paddleocr-js/dist/node/index.js

JavaScript wrapper for PaddleOCR, providing OCR capabilities in browser and Node.js

/*! For license information please see index.js.LICENSE.txt */
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o=(0,n(929).op)({atan2_:function(e,t){let n=(0,i.YT)(e,"a","atan2"),o=(0,i.YT)(t,"b","atan2");[n,o]=(0,s.makeTypesMatch)(n,o);const p={a:n,b:o};return a.T2.runKernel(r.lxb,p)}})},1411:(e,t,n)=>{"use strict";n.d(t,{S:()=>o});var a=n(1585),r=n(5119),s=n(7148),i=n(2855);function o(e,t="float32"){if((0,r.SA)(e),"complex64"===t){const t=o(e,"float32"),n=(0,i.U)(e,"float32");return(0,s.f)(t,n)}const n=(0,r.FZ)((0,r.Ze)(e),t);return a.T2.makeTensor(n,e,t)}},1455:e=>{"use strict";e.exports=require("node:fs/promises")},1585:(e,t,n)=>{"use strict";n.d(t,{T2:()=>w,Ye:()=>v});var a=n(5149),r=n(6574),s=n(1743),i=n(5441),o=n(7074),p=n(3673),l=n(3879),d=n(5119);class u{constructor(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new h)}profileKernel(e,t,n){let a;const s=()=>{a=n()};let i;const o=l.now();if(this.backendTimer.timerAvailable())i=this.backendTimer.time(s);else{s();for(const e of 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y{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class b{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new y}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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t=++this.pendingBackendInitId,a=n.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,p.i(`Initialization of backend ${e} failed`),p.i(n.stack||n.message)),!1)));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}}catch(t){return p.i(`Initialization of backend ${e} failed`),p.i(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(0===Object.keys(this.registryFactory).length)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort(((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority))}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,a=null;if(null==t){if("function"!==typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!==typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!==typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");a=e}return this.scopedRun((()=>this.startScope(a)),(()=>this.endScope(n)),(()=>(n=t(),n)))}scopedRun(e,t,n){e();try{const e=n();return t(),e}catch(e){throw t(),e}}nextTensorId(){return b.nextTensorId++}nextVariableId(){return b.nextVariableId++}clone(e){const t=w.runKernel(i.lzr,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e},n={dtype:"float32"};return w.runKernel(i.KXH,t,n)}})),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=(0,o._5)(e,this.backendName)))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const a=this.backend.numDataIds();let r=0;n.forEach((e=>{r+="complex64"===e.dtype?3:1}));const s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;let i,p;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=g(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(g(e)){const{kernelName:t,inputs:r,attrs:s}=e;null==this.backendName&&this.backend;const l=(0,o._5)(t,this.backendName);d.vA(null!=l,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),i=()=>{const e=this.backend.numDataIds();p=l.kernelFunc({inputs:r,attrs:s,backend:this.backend});const i=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,i);const o=i.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(a){const e=this.getTensorsForGradient(t,r,o);n=this.saveTensorsForBackwardMode(e)}return o}}else{const{forwardFunc:t}=e,r=e=>{a&&(n=e.map((e=>this.keep(this.clone(e)))))};i=()=>{const e=this.backend.numDataIds();p=this.tidy((()=>t(this.backend,r)));const n=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:u,attrs:c}=e,h=g(e)?null:e.backwardsFunc;let m;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(m=this.profiler.profileKernel(l,u,(()=>i())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(m),t=m.outputs):t=i()})),a&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map((e=>null!=u[e]?u[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:m.timeMs,extraInfo:m.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){const t=e.map((e=>this.keep(this.clone(e))));return t}getTensorsForGradient(e,t,n){const a=(0,o.vQ)(e);if(null!=a){const e=a.inputsToSave||[],r=a.outputsToSave||[];let s;a.saveAllInputs?(d.vA(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),s=Object.keys(t).map((e=>t[e]))):s=e.map((e=>t[e]));const i=n.filter(((e,t)=>r[t]));return s.concat(i)}return[]}makeTensor(e,t,n,a){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;"string"===n&&d.Kg(e[0])&&(r=e.map((e=>l.encodeString(e))));const s=a.write(r,t,n),i=new m.qY(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),"string"===n){const e=this.state.tensorInfo.get(s),t=(0,d.SL)(r);this.state.numBytes+=t-e.bytes,e.bytes=t}return i}makeTensorFromDataId(e,t,n,a){const r={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(r,a)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:a,dtype:r}=e,s=new m.qY(a,r,n,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),null!=a&&a!==e.dtype&&(e=e.cast(a));const r=new m.rT(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[r.name])throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*d.jv(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof m.rT||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const t=e.size*d.jv(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await 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