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t=this.tensorTrackersById.get(e);t&&(this.tensorTrackersById.delete(e),t.tensorWrapper&&this.releaseTensor(t.tensorWrapper))}async ensureTensor(e,t,n,r,i){uM(`verbose`,()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${t}, dataType: ${n}, shape: ${r}, copyOld: ${i}}`);let a=this.tensorTrackersById.get(t);if(!a)throw Error(`Tensor not found.`);return a.ensureTensor(e,n,r,i)}upload(e,t){let n=this.tensorTrackersById.get(e);if(!n)throw Error(`Tensor not found.`);n.upload(t)}async download(e,t){uM(`verbose`,()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${t?.byteLength}}`);let n=this.tensorTrackersById.get(e);if(!n)throw Error(`Tensor not found.`);return n.download(t)}releaseTensorsForSession(e){for(let t of this.freeTensors)t.sessionId===e&&t.destroy();this.freeTensors=this.freeTensors.filter(t=>t.sessionId!==e)}registerTensor(e,t,n,r){let i=this.getMLContext(e),a=gM(),o=new yM({sessionId:e,context:i,tensor:t,dataType:n,shape:r});return this.tensorTrackersById.set(a,new bM(this,o)),this.externalTensors.add(o),a}async getCachedTensor(e,t,n,r,i,a,o){let s=this.getMLContext(e);for(let[r,i]of this.freeTensors.entries())if(i.canReuseTensor(s,t,n)){uM(`verbose`,()=>`[WebNN] Reusing tensor {dataType: ${t}, ${o?`fallbackDataType: ${o},`:``} shape: ${n}`);let i=this.freeTensors.splice(r,1)[0];return i.sessionId=e,i}uM(`verbose`,()=>`[WebNN] MLContext.createTensor {dataType: ${t}, ${o?`fallbackDataType: ${o},`:``} shape: ${n}}`);let c=await s.createTensor({dataType:o??t,shape:n,dimensions:n,usage:r,writable:i,readable:a});return new yM({sessionId:e,context:s,tensor:c,dataType:t,shape:n,fallbackDataType:o})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},SM=(...e)=>new xM(...e)}),wM={};qk(wM,{WebNNBackend:()=>DM});var TM,EM,DM,OM=Kk(()=>{$j(),Aj(),rM(),CM(),dM(),TM=new Map([[1,`float32`],[10,`float16`],[6,`int32`],[12,`uint32`],[7,`int64`],[13,`uint64`],[22,`int4`],[21,`uint4`],[3,`int8`],[2,`uint8`],[9,`uint8`]]),EM=(e,t)=>{if(e===t)return!0;if(e===void 0||t===void 0)return!1;let n=Object.keys(e).sort(),r=Object.keys(t).sort();return n.length===r.length&&n.every((n,i)=>n===r[i]&&e[n]===t[n])},DM=class{constructor(e){this.tensorManager=SM(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],this.sessionGraphInputs=new Map,this.sessionGraphOutputs=new Map,this.temporaryGraphInputs=[],this.temporaryGraphOutputs=[],this.temporarySessionTensorIds=new Map,this.mlOpSupportLimitsBySessionId=new Map,cM(e.logLevel,!!e.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw Error(`No active session`);return this.activeSessionId}onRunStart(e){uM(`verbose`,()=>`[WebNN] onRunStart {sessionId: ${e}}`),this.activeSessionId=e}onRunEnd(e){uM(`verbose`,()=>`[WebNN] onRunEnd {sessionId: ${e}}`);let t=this.temporarySessionTensorIds.get(e);if(t){for(let e of t)uM(`verbose`,()=>`[WebNN] releasing temporary tensor {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e);this.temporarySessionTensorIds.delete(e),this.activeSessionId=void 0}}async createMLContext(e){if(e instanceof GPUDevice){let t=this.mlContextCache.findIndex(t=>t.gpuDevice===e);if(t!==-1)return this.mlContextCache[t].mlContext;{let t=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:t}),t}}else if(e===void 0){let e=this.mlContextCache.findIndex(e=>e.options===void 0&&e.gpuDevice===void 0);if(e!==-1)return this.mlContextCache[e].mlContext;{let e=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:e}),e}}let t=this.mlContextCache.findIndex(t=>EM(t.options,e));if(t!==-1)return this.mlContextCache[t].mlContext;{let t=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:t}),t}}registerMLContext(e,t){this.mlContextBySessionId.set(e,t);let n=this.sessionIdsByMLContext.get(t);n||(n=new Set,this.sessionIdsByMLContext.set(t,n)),n.add(e),this.mlOpSupportLimitsBySessionId.has(e)||this.mlOpSupportLimitsBySessionId.set(e,t.opSupportLimits()),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(e,this.temporaryGraphInputs),this.temporaryGraphInputs=[]),this.temporaryGraphOutputs.length>0&&(this.sessionGraphOutputs.set(e,this.temporaryGraphOutputs),this.temporaryGraphOutputs=[])}onReleaseSession(e){this.sessionGraphInputs.delete(e),this.sessionGraphOutputs.delete(e);let t=this.mlContextBySessionId.get(e);if(!t)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e),this.mlOpSupportLimitsBySessionId.delete(e);let n=this.sessionIdsByMLContext.get(t);if(n.delete(e),n.size===0){this.sessionIdsByMLContext.delete(t);let e=this.mlContextCache.findIndex(e=>e.mlContext===t);e!==-1&&this.mlContextCache.splice(e,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}getMLOpSupportLimits(e){return this.mlOpSupportLimitsBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){uM(`verbose`,()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,n,r,i){let a=TM.get(n);if(!a)throw Error(`Unsupported ONNX data type: ${n}`);return this.tensorManager.ensureTensor(e??this.currentSessionId,t,a,r,i)}async createTemporaryTensor(e,t,n){uM(`verbose`,()=>`[WebNN] createTemporaryTensor {onnxDataType: ${t}, shape: ${n}}`);let r=TM.get(t);if(!r)throw Error(`Unsupported ONNX data type: ${t}`);let i=this.tensorManager.reserveTensorId();await this.tensorManager.ensureTensor(e,i,r,n,!1);let a=this.temporarySessionTensorIds.get(e);return a?a.push(i):this.temporarySessionTensorIds.set(e,[i]),i}uploadTensor(e,t){if(!kj().shouldTransferToMLTensor)throw Error(`Trying to upload to a MLTensor while shouldTransferToMLTensor is false`);uM(`verbose`,()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${t.byteLength}}`),this.tensorManager.upload(e,t)}async downloadTensor(e,t){return this.tensorManager.download(e,t)}createMLTensorDownloader(e,t){return async()=>{let n=await this.tensorManager.download(e);return nM(n,t)}}registerMLTensor(e,t,n,r){let i=TM.get(n);if(!i)throw Error(`Unsupported ONNX data type: ${n}`);let a=this.tensorManager.registerTensor(e,t,i,r);return uM(`verbose`,()=>`[WebNN] registerMLTensor {tensor: ${t}, dataType: ${i}, dimensions: ${r}} -> {tensorId: ${a}}`),a}registerMLConstant(e,t,n,r,i,a,o=!1){if(!a)throw Error(`External mounted files are not available.`);let s=e;e.startsWith(`./`)&&(s=e.substring(2));let c=a.get(s);if(!c)throw Error(`File with name ${s} not found in preloaded 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