t,s){let o=li.get(t);if(!o)throw new Error(`Unsupported ONNX data type: ${t}`);let n=this.tensorManager.registerTensor(e,r,o,s);return It("verbose",()=>`[WebNN] registerMLTensor {tensor: ${r}, dataType: ${o}, dimensions: ${s}} -> {tensorId: ${n}}`),n}registerMLConstant(e,r,t,s,o,n,i=!1){if(!n)throw new Error("External mounted files are not available.");let a=e;e.startsWith("./")&&(a=e.substring(2));let l=n.get(a);if(!l)throw new Error(`File with name ${a} not found in preloaded files.`);if(r+t>l.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let u=l.slice(r,r+t).buffer,p;switch(o.dataType){case"float32":p=new Float32Array(u);break;case"float16":p=typeof Float16Array<"u"&&Float16Array.from?new Float16Array(u):new Uint16Array(u);break;case"int32":p=new Int32Array(u);break;case"uint32":p=new Uint32Array(u);break;case"int64":i?(p=qa(new Uint8Array(u),!1),o.dataType="int32"):p=new BigInt64Array(u);break;case"uint64":p=new BigUint64Array(u);break;case"int8":p=new Int8Array(u);break;case"int4":case"uint4":case"uint8":p=new Uint8Array(u);break;default:throw new Error(`Unsupported data type: ${o.dataType} in creating WebNN Constant from external data.`)}return It("verbose",()=>`[WebNN] registerMLConstant {dataType: ${o.dataType}, shape: ${o.shape}}} ${i?"(Note: it was int64 data type and registered to int32 as workaround)":""}`),s.constant(o,p)}registerGraphInput(e){this.temporaryGraphInputs.push(e)}isGraphInput(e,r){let t=this.sessionGraphInputs.get(e);return t?t.includes(r):!1}isInt64Supported(e){var r;return!!((r=this.mlContextBySessionId.get(e))!=null&&r.opSupportLimits().input.dataTypes.includes("int64"))}flush(){}}}),el=je(()=>{}),tl,ui,ci,Ad,Fd,rl,sl,Od,Dd,kv=je(()=>{Bs(),el(),tl=new Map([[64,250],[128,200],[256,200],[512,200],[2048,230],[4096,200],[8192,50],[16384,50],[32768,50],[65536,50],[131072,50],[262144,50],[524288,50],[1048576,50],[2097152,30],[4194304,20],[8388608,10],[12582912,10],[16777216,10],[26214400,15],[33554432,22],[44236800,2],[58982400,6],[67108864,6],[134217728,6],[167772160,6]]),ui=[],ci=e=>Math.ceil(Number(e)/16)*16,Ad=e=>{for(let r=0;r<ui.length;r++){let t=ui[r];if(e<=t)return t}return Math.ceil(e/16)*16},Fd=1,rl=()=>Fd++,sl=async(e,r,t,s)=>{let o=ci(t),n=e.device.createBuffer({size:o,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ});try{let i=e.getCommandEncoder();e.endComputePass(),i.copyBufferToBuffer(r,0,n,0,o),e.flush(),await n.mapAsync(GPUMapMode.READ);let a=n.getMappedRange();if(s){let l=s();return l.set(new Uint8Array(a,0,t)),l}else return new Uint8Array(a.slice(0,t))}finally{n.destroy()}},Od=class{constructor(e){this.backend=e,this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.buffersPending=[],this.capturedPendingBuffers=new Map;for(let[r]of tl)ui.push(r),this.freeBuffers.set(r,[]),this.freeUniformBuffers.set(r,[]);this.sessionCount=0}upload(e,r){let t=r.buffer,s=r.byteOffset,o=r.byteLength,n=ci(o),i=this.storageCache.get(e);if(!i)throw new Error("gpu data for uploading does not exist");if(Number(i.originalSize)!==o)throw new Error(`inconsistent data size. gpu data size=${i.originalSize}, data size=${o}`);let 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GpuDataManager.registerExternalBuffer(size=${r}) => id=${s}, buffer is the same, skip.`),s;if(this.backend.capturedCommandList.has(this.backend.currentSessionId))throw new Error(`Registering a different external buffer under graph capture mode is not supported yet.
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      struct Uniforms { ${e.join(", ")} };
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`)+this.internalVariables.map(e=>e.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=r=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(r)];return this.uniforms.map(r=>[e(r.type),r.length??1])}},Rd=(e,r)=>new Bd(e,r)}),jd,il,Nd,Vd,Ud,Wd,qr,Gd,Kd,Ws=je(()=>{ft(),yt(),or(),Tt(),jd=(e,r)=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(r.length!==0&&r.length!==e[0].dims.length)throw new Error(`perm size ${r.length} does not match input rank ${e[0].dims.length}`)},il=(e,r)=>r.length!==0?r:[...new Array(e).keys()].reverse(),Nd=(e,r)=>Me.sortBasedOnPerm(e,il(e.length,r)),Vd=(e,r,t,s)=>{let o=`fn perm(i: ${s.type.indices}) -> ${t.type.indices} {
    var a: ${t.type.indices};`;for(let n=0;n<r;++n)o+=`a[${e[n]}]=i[${n}];`;return o+="return a;}"},Ud=(e,r)=>{let t=[],s=[];for(let o=0;o<e.length;++o)e[o]!==1&&t.push(e[o]),e[r[o]]!==1&&s.push(r[o]);return{newShape:t,newPerm:s}},Wd=(e,r)=>{let t=0;for(let s=0;s<e.length;++s)if(r[e[s]]!==1){if(e[s]<t)return!1;t=e[s]}return!0},qr=(e,r)=>{let t=e.dataType,s=e.dims.length,o=il(s,r),n=Nd(e.dims,o),i=e.dims,a=n,l=s<2||Wd(o,e.dims),u;if(l)return u=f=>{let T=Pe("input",t,i,4),k=et("output",t,a,4);return`
  ${f.registerUniform("output_size","u32").declareVariables(T,k)}
  ${f.mainStart()}
    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    output[global_idx] = input[global_idx];
  }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let f=Me.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(f/64/4)},programUniforms:[{type:12,data:Math.ceil(f/4)}]}},getShaderSource:u};let{newShape:p,newPerm:c}=Ud(e.dims,o),d=Me.areEqual(c,[2,3,1]),_=Me.areEqual(c,[3,1,2]);if(p.length===2||d||_){i=d?[p[0],p[1]*p[2]]:_?[p[0]*p[1],p[2]]:p,a=[i[1],i[0]];let f=16;return u=T=>{let k=Pe("a",t,i.length),w=et("output",t,a.length);return`
  ${T.registerUniform("output_size","u32").declareVariables(k,w)}
  var<workgroup> tile : array<array<${w.type.value}, ${f+1}>, ${f}>;
  ${T.mainStart([f,f,1])}
    let stride = (uniforms.output_shape[1] - 1) / ${f} + 1;
    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${f}u + local_id.x;
    let input_row = workgroup_id_x * ${f}u + local_id.y;
    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
      tile[local_id.y][local_id.x] = ${k.getByIndices(`${k.type.indices}(input_row, input_col)`)};
    }
    workgroupBarrier();

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

  ${Vd(o,s,T,k)}

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

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

    ${k.setByOffset("global_idx",T.getByIndices("aIndices"))}
  }`},{name:"Transpose",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>{let f=Me.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:[{type:12,data:f},...at(i,a)]}},getShaderSource:u}},Gd=(e,r)=>{jd(e.inputs,r.perm),e.compute(qr(e.inputs[0],r.perm))},Kd=e=>zt({perm:e.perm})}),Hd,qd,Qd,Xd,Jd,Yd,Zd,ep,tp,rp,_s,sp,np,op,ip,ap,lp,up,cp,dp,pp,Iv=je(()=>{ft(),yt(),Tt(),ll(),Ws(),Hd={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},qd={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},Qd={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},Xd={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Jd=(e,r)=>{let t=[];for(let s=r-e;s<r;++s)t.push(s);return t},Yd=(e,r)=>{let t=[],s=e.length;for(let n=0;n<s;n++)r.indexOf(n)===-1&&t.push(e[n]);let o=r.map(n=>e[n]);return[t,o]},Zd=(e,r)=>{let t=e.length+r.length,s=[],o=0;for(let n=0;n<t;n++)r.indexOf(n)===-1?s.push(e[o++]):s.push(1);return s},ep=(e,r)=>{for(let t=0;t<e.length;++t)if(e[e.length-t-1]!==r-1-t)return!1;return!0},tp=(e,r)=>{let t=[];if(!ep(e,r)){for(let s=0;s<r;++s)e.indexOf(s)===-1&&t.push(s);e.forEach(s=>t.push(s))}return t},rp=(e,r,t,s,o,n,i)=>{let a=t[0].dims,l=Me.size(n),u=Me.size(i),p=Pe("_A",t[0].dataType,a),c=et("output",o,n),d=64;l===1&&(d=256);let _=`
          var<workgroup> aBestValues : array<f32, ${d}>;
       `,f=T=>`
        ${T.registerUniform("reduceSize","u32").declareVariables(p,c)}
        ${_}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${T.mainStart(d)}

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

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

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

         if (local_idx == 0u) {
          ${c.setByOffset("outputIndex",`${s==="mean"?`${c.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${c.type.storage}(${Xd[s]})`}`)};
         }
        }`;return{name:e,shaderCache:{hint:`${r};${d}`,inputDependencies:["type"]},getShaderSource:f,getRunData:()=>({outputs:[{dims:n,dataType:o}],dispatchGroup:{x:l},programUniforms:[{type:12,data:u}]})}},_s=(e,r,t,s)=>{let o=e.inputs.length===1?t:al(e.inputs,t),n=o.axes;n.length===0&&!o.noopWithEmptyAxes&&(n=e.inputs[0].dims.map((_,f)=>f));let i=Me.normalizeAxes(n,e.inputs[0].dims.length),a=i,l=e.inputs[0],u=tp(a,e.inputs[0].dims.length);u.length>0&&(l=e.compute(qr(e.inputs[0],u),{inputs:[0],outputs:[-1]})[0],a=Jd(a.length,l.dims.length));let[p,c]=Yd(l.dims,a),d=p;o.keepDims&&(d=Zd(p,i)),e.compute(rp(r,o.cacheKey,[l],s,e.inputs[0].dataType,d,c),{inputs:[l]})},sp=(e,r)=>{_s(e,"ReduceMeanShared",r,"mean")},np=(e,r)=>{_s(e,"ReduceL1Shared",r,"l1")},op=(e,r)=>{_s(e,"ReduceL2Shared",r,"l2")},ip=(e,r)=>{_s(e,"ReduceLogSumExpShared",r,"logSumExp")},ap=(e,r)=>{_s(e,"ReduceMaxShared",r,"max")},lp=(e,r)=>{_s(e,"ReduceMinShared",r,"min")},up=(e,r)=>{_s(e,"ReduceProdShared",r,"prod")},cp=(e,r)=>{_s(e,"ReduceSumShared",r,"sum")},dp=(e,r)=>{_s(e,"ReduceSumSquareShared",r,"sumSquare")},pp=(e,r)=>{_s(e,"ReduceLogSumShared",r,"logSum")}}),gs,hp,pi,al,ws,mp,fp,_p,gp,wp,Mp,bp,yp,vp,xp,Ms,Tp,Ep,Pp,Cp,Sp,$p,kp,Ip,Ap,Fp,ll=je(()=>{ft(),yt(),or(),Tt(),Iv(),gs=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},hp=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],pi=(e,r,t,s,o,n,i=!1,a=!1)=>{let l=[],u=t[0].dims,p=u.length,c=Me.normalizeAxes(o,p),d=!a&&c.length===0;u.forEach((T,k)=>{d||c.indexOf(k)>=0?i&&l.push(1):l.push(T)});let _=l.length,f=Me.size(l);return{name:e,shaderCache:r,getShaderSource:T=>{let k=[],w=Pe("_A",t[0].dataType,p),g=et("output",n,_),S=s(w,g,c),E=S[2];for(let v=0,M=0;v<p;v++)d||c.indexOf(v)>=0?(i&&M++,E=`for(var j${v}: u32 = 0; j${v} < ${u[v]}; j${v}++) {
                  ${S[2].includes("last_index")?`let last_index = j${v};`:""}
                  ${w.indicesSet("input_indices",v,`j${v}`)}
                  ${E}
                }`):(k.push(`${w.indicesSet("input_indices",v,g.indicesGet("output_indices",M))};`),M++);return`

        ${T.registerUniform("output_size","u32").declareVariables(w,g)}

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

          ${k.join(`
`)}
          ${S[0]}       // init ops for reduce max/min
          ${S[1]}
          ${E}
          ${S[3]}
          ${S.length===4?g.setByOffset("global_idx","value"):S.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:n}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:[{type:12,data:f},...at(u,l)]})}},al=(e,r)=>{let t=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(s=>t.push(Number(s))),zt({axes:t,keepDims:r.keepDims,noopWithEmptyAxes:r.noopWithEmptyAxes})},ws=(e,r,t,s)=>{let o=e.inputs,n=o.length===1?t:al(o,t);e.compute(pi(r,{hint:n.cacheKey,inputDependencies:["rank"]},[o[0]],n.noopWithEmptyAxes&&n.axes.length===0?hp:s,n.axes,o[0].dataType,n.keepDims,n.noopWithEmptyAxes),{inputs:[0]})},mp=(e,r)=>{gs(e.inputs),ws(e,"ReduceLogSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])},fp=(e,r)=>{gs(e.inputs),ws(e,"ReduceL1",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += abs(${t.getByIndices("input_indices")});`,""])},_p=(e,r)=>{gs(e.inputs),ws(e,"ReduceL2",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},gp=(e,r)=>{gs(e.inputs),ws(e,"ReduceLogSumExp",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += exp(${t.getByIndices("input_indices")});`,"value = log(value);"])},wp=(e,r)=>{gs(e.inputs),ws(e,"ReduceMax",r,(t,s,o)=>{let n=[];for(let i=0;i<t.rank;i++)(o.indexOf(i)>=0||o.length===0)&&n.push(t.indicesSet("input_indices",i,0));return[`${n.join(`
`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = max(value, ${t.getByIndices("input_indices")});`,""]})},Mp=(e,r)=>{gs(e.inputs),ws(e,"ReduceMean",r,(t,s,o)=>{let n=1;for(let i=0;i<t.rank;i++)(o.indexOf(i)>=0||o.length===0)&&(n*=e.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${t.getByIndices("input_indices")});`,`let value = ${s.type.value}(sum / ${n});`]})},bp=(e,r)=>{gs(e.inputs),ws(e,"ReduceMin",r,(t,s,o)=>{let n=[];for(let i=0;i<t.rank;i++)(o.indexOf(i)>=0||o.length===0)&&n.push(`input_indices[${i}] = 0;`);return[`${n.join(`
`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = min(value, ${t.getByIndices("input_indices")});`,""]})},yp=(e,r)=>{gs(e.inputs),ws(e,"ReduceProd",r,(t,s)=>[`var value = ${s.type.storage}(1);`,"",`value *= ${t.getByIndices("input_indices")};`,""])},vp=(e,r)=>{gs(e.inputs),ws(e,"ReduceSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,""])},xp=(e,r)=>{gs(e.inputs),ws(e,"ReduceSumSquare",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += t * t;`,""])},Ms=(e,r,t)=>{if(r.length===0)return t;let s=1,o=1;for(let n=0;n<r.length;n++)r.indexOf(n)===-1?s*=e[n]:o*=e[n];return o<32&&s>1024},Tp=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Mp(e,r):sp(e,r)},Ep=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?fp(e,r):np(e,r)},Pp=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?_p(e,r):op(e,r)},Cp=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?gp(e,r):ip(e,r)},Sp=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?wp(e,r):ap(e,r)},$p=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?bp(e,r):lp(e,r)},kp=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?yp(e,r):up(e,r)},Ip=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?vp(e,r):cp(e,r)},Ap=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?xp(e,r):dp(e,r)},Fp=(e,r)=>{Ms(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?mp(e,r):pp(e,r)}}),ul,Op,Dp,cl,Av=je(()=>{ft(),or(),ll(),ul=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Op=(e,r)=>{ul(e.inputs);let t=(s,o,n)=>{let i=[];for(let a=0;a<s.rank;a++)(n.indexOf(a)>=0||n.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(`
`)}`,`var value = ${s.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?"<=":"<"} value) {
         value = ${s.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};e.compute(pi("ArgMin",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},Dp=(e,r)=>{ul(e.inputs);let t=(s,o,n)=>{let i=[];for(let a=0;a<s.rank;a++)(n.indexOf(a)>=0||n.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(`
`)}`,`var value = ${s.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?">=":">"} value) {
         value = ${s.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};e.compute(pi("argMax",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},cl=e=>zt(e)}),Lp,hi,zp,Bp,Rp,mo,jp,Np,dl=je(()=>{ft(),yt(),el(),Tt(),Lp=(e,r)=>{let t=e[0],s=e[1],o=e[2],n=e[3],i=e[4],a=e[5];if(i&&a)throw new Error("Attention cannot have both past and attention_bias");if(t.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=t.dims[0],u=t.dims[1],p=t.dims[2];if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(s.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(s.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==s.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let c=o.dims[0]/3,d=c,_=d;if(r.qkvHiddenSizes.length>0){if(r.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let S of r.qkvHiddenSizes)if(S%r.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");c=r.qkvHiddenSizes[0],d=r.qkvHiddenSizes[1],_=r.qkvHiddenSizes[2]}let f=u;if(c!==d)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==c+d+_)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let T=0;if(i){if(d!==_)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==r.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==d/r.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');r.pastPresentShareBuffer||(T=i.dims[3])}let k=f+T,w=-1,g=0;if(n)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(a){if(a.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(a.dims[0]!==l||a.dims[1]!==r.numHeads||a.dims[2]!==u||a.dims[3]!==k)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:l,sequenceLength:u,pastSequenceLength:T,kvSequenceLength:f,totalSequenceLength:k,maxSequenceLength:w,inputHiddenSize:p,hiddenSize:c,vHiddenSize:_,headSize:Math.floor(c/r.numHeads),vHeadSize:Math.floor(_/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:g,scale:r.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},hi=(e,r,t)=>r&&e?`
      let total_sequence_length_input = u32(${r.getByOffset("0")});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${e==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;
      }
       `:`
    ${t?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,zp=(e,r,t,s,o,n,i,a)=>{let l=sr(i?1:n),u=64,p=n/l;p<u&&(u=32);let c=Math.ceil(n/l/u),d=[{type:12,data:r},{type:12,data:t},{type:12,data:s},{type:12,data:o},{type:12,data:p},{type:12,data:c}],_=Tr(e.dataType,l),f=zr(1,l),T=["type"];i&&T.push("type"),a&&T.push("type");let k=w=>{let g=et("x",e.dataType,e.dims,l),S=[g],E=i?Pe("seq_lens",i.dataType,i.dims):void 0;E&&S.push(E);let v=a?Pe("total_sequence_length_input",a.dataType,a.dims):void 0;v&&S.push(v);let M=zr(e.dataType),y=[{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, ${u}>;
  var<workgroup> thread_sum: array<f32, ${u}>;
  ${w.registerUniforms(y).declareVariables(...S)}
  ${w.mainStart([u,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;
    ${hi(E,v,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${u}) * 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 = ${f}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${f}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(l){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${l}`)}})()};
    workgroupBarrier();

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

    var sum_vector = ${f}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      sum_vector += exp(${f}(x[offset + i]) - max_value);
    }
    thread_sum[local_idx] = ${(()=>{switch(l){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${l}`)}})()};
    workgroupBarrier();

    var sum: f32 = 0;
    for (var i = 0u; i < ${u}; 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] = ${g.type.value}(${M}(1.0) / ${M}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${f}(x[offset + i]);
        x[offset + i] = ${g.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] = ${g.type.value}(${M}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${u};${_};${l}`,inputDependencies:T},getShaderSource:k,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:o,z:r*t},programUniforms:d})}},Bp=(e,r,t,s,o,n,i,a,l)=>{let u=i+n.kvSequenceLength,p=[n.batchSize,n.numHeads,n.sequenceLength,u],c=e>1&&s,d=n.kvNumHeads?n.kvNumHeads:n.numHeads,_=c?[n.batchSize,d,u,n.headSize]:void 0,f=n.nReps?n.nReps:1,T=n.scale===0?1/Math.sqrt(n.headSize):n.scale,k=sr(n.headSize),w=n.headSize/k,g=12,S={x:Math.ceil(u/g),y:Math.ceil(n.sequenceLength/g),z:n.batchSize*n.numHeads},E=[{type:12,data:n.sequenceLength},{type:12,data:w},{type:12,data:u},{type:12,data:n.numHeads},{type:12,data:n.headSize},{type:1,data:T},{type:12,data:i},{type:12,data:n.kvSequenceLength},{type:12,data:f}],v=c&&s&&Me.size(s.dims)>0,M=["type","type"];v&&M.push("type"),o&&M.push("type"),a&&M.push("type"),l&&M.push("type");let y=[{dims:p,dataType:r.dataType,gpuDataType:0}];c&&y.push({dims:_,dataType:r.dataType,gpuDataType:0});let C=F=>{let z=Pe("q",r.dataType,r.dims,k),K=Pe("key",t.dataType,t.dims,k),q=[z,K];if(v){let fe=Pe("past_key",s.dataType,s.dims,k);q.push(fe)}o&&q.push(Pe("attention_bias",o.dataType,o.dims));let R=a?Pe("seq_lens",a.dataType,a.dims):void 0;R&&q.push(R);let Z=l?Pe("total_sequence_length_input",l.dataType,l.dims):void 0;Z&&q.push(Z);let H=et("output",r.dataType,p),J=[H];c&&J.push(et("present_key",r.dataType,_,k));let Q=zr(1,k),se=[{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 = ${g}u;

  var<workgroup> tileQ: array<${z.type.storage}, ${g*g}>;
  var<workgroup> tileK: array<${z.type.storage}, ${g*g}>;
  ${F.registerUniforms(se).declareVariables(...q,...J)}
  ${F.mainStart([g,g,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${f===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${f===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;
    ${hi(R,Z,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${v&&c?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${c?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${Q}(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;
      ${v&&c?`
              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];
          }`}
      ${c?`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 += ${Q}(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(k){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: ${k}`)}})()};
        output[outputIdx] = ${H.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${k};${o!==void 0};${s!==void 0};${e}`,inputDependencies:M},getRunData:()=>({outputs:y,dispatchGroup:S,programUniforms:E}),getShaderSource:C}},Rp=(e,r,t,s,o,n,i=void 0,a=void 0)=>{let l=n+o.kvSequenceLength,u=o.nReps?o.nReps:1,p=o.vHiddenSize*u,c=e>1&&s,d=o.kvNumHeads?o.kvNumHeads:o.numHeads,_=c?[o.batchSize,d,l,o.headSize]:void 0,f=[o.batchSize,o.sequenceLength,p],T=12,k={x:Math.ceil(o.vHeadSize/T),y:Math.ceil(o.sequenceLength/T),z:o.batchSize*o.numHeads},w=[{type:12,data:o.sequenceLength},{type:12,data:l},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:p},{type:12,data:n},{type:12,data:o.kvSequenceLength},{type:12,data:u}],g=c&&s&&Me.size(s.dims)>0,S=["type","type"];g&&S.push("type"),i&&S.push("type"),a&&S.push("type");let E=[{dims:f,dataType:r.dataType,gpuDataType:0}];c&&E.push({dims:_,dataType:r.dataType,gpuDataType:0});let v=M=>{let y=Pe("probs",r.dataType,r.dims),C=Pe("v",t.dataType,t.dims),F=[y,C];g&&F.push(Pe("past_value",s.dataType,s.dims));let z=i?Pe("seq_lens",i.dataType,i.dims):void 0;i&&F.push(z);let K=a?Pe("total_sequence_length_input",a.dataType,a.dims):void 0;a&&F.push(K);let q=[et("output",r.dataType,f)];c&&q.push(et("present_value",r.dataType,_));let R=[{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 = ${T}u;
  var<workgroup> tileQ: array<${y.type.value}, ${T*T}>;
  var<workgroup> tileV: array<${y.type.value}, ${T*T}>;
  ${M.registerUniforms(R).declareVariables(...F,...q)}
  ${M.mainStart([T,T,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${u===1?"headIdx":"headIdx / uniforms.n_reps"};
   let kv_num_heads = ${u===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;
   ${hi(z,K,!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
   ${g&&c?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${c?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${y.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;
        ${g&&c?`
        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];
            }`}
        ${c?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

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

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:a,programUniforms:u}),getShaderSource:p},{inputs:l,outputs:[-1,-1,-1]})},Np=(e,r)=>{let t=Lp(e.inputs,r),[s,o,n]=jp(e,t);return mo(e,s,o,n,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t)}}),Vp,Up,Wp,Gp,Fv=je(()=>{ms(),ft(),yt(),or(),Tt(),Vp=(e,r)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let t=(s,o,n)=>{let i=o.length;if(i!==s.length)throw new Error(`${n}: num dimensions != ${i}`);o.forEach((a,l)=>{if(a!==s[l])throw new Error(`${n}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let s=r.format==="NHWC"?r.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,r.spatial?2:void 0);t(e[1].dims,s,"Invalid input scale"),t(e[2].dims,s,"Invalid input B"),t(e[3].dims,s,"Invalid input mean"),t(e[4].dims,s,"Invalid input var")}else t(e[1].dims,[1],"Invalid input scale"),t(e[2].dims,[1],"Invalid input B"),t(e[3].dims,[1],"Invalid input mean"),t(e[4].dims,[1],"Invalid input var")},Up=(e,r)=>{let{epsilon:t,spatial:s,format:o}=r,n=e[0].dims,i=s?sr(n[n.length-1]):1,a=o==="NHWC"&&n.length>1?i:1,l=Me.size(n)/i,u=s,p=u?n.length:n,c=Pe("x",e[0].dataType,e[0].dims,i),d=Pe("scale",e[1].dataType,e[1].dims,a),_=Pe("bias",e[2].dataType,e[2].dims,a),f=Pe("inputMean",e[3].dataType,e[3].dims,a),T=Pe("inputVar",e[4].dataType,e[4].dims,a),k=et("y",e[0].dataType,p,i),w=()=>{let S="";if(s)S=`let cOffset = ${n.length===1?"0u":o==="NHWC"?`outputIndices[${n.length-1}] / ${i}`:"outputIndices[1]"};`;else if(o==="NCHW")S=`
            ${k.indicesSet("outputIndices","0","0")}
            let cOffset = ${k.indicesToOffset("outputIndices")};`;else{S=`var cIndices = ${d.type.indices}(0);
                       cIndices[0] = outputIndices[${n.length-1}];`;for(let E=1;E<d.rank;E++)S+=`cIndices[${E}] = outputIndices[${E}];`;S+=`let cOffset = ${d.indicesToOffset("cIndices")};`}return S},g=S=>`
  const epsilon = ${t};
  ${S.registerUniform("outputSize","u32").declareVariables(c,d,_,f,T,k)}
  ${S.mainStart()}
  ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${k.offsetToIndices(`global_idx * ${i}`)};
    ${w()}
    let scale = ${d.getByOffset("cOffset")};
    let bias = ${_.getByOffset("cOffset")};
    let inputMean = ${f.getByOffset("cOffset")};
    let inputVar = ${T.getByOffset("cOffset")};
    let x = ${c.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${k.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${s}_${i}`,inputDependencies:u?["rank","type","type","type","type"]:void 0},getShaderSource:g,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u?[{type:12,data:l},...at(n)]:[{type:12,data:l}]})}},Wp=e=>zt(e),Gp=(e,r)=>{let{inputs:t,outputCount:s}=e,o=Wp({...r,outputCount:s});if(Xt.webgpu.validateInputContent&&Vp(t,o),r.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Up(t,o))}}),Kp,Hp,qp,Ov=je(()=>{yt(),Tt(),Kp=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")},Hp=e=>{let r=e[0].dims,t=e[0].dims[2],s=Me.size(r)/4,o=e[0].dataType,n=Pe("input",o,r,4),i=Pe("bias",o,[t],4),a=Pe("residual",o,r,4),l=et("output",o,r,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:u=>`
  const channels = ${t}u / 4;
  ${u.declareVariables(n,i,a,l)}

  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes(s)}
    let value = ${n.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${a.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},qp=e=>{Kp(e.inputs),e.compute(Hp(e.inputs))}}),Qp,Dt,Xp,Jp,Yp,Zp,eh,th,rh,sh,nh,oh,ih,ah,lh,uh,fo,ch,mi,dh,ph,hh,mh,fh,_h,gh,wh,Mh,bh,yh,vh,xh,Th,Eh,Ph,pl,Ch,hl,ml,Sh,$h,kh,Ih,Ah,Fh,fl=je(()=>{ft(),yt(),or(),Tt(),Qp=(e,r,t,s,o,n,i)=>{let a=Math.ceil(r/4),l="";typeof o=="string"?l=`${o}(a)`:l=o("a");let u=Pe("inputData",t,[a],4),p=et("outputData",s,[a],4),c=[{name:"vec_size",type:"u32"}];return i&&c.push(...i),`
      ${e.registerUniforms(c).declareVariables(u,p)}

  ${n??""}

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

    let a = ${u.getByOffset("global_idx")};
    ${p.setByOffset("global_idx",l)}
  }`},Dt=(e,r,t,s,o,n=e.dataType,i,a)=>{let l=[{type:12,data:Math.ceil(Me.size(e.dims)/4)}];return i&&l.push(...i),{name:r,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:u=>Qp(u,Me.size(e.dims),e.dataType,n,t,s,a),getRunData:u=>({outputs:[{dims:e.dims,dataType:n}],dispatchGroup:{x:Math.ceil(Me.size(u[0].dims)/64/4)},programUniforms:l})}},Xp=e=>{e.compute(Dt(e.inputs[0],"Abs","abs"))},Jp=e=>{e.compute(Dt(e.inputs[0],"Acos","acos"))},Yp=e=>{e.compute(Dt(e.inputs[0],"Acosh","acosh"))},Zp=e=>{e.compute(Dt(e.inputs[0],"Asin","asin"))},eh=e=>{e.compute(Dt(e.inputs[0],"Asinh","asinh"))},th=e=>{e.compute(Dt(e.inputs[0],"Atan","atan"))},rh=e=>{e.compute(Dt(e.inputs[0],"Atanh","atanh"))},sh=e=>zt(e),nh=(e,r)=>{let t;switch(r.to){case 10:t="vec4<f16>";break;case 1:t="vec4<f32>";break;case 12:t="vec4<u32>";break;case 6:t="vec4<i32>";break;case 9:t="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${r.to}`)}e.compute(Dt(e.inputs[0],"Cast",t,void 0,r.cacheKey,r.to))},oh=e=>{let r,t,s=e.length>=2&&e[1].data!==0,o=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:r=s?e[1].getFloat32Array()[0]:-34028234663852886e22,t=o?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:r=s?e[1].getUint16Array()[0]:64511,t=o?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return zt({min:r,max:t})},ih=(e,r)=>{let t=r||oh(e.inputs),s=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"Clip",o=>`clamp(${o}, vec4<${s}>(uniforms.min), vec4<${s}>(uniforms.max))`,void 0,t.cacheKey,void 0,[{type:e.inputs[0].dataType,data:t.min},{type:e.inputs[0].dataType,data:t.max}],[{name:"min",type:s},{name:"max",type:s}]),{inputs:[0]})},ah=e=>{e.compute(Dt(e.inputs[0],"Ceil","ceil"))},lh=e=>{e.compute(Dt(e.inputs[0],"Cos","cos"))},uh=e=>{e.compute(Dt(e.inputs[0],"Cosh","cosh"))},fo=e=>zt(e),ch=(e,r)=>{let t=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"Elu",s=>`elu_vf32(${s})`,`
  const elu_alpha_ = ${t}(${r.alpha});

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

  fn elu_vf32(v: vec4<${t}>) -> vec4<${t}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,r.cacheKey))},mi=(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));
}`,dh=e=>{let r=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"Erf",t=>`erf_vf32(${t})`,mi(r)))},ph=e=>{e.compute(Dt(e.inputs[0],"Exp","exp"))},hh=e=>{e.compute(Dt(e.inputs[0],"Floor","floor"))},mh=e=>{let r=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"Gelu",t=>`0.5 * ${t} * (1.0 + erf_vf32(${t} * 0.7071067811865475))`,mi(r)))},fh=(e,r)=>{let t=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"LeakyRelu",s=>`select(leaky_relu_alpha_ * ${s}, ${s}, ${s} >= vec4<${t}>(0.0))`,`const leaky_relu_alpha_ = ${t}(${r.alpha});`,r.cacheKey))},_h=e=>{e.compute(Dt(e.inputs[0],"Not",r=>`!${r}`))},gh=e=>{e.compute(Dt(e.inputs[0],"Neg",r=>`-${r}`))},wh=e=>{e.compute(Dt(e.inputs[0],"Reciprocal",r=>`1.0/${r}`))},Mh=e=>{let r=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"Relu",t=>`select(vec4<${r}>(0.0), ${t}, ${t} > vec4<${r}>(0.0))`))},bh=e=>{e.compute(Dt(e.inputs[0],"Sigmoid",r=>`(1.0 / (1.0 + exp(-${r})))`))},yh=e=>zt(e),vh=(e,r)=>{let t=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"HardSigmoid",s=>`max(vec4<${t}>(0.0), min(vec4<${t}>(1.0), ${r.alpha} * ${s} + vec4<${t}>(${r.beta})))`,void 0,r.cacheKey))},xh=e=>{e.compute(Dt(e.inputs[0],"Sin","sin"))},Th=e=>{e.compute(Dt(e.inputs[0],"Sinh","sinh"))},Eh=e=>{e.compute(Dt(e.inputs[0],"Sqrt","sqrt"))},Ph=e=>{e.compute(Dt(e.inputs[0],"Tan","tan"))},pl=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Ch=e=>{e.compute(Dt(e.inputs[0],"Tanh",pl))},hl=(e="f32")=>`
const fast_gelu_a: ${e} = 0.5;
const fast_gelu_b: ${e} = 0.7978845608028654;
const fast_gelu_c: ${e} = 0.035677408136300125;

fn tanh_v(v: vec4<${e}>) -> vec4<${e}> {
  return ${pl("v")};
}
`,ml=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Sh=e=>{let r=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"FastGelu",ml,hl(r),void 0,e.inputs[0].dataType))},$h=(e,r)=>{let t=zr(e.inputs[0].dataType);return e.compute(Dt(e.inputs[0],"ThresholdedRelu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${t}>(${r.alpha});`,r.cacheKey)),0},kh=e=>{e.compute(Dt(e.inputs[0],"Log","log"))},Ih=(e,r)=>`
const alpha = vec4<${e}>(${r});
const one = ${e}(1.0);
const zero = ${e}(0.0);

fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> {
  let v = x *alpha;
  var x1 : vec4<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Ah=e=>`quick_gelu_impl(${e})`,Fh=(e,r)=>{let t=zr(e.inputs[0].dataType);e.compute(Dt(e.inputs[0],"QuickGelu",Ah,Ih(t,r.alpha),r.cacheKey,e.inputs[0].dataType))}}),Oh,Dh,Lh,Dv=je(()=>{yt(),Tt(),fl(),Oh=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")},Dh=e=>{let r=e[0].dims.slice();r[2]=r[2]/2;let t=Pe("input",e[0].dataType,e[0].dims,4),s=Pe("bias",e[0].dataType,[e[0].dims[2]],4),o=et("output",e[0].dataType,r,4),n=Me.size(r)/4,i=Tr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:a=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

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

  ${mi(i)}

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

    ${o.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Lh=e=>{Oh(e.inputs),e.compute(Dh(e.inputs))}}),zh,Bh,bs,Rh,jh,Nh,Vh,Uh,Wh,Gh,Kh,Hh,qh,Lv=je(()=>{ft(),yt(),Tt(),zh=(e,r,t,s,o,n,i,a,l,u,p,c)=>{let d,_;typeof a=="string"?d=_=(g,S)=>`${a}((${g}),(${S}))`:typeof a=="function"?d=_=a:(d=a.scalar,_=a.vector);let f=et("outputData",p,s.length,4),T=Pe("aData",l,r.length,4),k=Pe("bData",u,t.length,4),w;if(o)if(n){let g=Me.size(r)===1,S=Me.size(t)===1,E=r.length>0&&r[r.length-1]%4===0,v=t.length>0&&t[t.length-1]%4===0;g||S?w=f.setByOffset("global_idx",_(g?`${T.type.value}(${T.getByOffset("0")}.x)`:T.getByOffset("global_idx"),S?`${k.type.value}(${k.getByOffset("0")}.x)`:k.getByOffset("global_idx"))):w=`
            let outputIndices = ${f.offsetToIndices("global_idx * 4u")};
            let offsetA = ${T.broadcastedIndicesToOffset("outputIndices",f)};
            let offsetB = ${k.broadcastedIndicesToOffset("outputIndices",f)};
            ${f.setByOffset("global_idx",_(i||E?T.getByOffset("offsetA / 4u"):`${T.type.value}(${T.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||v?k.getByOffset("offsetB / 4u"):`${k.type.value}(${k.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else w=f.setByOffset("global_idx",_(T.getByOffset("global_idx"),k.getByOffset("global_idx")));else{if(!n)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let g=(S,E,v="")=>{let M=`aData[indexA${E}][componentA${E}]`,y=`bData[indexB${E}][componentB${E}]`;return`
            let outputIndices${E} = ${f.offsetToIndices(`global_idx * 4u + ${E}u`)};
            let offsetA${E} = ${T.broadcastedIndicesToOffset(`outputIndices${E}`,f)};
            let offsetB${E} = ${k.broadcastedIndicesToOffset(`outputIndices${E}`,f)};
            let indexA${E} = offsetA${E} / 4u;
            let indexB${E} = offsetB${E} / 4u;
            let componentA${E} = offsetA${E} % 4u;
            let componentB${E} = offsetB${E} % 4u;
            ${S}[${E}] = ${v}(${d(M,y)});
          `};p===9?w=`
            var data = vec4<u32>(0);
            ${g("data",0,"u32")}
            ${g("data",1,"u32")}
            ${g("data",2,"u32")}
            ${g("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:w=`
            ${g("outputData[global_idx]",0)}
            ${g("outputData[global_idx]",1)}
            ${g("outputData[global_idx]",2)}
            ${g("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(T,k,f)}

        ${c??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${w}
      }`},Bh=(e,r,t,s,o,n,i=t.dataType)=>{let a=t.dims.map(T=>Number(T)??1),l=s.dims.map(T=>Number(T)??1),u=!Me.areEqual(a,l),p=a,c=Me.size(a),d=!1,_=!1,f=[u];if(u){let T=Nn.calcShape(a,l,!1);if(!T)throw new Error("Can't perform binary op on the given tensors");p=T.slice(),c=Me.size(p);let k=Me.size(a)===1,w=Me.size(l)===1,g=a.length>0&&a[a.length-1]%4===0,S=l.length>0&&l[l.length-1]%4===0;f.push(k),f.push(w),f.push(g),f.push(S);let E=1;for(let v=1;v<p.length;v++){let M=a[a.length-v],y=l[l.length-v];if(M===y)E*=M;else break}E%4===0?(_=!0,d=!0):(k||w||g||S)&&(d=!0)}else d=!0;return f.push(d),{name:e,shaderCache:{hint:r+f.map(T=>T.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:T=>zh(T,a,l,p,d,u,_,o,t.dataType,s.dataType,i,n),getRunData:()=>({outputs:[{dims:p,dataType:i}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:Math.ceil(Me.size(p)/4)},...at(a,l,p)]})}},bs=(e,r,t,s,o,n)=>{e.compute(Bh(r,o??"",e.inputs[0],e.inputs[1],t,s,n))},Rh=e=>{bs(e,"Add",(r,t)=>`${r}+${t}`)},jh=e=>{bs(e,"Div",(r,t)=>`${r}/${t}`)},Nh=e=>{bs(e,"Equal",{scalar:(r,t)=>`u32(${r}==${t})`,vector:(r,t)=>`vec4<u32>(${r}==${t})`},void 0,void 0,9)},Vh=e=>{bs(e,"Mul",(r,t)=>`${r}*${t}`)},Uh=e=>{let r=Pe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;bs(e,"Pow",{scalar:(t,s)=>`pow_custom(${t},${s})`,vector:(t,s)=>`pow_vector_custom(${t},${s})`},`
    fn pow_custom(a : ${r}, b : ${r}) -> ${r} {
      if (b == ${r}(0.0)) {
        return ${r}(1.0);
      } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) {
        return ${r}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${r}(1.0), round(f32(abs(b) % ${r}(2.0))) != 1.0) * ${r}(${r==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${r}>, b : vec4<${r}>) -> vec4<${r}> {
      // TODO: implement vectorized pow
      return vec4<${r}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},Wh=e=>{bs(e,"Sub",(r,t)=>`${r}-${t}`)},Gh=e=>{bs(e,"Greater",{scalar:(r,t)=>`u32(${r}>${t})`,vector:(r,t)=>`vec4<u32>(${r}>${t})`},void 0,void 0,9)},Kh=e=>{bs(e,"Less",{scalar:(r,t)=>`u32(${r}<${t})`,vector:(r,t)=>`vec4<u32>(${r}<${t})`},void 0,void 0,9)},Hh=e=>{bs(e,"GreaterOrEqual",{scalar:(r,t)=>`u32(${r}>=${t})`,vector:(r,t)=>`vec4<u32>(${r}>=${t})`},void 0,void 0,9)},qh=e=>{bs(e,"LessOrEqual",{scalar:(r,t)=>`u32(${r}<=${t})`,vector:(r,t)=>`vec4<u32>(${r}<=${t})`},void 0,void 0,9)}}),Qh,Xh,Jh,Yh,Zh,em,zv=je(()=>{ft(),yt(),or(),Tt(),Qh=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");let t=0,s=e[t],o=s.dataType,n=s.dims.length;e.forEach((i,a)=>{if(a!==t){if(i.dataType!==o)throw new Error("input tensors should be one type");if(i.dims.length!==n)throw new Error("input tensors should have the same shape");i.dims.forEach((l,u)=>{if(u!==r&&l!==s.dims[u])throw new Error("non concat dimensions must match")})}})},Xh=(e,r)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${r});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,Jh=(e,r)=>{let t=e.length,s=[];for(let o=0;o<t;++o){let n=r.setByOffset("global_idx",e[o].getByIndices("indices"));t===1?s.push(n):o===0?s.push(`if (inputIndex == ${o}u) { ${n} }`):o===t-1?s.push(`else { ${n} }`):s.push(`else if (inputIndex == ${o}) { ${n} }`)}return s.join(`
`)},Yh=(e,r,t,s)=>{let o=Me.size(t),n=new Array(e.length),i=new Array(e.length),a=0,l=[],u=[],p=[{type:12,data:o}];for(let T=0;T<e.length;++T)a+=e[T].dims[r],n[T]=a,u.push(e[T].dims.length),i[T]=Pe(`input${T}`,s,u[T]),l.push("rank"),p.push({type:12,data:n[T]});for(let T=0;T<e.length;++T)p.push(...at(e[T].dims));p.push(...at(t));let c=et("output",s,t.length),d=c.indicesGet("indices",r),_=Array.from(Array(n.length).keys()).map(T=>`uniforms.sizeInConcatAxis${T}`).join(","),f=T=>`

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

  ${Xh(n.length,_)}

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

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

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

    ${Jh(i,c)}
  }`;return{name:"Concat",shaderCache:{hint:`${r}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:t,dataType:s}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:p}),getShaderSource:f}},Zh=(e,r)=>{let t=e.inputs,s=t[0].dims,o=Me.normalizeAxis(r.axis,s.length);Qh(t,o);let n=s.slice();n[o]=t.reduce((a,l)=>a+(l.dims.length>o?l.dims[o]:0),0);let i=t.filter(a=>Me.size(a.dims)>0);e.compute(Yh(i,o,n,t[0].dataType),{inputs:i})},em=e=>zt({axis:e.axis})}),an,ln,un,_l,cn=je(()=>{ft(),yt(),an=(e,r,t="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${r}(0.0));`;case"Sigmoid":return`value = (${r}(1.0) / (${r}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${r}(${t}(uniforms.clip_min)), ${r}(${t}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${r}(0.0), min(${r}(1.0), ${t}(uniforms.alpha) * value + ${t}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${t}(uniforms.alpha) * value, value, value >= ${r}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},ln=(e,r)=>{e.activation==="Clip"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&r.push({type:1,data:e.alpha})},un=(e,r)=>{e.activation==="Clip"?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&r.push({name:"alpha",type:"f32"})},_l=e=>{let r=(e==null?void 0:e.activation)||"";if(r==="HardSigmoid"){let[t,s]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:r,alpha:t,beta:s}}else if(r==="Clip"){let[t,s]=(e==null?void 0:e.activation_params)||[xd,Td];return{activation:r,clipMax:s,clipMin:t}}else if(r==="LeakyRelu"){let[t]=(e==null?void 0:e.activation_params)||[.01];return{activation:r,alpha:t}}return{activation:r}}}),kr,tm,gl=je(()=>{kr=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},tm=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),rm,Bv=je(()=>{rm=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),_o,wl,Ml=je(()=>{ft(),yt(),Tt(),cn(),_o=(e,r,t,s,o)=>{let n=s-t;return`
      ${Array.from({length:t}).map((i,a)=>`
      if (${st(r.shape,a,r.rank)} != 1) {
        ${r.indicesSet(e,a,st(o,a+n,s))}
      } else {
        ${r.indicesSet(e,a,0)}
      }`).join("")}
`},wl=(e,r,t,s,o=!1,n)=>{let i=e[0].dims,a=e[1].dims,l=i[i.length-2],u=a[a.length-1],p=i[i.length-1],c=sr(u),d=sr(p),_=sr(l),f=Me.size(t)/c/_,T=e.length>2,k=s?s.slice(0,-2):t.slice(0,-2),w=[Me.size(k),l,u],g=[{type:12,data:f},{type:12,data:l},{type:12,data:u},{type:12,data:p}];ln(r,g),g.push(...at(k,i,a)),T&&g.push(...at(e[2].dims)),g.push(...at(w));let S=E=>{let v=ol("batch_dims",e[0].dataType,k.length),M=Pe("a",e[0].dataType,i.length,d),y=Pe("b",e[1].dataType,a.length,c),C=et("output",e[0].dataType,w.length,c),F=Tr(C.type.tensor),z=an(r,C.type.value,F),K=[M,y],q="";if(T){let H=o?c:1;K.push(Pe("bias",e[2].dataType,e[2].dims.length,H)),q=`${o?`value += bias[col / ${H}];`:`value += ${C.type.value}(bias[row + i]);`}`}let R=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];un(r,R);let Z=()=>{let H=`var a_data: ${M.type.value};`;for(let J=0;J<d;J++)H+=`
              let b_data${J} = b[(b_offset + (k + ${J}) * uniforms.N + col) / ${c}];`;for(let J=0;J<_;J++){H+=`a_data = a[(a_offset + (row + ${J}) * uniforms.K + k) / ${d}];`;for(let Q=0;Q<d;Q++)H+=`
            values[${J}] = fma(${y.type.value}(a_data${d===1?"":`[${Q}]`}), b_data${Q}, values[${J}]);
`}return H};return`
  ${E.registerUniforms(R).registerInternalVariables(v).declareVariables(...K,C)}
  ${E.mainStart()}
    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${c})) * ${c};
    var index1 = global_idx / (uniforms.N / ${c});
    let stride1 = uniforms.M / ${_};
    let row = (index1 % stride1) * ${_};
    let batch = index1 / stride1;

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

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

    var b_indices: ${y.type.indices};
    ${_o("b_indices",y,y.rank-2,v.rank,"batch_indices")}
    ${y.indicesSet("b_indices",y.rank-2,0)}
    ${y.indicesSet("b_indices",y.rank-1,0)}
    let b_offset = ${y.indicesToOffset("b_indices")};
    var values: array<${C.type.value}, ${_}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${d}) {
      ${Z()}
    }
    for (var i = 0u; i < ${_}u; i++) {
      var value = values[i];
      ${q}
      ${z}
      let cur_indices = ${C.type.indices}(batch, row + i, col);
      let offset = ${C.indicesToOffset("cur_indices")};
      ${C.setByOffset(`offset / ${c}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${c};${d};${_};${o}`,inputDependencies:T?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:g}),getShaderSource:S}}}),sm,nm,bl,yl,om,vl,im,fi,xl=je(()=>{ft(),yt(),Tt(),cn(),Ml(),gl(),sm=(e,r)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${r?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${r?", batchIndices":""});
        `,nm=(e,r)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${r===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${r===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${r===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,bl=(e,r,t="f32",s,o=!1,n=32,i=!1,a=32)=>{let l=r[1]*e[1],u=r[0]*e[0],p=o?l:n,c=o?n:l,d=p/r[0],_=n/r[1];if(!((o&&d===4&&e[1]===4||!o&&(d===3||d===4))&&p%r[0]===0&&n%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${d} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${d} must be 3 or 4.
  tileAWidth ${p} must be divisible by workgroupSize[0]${r[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${d}<${t}>, ${p/d}>, ${c}>;
var<workgroup> mm_Bsub: array<array<vec4<${t}>, ${u/e[0]}>, ${n}>;

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

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

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

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${_};
  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;
          ${sm(o,s)}
      }

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

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

          ${nm(o,d)}
      }

      workgroupBarrier();
  }

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

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

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

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

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

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

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

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${om(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<${t}, ${c}>, ${d}>;
  var<workgroup> mm_Bsub : array<array<${t}, ${p}>, ${n}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${n};

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

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kr(e,u)}) {
      let col = colIn * ${e};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${r?`value = value + ${o?"bias[colIn]":`${kr(e,u)}(bias[row])`};`:""}
        ${t}
        ${l.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},fi=(e,r,t,s,o=!1,n)=>{let i=e[0].dims,a=e[1].dims,l=i.slice(0,-2),u=a.slice(0,-2),p=s?s.slice(0,-2):t.slice(0,-2),c=Me.size(p),d=i[i.length-2],_=i[i.length-1],f=a[a.length-1],T=_%4===0&&f%4===0,k=d<=8?[4,1,1]:[4,4,1],w=[8,8,1],g=[Math.ceil(f/w[0]/k[0]),Math.ceil(d/w[1]/k[1]),Math.ceil(c/w[2]/k[2])],S=T?4:1,E=[...l,d,_/S],v=E.length,M=[...u,_,f/S],y=M.length,C=[c,d,f/S],F=[{type:6,data:d},{type:6,data:f},{type:6,data:_}];ln(r,F),F.push(...at(p,E,M));let z=["rank","rank"],K=e.length>2;K&&(F.push(...at(e[2].dims)),z.push("rank")),F.push(...at(C));let q=R=>{let Z=p.length,H=ol("batchDims",e[0].dataType,Z,1),J=Tr(e[0].dataType),Q=Pe("a",e[0].dataType,v,S),se=Pe("b",e[1].dataType,y,S),fe=et("result",e[0].dataType,C.length,S),ae=[Q,se];if(K){let _e=o?S:1;ae.push(Pe("bias",e[2].dataType,e[2].dims.length,_e))}let V=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];un(r,V);let A=Tr(fe.type.tensor),U=an(r,fe.type.value,A),ee=im(S,K,U,[H,Q,se,fe],o);return`
  ${R.registerUniforms(V).registerInternalVariables(H).declareVariables(...ae,fe)}
  ${ee}
  ${T?bl(k,w,J,H):vl(k,w,J,H)}
                   `};return{name:"MatMul",shaderCache:{hint:`${k};${r.activation};${T};${o}`,inputDependencies:z},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:g[0],y:g[1],z:g[2]},programUniforms:F}),getShaderSource:q}}}),am,lm,Rv=je(()=>{ft(),Bs(),Tt(),cn(),gl(),Bv(),xl(),am=(e,r,t,s,o=!1,n,i=4,a=4,l=4,u="f32")=>{let p=F=>{switch(F){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${u}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${F} is not supported.`)}},c=F=>{switch(F){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 ${F} is not supported.`)}},d=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,_=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,f=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",T=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",k=e?"row":"col",w=e?"col":"row",g=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${k} / outWidth;
    let outCol = ${k} % outWidth;

    let WRow = ${w} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${w} / inChannels % i32(uniforms.w_shape[1]);
    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
    let xCh = ${w} % inChannels;
    var resData = ${kr(i,u)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${T}) {
      ${d}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${p(i)}
    }
    return resData;`,S=e?r&&s?`
    let col = colIn * ${i};
    ${g}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${g}
    }
    return ${kr(i,u)}(0.0);`:s&&t?`
    let col = colIn * ${i};
    ${g}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${g}
    }
    return ${kr(i,u)}(0.0);`,E=e?s&&t?c(a):`
    let col = colIn * ${a};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${c(a)}
    }
    return ${kr(a,u)}(0.0);`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
      ${c(a)}
    }
    return ${kr(a,u)}(0.0);`,v=kr(l,u),M=kr(e?i:a,u),y=kr(e?a:i,u),C=an(n,v,u);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${M} {
      ${e?S:E}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${v}) {
      let col = colIn * ${l};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${_}
      ${tm(o)}
      ${C}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},lm=(e,r,t,s,o,n,i,a,l)=>{let u=r.format==="NHWC",p=u?e[0].dims[3]:e[0].dims[1],c=t[0],d=u?t[2]:t[3],_=u?t[1]:t[2],f=u?t[3]:t[1],T=u&&(p%4===0||p%3===0)&&f%4===0,k=u?f:d*_,w=u?d*_:f,g=[8,8,1],S=s<=8?[4,1,1]:[4,4,1],E=[Math.ceil(k/g[0]/S[0]),Math.ceil(w/g[1]/S[1]),Math.ceil(c/g[2]/S[2])];It("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let v=T?u&&p%4!==0?3:4:1,M=g[1]*S[1],y=g[0]*S[0],C=Math.max(g[0]*v,g[1]),F=s%M===0,z=o%y===0,K=n%C===0,q=T?[v,4,4]:[1,1,1],R=[{type:6,data:s},{type:6,data:o},{type:6,data:n},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];ln(r,R),R.push(...at(e[0].dims,e[1].dims));let Z=["rank","rank"];i&&(R.push(...at(e[2].dims)),Z.push("rank")),R.push(...at(t));let H=J=>{let Q=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];un(r,Q);let se=T?4:1,fe=Tr(e[0].dataType),ae=`
      fn setOutputAtIndex(flatIndex : i32, value : ${T?`vec4<${fe}>`:fe}) {
        result[flatIndex] = ${T?`vec4<${fe}>`:fe}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${T?`vec4<${fe}>`:fe}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${T?"/ 4":""}, value);
      }`,V=Pe("x",e[0].dataType,e[0].dims.length,v===3?1:v),A=Pe("w",e[1].dataType,e[1].dims.length,se),U=[V,A],ee=et("result",e[0].dataType,t.length,se);if(i){let _e=Pe("bias",e[2].dataType,e[2].dims.length,se);U.push(_e),ae+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${T?`vec4<${fe}>`:fe} {
          return bias[coords.${u?"w":"y"}${T?"/ 4":""}];
        }`}return`
        ${rm("uniforms.result_strides")}
        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
        ${J.registerUniforms(Q).declareVariables(...U,ee)}
        ${ae}
        ${am(u,F,z,K,i,r,q[0],q[1],q[2],fe)}
        ${T?bl(S,g,fe,void 0,!u,C):vl(S,g,fe,void 0,!u,C,!1,void 0,a)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${v};${T};${F};${z};${K};${M};${y};${C}`,inputDependencies:Z},getRunData:()=>({outputs:[{dims:l?l(t):t,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:R}),getShaderSource:H}}}),um,Tl,go,cm,El,dm,pm,hm,jv=je(()=>{ft(),Bs(),yt(),Tt(),cn(),gl(),um=e=>{let r=1;for(let t=0;t<e.length;t++)r*=e[t];return r},Tl=e=>typeof e=="number"?[e,e,e]:e,go=(e,r)=>r<=1?e:e+(e-1)*(r-1),cm=(e,r,t,s=1)=>{let o=go(r,s);return Math.floor((e[0]*(t-1)-t+o)/2)},El=(e,r,t,s,o)=>{o==null&&(o=cm(e,r[0],s[0]));let n=[0,0,0,t];for(let i=0;i<3;i++)e[i]+2*o>=r[i]&&(n[i]=Math.trunc((e[i]-r[i]+2*o)/s[i]+1));return n},dm=(e,r,t,s,o,n,i,a,l,u)=>{let p,c,d,_;if(e==="VALID"&&(e=0),typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e};let f=El([r,t,s,1],[a,l,u],1,[o,n,i],e);c=f[0],d=f[1],_=f[2]}else if(Array.isArray(e)){if(!e.every((T,k,w)=>T===w[0]))throw Error(`Unsupported padding parameter: ${e}`);p={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let f=El([r,t,s,1],[a,l,u],1,[o,n,i],e[0]);c=f[0],d=f[1],_=f[2]}else if(e==="SAME_UPPER"){c=Math.ceil(r/o),d=Math.ceil(t/n),_=Math.ceil(s/i);let f=(c-1)*o+a-r,T=(d-1)*n+l-t,k=(_-1)*i+u-s,w=Math.floor(f/2),g=f-w,S=Math.floor(T/2),E=T-S,v=Math.floor(k/2),M=k-v;p={top:S,bottom:E,left:v,right:M,front:w,back:g}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:c,outHeight:d,outWidth:_}},pm=(e,r,t,s,o,n=!1,i="channelsLast")=>{let a,l,u,p,c;if(i==="channelsLast")[a,l,u,p,c]=e;else if(i==="channelsFirst")[a,c,l,u,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[d,,_,f,T]=r,[k,w,g]=Tl(t),[S,E,v]=Tl(s),M=go(_,S),y=go(f,E),C=go(T,v),{padInfo:F,outDepth:z,outHeight:K,outWidth:q}=dm(o,l,u,p,k,w,g,M,y,C),R=n?d*c:d,Z=[0,0,0,0,0];return i==="channelsFirst"?Z=[a,R,z,K,q]:i==="channelsLast"&&(Z=[a,z,K,q,R]),{batchSize:a,dataFormat:i,inDepth:l,inHeight:u,inWidth:p,inChannels:c,outDepth:z,outHeight:K,outWidth:q,outChannels:R,padInfo:F,strideDepth:k,strideHeight:w,strideWidth:g,filterDepth:_,filterHeight:f,filterWidth:T,effectiveFilterDepth:M,effectiveFilterHeight:y,effectiveFilterWidth:C,dilationDepth:S,dilationHeight:E,dilationWidth:v,inShape:e,outShape:Z,filterShape:r}},hm=(e,r,t,s,o,n)=>{let i=n==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let a=[64,1,1],l={x:t.map((k,w)=>w)},u=[Math.ceil(um(l.x.map(k=>t[k]))/a[0]),1,1];It("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${u}`);let p=1,c=Me.size(t),d=[{type:12,data:c},{type:12,data:s},{type:12,data:o},{type:12,data:r.strides},{type:12,data:r.dilations}];ln(r,d),d.push(...at(e[0].dims,e[1].dims));let _=["rank","rank"],f=e.length===3;f&&(d.push(...at(e[2].dims)),_.push("rank")),d.push(...at(t));let T=k=>{let w=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:s.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:r.strides.length},{name:"dilations",type:"u32",length:r.dilations.length}];un(r,w);let g=1,S=Tr(e[0].dataType),E=Pe("x",e[0].dataType,e[0].dims.length,p),v=Pe("W",e[1].dataType,e[1].dims.length,g),M=[E,v],y=et("result",e[0].dataType,t.length,g),C="";if(f){let K=Pe("bias",e[2].dataType,e[2].dims.length,g);M.push(K),C+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${S} {
          return bias[${i?st("coords",4,5):st("coords",1,5)}];
        }`}let F=kr(p,S),z=an(r,F,S);return`
            ${C}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${E.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 ${v.getByIndices("aIndices")};
            }
          ${k.registerUniforms(w).declareVariables(...M,y)}
          ${k.mainStart()}
          ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${y.offsetToIndices("global_idx")};
              let batch = ${st("coords",0,E.rank)};
              let d2 = ${i?st("coords",E.rank-1,E.rank):st("coords",1,E.rank)};
              let xFRCCorner = vec3<u32>(${i?st("coords",1,E.rank):st("coords",2,E.rank)},
              ${i?st("coords",2,E.rank):st("coords",3,E.rank)},
              ${i?st("coords",3,E.rank):st("coords",4,E.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${i?st("uniforms.x_shape",1,E.rank):st("uniforms.x_shape",2,E.rank)};
              let xShapeZ = ${i?st("uniforms.x_shape",2,E.rank):st("uniforms.x_shape",3,E.rank)};
              let xShapeW = ${i?st("uniforms.x_shape",3,E.rank):st("uniforms.x_shape",4,E.rank)};
              let xShapeU = ${i?st("uniforms.x_shape",4,E.rank):st("uniforms.x_shape",1,E.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);
                    }
                  }
                }
              }
              ${f?"value = value + getBiasByOutputCoords(coords)":""};
              ${z}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${r.cacheKey};${i};${p};${f}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:u[0],y:u[1],z:u[2]},programUniforms:d}),getShaderSource:T}}}),mm,fm,Nv=je(()=>{ft(),yt(),Tt(),cn(),mm=(e,r,t,s)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",i=e[0].dims,a=e[1].dims,l=r.format==="NHWC",u=l?t[3]:t[1],p=u/r.group,c=l&&p>=4?sr(u):1,d=Me.size(t)/c,_=[{type:12,data:d},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:p}];ln(r,_),_.push(...at(i,[a[0],a[1],a[2],a[3]/c]));let f=o?["rank","rank","rank"]:["rank","rank"];_.push(...at([t[0],t[1],t[2],t[3]/c]));let T=k=>{let w=et("output",e[0].dataType,t.length,c),g=Tr(w.type.tensor),S=an(r,w.type.value,g),E=Pe("x",e[0].dataType,i.length),v=Pe("w",e[1].dataType,a.length,c),M=[E,v];o&&M.push(Pe("b",e[2].dataType,e[2].dims,c));let y=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:r.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];un(r,y);let C=l?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

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

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

          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
            let input_channel = in_channel_offset + wInChannel;
            let xVal = ${E.get("batch","xHeight","xWidth","input_channel")};
            let wVal = ${v.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 = ${E.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${v.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${k.registerUniforms(y).declareVariables(...M,w)}

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

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

    var value: ${w.type.value} = ${w.type.value}(0);
    ${C}
    ${n}
    ${S}
    ${w.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${r.cacheKey}_${c}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:_}),getShaderSource:T}},fm=(e,r,t,s)=>{let o=e.length>2,n=sr(t[3]),i=sr(t[2]),a=Me.size(t)/n/i,l=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/n],u=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/n],p=[t[0],t[1],t[2],t[3]/n],c=[{type:12,data:a},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];ln(r,c),c.push(...at(l,u,p));let d=(i-1)*r.strides[1]+u[1],_=f=>{let T=et("output",e[0].dataType,p.length,n),k=Tr(T.type.tensor),w=an(r,T.type.value,k),g=Pe("x",e[0].dataType,l.length,n),S=Pe("w",e[1].dataType,u.length,n),E=[g,S];o&&E.push(Pe("b",e[2].dataType,e[2].dims,n));let v=o?"value += b[output_channel];":"",M=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return un(r,M),`
  ${f.registerUniforms(M).declareVariables(...E,T)}
  ${f.mainStart()}
    ${f.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<${g.type.value}, ${d}>;
    var values: array<${T.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 < ${u[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${d}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${g.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${g.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${u[1]}; w_width++) {
          let w_val = ${S.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];
      ${v}
      ${w}
      ${T.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${n};${i};${d};${u[0]};${u[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c}),getShaderSource:_}}}),_m,_i,gm,gi,Pl,Cl,wm,Mm,Sl,Vv=je(()=>{yt(),Rv(),jv(),xl(),Nv(),cn(),Ml(),Ws(),_m=(e,r,t,s,o,n)=>{let i=e[0],a=e.slice(n?1:2,n?3:4),l=a.length,u=r[0],p=r.slice(2).map((d,_)=>d+(d-1)*(t[_]-1)),c=a.map((d,_)=>d+s[_]+s[_+l]).map((d,_)=>Math.floor((d-p[_]+o[_])/o[_]));return c.splice(0,0,i),c.splice(n?3:1,0,u),c},_i=[2,3,1,0],gm=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],s=e[1].dims[1]*r.group;if(t!==s)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let o=e[0].dims.length-2;if(r.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(r.strides.length!==o)throw new Error(`strides should be ${o}D`);if(r.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},gi=(e,r)=>{let t=e.kernelShape.slice();t.length<r[1].dims.length-2&&t.push(...Array(r[1].dims.length-2-t.length).fill(0));for(let n=2;n<r[1].dims.length;++n)t[n-2]===0&&(t[n-2]=r[1].dims[n]);let s=e.pads.slice();ai.adjustPadsBasedOnAutoPad(r[0].dims,e.strides,e.dilations,t,s,e.format==="NHWC",e.autoPad);let o=Object.assign({},e);return Object.assign(o,{kernelShape:t,pads:s}),o},Pl=e=>{let r=_l(e),t=e.format,s=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,n=e.group,i=e.kernel_shape,a=e.pads,l=e.strides,u=e.w_is_const();return{autoPad:s,format:t,dilations:o,group:n,kernelShape:i,pads:a,strides:l,wIsConst:u,...r,cacheKey:`${e.format};${r.activation};`}},Cl=(e,r,t,s)=>{let o=t.format==="NHWC",n=_m(r[0].dims,r[1].dims,t.dilations,t.pads,t.strides,o);if(t.group!==1){let M=[r[0]];if(o){let y=e.kernelCustomData.wT??e.compute(qr(r[1],_i),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=y),M.push(y)}else M.push(r[1]);r.length===3&&M.push(r[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&r[1].dims[0]===t.group&&r[1].dims[1]===1&&t.dilations[0]===1&&t.dilations[1]===1?e.compute(fm(M,t,n,s),{inputs:M}):e.compute(mm(M,t,n,s),{inputs:M});return}let i=r.length===3,a=r[0].dims[o?1:2],l=r[0].dims[o?2:3],u=r[0].dims[o?3:1],p=r[1].dims[2],c=r[1].dims[3],d=n[o?1:2],_=n[o?2:3],f=n[o?3:1],T=o&&p===a&&c===l&&t.pads[0]===0&&t.pads[1]===0;if(T||p===1&&c===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let M=n[0],y,C,F,z=[];if(o){let R=e.kernelCustomData.wT??e.compute(qr(r[1],_i),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=R),T){let Z=a*l*u;y=r[0].reshape([1,M,Z]),C=R.reshape([1,Z,f]),F=[1,M,f]}else y=r[0].reshape([M,a*l,u]),C=R.reshape([1,u,f]),F=[M,d*_,f];z.push(y),z.push(C)}else y=r[0].reshape([M,u,a*l]),C=r[1].reshape([1,f,u]),F=[M,f,d*_],z.push(C),z.push(y);i&&z.push(r[2]);let K=F[2],q=z[0].dims[z[0].dims.length-1];K<8&&q<8?e.compute(wl(z,t,n,F,o,s),{inputs:z}):e.compute(fi(z,t,n,F,o,s),{inputs:z});return}let k=!0,w=e.kernelCustomData.wT??e.compute(qr(r[1],_i),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=w);let g=[r[0],w];i&&g.push(r[2]);let S=o?d*_:f,E=o?f:d*_,v=p*c*u;e.compute(lm(g,t,n,S,E,v,i,k,s),{inputs:g})},wm=(e,r)=>{let t=r.format==="NHWC",s=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&s.push(e.inputs[2]);let o=[0,r.pads[0],0,r.pads[1]],n=[1].concat(r.strides),i=[1].concat(r.dilations),a=[1].concat(r.kernelShape),l=gi({...r,pads:o,strides:n,dilations:i,kernelShape:a},s);Cl(e,s,l,u=>t?[u[0],u[2],u[3]]:[u[0],u[1],u[3]])},Mm=(e,r,t)=>{let s=t.format==="NHWC"?"channelsLast":"channelsFirst",o=gi(t,r),n=t.autoPad==="NOTSET"?t.pads:t.autoPad,i=pm(r[0].dims,r[1].dims,t.strides,t.dilations,n,!1,s);e.compute(hm(r,o,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],s))},Sl=(e,r)=>{if(gm(e.inputs,r),e.inputs[0].dims.length===3)wm(e,r);else if(e.inputs[0].dims.length===5)Mm(e,e.inputs,r);else{let t=gi(r,e.inputs);Cl(e,e.inputs,t)}}}),bm,Uv=je(()=>{ft(),Bs(),yt(),Tt(),bm=(e,r,t)=>{let s=e.length>2,o=r.outputShape,n=r.format==="NHWC",i=r.group,a=e[1].dims,l=a[2]/i,u=a[3],p=n?sr(l):1,c=n&&u===1&&l>=4,d=c?Math.floor(l/4)*4:Math.floor(l/p)*p,_=l-d,f=n?sr(u):1,T=n?u===1?p:f:1,k=Me.size(o)/f,w=[Math.ceil(k/64),1,1];It("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${w}`);let g=["rank","rank"],S=[r.strides[0],r.strides[1]],E=[r.kernelShape[n?1:2],r.kernelShape[n?2:3]],v=[r.dilations[0],r.dilations[1]],M=[E[0]+(r.dilations[0]<=1?0:(r.kernelShape[n?1:2]-1)*(r.dilations[0]-1)),E[1]+(r.dilations[1]<=1?0:(r.kernelShape[n?2:3]-1)*(r.dilations[1]-1))],y=[M[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),M[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],C=[{type:12,data:k},{type:12,data:S},{type:12,data:E},{type:12,data:v},{type:12,data:M},{type:6,data:y},{type:12,data:d},{type:12,data:l},{type:12,data:u},...at(e[0].dims,e[1].dims)];s&&(C.push(...at(e[2].dims)),g.push("rank")),C.push(...at(o));let F=z=>{let K=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:S.length},{name:"filter_dims",type:"u32",length:E.length},{name:"dilations",type:"u32",length:E.length},{name:"effective_filter_dims",type:"u32",length:M.length},{name:"pads",type:"i32",length:y.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],q=Tr(e[0].dataType),R=n?1:2,Z=n?2:3,H=n?3:1,J=Pe("W",e[1].dataType,e[1].dims.length,T),Q=Pe("Dy",e[0].dataType,e[0].dims.length,p),se=[Q,J];s&&se.push(Pe("bias",e[2].dataType,[o[H]].length,f));let fe=et("result",e[0].dataType,o.length,f),ae=()=>{let U="";if(c)p===4?U+=`
        let xValue = ${Q.getByOffset("x_offset")};
        let wValue = ${J.getByOffset("w_offset")};
        dotProd = dotProd + dot(xValue, wValue);
        x_offset += 1u;
        w_offset += 1u;`:p===2?U+=`
          dotProd = dotProd + dot(vec4<${q}>(${Q.getByOffset("x_offset")}, ${Q.getByOffset("x_offset + 1u")}), vec4<${q}>(${J.getByOffset("w_offset")}, ${J.getByOffset("w_offset + 1u")}));
          x_offset += 2u;
          w_offset += 2u;`:p===1&&(U+=`
          dotProd = dotProd + dot(vec4<${q}>(${Q.getByOffset("x_offset")}, ${Q.getByOffset("x_offset + 1u")}, ${Q.getByOffset("x_offset + 2u")}, ${Q.getByOffset("x_offset + 3u")}), vec4<${q}>(${J.getByOffset("w_offset")}, ${J.getByOffset("w_offset + 1u")}, ${J.getByOffset("w_offset + 2u")}, ${J.getByOffset("w_offset + 3u")}));
          x_offset += 4u;
          w_offset += 4u;`);else if(U+=`
                  let xValue = ${n?Q.getByOffset(`${Q.indicesToOffset(`${Q.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):Q.get("batch","inputChannel","idyR","idyC")};
        `,p===1)U+=`
          let w_offset = ${J.indicesToOffset(`${J.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
          let wValue = ${J.getByOffset(`w_offset / ${T}`)};
          dotProd = dotProd + xValue * wValue;`;else for(let ee=0;ee<p;ee++)U+=`
            let wValue${ee} = ${J.getByOffset(`${J.indicesToOffset(`${J.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${ee}, wOutChannel)`)} / ${T}`)};
            dotProd = dotProd + xValue[${ee}] * wValue${ee};`;return U},V=()=>{if(_===0)return"";if(!c)throw new Error(`packInputAs4 ${c} is not true.`);let U="";if(p===1){U+="dotProd = dotProd";for(let ee=0;ee<_;ee++)U+=`
            + ${Q.getByOffset(`x_offset + ${ee}`)} * ${J.getByOffset(`w_offset + ${ee}`)}`;U+=";"}else if(p===2){if(_!==2)throw new Error(`Invalid inputChannelsRemainder ${_}.`);U+=`
          let xValue = ${Q.getByOffset("x_offset")};
          let wValue = ${J.getByOffset("w_offset")};
          dotProd = dotProd + dot(xValue, wValue);`}return U},A=`
            let outputIndices = ${fe.offsetToIndices(`global_idx * ${f}`)};
            let batch = ${fe.indicesGet("outputIndices",0)};
            let d1 = ${fe.indicesGet("outputIndices",H)};
            let r = ${fe.indicesGet("outputIndices",R)};
            let c = ${fe.indicesGet("outputIndices",Z)};
            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 = ${fe.type.value}(0.0);
            var wR: u32 = 0;
            if (uniforms.dilations.x == 1) {
              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
            }
            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${q}(dyRCorner) + ${q}(wR)) / ${q}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${q}(uniforms.Dy_shape[${R}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);
              var wC: u32 = 0;
              if (uniforms.dilations.y == 1) {
                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
              }
              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${q}(dyCCorner) + ${q}(wC)) / ${q}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${q}(uniforms.Dy_shape[${Z}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                ${c?`
                var x_offset = ${Q.indicesToOffset(`${Q.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p};
                var w_offset = ${J.indicesToOffset(`${J.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${T};
                  `:""}
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${c?4:p}) {
                  ${ae()}
                  inputChannel = inputChannel + ${c?4:p};
                }
                ${V()}
                wC = wC + uniforms.strides.y - 1;
              }
              wR = wR + uniforms.strides[0] - 1;
            }
            let value = dotProd${s?` + bias[d1 / ${f}]`:""};
            ${fe.setByOffset("global_idx","value")};
          `;return`
    ${z.registerUniforms(K).declareVariables(...se,fe)}
      ${z.mainStart()}
      ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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        ${T.setByOffset("global_idx","value")}
      `}else w=`
      let outputIndices = ${T.offsetToIndices("global_idx")};
      ${k("")};
      let value = ${_.getByIndices("dataIndices")};
      ${T.setByOffset("global_idx","value")};
      `;return`
      ${d.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(_,f,T)}
      ${d.mainStart()}
        ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${w}
      }`};return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:c}},Jm=e=>zt({axis:e.axis}),Ym=(e,r)=>{let t=e.inputs;Qm(t),e.compute(Xm(e.inputs,r))}}),Zm,ef,tf,Jv=je(()=>{ft(),yt(),Tt(),Zm=(e,r,t,s,o,n,i,a,l)=>{let u=[{type:12,data:n},{type:12,data:s},{type:12,data:o},{type:12,data:t},{type:12,data:i},{type:12,data:a},{type:12,data:l}],p=[n];u.push(...at(r.dims,p));let c=d=>{let _=Pe("indices_data",r.dataType,r.dims.length),f=et("input_slice_offsets_data",12,1,1),T=[_,f],k=[{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:t.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
  ${d.registerUniforms(k).declareVariables(...T)}
  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

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

    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
  }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${t.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:u}),getShaderSource:c},{inputs:[r],outputs:[-1]})[0]},ef=(e,r)=>{let t=e.inputs,s=t[0].dims,o=t[0].dataType,n=t[1].dims,i=n[n.length-1],a=Me.sizeToDimension(n,n.length-1),l=Me.sizeFromDimension(s,r.batchDims+i),u=Me.sizeToDimension(s,r.batchDims),p=Me.sizeFromDimension(s,r.batchDims),c=a/u,d=new Array(i),_=l;for(let E=0;E<i;++E)d[i-1-E]=_,_*=s[r.batchDims+i-1-E];let f=Zm(e,t[1],d,r.batchDims,s,a,c,p,i),T=r.batchDims+i;if(T>s.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let k=n.slice(0,-1).concat(s.slice(T)),w=Me.size(k),g=[{type:12,data:w},{type:12,data:l},...at(t[0].dims,f.dims,k)],S=E=>{let v=Pe("data",t[0].dataType,t[0].dims.length),M=Pe("slice_offsets",12,f.dims.length),y=et("output",t[0].dataType,k.length);return`
          ${E.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(v,M,y)}
            ${E.mainStart()}
            ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`};e.compute({name:"GatherND",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:k,dataType:o}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:g}),getShaderSource:S},{inputs:[t[0],f]})},tf=e=>({batchDims:e.batch_dims,cacheKey:""})}),rf,sf,nf,of,Yv=je(()=>{ft(),yt(),or(),Tt(),rf=(e,r)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let t=Me.normalizeAxis(r.quantizeAxis,e[0].dims.length),s=r.blockSize,o=e[0],n=e[2],i=e.length===4?e[3]:void 0;if(n.dims.length!==o.dims.length||!o.dims.map((a,l)=>l===t?Math.ceil(a/s)===n.dims[l]:a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==n.dims.length||!i.dims.map((a,l)=>a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},sf=(e,r)=>{let t=e[0].dims,s=e[1].dims,o=t.length,n=Me.normalizeAxis(r.gatherAxis,o),i=Me.normalizeAxis(r.quantizeAxis,o),a=t.slice(0);a.splice(n,1,...s);let l=Me.size(a),u=e[2].dataType,p=e[0].dataType===22,c=[{type:12,data:l},{type:12,data:i},{type:12,data:n},{type:12,data:r.blockSize},...at(...e.map((_,f)=>_.dims),a)],d=_=>{let f=Pe("data",e[0].dataType,e[0].dims.length),T=Pe("inputIndices",e[1].dataType,e[1].dims.length),k=Pe("scales",e[2].dataType,e[2].dims.length),w=e.length>3?Pe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,g=et("output",u,a.length),S=[f,T,k];w&&S.push(w);let E=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${_.registerUniforms(E).declareVariables(...S,g)}
        ${_.mainStart()}
        let output_indices = ${g.offsetToIndices("global_idx")};
        var indices_indices = ${T.type.indices}(0);
        ${s.length>1?`
          for (var i: u32 = 0; i < ${s.length}; i++) {
            let index = ${g.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${T.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${g.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${f.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${g.indicesGet("output_indices","i")};
          ${f.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${T.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${t[n]};
        }
        ${f.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${a.length}; i++) {
          let index = ${g.indicesGet("output_indices",`i + ${s.length} - 1`)};
          ${f.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${f.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${f.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${k.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${k.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${k.getByIndices("scale_indices")};
        ${w?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${w.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${w.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${zr(u)}(quantized_data - zero_point) * scale;
        ${g.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${r.cacheKey};${e.filter((_,f)=>f!==1).map(_=>_.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(_,f)=>"rank")},getRunData:()=>({outputs:[{dims:a,dataType:u}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:c}),getShaderSource:d}},nf=(e,r)=>{let t=e.inputs;rf(t,r),e.compute(sf(e.inputs,r))},of=e=>zt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),af,lf,uf,cf,Zv=je(()=>{ft(),yt(),or(),Tt(),af=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.`)},lf=(e,r)=>{let t=e[0].dims,s=e[0].dataType,o=t.length,n=e[1].dims,i=e[1].dataType,a=Me.normalizeAxis(r.axis,o),l=t[a],u=n.slice(0),p=Me.size(u),c=Pe("input",s,o),d=Pe("indicesInput",i,n.length),_=et("output",s,u.length),f=[{type:12,data:p},{type:6,data:l},{type:12,data:a}];return f.push(...at(t,n,u)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:f}),getShaderSource:T=>`
      ${T.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(c,d,_)}
      ${T.mainStart()}
      ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${_.setByOffset("global_idx","value")};
  }`}},uf=e=>zt({axis:e.axis}),cf=(e,r)=>{let t=e.inputs;af(t),e.compute(lf(e.inputs,r))}}),df,pf,hf,mf,ex=je(()=>{ft(),yt(),Tt(),df=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")},pf=(e,r)=>{let t=e[0].dims.slice(),s=e[1].dims.slice(),[o,n,i]=vd.getShapeOfGemmResult(t,r.transA,s,r.transB,e.length===3?e[2].dims:void 0),a=[o,n];if(!a)throw new Error("Can't use gemm on the given tensors");let l=16,u=Math.ceil(n/l),p=Math.ceil(o/l),c=!0,d=Me.size(a),_=[{type:12,data:c?u:d},{type:12,data:o},{type:12,data:n},{type:12,data:i},{type:1,data:r.alpha},{type:1,data:r.beta}],f=["type","type"];e.length===3&&(_.push(...at(e[2].dims)),f.push("rank")),_.push(...at(a));let T=w=>{let g="";r.transA&&r.transB?g="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":r.transA&&!r.transB?g="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!r.transA&&r.transB?g="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!r.transA&&!r.transB&&(g="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let S=r.alpha===1?"":"value *= uniforms.alpha;",E=Pe("a",e[0].dataType,e[0].dims),v=Pe("b",e[1].dataType,e[1].dims),M=E.type.value,y=null,C=[E,v];e.length===3&&(y=Pe("c",e[2].dataType,e[2].dims.length),C.push(y));let F=et("output",e[0].dataType,a.length);C.push(F);let z=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
  ${w.registerUniforms(z).declareVariables(...C)}

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

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

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

    ${S}
    ${y!=null?`let cOffset = ${y.broadcastedIndicesToOffset("vec2(m, n)",F)}; value += ${M}(uniforms.beta) * ${y.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`},k=w=>{let g=Pe("a",e[0].dataType,e[0].dims),S=Pe("b",e[1].dataType,e[1].dims),E=null,v=[g,S];e.length===3&&(E=Pe("c",e[2].dataType,e[2].dims.length),v.push(E));let M=et("output",e[0].dataType,a.length);v.push(M);let y=[{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"}],C="",F="";r.transA&&r.transB?(F=`
      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] = ${g.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] = ${S.type.value}(0);
      }
      `,C="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):r.transA&&!r.transB?(F=`
      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] = ${g.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] = ${S.type.value}(0);
      }
      `,C="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!r.transA&&r.transB?(F=`
      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] = ${g.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] = ${S.type.value}(0);
      }
      `,C="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!r.transA&&!r.transB&&(F=`
      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] = ${g.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] = ${S.type.value}(0);
      }
      `,C="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let z=r.alpha===1?"":"value *= uniforms.alpha;";return`
  ${w.registerUniforms(y).declareVariables(...v)}
  var<workgroup> tile_a: array<array<${g.type.storage}, ${l}>, ${l}>;
  var<workgroup> tile_b: array<array<${S.type.storage}, ${l}>, ${l}>;
  ${w.mainStart([l,l,1])}
    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${l};
    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${l};
    let num_tiles = (uniforms.K - 1) / ${l} + 1;
    var k_start = 0u;
    var value = ${M.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${F}
      k_start = k_start + ${l};
      workgroupBarrier();

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

    ${z}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${E!=null?`let cOffset = ${E.broadcastedIndicesToOffset("vec2(m, n)",M)}; value += ${M.type.value}(uniforms.beta) * ${E.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`};return c?{name:"GemmShared",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:u*p},programUniforms:_}),getShaderSource:k}:{name:"Gemm",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:_}),getShaderSource:T}},hf=e=>{let r=e.transA,t=e.transB,s=e.alpha,o=e.beta;return{transA:r,transB:t,alpha:s,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},mf=(e,r)=>{df(e.inputs),e.compute(pf(e.inputs,r))}}),Ss,Rs,dn,pn,ff,_f,gf,wf,Mf,bf,yf,vf,xf,Tf,tx=je(()=>{ft(),yt(),or(),Tt(),[Ss,Rs,dn,pn]=[0,1,2,3],ff=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 ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},_f=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,gf=e=>`
  fn gs_bicubic_interpolate(p: mat4x4<${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;
  }
`,wf=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));
    `}
  }
`,Mf=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);
      }`:""}
`,bf=(e,r,t)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${r} {
     var pixel = ${r}(0);
     var indices = vec4<u32>(0);
     indices[${Ss}] = batch;
     indices[${Rs}] = channel;`+(()=>{switch(t.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${dn}] = u32(r);
            indices[${pn}] = u32(c);
          } else {
            return ${r}(0);
          }
        `;case"border":return`
          indices[${dn}] = u32(clamp(r, 0, H - 1));
          indices[${pn}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${dn}] = gs_reflect(r, border[1], border[3]);
          indices[${pn}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${t.paddingMode} is not supported`)}})()+`
    return ${e.getByIndices("indices")};
  }
`,yf=(e,r,t)=>(()=>{switch(t.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Ss}], indices[${Rs}], 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[${Ss}], indices[${Rs}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Ss}], indices[${Rs}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Ss}], indices[${Rs}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Ss}], indices[${Rs}], border);

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

          let dx = x - f32(x0 + 1);
          let dy = y - f32(y0 + 1);
          let result = gs_bicubic_interpolate(p, dx, dy);
        `;default:throw new Error(`mode ${t.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,vf=(e,r)=>{let t=Pe("x",e[0].dataType,e[0].dims.length),s=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=Pe("grid",e[1].dataType,s.length,2),n=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];r.format==="NHWC"&&(n=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[Ss,Rs,dn,pn]=[0,3,1,2]);let i=et("output",e[0].dataType,n.length),a=t.type.value,l=Me.size(n),u=[{type:12,data:l},...at(e[0].dims,s,n)],p=c=>`
  ${c.registerUniform("output_size","u32").declareVariables(t,o,i)}
  ${_f}
  ${gf(a)}
  ${wf(r)}
  ${Mf(r)}
  ${bf(t,a,r)}

  ${c.mainStart()}
    ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${dn}]);
      let W_in = i32(uniforms.x_shape[${pn}]);

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

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

      ${yf(i,a,r)}
  }`;return{name:"GridSample",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:["type","type"]},getRunData:c=>{let d=Me.size(n);return{outputs:[{dims:n,dataType:c[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:u}},getShaderSource:p}},xf=(e,r)=>{ff(e.inputs),e.compute(vf(e.inputs,r))},Tf=e=>zt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Nr,Ef,Pf,Ol,Cf,Mo,Sf,$f=je(()=>{ft(),yt(),or(),el(),dl(),Tt(),Ws(),Nr=(e,r)=>e.length>r&&e[r].dims.length>0?e[r]:void 0,Ef=(e,r)=>{let t=e[0],s=Nr(e,1),o=Nr(e,2),n=Nr(e,3),i=Nr(e,4),a=Nr(e,5),l=Nr(e,6),u=Nr(e,7);if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=t.dims[0],c=t.dims[1],d=t.dims.length===3?t.dims[2]:r.numHeads*t.dims[4],_=c,f=0,T=0,k=Math.floor(d/r.numHeads);if(l&&u&&Me.size(l.dims)&&Me.size(u.dims)){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==p||l.dims[1]!==r.numHeads||l.dims[3]!==k)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[0]!==p||u.dims[1]!==r.numHeads||u.dims[3]!==k)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(u.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');f=l.dims[2],T=l.dims[2]}else if(l&&Me.size(l.dims)||u&&Me.size(u.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w;if(s&&Me.size(s.dims)>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(s.dims[2]!==t.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');w=2,_=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==k)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.');w=5,_=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==k)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');w=0,_=s.dims[2]}}else{if(t.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(t.dims[2]!==r.numHeads||t.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}if(n&&Me.size(n.dims)>0){if(n.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(s&&s.dims.length===5&&s.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let g=f+_,S=0;if(i&&Me.size(i.dims)>0){S=8;let y=i.dims;throw y.length===1?y[0]===p?S=1:y[0]===3*p+2&&(S=3):y.length===2&&y[0]===p&&y[1]===g&&(S=5),S===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let E=!1,v=d;if(o&&Me.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(t.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(_!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');v=o.dims[2]}else{if(_!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');v=o.dims[1]*o.dims[3],E=!0}}let M=!1;if(i&&Me.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(a&&Me.size(a.dims)>0){if(a.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(a.dims[0]!==p||a.dims[1]!==r.numHeads||a.dims[2]!==c||a.dims[3]!==g)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:c,pastSequenceLength:f,kvSequenceLength:_,totalSequenceLength:g,maxSequenceLength:T,inputHiddenSize:0,hiddenSize:d,vHiddenSize:v,headSize:k,vHeadSize:Math.floor(v/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:S,scale:r.scale,broadcastResPosBias:M,passPastInKv:E,qkvFormat:w}},Pf=e=>zt({...e}),Ol=zt({perm:[0,2,1,3]}),Cf=(e,r,t,s,o,n,i)=>{let a=[s,o,n],l=Me.size(a),u=[{type:12,data:l},{type:12,data:i},{type:12,data:n}],p=c=>{let d=et("qkv_with_bias",r.dataType,a),_=Pe("qkv",r.dataType,a),f=Pe("bias",t.dataType,a),T=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${c.registerUniforms(T).declareVariables(_,f,d)}
  ${c.mainStart()}
    ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:p},{inputs:[r,t],outputs:[-1]})[0]},Mo=(e,r,t,s,o,n,i,a)=>{let l=n;if(i&&Me.size(i.dims)>0){if(s===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=Cf(e,n,i,r,s,t*o,a),l=l.reshape([r,s,t,o]),t===1||s===1?l:e.compute(qr(l,Ol.perm),{inputs:[l],outputs:[-1]})[0]}else return n.dims.length===3&&(l=n.reshape([r,s,t,o])),t===1||s===1?l:e.compute(qr(l,Ol.perm),{inputs:[l],outputs:[-1]})[0]},Sf=(e,r)=>{let t=Ef(e.inputs,r),s=e.inputs[0],o=Nr(e.inputs,1),n=Nr(e.inputs,2),i=Nr(e.inputs,3),a=Nr(e.inputs,4),l=Nr(e.inputs,5),u=Nr(e.inputs,6),p=Nr(e.inputs,7);if(s.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 c=o&&n&&o.dims.length===4&&n.dims.length===4,d=Mo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,s,i,0);if(c)return mo(e,d,o,n,a,void 0,u,p,l,t);if(!o||!n)throw new Error("key and value must be provided");let _=Mo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,o,i,t.hiddenSize),f=Mo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,n,i,2*t.hiddenSize);mo(e,d,_,f,a,void 0,u,p,l,t)}}),kf,If,Af,Ff,Dl,Of,Df,Lf=je(()=>{ft(),yt(),or(),Tt(),kf=e=>{if(!e||e.length<1)throw new Error("too few inputs")},If=(e,r)=>{let t=[],s=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>t.push(Number(o))),s=t.length),zt({numOutputs:s,axis:r.axis,splitSizes:t})},Af=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${st("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,Ff=e=>{let r=e.length,t=[];for(let s=0;s<r;++s){let o=e[s].setByIndices("indices","input[global_idx]");r===1?t.push(o):s===0?t.push(`if (output_number == ${s}u) { ${o} }`):s===r-1?t.push(`else { ${o} }`):t.push(`else if (output_number == ${s}) { ${o} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${t.join(`
`)}
      }`},Dl=(e,r)=>{let t=e[0].dims,s=Me.size(t),o=e[0].dataType,n=Me.normalizeAxis(r.axis,t.length),i=new Array(r.numOutputs),a=Pe("input",o,t.length),l=new Array(r.numOutputs),u=[],p=[],c=0,d=[{type:12,data:s}];for(let f=0;f<r.numOutputs;f++){c+=r.splitSizes[f],l[f]=c;let T=t.slice();T[n]=r.splitSizes[f],p.push(T),i[f]=et(`output${f}`,o,T.length),u.push({dims:p[f],dataType:e[0].dataType})}d.push({type:12,data:l},...at(t,...p));let _=f=>`
  ${f.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(a,...i)}
  ${Af(l.length)}
  ${Ff(i)}

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

    var indices = ${a.offsetToIndices("global_idx")};
    var index = ${a.indicesGet("indices",n)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${st("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${a.indicesSet("indices",n,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:u,dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d})}},Of=(e,r)=>{kf(e.inputs);let t=e.inputs.length===1?r:If(e.inputs,r);e.compute(Dl(e.inputs,t),{inputs:[0]})},Df=e=>{let r=e.axis,t=e.splitSizes,s=e.numOutputs<0?t.length:e.numOutputs;if(s!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return zt({axis:r,numOutputs:s,splitSizes:t})}}),zf,Mi,Bf,Rf=je(()=>{ft(),yt(),or(),Tt(),zf=(e,r)=>{let[t,s,o,n]=e,{numHeads:i,rotaryEmbeddingDim:a}=r;if(t.dims.length!==3&&t.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${t.dims.length}`);if(!Me.areEqual(s.dims,[])&&!Me.areEqual(s.dims,[1])&&s.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${s.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(n.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${n.dims.length}`);if(!Me.areEqual(o.dims,n.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(a>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=t.dims[0],u=t.dims[t.dims.length-2],p=o.dims[0],c=Me.sizeFromDimension(t.dims,1)/u,d=a===0?o.dims[1]*2:c/i;if(a>d)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(s.dims.length===2){if(l!==s.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${s.dims[0]}`);if(u!==s.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${s.dims[1]}`)}if(d/2!==o.dims[1]&&a/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(u>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Mi=(e,r)=>{let{interleaved:t,numHeads:s,rotaryEmbeddingDim:o,scale:n}=r,i=e[0].dims[0],a=Me.sizeFromDimension(e[0].dims,1),l=e[0].dims[e[0].dims.length-2],u=a/l,p=e[2].dims[1],c=o===0?p*2:u/s,d=new Array(i,l,u/c,c-p),_=Me.computeStrides(d),f=[{type:1,data:n},{type:12,data:d},{type:12,data:_},...e[0].dims.length===3?new Array({type:12,data:[a,u,c,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[a,c,l*c,1]}):[],...at(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],T=k=>{let w=Pe("input",e[0].dataType,e[0].dims.length),g=Pe("position_ids",e[1].dataType,e[1].dims.length),S=Pe("cos_cache",e[2].dataType,e[2].dims.length),E=Pe("sin_cache",e[3].dataType,e[3].dims.length),v=et("output",e[0].dataType,e[0].dims.length);return k.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:d.length},{name:"global_strides",type:"u32",length:_.length},{name:"input_output_strides",type:"u32",length:_.length}]),`
        ${k.declareVariables(w,g,S,E,v)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${g.broadcastedIndicesToOffset("bsnh.xy",et("",g.type.tensor,2))};
            let position_id =
                u32(${g.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${t});
            let j = i + select(half_rotary_emb_dim, 1, ${t});
            let re = ${w.getByOffset("i")} * ${S.get("position_id","bsnh[3]")} -
                ${w.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};
            ${v.setByOffset("i","re")}
            let im = ${w.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} +
                ${w.getByOffset("j")} * ${S.get("position_id","bsnh[3]")};
            ${v.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${v.setByOffset("k",w.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:zt({interleaved:t}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:T,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Me.size(d)/Vn)},programUniforms:f})}},Bf=(e,r)=>{zf(e.inputs,r),e.compute(Mi(e.inputs,r))}}),jf,Nf,Ll,Vf,Uf,rx=je(()=>{or(),ft(),dl(),$f(),Lf(),Ws(),Rf(),Tt(),jf=(e,r)=>{if(r.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let t=e[0],s=e[1],o=e[2],n=e[3],i=e[4];if(r.doRotary!==0&&e.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(r.localWindowSize!==-1)throw new Error("Local attention is not supported");if(r.softcap!==0)throw new Error("Softcap is not supported");if(r.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(r.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let a=!1,l=t.dims[0],u=t.dims[1],p=t.dims.length===3?a?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],c=u,d=0,_=!s||s.dims.length===0,f=Math.floor(_?p/(r.numHeads+2*r.kvNumHeads):p/r.numHeads);_&&(p=f*r.numHeads);let T=n&&n.dims.length!==0,k=i&&i.dims.length!==0;if(T&&n.dims.length===4&&n.dims[0]===l&&n.dims[1]!==r.kvNumHeads&&n.dims[2]===r.kvNumHeads&&n.dims[3]===f)throw new Error("BSNH pastKey/pastValue is not supported");if(T&&k){if(n.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');d=n.dims[2]}else if(T||k)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w=1;if(s&&s.dims.length>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(t.dims[2]%s.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');c=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==f)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.');c=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==f)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');c=s.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}let g=0,S=!1,E=r.kvNumHeads?f*r.kvNumHeads:p;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(t.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(c!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');E=o.dims[2]}else{if(c!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');E=o.dims[1]*o.dims[3],S=!0}}let v=e.length>4?e[5]:void 0;if(v&&v.dims.length!==1&&v.dims[0]!==l)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:l,sequenceLength:u,pastSequenceLength:d,kvSequenceLength:c,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:p,vHiddenSize:E,headSize:f,vHeadSize:Math.floor(E/r.kvNumHeads),numHeads:r.numHeads,kvNumHeads:r.kvNumHeads,nReps:r.numHeads/r.kvNumHeads,pastPresentShareBuffer:!1,maskType:g,scale:r.scale,broadcastResPosBias:!1,passPastInKv:S,qkvFormat:w}},Nf=zt({perm:[0,2,1,3]}),Ll=(e,r,t)=>{let s=r,o=t.kvNumHeads;return r.dims.length===3&&t.kvSequenceLength!==0&&(s=r.reshape([t.batchSize,t.kvSequenceLength,o,t.headSize]),s=e.compute(qr(s,Nf.perm),{inputs:[s],outputs:[-1]})[0]),s},Vf=(e,r,t,s)=>{let o=7,n=["type","type"],i=[e*r],a=e*r,l=[{type:12,data:a},{type:12,data:r},{type:12,data:e}],u=p=>{let c=Pe("seq_lens",t.dataType,t.dims),d=Pe("total_seq_lens",s.dataType,s.dims),_=et("pos_ids",o,i),f=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return`
  ${p.registerUniforms(f).declareVariables(c,d,_)}
  ${p.mainStart()}
    ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let total_sequence_length = u32(${d.getByOffset("0")});
    let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
    let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
    let batch_idx = global_idx / uniforms.sequence_length;
    let sequence_idx = i32(global_idx % uniforms.sequence_length);
    var pos_id: i32 = 0;
    let seqlen = ${c.getByOffset("batch_idx")};
    let total_seqlen = seqlen + 1;
    if (is_first_prompt) {
      if (sequence_idx < total_seqlen) {
        pos_id = sequence_idx;
      } else {
        pos_id = 1;
      }
      ${_.setByOffset("global_idx","pos_id")}
    } else if (is_subsequent_prompt) {
      let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
      if (past_seqlen + sequence_idx < total_seqlen) {
        pos_id = past_seqlen + sequence_idx;
      } else {
        pos_id = 1;
      }
      ${_.setByOffset("global_idx","pos_id")}
    } else if (global_idx < uniforms.batch_size) {
      ${_.setByOffset("global_idx","seqlen")}
    };
  }
  `};return{name:"GeneratePositionIds",shaderCache:{hint:`${e};${r}`,inputDependencies:n},getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:l}),getShaderSource:u}},Uf=(e,r)=>{var E;let t=jf(e.inputs,r);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((E=e.inputs[1])==null?void 0:E.dims.length)===5)throw new Error("Packed KV is not implemented");let s=e.inputs[0],o=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,n=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,i=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,l=e.inputs.length>4?e.inputs[5]:void 0,u=e.inputs.length>5?e.inputs[6]:void 0,p=t.kvNumHeads?t.kvNumHeads:t.numHeads,c=zt({axis:2,numOutputs:3,splitSizes:[t.numHeads*t.headSize,p*t.headSize,p*t.headSize]}),[d,_,f]=!o&&!n?e.compute(Dl([s],c),{inputs:[s],outputs:[-1,-1,-1]}):[s,o,n],T,k;if(r.doRotary){let v=e.compute(Vf(t.batchSize,t.sequenceLength,l,u),{inputs:[l,u],outputs:[-1]})[0],M=e.inputs[7],y=e.inputs[8],C=zt({interleaved:r.rotaryInterleaved!==0,numHeads:t.numHeads,rotaryEmbeddingDim:0,scale:r.scale}),F=[d,v,M,y],z=[-1];T=e.compute(Mi(F,C),{inputs:F,outputs:z})[0],F.splice(0,1,_);let K=zt({interleaved:r.rotaryInterleaved!==0,numHeads:t.kvNumHeads,rotaryEmbeddingDim:0,scale:r.scale});k=e.compute(Mi(F,K),{inputs:F,outputs:z})[0]}let w=Mo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,r.doRotary?T:d,void 0,0),g=Ll(e,r.doRotary?k:_,t),S=Ll(e,f,t);mo(e,w,g,S,void 0,void 0,i,a,void 0,t,l,u)}}),zl,Wf,Gf,Kf,sx=je(()=>{ft(),yt(),Ws(),Tt(),zl=(e,r,t,s,o,n,i,a)=>{let l=sr(n),u=l===1?"f32":`vec${l}f`,p=l===1?"vec2f":`mat2x${l}f`,c=o*i,d=64;c===1&&(d=256);let _=[o,i,n/l],f=[o,i,2],T=["rank","type","type"],k=[];k.push(...at(_,f));let w=g=>{let S=Pe("x",r.dataType,3,l),E=Pe("scale",t.dataType,t.dims),v=Pe("bias",s.dataType,s.dims),M=et("output",1,3,2),y=[S,E,v,M];return`
  var<workgroup> workgroup_shared : array<${p}, ${d}>;
  const workgroup_size = ${d}u;
  ${g.declareVariables(...y)}
  ${g.mainStart(d)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${u}(0);
    var squared_sum = ${u}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${u}(${S.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${p}(sum, squared_sum);
    workgroupBarrier();

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

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

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

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${E(0)}, ${E(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:k},{inputs:[r[0],T]})},Kf=(e,r)=>{r.format==="NHWC"?Gf(e,e.inputs,r):Wf(e,e.inputs,r)}}),Hf,qf,Qf,nx=je(()=>{ft(),yt(),Tt(),Hf=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},qf=(e,r,t)=>{let s=r.simplified,o=e[0].dims,n=e[1],i=!s&&e[2],a=o,l=Me.normalizeAxis(r.axis,o.length),u=Me.sizeToDimension(o,l),p=Me.sizeFromDimension(o,l),c=Me.size(n.dims),d=i?Me.size(i.dims):0;if(c!==p||i&&d!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${c} and bias size of ${d}`);let _=[];for(let v=0;v<o.length;++v)v<l?_.push(o[v]):_.push(1);let f=sr(p),T=["type","type"],k=[{type:12,data:u},{type:1,data:p},{type:12,data:Math.floor(p/f)},{type:1,data:r.epsilon}];i&&T.push("type");let w=t>1,g=t>2,S=v=>{let M=Tr(e[0].dataType),y=[Pe("x",e[0].dataType,e[0].dims,f),Pe("scale",n.dataType,n.dims,f)];i&&y.push(Pe("bias",i.dataType,i.dims,f)),y.push(et("output",e[0].dataType,a,f)),w&&y.push(et("mean_data_output",1,_)),g&&y.push(et("inv_std_output",1,_));let C=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${v.registerUniforms(C).declareVariables(...y)}
  ${v.mainStart()}
    ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${nl("f32",f)};
    var mean_square_vector = ${nl("f32",f)};

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

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

    ${w?"mean_data_output[global_idx] = mean":""};
    ${g?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},E=[{dims:a,dataType:e[0].dataType}];return w&&E.push({dims:_,dataType:1}),g&&E.push({dims:_,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${f};${t};${s}`,inputDependencies:T},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:k}),getShaderSource:S}},Qf=(e,r)=>{Hf(e.inputs),e.compute(qf(e.inputs,r,e.outputCount))}}),Xf,Jf,ox=je(()=>{yt(),Ml(),xl(),Xf=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.")},Jf=e=>{Xf(e.inputs);let r=Nn.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can't use matmul on the given tensors");let t=r[r.length-1],s=e.inputs[0].dims[e.inputs[0].dims.length-1];if(t<8&&s<8)e.compute(wl(e.inputs,{activation:""},r));else{let o=r[r.length-2],n=Me.size(e.inputs[0].dims.slice(0,-2)),i=Me.size(e.inputs[1].dims.slice(0,-2));if(n!==1&&o===1&&i===1){let a=e.inputs[0].reshape([1,n,s]),l=e.inputs[1].reshape([1,s,t]),u=[1,n,t],p=[a,l];e.compute(fi(p,{activation:""},r,u),{inputs:p})}else e.compute(fi(e.inputs,{activation:""},r))}}}),Yf,Zf,e_,t_,r_,ix=je(()=>{ft(),yt(),or(),Tt(),Yf=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],s=t.dims.length;if(t.dims[s-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((r.k+r.blockSize-1)/r.blockSize),n=r.blockSize/8*r.bits,i=e[1];if(!Me.areEqual(i.dims,[r.n,o,n]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let a=e[2].dims;if(Me.size(a)!==r.n*o)throw new Error("scales input size error.");if(e.length===4){let l=e[3].dims,u=r.bits>4?r.n*o:r.n*Math.floor((o+1)/2);if(Me.size(l)!==u)throw new Error("zeroPoints input size error.")}},Zf=(e,r)=>{let t=e[0].dims,s=t.length,o=t[s-2],n=r.k,i=r.n,a=t.slice(0,s-2),l=Me.size(a),u=e[1].dims[2]/4,p=e[0].dataType,c=sr(r.k),d=sr(u),_=sr(i),f=a.concat([o,i]),T=o>1&&i/_%2===0?2:1,k=Me.size(f)/_/T,w=64,g=[],S=[l,o,n/c],E=Me.convertShape(e[1].dims).slice();E.splice(-1,1,u/d),g.push(...at(S)),g.push(...at(E)),g.push(...at(e[2].dims)),e.length===4&&g.push(...at(Me.convertShape(e[3].dims)));let v=[l,o,i/_];g.push(...at(v));let M=y=>{let C=S.length,F=Pe("a",e[0].dataType,C,c),z=Pe("b",12,E.length,d),K=Pe("scales",e[2].dataType,e[2].dims.length),q=[F,z,K],R=e.length===4?Pe("zero_points",12,e[3].dims.length):void 0;R&&q.push(R);let Z=v.length,H=et("output",e[0].dataType,Z,_),J=Tr(e[0].dataType),Q=(()=>{switch(c){case 1:return`array<${J}, 8>`;case 2:return`mat4x2<${J}>`;case 4:return`mat2x4<${J}>`;default:throw new Error(`${c}-component is not supported.`)}})(),se=()=>{let V=`
          // reuse a data
            var input_offset = ${F.indicesToOffset(`${F.type.indices}(batch, row, word_offset)`)};
            var a_data: ${Q};
            for (var j: u32 = 0; j < ${8/c}; j++) {
              a_data[j] = ${F.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let A=0;A<_*T;A++)V+=`
            b_value = ${d===1?`b${A}_data`:`b${A}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${Q}(${Array.from({length:4},(U,ee)=>`${J}(b_value_lower[${ee}]), ${J}(b_value_upper[${ee}])`).join(", ")});
            b_dequantized_values = ${c===1?`${Q}(${Array.from({length:8},(U,ee)=>`(b_quantized_values[${ee}] - ${R?`zero_point${A}`:"zero_point"}) * scale${A}`).join(", ")});`:`(b_quantized_values - ${Q}(${Array(8).fill(`${R?`zero_point${A}`:"zero_point"}`).join(",")})) * scale${A};`};
            workgroup_shared[local_id.x * ${T} + ${Math.floor(A/_)}]${_>1?`[${A%_}]`:""} += ${Array.from({length:8/c},(U,ee)=>`${c===1?`a_data[${ee}] * b_dequantized_values[${ee}]`:`dot(a_data[${ee}], b_dequantized_values[${ee}])`}`).join(" + ")};
          `;return V},fe=()=>{let V=`
            var col_index = col * ${_};
            ${R?`
            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
            var zero_point_byte_count: u32;
            var zero_point_word_index: u32;
            var zero_point_byte_offset: u32;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32;
            var zero_point_word: u32;`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${J}(8);`}
            `;for(let A=0;A<_*T;A++)V+=`
            let scale${A} = ${K.getByOffset("col_index * nBlocksPerCol + block")};
            ${R?`
            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 = ${R.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${A} = ${J}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return V},ae=()=>{let V=`col_index = col * ${_};`;for(let A=0;A<_*T;A++)V+=`
            let b${A}_data = ${z.getByIndices(`${z.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return V+=`
            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: ${Q};
            var b_dequantized_values: ${Q};`,V};return`
        var<workgroup> workgroup_shared: array<${H.type.value}, ${T*w}>;
        ${y.declareVariables(...q,H)}
        ${y.mainStart([w,1,1])}
          let output_indices = ${H.offsetToIndices(`(global_idx / ${w}) * ${T}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let nBlocksPerCol = uniforms.b_shape[1];

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

          if (local_id.x < ${T}) {
            var output_value: ${H.type.value} = ${H.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${w}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${T};
            }
            ${H.setByIndices(`${H.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${r.blockSize};${r.bits};${c};${d};${_};${T};${w}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:f,dataType:p}],dispatchGroup:{x:k},programUniforms:g}),getShaderSource:M}},e_=(e,r)=>{let t=e[0].dims,s=t.length,o=t[s-2],n=r.k,i=r.n,a=t.slice(0,s-2),l=Me.size(a),u=e[1].dims[2]/4,p=e[0].dataType,c=sr(r.k),d=sr(u),_=a.concat([o,i]),f=128,T=i%8===0?8:i%4===0?4:1,k=f/T,w=k*d*8,g=w/c,S=w/r.blockSize,E=Me.size(_)/T,v=[],M=[l,o,n/c],y=Me.convertShape(e[1].dims).slice();y.splice(-1,1,u/d),v.push(...at(M)),v.push(...at(y)),v.push(...at(e[2].dims)),e.length===4&&v.push(...at(Me.convertShape(e[3].dims)));let C=[l,o,i];v.push(...at(C));let F=z=>{let K=M.length,q=Pe("a",e[0].dataType,K,c),R=Pe("b",12,y.length,d),Z=Pe("scales",e[2].dataType,e[2].dims.length),H=[q,R,Z],J=e.length===4?Pe("zero_points",12,e[3].dims.length):void 0;J&&H.push(J);let Q=C.length,se=et("output",e[0].dataType,Q),fe=Tr(e[0].dataType),ae=()=>{switch(c){case 1:return`
          let a_data0 = vec4<${fe}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${fe}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${fe}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${fe}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${c}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${q.type.value}, ${g}>;
        var<workgroup> inter_results: array<array<${se.type.value}, ${k}>, ${T}>;
        ${z.declareVariables(...H,se)}
        ${z.mainStart([k,T,1])}
          let output_indices = ${se.offsetToIndices(`workgroup_index * ${T}`)};
          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) / ${S} + 1;

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

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

          if (local_idx < ${T}) {
            var output_value: ${se.type.value} = ${se.type.value}(0);
            for (var b = 0u; b < ${k}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${se.setByIndices(`${se.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${r.blockSize};${c};${d};${k};${T}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:_,dataType:p}],dispatchGroup:{x:E},programUniforms:v}),getShaderSource:F}},t_=(e,r)=>{Yf(e.inputs,r),r.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(e_(e.inputs,r)):e.compute(Zf(e.inputs,r))},r_=e=>zt(e)}),s_,n_,o_,i_,a_,l_,u_,c_,d_,ax=je(()=>{ft(),yt(),Tt(),s_=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},n_=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
            k = i32(${e.indicesGet("indices",o)}) - ${st("uniforms.pads",o,t)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${st("uniforms.x_shape",o,r)})) {
              break;
            }
            offset += k * i32(${st("uniforms.x_strides",o,r)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${s}
            value = x[offset];
          }
      `},o_=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
                k = i32(${e.indicesGet("indices",o)}) - ${st("uniforms.pads",o,t)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${st("uniforms.x_shape",o,r)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${st("uniforms.x_shape",o,r)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${st("uniforms.x_strides",o,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},i_=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
                k = i32(${e.indicesGet("indices",o)}) - ${st("uniforms.pads",o,t)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${st("uniforms.x_shape",o,r)})) {
                  k = i32(${st("uniforms.x_shape",o,r)}) - 1;
                }
                offset += k * i32(${st("uniforms.x_strides",o,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},a_=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
                k = i32(${e.indicesGet("indices",o)}) - ${st("uniforms.pads",o,t)};
                if (k < 0)  {
                  k += i32(${st("uniforms.x_shape",o,r)}]);
                }
                if (k >= i32(${st("uniforms.x_shape",o,r)})) {
                  k -= i32(${st("uniforms.x_shape",o,r)});
                }
                offset += k * i32(${st("uniforms.x_strides",o,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},l_=(e,r,t)=>{switch(t.mode){case 0:return n_(e,r,t.pads.length);case 1:return o_(e,r,t.pads.length);case 2:return i_(e,r,t.pads.length);case 3:return a_(e,r,t.pads.length);default:throw new Error("Invalid mode")}},u_=(e,r)=>{let t=Me.padShape(e[0].dims.slice(),r.pads),s=e[0].dims,o=Me.size(t),n=[{type:12,data:o},{type:6,data:r.pads}],i=e.length>=3&&e[2].data;r.mode===0&&n.push({type:i?e[2].dataType:1,data:r.value}),n.push(...at(e[0].dims,t));let a=["rank"],l=u=>{let p=et("output",e[0].dataType,t.length),c=Pe("x",e[0].dataType,s.length),d=c.type.value,_=l_(p,s.length,r),f=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&f.push({name:"constant_value",type:i?d:"f32"}),`
            ${u.registerUniforms(f).declareVariables(c,p)}
            ${u.mainStart()}
            ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${d}(0);
            ${_}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${r.mode}${i}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Me.size(t)/64)},programUniforms:n}),getShaderSource:l}},c_=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),s=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,o=e[0].dims.length,n=new Int32Array(2*o).fill(0);if(e.length>=4){let a=e[3].getBigInt64Array();for(let l=0;l<a.length;l++)n[Number(a[l])]=Number(t[l]),n[Number(a[l])+o]=Number(t[l+a.length])}else t.forEach((a,l)=>n[Number(l)]=Number(a));let i=[];return n.forEach(a=>i.push(a)),{mode:r.mode,value:s,pads:i}}else return r},d_=(e,r)=>{s_(e.inputs);let t=c_(e.inputs,r);e.compute(u_(e.inputs,t),{inputs:[0]})}}),bo,Bl,Rl,jl,Nl,p_,h_,Vl,Ul,m_,f_,Wl,__,g_,Gl,w_,M_,b_,y_,lx=je(()=>{ms(),ft(),yt(),Tt(),bo=e=>{if(Xt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Bl=(e,r,t)=>{let s=r.format==="NHWC",o=e.dims.slice();s&&o.splice(1,0,o.pop());let n=Object.hasOwnProperty.call(r,"dilations"),i=r.kernelShape.slice(),a=r.strides.slice(),l=n?r.dilations.slice():[],u=r.pads.slice();ai.adjustPoolAttributes(t,o,i,a,l,u);let p=ai.computePoolOutputShape(t,o,a,l,i,u,r.autoPad),c=Object.assign({},r);n?Object.assign(c,{kernelShape:i,strides:a,pads:u,dilations:l,cacheKey:r.cacheKey}):Object.assign(c,{kernelShape:i,strides:a,pads:u,cacheKey:r.cacheKey});let d=p.slice();return d.push(d.splice(1,1)[0]),[c,s?d:p]},Rl=(e,r)=>{let t=r.format==="NHWC",s=Me.size(e),o=Me.size(r.kernelShape),n=[{type:12,data:s},{type:12,data:o}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let a=r.kernelShape[r.kernelShape.length-1],l=r.strides[r.strides.length-1],u=r.pads[r.pads.length/2-1],p=r.pads[r.pads.length-1],c=!!(u+p);n.push({type:12,data:a},{type:12,data:l},{type:12,data:u},{type:12,data:p}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let d=!1;if(r.kernelShape.length===2){let _=r.kernelShape[r.kernelShape.length-2],f=r.strides[r.strides.length-2],T=r.pads[r.pads.length/2-2],k=r.pads[r.pads.length-2];d=!!(T+k),n.push({type:12,data:_},{type:12,data:f},{type:12,data:T},{type:12,data:k}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[n,i,!0,c,d]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let a=Me.computeStrides(r.kernelShape);n.push({type:12,data:a},{type:12,data:r.pads},{type:12,data:r.strides}),i.push({name:"kernelStrides",type:"u32",length:a.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length});let l=r.pads.reduce((u,p)=>u+p);return[n,i,!!l,!1,!1]}},jl=(e,r,t,s,o,n,i,a,l,u,p,c)=>{let d=o.format==="NHWC",_=r.type.value,f=et("output",r.type.tensor,s);if(o.kernelShape.length<=2){let T="",k="",w="",g=t-(d?2:1);if(p?T=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${g}] < 0 || xIndices[${g}]
                      >= uniforms.x_shape[${g}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${r.indicesToOffset("xIndices")}];
                  ${n}
                }`:T=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${r.indicesToOffset("xIndices")}];
                  ${n}
                }`,o.kernelShape.length===2){let S=t-(d?3:2);c?k=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${S}] < 0 || xIndices[${S}] >= uniforms.x_shape[${S}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:k=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j;
                `,w=`
              }
            `}return`
            ${e.registerUniforms(l).declareVariables(r,f)}

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

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

              var value = ${_}(${a});
              var pad = 0;
              ${k}
              ${T}
              ${w}
              ${i}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},Nl=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,p_=e=>`${Nl(e)};${e.countIncludePad}`,h_=e=>`${Nl(e)};${e.storageOrder};${e.dilations}`,Vl=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}),Ul=(e,r,t,s)=>{let[o,n]=Bl(r,s,t),i=Pe("x",r.dataType,r.dims.length),a=i.type.value,l="value += x_val;",u="";o.countIncludePad?u+=`value /= ${a}(uniforms.kernelSize);`:u+=`value /= ${a}(i32(uniforms.kernelSize) - pad);`;let[p,c,d,_,f]=Rl(n,o);p.push(...at(r.dims,n));let T=["rank"];return{name:e,shaderCache:{hint:`${s.cacheKey};${d};${_};${f}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Me.size(n)/64)},programUniforms:p}),getShaderSource:k=>jl(k,i,r.dims.length,n.length,o,l,u,0,c,d,_,f)}},m_=e=>{let r=e.count_include_pad!==0,t=Vl(e);if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let s={countIncludePad:r,...t,cacheKey:""};return{...s,cacheKey:p_(s)}},f_=(e,r)=>{bo(e.inputs),e.compute(Ul("AveragePool",e.inputs[0],!1,r))},Wl={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},__=e=>{let r=e.format;return{format:r,...Wl,cacheKey:r}},g_=(e,r)=>{bo(e.inputs),e.compute(Ul("GlobalAveragePool",e.inputs[0],!0,r))},Gl=(e,r,t,s)=>{let[o,n]=Bl(r,s,t),i=`
      value = max(x_val, value);
    `,a="",l=Pe("x",r.dataType,r.dims.length),u=["rank"],[p,c,d,_,f]=Rl(n,o);return p.push(...at(r.dims,n)),{name:e,shaderCache:{hint:`${s.cacheKey};${d};${_};${f}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Me.size(n)/64)},programUniforms:p}),getShaderSource:T=>jl(T,l,r.dims.length,n.length,o,i,a,r.dataType===10?-65504:-1e5,c,d,_,f)}},w_=(e,r)=>{bo(e.inputs),e.compute(Gl("MaxPool",e.inputs[0],!1,r))},M_=e=>{let r=e.storage_order,t=e.dilations,s=Vl(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let o={storageOrder:r,dilations:t,...s,cacheKey:""};return{...o,cacheKey:h_(o)}},b_=e=>{let r=e.format;return{format:r,...Wl,cacheKey:r}},y_=(e,r)=>{bo(e.inputs),e.compute(Gl("GlobalMaxPool",e.inputs[0],!0,r))}}),v_,x_,T_,E_,ux=je(()=>{ft(),yt(),or(),Tt(),v_=(e,r)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((t,s)=>t===e[2].dims[s]).reduce((t,s)=>t&&s,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(r.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((o,n)=>n===r.axis||o===e[0].dims[n]).reduce((o,n)=>o&&n,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let t=e[0].dims[r.axis],s=e[1].dims[r.axis];if(r.blockSize<Math.ceil(t/s)||r.blockSize>Math.ceil(t/(s-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},x_=(e,r)=>{let t=Me.normalizeAxis(r.axis,e[0].dims.length),s=e[0].dataType,o=s===3,n=e[0].dims,i=e[1].dataType,a=Me.size(n),l=s===3||s===2,u=l?[Math.ceil(Me.size(e[0].dims)/4)]:e[0].dims,p=e[1].dims,c=e.length>2?e[2]:void 0,d=c?l?[Math.ceil(Me.size(c.dims)/4)]:c.dims:void 0,_=p.length===0||p.length===1&&p[0]===1,f=_===!1&&p.length===1,T=sr(a),k=_&&(!l||T===4),w=k?T:1,g=k&&!l?T:1,S=Pe("input",l?12:s,u.length,g),E=Pe("scale",i,p.length),v=c?Pe("zero_point",l?12:s,d.length):void 0,M=et("output",i,n.length,w),y=[S,E];v&&y.push(v);let C=[u,p];c&&C.push(d);let F=[{type:12,data:a/w},{type:12,data:t},{type:12,data:r.blockSize},...at(...C,n)],z=K=>{let q=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${K.registerUniforms(q).declareVariables(...y,M)}
      ${K.mainStart()}
          ${K.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${M.offsetToIndices("global_idx")};

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

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

          // Set zero-point input
          ${v?_?l?`
                let zero_point_input = ${v.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 = ${v.getByOffset("0")}`:f?l?`
                let zero_point_index = ${M.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${v.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 = ${M.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${v.getByOffset("zero_point_index")};`:l?`
                let zero_point_offset = ${E.indicesToOffset("scale_indices")};
                let zero_point_input = ${v.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 = ${v.getByIndices("scale_indices")};`:`let zero_point_value = ${l?o?"i32":"u32":S.type.value}(0);`};
      // Compute and write output
      ${M.setByOffset("global_idx",`${M.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:r.cacheKey,inputDependencies:v?["rank","rank","rank"]:["rank","rank"]},getShaderSource:z,getRunData:()=>({outputs:[{dims:n,dataType:i}],dispatchGroup:{x:Math.ceil(a/w/64),y:1,z:1},programUniforms:F})}},T_=(e,r)=>{v_(e.inputs,r),e.compute(x_(e.inputs,r))},E_=e=>zt({axis:e.axis,blockSize:e.blockSize})}),P_,C_,S_,cx=je(()=>{ms(),ft(),Tt(),P_=(e,r,t)=>{let s=e===r,o=e<r&&t<0,n=e>r&&t>0;if(s||o||n)throw new Error("Range these inputs' contents are invalid.")},C_=(e,r,t,s)=>{let o=Math.abs(Math.ceil((r-e)/t)),n=[o],i=o,a=[{type:12,data:i},{type:s,data:e},{type:s,data:t},...at(n)],l=u=>{let p=et("output",s,n.length),c=p.type.value,d=[{name:"outputSize",type:"u32"},{name:"start",type:c},{name:"delta",type:c}];return`
        ${u.registerUniforms(d).declareVariables(p)}
        ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${c}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${s}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:n,dataType:s}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:a})}},S_=e=>{let r=0,t=0,s=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],s=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],s=e.inputs[2].getFloat32Array()[0]),Xt.webgpu.validateInputContent&&P_(r,t,s),e.compute(C_(r,t,s,e.inputs[0].dataType),{inputs:[]})}}),$_,Kl,Hl,k_,I_,A_,dx=je(()=>{ft(),yt(),or(),Tt(),$_=(e,r,t,s)=>{if(e!=="none"&&s!=="i32"&&s!=="u32"&&s!=="f32")throw new Error(`Input ${s} is not supported with reduction ${e}.`);let o=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,n=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${r}, oldValue, newValue);
                  if res.exchanged {
                    break;
                  }
                  oldValue = res.old_value;
                }
              }`;switch(e){case"none":return`${r}=${t};`;case"add":return s==="i32"||s==="u32"?`atomicAdd(&${r}, bitcast<${s}>(${t}));`:`
              ${o}bitcast<${s}>(oldValue) + (${t})${n}`;case"max":return s==="i32"||s==="u32"?`atomicMax(&${r}, bitcast<${s}>(${t}));`:`
                ${o}max(bitcast<f32>(oldValue), (${t}))${n}`;case"min":return s==="i32"||s==="u32"?`atomicMin(&${r}, bitcast<${s}>(${t}));`:`${o}min(bitcast<${s}>(oldValue), (${t}))${n}`;case"mul":return`${o}(bitcast<${s}>(oldValue) * (${t}))${n}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Kl=(e,r)=>`${e===1?`
    let element_count_dim = uniforms.output_strides;
    let dim_value = uniforms.output_shape;`:`
    let element_count_dim = uniforms.output_strides[${r?"i - indices_start":"i"}];
    let dim_value = uniforms.output_shape[${r?"i - indices_start":"i"} + uniforms.last_index_dimension];`}
    
    if (index >= 0) {
      if (index >= i32(dim_value)) {
        index = i32(dim_value - 1);
      }
    } else {
      if (index < -i32(dim_value)) {
        index = 0;
      } else {
        index += i32(dim_value);
      }
    }
    data_offset += u32((u32(index) * element_count_dim));`,Hl=(e,r,t)=>`for (var i = 0u; i < uniforms.num_updates_elements; i++) {
        let value = updates[uniforms.num_updates_elements * ${t?"global_idx":"idx"} + i];
        ${$_(e.reduction,"output[data_offset + i]","value",r)}
      }`,k_=(e,r)=>{let t=e[0].dims,s=e[1].dims,o=t,n=1,i=Math.ceil(Me.size(s)/n),a=s[s.length-1],l=Me.sizeFromDimension(t,a),u=Me.sizeFromDimension(s,0)/a,p=[{type:12,data:i},{type:12,data:a},{type:12,data:l},...at(e[1].dims,e[2].dims,o)],c=d=>{let _=Pe("indices",e[1].dataType,e[1].dims.length),f=Pe("updates",e[2].dataType,e[2].dims.length,n),T=r.reduction!=="none"&&r.reduction!==""?zd("output",e[0].dataType,o.length):et("output",e[0].dataType,o.length,n);return`
      ${d.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(_,f,T)}
      ${d.mainStart()}
        ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
  var hasDuplicates = false;
  if (${r.reduction==="none"}) {
    for (var i = 0; i < ${u}; i = i + 1) {
      for (var j = i + 1; j < ${u}; j = j + 1) {
        var index_i = i32(indices[i].x);
        var index_j = i32(indices[j].x);
        if (index_i == index_j) {
          hasDuplicates = true;
          break;
        }
      }
      if (hasDuplicates) {
        break;
      }
    }
  }

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

  var data_offset = 0u;
  var indices_start = uniforms.last_index_dimension * global_idx;
  var indices_end = indices_start + uniforms.last_index_dimension;
  for (var i = indices_start; i < indices_end; i++) {
    var index = i32(indices[i].x);
    ${Kl(t.length,!0)}
  }
  ${Hl(r,T.type.value,!0)}
  }`};return{name:"ScatterND",shaderCache:{hint:`${r.cacheKey}_${r.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}),getShaderSource:c}},I_=e=>zt({reduction:e.reduction}),A_=(e,r)=>{e.compute(k_(e.inputs,r),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),F_,O_,D_,ql,L_,z_,B_,R_,j_,N_,V_,U_,Ql,W_,G_,K_,H_,q_,Q_,X_,px=je(()=>{ft(),yt(),or(),Tt(),F_=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(r.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},O_=(e,r,t)=>{r.every(o=>o>=0&&o<t||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let s=new Array(t).fill(1);return r.forEach((o,n)=>s[o]=e[n]),s},D_=(e,r,t,s,o,n)=>{let[i,a,l]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],u=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(p=>n.push(p));else if(r.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(a>0&&e.length>a&&e[a].dims.length===1&&e[a].dims[0]>0){if(e[a].getFloat32Array().forEach(p=>s.push(p)),s.length!==0&&s.length!==u&&t>=18&&s.length!==r.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");F_(s,r),r.axes.length>0&&O_(s,r.axes,u).forEach((p,c)=>s[c]=p)}if(l>0&&e.length>l&&e[l].dims.length===1&&e[l].dims[0]>0&&(e[l].getBigInt64Array().forEach(p=>o.push(Number(p))),o.length!==0&&o.length!==u&&t>=18&&o.length!==r.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(r.axes.length>0){if(s.length!==0&&s.length!==r.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(o.length!==0&&o.length!==r.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof s<"u"&&typeof o<"u"&&s.length>0&&o.length>u)throw new Error("Resize requires only of scales or sizes to be specified")},ql=(e,r,t,s)=>`
  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
  // offset-by-one error later in floor().
  let big = (${e}) * (${r});
  let whole = ${s}(big / (${t}));
  let fract = ${s}(big % (${t})) / ${s}(${t});
  return whole + fract;
`,L_=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return`
          if (xScale < 1.0 || floor(xScale) != xScale) {
            return ${r}(xResized) / ${r}(xScale);
          } else {
            ${ql("xResized","lengthOriginal","lengthResized",r)}
          }
        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    ${ql("xResized","lengthOriginal - 1","lengthResized - 1",r)}
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${r}(roiStart) * ${r}(lengthOriginal - 1) +
                        (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) /
                        ${r}(lengthResized - 1);
                  } else {
                    return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized);
                  const adjustment = ${r}(lengthResized) / outputWidth;
                  const center = ${r}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",z_=(e,r,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(r<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",B_=(e,r,t)=>{let s=new Array(t).fill(0).concat(new Array(t).fill(1)),o=e.length===0?s:e.slice();return r.length>0?(r.forEach((n,i)=>{s[n]=o[i],s[i+t]=o[r.length+i]}),s):o},R_=(e,r,t,s)=>{let o=[];if(t.length>0)if(s.length>0){if(e.forEach(n=>o.push(n)),Math.max(...s)>e.length)throw new Error("axes is out of bound");s.forEach((n,i)=>o[n]=t[i])}else t.forEach(n=>o.push(n));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((n,i)=>Math.round(n*r[i]))}return o},j_=(e,r,t)=>{let s=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(n=>r[n]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(n=>r[n]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let o=e.slice();return t.axes.length>0?(t.axes.forEach(n=>r[n]=s),t.axes.forEach(n=>o[n]=Math.round(e[n]*r[n]))):(r.fill(s,0,r.length),o.forEach((n,i)=>o[i]=Math.round(n*r[i]))),o},N_=(e,r,t,s,o)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> {
      var original_indices: array<${e.type.value}, ${t.length}>;
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${st("uniforms.scales","i",s)};
        var roi_low = ${st("uniforms.roi","i",o)};
        var roi_hi = ${st("uniforms.roi",`i + ${r.length}`,o)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${st("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${st("uniforms.output_shape","i",t.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,V_=(e,r,t,s,o,n,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${s.length}; i++) {
        var output_index = ${r.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${st("uniforms.scales","i",o)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${st("uniforms.roi","i",n)};
          var roi_hi = ${st("uniforms.roi",`i + ${t.length}`,n)};
          var input_shape_i = ${st("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${st("uniforms.output_shape","i",s.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${r.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet("input_indices","i","input_index")}
      }
      return input_indices;
    }`,U_=(e,r)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${st("uniforms.input_shape","i",r.length)}) {
          return false;
        }
      }
      return true;
    }`,Ql=(e,r,t,s)=>e.rank>s?`
    ${e.indicesSet("input_indices",r,"channel")};
    ${e.indicesSet("input_indices",t,"batch")};
`:"",W_=(e,r,t,s,o)=>{let[n,i,a,l]=t.length===2?[-1,0,1,-1]:[0,2,3,1],u=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${u} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(row, ${t[i]} - 1))`)};
      ${e.indicesSet("input_indices",a,`max(0, min(col, ${t[a]} - 1))`)};
      ${Ql(e,l,n,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

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

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

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

      var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${p} = abs(depth - ${p}(depth1));
      var dx2: ${p} = abs(${p}(depth2) - depth);
      var dy1: ${p} = abs(height - ${p}(height1));
      var dy2: ${p} = abs(${p}(height2) - height);
      var dz1: ${p} = abs(width - ${p}(width1));
      var dz2: ${p} = abs(${p}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},H_=(e,r,t,s,o,n)=>{let i=e.dims,a=B_(n,r.axes,i.length),l=R_(i,s,o,r.axes),u=s.slice();s.length===0&&(u=i.map((g,S)=>g===0?1:l[S]/g),r.keepAspectRatioPolicy!=="stretch"&&(l=j_(i,u,r)));let p=et("output",e.dataType,l.length),c=Pe("input",e.dataType,i.length),d=Me.size(l),_=i.length===l.length&&i.every((g,S)=>g===l[S]),f=r.coordinateTransformMode==="tf_crop_and_resize",T=r.extrapolationValue,k=c.type.value,w=g=>`
      ${_?"":`
      ${L_(r.coordinateTransformMode,k)};
      ${(()=>{switch(r.mode){case"nearest":return`
              ${U_(c,i)};
              ${z_(r.nearestMode,t,k)};
              ${V_(c,p,i,l,u.length,a.length,f)};
              `;case"linear":return`
              ${N_(p,i,l,u.length,a.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${W_(c,p,i,f,T)}`;if(i.length===3||i.length===5)return`${K_(c,p,i,f,T)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${G_(c,p,i,l,u,a,r.cubicCoeffA,f,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${g.registerUniform("output_size","u32").registerUniform("scales","f32",u.length).registerUniform("roi","f32",a.length).declareVariables(c,p)}
      ${g.mainStart()}
        ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${_?"output[global_idx] = input[global_idx];":`
        let output_indices = ${p.offsetToIndices("global_idx")};
        var input_indices: ${c.type.indices};
        ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${c.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${r.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${u.length>0?r.mode==="cubic"?u:u.length:""}|${o.length>0?o:""}|${a.length>0?a:""}|${_}|${r.mode==="nearest"?i.length:i}`,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:[{type:12,data:d},{type:1,data:u},{type:1,data:a},...at(i,l)]})}},q_=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},Q_=(e,r)=>{let t=[],s=[],o=[],n=q_(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");D_(e.inputs,r,n,t,s,o),e.compute(H_(e.inputs[0],r,n,t,s,o),{inputs:[0]})},X_=e=>{let r=e.antialias,t=e.axes,s=e.coordinateTransformMode,o=e.cubicCoeffA,n=e.excludeOutside!==0,i=e.extrapolationValue,a=e.keepAspectRatioPolicy,l=e.mode,u=e.nearestMode===""?"simple":e.nearestMode;return zt({antialias:r,axes:t,coordinateTransformMode:s,cubicCoeffA:o,excludeOutside:n,extrapolationValue:i,keepAspectRatioPolicy:a,mode:l,nearestMode:u})}}),J_,Y_,Z_,hx=je(()=>{ft(),yt(),Tt(),J_=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],s=e[2];if(r.dataType!==t.dataType||r.dataType!==s.dataType)throw new Error("All inputs must have the same data type");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Input must be 2D or 3D");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=r.dims[r.dims.length-1],n=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==n)throw new Error("Skip must have the same sequence length as input");if(s.dims.length!==1)throw new Error("Gamma must be 1D");if(s.dims[s.dims.length-1]!==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")}},Y_=(e,r,t,s)=>{let o=r.simplified,n=e[0].dims,i=Me.size(n),a=n,l=i,u=n.slice(-1)[0],p=s?n.slice(0,-1).concat(1):[],c=!o&&e.length>3,d=e.length>4,_=s&&t>1,f=s&&t>2,T=t>3,k=64,w=sr(u),g=[{type:12,data:l},{type:12,data:w},{type:12,data:u},{type:1,data:r.epsilon}],S=v=>{let M=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],y=[Pe("x",e[0].dataType,e[0].dims,w),Pe("skip",e[1].dataType,e[1].dims,w),Pe("gamma",e[2].dataType,e[2].dims,w)];c&&y.push(Pe("beta",e[3].dataType,e[3].dims,w)),d&&y.push(Pe("bias",e[4].dataType,e[4].dims,w)),y.push(et("output",e[0].dataType,a,w)),_&&y.push(et("mean_output",1,p)),f&&y.push(et("inv_std_output",1,p)),T&&y.push(et("input_skip_bias_sum",e[0].dataType,a,w));let C=Tr(e[0].dataType),F=Tr(1,w);return`

      ${v.registerUniforms(M).declareVariables(...y)}
      var<workgroup> sum_shared : array<${F}, ${k}>;
      var<workgroup> sum_squared_shared : array<${F}, ${k}>;

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

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

        var reduce_size : u32 = ${k};
        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 = ${Us("sum",w)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Us("square_sum",w)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon);
        ${_?"mean_output[global_idx] = mean;":""}
        ${f?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${o?"":`- ${C}(mean)`}) *
            ${C}(inv_std_dev) * gamma[offset1d + i]
            ${c?"+ beta[offset1d + i]":""};
        }
      }`},E=[{dims:a,dataType:e[0].dataType}];return t>1&&E.push({dims:p,dataType:1}),t>2&&E.push({dims:p,dataType:1}),t>3&&E.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${w};${_};${f};${T}`,inputDependencies:e.map((v,M)=>"type")},getShaderSource:S,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/u)},programUniforms:g})}},Z_=(e,r)=>{J_(e.inputs);let t=[0];e.outputCount>1&&t.push(-3),e.outputCount>2&&t.push(-3),e.outputCount>3&&t.push(3),e.compute(Y_(e.inputs,r,e.outputCount,!1),{outputs:t})}}),eg,yo,tg,Xl,rg,sg,ng,og,mx=je(()=>{ft(),yt(),or(),Tt(),eg=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((t,s)=>{if(e[s+1].dataType!==6&&e[s+1].dataType!==7)throw new Error(`Input ${s} must be an array of int32 or int64`)})},yo=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(s=>t.push(Number(s)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(s=>t.push(Number(s)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},tg=(e,r)=>{if(e.length>1){let t=yo(e,1),s=yo(e,2),o=yo(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),zt({starts:t,ends:s,axes:o})}else return r},Xl=(e,r,t,s,o)=>{let n=e;return e<0&&(n+=t[s[r]]),o[r]<0?Math.max(0,Math.min(n,t[s[r]]-1)):Math.max(0,Math.min(n,t[s[r]]))},rg=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${t.length}; i >= 0; i--) {
            let input_shape_i = ${st("uniforms.input_shape","i",t.length)};
            let steps_i = ${st("uniforms.steps","i",t.length)};
            let signs_i = ${st("uniforms.signs","i",t.length)};
            let starts_i = ${st("uniforms.starts","i",t.length)};
            var output_index = ${r.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,sg=(e,r)=>{let t=e[0].dims,s=Me.size(t),o=r.axes.length>0?Me.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],n=yo(e,4);n.forEach(w=>w!==0||(()=>{throw new Error("step cannot be 0")})),n.length===0&&(n=Array(o.length).fill(1));let i=r.starts.map((w,g)=>Xl(w,g,t,o,n)),a=r.ends.map((w,g)=>Xl(w,g,t,o,n));if(o.length!==i.length||o.length!==a.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==t.length)for(let w=0;w<t.length;++w)o.includes(w)||(i.splice(w,0,0),a.splice(w,0,t[w]),n.splice(w,0,1));let l=n.map(w=>Math.sign(w));n.forEach((w,g,S)=>{if(w<0){let E=(a[g]-i[g])/w,v=i[g],M=v+E*n[g];i[g]=M,a[g]=v,S[g]=-w}});let u=t.slice(0);o.forEach((w,g)=>{u[w]=Math.ceil((a[w]-i[w])/n[w])});let p={dims:u,dataType:e[0].dataType},c=et("output",e[0].dataType,u.length),d=Pe("input",e[0].dataType,e[0].dims.length),_=Me.size(u),f=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:n.length}],T=[{type:12,data:_},{type:12,data:i},{type:6,data:l},{type:12,data:n},...at(e[0].dims,u)],k=w=>`
      ${w.registerUniforms(f).declareVariables(d,c)}
        ${rg(d,c,t)}
        ${w.mainStart()}
          ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${c.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${c.setByOffset("global_idx",d.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${i.length}_${n.length}`,inputDependencies:["rank"]},getShaderSource:k,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:T})}},ng=(e,r)=>{eg(e.inputs,r);let t=tg(e.inputs,r);e.compute(sg(e.inputs,t),{inputs:[0]})},og=e=>{let r=e.starts,t=e.ends,s=e.axes;return zt({starts:r,ends:t,axes:s})}}),ig,ag,lg,ug,fx=je(()=>{ft(),yt(),or(),Ws(),Tt(),ig=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},ag=(e,r)=>{let t=e.inputs[0],s=t.dims,o=Me.size(s),n=s.length,i=Me.normalizeAxis(r.axis,n),a=i<s.length-1,l,u=[];a?(u=Array.from({length:n},(y,C)=>C),u[i]=n-1,u[n-1]=i,l=e.compute(qr(t,u),{inputs:[t],outputs:[-1]})[0]):l=t;let p=l.dims,c=p[n-1],d=o/c,_=sr(c),f=c/_,T=64;d===1&&(T=256);let k=(y,C)=>C===4?`max(max(${y}.x, ${y}.y), max(${y}.z, ${y}.w))`:C===2?`max(${y}.x, ${y}.y)`:C===3?`max(max(${y}.x, ${y}.y), ${y}.z)`:y,w=Pe("x",l.dataType,l.dims,_),g=et("result",l.dataType,l.dims,_),S=w.type.value,E=Tr(l.dataType)==="f32"?`var threadMax = ${S}(-3.402823e+38f);`:`var threadMax = ${S}(-65504.0h);`,v=y=>`
      var<workgroup> rowMaxShared : ${S};
      var<workgroup> rowSumShared : ${S};
      var<workgroup> threadShared : array<${S}, ${T}>;

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

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

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

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

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

        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
          if (lindex < currSize) {
            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowSumShared = ${S}(${Us("threadShared[0]",_)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`,M=e.compute({name:"Softmax",shaderCache:{hint:`${_};${T}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:p,dataType:l.dataType}],dispatchGroup:{x:d},programUniforms:[{type:6,data:f}]}),getShaderSource:v},{inputs:[l],outputs:[a?-1:0]})[0];a&&e.compute(qr(M,u),{inputs:[M]})},lg=(e,r)=>{ig(e.inputs),ag(e,r)},ug=e=>zt({axis:e.axis})}),Jl,cg,dg,pg,hg,_x=je(()=>{ft(),yt(),Tt(),Jl=e=>Array.from(e.getBigInt64Array(),Number),cg=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Jl(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},dg=(e,r)=>{let t=[];for(let s=0;s<e.length;++s)t.push(e[s]*r[s]);return t},pg=(e,r)=>{let t=e[0].dims,s=r??Jl(e[1]),o=dg(t,s),n=Me.size(o),i=e[0].dataType,a=Pe("input",i,t.length),l=et("output",i,o.length),u=p=>`
      const inputShape = ${a.indices(...t)};
      ${p.registerUniform("output_size","u32").declareVariables(a,l)}
      ${p.mainStart()}
      ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${l.offsetToIndices("global_idx")};
      var input_indices: ${a.type.indices};
      for (var i = 0; i < ${t.length}; i++) {
        let input_dim_i = ${a.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;

        ${a.indicesSet("input_indices","i","input_dim_value")}
      }
      ${l.setByOffset("global_idx",a.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${s}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:12,data:n},...at(e[0].dims,o)]}),getShaderSource:u}},hg=e=>{cg(e.inputs),e.compute(pg(e.inputs),{inputs:[0]})}}),mg,fg,_g,gx=je(()=>{ft(),yt(),Tt(),mg=(e,r,t,s,o)=>{let n=et("output_data",o,t.length,4),i=Pe("a_data",r[1].dataType,r[1].dims.length,4),a=Pe("b_data",r[2].dataType,r[2].dims.length,4),l=Pe("c_data",r[0].dataType,r[0].dims.length,4),u,p=(c,d,_)=>`select(${d}, ${c}, ${_})`;if(!s)u=n.setByOffset("global_idx",p(i.getByOffset("global_idx"),a.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let c=(d,_,f="")=>{let T=`a_data[index_a${_}][component_a${_}]`,k=`b_data[index_b${_}][component_b${_}]`,w=`bool(c_data[index_c${_}] & (0xffu << (component_c${_} * 8)))`;return`
            let output_indices${_} = ${n.offsetToIndices(`global_idx * 4u + ${_}u`)};
            let offset_a${_} = ${i.broadcastedIndicesToOffset(`output_indices${_}`,n)};
            let offset_b${_} = ${a.broadcastedIndicesToOffset(`output_indices${_}`,n)};
            let offset_c${_} = ${l.broadcastedIndicesToOffset(`output_indices${_}`,n)};
            let index_a${_} = offset_a${_} / 4u;
            let index_b${_} = offset_b${_} / 4u;
            let index_c${_} = offset_c${_} / 4u;
            let component_a${_} = offset_a${_} % 4u;
            let component_b${_} = offset_b${_} % 4u;
            let component_c${_} = offset_c${_} % 4u;
            ${d}[${_}] = ${f}(${p(T,k,w)});
          `};o===9?u=`
            var data = vec4<u32>(0);
            ${c("data",0,"u32")}
            ${c("data",1,"u32")}
            ${c("data",2,"u32")}
            ${c("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:u=`
            ${c("output_data[global_idx]",0)}
            ${c("output_data[global_idx]",1)}
            ${c("output_data[global_idx]",2)}
            ${c("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(l,i,a,n)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${u}
      }`},fg=e=>{let r=e[1].dims,t=e[2].dims,s=e[0].dims,o=e[1].dataType,n=!(Me.areEqual(r,t)&&Me.areEqual(t,s)),i=r,a=Me.size(r);if(n){let u=Nn.calcShape(Nn.calcShape(r,t,!1),s,!1);if(!u)throw new Error("Can't perform where op on the given tensors");i=u,a=Me.size(i)}let l=Math.ceil(a/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:u=>mg(u,e,i,n,o),getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:Math.ceil(a/64/4)},programUniforms:[{type:12,data:l},...at(s,r,t,i)]})}},_g=e=>{e.compute(fg(e.inputs))}}),gg,wx=je(()=>{Av(),dl(),Fv(),Ov(),Dv(),Lv(),zv(),Vv(),Wv(),Gv(),Kv(),Hv(),qv(),Qv(),Xv(),Jv(),Yv(),Zv(),ex(),tx(),rx(),sx(),nx(),ox(),ix(),$f(),ax(),lx(),ux(),cx(),dx(),ll(),px(),Rf(),hx(),mx(),fx(),Lf(),_x(),Ws(),fl(),gx(),gg=new Map([["Abs",[Xp]],["Acos",[Jp]],["Acosh",[Yp]],["Add",[Rh]],["ArgMax",[Dp,cl]],["ArgMin",[Op,cl]],["Asin",[Zp]],["Asinh",[eh]],["Atan",[th]],["Atanh",[rh]],["Attention",[Np]],["AveragePool",[f_,m_]],["BatchNormalization",[Gp]],["BiasAdd",[qp]],["BiasSplitGelu",[Lh]],["Cast",[nh,sh]],["Ceil",[ah]],["Clip",[ih]],["Concat",[Zh,em]],["Conv",[Sl,Pl]],["ConvTranspose",[Cm,Tm]],["Cos",[lh]],["Cosh",[uh]],["CumSum",[$m,km]],["DepthToSpace",[Om,Dm]],["DequantizeLinear",[T_,E_]],["Div",[jh]],["Einsum",[Nm,Vm]],["Elu",[ch,fo]],["Equal",[Nh]],["Erf",[dh]],["Exp",[ph]],["Expand",[Km]],["FastGelu",[qm]],["Floor",[hh]],["FusedConv",[Sl,Pl]],["Gather",[Ym,Jm]],["GatherElements",[cf,uf]],["GatherBlockQuantized",[nf,of]],["GatherND",[ef,tf]],["Gelu",[mh]],["Gemm",[mf,hf]],["GlobalAveragePool",[g_,__]],["GlobalMaxPool",[y_,b_]],["Greater",[Gh]],["GreaterOrEqual",[Hh]],["GridSample",[xf,Tf]],["GroupQueryAttention",[Uf]],["HardSigmoid",[vh,yh]],["InstanceNormalization",[Kf]],["LayerNormalization",[Qf]],["LeakyRelu",[fh,fo]],["Less",[Kh]],["LessOrEqual",[qh]],["Log",[kh]],["MatMul",[Jf]],["MatMulNBits",[t_,r_]],["MaxPool",[w_,M_]],["Mul",[Vh]],["MultiHeadAttention",[Sf,Pf]],["Neg",[gh]],["Not",[_h]],["Pad",[d_]],["Pow",[Uh]],["QuickGelu",[Fh,fo]],["Range",[S_]],["Reciprocal",[wh]],["ReduceMin",[$p]],["ReduceMean",[Tp]],["ReduceMax",[Sp]],["ReduceSum",[Ip]],["ReduceProd",[kp]],["ReduceL1",[Ep]],["ReduceL2",[Pp]],["ReduceLogSum",[Fp]],["ReduceLogSumExp",[Cp]],["ReduceSumSquare",[Ap]],["Relu",[Mh]],["Resize",[Q_,X_]],["RotaryEmbedding",[Bf]],["ScatterND",[A_,I_]],["Sigmoid",[bh]],["Sin",[xh]],["Sinh",[Th]],["Slice",[ng,og]],["SkipLayerNormalization",[Z_]],["Split",[Of,Df]],["Sqrt",[Eh]],["Softmax",[lg,ug]],["Sub",[Wh]],["Tan",[Ph]],["Tanh",[Ch]],["ThresholdedRelu",[$h,fo]],["Tile",[hg]],["Transpose",[Gd,Kd]],["Where",[_g]]])}),wg,Mx=je(()=>{ms(),Bs(),Tt(),wg=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,r){this.repo.set(e,r)}run(e,r,t,s,o){hs(e.programInfo.name);let n=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let a=[];for(let u of r)a.push({binding:a.length,resource:{buffer:u.buffer}});for(let u of t)a.push({binding:a.length,resource:{buffer:u.buffer}});o&&a.push({binding:a.length,resource:o});let l=n.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:a,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let u={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:l,dispatchGroup:s};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(u)}i.setPipeline(e.computePipeline),i.setBindGroup(0,l),i.dispatchWorkgroups(...s),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),ts(e.programInfo.name)}dispose(){}build(e,r){hs(e.name);let t=this.backend.device,s=[];[{feature:"shader-f16",extension:"f16"},{feature:"subgroups",extension:"subgroups"}].forEach(u=>{t.features.has(u.feature)&&s.push(`enable ${u.extension};`)});let o=Rd(r,this.backend.device.limits),n=e.getShaderSource(o),i=`${s.join(`
`)}
${o.additionalImplementations}
${n}`,a=t.createShaderModule({code:i,label:e.name});It("verbose",()=>`[WebGPU] ${e.name} shader code: ${i}`);let l=t.createComputePipeline({compute:{module:a,entryPoint:"main"},layout:"auto",label:e.name});return ts(e.name),{programInfo:e,computePipeline:l,uniformVariablesInfo:o.variablesInfo}}normalizeDispatchGroupSize(e){let r=typeof e=="number"?e:e.x,t=typeof e=="number"?1:e.y||1,s=typeof e=="number"?1:e.z||1,o=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(r<=o&&t<=o&&s<=o)return[r,t,s];let n=r*t*s,i=Math.ceil(Math.sqrt(n));if(i>o){if(i=Math.ceil(Math.cbrt(n)),i>o)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),Mg={};Rn(Mg,{WebGpuBackend:()=>xg});var bg,yg,vg,xg,bx=je(()=>{ms(),ft(),Bs(),Ed(),kv(),wx(),Mx(),bg=(e,r)=>{if(r.length!==e.length)throw new Error(`inputDependencies length ${r.length} is not equal to inputTensors length ${e.length}.`);let t=[];for(let s=0;s<e.length;++s){let o=e[s].dataType;switch(r[s]){case"none":{t.push("");break}case"type":{t.push(`${o}`);break}case"rank":{let n=e[s].dims.length;t.push(`${o};${n}`);break}case"dims":{let n=e[s].dims.join(",");t.push(`${o};${n}`);break}default:throw new Error(`unsupported input dependency: ${r[s]}`)}}return t.join("|")},yg=(e,r,t)=>{var o,n;let s=e.name;return(o=e.shaderCache)!=null&&o.hint&&(s+="["+e.shaderCache.hint+"]"),s+=":"+t+`:${bg(r,((n=e.shaderCache)==null?void 0:n.inputDependencies)??new Array(r.length).fill("dims"))}`,s},vg=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},xg=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. 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r.requestAdapterInfo()),this.gpuDataManager=Dd(this),this.programManager=new wg(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Ka(e.logLevel,!!e.debug),this.device.onuncapturederror=n=>{n.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${n.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:r,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),r={};this.queryType==="at-passes"&&(r.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(r)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;hs(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var s;let r=new BigUint64Array(e.getMappedRange()),t=this.pendingQueries.get(e);for(let o=0;o<r.length/2;o++){let n=t[o],i=n.kernelId,a=this.kernels.get(i),l=a.kernelType,u=a.kernelName,p=n.programName,c=n.inputTensorViews,d=n.outputTensorViews,_=r[o*2],f=r[o*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=_);let T=Number(_-this.queryTimeBase),k=Number(f-this.queryTimeBase);if(!Number.isSafeInteger(T)||!Number.isSafeInteger(k))throw new RangeError("incorrect timestamp range");if((s=this.env.webgpu.profiling)!=null&&s.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:c.map(w=>({dims:w.dims,dataType:zs(w.dataType)})),outputsMetadata:d.map(w=>({dims:w.dims,dataType:zs(w.dataType)})),kernelId:i,kernelType:l,kernelName:u,programName:p,startTime:T,endTime:k});else{let w="";c.forEach((S,E)=>{w+=`input[${E}]: [${S.dims}] | ${zs(S.dataType)}, `});let g="";d.forEach((S,E)=>{g+=`output[${E}]: [${S.dims}] | ${zs(S.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${u}|${p}" ${w}${g}execution time: ${k-T} ns`)}uo("GPU",`${p}::${_}::${f}`)}e.unmap(),this.pendingQueries.delete(e)}),ts()}run(e,r,t,s,o,n){hs(e.name);let i=[];for(let g=0;g<r.length;++g){let S=r[g].data;if(S===0)continue;let E=this.gpuDataManager.get(S);if(!E)throw new Error(`no GPU data for input: ${S}`);i.push(E)}let{outputs:a,dispatchGroup:l,programUniforms:u}=e.getRunData(r),p=t.length===0?a.map((g,S)=>S):t;if(p.length!==a.length)throw new Error(`Output size ${p.length} must be equal to ${a.length}.`);let c=[],d=[];for(let g=0;g<a.length;++g){if(!Number.isInteger(p[g])||p[g]<-3||p[g]>=n)throw new Error(`Invalid output index: ${p[g]}`);if(p[g]===-3)continue;let S=p[g]===-1,E=p[g]===-2,v=S||E?o(a[g].dataType,a[g].dims):s(p[g],a[g].dataType,a[g].dims);if(c.push(v),v.data===0)continue;let M=this.gpuDataManager.get(v.data);if(!M)throw new Error(`no GPU data for output: ${v.data}`);if(S&&this.temporaryData.push(M),E){let y=this.kernelPersistentData.get(this.currentKernelId);y||(y=[],this.kernelPersistentData.set(this.currentKernelId,y)),y.push(M)}d.push(M)}if(i.length!==r.length||d.length!==c.length){if(d.length===0)return ts(e.name),c;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let _;if(u){let g=0,S=[];u.forEach(y=>{let C=typeof y.data=="number"?[y.data]:y.data;if(C.length===0)return;let F=y.type===10?2:4,z,K;y.type===10?(K=C.length>4?16:C.length>2?8:C.length*F,z=C.length>4?16:F*C.length):(K=C.length<=2?C.length*F:16,z=16),g=Math.ceil(g/K)*K,S.push(g);let q=y.type===10?8:4;g+=C.length>4?Math.ceil(C.length/q)*z:C.length*F});let E=16;g=Math.ceil(g/E)*E;let v=new ArrayBuffer(g);u.forEach((y,C)=>{let F=S[C],z=typeof y.data=="number"?[y.data]:y.data;if(y.type===6)new Int32Array(v,F,z.length).set(z);else if(y.type===12)new Uint32Array(v,F,z.length).set(z);else if(y.type===10)new Uint16Array(v,F,z.length).set(z);else if(y.type===1)new Float32Array(v,F,z.length).set(z);else throw new Error(`Unsupported uniform type: ${zs(y.type)}`)});let M=this.gpuDataManager.create(g,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(M.buffer,0,v,0,g),this.gpuDataManager.release(M.id),_={offset:0,size:g,buffer:M.buffer}}let f=this.programManager.normalizeDispatchGroupSize(l),T=f[1]===1&&f[2]===1,k=yg(e,r,T),w=this.programManager.getArtifact(k);if(w||(w=this.programManager.build(e,f),this.programManager.setArtifact(k,w),It("info",()=>`[artifact] key: ${k}, programName: ${e.name}`)),u&&w.uniformVariablesInfo){if(u.length!==w.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${w.uniformVariablesInfo.length}, got ${u.length} in program "${w.programInfo.name}".`);for(let g=0;g<u.length;g++){let S=u[g],E=S.type,v=typeof S.data=="number"?1:S.data.length,[M,y]=w.uniformVariablesInfo[g];if(E!==M||v!==y)throw new Error(`Uniform variable ${g} mismatch: expect type ${M} with size ${y}, got type ${E} with size ${v} in program "${w.programInfo.name}".`)}}if(It("info",()=>`[ProgramManager] run "${e.name}" (key=${k}) with ${f[0]}x${f[1]}x${f[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let g={kernelId:this.currentKernelId,programName:w.programInfo.name,inputTensorViews:r,outputTensorViews:c};this.pendingKernels.push(g),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(g)}return this.programManager.run(w,i,d,f,_),ts(e.name),c}upload(e,r){this.gpuDataManager.upload(e,r)}memcpy(e,r){this.gpuDataManager.memcpy(e,r)}async download(e,r){await this.gpuDataManager.download(e,r)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,r,t,s){let o=gg.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let n={kernelType:e,kernelName:s,kernelEntry:o[0],attributes:[o[1],t]};this.kernels.set(r,n)}releaseKernel(e){let r=this.kernelPersistentData.get(e);if(r){for(let t of r)this.gpuDataManager.release(t.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,r,t){let s=this.kernels.get(e);if(!s)throw new Error(`kernel not created: ${e}`);let o=s.kernelType,n=s.kernelName,i=s.kernelEntry,a=s.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${n}" is not allowed to be called recursively`);this.currentKernelId=e,a[0]&&(a[1]=a[0](a[1]),a[0]=void 0),It("info",()=>`[WebGPU] Start to run kernel "[${o}] ${n}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),i(r,a[1]),0}catch(u){return t.push(Promise.resolve(`[WebGPU] Kernel "[${o}] ${n}" failed. ${u}`)),1}finally{l&&t.push(this.device.popErrorScope().then(u=>u?`GPU validation error for kernel "[${o}] ${n}": ${u.message}`:null));for(let u of this.temporaryData)this.gpuDataManager.release(u.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,r,t,s){let o=this.sessionExternalDataMapping.get(e);o||(o=new Map,this.sessionExternalDataMapping.set(e,o));let n=o.get(r),i=this.gpuDataManager.registerExternalBuffer(t,s,n);return o.set(r,[i,t]),i}unregisterBuffers(e){let r=this.sessionExternalDataMapping.get(e);r&&(r.forEach(t=>this.gpuDataManager.unregisterExternalBuffer(t[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let r=this.gpuDataManager.get(e);if(!r)throw new Error(`no GPU data for buffer: ${e}`);return r.buffer}createDownloader(e,r,t){return async()=>{let s=await sl(this,e,r);return Ha(s.buffer,t)}}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(){It("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(){It("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){It("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),t=e.length;this.pendingKernels=[];for(let s=0;s<t;s++){let o=this.getComputePassEncoder(),n=e[s];this.writeTimestamp(this.pendingDispatchNumber*2),o.setPipeline(n.computePipeline),o.setBindGroup(0,n.bindGroup),o.dispatchWorkgroups(...n.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(r[s]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),Tg={};Rn(Tg,{init:()=>Pg});var bi,Eg,Pg,yx=je(()=>{ft(),Bs(),yt(),$v(),bi=class W0{constructor(r,t,s,o){this.module=r,this.dataType=t,this.data=s,this.dims=o}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,r)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,r)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,r)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,r)}reshape(r){if(Me.size(r)!==Me.size(this.dims))throw new Error("Invalid new shape");return new W0(this.module,this.dataType,this.data,r)}},Eg=class{constructor(e,r,t){this.module=e,this.backend=r,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=r.adapterInfo;let s=e.PTR_SIZE,o=t/e.PTR_SIZE,n=s===4?"i32":"i64";this.opKernelContext=Number(e.getValue(s*o++,n));let i=Number(e.getValue(s*o++,n));this.outputCount=Number(e.getValue(s*o++,n)),this.customDataOffset=Number(e.getValue(s*o++,"*")),this.customDataSize=Number(e.getValue(s*o++,n));let a=[];for(let l=0;l<i;l++){let u=Number(e.getValue(s*o++,n)),p=Number(e.getValue(s*o++,"*")),c=Number(e.getValue(s*o++,n)),d=[];for(let _=0;_<c;_++)d.push(Number(e.getValue(s*o++,n)));a.push(new bi(e,u,p,d))}this.inputs=a}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,r){var i;let t=((i=r==null?void 0:r.inputs)==null?void 0:i.map(a=>typeof a=="number"?this.inputs[a]:a))??this.inputs,s=(r==null?void 0:r.outputs)??[],o=(a,l,u)=>new bi(this.module,l,this.output(a,u),u),n=(a,l)=>{let u=on(a,l);if(!u)throw new Error(`Unsupported data type: ${a}`);let p=u>0?this.backend.gpuDataManager.create(u).id:0;return new bi(this.module,a,p,l)};return this.backend.run(e,t,s,o,n,this.outputCount)}output(e,r){let t=this.module.stackSave();try{let s=this.module.PTR_SIZE,o=s===4?"i32":"i64",n=this.module.stackAlloc((1+r.length)*s);this.module.setValue(n,r.length,o);for(let i=0;i<r.length;i++)this.module.setValue(n+s*(i+1),r[i],o);return this.module._JsepOutput(this.opKernelContext,e,n)}catch(s){throw new Error(`Failed to generate kernel's output[${e}] with dims [${r}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${s}`)}finally{this.module.stackRestore(t)}}},Pg=async(e,r,t,s)=>{let o=r.jsepInit;if(!o)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(e==="webgpu"){let n=(bx(),io(Mg)).WebGpuBackend,i=new n;await i.initialize(t,s),o("webgpu",[i,a=>i.alloc(Number(a)),a=>i.free(a),(a,l,u,p=!1)=>{if(p)It("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${Number(a)}, dst=${Number(l)}, size=${Number(u)}`),i.memcpy(Number(a),Number(l));else{It("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${Number(a)}, gpuDataId=${Number(l)}, size=${Number(u)}`);let c=r.HEAPU8.subarray(Number(a>>>0),Number(a>>>0)+Number(u));i.upload(Number(l),c)}},async(a,l,u)=>{It("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${a}, dataOffset=${l}, size=${u}`),await i.download(Number(a),()=>r.HEAPU8.subarray(Number(l)>>>0,Number(l+u)>>>0))},(a,l,u)=>i.createKernel(a,Number(l),u,r.UTF8ToString(r._JsepGetNodeName(Number(l)))),a=>i.releaseKernel(a),(a,l,u,p)=>{It("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${u}, kernel=${a}, contextDataOffset=${l}`);let c=new Eg(r,i,Number(l));return i.computeKernel(Number(a),c,p)},()=>i.captureBegin(),()=>i.captureEnd(),()=>i.replay()])}else{let n=new Id(t);o("webnn",[n,()=>n.reserveTensorId(),i=>n.releaseTensorId(i),async(i,a,l,u,p)=>n.ensureTensor(i,a,l,u,p),(i,a)=>{n.uploadTensor(i,a)},async(i,a)=>n.downloadTensor(i,a)])}}}),Cg,Yl,Zl,Gs,Sg,eu,yi,tu,ru,su,nu,ou,iu,$g=je(()=>{Pv(),Cv(),ft(),nn(),ja(),fd(),Cg=(e,r)=>{Qt()._OrtInit(e,r)!==0&&Gt("Can't initialize onnxruntime.")},Yl=async e=>{Cg(e.wasm.numThreads,ii(e.logLevel))},Zl=async(e,r)=>{var t,s;(s=(t=Qt()).asyncInit)==null||s.call(t);{let o=(yx(),io(Tg)).init;if(r==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let n=e.webgpu.adapter;if(n){if(typeof n.limits!="object"||typeof n.features!="object"||typeof n.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let i=e.webgpu.powerPreference;if(i!==void 0&&i!=="low-power"&&i!=="high-performance")throw new Error(`Invalid powerPreference setting: "${i}"`);let a=e.webgpu.forceFallbackAdapter;if(a!==void 0&&typeof a!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${a}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:i,forceFallbackAdapter:a}),!n)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.')}await o("webgpu",Qt(),e,n)}if(r==="webnn"){if(typeof navigator>"u"||!navigator.ml)throw new Error("WebNN is not supported in current environment");await o("webnn",Qt(),e)}}},Gs=new Map,Sg=e=>{let r=Qt(),t=r.stackSave();try{let s=r.PTR_SIZE,o=r.stackAlloc(2*s);r._OrtGetInputOutputCount(e,o,o+s)!==0&&Gt("Can't get session input/output count.");let n=s===4?"i32":"i64";return[Number(r.getValue(o,n)),Number(r.getValue(o+s,n))]}finally{r.stackRestore(t)}},eu=(e,r)=>{let t=Qt(),s=t.stackSave(),o=0;try{let n=t.PTR_SIZE,i=t.stackAlloc(2*n);t._OrtGetInputOutputMetadata(e,r,i,i+n)!==0&&Gt("Can't get session input/output metadata.");let a=Number(t.getValue(i,"*"));o=Number(t.getValue(i+n,"*"));let l=t.HEAP32[o/4];if(l===0)return[a,0];let u=t.HEAPU32[o/4+1],p=[];for(let c=0;c<u;c++){let d=Number(t.getValue(o+8+c*n,"*"));p.push(d!==0?t.UTF8ToString(d):Number(t.getValue(o+8+(c+u)*n,"*")))}return[a,l,p]}finally{t.stackRestore(s),o!==0&&t._OrtFree(o)}},yi=e=>{let r=Qt(),t=r._malloc(e.byteLength);if(t===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${e.byteLength}.`);return r.HEAPU8.set(e,t),[t,e.byteLength]},tu=async(e,r)=>{var c,d,_,f;let t,s,o=Qt();Array.isArray(e)?[t,s]=e:e.buffer===o.HEAPU8.buffer?[t,s]=[e.byteOffset,e.byteLength]:[t,s]=yi(e);let n=0,i=0,a=0,l=[],u=[],p=[];try{if([i,l]=await md(r),(r==null?void 0:r.externalData)&&o.mountExternalData){let C=[];for(let F of r.externalData){let z=typeof F=="string"?F:F.path;C.push(Ga(typeof F=="string"?F:F.data).then(K=>{o.mountExternalData(z,K)}))}await Promise.all(C)}for(let C of(r==null?void 0:r.executionProviders)??[])if((typeof C=="string"?C:C.name)==="webnn"){if(o.shouldTransferToMLTensor=!1,typeof C!="string"){let F=C,z=F==null?void 0:F.context,K=F==null?void 0:F.gpuDevice,q=F==null?void 0:F.deviceType,R=F==null?void 0:F.powerPreference;z?o.currentContext=z:K?o.currentContext=await o.webnnCreateMLContext(K):o.currentContext=await o.webnnCreateMLContext({deviceType:q,powerPreference:R})}else o.currentContext=await o.webnnCreateMLContext();break}n=await o._OrtCreateSession(t,s,i),(c=o.webgpuOnCreateSession)==null||c.call(o,n),n===0&&Gt("Can't create a session."),(d=o.jsepOnCreateSession)==null||d.call(o),o.currentContext&&(o.webnnRegisterMLContext(n,o.currentContext),o.currentContext=void 0,o.shouldTransferToMLTensor=!0);let[T,k]=Sg(n),w=!!(r!=null&&r.enableGraphCapture),g=[],S=[],E=[],v=[],M=[];for(let C=0;C<T;C++){let[F,z,K]=eu(n,C);F===0&&Gt("Can't get an input name."),u.push(F);let q=o.UTF8ToString(F);g.push(q),E.push(z===0?{name:q,isTensor:!1}:{name:q,isTensor:!0,type:zs(z),shape:K})}for(let C=0;C<k;C++){let[F,z,K]=eu(n,C+T);F===0&&Gt("Can't get an output name."),p.push(F);let q=o.UTF8ToString(F);S.push(q),v.push(z===0?{name:q,isTensor:!1}:{name:q,isTensor:!0,type:zs(z),shape:K});{if(w&&(r==null?void 0:r.preferredOutputLocation)===void 0){M.push("gpu-buffer");continue}let R=typeof(r==null?void 0:r.preferredOutputLocation)=="string"?r.preferredOutputLocation:((_=r==null?void 0:r.preferredOutputLocation)==null?void 0:_[q])??"cpu";if(R!=="cpu"&&R!=="cpu-pinned"&&R!=="gpu-buffer"&&R!=="ml-tensor")throw new Error(`Not supported preferred output location: ${R}.`);if(w&&R!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${R}. 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Reset it to \`false\` and ignore SIMD feature checking.`),Xt.wasm.simd=!1),typeof Xt.wasm.proxy!="boolean"&&(Xt.wasm.proxy=!1),typeof Xt.wasm.trace!="boolean"&&(Xt.wasm.trace=!1),typeof Xt.wasm.numThreads!="number"||!Number.isInteger(Xt.wasm.numThreads)||Xt.wasm.numThreads<=0)if(typeof self<"u"&&!self.crossOriginIsolated)Xt.wasm.numThreads=1;else{let r=typeof navigator>"u"?uv("node:os").cpus().length:navigator.hardwareConcurrency;Xt.wasm.numThreads=Math.min(4,Math.ceil((r||1)/2))}},cu=class{async init(e){uu(),await Ig(),await Ag(e)}async createInferenceSessionHandler(e,r){let t=new jg;return await t.loadModel(e,r),t}},Vg=new cu});ms(),ms(),ms();var Tx="1.22.0-dev.20250409-89f8206ba4",Ex=Hc;{let e=(xx(),io(Ng)).wasmBackend;rn("webgpu",e,5),rn("webnn",e,5),rn("cpu",e,10),rn("wasm",e,10)}Object.defineProperty(Xt.versions,"web",{value:Tx,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.
 * =============================================================================
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s.Callable{constructor(M){super(),this.image_mean=M.image_mean??M.mean,this.image_std=M.image_std??M.std,this.resample=M.resample??2,this.do_rescale=M.do_rescale??!0,this.rescale_factor=M.rescale_factor??1/255,this.do_normalize=M.do_normalize,this.do_thumbnail=M.do_thumbnail,this.size=M.size??M.image_size,this.do_resize=M.do_resize??this.size!==void 0,this.size_divisibility=M.size_divisibility??M.size_divisor,this.do_center_crop=M.do_center_crop,this.crop_size=M.crop_size,this.do_convert_rgb=M.do_convert_rgb??!0,this.do_crop_margin=M.do_crop_margin,this.pad_size=M.pad_size,this.do_pad=M.do_pad,this.min_pixels=M.min_pixels,this.max_pixels=M.max_pixels,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=M.do_flip_channel_order??!1,this.config=M}async thumbnail(M,y,C=2){const F=M.height,z=M.width,K=y.height,q=y.width;let R=Math.min(F,K),Z=Math.min(z,q);return R===F&&Z===z?M:(F>z?Z=Math.floor(z*R/F):z>F&&(R=Math.floor(F*Z/z)),await M.resize(Z,R,{resample:C}))}async crop_margin(M,y=200){const C=M.clone().grayscale(),F=(0,n.min)(C.data)[0],K=(0,n.max)(C.data)[0]-F;if(K===0)return M;const q=y/255;let R=C.width,Z=C.height,H=0,J=0;const Q=C.data;for(let se=0;se<C.height;++se){const fe=se*C.width;for(let ae=0;ae<C.width;++ae)(Q[fe+ae]-F)/K<q&&(R=Math.min(R,ae),Z=Math.min(Z,se),H=Math.max(H,ae),J=Math.max(J,se))}return M=await M.crop([R,Z,H,J]),M}pad_image(M,y,C,{mode:F="constant",center:z=!1,constant_values:K=0}={}){const[q,R,Z]=y;let H,J;if(typeof C=="number"?(H=C,J=C):C==="square"?H=J=Math.max(q,R):(H=C.width,J=C.height),H!==R||J!==q){const Q=new Float32Array(H*J*Z);if(Array.isArray(K))for(let ae=0;ae<Q.length;++ae)Q[ae]=K[ae%Z];else K!==0&&Q.fill(K);const[se,fe]=z?[Math.floor((H-R)/2),Math.floor((J-q)/2)]:[0,0];for(let ae=0;ae<q;++ae){const V=(ae+fe)*H,A=ae*R;for(let U=0;U<R;++U){const ee=(V+U+se)*Z,_e=(A+U)*Z;for(let le=0;le<Z;++le)Q[ee+le]=M[_e+le]}}if(F==="symmetric"){if(z)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const ae=q-1,V=R-1;for(let A=0;A<J;++A){const U=A*H,ee=(0,i.calculateReflectOffset)(A,ae)*R;for(let _e=0;_e<H;++_e){if(A<q&&_e<R)continue;const le=(U+_e)*Z,ye=(ee+(0,i.calculateReflectOffset)(_e,V))*Z;for(let ze=0;ze<Z;++ze)Q[le+ze]=M[ye+ze]}}}M=Q,y=[J,H,Z]}return[M,y]}rescale(M){for(let y=0;y<M.length;++y)M[y]=this.rescale_factor*M[y]}get_resize_output_image_size(M,y){const[C,F]=M.size;let z,K;if(this.do_thumbnail){const{height:q,width:R}=y;z=Math.min(q,R)}else Number.isInteger(y)?(z=y,K=this.config.max_size??z):y!==void 0&&(z=y.shortest_edge,K=y.longest_edge);if(z!==void 0||K!==void 0){const q=z===void 0?1:Math.max(z/C,z/F),R=C*q,Z=F*q,H=K===void 0?1:Math.min(K/R,K/Z);let J=Math.floor(Number((R*H).toFixed(2))),Q=Math.floor(Number((Z*H).toFixed(2)));return this.size_divisibility!==void 0&&([J,Q]=p([J,Q],this.size_divisibility)),[J,Q]}else if(y!==void 0&&y.width!==void 0&&y.height!==void 0){let q=y.width,R=y.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Z=R/F,H=q/C;Math.abs(1-H)<Math.abs(1-Z)?Z=H:H=Z,R=u(Z*F,this.config.ensure_multiple_of),q=u(H*C,this.config.ensure_multiple_of)}return[q,R]}else{if(this.size_divisibility!==void 0)return p([C,F],this.size_divisibility);if(this.min_pixels!==void 0&&this.max_pixels!==void 0){const q=this.config.patch_size*this.config.merge_size;return w(F,C,q,this.min_pixels,this.max_pixels)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(y)}`)}}async resize(M){const[y,C]=this.get_resize_output_image_size(M,this.size);return await M.resize(y,C,{resample:this.resample})}async preprocess(M,{do_normalize:y=null,do_pad:C=null,do_convert_rgb:F=null,do_convert_grayscale:z=null,do_flip_channel_order:K=null}={}){this.do_crop_margin&&(M=await this.crop_margin(M));const[q,R]=M.size;if(F??this.do_convert_rgb?M=M.rgb():z&&(M=M.grayscale()),this.do_resize&&(M=await this.resize(M)),this.do_thumbnail&&(M=await this.thumbnail(M,this.size,this.resample)),this.do_center_crop){let se,fe;Number.isInteger(this.crop_size)?(se=this.crop_size,fe=this.crop_size):(se=this.crop_size.width,fe=this.crop_size.height),M=await M.center_crop(se,fe)}const Z=[M.height,M.width];let H=Float32Array.from(M.data),J=[M.height,M.width,M.channels];if(this.do_rescale&&this.rescale(H),y??this.do_normalize){let se=this.image_mean;Array.isArray(this.image_mean)||(se=new Array(M.channels).fill(se));let fe=this.image_std;if(Array.isArray(this.image_std)||(fe=new Array(M.channels).fill(se)),se.length!==M.channels||fe.length!==M.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${se.length}) and \`image_std\` (${fe.length}) must match the number of channels in the image (${M.channels}).`);for(let ae=0;ae<H.length;ae+=M.channels)for(let V=0;V<M.channels;++V)H[ae+V]=(H[ae+V]-se[V])/fe[V]}if(C??this.do_pad){if(this.pad_size)[H,J]=this.pad_image(H,[M.height,M.width,M.channels],this.pad_size);else if(this.size_divisibility){const[se,fe]=p([J[1],J[0]],this.size_divisibility);[H,J]=this.pad_image(H,J,{width:se,height:fe})}}if(K??this.do_flip_channel_order){if(J[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let se=0;se<H.length;se+=3){const fe=H[se];H[se]=H[se+2],H[se+2]=fe}}const Q=new o.Tensor("float32",H,J).permute(2,0,1);return{original_size:[R,q],reshaped_input_size:Z,pixel_values:Q}}async _call(M,...y){Array.isArray(M)||(M=[M]);const C=await Promise.all(M.map(z=>this.preprocess(z)));return{pixel_values:(0,o.stack)(C.map(z=>z.pixel_values),0),original_sizes:C.map(z=>z.original_size),reshaped_input_sizes:C.map(z=>z.reshaped_input_size)}}static async from_pretrained(M,y){const C=await(0,a.getModelJSON)(M,l.IMAGE_PROCESSOR_NAME,!0,y);return new 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n{constructor(M,y,C){super(),this.prompt_length_to_skip=M,this.min_new_tokens=y,this.eos_token_id=Array.isArray(C)?C:[C]}_call(M,y){for(let C=0;C<M.length;++C)if(M[C].length-this.prompt_length_to_skip<this.min_new_tokens){const z=y[C].data;for(const K of this.eos_token_id)z[K]=-1/0}return y}}class k extends n{constructor(M,y){super(),this.bad_words_ids=M,this.eos_token_id=Array.isArray(y)?y:[y]}_call(M,y){for(let C=0;C<M.length;++C){const F=y[C].data,z=M[C];for(const K of this.bad_words_ids){if(z.length<K.length-1)continue;let q=!0;for(let R=1;R<=K.length-1;++R)if(K.at(-R-1)!=z.at(-R)){q=!1;break}q&&(F[K.at(-1)]=-1/0)}}return y}}class w extends n{constructor(M){if(super(),M<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${M}.`);this.guidance_scale=M}_call(M,y){if(y.dims[0]!==2*M.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 ${y.dims[0]} for the logits and ${M.length} for the input ids.`);const C=M.length,F=y.slice([0,C],null),z=y.slice([C,y.dims[0]],null);for(let K=0;K<z.data.length;++K)z.data[K]+=(F.data[K]-z.data[K])*this.guidance_scale;return z}}class g extends i{constructor(M){super(),this.temperature=M}_call(M,y){const C=y.data;for(let F=0;F<C.length;++F)C[F]/=this.temperature;return y}}class S extends i{constructor(M,{filter_value:y=-1/0,min_tokens_to_keep:C=1}={}){if(super(),M<0||M>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${M}`);if(!Number.isInteger(C)||C<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${C}`);this.top_p=M,this.filter_value=y,this.min_tokens_to_keep=C}}class E extends i{constructor(M,{filter_value:y=-1/0,min_tokens_to_keep:C=1}={}){if(super(),!Number.isInteger(M)||M<0)throw new Error(`\`top_k\` must be a positive integer, but is ${M}`);this.top_k=Math.max(M,C),this.filter_value=y}}},"./src/generation/logits_sampler.js":(e,r,t)=>{t.r(r),t.d(r,{LogitsSampler:()=>i});var s=t("./src/utils/generic.js"),o=t("./src/utils/tensor.js"),n=t("./src/utils/maths.js");t("./src/generation/configuration_utils.js");class i extends s.Callable{constructor(c){super(),this.generation_config=c}async _call(c){return this.sample(c)}async sample(c){throw Error("sample should be implemented in subclasses.")}getLogits(c,d){let _=c.dims.at(-1),f=c.data;if(d===-1)f=f.slice(-_);else{let T=d*_;f=f.slice(T,T+_)}return f}randomSelect(c){let d=0;for(let f=0;f<c.length;++f)d+=c[f];let _=Math.random()*d;for(let f=0;f<c.length;++f)if(_-=c[f],_<=0)return f;return 0}static getSampler(c){if(c.do_sample)return new l(c);if(c.num_beams>1)return new u(c);if(c.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${c.num_return_sequences}.`);return new a(c)}}class a extends i{async sample(c){const d=(0,n.max)(c.data)[1];return[[BigInt(d),0]]}}class l extends i{async sample(c){let d=c.dims.at(-1);this.generation_config.top_k>0&&(d=Math.min(this.generation_config.top_k,d));const[_,f]=await(0,o.topk)(c,d),T=(0,n.softmax)(_.data);return Array.from({length:this.generation_config.num_beams},()=>{const k=this.randomSelect(T);return[f.data[k],Math.log(T[k])]})}}class u extends i{async sample(c){let d=c.dims.at(-1);this.generation_config.top_k>0&&(d=Math.min(this.generation_config.top_k,d));const[_,f]=await(0,o.topk)(c,d),T=(0,n.softmax)(_.data);return Array.from({length:this.generation_config.num_beams},(k,w)=>[f.data[w],Math.log(T[w])])}}},"./src/generation/stopping_criteria.js":(e,r,t)=>{t.r(r),t.d(r,{EosTokenCriteria:()=>a,InterruptableStoppingCriteria:()=>l,MaxLengthCriteria:()=>i,StoppingCriteria:()=>o,StoppingCriteriaList:()=>n});var s=t("./src/utils/generic.js");class o extends s.Callable{_call(p,c){throw Error("StoppingCriteria needs to be subclassed")}}class n extends s.Callable{constructor(){super(),this.criteria=[]}push(p){this.criteria.push(p)}extend(p){p instanceof n?p=p.criteria:p instanceof o&&(p=[p]),this.criteria.push(...p)}_call(p,c){const d=new Array(p.length).fill(!1);for(const _ of this.criteria){const f=_(p,c);for(let T=0;T<d.length;++T)d[T]||(d[T]=f[T])}return d}[Symbol.iterator](){return this.criteria.values()}}class i extends o{constructor(p,c=null){super(),this.max_length=p,this.max_position_embeddings=c}_call(p){return p.map(c=>c.length>=this.max_length)}}class a extends o{constructor(p){super(),Array.isArray(p)||(p=[p]),this.eos_token_id=p}_call(p,c){return p.map(d=>{const _=d.at(-1);return this.eos_token_id.some(f=>_==f)})}}class l extends o{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(p,c){return new Array(p.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(e,r,t)=>{t.r(r),t.d(r,{BaseStreamer:()=>i,TextStreamer:()=>l,WhisperTextStreamer:()=>u});var s=t("./src/utils/core.js"),o=t("./src/tokenizers.js"),n=t("./src/env.js");class i{put(c){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const a=n.apis.IS_PROCESS_AVAILABLE?p=>process.stdout.write(p):p=>console.log(p);class l extends i{constructor(c,{skip_prompt:d=!1,callback_function:_=null,token_callback_function:f=null,skip_special_tokens:T=!0,decode_kwargs:k={},...w}={}){super(),this.tokenizer=c,this.skip_prompt=d,this.callback_function=_??a,this.token_callback_function=f,this.decode_kwargs={skip_special_tokens:T,...k,...w},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(c){var k;if(c.length>1)throw Error("TextStreamer only supports batch size of 1");const d=this.next_tokens_are_prompt;if(d&&(this.next_tokens_are_prompt=!1,this.skip_prompt))return;const _=c[0];(k=this.token_callback_function)==null||k.call(this,_),this.token_cache=(0,s.mergeArrays)(this.token_cache,_);const f=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let T;d||f.endsWith(`
`)?(T=f.slice(this.print_len),this.token_cache=[],this.print_len=0):f.length>0&&(0,o.is_chinese_char)(f.charCodeAt(f.length-1))?(T=f.slice(this.print_len),this.print_len+=T.length):(T=f.slice(this.print_len,f.lastIndexOf(" ")+1),this.print_len+=T.length),this.on_finalized_text(T,!1)}end(){let c;this.token_cache.length>0?(c=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):c="",this.next_tokens_are_prompt=!0,this.on_finalized_text(c,!0)}on_finalized_text(c,d){var _,f;c.length>0&&((_=this.callback_function)==null||_.call(this,c)),d&&this.callback_function===a&&n.apis.IS_PROCESS_AVAILABLE&&((f=this.callback_function)==null||f.call(this,`
`))}}class u extends l{constructor(c,{skip_prompt:d=!1,callback_function:_=null,token_callback_function:f=null,on_chunk_start:T=null,on_chunk_end:k=null,on_finalize:w=null,time_precision:g=.02,skip_special_tokens:S=!0,decode_kwargs:E={}}={}){super(c,{skip_prompt:d,skip_special_tokens:S,callback_function:_,token_callback_function:f,decode_kwargs:E}),this.timestamp_begin=c.timestamp_begin,this.on_chunk_start=T,this.on_chunk_end=k,this.on_finalize=w,this.time_precision=g,this.waiting_for_timestamp=!1}put(c){var _,f;if(c.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const d=c[0];if(d.length===1){const T=Number(d[0])-this.timestamp_begin;if(T>=0){const k=T*this.time_precision;this.waiting_for_timestamp?(_=this.on_chunk_end)==null||_.call(this,k):(f=this.on_chunk_start)==null||f.call(this,k),this.waiting_for_timestamp=!this.waiting_for_timestamp,c=[[]]}}return super.put(c)}end(){var 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E.Musicgen:this.can_generate=!0,this._forward=Q,this._prepare_inputs_for_generation=pe;break;case E.EncoderDecoder:this._forward=Q;break;case E.ImageTextToText:this.can_generate=!0,this._forward=le,this._prepare_inputs_for_generation=W;break;case E.AudioTextToText:this.can_generate=!0,this._forward=_e,this._prepare_inputs_for_generation=W;break;case E.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=W;break;case E.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=re;break;case E.AutoEncoder:this._forward=fe;break;default:this._forward=se;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var ne;const O=[];for(const ge of Object.values(this.sessions))(ne=ge==null?void 0:ge.handler)!=null&&ne.dispose&&O.push(ge.handler.dispose());return await Promise.all(O)}static async from_pretrained(O,{progress_callback:ne=null,config:ge=null,cache_dir:he=null,local_files_only:Ee=!1,revision:De="main",model_file_name:Ne=null,subfolder:Xe="onnx",device:mt=null,dtype:wt=null,use_external_data_format:dt=null,session_options:Pt={}}={}){let gt={progress_callback:ne,config:ge,cache_dir:he,local_files_only:Ee,revision:De,model_file_name:Ne,subfolder:Xe,device:mt,dtype:wt,use_external_data_format:dt,session_options:Pt};const Et=y.get(this),ot=v.get(Et);ge=gt.config=await s.AutoConfig.from_pretrained(O,gt);let $t;if(ot===E.DecoderOnly)$t=await Promise.all([F(O,{model:gt.model_file_name??"model"},gt),z(O,{generation_config:"generation_config.json"},gt)]);else if(ot===E.Seq2Seq||ot===E.Vision2Seq)$t=await Promise.all([F(O,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},gt),z(O,{generation_config:"generation_config.json"},gt)]);else if(ot===E.MaskGeneration)$t=await Promise.all([F(O,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},gt)]);else if(ot===E.EncoderDecoder)$t=await Promise.all([F(O,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},gt)]);else if(ot===E.ImageTextToText){const qt={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};ge.is_encoder_decoder&&(qt.model="encoder_model"),$t=await Promise.all([F(O,qt,gt),z(O,{generation_config:"generation_config.json"},gt)])}else if(ot===E.AudioTextToText){const qt={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};$t=await Promise.all([F(O,qt,gt),z(O,{generation_config:"generation_config.json"},gt)])}else if(ot===E.Musicgen)$t=await Promise.all([F(O,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},gt),z(O,{generation_config:"generation_config.json"},gt)]);else if(ot===E.MultiModality)$t=await Promise.all([F(O,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},gt),z(O,{generation_config:"generation_config.json"},gt)]);else if(ot===E.Phi3V)$t=await Promise.all([F(O,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},gt),z(O,{generation_config:"generation_config.json"},gt)]);else if(ot===E.AutoEncoder)$t=await Promise.all([F(O,{encoder_model:"encoder_model",decoder_model:"decoder_model"},gt)]);else{if(ot!==E.EncoderOnly){const qt=Et??(ge==null?void 0:ge.model_type);qt!=="custom"&&console.warn(`Model type for '${qt}' not found, assuming encoder-only architecture. Please report this at ${u.GITHUB_ISSUE_URL}.`)}$t=await Promise.all([F(O,{model:gt.model_file_name??"model"},gt)])}return new this(ge,...$t)}async _call(O){return await this.forward(O)}async forward(O){return await this._forward(this,O)}get generation_config(){var O;return((O=this.configs)==null?void 0:O.generation_config)??null}_get_logits_warper(O){const ne=new p.LogitsProcessorList;return O.temperature!==null&&O.temperature!==1&&ne.push(new p.TemperatureLogitsWarper(O.temperature)),O.top_k!==null&&O.top_k!==0&&ne.push(new p.TopKLogitsWarper(O.top_k)),O.top_p!==null&&O.top_p<1&&ne.push(new p.TopPLogitsWarper(O.top_p)),ne}_get_logits_processor(O,ne,ge=null){const he=new p.LogitsProcessorList;if(O.repetition_penalty!==null&&O.repetition_penalty!==1&&he.push(new p.RepetitionPenaltyLogitsProcessor(O.repetition_penalty)),O.no_repeat_ngram_size!==null&&O.no_repeat_ngram_size>0&&he.push(new p.NoRepeatNGramLogitsProcessor(O.no_repeat_ngram_size)),O.bad_words_ids!==null&&he.push(new p.NoBadWordsLogitsProcessor(O.bad_words_ids,O.eos_token_id)),O.min_length!==null&&O.eos_token_id!==null&&O.min_length>0&&he.push(new p.MinLengthLogitsProcessor(O.min_length,O.eos_token_id)),O.min_new_tokens!==null&&O.eos_token_id!==null&&O.min_new_tokens>0&&he.push(new p.MinNewTokensLengthLogitsProcessor(ne,O.min_new_tokens,O.eos_token_id)),O.forced_bos_token_id!==null&&he.push(new p.ForcedBOSTokenLogitsProcessor(O.forced_bos_token_id)),O.forced_eos_token_id!==null&&he.push(new p.ForcedEOSTokenLogitsProcessor(O.max_length,O.forced_eos_token_id)),O.begin_suppress_tokens!==null){const Ee=ne>1||O.forced_bos_token_id===null?ne:ne+1;he.push(new p.SuppressTokensAtBeginLogitsProcessor(O.begin_suppress_tokens,Ee))}return O.guidance_scale!==null&&O.guidance_scale>1&&he.push(new p.ClassifierFreeGuidanceLogitsProcessor(O.guidance_scale)),ge!==null&&he.extend(ge),he}_prepare_generation_config(O,ne,ge=c.GenerationConfig){const he={...this.config};for(const De of["decoder","generator","text_config"])De in he&&Object.assign(he,he[De]);const Ee=new ge(he);return Object.assign(Ee,this.generation_config??{}),O&&Object.assign(Ee,O),ne&&Object.assign(Ee,(0,a.pick)(ne,Object.getOwnPropertyNames(Ee))),Ee}_get_stopping_criteria(O,ne=null){const ge=new T.StoppingCriteriaList;return O.max_length!==null&&ge.push(new T.MaxLengthCriteria(O.max_length,this.config.max_position_embeddings??null)),O.eos_token_id!==null&&ge.push(new T.EosTokenCriteria(O.eos_token_id)),ne&&ge.extend(ne),ge}_validate_model_class(){if(!this.can_generate){const O=[_c,gc,fc,mc],ne=y.get(this.constructor),ge=new Set,he=this.config.model_type;for(const De of O){const Ne=De.get(he);Ne&&ge.add(Ne[0])}let Ee=`The current model class (${ne}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw ge.size>0&&(Ee+=` Please use the following class instead: ${[...ge].join(", ")}`),Error(Ee)}}prepare_inputs_for_generation(...O){return this._prepare_inputs_for_generation(this,...O)}_update_model_kwargs_for_generation({generated_input_ids:O,outputs:ne,model_inputs:ge,is_encoder_decoder:he}){return ge.past_key_values=this.getPastKeyValues(ne,ge.past_key_values),ge.input_ids=new d.Tensor("int64",O.flat(),[O.length,1]),he||(ge.attention_mask=(0,d.cat)([ge.attention_mask,(0,d.ones)([ge.attention_mask.dims[0],1])],1)),ge.position_ids=null,ge}_prepare_model_inputs({inputs:O,bos_token_id:ne,model_kwargs:ge}){const he=(0,a.pick)(ge,this.forward_params),Ee=this.main_input_name;if(Ee in he){if(O)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else he[Ee]=O;return{inputs_tensor:he[Ee],model_inputs:he,model_input_name:Ee}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:O,model_inputs:ne,model_input_name:ge,generation_config:he}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!ne.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:De,pixel_values:Ne,attention_mask:Xe,...mt}=ne,wt=await this._prepare_inputs_embeds(ne);ne={...mt,...(0,a.pick)(wt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ee}=await se(this,ne);if(he.guidance_scale!==null&&he.guidance_scale>1)Ee=(0,d.cat)([Ee,(0,d.full_like)(Ee,0)],0),"attention_mask"in ne&&(ne.attention_mask=(0,d.cat)([ne.attention_mask,(0,d.zeros_like)(ne.attention_mask)],0));else if(ne.decoder_input_ids){const De=H(ne.decoder_input_ids).dims[0];if(De!==Ee.dims[0]){if(Ee.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ee.dims[0]}) than the decoder inputs (${De}).`);Ee=(0,d.cat)(Array.from({length:De},()=>Ee),0)}}return ne.encoder_outputs=Ee,ne}_prepare_decoder_input_ids_for_generation({batch_size:O,model_input_name:ne,model_kwargs:ge,decoder_start_token_id:he,bos_token_id:Ee,generation_config:De}){let{decoder_input_ids:Ne,...Xe}=ge;if(!(Ne instanceof d.Tensor)){if(Ne)Array.isArray(Ne[0])||(Ne=Array.from({length:O},()=>Ne));else if(he??(he=Ee),this.config.model_type==="musicgen")Ne=Array.from({length:O*this.config.decoder.num_codebooks},()=>[he]);else if(Array.isArray(he)){if(he.length!==O)throw new Error(`\`decoder_start_token_id\` expcted to have length ${O} but got ${he.length}`);Ne=he}else Ne=Array.from({length:O},()=>[he]);Ne=H(Ne)}return ge.decoder_attention_mask=(0,d.ones_like)(Ne),{input_ids:Ne,model_inputs:Xe}}async generate({inputs:O=null,generation_config:ne=null,logits_processor:ge=null,stopping_criteria:he=null,streamer:Ee=null,...De}){this._validate_model_class(),ne=this._prepare_generation_config(ne,De);let{inputs_tensor:Ne,model_inputs:Xe,model_input_name:mt}=this._prepare_model_inputs({inputs:O,model_kwargs:De});const wt=this.config.is_encoder_decoder;wt&&("encoder_outputs"in Xe||(Xe=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Ne,model_inputs:Xe,model_input_name:mt,generation_config:ne})));let dt;wt?{input_ids:dt,model_inputs:Xe}=this._prepare_decoder_input_ids_for_generation({batch_size:Xe[mt].dims.at(0),model_input_name:mt,model_kwargs:Xe,decoder_start_token_id:ne.decoder_start_token_id,bos_token_id:ne.bos_token_id,generation_config:ne}):dt=Xe[mt];let Pt=dt.dims.at(-1);ne.max_new_tokens!==null&&(ne.max_length=Pt+ne.max_new_tokens);const gt=this._get_logits_processor(ne,Pt,ge),Et=this._get_stopping_criteria(ne,he),ot=Xe[mt].dims.at(0),$t=k.LogitsSampler.getSampler(ne),qt=new Array(ot).fill(0),tr=dt.tolist();Ee&&Ee.put(tr);let lr,nr={};for(;;){if(Xe=this.prepare_inputs_for_generation(tr,Xe,ne),lr=await this.forward(Xe),ne.output_attentions&&ne.return_dict_in_generate){const Ur=this.getAttentions(lr);for(const cs in Ur)cs in nr||(nr[cs]=[]),nr[cs].push(Ur[cs])}const Ct=lr.logits.slice(null,-1,null),vr=gt(tr,Ct),Yr=[];for(let Ur=0;Ur<vr.dims.at(0);++Ur){const cs=vr[Ur],ca=await $t(cs);for(const[Fn,da]of ca){const pa=BigInt(Fn);qt[Ur]+=da,tr[Ur].push(pa),Yr.push([pa]);break}}if(Ee&&Ee.put(Yr),Et(tr).every(Ur=>Ur))break;Xe=this._update_model_kwargs_for_generation({generated_input_ids:Yr,outputs:lr,model_inputs:Xe,is_encoder_decoder:wt})}Ee&&Ee.end();const _r=this.getPastKeyValues(lr,Xe.past_key_values,!0),$r=new d.Tensor("int64",tr.flat(),[tr.length,tr[0].length]);if(ne.return_dict_in_generate)return{sequences:$r,past_key_values:_r,...nr};for(const Ct of Object.values(lr))Ct.location==="gpu-buffer"&&Ct.dispose();return $r}getPastKeyValues(O,ne,ge=!1){const he=Object.create(null);for(const Ee in O)if(Ee.startsWith("present")){const De=Ee.replace("present","past_key_values"),Ne=Ee.includes("encoder");if(Ne&&ne?he[De]=ne[De]:he[De]=O[Ee],ne&&(!Ne||ge)){const Xe=ne[De];Xe.location==="gpu-buffer"&&Xe.dispose()}}return he}getAttentions(O){const ne={};for(const ge of["cross_attentions","encoder_attentions","decoder_attentions"])for(const he in O)he.startsWith(ge)&&(ge in ne||(ne[ge]=[]),ne[ge].push(O[he]));return ne}addPastKeyValues(O,ne){var ge,he,Ee;if(ne)Object.assign(O,ne);else{const De=this.sessions.decoder_model_merged??this.sessions.model,Ne=((ge=De==null?void 0:De.config)==null?void 0:ge.kv_cache_dtype)??"float32",Xe=Ne==="float16"?new d.DataTypeMap.float16:[],mt=((Ee=(he=O[this.main_input_name]??O.attention_mask)==null?void 0:he.dims)==null?void 0:Ee[0])??1,wt=(0,s.getKeyValueShapes)(this.config,{batch_size:mt});for(const dt in wt)O[dt]=new d.Tensor(Ne,Xe,wt[dt])}}async encode_image({pixel_values:O}){const ne=(await R(this.sessions.vision_encoder,{pixel_values:O})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${ne.dims[1]}).`),this.config.num_image_tokens=ne.dims[1]),ne}async encode_text({input_ids:O}){return(await R(this.sessions.embed_tokens,{input_ids:O})).inputs_embeds}async encode_audio({audio_values:O}){return(await R(this.sessions.audio_encoder,{audio_values:O})).audio_features}}class be{}class we extends be{constructor({last_hidden_state:P,hidden_states:O=null,attentions:ne=null}){super(),this.last_hidden_state=P,this.hidden_states=O,this.attentions=ne}}class Se extends G{}class Ce extends Se{}class $e extends Se{async _call(P){return new Fr(await super._call(P))}}class Fe extends Se{async _call(P){return new xt(await super._call(P))}}class Be extends Se{async _call(P){return new Sr(await super._call(P))}}class He extends Se{async _call(P){return new Rr(await super._call(P))}}class qe extends G{}class ke extends qe{}class Ve extends qe{async _call(P){return new Fr(await super._call(P))}}class Ze extends qe{async _call(P){return new xt(await super._call(P))}}class nt extends qe{async _call(P){return new Sr(await super._call(P))}}class lt extends G{}class Ge extends lt{}class Ie extends G{}class pt extends Ie{}class St extends Ie{async _call(P){return new Fr(await super._call(P))}}class Vt extends Ie{async _call(P){return new xt(await super._call(P))}}class Rt extends Ie{async _call(P){return new Sr(await super._call(P))}}class gr extends Ie{async _call(P){return new Rr(await super._call(P))}}class ir extends G{}class Mt extends ir{}class rs extends ir{async _call(P){return new Fr(await super._call(P))}}class D extends ir{async _call(P){return new xt(await super._call(P))}}class oe extends ir{async _call(P){return new Sr(await super._call(P))}}class B extends ir{async _call(P){return new Rr(await super._call(P))}}class te extends G{}class me extends te{}class Oe extends te{async _call(P){return new Fr(await super._call(P))}}class ve extends te{async _call(P){return new xt(await super._call(P))}}class vt extends te{async _call(P){return new Sr(await super._call(P))}}class Ft extends te{async _call(P){return new Rr(await super._call(P))}}class ht extends G{}class ut extends ht{}class rt extends ht{async _call(P){return new Fr(await super._call(P))}}class jt extends ht{async _call(P){return new xt(await super._call(P))}}class Ht extends ht{async _call(P){return new Sr(await super._call(P))}}class wr extends ht{async _call(P){return new Rr(await super._call(P))}}class Jt extends G{}class Or extends Jt{}class ss extends Jt{async _call(P){return new Fr(await super._call(P))}}class ys extends Jt{async _call(P){return new xt(await super._call(P))}}class ns extends Jt{async _call(P){return new Sr(await super._call(P))}}class $s extends Jt{async _call(P){return new Rr(await super._call(P))}}class Vr extends G{}class ks extends Vr{}class Qr extends Vr{async _call(P){return new Fr(await super._call(P))}}class vs extends Vr{async _call(P){return new xt(await super._call(P))}}class Is extends Vr{async _call(P){return new Sr(await super._call(P))}}class As extends Vr{async _call(P){return new Rr(await super._call(P))}}class ar extends G{}class Fs extends ar{}class Er extends ar{async _call(P){return new xt(await super._call(P))}}class xs extends ar{async _call(P){return new Sr(await super._call(P))}}class Br extends ar{async _call(P){return new Rr(await super._call(P))}}class Ae extends ar{async _call(P){return new Fr(await super._call(P))}}class Je extends G{}class it extends Je{}class Nt extends Je{async _call(P){return new Fr(await super._call(P))}}class os extends Je{async _call(P){return new xt(await super._call(P))}}class is extends Je{async _call(P){return new Sr(await super._call(P))}}class ur extends G{}class as extends ur{}class cr extends ur{async _call(P){return new Fr(await super._call(P))}}class hr extends ur{async _call(P){return new xt(await super._call(P))}}class ls extends ur{async _call(P){return new Rr(await super._call(P))}}class Ts extends G{}class fn extends Ts{}class _n extends Ts{async _call(P){return new Fr(await super._call(P))}}class gn extends Ts{async _call(P){return new xt(await super._call(P))}}class wn extends Ts{async _call(P){return new Sr(await super._call(P))}}class Mn extends Ts{async _call(P){return new Rr(await super._call(P))}}class Os extends G{}class Hs extends Os{}class bn extends Os{async _call(P){return new Fr(await super._call(P))}}class yn extends Os{async _call(P){return new xt(await super._call(P))}}class vn extends Os{async _call(P){return new Rr(await super._call(P))}}class Ds extends G{}class de extends Ds{}class $ extends Ds{async _call(P){return new xt(await super._call(P))}}class j extends Ds{async _call(P){return new Rr(await super._call(P))}}class X extends Ds{async _call(P){return new Fr(await super._call(P))}}class ie extends G{constructor(){super(...arguments);Y(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class ce extends ie{}class xe extends ie{}class Re extends G{}class Qe extends Re{}class We extends Re{}class Ye extends G{}class _t extends Ye{}class Ot extends Ye{}class At extends G{}class Yt extends At{}class Ut extends At{}class mr extends At{async _call(P){return new xt(await super._call(P))}}class Mr extends G{}class Pr extends Mr{}class Cr extends Mr{}class Zt extends Mr{async _call(P){return new xt(await super._call(P))}}class Es extends Mr{}class Kt extends G{}class fr extends Kt{}class Dr extends Kt{}class Xr extends G{}class Jr extends Xr{}class Ir extends Xr{}class Lr extends G{}class br extends Lr{}class er extends Lr{async _call(P){return new Fr(await super._call(P))}}class dr extends Lr{async _call(P){return new xt(await super._call(P))}}class pr extends Lr{async _call(P){return new Sr(await super._call(P))}}class Ar extends Lr{async _call(P){return new Rr(await super._call(P))}}class us extends G{}class xn extends us{}class Ti extends us{async _call(P){return new Fr(await super._call(P))}}class Ei extends us{async _call(P){return new xt(await super._call(P))}}class Pi extends us{async _call(P){return new Sr(await super._call(P))}}class To extends us{async _call(P){return new Rr(await super._call(P))}}class qs extends G{}class Ci extends qs{}class Si extends qs{async _call(P){return new Fr(await super._call(P))}}class $i extends qs{async _call(P){return new xt(await super._call(P))}}class ki extends qs{async _call(P){return new Sr(await super._call(P))}}class Ii extends qs{async _call(P){return new Rr(await super._call(P))}}class Eo extends G{}class Ai extends Eo{}class Fi extends Eo{}class Po extends G{constructor(){super(...arguments);Y(this,"requires_attention_mask",!1);Y(this,"main_input_name","input_features");Y(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Oi extends Po{}class Co extends Po{_prepare_generation_config(P,O){return super._prepare_generation_config(P,O,g.WhisperGenerationConfig)}_retrieve_init_tokens(P){const O=[P.decoder_start_token_id];let ne=P.language;const ge=P.task;if(P.is_multilingual){ne||(console.warn("No language specified - defaulting to English (en)."),ne="en");const Ee=`<|${(0,S.whisper_language_to_code)(ne)}|>`;O.push(P.lang_to_id[Ee]),O.push(P.task_to_id[ge??"transcribe"])}else if(ne||ge)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!P.return_timestamps&&P.no_timestamps_token_id&&O.at(-1)!==P.no_timestamps_token_id?O.push(P.no_timestamps_token_id):P.return_timestamps&&O.at(-1)===P.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),O.pop()),O.filter(he=>he!=null)}async generate({inputs:P=null,generation_config:O=null,logits_processor:ne=null,stopping_criteria:ge=null,...he}){O=this._prepare_generation_config(O,he);const Ee=he.decoder_input_ids??this._retrieve_init_tokens(O);if(O.return_timestamps&&(ne??(ne=new p.LogitsProcessorList),ne.push(new p.WhisperTimeStampLogitsProcessor(O,Ee))),O.begin_suppress_tokens&&(ne??(ne=new p.LogitsProcessorList),ne.push(new p.SuppressTokensAtBeginLogitsProcessor(O.begin_suppress_tokens,Ee.length))),O.return_token_timestamps){if(!O.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.");O.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),O.output_attentions=!0,O.return_dict_in_generate=!0}const De=await super.generate({inputs:P,generation_config:O,logits_processor:ne,decoder_input_ids:Ee,...he});return O.return_token_timestamps&&(De.token_timestamps=this._extract_token_timestamps(De,O.alignment_heads,O.num_frames)),De}_extract_token_timestamps(P,O,ne=null,ge=.02){if(!P.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`.");ne==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 he=this.config.median_filter_width;he===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),he=7);const Ee=P.cross_attentions,De=Array.from({length:this.config.decoder_layers},(Et,ot)=>(0,d.cat)(Ee.map($t=>$t[ot]),2)),Ne=(0,d.stack)(O.map(([Et,ot])=>{if(Et>=De.length)throw new Error(`Layer index ${Et} is out of bounds for cross attentions (length ${De.length}).`);return ne?De[Et].slice(null,ot,null,[0,ne]):De[Et].slice(null,ot)})).transpose(1,0,2,3),[Xe,mt]=(0,d.std_mean)(Ne,-2,0,!0),wt=Ne.clone();for(let Et=0;Et<wt.dims[0];++Et){const ot=wt[Et];for(let $t=0;$t<ot.dims[0];++$t){const qt=ot[$t],tr=Xe[Et][$t][0].data,lr=mt[Et][$t][0].data;for(let nr=0;nr<qt.dims[0];++nr){let _r=qt[nr].data;for(let $r=0;$r<_r.length;++$r)_r[$r]=(_r[$r]-lr[$r])/tr[$r];_r.set((0,f.medianFilter)(_r,he))}}}const dt=[(0,d.mean)(wt,1)],Pt=P.sequences.dims,gt=new d.Tensor("float32",new Float32Array(Pt[0]*Pt[1]),Pt);for(let Et=0;Et<Pt[0];++Et){const ot=dt[Et].neg().squeeze_(0),[$t,qt]=(0,f.dynamic_time_warping)(ot.tolist()),tr=Array.from({length:$t.length-1},(_r,$r)=>$t[$r+1]-$t[$r]),lr=(0,a.mergeArrays)([1],tr).map(_r=>!!_r),nr=[];for(let _r=0;_r<lr.length;++_r)lr[_r]&&nr.push(qt[_r]*ge);gt[Et].data.set(nr,1)}return gt}}class So extends Co{}class Kn extends G{constructor(){super(...arguments);Y(this,"requires_attention_mask",!1);Y(this,"main_input_name","input_values");Y(this,"forward_params",["input_values","decoder_input_ids","past_key_values"])}}class Di extends Kn{}class Li extends Kn{}class $o extends G{constructor(){super(...arguments);Y(this,"main_input_name","pixel_values");Y(this,"forward_params",["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"])}}class ko extends G{constructor(){super(...arguments);Y(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class Tn extends ko{_merge_input_ids_with_image_features({inputs_embeds:P,image_features:O,input_ids:ne,attention_mask:ge}){const he=this.config.image_token_index,De=ne.tolist().map(dt=>dt.findIndex(Pt=>Pt==he)),Ne=De.every(dt=>dt===-1),Xe=De.every(dt=>dt!==-1);if(!Ne&&!Xe)throw new Error("Every input should contain either 0 or 1 image token.");if(Ne)return{inputs_embeds:P,attention_mask:ge};const mt=[],wt=[];for(let dt=0;dt<De.length;++dt){const Pt=De[dt],gt=P[dt],Et=O[dt],ot=ge[dt];mt.push((0,d.cat)([gt.slice([0,Pt]),Et,gt.slice([Pt+1,gt.dims[0]])],0)),wt.push((0,d.cat)([ot.slice([0,Pt]),(0,d.ones)([Et.dims[0]]),ot.slice([Pt+1,ot.dims[0]])],0))}return{inputs_embeds:(0,d.stack)(mt,0),attention_mask:(0,d.stack)(wt,0)}}}class Qs extends Tn{}class zi extends Tn{}class Io extends G{constructor(){super(...arguments);Y(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"]);Y(this,"main_input_name","inputs_embeds")}}class Bi extends Io{_merge_input_ids_with_image_features({inputs_embeds:P,image_features:O,input_ids:ne,attention_mask:ge}){return{inputs_embeds:(0,d.cat)([O,P],1),attention_mask:(0,d.cat)([(0,d.ones)(O.dims.slice(0,2)),ge],1)}}async _prepare_inputs_embeds({input_ids:P,pixel_values:O,inputs_embeds:ne,attention_mask:ge}){if(!P&&!O)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let he,Ee;return P&&(he=await this.encode_text({input_ids:P})),O&&(Ee=await this.encode_image({pixel_values:O})),he&&Ee?{inputs_embeds:ne,attention_mask:ge}=this._merge_input_ids_with_image_features({inputs_embeds:he,image_features:Ee,input_ids:P,attention_mask:ge}):ne=he||Ee,{inputs_embeds:ne,attention_mask:ge}}async forward({input_ids:P,pixel_values:O,attention_mask:ne,decoder_input_ids:ge,decoder_attention_mask:he,encoder_outputs:Ee,past_key_values:De,inputs_embeds:Ne,decoder_inputs_embeds:Xe}){if(Ne||({inputs_embeds:Ne,attention_mask:ne}=await this._prepare_inputs_embeds({input_ids:P,pixel_values:O,inputs_embeds:Ne,attention_mask:ne})),!Ee){let{last_hidden_state:dt}=await se(this,{inputs_embeds:Ne,attention_mask:ne});Ee=dt}if(!Xe){if(!ge)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");Xe=await this.encode_text({input_ids:ge})}return await ae(this,{inputs_embeds:Xe,attention_mask:he,encoder_attention_mask:ne,encoder_hidden_states:Ee,past_key_values:De},!0)}}class Ri extends G{constructor(){super(...arguments);Y(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class ji extends Ri{_merge_input_ids_with_image_features(P){const O=P.image_features.dims.at(-1),ne=P.image_features.view(-1,O);return A({image_token_id:this.config.image_token_index,...P,image_features:ne})}}class Hn extends G{constructor(){super(...arguments);Y(this,"forward_params",["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"])}}class En extends Hn{async encode_image({pixel_values:P,pixel_attention_mask:O}){return(await R(this.sessions.vision_encoder,{pixel_values:P,pixel_attention_mask:O})).image_features}_merge_input_ids_with_image_features(P){const O=P.image_features.dims.at(-1),ne=P.image_features.view(-1,O);return A({image_token_id:this.config.image_token_id,...P,image_features:ne})}}class Ao extends En{}class Ni extends G{constructor(){super(...arguments);Y(this,"forward_params",["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"])}}class Fo extends Ni{async forward({input_ids:P=null,attention_mask:O=null,pixel_values:ne=null,image_sizes:ge=null,position_ids:he=null,inputs_embeds:Ee=null,past_key_values:De=null,generation_config:Ne=null,logits_processor:Xe=null,...mt}){if(!Ee){let dt;if(ne&&P.dims[1]!==1){if(!ge)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:dt}=await R(this.sessions.vision_encoder,{pixel_values:ne,image_sizes:ge}))}else{const Pt=this.config.normalized_config.hidden_size;dt=new d.Tensor("float32",[],[0,Pt])}({inputs_embeds:Ee}=await R(this.sessions.prepare_inputs_embeds,{input_ids:P,image_features:dt}))}return await ae(this,{inputs_embeds:Ee,past_key_values:De,attention_mask:O,position_ids:he,generation_config:Ne,logits_processor:Xe},!1)}}class Ns extends G{}class Vi extends Ns{}class pu extends Ns{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"text_model"})}}class Ui extends Ns{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"text_model"})}}class hu extends Ns{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"vision_model"})}}class Wi extends Ns{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"vision_model"})}}class Xs extends G{}class Js extends Xs{}class Oo extends Xs{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"text_model"})}}class Gi extends Ns{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"vision_model"})}}class Ki extends G{}class qn extends Ki{}class Ys extends G{}class Do extends Ys{async forward(P){const O=!P.input_ids,ne=!P.pixel_values;if(O&&ne)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(O&&(P.input_ids=(0,d.ones)([P.pixel_values.dims[0],1])),ne){const{image_size:Xe}=this.config.vision_config;P.pixel_values=(0,d.full)([0,3,Xe,Xe],0)}const{text_embeddings:ge,image_embeddings:he,l2norm_text_embeddings:Ee,l2norm_image_embeddings:De}=await super.forward(P),Ne={};return O||(Ne.text_embeddings=ge,Ne.l2norm_text_embeddings=Ee),ne||(Ne.image_embeddings=he,Ne.l2norm_image_embeddings=De),Ne}}class Lo extends Ys{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"text_model"})}}class zo extends Ys{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"vision_model"})}}class Qn extends G{}class Bo extends Qn{}class Ro extends Qn{}class jo extends G{}class No extends jo{}class Vo extends jo{}class Zs extends G{}class ct extends Zs{}class Hi extends Zs{}class Xn extends G{}class Uo extends Xn{}class Pn extends Xn{}class Gr extends G{}class Cn extends Gr{}class Jn extends Gr{}class Yn extends G{}class Wo extends Yn{}class Go extends Yn{}class Sn extends G{}class Ko extends Sn{}class Ho extends Sn{}class Zn extends G{}class $n extends Zn{}class eo extends Zn{}class kn extends G{}class qo extends kn{}class Qo extends kn{}class to extends G{}class Xo extends to{}class Jo extends to{}class ro extends G{}class h extends ro{}class x extends ro{}class I extends G{}class L extends I{}class N extends I{}class ue extends G{}class Te extends ue{}class Le extends ue{}class Ke extends G{}class tt extends Ke{}class bt extends Ke{}class kt extends G{}class Wt extends kt{}class yr extends kt{}class Ls extends G{}class qi extends Ls{}class Wg extends Ls{}class mu extends G{}class Gg extends mu{}class Kg extends mu{}class fu extends G{}class Hg extends fu{}class qg extends fu{}class _u extends G{}class Qg extends _u{}class Xg extends _u{}class gu extends G{}class Jg extends gu{}class Yg extends gu{}class wu extends G{}class Zg extends wu{}class ew extends wu{}class Mu extends G{}class tw extends Mu{}class rw extends Mu{}class bu extends G{}class sw extends bu{}class nw extends bu{}class ow extends G{constructor(){super(...arguments);Y(this,"forward_params",["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"])}}class iw extends ow{get_rope_index(P,O,ne,ge){const{vision_config:he,image_token_id:Ee,video_token_id:De,vision_start_token_id:Ne}=this.config,Xe=he.spatial_merge_size??2,mt=[];if(O||ne){let wt=P.tolist();ge||(ge=(0,d.ones_like)(P));const dt=ge.tolist(),Pt=Array.from({length:3},qt=>Array.from({length:P.dims[0]},tr=>Array.from({length:P.dims[1]},lr=>1))),gt=O?O.tolist():[],Et=ne?ne.tolist():[];let ot=0,$t=0;for(let qt=0;qt<wt.length;++qt){const tr=wt[qt].filter((rr,xr)=>dt[qt][xr]==1),nr=tr.reduce((rr,xr,tn)=>(xr==Ne&&rr.push(tn),rr),[]).map(rr=>tr[rr+1]),_r=nr.filter(rr=>rr==Ee).length,$r=nr.filter(rr=>rr==De).length;let Ct=[],vr=0,Yr=_r,An=$r;for(let rr=0;rr<nr.length;++rr){const xr=tr.findIndex((Dn,Ps)=>Ps>vr&&Dn==Ee),tn=tr.findIndex((Dn,Ps)=>Ps>vr&&Dn==De),On=Yr>0&&xr!==-1?xr:tr.length+1,oo=An>0&&tn!==-1?tn:tr.length+1;let ha,Mc,bc,yc;On<oo?([Mc,bc,yc]=gt[ot],++ot,--Yr,ha=On):([Mc,bc,yc]=Et[$t],++$t,--An,ha=oo);const[eT,vc,ma]=[Number(Mc),Math.floor(Number(bc)/Xe),Math.floor(Number(yc)/Xe)],xc=ha-vr,N0=Ct.length>0?(0,f.max)(Ct.at(-1))[0]+1:0;Ct.push(Array.from({length:3*xc},(Dn,Ps)=>N0+Ps%xc));const Tc=xc+N0,fa=eT*vc*ma,tT=Array.from({length:fa},(Dn,Ps)=>Tc+Math.floor(Ps/(vc*ma))),rT=Array.from({length:fa},(Dn,Ps)=>Tc+Math.floor(Ps/ma)%vc),sT=Array.from({length:fa},(Dn,Ps)=>Tc+Ps%ma);Ct.push([tT,rT,sT].flat()),vr=ha+fa}if(vr<tr.length){const rr=Ct.length>0?(0,f.max)(Ct.at(-1))[0]+1:0,xr=tr.length-vr;Ct.push(Array.from({length:3*xr},(tn,On)=>rr+On%xr))}const Ur=Ct.reduce((rr,xr)=>rr+xr.length,0),cs=new Array(Ur);let ca=0;for(let rr=0;rr<3;++rr)for(let xr=0;xr<Ct.length;++xr){const tn=Ct[xr],On=tn.length/3;for(let oo=rr*On;oo<(rr+1)*On;++oo)cs[ca++]=tn[oo]}let Fn=0;const da=dt[qt];for(let rr=0;rr<da.length;++rr)if(da[rr]==1){for(let xr=0;xr<3;++xr)Pt[xr][qt][rr]=cs[xr*Ur/3+Fn];++Fn}const pa=(0,f.max)(cs)[0];mt.push(pa+1-wt[qt].length)}return[new d.Tensor("int64",Pt.flat(1/0),[3,P.dims[0],P.dims[1]]),new d.Tensor("int64",mt,[mt.length,1])]}else if(ge){const{data:wt,dims:dt}=ye(ge),Pt=BigInt64Array.from({length:3*wt.length},(Et,ot)=>wt[ot%wt.length]),gt=Array.from({length:dt[0]},(Et,ot)=>(0,f.max)(wt.subarray(dt[1]*ot,dt[1]*(ot+1)))[0]+1n+BigInt(dt[1]));return[new d.Tensor("int64",Pt,[3,...dt]),new d.Tensor("int64",gt,[gt.length,1])]}else{const[wt,dt]=P.dims,Pt=BigInt64Array.from({length:3*wt*dt},(gt,Et)=>BigInt(Math.floor(Et%dt/wt)));return[new d.Tensor("int64",Pt,[3,...P.dims]),(0,d.zeros)([wt,1])]}}async encode_image({pixel_values:P,image_grid_thw:O}){return(await R(this.sessions.vision_encoder,{pixel_values:P,grid_thw:O})).image_features}_merge_input_ids_with_image_features(P){return A({image_token_id:this.config.image_token_id,...P})}prepare_inputs_for_generation(P,O,ne){if(O.attention_mask&&!O.position_ids)if(!O.past_key_values)[O.position_ids,O.rope_deltas]=this.get_rope_index(O.input_ids,O.image_grid_thw,O.video_grid_thw,O.attention_mask);else{O.pixel_values=null;const ge=BigInt(Object.values(O.past_key_values)[0].dims.at(-2)),he=O.rope_deltas.map(Ee=>ge+Ee);O.position_ids=(0,d.stack)([he,he,he],0)}return O}}class yu extends G{}class aw extends yu{}class lw extends yu{}class vu extends G{}class uw extends vu{}class cw extends vu{}class xu extends G{}class dw extends xu{}class pw extends xu{}class Tu extends G{}class hw extends Tu{}class mw extends Tu{}class Eu extends G{}class fw extends Eu{}class _w extends Eu{}class Pu extends G{}class gw extends Pu{}class ww extends Pu{async _call(P){return new xt(await super._call(P))}}class Cu extends G{}class Mw extends Cu{}class bw extends Cu{async _call(P){return new xt(await super._call(P))}}class yw extends G{}class vw extends yw{}class Su extends G{}class xw extends Su{}class Tw extends Su{async _call(P){return new xt(await super._call(P))}}class Ew extends G{}class Pw extends Ew{}class $u extends G{}class Cw extends $u{}class Sw extends $u{async _call(P){return new xt(await super._call(P))}}class $w extends G{}class kw extends $w{}class ku extends G{}class Iw extends ku{}class Aw extends ku{async _call(P){return new xt(await super._call(P))}}class Fw extends G{}class Ow extends Fw{async _call(P){return new R0(await super._call(P))}}class Iu extends G{}class Dw extends Iu{}class Lw extends Iu{async _call(P){return new xt(await super._call(P))}}class Au extends G{}class zw extends Au{}class Bw extends Au{async _call(P){return new xt(await super._call(P))}}class Fu extends G{}class Rw extends Fu{}class jw extends Fu{}class Ou extends G{}class Nw extends Ou{}class Vw extends Ou{}class Du extends G{}class Uw extends Du{}class Ww extends Du{async _call(P){return new xt(await super._call(P))}}class Qi extends G{}class Gw extends Qi{}class Kw extends Qi{async _call(P){return new zu(await super._call(P))}}class Lu extends Qi{async _call(P){return new Hw(await super._call(P))}}class zu extends be{constructor({logits:P,pred_boxes:O}){super(),this.logits=P,this.pred_boxes=O}}class Hw extends be{constructor({logits:P,pred_boxes:O,pred_masks:ne}){super(),this.logits=P,this.pred_boxes=O,this.pred_masks=ne}}class Bu extends G{}class qw extends Bu{}class Qw extends Bu{async _call(P){return new Yo(await super._call(P))}}class Yo extends be{constructor({logits:P,pred_boxes:O}){super(),this.logits=P,this.pred_boxes=O}}class Ru extends G{}class Xw extends Ru{}class Jw extends Ru{async _call(P){return new Yw(await super._call(P))}}class Yw extends Yo{}class ju extends G{}class Zw extends ju{}class eM extends ju{async _call(P){return new tM(await super._call(P))}}class tM extends Yo{}class Nu extends G{}class rM extends Nu{}class sM extends Nu{async _call(P){return new Yo(await super._call(P))}}class Vu extends G{}class nM extends Vu{}class oM extends Vu{async _call(P){return new iM(await super._call(P))}}class iM extends zu{}class Uu extends G{}class aM extends Uu{}class lM extends Uu{async _call(P){return new xt(await super._call(P))}}class Wu extends G{}class uM extends Wu{}class cM extends Wu{async _call(P){return new xt(await super._call(P))}}class Gu extends G{}class dM extends Gu{}class pM extends Gu{async _call(P){return new xt(await super._call(P))}}class Xi extends G{}class hM extends Xi{}class mM extends Xi{async _call(P){return new xt(await super._call(P))}}class fM extends Xi{}class Ku extends G{}class _M extends Ku{}class gM extends Ku{}class Hu extends G{}class wM extends Hu{}class MM extends Hu{}class bM extends G{}class yM extends bM{}class Ji extends G{}class vM extends Ji{}class xM extends Ji{}class TM extends Ji{}class EM extends G{}class PM extends EM{}class CM extends G{}class SM extends CM{}class $M extends G{}class kM extends $M{}class qu extends G{}class IM extends qu{}class AM extends qu{}class Qu extends G{}class FM extends Qu{}class OM extends Qu{}class DM extends G{}class LM extends DM{}class Xu extends G{}class zM extends Xu{}class BM extends Xu{async _call(P){return new xt(await super._call(P))}}class Ju extends G{}class RM extends Ju{}class jM extends Ju{async _call(P){return new xt(await super._call(P))}}class Yu extends G{}class NM extends Yu{}class VM extends Yu{async _call(P){return new xt(await super._call(P))}}class Zu extends G{}class UM extends Zu{}class WM extends Zu{async _call(P){return new xt(await super._call(P))}}class GM extends G{}class KM extends GM{}class ec extends G{}class HM extends ec{}class qM extends ec{async _call(P){return new QM(await super._call(P))}}class QM extends be{constructor({logits:P,pred_boxes:O}){super(),this.logits=P,this.pred_boxes=O}}class XM extends G{}class JM extends XM{async get_image_embeddings({pixel_values:P}){return await se(this,{pixel_values:P})}async forward(P){if((!P.image_embeddings||!P.image_positional_embeddings)&&(P={...P,...await this.get_image_embeddings(P)}),!P.input_labels&&P.input_points){const ne=P.input_points.dims.slice(0,-1),ge=ne.reduce((he,Ee)=>he*Ee,1);P.input_labels=new d.Tensor("int64",new BigInt64Array(ge).fill(1n),ne)}const O={image_embeddings:P.image_embeddings,image_positional_embeddings:P.image_positional_embeddings};return P.input_points&&(O.input_points=P.input_points),P.input_labels&&(O.input_labels=P.input_labels),P.input_boxes&&(O.input_boxes=P.input_boxes),await R(this.sessions.prompt_encoder_mask_decoder,O)}async _call(P){return new YM(await super._call(P))}}class YM extends be{constructor({iou_scores:P,pred_masks:O}){super(),this.iou_scores=P,this.pred_masks=O}}class tc extends G{}class ZM extends tc{}class eb extends tc{}class rc extends G{}class tb extends rc{}class rb extends rc{}class en extends G{}class sb extends en{}class nb extends en{async _call(P){return new In(await super._call(P))}}class ob extends en{async _call(P){return new xt(await super._call(P))}}class ib extends en{async _call(P){return new Sr(await super._call(P))}}class sc extends G{}class ab extends sc{}class lb extends sc{async _call(P){return new Sr(await super._call(P))}}class ub extends G{}class cb extends ub{}class Yi extends G{}class db extends Yi{}class pb extends Yi{async _call(P){return new In(await super._call(P))}}class hb extends Yi{async _call(P){return new xt(await super._call(P))}}class Zo extends G{}class mb extends Zo{}class fb extends Zo{async _call(P){return new In(await super._call(P))}}class _b extends Zo{async _call(P){return new xt(await super._call(P))}}class gb extends Zo{async _call(P){return new Sr(await super._call(P))}}class Zi extends G{}class wb extends Zi{}class Mb extends Zi{async _call(P){return new In(await super._call(P))}}class bb extends Zi{async _call(P){return new xt(await super._call(P))}}class Rx extends G{}class yb extends en{}class vb extends en{async _call(P){return new In(await super._call(P))}}class xb extends en{async _call(P){return new xt(await super._call(P))}}class so extends G{}class Tb extends so{}class Eb extends so{async _call(P){return new In(await super._call(P))}}class Pb extends so{async _call(P){return new xt(await super._call(P))}}class Cb extends so{async _call(P){return new B0(await super._call(P))}}class Sb extends so{async _call(P){return new Sr(await super._call(P))}}class $b extends G{}class kb extends $b{}class ea extends G{}class jx extends ea{}class Ib extends ea{}class Ab extends ea{async generate_speech(P,O,{threshold:ne=.5,minlenratio:ge=0,maxlenratio:he=20,vocoder:Ee=null}={}){const De={input_ids:P},{encoder_outputs:Ne,encoder_attention_mask:Xe}=await se(this,De),mt=Ne.dims[1]/this.config.reduction_factor,wt=Math.floor(mt*he),dt=Math.floor(mt*ge),Pt=this.config.num_mel_bins;let gt=[],Et=null,ot=null,$t=0;for(;;){++$t;const lr=J(!!ot);let nr;ot?nr=ot.output_sequence_out:nr=new d.Tensor("float32",new Float32Array(Pt),[1,1,Pt]);let _r={use_cache_branch:lr,output_sequence:nr,encoder_attention_mask:Xe,speaker_embeddings:O,encoder_hidden_states:Ne};this.addPastKeyValues(_r,Et),ot=await R(this.sessions.decoder_model_merged,_r),Et=this.getPastKeyValues(ot,Et);const{prob:$r,spectrum:Ct}=ot;if(gt.push(Ct),$t>=dt&&(Array.from($r.data).filter(vr=>vr>=ne).length>0||$t>=wt))break}const qt=(0,d.cat)(gt),{waveform:tr}=await R(Ee.sessions.model,{spectrogram:qt});return{spectrogram:qt,waveform:tr}}}class Fb extends G{constructor(){super(...arguments);Y(this,"main_input_name","spectrogram")}}class Ob extends G{}class Db extends Ob{}class nc extends G{}class Lb extends nc{}class zb extends nc{}class oc extends G{}class Bb extends oc{}class Rb extends oc{}class ic extends G{}class jb extends ic{}class Nb extends ic{}class ta extends G{}class Vb extends ta{}class Ub extends ta{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"text_model"})}}class Wb extends ta{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"audio_model"})}}class Gb extends G{}class ac extends Gb{async _call(P){return new j0(await super._call(P))}}class ra extends G{}class Nx extends ra{}class Kb extends ra{}class Hb extends ra{}class lc extends G{}class qb extends lc{}class Qb extends lc{}class uc extends G{}class Xb extends uc{}class Jb extends uc{async _call(P){return new xt(await super._call(P))}}class cc extends G{}class Vx extends cc{}class Ux extends cc{}class dc extends G{constructor(){super(...arguments);Y(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(O){const[ne,ge]=O.dims,he=this.config.decoder.num_codebooks,Ee=ge-he;let De=0;for(let mt=0;mt<O.size;++mt){if(O.data[mt]===this.config.decoder.pad_token_id)continue;const wt=mt%ge,dt=Math.floor(mt/ge)%he,Pt=wt-dt;Pt>0&&Pt<=Ee&&(O.data[De++]=O.data[mt])}const Ne=Math.floor(ne/he),Xe=De/(Ne*he);return new d.Tensor(O.type,O.data.slice(0,De),[Ne,he,Xe])}prepare_inputs_for_generation(O,ne,ge){let he=structuredClone(O);for(let De=0;De<he.length;++De)for(let Ne=0;Ne<he[De].length;++Ne)De%this.config.decoder.num_codebooks>=Ne&&(he[De][Ne]=BigInt(this.config.decoder.pad_token_id));return ge.guidance_scale!==null&&ge.guidance_scale>1&&(he=he.concat(he)),super.prepare_inputs_for_generation(he,ne,ge)}async generate(O){const ne=await super.generate(O),ge=this._apply_and_filter_by_delay_pattern_mask(ne).unsqueeze_(0),{audio_values:he}=await R(this.sessions.encodec_decode,{audio_codes:ge});return he}}class sa extends G{}class Yb extends sa{}class Zb extends sa{async _call(P){return new xt(await super._call(P))}}class ey extends sa{}class na extends G{}class ty extends na{}class ry extends na{async _call(P){return new xt(await super._call(P))}}class sy extends na{}class oa extends G{}class ny extends oa{}class oy extends oa{async _call(P){return new xt(await super._call(P))}}class iy extends oa{}class ia extends G{}class ay extends ia{}class ly extends ia{async _call(P){return new xt(await super._call(P))}}class uy extends ia{}class cy extends G{}class dy extends cy{}class py extends G{}class hy extends py{constructor(...O){super(...O);Y(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(O){const ne=this._generation_mode??"text";let ge;if(ne==="text"||!O.past_key_values){const Xe=this.sessions.prepare_inputs_embeds,mt=(0,a.pick)(O,Xe.inputNames);ge=await R(Xe,mt)}else{const Xe=this.sessions.gen_img_embeds,mt=(0,a.pick)({image_ids:O.input_ids},Xe.inputNames);ge=await R(Xe,mt)}const he={...O,...ge},Ee=await ae(this,he),De=this.sessions[ne==="text"?"lm_head":"gen_head"];if(!De)throw new Error(`Unable to find "${De}" generation head`);const Ne=await R(De,(0,a.pick)(Ee,De.inputNames));return{...ge,...Ee,...Ne}}async generate(O){return this._generation_mode="text",super.generate(O)}async generate_images(O){this._generation_mode="image";const ne=(O.inputs??O[this.main_input_name]).dims[1],he=(await super.generate(O)).slice(null,[ne,null]),Ee=this.sessions.image_decode,{decoded_image:De}=await R(Ee,{generated_tokens:he}),Ne=De.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),Xe=[];for(const mt of Ne){const wt=_.RawImage.fromTensor(mt);Xe.push(wt)}return Xe}}class my extends be{constructor({char_logits:P,bpe_logits:O,wp_logits:ne}){super(),this.char_logits=P,this.bpe_logits=O,this.wp_logits=ne}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class fy extends G{}class _y extends fy{async _call(P){return new my(await super._call(P))}}class pc extends G{}class gy extends pc{}class wy extends pc{}class hc extends G{}class My extends hc{}class by extends hc{}class yy extends G{constructor(){super(...arguments);Y(this,"forward_params",["input_ids","attention_mask","position_ids","audio_values","past_key_values"])}}class vy extends yy{_merge_input_ids_with_audio_features(P){const O=P.audio_features.dims.at(-1),ne=P.audio_features.view(-1,O);return U({audio_token_id:this.config.ignore_index,...P,audio_features:ne})}}class aa extends G{constructor(){super(...arguments);Y(this,"main_input_name","input_values");Y(this,"forward_params",["input_values"])}}class xy extends be{constructor({audio_codes:P}){super(),this.audio_codes=P}}class Ty extends be{constructor({audio_values:P}){super(),this.audio_values=P}}class Ey extends aa{async encode(P){return new xy(await R(this.sessions.encoder_model,P))}async decode(P){return new Ty(await R(this.sessions.decoder_model,P))}}class Py extends aa{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"encoder_model"})}}class Cy extends aa{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"decoder_model"})}}class la extends G{constructor(){super(...arguments);Y(this,"main_input_name","input_values");Y(this,"forward_params",["input_values"])}}class Sy extends be{constructor({audio_codes:P}){super(),this.audio_codes=P}}class $y extends be{constructor({audio_values:P}){super(),this.audio_values=P}}class ky extends la{async encode(P){return new Sy(await R(this.sessions.encoder_model,P))}async decode(P){return new $y(await R(this.sessions.decoder_model,P))}}class Iy extends la{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"encoder_model"})}}class Ay extends la{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"decoder_model"})}}class ua extends G{constructor(){super(...arguments);Y(this,"main_input_name","input_values");Y(this,"forward_params",["input_values"])}}class Fy extends ua{async encode(P){return await R(this.sessions.encoder_model,P)}async decode(P){return await R(this.sessions.decoder_model,P)}}class Oy extends ua{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"encoder_model"})}}class Dy extends ua{static async from_pretrained(P,O={}){return super.from_pretrained(P,{...O,model_file_name:O.model_file_name??"decoder_model"})}}class Lt{static async from_pretrained(P,{progress_callback:O=null,config:ne=null,cache_dir:ge=null,local_files_only:he=!1,revision:Ee="main",model_file_name:De=null,subfolder:Ne="onnx",device:Xe=null,dtype:mt=null,use_external_data_format:wt=null,session_options:dt={}}={}){const Pt={progress_callback:O,config:ne,cache_dir:ge,local_files_only:he,revision:Ee,model_file_name:De,subfolder:Ne,device:Xe,dtype:mt,use_external_data_format:wt,session_options:dt};if(Pt.config=await s.AutoConfig.from_pretrained(P,Pt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const gt=Pt.config.model_type;for(const Et of this.MODEL_CLASS_MAPPINGS){let ot=Et.get(gt);if(!ot){for(const $t of Et.values())if($t[0]===gt){ot=$t;break}if(!ot)continue}return await ot[1].from_pretrained(P,Pt)}if(this.BASE_IF_FAIL)return a0.has(gt)||console.warn(`Unknown model class "${gt}", attempting to construct from base class.`),await G.from_pretrained(P,Pt);throw Error(`Unsupported model type: ${gt}`)}}Y(Lt,"MODEL_CLASS_MAPPINGS",null),Y(Lt,"BASE_IF_FAIL",!1);const Wx=new 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Map([["t5",["T5Model",ce]],["longt5",["LongT5Model",Qe]],["mt5",["MT5Model",_t]],["bart",["BartModel",Yt]],["mbart",["MBartModel",Pr]],["marian",["MarianModel",ZM]],["whisper",["WhisperModel",Oi]],["m2m_100",["M2M100Model",tb]],["blenderbot",["BlenderbotModel",fr]],["blenderbot-small",["BlenderbotSmallModel",Jr]]]),Kx=new Map([["mimi",["MimiModel",Ey]],["dac",["DacModel",ky]],["snac",["SnacModel",Fy]]]),Hx=new Map([["bloom",["BloomModel",dw]],["jais",["JAISModel",ct]],["gpt2",["GPT2Model",No]],["gptj",["GPTJModel",Wo]],["gpt_bigcode",["GPTBigCodeModel",Ko]],["gpt_neo",["GPTNeoModel",Uo]],["gpt_neox",["GPTNeoXModel",Cn]],["codegen",["CodeGenModel",$n]],["llama",["LlamaModel",qo]],["exaone",["ExaoneModel",L]],["olmo",["OlmoModel",tt]],["olmo2",["Olmo2Model",Wt]],["mobilellm",["MobileLLMModel",Te]],["granite",["GraniteModel",qi]],["cohere",["CohereModel",Gg]],["gemma",["GemmaModel",Hg]],["gemma2",["Gemma2Model",Qg]],["gemma3_text",["Gemma3Model",Jg]],["helium",["HeliumModel",Xo]],["glm",["GlmModel",h]],["openelm",["OpenELMModel",Zg]],["qwen2",["Qwen2Model",tw]],["qwen3",["Qwen3Model",sw]],["phi",["PhiModel",aw]],["phi3",["Phi3Model",uw]],["mpt",["MptModel",hw]],["opt",["OPTModel",fw]],["mistral",["MistralModel",Lb]],["starcoder2",["Starcoder2Model",Bb]],["falcon",["FalconModel",jb]],["stablelm",["StableLmModel",qb]]]),mc=new Map([["speecht5",["SpeechT5ForSpeechToText",Ib]],["whisper",["WhisperForConditionalGeneration",Co]],["lite-whisper",["LiteWhisperForConditionalGeneration",So]],["moonshine",["MoonshineForConditionalGeneration",Li]]]),Ly=new Map([["speecht5",["SpeechT5ForTextToSpeech",Ab]]]),zy=new Map([["vits",["VitsModel",ac]],["musicgen",["MusicgenForConditionalGeneration",dc]]]),By=new Map([["bert",["BertForSequenceClassification",Fe]],["modernbert",["ModernBertForSequenceClassification",Ze]],["roformer",["RoFormerForSequenceClassification",Vt]],["electra",["ElectraForSequenceClassification",ve]],["esm",["EsmForSequenceClassification",os]],["convbert",["ConvBertForSequenceClassification",D]],["camembert",["CamembertForSequenceClassification",jt]],["deberta",["DebertaForSequenceClassification",ys]],["deberta-v2",["DebertaV2ForSequenceClassification",vs]],["mpnet",["MPNetForSequenceClassification",gn]],["albert",["AlbertForSequenceClassification",$]],["distilbert",["DistilBertForSequenceClassification",Er]],["roberta",["RobertaForSequenceClassification",dr]],["xlm",["XLMForSequenceClassification",Ei]],["xlm-roberta",["XLMRobertaForSequenceClassification",$i]],["bart",["BartForSequenceClassification",mr]],["mbart",["MBartForSequenceClassification",Zt]],["mobilebert",["MobileBertForSequenceClassification",hr]],["squeezebert",["SqueezeBertForSequenceClassification",yn]]]),Ry=new Map([["bert",["BertForTokenClassification",Be]],["modernbert",["ModernBertForTokenClassification",nt]],["roformer",["RoFormerForTokenClassification",Rt]],["electra",["ElectraForTokenClassification",vt]],["esm",["EsmForTokenClassification",is]],["convbert",["ConvBertForTokenClassification",oe]],["camembert",["CamembertForTokenClassification",Ht]],["deberta",["DebertaForTokenClassification",ns]],["deberta-v2",["DebertaV2ForTokenClassification",Is]],["mpnet",["MPNetForTokenClassification",wn]],["distilbert",["DistilBertForTokenClassification",xs]],["roberta",["RobertaForTokenClassification",pr]],["xlm",["XLMForTokenClassification",Pi]],["xlm-roberta",["XLMRobertaForTokenClassification",ki]]]),fc=new Map([["t5",["T5ForConditionalGeneration",xe]],["longt5",["LongT5ForConditionalGeneration",We]],["mt5",["MT5ForConditionalGeneration",Ot]],["bart",["BartForConditionalGeneration",Ut]],["mbart",["MBartForConditionalGeneration",Cr]],["marian",["MarianMTModel",eb]],["m2m_100",["M2M100ForConditionalGeneration",rb]],["blenderbot",["BlenderbotForConditionalGeneration",Dr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Ir]]]),_c=new Map([["bloom",["BloomForCausalLM",pw]],["gpt2",["GPT2LMHeadModel",Vo]],["jais",["JAISLMHeadModel",Hi]],["gptj",["GPTJForCausalLM",Go]],["gpt_bigcode",["GPTBigCodeForCausalLM",Ho]],["gpt_neo",["GPTNeoForCausalLM",Pn]],["gpt_neox",["GPTNeoXForCausalLM",Jn]],["codegen",["CodeGenForCausalLM",eo]],["llama",["LlamaForCausalLM",Qo]],["exaone",["ExaoneForCausalLM",N]],["olmo",["OlmoForCausalLM",bt]],["olmo2",["Olmo2ForCausalLM",yr]],["mobilellm",["MobileLLMForCausalLM",Le]],["granite",["GraniteForCausalLM",Wg]],["cohere",["CohereForCausalLM",Kg]],["gemma",["GemmaForCausalLM",qg]],["gemma2",["Gemma2ForCausalLM",Xg]],["gemma3_text",["Gemma3ForCausalLM",Yg]],["helium",["HeliumForCausalLM",Jo]],["glm",["GlmForCausalLM",x]],["openelm",["OpenELMForCausalLM",ew]],["qwen2",["Qwen2ForCausalLM",rw]],["qwen3",["Qwen3ForCausalLM",nw]],["phi",["PhiForCausalLM",lw]],["phi3",["Phi3ForCausalLM",cw]],["mpt",["MptForCausalLM",mw]],["opt",["OPTForCausalLM",_w]],["mbart",["MBartForCausalLM",Es]],["mistral",["MistralForCausalLM",zb]],["starcoder2",["Starcoder2ForCausalLM",Rb]],["falcon",["FalconForCausalLM",Nb]],["trocr",["TrOCRForCausalLM",Db]],["stablelm",["StableLmForCausalLM",Qb]],["phi3_v",["Phi3VForCausalLM",Fo]]]),qx=new Map([["multi_modality",["MultiModalityCausalLM",hy]]]),jy=new Map([["bert",["BertForMaskedLM",$e]],["modernbert",["ModernBertForMaskedLM",Ve]],["roformer",["RoFormerForMaskedLM",St]],["electra",["ElectraForMaskedLM",Oe]],["esm",["EsmForMaskedLM",Nt]],["convbert",["ConvBertForMaskedLM",rs]],["camembert",["CamembertForMaskedLM",rt]],["deberta",["DebertaForMaskedLM",ss]],["deberta-v2",["DebertaV2ForMaskedLM",Qr]],["mpnet",["MPNetForMaskedLM",_n]],["albert",["AlbertForMaskedLM",X]],["distilbert",["DistilBertForMaskedLM",Ae]],["roberta",["RobertaForMaskedLM",er]],["xlm",["XLMWithLMHeadModel",Ti]],["xlm-roberta",["XLMRobertaForMaskedLM",Si]],["mobilebert",["MobileBertForMaskedLM",cr]],["squeezebert",["SqueezeBertForMaskedLM",bn]]]),Ny=new Map([["bert",["BertForQuestionAnswering",He]],["roformer",["RoFormerForQuestionAnswering",gr]],["electra",["ElectraForQuestionAnswering",Ft]],["convbert",["ConvBertForQuestionAnswering",B]],["camembert",["CamembertForQuestionAnswering",wr]],["deberta",["DebertaForQuestionAnswering",$s]],["deberta-v2",["DebertaV2ForQuestionAnswering",As]],["mpnet",["MPNetForQuestionAnswering",Mn]],["albert",["AlbertForQuestionAnswering",j]],["distilbert",["DistilBertForQuestionAnswering",Br]],["roberta",["RobertaForQuestionAnswering",Ar]],["xlm",["XLMForQuestionAnswering",To]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Ii]],["mobilebert",["MobileBertForQuestionAnswering",ls]],["squeezebert",["SqueezeBertForQuestionAnswering",vn]]]),gc=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",$o]],["idefics3",["Idefics3ForConditionalGeneration",En]],["smolvlm",["SmolVLMForConditionalGeneration",Ao]]]),Vy=new Map([["llava",["LlavaForConditionalGeneration",Tn]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Qs]],["moondream1",["Moondream1ForConditionalGeneration",zi]],["florence2",["Florence2ForConditionalGeneration",Bi]],["qwen2-vl",["Qwen2VLForConditionalGeneration",iw]],["idefics3",["Idefics3ForConditionalGeneration",En]],["smolvlm",["SmolVLMForConditionalGeneration",Ao]],["paligemma",["PaliGemmaForConditionalGeneration",ji]]]),Uy=new Map([["ultravox",["UltravoxModel",vy]]]),Qx=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",$o]]]),Wy=new Map([["vit",["ViTForImageClassification",ww]],["ijepa",["IJepaForImageClassification",bw]],["pvt",["PvtForImageClassification",Tw]],["vit_msn",["ViTMSNForImageClassification",Sw]],["fastvit",["FastViTForImageClassification",Aw]],["mobilevit",["MobileViTForImageClassification",Lw]],["mobilevitv2",["MobileViTV2ForImageClassification",Bw]],["beit",["BeitForImageClassification",Ww]],["deit",["DeiTForImageClassification",lM]],["hiera",["HieraForImageClassification",cM]],["convnext",["ConvNextForImageClassification",BM]],["convnextv2",["ConvNextV2ForImageClassification",jM]],["dinov2",["Dinov2ForImageClassification",VM]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",WM]],["resnet",["ResNetForImageClassification",pM]],["swin",["SwinForImageClassification",mM]],["segformer",["SegformerForImageClassification",Kb]],["efficientnet",["EfficientNetForImageClassification",Jb]],["mobilenet_v1",["MobileNetV1ForImageClassification",Zb]],["mobilenet_v2",["MobileNetV2ForImageClassification",ry]],["mobilenet_v3",["MobileNetV3ForImageClassification",oy]],["mobilenet_v4",["MobileNetV4ForImageClassification",ly]]]),Gy=new Map([["detr",["DetrForObjectDetection",Kw]],["rt_detr",["RTDetrForObjectDetection",Qw]],["rt_detr_v2",["RTDetrV2ForObjectDetection",Jw]],["rf_detr",["RFDetrForObjectDetection",eM]],["d_fine",["DFineForObjectDetection",sM]],["table-transformer",["TableTransformerForObjectDetection",oM]],["yolos",["YolosForObjectDetection",qM]]]),Ky=new Map([["owlvit",["OwlViTForObjectDetection",jw]],["owlv2",["Owlv2ForObjectDetection",Vw]],["grounding-dino",["GroundingDinoForObjectDetection",KM]]]),no=new Map([["detr",["DetrForSegmentation",Lu]],["clipseg",["CLIPSegForImageSegmentation",Ro]]]),Hy=new Map([["segformer",["SegformerForSemanticSegmentation",Hb]],["sapiens",["SapiensForSemanticSegmentation",vM]],["swin",["SwinForSemanticSegmentation",fM]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",ey]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",sy]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",iy]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",uy]]]),qy=new Map([["detr",["DetrForSegmentation",Lu]],["maskformer",["MaskFormerForInstanceSegmentation",AM]]]),Qy=new Map([["sam",["SamModel",JM]]]),Xy=new Map([["wav2vec2",["Wav2Vec2ForCTC",nb]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Mb]],["unispeech",["UniSpeechForCTC",pb]],["unispeech-sat",["UniSpeechSatForCTC",fb]],["wavlm",["WavLMForCTC",Eb]],["hubert",["HubertForCTC",vb]]]),Jy=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",ob]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",bb]],["unispeech",["UniSpeechForSequenceClassification",hb]],["unispeech-sat",["UniSpeechSatForSequenceClassification",_b]],["wavlm",["WavLMForSequenceClassification",Pb]],["hubert",["HubertForSequenceClassification",xb]],["audio-spectrogram-transformer",["ASTForAudioClassification",Fi]]]),Yy=new Map([["wavlm",["WavLMForXVector",Cb]]]),Zy=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",gb]],["wavlm",["WavLMForAudioFrameClassification",Sb]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",ib]],["pyannote",["PyAnnoteForAudioFrameClassification",lb]]]),e0=new Map([["vitmatte",["VitMatteForImageMatting",Ow]]]),Xx=new Map([["patchtst",["PatchTSTForPrediction",wy]],["patchtsmixer",["PatchTSMixerForPrediction",by]]]),t0=new Map([["swin2sr",["Swin2SRForImageSuperResolution",gM]]]),r0=new Map([["dpt",["DPTForDepthEstimation",MM]],["depth_anything",["DepthAnythingForDepthEstimation",yM]],["glpn",["GLPNForDepthEstimation",OM]],["sapiens",["SapiensForDepthEstimation",xM]],["depth_pro",["DepthProForDepthEstimation",PM]],["metric3d",["Metric3DForDepthEstimation",SM]],["metric3dv2",["Metric3Dv2ForDepthEstimation",kM]]]),s0=new Map([["sapiens",["SapiensForNormalEstimation",TM]]]),n0=new Map([["vitpose",["VitPoseForPoseEstimation",vw]]]),o0=new Map([["clip",["CLIPVisionModelWithProjection",Wi]],["siglip",["SiglipVisionModel",Gi]],["jina_clip",["JinaCLIPVisionModel",zo]]]),i0=[[Wx,E.EncoderOnly],[Gx,E.EncoderDecoder],[Hx,E.DecoderOnly],[Kx,E.AutoEncoder],[By,E.EncoderOnly],[Ry,E.EncoderOnly],[fc,E.Seq2Seq],[mc,E.Seq2Seq],[_c,E.DecoderOnly],[qx,E.MultiModality],[jy,E.EncoderOnly],[Ny,E.EncoderOnly],[gc,E.Vision2Seq],[Vy,E.ImageTextToText],[Uy,E.AudioTextToText],[Wy,E.EncoderOnly],[no,E.EncoderOnly],[qy,E.EncoderOnly],[Hy,E.EncoderOnly],[e0,E.EncoderOnly],[Xx,E.EncoderOnly],[t0,E.EncoderOnly],[r0,E.EncoderOnly],[s0,E.EncoderOnly],[n0,E.EncoderOnly],[Gy,E.EncoderOnly],[Ky,E.EncoderOnly],[Qy,E.MaskGeneration],[Xy,E.EncoderOnly],[Jy,E.EncoderOnly],[Ly,E.Seq2Seq],[zy,E.EncoderOnly],[Yy,E.EncoderOnly],[Zy,E.EncoderOnly],[o0,E.EncoderOnly]];for(const[b,P]of i0)for(const[O,ne]of b.values())v.set(O,P),y.set(ne,O),M.set(O,ne);const Jx=[["MusicgenForConditionalGeneration",dc,E.Musicgen],["Phi3VForCausalLM",Fo,E.Phi3V],["CLIPTextModelWithProjection",Ui,E.EncoderOnly],["SiglipTextModel",Oo,E.EncoderOnly],["JinaCLIPTextModel",Lo,E.EncoderOnly],["ClapTextModelWithProjection",Ub,E.EncoderOnly],["ClapAudioModelWithProjection",Wb,E.EncoderOnly],["DacEncoderModel",Iy,E.EncoderOnly],["DacDecoderModel",Ay,E.EncoderOnly],["MimiEncoderModel",Py,E.EncoderOnly],["MimiDecoderModel",Cy,E.EncoderOnly],["SnacEncoderModel",Oy,E.EncoderOnly],["SnacDecoderModel",Dy,E.EncoderOnly]];for(const[b,P,O]of Jx)v.set(b,O),y.set(P,b),M.set(b,P);const a0=new Map([["modnet",no],["birefnet",no],["isnet",no],["ben",no]]);for(const[b,P]of a0.entries())P.set(b,["PreTrainedModel",G]),v.set(b,E.EncoderOnly),y.set(G,b),M.set(b,G);class wc extends Lt{}Y(wc,"MODEL_CLASS_MAPPINGS",i0.map(P=>P[0])),Y(wc,"BASE_IF_FAIL",!0);class l0 extends Lt{}Y(l0,"MODEL_CLASS_MAPPINGS",[By]);class u0 extends Lt{}Y(u0,"MODEL_CLASS_MAPPINGS",[Ry]);class c0 extends Lt{}Y(c0,"MODEL_CLASS_MAPPINGS",[fc]);class d0 extends Lt{}Y(d0,"MODEL_CLASS_MAPPINGS",[mc]);class p0 extends Lt{}Y(p0,"MODEL_CLASS_MAPPINGS",[Ly]);class h0 extends Lt{}Y(h0,"MODEL_CLASS_MAPPINGS",[zy]);class m0 extends Lt{}Y(m0,"MODEL_CLASS_MAPPINGS",[_c]);class f0 extends Lt{}Y(f0,"MODEL_CLASS_MAPPINGS",[jy]);class _0 extends Lt{}Y(_0,"MODEL_CLASS_MAPPINGS",[Ny]);class g0 extends Lt{}Y(g0,"MODEL_CLASS_MAPPINGS",[gc]);class w0 extends Lt{}Y(w0,"MODEL_CLASS_MAPPINGS",[Wy]);class M0 extends Lt{}Y(M0,"MODEL_CLASS_MAPPINGS",[no]);class b0 extends Lt{}Y(b0,"MODEL_CLASS_MAPPINGS",[Hy]);class y0 extends Lt{}Y(y0,"MODEL_CLASS_MAPPINGS",[qy]);class v0 extends Lt{}Y(v0,"MODEL_CLASS_MAPPINGS",[Gy]);class x0 extends Lt{}Y(x0,"MODEL_CLASS_MAPPINGS",[Ky]);class T0 extends Lt{}Y(T0,"MODEL_CLASS_MAPPINGS",[Qy]);class E0 extends Lt{}Y(E0,"MODEL_CLASS_MAPPINGS",[Xy]);class P0 extends Lt{}Y(P0,"MODEL_CLASS_MAPPINGS",[Jy]);class C0 extends Lt{}Y(C0,"MODEL_CLASS_MAPPINGS",[Yy]);class S0 extends Lt{}Y(S0,"MODEL_CLASS_MAPPINGS",[Zy]);class $0 extends Lt{}Y($0,"MODEL_CLASS_MAPPINGS",[Qx]);class k0 extends Lt{}Y(k0,"MODEL_CLASS_MAPPINGS",[e0]);class I0 extends Lt{}Y(I0,"MODEL_CLASS_MAPPINGS",[t0]);class A0 extends Lt{}Y(A0,"MODEL_CLASS_MAPPINGS",[r0]);class F0 extends Lt{}Y(F0,"MODEL_CLASS_MAPPINGS",[s0]);class O0 extends Lt{}Y(O0,"MODEL_CLASS_MAPPINGS",[n0]);class D0 extends Lt{}Y(D0,"MODEL_CLASS_MAPPINGS",[o0]);class L0 extends Lt{}Y(L0,"MODEL_CLASS_MAPPINGS",[Vy]);class z0 extends Lt{}Y(z0,"MODEL_CLASS_MAPPINGS",[Uy]);class Yx extends be{constructor({logits:P,past_key_values:O,encoder_outputs:ne,decoder_attentions:ge=null,cross_attentions:he=null}){super(),this.logits=P,this.past_key_values=O,this.encoder_outputs=ne,this.decoder_attentions=ge,this.cross_attentions=he}}class xt extends be{constructor({logits:P,...O}){super(),this.logits=P;const ne=Object.values(O);ne.length>0&&(this.attentions=ne)}}class B0 extends be{constructor({logits:P,embeddings:O}){super(),this.logits=P,this.embeddings=O}}class Sr extends be{constructor({logits:P}){super(),this.logits=P}}class Fr extends be{constructor({logits:P}){super(),this.logits=P}}class Rr extends be{constructor({start_logits:P,end_logits:O}){super(),this.start_logits=P,this.end_logits=O}}class In extends be{constructor({logits:P}){super(),this.logits=P}}class Zx extends be{constructor({logits:P,past_key_values:O}){super(),this.logits=P,this.past_key_values=O}}class R0 extends be{constructor({alphas:P}){super(),this.alphas=P}}class j0 extends be{constructor({waveform:P,spectrogram:O}){super(),this.waveform=P,this.spectrogram=O}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a);const l=this.config.sampling_rate,u=(0,o.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(l/2),l,null,"kaldi",!0);this.mel_filters=u,this.window=(0,o.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(a,l){return(0,o.spectrogram)(a,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:l,transpose:!0})}async _call(a){(0,s.validate_audio_inputs)(a,"ASTFeatureExtractor");const l=await this._extract_fbank_features(a,this.config.max_length);if(this.config.do_normalize){const u=this.std*2,p=l.data;for(let c=0;c<p.length;++c)p[c]=(p[c]-this.mean)/u}return{input_values:l.unsqueeze_(0)}}}},"./src/models/auto/feature_extraction_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoFeatureExtractor:()=>i});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js");t("./src/base/feature_extraction_utils.js");var n=t("./src/models/feature_extractors.js");class i{static async from_pretrained(l,u={}){const p=await(0,o.getModelJSON)(l,s.FEATURE_EXTRACTOR_NAME,!0,u),c=p.feature_extractor_type,d=n[c];if(!d)throw new Error(`Unknown feature_extractor_type: '${c}'. Please report this at ${s.GITHUB_ISSUE_URL}.`);return new d(p)}}},"./src/models/auto/image_processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoImageProcessor:()=>a});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js"),n=t("./src/base/image_processors_utils.js"),i=t("./src/models/image_processors.js");class a{static async from_pretrained(u,p={}){const c=await(0,o.getModelJSON)(u,s.IMAGE_PROCESSOR_NAME,!0,p),d=c.image_processor_type??c.feature_extractor_type;let _=i[d];return _||(d!==void 0&&console.warn(`Image processor type '${d}' not found, assuming base ImageProcessor. Please report this at ${s.GITHUB_ISSUE_URL}.`),_=n.ImageProcessor),new _(c)}}},"./src/models/auto/processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoProcessor:()=>u});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js"),n=t("./src/base/processing_utils.js"),i=t("./src/models/processors.js"),a=t("./src/models/image_processors.js"),l=t("./src/models/feature_extractors.js");class u{static async from_pretrained(c,d={}){const _=await(0,o.getModelJSON)(c,s.IMAGE_PROCESSOR_NAME,!0,d),{image_processor_type:f,feature_extractor_type:T,processor_class:k}=_;if(k&&i[k])return i[k].from_pretrained(c,d);if(!f&&!T)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const w={};if(f){const S=a[f];if(!S)throw new Error(`Unknown image_processor_type: '${f}'.`);w.image_processor=new S(_)}if(T){const S=a[T];if(S)w.image_processor=new S(_);else{const E=l[T];if(!E)throw new Error(`Unknown feature_extractor_type: '${T}'.`);w.feature_extractor=new E(_)}}const g={};return new n.Processor(g,w)}}},"./src/models/beit/image_processing_beit.js":(e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(e,r,t)=>{t.r(r),t.d(r,{BitImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(e,r,t)=>{t.r(r),t.d(r,{ChineseCLIPFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(e,r,t)=>{t.r(r),t.d(r,{ClapFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a),this.mel_filters=(0,o.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,o.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,o.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(a,l,u,p){let c;const d=a.length-l;if(d>0)if(u==="rand_trunc"){const _=Math.floor(Math.random()*(d+1));a=a.subarray(_,_+l),c=await this._extract_fbank_features(a,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${u}" not implemented`);else{if(d<0){let _=new Float64Array(l);if(_.set(a),p==="repeat")for(let f=a.length;f<l;f+=a.length)_.set(a.subarray(0,Math.min(a.length,l-f)),f);else if(p==="repeatpad")for(let 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o{}},"./src/models/convnext/image_processing_convnext.js":(e,r,t)=>{t.r(r),t.d(r,{ConvNextFeatureExtractor:()=>n,ConvNextImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(a){super(a),this.crop_pct=this.config.crop_pct??224/256}async resize(a){var u;const l=(u=this.size)==null?void 0:u.shortest_edge;if(l===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(l<384){const p=Math.floor(l/this.crop_pct),[c,d]=this.get_resize_output_image_size(a,{shortest_edge:p});a=await a.resize(c,d,{resample:this.resample}),a=await a.center_crop(l,l)}else a=await a.resize(l,l,{resample:this.resample});return a}}class n extends o{}},"./src/models/dac/feature_extraction_dac.js":(e,r,t)=>{t.r(r),t.d(r,{DacFeatureExtractor:()=>o});var s=t("./src/models/encodec/feature_extraction_encodec.js");class o extends s.EncodecFeatureExtractor{}},"./src/models/deit/image_processing_deit.js":(e,r,t)=>{t.r(r),t.d(r,{DeiTFeatureExtractor:()=>n,DeiTImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/detr/image_processing_detr.js":(e,r,t)=>{t.r(r),t.d(r,{DetrFeatureExtractor:()=>i,DetrImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{async _call(l){const u=await super._call(l),p=[u.pixel_values.dims[0],64,64],c=(0,o.full)(p,1n);return{...u,pixel_mask:c}}post_process_object_detection(...l){return(0,s.post_process_object_detection)(...l)}post_process_panoptic_segmentation(...l){return(0,s.post_process_panoptic_segmentation)(...l)}post_process_instance_segmentation(...l){return(0,s.post_process_instance_segmentation)(...l)}}class i extends n{}},"./src/models/donut/image_processing_donut.js":(e,r,t)=>{t.r(r),t.d(r,{DonutFeatureExtractor:()=>n,DonutImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{pad_image(a,l,u,p={}){const[c,d,_]=l;let f=this.image_mean;Array.isArray(this.image_mean)||(f=new Array(_).fill(f));let T=this.image_std;Array.isArray(T)||(T=new Array(_).fill(f));const k=f.map((w,g)=>-w/T[g]);return super.pad_image(a,l,u,{center:!0,constant_values:k,...p})}}class n extends o{}},"./src/models/dpt/image_processing_dpt.js":(e,r,t)=>{t.r(r),t.d(r,{DPTFeatureExtractor:()=>n,DPTImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/efficientnet/image_processing_efficientnet.js":(e,r,t)=>{t.r(r),t.d(r,{EfficientNetImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){super(i),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(a=>a*a))}}},"./src/models/encodec/feature_extraction_encodec.js":(e,r,t)=>{t.r(r),t.d(r,{EncodecFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js"),o=t("./src/utils/tensor.js");class n extends s.FeatureExtractor{async _call(a){(0,s.validate_audio_inputs)(a,"EncodecFeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));const l=this.config.feature_size;if(a.length%l!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${l}).`);const u=[1,l,a.length/l];return{input_values:new o.Tensor("float32",a,u)}}}},"./src/models/feature_extractors.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>s.ASTFeatureExtractor,ClapFeatureExtractor:()=>n.ClapFeatureExtractor,DacFeatureExtractor:()=>i.DacFeatureExtractor,EncodecFeatureExtractor:()=>o.EncodecFeatureExtractor,ImageFeatureExtractor:()=>T.ImageProcessor,MoonshineFeatureExtractor:()=>a.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>l.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>u.SeamlessM4TFeatureExtractor,SnacFeatureExtractor:()=>p.SnacFeatureExtractor,SpeechT5FeatureExtractor:()=>c.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>d.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>_.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>f.WhisperFeatureExtractor});var s=t("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),o=t("./src/models/encodec/feature_extraction_encodec.js"),n=t("./src/models/clap/feature_extraction_clap.js"),i=t("./src/models/dac/feature_extraction_dac.js"),a=t("./src/models/moonshine/feature_extraction_moonshine.js"),l=t("./src/models/pyannote/feature_extraction_pyannote.js"),u=t("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),p=t("./src/models/snac/feature_extraction_snac.js"),c=t("./src/models/speecht5/feature_extraction_speecht5.js"),d=t("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),_=t("./src/models/wespeaker/feature_extraction_wespeaker.js"),f=t("./src/models/whisper/feature_extraction_whisper.js"),T=t("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(e,r,t)=>{t.r(r),t.d(r,{Florence2Processor:()=>i});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");class i extends s.Processor{constructor(l,u){super(l,u);const{tasks_answer_post_processing_type:p,task_prompts_without_inputs:c,task_prompts_with_input:d}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(p??{})),this.task_prompts_without_inputs=new Map(Object.entries(c??{})),this.task_prompts_with_input=new Map(Object.entries(d??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(l){typeof l=="string"&&(l=[l]);const u=[];for(const p of l)if(this.task_prompts_without_inputs.has(p))u.push(this.task_prompts_without_inputs.get(p));else{for(const[c,d]of this.task_prompts_with_input)if(p.includes(c)){u.push(d.replaceAll("{input}",p).replaceAll(c,""));break}u.length!==l.length&&u.push(p)}return u}post_process_generation(l,u,p){const c=this.tasks_answer_post_processing_type.get(u)??"pure_text";l=l.replaceAll("<s>","").replaceAll("</s>","");let d;switch(c){case"pure_text":d=l;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const _=c==="ocr"?"quad_boxes":"bboxes",f=l.matchAll(this.regexes[_]),T=[],k=[];for(const[w,g,...S]of f)T.push(g?g.trim():T.at(-1)??""),k.push(S.map((E,v)=>(Number(E)+.5)/this.size_per_bin*p[v%2]));d={labels:T,[_]:k};break;default:throw new Error(`Task "${u}" (of type "${c}") not yet implemented.`)}return{[u]:d}}async _call(l,u=null,p={}){if(!l&&!u)throw new Error("Either text or images must be provided");const c=await this.image_processor(l,p),d=u?this.tokenizer(u,p):{};return{...c,...d}}}Y(i,"tokenizer_class",n.AutoTokenizer),Y(i,"image_processor_class",o.AutoImageProcessor)},"./src/models/glpn/image_processing_glpn.js":(e,r,t)=>{t.r(r),t.d(r,{GLPNFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/grounding_dino/image_processing_grounding_dino.js":(e,r,t)=>{t.r(r),t.d(r,{GroundingDinoImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{async _call(a){const l=await super._call(a),u=l.pixel_values.dims,p=(0,o.ones)([u[0],u[2],u[3]]);return{...l,pixel_mask:p}}}},"./src/models/grounding_dino/processing_grounding_dino.js":(e,r,t)=>{t.r(r),t.d(r,{GroundingDinoProcessor:()=>l});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),i=t("./src/base/image_processors_utils.js");function a(u,p){const d=u.dims.at(-1)-1,_=u.tolist();_.fill(!1,0,1),_.fill(!1,d);const f=p.tolist();return _.map((T,k)=>T?k:null).filter(T=>T!==null).map(T=>f[T])}class l extends s.Processor{async _call(p,c,d={}){const _=p?await this.image_processor(p,d):{};return{...c?this.tokenizer(c,d):{},..._}}post_process_grounded_object_detection(p,c,{box_threshold:d=.25,text_threshold:_=.25,target_sizes:f=null}={}){const{logits:T,pred_boxes:k}=p,w=T.dims[0];if(f!==null&&f.length!==w)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const g=T.dims.at(1),S=T.sigmoid(),E=S.max(-1).tolist(),v=k.tolist().map(y=>y.map(C=>(0,i.center_to_corners_format)(C))),M=[];for(let y=0;y<w;++y){const C=f!==null?f[y]:null;C!==null&&(v[y]=v[y].map(R=>R.map((Z,H)=>Z*C[(H+1)%2])));const F=E[y],z=[],K=[],q=[];for(let R=0;R<g;++R){const Z=F[R];if(Z<=d)continue;const H=v[y][R],J=S[y][R];z.push(Z),q.push(H);const Q=a(J.gt(_),c[y]);K.push(Q)}M.push({scores:z,boxes:q,labels:this.batch_decode(K)})}return M}}Y(l,"tokenizer_class",n.AutoTokenizer),Y(l,"image_processor_class",o.AutoImageProcessor)},"./src/models/idefics3/image_processing_idefics3.js":(e,r,t)=>{t.r(r),t.d(r,{Idefics3ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{constructor(a){super(a),this.do_image_splitting=a.do_image_splitting??!0,this.max_image_size=a.max_image_size}get_resize_for_vision_encoder(a,l){let[u,p]=a.dims.slice(-2);const c=p/u;return p>=u?(p=Math.ceil(p/l)*l,u=Math.floor(p/c),u=Math.ceil(u/l)*l):(u=Math.ceil(u/l)*l,p=Math.floor(u*c),p=Math.ceil(p/l)*l),{height:u,width:p}}async _call(a,{do_image_splitting:l=null,return_row_col_info:u=!1}={}){let p;if(!Array.isArray(a))p=[[a]];else{if(a.length===0||!a[0])throw new Error("No images provided.");Array.isArray(a[0])?p=a:p=[a]}let c=[],d=[],_=[];const f=[],T=[];for(const y of p){let C=await Promise.all(y.map(K=>this.preprocess(K)));f.push(...C.map(K=>K.original_size)),T.push(...C.map(K=>K.reshaped_input_size)),C.forEach(K=>K.pixel_values.unsqueeze_(0));const{longest_edge:F}=this.max_image_size;let z;if(l??this.do_image_splitting){let K=new Array(C.length),q=new Array(C.length);z=await Promise.all(C.map(async(R,Z)=>{const H=this.get_resize_for_vision_encoder(R.pixel_values,F),J=await(0,o.interpolate_4d)(R.pixel_values,{size:[H.height,H.width]}),{frames:Q,num_splits_h:se,num_splits_w:fe}=await this.split_image(J,this.max_image_size);return K[Z]=se,q[Z]=fe,(0,o.cat)(Q,0)})),d.push(K),_.push(q)}else{const K=[F,F];z=await Promise.all(C.map(q=>(0,o.interpolate_4d)(q.pixel_values,{size:K}))),d.push(new Array(C.length).fill(0)),_.push(new Array(C.length).fill(0))}c.push((0,o.cat)(z,0))}const k=c.length,[w,g,S,E]=c[0].dims;let v,M;if(k===1)v=c[0].unsqueeze_(0),M=(0,o.full)([k,w,S,E],!0);else{const y=Math.max(...c.map(z=>z.dims.at(0)));M=(0,o.full)([k,y,S,E],!0);const C=M.data,F=y*S*E;for(let z=0;z<k;++z){const K=c[z].dims[0];if(K<y){c[z]=(0,o.cat)([c[z],(0,o.full)([y-K,g,S,E],0)],0);const q=z*F+K*S*E,R=(z+1)*F;C.fill(!1,q,R)}}v=(0,o.stack)(c,0)}return{pixel_values:v,pixel_attention_mask:M,original_sizes:f,reshaped_input_sizes:T,...u?{rows:d,cols:_}:{}}}async split_image(a,{longest_edge:l}){const u=l,p=l,c=[],[d,_]=a.dims.slice(-2);let f=0,T=0;if(d>u||_>p){f=Math.ceil(d/u),T=Math.ceil(_/p);const k=Math.ceil(d/f),w=Math.ceil(_/T);for(let E=0;E<f;++E)for(let v=0;v<T;++v){let M,y,C,F;E===f-1?(y=d-k,F=d):(y=E*k,F=(E+1)*k),v===T-1?(M=_-w,C=_):(M=v*w,C=(v+1)*w);const z=[y,M],K=[F,C],q=await(0,o.slice)(a,z,K,[2,3]);c.push(q)}const g=u,S=p;(d!==g||_!==S)&&(a=await(0,o.interpolate_4d)(a,{size:[g,S]}))}return c.push(a),{frames:c,num_splits_h:f,num_splits_w:T}}}},"./src/models/idefics3/processing_idefics3.js":(e,r,t)=>{t.r(r),t.d(r,{Idefics3Processor:()=>p});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");t("./src/utils/image.js");var i=t("./src/utils/core.js");function a(c,d,_,f,T,k){let w="";for(let g=0;g<d;++g){for(let S=0;S<_;++S)w+=f+`<row_${g+1}_col_${S+1}>`+T.repeat(c);w+=`
`}return w+=`
${f}${k}`+T.repeat(c)+`${f}`,w}function l(c,d,_,f){return`${d}${f}`+_.repeat(c)+`${d}`}function u(c,d,_,f,T,k){return c===0&&d===0?l(_,f,T,k):a(_,c,d,f,T,k)}class p extends s.Processor{constructor(){super(...arguments);Y(this,"fake_image_token","<fake_token_around_image>");Y(this,"image_token","<image>");Y(this,"global_img_token","<global-img>")}async _call(_,f=null,T={}){T.return_row_col_info??(T.return_row_col_info=!0);let k;f&&(k=await this.image_processor(f,T)),Array.isArray(_)||(_=[_]);const w=k.rows??[new Array(_.length).fill(0)],g=k.cols??[new Array(_.length).fill(0)],S=this.config.image_seq_len,E=[],v=[];for(let y=0;y<_.length;++y){const C=_[y],F=w[y],z=g[y];E.push((0,i.count)(C,this.image_token));const K=F.map((Z,H)=>u(Z,z[H],S,this.fake_image_token,this.image_token,this.global_img_token)),q=C.split(this.image_token);if(q.length===0)throw new Error("The image token should be present in the text.");let R=q[0];for(let Z=0;Z<K.length;++Z)R+=K[Z]+q[Z+1];v.push(R)}return{...this.tokenizer(v),...k}}}Y(p,"image_processor_class",o.AutoImageProcessor),Y(p,"tokenizer_class",n.AutoTokenizer),Y(p,"uses_processor_config",!0)},"./src/models/image_processors.js":(e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>s.BeitFeatureExtractor,BitImageProcessor:()=>o.BitImageProcessor,CLIPFeatureExtractor:()=>i.CLIPFeatureExtractor,CLIPImageProcessor:()=>i.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>n.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>a.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>a.ConvNextImageProcessor,DPTFeatureExtractor:()=>c.DPTFeatureExtractor,DPTImageProcessor:()=>c.DPTImageProcessor,DeiTFeatureExtractor:()=>l.DeiTFeatureExtractor,DeiTImageProcessor:()=>l.DeiTImageProcessor,DetrFeatureExtractor:()=>u.DetrFeatureExtractor,DetrImageProcessor:()=>u.DetrImageProcessor,DonutFeatureExtractor:()=>p.DonutFeatureExtractor,DonutImageProcessor:()=>p.DonutImageProcessor,EfficientNetImageProcessor:()=>d.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>_.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>f.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>T.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>w.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>g.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>S.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>E.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>E.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>v.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>v.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>M.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>M.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>y.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>y.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>C.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>C.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>F.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>F.MobileViTImageProcessor,NougatImageProcessor:()=>z.NougatImageProcessor,OwlViTFeatureExtractor:()=>q.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>q.OwlViTImageProcessor,Owlv2ImageProcessor:()=>K.Owlv2ImageProcessor,Phi3VImageProcessor:()=>R.Phi3VImageProcessor,PvtImageProcessor:()=>Z.PvtImageProcessor,Qwen2VLImageProcessor:()=>H.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>J.RTDetrImageProcessor,SamImageProcessor:()=>Q.SamImageProcessor,SegformerFeatureExtractor:()=>se.SegformerFeatureExtractor,SegformerImageProcessor:()=>se.SegformerImageProcessor,SiglipImageProcessor:()=>fe.SiglipImageProcessor,SmolVLMImageProcessor:()=>ae.SmolVLMImageProcessor,Swin2SRImageProcessor:()=>V.Swin2SRImageProcessor,VLMImageProcessor:()=>k.VLMImageProcessor,ViTFeatureExtractor:()=>A.ViTFeatureExtractor,ViTImageProcessor:()=>A.ViTImageProcessor,VitMatteImageProcessor:()=>U.VitMatteImageProcessor,VitPoseImageProcessor:()=>ee.VitPoseImageProcessor,YolosFeatureExtractor:()=>_e.YolosFeatureExtractor,YolosImageProcessor:()=>_e.YolosImageProcessor});var s=t("./src/models/beit/image_processing_beit.js"),o=t("./src/models/bit/image_processing_bit.js"),n=t("./src/models/chinese_clip/image_processing_chinese_clip.js"),i=t("./src/models/clip/image_processing_clip.js"),a=t("./src/models/convnext/image_processing_convnext.js"),l=t("./src/models/deit/image_processing_deit.js"),u=t("./src/models/detr/image_processing_detr.js"),p=t("./src/models/donut/image_processing_donut.js"),c=t("./src/models/dpt/image_processing_dpt.js"),d=t("./src/models/efficientnet/image_processing_efficientnet.js"),_=t("./src/models/glpn/image_processing_glpn.js"),f=t("./src/models/grounding_dino/image_processing_grounding_dino.js"),T=t("./src/models/idefics3/image_processing_idefics3.js"),k=t("./src/models/janus/image_processing_janus.js"),w=t("./src/models/jina_clip/image_processing_jina_clip.js"),g=t("./src/models/llava_onevision/image_processing_llava_onevision.js"),S=t("./src/models/mask2former/image_processing_mask2former.js"),E=t("./src/models/maskformer/image_processing_maskformer.js"),v=t("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),M=t("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),y=t("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),C=t("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),F=t("./src/models/mobilevit/image_processing_mobilevit.js"),z=t("./src/models/nougat/image_processing_nougat.js"),K=t("./src/models/owlv2/image_processing_owlv2.js"),q=t("./src/models/owlvit/image_processing_owlvit.js"),R=t("./src/models/phi3_v/image_processing_phi3_v.js"),Z=t("./src/models/pvt/image_processing_pvt.js"),H=t("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),J=t("./src/models/rt_detr/image_processing_rt_detr.js"),Q=t("./src/models/sam/image_processing_sam.js"),se=t("./src/models/segformer/image_processing_segformer.js"),fe=t("./src/models/siglip/image_processing_siglip.js"),ae=t("./src/models/smolvlm/image_processing_smolvlm.js"),V=t("./src/models/swin2sr/image_processing_swin2sr.js"),A=t("./src/models/vit/image_processing_vit.js"),U=t("./src/models/vitmatte/image_processing_vitmatte.js"),ee=t("./src/models/vitpose/image_processing_vitpose.js"),_e=t("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(e,r,t)=>{t.r(r),t.d(r,{VLMImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){super({do_pad:!0,pad_size:{width:i.image_size,height:i.image_size},...i}),this.constant_values=this.config.background_color.map(a=>a*this.rescale_factor)}pad_image(i,a,l,u){return super.pad_image(i,a,l,{constant_values:this.constant_values,center:!0,...u})}}},"./src/models/janus/processing_janus.js":(e,r,t)=>{t.r(r),t.d(r,{VLChatProcessor:()=>u});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),i=t("./src/utils/core.js"),a=t("./src/utils/tensor.js"),l=t("./src/utils/image.js");class u extends s.Processor{constructor(c,d){super(c,d),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(c,{images:d=null,chat_template:_="default"}={}){d?Array.isArray(d)||(d=[d]):d=await Promise.all(c.filter(z=>z.images).flatMap(z=>z.images).map(z=>l.RawImage.read(z)));const f=this.tokenizer,T=f.apply_chat_template(c,{tokenize:!1,add_generation_prompt:!0,chat_template:_}),k=z=>f.encode(z,{add_special_tokens:!1}),w=T.split(this.image_tag),g=w.length-1;if(d.length!==g)throw new Error(`Number of images provided (${d.length}) does not match number of "${this.image_tag}" image tags (${g})`);const[S,E,v]=f.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let M=k(w[0]),y=new Array(M.length).fill(!1);for(let z=1;z<w.length;++z){const K=new Array(this.num_image_tokens).fill(S),q=k(w[z]);M=(0,i.mergeArrays)(M,[E],K,[v],q);const R=new Array(this.num_image_tokens).fill(!0);y=(0,i.mergeArrays)(y,[!1],R,[!1],new Array(q.length).fill(!1))}const C=[1,M.length],F={input_ids:new a.Tensor("int64",M,C),attention_mask:new a.Tensor("int64",new Array(M.length).fill(1),C),images_seq_mask:new a.Tensor("bool",y,C),images_emb_mask:new a.Tensor("bool",new Array(g*this.num_image_tokens).fill(!0),[1,g,this.num_image_tokens])};if(d&&d.length>0){const z=await this.image_processor(d);return z.pixel_values.unsqueeze_(0),{...F,...z}}return F}}Y(u,"image_processor_class",o.AutoImageProcessor),Y(u,"tokenizer_class",n.AutoTokenizer),Y(u,"uses_processor_config",!0)},"./src/models/jina_clip/image_processing_jina_clip.js":(e,r,t)=>{t.r(r),t.d(r,{JinaCLIPImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){const{resize_mode:a,fill_color:l,interpolation:u,size:p,...c}=i,d=a==="squash"?{width:p,height:p}:a==="shortest"?{shortest_edge:p}:{longest_edge:p},_=u==="bicubic"?3:2;super({...c,size:d,resample:_,do_center_crop:!0,crop_size:p,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(e,r,t)=>{t.r(r),t.d(r,{JinaCLIPProcessor:()=>i});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");class i extends s.Processor{async _call(l=null,u=null,p={}){if(!l&&!u)throw new Error("Either text or images must be provided");const c=l?this.tokenizer(l,p):{},d=u?await this.image_processor(u,p):{};return{...c,...d}}}Y(i,"tokenizer_class",n.AutoTokenizer),Y(i,"image_processor_class",o.AutoImageProcessor)},"./src/models/llava_onevision/image_processing_llava_onevision.js":(e,r,t)=>{t.r(r),t.d(r,{LlavaOnevisionImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/mask2former/image_processing_mask2former.js":(e,r,t)=>{t.r(r),t.d(r,{Mask2FormerImageProcessor:()=>o});var s=t("./src/models/maskformer/image_processing_maskformer.js");class o extends s.MaskFormerImageProcessor{}},"./src/models/maskformer/image_processing_maskformer.js":(e,r,t)=>{t.r(r),t.d(r,{MaskFormerFeatureExtractor:()=>n,MaskFormerImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{post_process_panoptic_segmentation(...a){return(0,s.post_process_panoptic_segmentation)(...a)}post_process_instance_segmentation(...a){return(0,s.post_process_instance_segmentation)(...a)}}class n extends o{}},"./src/models/mgp_str/processing_mgp_str.js":(e,r,t)=>{t.r(r),t.d(r,{MgpstrProcessor:()=>l});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),i=t("./src/utils/maths.js");const a={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class l extends s.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(p,c){if(!a.hasOwnProperty(c))throw new Error(`Format ${c} is not supported.`);const[d,_]=a[c],f=this[d].bind(this),[T,k]=p.dims,w=[],g=[],S=p.tolist();for(let v=0;v<T;++v){const M=S[v],y=[],C=[];for(let z=1;z<k;++z){const[K,q]=(0,i.max)((0,i.softmax)(M[z]));if(C.push(K),q==_)break;y.push(q)}const F=C.length>0?C.reduce((z,K)=>z*K,1):0;g.push(y),w.push(F)}return[f(g),w]}char_decode(p){return this.char_tokenizer.batch_decode(p).map(c=>c.replaceAll(" ",""))}bpe_decode(p){return this.bpe_tokenizer.batch_decode(p)}wp_decode(p){return this.wp_tokenizer.batch_decode(p).map(c=>c.replaceAll(" ",""))}batch_decode([p,c,d]){const[_,f]=this._decode_helper(p,"char"),[T,k]=this._decode_helper(c,"bpe"),[w,g]=this._decode_helper(d,"wp"),S=[],E=[];for(let v=0;v<_.length;++v){const[M,y]=(0,i.max)([f[v],k[v],g[v]]);S.push([_[v],T[v],w[v]][y]),E.push(M)}return{generated_text:S,scores:E,char_preds:_,bpe_preds:T,wp_preds:w}}static async from_pretrained(...p){const c=await super.from_pretrained(...p),d=await n.AutoTokenizer.from_pretrained("Xenova/gpt2"),_=await n.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return 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this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=a([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=a([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=a([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=a([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=a([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=a([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=a([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}}Y(l,"session_options",{})},"./src/pipelines.js":(e,r,t)=>{t.r(r),t.d(r,{AudioClassificationPipeline:()=>q,AutomaticSpeechRecognitionPipeline:()=>Z,BackgroundRemovalPipeline:()=>se,DepthEstimationPipeline:()=>_e,DocumentQuestionAnsweringPipeline:()=>A,FeatureExtractionPipeline:()=>z,FillMaskPipeline:()=>S,ImageClassificationPipeline:()=>J,ImageFeatureExtractionPipeline:()=>K,ImageSegmentationPipeline:()=>Q,ImageToImagePipeline:()=>ee,ImageToTextPipeline:()=>H,ObjectDetectionPipeline:()=>ae,Pipeline:()=>T,QuestionAnsweringPipeline:()=>g,SummarizationPipeline:()=>v,Text2TextGenerationPipeline:()=>E,TextClassificationPipeline:()=>k,TextGenerationPipeline:()=>C,TextToAudioPipeline:()=>U,TokenClassificationPipeline:()=>w,TranslationPipeline:()=>M,ZeroShotAudioClassificationPipeline:()=>R,ZeroShotClassificationPipeline:()=>F,ZeroShotImageClassificationPipeline:()=>fe,ZeroShotObjectDetectionPipeline:()=>V,pipeline:()=>ze});var s=t("./src/tokenizers.js"),o=t("./src/models.js"),n=t("./src/models/auto/processing_auto.js");t("./src/base/processing_utils.js");var i=t("./src/utils/generic.js"),a=t("./src/utils/core.js"),l=t("./src/utils/maths.js"),u=t("./src/utils/audio.js"),p=t("./src/utils/tensor.js"),c=t("./src/utils/image.js");async function d(pe){return Array.isArray(pe)||(pe=[pe]),await Promise.all(pe.map(W=>c.RawImage.read(W)))}async function _(pe,W){return Array.isArray(pe)||(pe=[pe]),await Promise.all(pe.map(re=>typeof re=="string"||re instanceof URL?(0,u.read_audio)(re,W):re instanceof Float64Array?new Float32Array(re):re))}function f(pe,W){W&&(pe=pe.map(Se=>Se|0));const[re,G,be,we]=pe;return{xmin:re,ymin:G,xmax:be,ymax:we}}class T extends i.Callable{constructor({task:W,model:re,tokenizer:G=null,processor:be=null}){super(),this.task=W,this.model=re,this.tokenizer=G,this.processor=be}async dispose(){await this.model.dispose()}}class k extends T{constructor(W){super(W)}async _call(W,{top_k:re=1}={}){const G=this.tokenizer(W,{padding:!0,truncation:!0}),be=await this.model(G),we=this.model.config.problem_type==="multi_label_classification"?$e=>$e.sigmoid():$e=>new p.Tensor("float32",(0,l.softmax)($e.data),$e.dims),Se=this.model.config.id2label,Ce=[];for(const $e of be.logits){const Fe=we($e),Be=await(0,p.topk)(Fe,re),He=Be[0].tolist(),ke=Be[1].tolist().map((Ve,Ze)=>({label:Se?Se[Ve]:`LABEL_${Ve}`,score:He[Ze]}));re===1?Ce.push(...ke):Ce.push(ke)}return Array.isArray(W)||re===1?Ce:Ce[0]}}class w extends T{constructor(W){super(W)}async _call(W,{ignore_labels:re=["O"]}={}){const G=Array.isArray(W),be=this.tokenizer(G?W:[W],{padding:!0,truncation:!0}),Se=(await this.model(be)).logits,Ce=this.model.config.id2label,$e=[];for(let Fe=0;Fe<Se.dims[0];++Fe){const Be=be.input_ids[Fe],He=Se[Fe],qe=[];for(let ke=0;ke<He.dims[0];++ke){const Ve=He[ke],Ze=(0,l.max)(Ve.data)[1],nt=Ce?Ce[Ze]:`LABEL_${Ze}`;if(re.includes(nt))continue;const lt=this.tokenizer.decode([Be[ke].item()],{skip_special_tokens:!0});if(lt==="")continue;const Ge=(0,l.softmax)(Ve.data);qe.push({entity:nt,score:Ge[Ze],index:ke,word:lt})}$e.push(qe)}return G?$e:$e[0]}}class g extends T{constructor(W){super(W)}async _call(W,re,{top_k:G=1}={}){const be=this.tokenizer(W,{text_pair:re,padding:!0,truncation:!0}),{start_logits:we,end_logits:Se}=await this.model(be),Ce=be.input_ids.tolist(),$e=be.attention_mask.tolist(),Fe=this.tokenizer.all_special_ids,Be=[];for(let He=0;He<we.dims[0];++He){const qe=Ce[He],ke=qe.findIndex(Ie=>Ie==this.tokenizer.sep_token_id);$e[He].map((Ie,pt)=>Ie==1&&(pt===0||pt>ke&&Fe.findIndex(St=>St==qe[pt])===-1));const Ve=we[He].tolist(),Ze=Se[He].tolist();for(let Ie=1;Ie<Ve.length;++Ie)($e[He]==0||Ie<=ke||Fe.findIndex(pt=>pt==qe[Ie])!==-1)&&(Ve[Ie]=-1/0,Ze[Ie]=-1/0);const nt=(0,l.softmax)(Ve).map((Ie,pt)=>[Ie,pt]),lt=(0,l.softmax)(Ze).map((Ie,pt)=>[Ie,pt]);nt[0][0]=0,lt[0][0]=0;const Ge=(0,a.product)(nt,lt).filter(Ie=>Ie[0][1]<=Ie[1][1]).map(Ie=>[Ie[0][1],Ie[1][1],Ie[0][0]*Ie[1][0]]).sort((Ie,pt)=>pt[2]-Ie[2]);for(let Ie=0;Ie<Math.min(Ge.length,G);++Ie){const[pt,St,Vt]=Ge[Ie],Rt=qe.slice(pt,St+1),gr=this.tokenizer.decode(Rt,{skip_special_tokens:!0});Be.push({answer:gr,score:Vt})}}return G===1?Be[0]:Be}}class S extends T{constructor(W){super(W)}async _call(W,{top_k:re=5}={}){const G=this.tokenizer(W,{padding:!0,truncation:!0}),{logits:be}=await this.model(G),we=[],Se=G.input_ids.tolist();for(let Ce=0;Ce<Se.length;++Ce){const $e=Se[Ce],Fe=$e.findIndex(Ve=>Ve==this.tokenizer.mask_token_id);if(Fe===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Be=be[Ce][Fe],He=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Be.data),Be.dims),re),qe=He[0].tolist(),ke=He[1].tolist();we.push(ke.map((Ve,Ze)=>{const nt=$e.slice();return nt[Fe]=Ve,{score:qe[Ze],token:Number(Ve),token_str:this.tokenizer.decode([Ve]),sequence:this.tokenizer.decode(nt,{skip_special_tokens:!0})}}))}return Array.isArray(W)?we:we[0]}}class E extends T{constructor(re){super(re);Y(this,"_key","generated_text")}async _call(re,G={}){Array.isArray(re)||(re=[re]),this.model.config.prefix&&(re=re.map(Fe=>this.model.config.prefix+Fe));const be=this.model.config.task_specific_params;be&&be[this.task]&&be[this.task].prefix&&(re=re.map(Fe=>be[this.task].prefix+Fe));const we=this.tokenizer,Se={padding:!0,truncation:!0};let Ce;this instanceof M&&"_build_translation_inputs"in we?Ce=we._build_translation_inputs(re,Se,G):Ce=we(re,Se);const $e=await this.model.generate({...Ce,...G});return we.batch_decode($e,{skip_special_tokens:!0}).map(Fe=>({[this._key]:Fe}))}}class v extends E{constructor(re){super(re);Y(this,"_key","summary_text")}}class M extends E{constructor(re){super(re);Y(this,"_key","translation_text")}}function y(pe){return Array.isArray(pe)&&pe.every(W=>"role"in W&&"content"in W)}class C extends T{constructor(W){super(W)}async _call(W,re={}){let G=!1,be=!1,we;if(typeof W=="string")we=W=[W];else if(Array.isArray(W)&&W.every(ke=>typeof ke=="string"))G=!0,we=W;else{if(y(W))W=[W];else if(Array.isArray(W)&&W.every(y))G=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");be=!0,we=W.map(ke=>this.tokenizer.apply_chat_template(ke,{tokenize:!1,add_generation_prompt:!0}))}const Se=re.add_special_tokens??!1,Ce=be?!1:re.return_full_text??!0;this.tokenizer.padding_side="left";const $e=this.tokenizer(we,{add_special_tokens:Se,padding:!0,truncation:!0}),Fe=await this.model.generate({...$e,...re}),Be=this.tokenizer.batch_decode(Fe,{skip_special_tokens:!0});let He;!Ce&&$e.input_ids.dims.at(-1)>0&&(He=this.tokenizer.batch_decode($e.input_ids,{skip_special_tokens:!0}).map(ke=>ke.length));const qe=Array.from({length:W.length},ke=>[]);for(let ke=0;ke<Be.length;++ke){const Ve=Math.floor(ke/Fe.dims[0]*W.length);He&&(Be[ke]=Be[ke].slice(He[Ve])),qe[Ve].push({generated_text:be?[...W[Ve],{role:"assistant",content:Be[ke]}]:Be[ke]})}return!G&&qe.length===1?qe[0]:qe}}class F extends T{constructor(W){super(W),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([re,G])=>[re.toLowerCase(),G])),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(W,re,{hypothesis_template:G="This example is {}.",multi_label:be=!1}={}){const we=Array.isArray(W);we||(W=[W]),Array.isArray(re)||(re=[re]);const Se=re.map(Fe=>G.replace("{}",Fe)),Ce=be||re.length===1,$e=[];for(const Fe of W){const Be=[];for(const ke of Se){const Ve=this.tokenizer(Fe,{text_pair:ke,padding:!0,truncation:!0}),Ze=await this.model(Ve);Ce?Be.push([Ze.logits.data[this.contradiction_id],Ze.logits.data[this.entailment_id]]):Be.push(Ze.logits.data[this.entailment_id])}const qe=(Ce?Be.map(ke=>(0,l.softmax)(ke)[1]):(0,l.softmax)(Be)).map((ke,Ve)=>[ke,Ve]).sort((ke,Ve)=>Ve[0]-ke[0]);$e.push({sequence:Fe,labels:qe.map(ke=>re[ke[1]]),scores:qe.map(ke=>ke[0])})}return we?$e:$e[0]}}class z extends T{constructor(W){super(W)}async _call(W,{pooling:re="none",normalize:G=!1,quantize:be=!1,precision:we="binary"}={}){const Se=this.tokenizer(W,{padding:!0,truncation:!0}),Ce=await this.model(Se);let $e=Ce.last_hidden_state??Ce.logits??Ce.token_embeddings;if(re!=="none")if(re==="mean")$e=(0,p.mean_pooling)($e,Se.attention_mask);else if(re==="cls")$e=$e.slice(null,0);else throw Error(`Pooling method '${re}' not supported.`);return G&&($e=$e.normalize(2,-1)),be&&($e=(0,p.quantize_embeddings)($e,we)),$e}}class K extends T{constructor(W){super(W)}async _call(W,{pool:re=null}={}){const G=await d(W),{pixel_values:be}=await this.processor(G),we=await this.model({pixel_values:be});let Se;if(re){if(!("pooler_output"in we))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Se=we.pooler_output}else Se=we.last_hidden_state??we.logits??we.image_embeds;return Se}}class q extends T{constructor(W){super(W)}async _call(W,{top_k:re=5}={}){const G=this.processor.feature_extractor.config.sampling_rate,be=await _(W,G),we=this.model.config.id2label,Se=[];for(const Ce of be){const $e=await this.processor(Ce),Be=(await this.model($e)).logits[0],He=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Be.data),Be.dims),re),qe=He[0].tolist(),Ve=He[1].tolist().map((Ze,nt)=>({label:we?we[Ze]:`LABEL_${Ze}`,score:qe[nt]}));Se.push(Ve)}return Array.isArray(W)?Se:Se[0]}}class R extends T{constructor(W){super(W)}async _call(W,re,{hypothesis_template:G="This is a sound of {}."}={}){const be=!Array.isArray(W);be&&(W=[W]);const we=re.map(Be=>G.replace("{}",Be)),Se=this.tokenizer(we,{padding:!0,truncation:!0}),Ce=this.processor.feature_extractor.config.sampling_rate,$e=await _(W,Ce),Fe=[];for(const Be of $e){const He=await this.processor(Be),qe=await this.model({...Se,...He}),ke=(0,l.softmax)(qe.logits_per_audio.data);Fe.push([...ke].map((Ve,Ze)=>({score:Ve,label:re[Ze]})))}return be?Fe[0]:Fe}}class Z extends T{constructor(W){super(W)}async _call(W,re={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(W,re);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(W,re);case"moonshine":return this._call_moonshine(W,re);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(W,re){re.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),re.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const G=!Array.isArray(W);G&&(W=[W]);const be=this.processor.feature_extractor.config.sampling_rate,we=await _(W,be),Se=[];for(const Ce of we){const $e=await this.processor(Ce),Be=(await this.model($e)).logits[0],He=[];for(const ke of Be)He.push((0,l.max)(ke.data)[1]);const qe=this.tokenizer.decode(He);Se.push({text:qe})}return G?Se[0]:Se}async _call_whisper(W,re){const G=re.return_timestamps??!1,be=re.chunk_length_s??0,we=re.force_full_sequences??!1;let Se=re.stride_length_s??null;const Ce={...re};G==="word"&&(Ce.return_token_timestamps=!0,Ce.return_timestamps=!1);const $e=!Array.isArray(W);$e&&(W=[W]);const Fe=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Be=this.processor.feature_extractor.config.hop_length,He=this.processor.feature_extractor.config.sampling_rate,qe=await _(W,He),ke=[];for(const Ve of qe){let Ze=[];if(be>0){if(Se===null)Se=be/6;else if(be<=Se)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Ge=He*be,Ie=He*Se,pt=Ge-2*Ie;let St=0;for(;;){const Vt=St+Ge,Rt=Ve.subarray(St,Vt),gr=await this.processor(Rt),ir=St===0,Mt=Vt>=Ve.length;if(Ze.push({stride:[Rt.length,ir?0:Ie,Mt?0:Ie],input_features:gr.input_features,is_last:Mt}),Mt)break;St+=pt}}else Ze=[{stride:[Ve.length,0,0],input_features:(await this.processor(Ve)).input_features,is_last:!0}];for(const Ge of Ze){Ce.num_frames=Math.floor(Ge.stride[0]/Be);const Ie=await this.model.generate({inputs:Ge.input_features,...Ce});G==="word"?(Ge.tokens=Ie.sequences.tolist()[0],Ge.token_timestamps=Ie.token_timestamps.tolist()[0].map(pt=>(0,l.round)(pt,2))):Ge.tokens=Ie[0].tolist(),Ge.stride=Ge.stride.map(pt=>pt/He)}const[nt,lt]=this.tokenizer._decode_asr(Ze,{time_precision:Fe,return_timestamps:G,force_full_sequences:we});ke.push({text:nt,...lt})}return $e?ke[0]:ke}async _call_moonshine(W,re){const G=!Array.isArray(W);G&&(W=[W]);const be=this.processor.feature_extractor.config.sampling_rate,we=await _(W,be),Se=[];for(const Ce of we){const $e=await this.processor(Ce),Fe=Math.floor(Ce.length/be)*6,Be=await this.model.generate({max_new_tokens:Fe,...re,...$e}),He=this.processor.batch_decode(Be,{skip_special_tokens:!0})[0];Se.push({text:He})}return G?Se[0]:Se}}class H extends T{constructor(W){super(W)}async _call(W,re={}){const G=Array.isArray(W),be=await d(W),{pixel_values:we}=await this.processor(be),Se=[];for(const Ce of we){Ce.dims=[1,...Ce.dims];const $e=await this.model.generate({inputs:Ce,...re}),Fe=this.tokenizer.batch_decode($e,{skip_special_tokens:!0}).map(Be=>({generated_text:Be.trim()}));Se.push(Fe)}return G?Se:Se[0]}}class J extends T{constructor(W){super(W)}async _call(W,{top_k:re=5}={}){const G=await d(W),{pixel_values:be}=await this.processor(G),we=await this.model({pixel_values:be}),Se=this.model.config.id2label,Ce=[];for(const $e of we.logits){const Fe=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)($e.data),$e.dims),re),Be=Fe[0].tolist(),qe=Fe[1].tolist().map((ke,Ve)=>({label:Se?Se[ke]:`LABEL_${ke}`,score:Be[Ve]}));Ce.push(qe)}return Array.isArray(W)?Ce:Ce[0]}}class Q extends T{constructor(W){super(W),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(W,{threshold:re=.5,mask_threshold:G=.5,overlap_mask_area_threshold:be=.8,label_ids_to_fuse:we=null,target_sizes:Se=null,subtask:Ce=null}={}){if(Array.isArray(W)&&W.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const Fe=await d(W),Be=Fe.map(Ge=>[Ge.height,Ge.width]),He=await this.processor(Fe),{inputNames:qe,outputNames:ke}=this.model.sessions.model;if(!qe.includes("pixel_values")){if(qe.length!==1)throw Error(`Expected a single input name, but got ${qe.length} inputs: ${qe}.`);const Ge=qe[0];if(Ge in He)throw Error(`Input name 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d(W),Se=re.map(qe=>G.replace("{}",qe)),Ce=this.tokenizer(Se,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:$e}=await this.processor(we),Fe=await this.model({...Ce,pixel_values:$e}),Be=this.model.config.model_type==="siglip"?qe=>qe.sigmoid().data:qe=>(0,l.softmax)(qe.data),He=[];for(const qe of Fe.logits_per_image){const Ve=[...Be(qe)].map((Ze,nt)=>({score:Ze,label:re[nt]}));Ve.sort((Ze,nt)=>nt.score-Ze.score),He.push(Ve)}return be?He:He[0]}}class ae extends T{constructor(W){super(W)}async _call(W,{threshold:re=.9,percentage:G=!1}={}){const be=Array.isArray(W);if(be&&W.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const we=await d(W),Se=G?null:we.map(ke=>[ke.height,ke.width]),{pixel_values:Ce,pixel_mask:$e}=await this.processor(we),Fe=await 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lt=this.processor.image_processor.post_process_object_detection(Ze,G,ke,!0)[0];nt=lt.boxes.map((Ge,Ie)=>({score:lt.scores[Ie],label:re[lt.classes[Ie]],box:f(Ge,!we)}))}nt.sort((lt,Ge)=>Ge.score-lt.score),be!==null&&(nt=nt.slice(0,be)),Be.push(nt)}return Se?Be:Be[0]}}class A extends T{constructor(W){super(W)}async _call(W,re,G={}){const be=(await d(W))[0],{pixel_values:we}=await this.processor(be),Se=`<s_docvqa><s_question>${re}</s_question><s_answer>`,Ce=this.tokenizer(Se,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,$e=await this.model.generate({inputs:we,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ce,...G}),Be=this.tokenizer.batch_decode($e)[0].match(/<s_answer>(.*?)<\/s_answer>/);let He=null;return Be&&Be.length>=2&&(He=Be[1].trim()),[{answer:He}]}}class U extends T{constructor(re){super(re);Y(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=re.vocoder??null}async _call(re,{speaker_embeddings:G=null}={}){return this.processor?this._call_text_to_spectrogram(re,{speaker_embeddings:G}):this._call_text_to_waveform(re)}async _call_text_to_waveform(re){const G=this.tokenizer(re,{padding:!0,truncation:!0}),{waveform:be}=await this.model(G),we=this.model.config.sampling_rate;return new u.RawAudio(be.data,we)}async _call_text_to_spectrogram(re,{speaker_embeddings:G}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await o.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof G=="string"||G instanceof URL)&&(G=new Float32Array(await(await fetch(G)).arrayBuffer())),G instanceof Float32Array)G=new p.Tensor("float32",G,[1,G.length]);else if(!(G instanceof p.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:be}=this.tokenizer(re,{padding:!0,truncation:!0}),{waveform:we}=await this.model.generate_speech(be,G,{vocoder:this.vocoder}),Se=this.processor.feature_extractor.config.sampling_rate;return new u.RawAudio(we.data,Se)}}class ee extends T{constructor(W){super(W)}async _call(W){const re=await d(W),G=await this.processor(re),be=await this.model(G),we=[];for(const Se of be.reconstruction){const Ce=Se.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");we.push(c.RawImage.fromTensor(Ce))}return we.length>1?we:we[0]}}class _e extends T{constructor(W){super(W)}async _call(W){const re=await d(W),G=await this.processor(re),{predicted_depth:be}=await this.model(G),we=[];for(let Se=0;Se<re.length;++Se){const Ce=be[Se],[$e,Fe]=Ce.dims.slice(-2),[Be,He]=re[Se].size,qe=(await(0,p.interpolate_4d)(Ce.view(1,1,$e,Fe),{size:[He,Be],mode:"bilinear"})).view(He,Be),ke=qe.min().item(),Ve=qe.max().item(),Ze=qe.sub(ke).div_(Ve-ke).mul_(255).to("uint8").unsqueeze(0),nt=c.RawImage.fromTensor(Ze);we.push({predicted_depth:qe,depth:nt})}return we.length>1?we:we[0]}}const 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Ye=ie.slice(xe,Qe).join("");if(xe>0&&(Ye=this.config.continuing_subword_prefix+Ye),this.tokens_to_ids.has(Ye)){We=Ye;break}--Qe}if(We===null){ce=!0;break}Re.push(We),xe=Qe}ce?j.push(this.unk_token):j.push(...Re)}return j}}class R extends K{constructor($,j){super($);const X=$.vocab.length;this.vocab=new Array(X),this.scores=new Array(X);for(let ie=0;ie<X;++ie)[this.vocab[ie],this.scores[ie]]=$.vocab[ie];this.unk_token_id=$.unk_id,this.unk_token=this.vocab[$.unk_id],this.tokens_to_ids=new Map(this.vocab.map((ie,ce)=>[ie,ce])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=j.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,i.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new l.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes($){const j=$.chars,X=1;let ie=0;for(;ie<j.length;){let ce=!1;const xe=j.slice(ie).join(""),Re=this.trie.commonPrefixSearch(xe);for(const Qe of Re){const We=this.tokens_to_ids.get(Qe),Ye=this.scores[We],_t=(0,o.len)(Qe);$.insert(ie,_t,Ye,We),!ce&&_t===X&&(ce=!0)}ce||$.insert(ie,X,this.unk_score,this.unk_token_id),ie+=X}}tokenize($){const j=new l.TokenLattice($,this.bos_token_id,this.eos_token_id);return this.populateNodes(j),j.tokens()}encode($){const j=[];for(const X of $){const ie=this.tokenize(X);j.push(...ie)}return j}}const Z=(()=>{const de=[...Array.from({length:94},(ie,ce)=>ce+33),...Array.from({length:12},(ie,ce)=>ce+161),...Array.from({length:82},(ie,ce)=>ce+174)],$=de.slice();let j=0;for(let ie=0;ie<256;++ie)de.includes(ie)||(de.push(ie),$.push(256+j),j+=1);const X=$.map(ie=>String.fromCharCode(ie));return Object.fromEntries(de.map((ie,ce)=>[ie,X[ce]]))})(),H=(0,o.reverseDictionary)(Z);class J extends K{constructor($){super($),this.tokens_to_ids=f($.vocab),this.unk_token_id=this.tokens_to_ids.get($.unk_token),this.unk_token=$.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[X,ie]of this.tokens_to_ids)this.vocab[ie]=X;const j=Array.isArray($.merges[0]);this.merges=j?$.merges:$.merges.map(X=>X.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((X,ie)=>[JSON.stringify(X),ie])),this.end_of_word_suffix=$.end_of_word_suffix,this.continuing_subword_suffix=$.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new l.LRUCache(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe($){if($.length===0)return[];const j=this.cache.get($);if(j!==void 0)return j;const X=Array.from($);this.end_of_word_suffix&&(X[X.length-1]+=this.end_of_word_suffix);let ie=[];if(X.length>1){const ce=new l.PriorityQueue((Qe,We)=>Qe.score<We.score);let xe={token:X[0],bias:0,prev:null,next:null},Re=xe;for(let Qe=1;Qe<X.length;++Qe){const We={bias:Qe/X.length,token:X[Qe],prev:Re,next:null};Re.next=We,this._add_node(ce,Re),Re=We}for(;!ce.isEmpty();){const Qe=ce.pop();if(Qe.deleted||!Qe.next||Qe.next.deleted)continue;if(Qe.deleted=!0,Qe.next.deleted=!0,Qe.prev){const Ye={...Qe.prev};Qe.prev.deleted=!0,Qe.prev=Ye,Ye.prev?Ye.prev.next=Ye:xe=Ye}const We={token:Qe.token+Qe.next.token,bias:Qe.bias,prev:Qe.prev,next:Qe.next.next};We.prev?(We.prev.next=We,this._add_node(ce,We.prev)):xe=We,We.next&&(We.next.prev=We,this._add_node(ce,We))}for(let Qe=xe;Qe!==null;Qe=Qe.next)ie.push(Qe.token)}else ie=X;if(this.continuing_subword_suffix)for(let ce=0;ce<ie.length-1;++ce)ie[ce]+=this.continuing_subword_suffix;return $.length<this.max_length_to_cache&&this.cache.put($,ie),ie}_add_node($,j){const X=this.bpe_ranks.get(JSON.stringify([j.token,j.next.token]));X!==void 0&&(j.score=X+j.bias,$.push(j))}encode($){const j=[];for(const X of $){if(this.ignore_merges&&this.tokens_to_ids.has(X)){j.push(X);continue}const ie=this.bpe(X);for(const ce of ie)if(this.tokens_to_ids.has(ce))j.push(ce);else if(this.byte_fallback){const xe=Array.from(this.text_encoder.encode(ce)).map(Re=>`<0x${Re.toString(16).toUpperCase().padStart(2,"0")}>`);xe.every(Re=>this.tokens_to_ids.has(Re))?j.push(...xe):j.push(this.unk_token)}else j.push(this.unk_token)}return j}}class Q extends K{constructor($,j){super($),this.tokens_to_ids=f(j.target_lang?$.vocab[j.target_lang]:$.vocab),this.bos_token=j.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=j.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=j.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=j.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[X,ie]of this.tokens_to_ids)this.vocab[ie]=X}encode($){return $}}class se extends s.Callable{constructor($){super(),this.config=$}static fromConfig($){if($===null)return null;switch($.type){case"BertNormalizer":return new pe($);case"Precompiled":return new Mt($);case"Sequence":return new Ue($);case"Replace":return new fe($);case"NFC":return new V($);case"NFD":return new A($);case"NFKC":return new U($);case"NFKD":return new ee($);case"Strip":return new _e($);case"StripAccents":return new le($);case"Lowercase":return new ye($);case"Prepend":return new ze($);default:throw new Error(`Unknown Normalizer type: ${$.type}`)}}normalize($){throw Error("normalize should be implemented in subclass.")}_call($){return this.normalize($)}}class fe extends se{normalize($){const j=_(this.config.pattern);return j===null?$:$.replaceAll(j,this.config.content)}}class ae extends se{constructor(){super(...arguments);Y(this,"form")}normalize(j){return j=j.normalize(this.form),j}}class V extends ae{constructor(){super(...arguments);Y(this,"form","NFC")}}class A extends ae{constructor(){super(...arguments);Y(this,"form","NFD")}}class U extends ae{constructor(){super(...arguments);Y(this,"form","NFKC")}}class ee extends ae{constructor(){super(...arguments);Y(this,"form","NFKD")}}class _e extends se{normalize($){return this.config.strip_left&&this.config.strip_right?$=$.trim():(this.config.strip_left&&($=$.trimStart()),this.config.strip_right&&($=$.trimEnd())),$}}class le extends se{normalize($){return $=w($),$}}class ye extends se{normalize($){return $=$.toLowerCase(),$}}class ze extends se{normalize($){return $=this.config.prepend+$,$}}class Ue extends se{constructor($){super($),this.normalizers=$.normalizers.map(j=>se.fromConfig(j))}normalize($){return this.normalizers.reduce((j,X)=>X.normalize(j),$)}}class pe extends se{_tokenize_chinese_chars($){const j=[];for(let X=0;X<$.length;++X){const ie=$[X],ce=ie.charCodeAt(0);S(ce)?(j.push(" "),j.push(ie),j.push(" 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`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test($)}}_clean_text($){const j=[];for(const X of $){const ie=X.charCodeAt(0);ie===0||ie===65533||this._is_control(X)||(/^\s$/.test(X)?j.push(" "):j.push(X))}return j.join("")}normalize($){return this.config.clean_text&&($=this._clean_text($)),this.config.handle_chinese_chars&&($=this._tokenize_chinese_chars($)),this.config.lowercase?($=$.toLowerCase(),this.config.strip_accents!==!1&&($=this.stripAccents($))):this.config.strip_accents&&($=this.stripAccents($)),$}}class W extends s.Callable{static fromConfig($){if($===null)return null;switch($.type){case"BertPreTokenizer":return new re($);case"Sequence":return new rs($);case"Whitespace":return new D($);case"WhitespaceSplit":return new oe($);case"Metaspace":return new gr($);case"ByteLevel":return new G($);case"Split":return new be($);case"Punctuation":return new we($);case"Digits":return new Se($);case"Replace":return new B($);default:throw new Error(`Unknown PreTokenizer type: ${$.type}`)}}pre_tokenize_text($,j){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize($,j){return(Array.isArray($)?$.map(X=>this.pre_tokenize_text(X,j)):this.pre_tokenize_text($,j)).flat()}_call($,j){return this.pre_tokenize($,j)}}class re extends W{constructor($){super(),this.pattern=new RegExp(`[^\\s${M}]+|[${M}]`,"gu")}pre_tokenize_text($,j){return $.trim().match(this.pattern)||[]}}class G extends W{constructor($){super(),this.config=$,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Z,this.text_encoder=new TextEncoder}pre_tokenize_text($,j){return this.add_prefix_space&&!$.startsWith(" ")&&($=" "+$),(this.use_regex?$.match(this.pattern)||[]:[$]).map(ie=>Array.from(this.text_encoder.encode(ie),ce=>this.byte_encoder[ce]).join(""))}}class be extends W{constructor($){super(),this.config=$,this.pattern=_(this.config.pattern,this.config.invert)}pre_tokenize_text($,j){var X;return this.pattern===null?[]:this.config.invert?$.match(this.pattern)||[]:((X=this.config.behavior)==null?void 0:X.toLowerCase())==="removed"?$.split(this.pattern).filter(ie=>ie):d($,this.pattern)}}class we extends W{constructor($){super(),this.config=$,this.pattern=new RegExp(`[^${M}]+|[${M}]+`,"gu")}pre_tokenize_text($,j){return $.match(this.pattern)||[]}}class Se extends W{constructor($){super(),this.config=$;const j=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(j,"gu")}pre_tokenize_text($,j){return $.match(this.pattern)||[]}}class Ce extends s.Callable{constructor($){super(),this.config=$}static fromConfig($){if($===null)return null;switch($.type){case"TemplateProcessing":return new Be($);case"ByteLevel":return new He($);case"RobertaProcessing":return new Fe($);case"BertProcessing":return new $e($);case"Sequence":return new qe($);default:throw new Error(`Unknown PostProcessor type: ${$.type}`)}}post_process($,...j){throw Error("post_process should be implemented in subclass.")}_call($,...j){return this.post_process($,...j)}}class $e extends Ce{constructor($){super($),this.cls=$.cls[0],this.sep=$.sep[0]}post_process($,j=null,{add_special_tokens:X=!0}={}){X&&($=(0,o.mergeArrays)([this.cls],$,[this.sep]));let ie=new Array($.length).fill(0);if(j!==null){const ce=X&&this instanceof Fe?[this.sep]:[],xe=X?[this.sep]:[];$=(0,o.mergeArrays)($,ce,j,xe),ie=(0,o.mergeArrays)(ie,new Array(j.length+ce.length+xe.length).fill(1))}return{tokens:$,token_type_ids:ie}}}class Fe extends $e{}class Be extends Ce{constructor($){super($),this.single=$.single,this.pair=$.pair}post_process($,j=null,{add_special_tokens:X=!0}={}){const ie=j===null?this.single:this.pair;let ce=[],xe=[];for(const Re of ie)"SpecialToken"in Re?X&&(ce.push(Re.SpecialToken.id),xe.push(Re.SpecialToken.type_id)):"Sequence"in Re&&(Re.Sequence.id==="A"?(ce=(0,o.mergeArrays)(ce,$),xe=(0,o.mergeArrays)(xe,new Array($.length).fill(Re.Sequence.type_id))):Re.Sequence.id==="B"&&(ce=(0,o.mergeArrays)(ce,j),xe=(0,o.mergeArrays)(xe,new Array(j.length).fill(Re.Sequence.type_id))));return{tokens:ce,token_type_ids:xe}}}class He extends Ce{post_process($,j=null){return j&&($=(0,o.mergeArrays)($,j)),{tokens:$}}}class qe extends Ce{constructor($){super($),this.processors=$.processors.map(j=>Ce.fromConfig(j))}post_process($,j=null,X={}){let ie;for(const ce of this.processors)if(ce instanceof He)$=ce.post_process($).tokens,j&&(j=ce.post_process(j).tokens);else{const xe=ce.post_process($,j,X);$=xe.tokens,ie=xe.token_type_ids}return{tokens:$,token_type_ids:ie}}}class ke extends s.Callable{constructor($){super(),this.config=$,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=$.trim_offsets}static fromConfig($){if($===null)return null;switch($.type){case"WordPiece":return new Ge($);case"Metaspace":return new ir($);case"ByteLevel":return new Ie($);case"Replace":return new Ve($);case"ByteFallback":return new Ze($);case"Fuse":return new nt($);case"Strip":return new lt($);case"Sequence":return new St($);case"CTC":return new pt($);case"BPEDecoder":return new Vt($);default:throw new Error(`Unknown Decoder type: ${$.type}`)}}_call($){return this.decode($)}decode($){return this.decode_chain($).join("")}decode_chain($){throw Error("`decode_chain` should be implemented in subclass.")}}class Ve extends ke{decode_chain($){const j=_(this.config.pattern);return j===null?$:$.map(X=>X.replaceAll(j,this.config.content))}}class Ze extends ke{constructor($){super($),this.text_decoder=new TextDecoder}decode_chain($){const j=[];let X=[];for(const ie of $){let ce=null;if(ie.length===6&&ie.startsWith("<0x")&&ie.endsWith(">")){const xe=parseInt(ie.slice(3,5),16);isNaN(xe)||(ce=xe)}if(ce!==null)X.push(ce);else{if(X.length>0){const xe=this.text_decoder.decode(Uint8Array.from(X));j.push(xe),X=[]}j.push(ie)}}if(X.length>0){const ie=this.text_decoder.decode(Uint8Array.from(X));j.push(ie),X=[]}return j}}class nt extends ke{decode_chain($){return[$.join("")]}}class lt extends ke{constructor($){super($),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain($){return $.map(j=>{let X=0;for(let ce=0;ce<this.start&&j[ce]===this.content;++ce){X=ce+1;continue}let ie=j.length;for(let ce=0;ce<this.stop;++ce){const xe=j.length-ce-1;if(j[xe]===this.content){ie=xe;continue}else break}return j.slice(X,ie)})}}class Ge extends ke{constructor($){super($),this.cleanup=$.cleanup}decode_chain($){return $.map((j,X)=>(X!==0&&(j.startsWith(this.config.prefix)?j=j.replace(this.config.prefix,""):j=" 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this.cleanup&&(ie=k(ie).replaceAll(this.word_delimiter_token," ").trim()),ie}decode_chain($){return[this.convert_tokens_to_string($)]}}class St extends ke{constructor($){super($),this.decoders=$.decoders.map(j=>ke.fromConfig(j))}decode_chain($){return this.decoders.reduce((j,X)=>X.decode_chain(j),$)}}class Vt extends ke{constructor($){super($),this.suffix=this.config.suffix}decode_chain($){return $.map((j,X)=>j.replaceAll(this.suffix,X===$.length-1?"":" "))}}class Rt extends ke{decode_chain($){let j="";for(let X=1;X<$.length;X+=2)j+=$[X];return[j]}}class gr extends W{constructor($){super(),this.addPrefixSpace=$.add_prefix_space,this.replacement=$.replacement,this.strRep=$.str_rep||this.replacement,this.prepend_scheme=$.prepend_scheme??"always"}pre_tokenize_text($,{section_index:j=void 0}={}){let X=$.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!X.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&j===0)&&(X=this.strRep+X),[X]}}class ir extends ke{constructor($){super($),this.addPrefixSpace=$.add_prefix_space,this.replacement=$.replacement}decode_chain($){const j=[];for(let X=0;X<$.length;++X){let ie=$[X].replaceAll(this.replacement," ");this.addPrefixSpace&&X==0&&ie.startsWith(" ")&&(ie=ie.substring(1)),j.push(ie)}return j}}class Mt extends se{constructor($){super($),this.charsmap=$.precompiled_charsmap}normalize($){return $=$.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),$=$.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),$.includes("～")?$=$.split("～").map(X=>X.normalize("NFKC")).join("～"):$=$.normalize("NFKC"),$}}class rs extends W{constructor($){super(),this.tokenizers=$.pretokenizers.map(j=>W.fromConfig(j))}pre_tokenize_text($,j){return this.tokenizers.reduce((X,ie)=>ie.pre_tokenize(X,j),[$])}}class D extends W{constructor($){super()}pre_tokenize_text($,j){return $.match(/\w+|[^\w\s]+/g)||[]}}class oe extends W{constructor($){super()}pre_tokenize_text($,j){return v($)}}class B extends W{constructor($){super(),this.config=$,this.pattern=_(this.config.pattern),this.content=this.config.content}pre_tokenize_text($,j){return this.pattern===null?[$]:[$.replaceAll(this.pattern,this.config.content)]}}const te=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function me(de,$,j,X){for(const ie of Object.keys(de)){const ce=$-de[ie].length,xe=j(ie),Re=new Array(ce).fill(xe);de[ie]=X==="right"?(0,o.mergeArrays)(de[ie],Re):(0,o.mergeArrays)(Re,de[ie])}}function Oe(de,$){for(const j of Object.keys(de))de[j].length=$}class ve extends s.Callable{constructor(j,X){super();Y(this,"return_token_type_ids",!1);Y(this,"padding_side","right");this._tokenizer_config=X,this.normalizer=se.fromConfig(j.normalizer),this.pre_tokenizer=W.fromConfig(j.pre_tokenizer),this.model=K.fromConfig(j.model,X),this.post_processor=Ce.fromConfig(j.post_processor),this.decoder=ke.fromConfig(j.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ie of j.added_tokens){const ce=new z(ie);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=X.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_splitter=new l.DictionarySplitter(this.added_tokens.map(ie=>ie.content)),this.added_tokens_map=new Map(this.added_tokens.map(ie=>[ie.content,ie])),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=X.model_max_length,this.remove_space=X.remove_space,this.clean_up_tokenization_spaces=X.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=X.do_lowercase_and_remove_accent??!1,X.padding_side&&(this.padding_side=X.padding_side),this.legacy=!1,this.chat_template=X.chat_template??null,Array.isArray(this.chat_template)){const ie=Object.create(null);for(const{name:ce,template:xe}of this.chat_template){if(typeof ce!="string"||typeof xe!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ie[ce]=xe}this.chat_template=ie}this._compiled_template_cache=new Map}getToken(...j){for(const X of j){const ie=this._tokenizer_config[X];if(ie)if(typeof ie=="object"){if(ie.__type==="AddedToken")return ie.content;throw Error(`Unknown token: ${ie}`)}else return ie}return null}static async from_pretrained(j,{progress_callback:X=null,config:ie=null,cache_dir:ce=null,local_files_only:xe=!1,revision:Re="main",legacy:Qe=null}={}){const We=await c(j,{progress_callback:X,config:ie,cache_dir:ce,local_files_only:xe,revision:Re,legacy:Qe});return new this(...We)}_call(j,{text_pair:X=null,add_special_tokens:ie=!0,padding:ce=!1,truncation:xe=null,max_length:Re=null,return_tensor:Qe=!0,return_token_type_ids:We=null}={}){const Ye=Array.isArray(j);let _t;if(Ye){if(j.length===0)throw Error("text array must be non-empty");if(X!==null){if(Array.isArray(X)){if(j.length!==X.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");_t=j.map((At,Yt)=>this._encode_plus(At,{text_pair:X[Yt],add_special_tokens:ie,return_token_type_ids:We}))}else _t=j.map(At=>this._encode_plus(At,{add_special_tokens:ie,return_token_type_ids:We}))}else{if(j==null)throw Error("text may not be null or undefined");if(Array.isArray(X))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");_t=[this._encode_plus(j,{text_pair:X,add_special_tokens:ie,return_token_type_ids:We})]}if(Re===null?Re=this.model_max_length:xe===null&&(ce===!0?(console.warn("`max_length` is ignored when `padding: true` and there is no truncation strategy. To pad to max length, use `padding: 'max_length'`."),Re=this.model_max_length):ce===!1&&(console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation: true` to explicitly truncate examples to max length."),xe=!0)),ce===!0&&(Re=Math.min((0,i.max)(_t.map(At=>At.input_ids.length))[0],Re??1/0)),Re=Math.min(Re,this.model_max_length??1/0),ce||xe)for(let At=0;At<_t.length;++At)_t[At].input_ids.length!==Re&&(_t[At].input_ids.length>Re?xe&&Oe(_t[At],Re):ce&&me(_t[At],Re,Yt=>Yt==="input_ids"?this.pad_token_id:0,this.padding_side));const Ot={};if(Qe){if(!(ce&&xe)&&_t.some(Yt=>{var Ut;for(const mr of Object.keys(Yt))if(Yt[mr].length!==((Ut=_t[0][mr])==null?void 0:Ut.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 At=[_t.length,_t[0].input_ids.length];for(const Yt of Object.keys(_t[0]))Ot[Yt]=new a.Tensor("int64",BigInt64Array.from(_t.flatMap(Ut=>Ut[Yt]).map(BigInt)),At)}else{for(const At of Object.keys(_t[0]))Ot[At]=_t.map(Yt=>Yt[At]);if(!Ye)for(const At of Object.keys(Ot))Ot[At]=Ot[At][0]}return Ot}_encode_text(j){if(j===null)return null;const X=this.added_tokens_splitter.split(j);for(let ce=0;ce<X.length;++ce){const xe=this.added_tokens_map.get(X[ce]);xe&&(xe.lstrip&&ce>0&&(X[ce-1]=X[ce-1].trimEnd()),xe.rstrip&&ce<X.length-1&&(X[ce+1]=X[ce+1].trimStart()))}return X.flatMap((ce,xe)=>{if(ce.length===0)return[];if(this.added_tokens_map.has(ce))return[ce];if(this.remove_space===!0&&(ce=ce.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(ce=g(ce)),this.normalizer!==null&&(ce=this.normalizer(ce)),ce.length===0)return[];const Re=this.pre_tokenizer!==null?this.pre_tokenizer(ce,{section_index:xe}):[ce];return this.model(Re)})}_encode_plus(j,{text_pair:X=null,add_special_tokens:ie=!0,return_token_type_ids:ce=null}={}){const{tokens:xe,token_type_ids:Re}=this._tokenize_helper(j,{pair:X,add_special_tokens:ie}),Qe=this.model.convert_tokens_to_ids(xe),We={input_ids:Qe,attention_mask:new Array(Qe.length).fill(1)};return(ce??this.return_token_type_ids)&&Re&&(We.token_type_ids=Re),We}_tokenize_helper(j,{pair:X=null,add_special_tokens:ie=!1}={}){const ce=this._encode_text(j),xe=this._encode_text(X);return this.post_processor?this.post_processor(ce,xe,{add_special_tokens:ie}):{tokens:(0,o.mergeArrays)(ce??[],xe??[])}}tokenize(j,{pair:X=null,add_special_tokens:ie=!1}={}){return this._tokenize_helper(j,{pair:X,add_special_tokens:ie}).tokens}encode(j,{text_pair:X=null,add_special_tokens:ie=!0,return_token_type_ids:ce=null}={}){return this._encode_plus(j,{text_pair:X,add_special_tokens:ie,return_token_type_ids:ce}).input_ids}batch_decode(j,X={}){return j instanceof a.Tensor&&(j=j.tolist()),j.map(ie=>this.decode(ie,X))}decode(j,X={}){if(j instanceof a.Tensor&&(j=T(j)),!Array.isArray(j)||j.length===0||!(0,o.isIntegralNumber)(j[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(j,X)}decode_single(j,{skip_special_tokens:X=!1,clean_up_tokenization_spaces:ie=null}){let ce=this.model.convert_ids_to_tokens(j);X&&(ce=ce.filter(Re=>!this.special_tokens.includes(Re)));let xe=this.decoder?this.decoder(ce):ce.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(xe=xe.replaceAll(this.decoder.end_of_word_suffix," "),X&&(xe=xe.trim())),(ie??this.clean_up_tokenization_spaces)&&(xe=k(xe)),xe}get_chat_template({chat_template:j=null,tools:X=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ie=this.chat_template;if(j!==null&&Object.hasOwn(ie,j))j=ie[j];else if(j===null)if(X!==null&&"tool_use"in ie)j=ie.tool_use;else if("default"in ie)j=ie.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(ie).sort()}.`)}else if(j===null)if(this.chat_template)j=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 j}apply_chat_template(j,{tools:X=null,documents:ie=null,chat_template:ce=null,add_generation_prompt:xe=!1,tokenize:Re=!0,padding:Qe=!1,truncation:We=!1,max_length:Ye=null,return_tensor:_t=!0,return_dict:Ot=!1,tokenizer_kwargs:At={},...Yt}={}){if(ce=this.get_chat_template({chat_template:ce,tools:X}),typeof ce!="string")throw Error(`chat_template must be a string, but got ${typeof ce}`);let Ut=this._compiled_template_cache.get(ce);Ut===void 0&&(Ut=new u.Template(ce),this._compiled_template_cache.set(ce,Ut));const mr=Object.create(null);for(const Pr of te){const Cr=this.getToken(Pr);Cr&&(mr[Pr]=Cr)}const Mr=Ut.render({messages:j,add_generation_prompt:xe,tools:X,documents:ie,...mr,...Yt});if(Re){const Pr=this._call(Mr,{add_special_tokens:!1,padding:Qe,truncation:We,max_length:Ye,return_tensor:_t,...At});return Ot?Pr:Pr.input_ids}return Mr}}class vt extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class Ft extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class ht extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class ut extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class rt extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class jt extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class Ht extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class wr extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class Jt extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class Or extends ve{}class ss extends ve{}class ys extends ve{constructor(j,X){super(j,X);Y(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 ns extends ve{constructor(){super(...arguments);Y(this,"return_token_type_ids",!0)}}class $s extends ve{}class Vr extends ve{}class ks extends ve{}class Qr extends ve{constructor($,j){super($,j),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(X=>this.languageRegex.test(X)),this.lang_to_token=X=>X}_build_translation_inputs($,j,X){return ur(this,$,j,X)}}class vs extends Qr{}class Is extends ve{}class As extends ve{}const ar="▁";class Fs extends ve{constructor(j,X){super(j,X);Y(this,"padding_side","left");this.legacy=X.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new gr({replacement:ar,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(j){if(j===null)return null;if(this.legacy||j.length===0)return super._encode_text(j);let X=super._encode_text(ar+j.replaceAll(ar," "));return X.length>1&&X[0]===ar&&this.special_tokens.includes(X[1])&&(X=X.slice(1)),X}}class Er extends ve{}class xs extends ve{}class Br extends ve{}class Ae extends ve{}class Je extends ve{}class it extends ve{}class Nt extends ve{}class os extends ve{}class is extends ve{}function ur(de,$,j,X){if(!("language_codes"in de)||!Array.isArray(de.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in de)||!(de.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in de)||typeof de.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ie=X.src_lang,ce=X.tgt_lang;if(!de.language_codes.includes(ce))throw new Error(`Target language code "${ce}" is not valid. Must be one of: {${de.language_codes.join(", ")}}`);if(ie!==void 0){if(!de.language_codes.includes(ie))throw new Error(`Source language code "${ie}" is not valid. Must be one of: {${de.language_codes.join(", ")}}`);for(const xe of de.post_processor.config.single)if("SpecialToken"in xe&&de.languageRegex.test(xe.SpecialToken.id)){xe.SpecialToken.id=de.lang_to_token(ie);break}}return X.forced_bos_token_id=de.model.convert_tokens_to_ids([de.lang_to_token(ce)])[0],de._call($,j)}class as extends ve{constructor($,j){super($,j),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(X=>this.languageRegex.test(X)),this.lang_to_token=X=>X}_build_translation_inputs($,j,X){return ur(this,$,j,X)}}class cr extends ve{constructor($,j){super($,j),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(X=>this.languageRegex.test(X)).map(X=>X.slice(2,-2)),this.lang_to_token=X=>`__${X}__`}_build_translation_inputs($,j,X){return ur(this,$,j,X)}}class hr extends ve{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr($,{return_timestamps:j=!1,return_language:X=!1,time_precision:ie=null,force_full_sequences:ce=!0}={}){if(ie===null)throw Error("Must specify time_precision");let xe=null;const Re=j==="word";function Qe(){return{language:xe,timestamp:[null,null],text:""}}const We=[];let Ye=Qe(),_t=0;const Ot=this.timestamp_begin,Yt=Ot+1500;let Ut=[],mr=[],Mr=!1,Pr=null;const Cr=new Set(this.all_special_ids);for(const Kt of $){const fr=Kt.tokens,Dr=Re?Kt.token_timestamps:null;let Xr=null,Jr=Ot;if("stride"in Kt){const[br,er,dr]=Kt.stride;if(_t-=er,Pr=br-dr,er&&(Jr=er/ie+Ot),dr)for(let pr=fr.length-1;pr>=0;--pr){const Ar=Number(fr[pr]);if(Ar>=Ot){if(Xr!==null&&(Ar-Ot)*ie<Pr)break;Xr=Ar}}}let Ir=[],Lr=[];for(let br=0;br<fr.length;++br){const er=Number(fr[br]);if(Cr.has(er)){const dr=this.decode([er]),pr=p.WHISPER_LANGUAGE_MAPPING.get(dr.slice(2,-2));if(pr!==void 0){if(xe!==null&&pr!==xe&&!j){Ut.push(Ir);const Ar=this.findLongestCommonSequence(Ut)[0],us=this.decode(Ar);Ye.text=us,We.push(Ye),Ut=[],Ir=[],Ye=Qe()}xe=Ye.language=pr}}else if(er>=Ot&&er<=Yt){const dr=(er-Ot)*ie+_t,pr=(0,i.round)(dr,2);if(Xr!==null&&er>=Xr)Mr=!0;else if(Mr||Ut.length>0&&er<Jr)Mr=!1;else if(Ye.timestamp[0]===null)Ye.timestamp[0]=pr;else if(pr!==Ye.timestamp[0]){Ye.timestamp[1]=pr,Ut.push(Ir),Re&&mr.push(Lr);const[Ar,us]=this.findLongestCommonSequence(Ut,mr),xn=this.decode(Ar);Ye.text=xn,Re&&(Ye.words=this.collateWordTimestamps(Ar,us,xe)),We.push(Ye),Ut=[],Ir=[],mr=[],Lr=[],Ye=Qe()}}else if(Ir.push(er),Re){let dr=(0,i.round)(Dr[br]+_t,2),pr;if(br+1<Dr.length){pr=(0,i.round)(Dr[br+1]+_t,2);const Ar=this.decode([er]);y.test(Ar)&&(pr=(0,i.round)(Math.min(dr+ie,pr),2))}else pr=null;Lr.push([dr,pr])}}if("stride"in Kt){const[br,er,dr]=Kt.stride;_t+=br-dr}Ir.length>0?(Ut.push(Ir),Re&&mr.push(Lr)):Ut.every(br=>br.length===0)&&(Ye=Qe(),Ut=[],Ir=[],mr=[],Lr=[])}if(Ut.length>0){if(ce&&j)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[Kt,fr]=this.findLongestCommonSequence(Ut,mr),Dr=this.decode(Kt);Ye.text=Dr,Re&&(Ye.words=this.collateWordTimestamps(Kt,fr,xe)),We.push(Ye)}let Zt=Object.create(null);const Es=We.map(Kt=>Kt.text).join("");if(j||X){for(let Kt=0;Kt<We.length;++Kt){const fr=We[Kt];j||delete fr.timestamp,X||delete fr.language}if(Re){const Kt=[];for(const fr of We)for(const Dr of fr.words)Kt.push(Dr);Zt={chunks:Kt}}else Zt={chunks:We}}return[Es,Zt]}findLongestCommonSequence($,j=null){let X=$[0],ie=X.length,ce=[];const xe=Array.isArray(j)&&j.length>0;let Re=xe?[]:null,Qe=xe?j[0]:null;for(let We=1;We<$.length;++We){const Ye=$[We];let _t=0,Ot=[ie,ie,0,0];const At=Ye.length;for(let Zt=1;Zt<ie+At;++Zt){const Es=Math.max(0,ie-Zt),Kt=Math.min(ie,ie+At-Zt),fr=X.slice(Es,Kt),Dr=Math.max(0,Zt-ie),Xr=Math.min(At,Zt),Jr=Ye.slice(Dr,Xr);if(fr.length!==Jr.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. 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Therefore, you may experience slightly inaccurate results.')}_encode_text($){if($===null)return null;const[j,...X]=$.trim().split(this.languageRegex);if(X.length===0)return super._encode_text(j);if(X.length===2){const[ie,ce]=X;return this.supported_language_codes.includes(ie)||console.warn(`Unsupported language code "${ie}" detected, which may lead to unexpected behavior. 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k.metadata()).channels,{data:S,info:E}=await k.rotate().raw().toBuffer({resolveWithObject:!0}),v=new f(new Uint8ClampedArray(S),E.width,E.height,E.channels);return g!==void 0&&g!==E.channels&&v.convert(g),v};else throw new Error("Unable to load image processing library.");const d={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},_=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class f{constructor(w,g,S,E){this.data=w,this.width=g,this.height=S,this.channels=E}get size(){return[this.width,this.height]}static async read(w){if(w instanceof f)return w;if(typeof w=="string"||w instanceof URL)return await this.fromURL(w);if(w instanceof Blob)return await this.fromBlob(w);if(typeof HTMLCanvasElement<"u"&&w instanceof HTMLCanvasElement||typeof OffscreenCanvas<"u"&&w instanceof OffscreenCanvas)return this.fromCanvas(w);throw new Error(`Unsupported input type: ${typeof w}`)}static fromCanvas(w){if(!c)throw new Error("fromCanvas() is only supported in browser environments.");const S=w.getContext("2d").getImageData(0,0,w.width,w.height).data;return new f(S,w.width,w.height,4)}static async fromURL(w){const g=await(0,o.getFile)(w);if(g.status!==200)throw new Error(`Unable to read image from "${w}" (${g.status} ${g.statusText})`);const S=await g.blob();return this.fromBlob(S)}static async fromBlob(w){if(c){const g=await p(w),S=l(g.width,g.height).getContext("2d");return S.drawImage(g,0,0),new this(S.getImageData(0,0,g.width,g.height).data,g.width,g.height,4)}else{const g=a(await w.arrayBuffer());return await p(g)}}static fromTensor(w,g="CHW"){if(w.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${w.dims.length} dimensions.`);if(g==="CHW")w=w.transpose(1,2,0);else if(g!=="HWC")throw new Error(`Unsupported channel format: ${g}`);if(!(w.data instanceof Uint8ClampedArray||w.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${w.type}`);switch(w.dims[2]){case 1:case 2:case 3:case 4:return new f(w.data,w.dims[1],w.dims[0],w.dims[2]);default:throw new Error(`Unsupported number of channels: ${w.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const w=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let g=0,S=0;g<this.data.length;g+=this.channels){const E=this.data[g],v=this.data[g+1],M=this.data[g+2];w[S++]=Math.round(.2989*E+.587*v+.114*M)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(w,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const w=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let g=0,S=0;g<this.data.length;++g)w[S++]=this.data[g],w[S++]=this.data[g],w[S++]=this.data[g];break;case 4:for(let g=0,S=0;g<this.data.length;g+=4)w[S++]=this.data[g],w[S++]=this.data[g+1],w[S++]=this.data[g+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(w,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const w=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let g=0,S=0;g<this.data.length;++g)w[S++]=this.data[g],w[S++]=this.data[g],w[S++]=this.data[g],w[S++]=255;break;case 3:for(let g=0,S=0;g<this.data.length;g+=3)w[S++]=this.data[g],w[S++]=this.data[g+1],w[S++]=this.data[g+2],w[S++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(w,this.width,this.height,4)}putAlpha(w){if(w.width!==this.width||w.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${w.width}x${w.height}`);if(w.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${w.channels}`);const g=this.data,S=w.data,E=this.width*this.height;if(this.channels===3){const v=new Uint8ClampedArray(E*4);for(let M=0,y=0,C=0;M<E;++M)v[C++]=g[y++],v[C++]=g[y++],v[C++]=g[y++],v[C++]=S[M];return this._update(v,this.width,this.height,4)}else if(this.channels===4){for(let v=0;v<E;++v)g[4*v+3]=S[v];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(w,g,{resample:S=2}={}){if(this.width===w&&this.height===g)return this;let E=d[S]??S;const v=(0,s.isNullishDimension)(w),M=(0,s.isNullishDimension)(g);if(v&&M)return this;if(v?w=g/this.height*this.width:M&&(g=w/this.width*this.height),c){const y=this.channels,C=this.toCanvas(),F=l(w,g).getContext("2d");return F.drawImage(C,0,0,w,g),new f(F.getImageData(0,0,w,g).data,w,g,4).convert(y)}else{let y=this.toSharp();switch(E){case"box":case"hamming":(E==="box"||E==="hamming")&&(console.warn(`Resampling method ${E} is not yet supported. Using bilinear instead.`),E="bilinear");case"nearest":case"bilinear":case"bicubic":y=y.affine([w/this.width,0,0,g/this.height],{interpolator:E});break;case"lanczos":y=y.resize({width:w,height:g,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${E} is not supported.`)}return await p(y)}}async pad([w,g,S,E]){if(w=Math.max(w,0),g=Math.max(g,0),S=Math.max(S,0),E=Math.max(E,0),w===0&&g===0&&S===0&&E===0)return this;if(c){const v=this.channels,M=this.toCanvas(),y=this.width+w+g,C=this.height+S+E,F=l(y,C).getContext("2d");return F.drawImage(M,0,0,this.width,this.height,w,S,this.width,this.height),new f(F.getImageData(0,0,y,C).data,y,C,4).convert(v)}else{const v=this.toSharp().extend({left:w,right:g,top:S,bottom:E});return await p(v)}}async crop([w,g,S,E]){if(w=Math.max(w,0),g=Math.max(g,0),S=Math.min(S,this.width-1),E=Math.min(E,this.height-1),w===0&&g===0&&S===this.width-1&&E===this.height-1)return this;const v=S-w+1,M=E-g+1;if(c){const y=this.channels,C=this.toCanvas(),F=l(v,M).getContext("2d");return F.drawImage(C,w,g,v,M,0,0,v,M),new f(F.getImageData(0,0,v,M).data,v,M,4).convert(y)}else{const y=this.toSharp().extract({left:w,top:g,width:v,height:M});return await p(y)}}async center_crop(w,g){if(this.width===w&&this.height===g)return this;const S=(this.width-w)/2,E=(this.height-g)/2;if(c){const v=this.channels,M=this.toCanvas(),y=l(w,g).getContext("2d");let C=0,F=0,z=0,K=0;return S>=0?C=S:z=-S,E>=0?F=E:K=-E,y.drawImage(M,C,F,w,g,z,K,w,g),new f(y.getImageData(0,0,w,g).data,w,g,4).convert(v)}else{let v=this.toSharp();if(S>=0&&E>=0)v=v.extract({left:Math.floor(S),top:Math.floor(E),width:w,height:g});else if(S<=0&&E<=0){const M=Math.floor(-E),y=Math.floor(-S);v=v.extend({top:M,left:y,right:w-this.width-y,bottom:g-this.height-M})}else{let M=[0,0],y=0;E<0?(M[0]=Math.floor(-E),M[1]=g-this.height-M[0]):y=Math.floor(E);let 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  !*** ./src/transformers.js ***!
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Ax=m.full;m.full_like,m.getKeyValueShapes,m.hamming,m.hanning,m.interpolate,m.interpolate_4d,m.interpolate_data,m.is_chinese_char,m.layer_norm,m.load_image,m.load_video,m.log_softmax,m.magnitude,m.matmul,m.max,m.mean,m.mean_pooling,m.medianFilter,m.mel_filter_bank,m.min,m.ones,m.ones_like,m.permute,m.permute_data,m.pipeline,m.quantize_embeddings,m.rand,m.read_audio,m.rfft,m.round,m.slice,m.softmax,m.spectrogram,m.stack,m.std_mean,m.topk,m.window_function,m.zeros,m.zeros_like;async function Fx(){try{if(Ox())return{supported:!1,isNode:!0,reason:"",fp16Supported:!1};if(typeof navigator>"u"||!navigator.gpu)return{supported:!1,isNode:!1,reason:"WebGPU is not available (navigator.gpu is undefined)",fp16Supported:!1};const e=await navigator.gpu.requestAdapter();return e?{supported:!0,isNode:!1,reason:"",adapter:e,fp16Supported:e.features.has("shader-f16")}:{supported:!1,isNode:!1,reason:"WebGPU is not supported (no adapter found)",fp16Supported:!1}}catch(e){return{supported:!1,isNode:!1,reason:e instanceof Error?e.toString():String(e),fp16Supported:!1}}}function Ox(){return typeof process<"u"&&"versions"in process&&process.versions!=null&&typeof process.versions=="object"&&"node"in process.versions&&process.versions.node!=null}async function Dx(){return Fx().then(e=>e.supported)}const Lx=64;class Gn{static async getInstance(r){return this.model_id="onnx-community/whisper-base",this.tokenizer??(this.tokenizer=$x.from_pretrained(this.model_id,{progress_callback:r})),this.processor??(this.processor=Sx.from_pretrained(this.model_id,{progress_callback:r})),this.model??(this.model=Ix.from_pretrained(this.model_id,{dtype:{encoder_model:"fp32",decoder_model_merged:"q4"},device:await Dx()?"webgpu":"wasm",progress_callback:r})),Promise.all([this.tokenizer,this.processor,this.model])}}Y(Gn,"model_id",null),Y(Gn,"tokenizer"),Y(Gn,"processor"),Y(Gn,"model");let du=!1;async function zx({audio:e,language:r}){if(du)return;du=!0,self.postMessage({status:"start"});const[t,s,o]=await Gn.getInstance();let n,i=0;const a=d=>{n??(n=performance.now());let _;i++>0&&(_=i/(performance.now()-n)*1e3),self.postMessage({status:"update",output:d,tps:_,numTokens:i})},l=new kx(t,{skip_prompt:!0,decode_kwargs:{skip_special_tokens:!0},callback_function:a}),u=await s(e),p=await o.generate({...u,max_new_tokens:Lx,language:r,streamer:l}),c=t.batch_decode(p,{skip_special_tokens:!0});self.postMessage({status:"complete",output:c}),du=!1}async function Bx(){self.postMessage({status:"loading",data:"Loading model..."});const[e,r,t]=await Gn.getInstance(s=>{self.postMessage(s)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."}),await t.generate({input_features:Ax([1,80,3e3],0),max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async e=>{const{type:r,data:t}=e.data;switch(r){case"load":Bx();break;case"generate":zx(t);break}})})();