From de7ef14bf59df357cc82758fa89608c58ecdb84d Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 13 Mar 2021 12:21:32 -0500 Subject: [PATCH] update docs --- CHANGELOG.md | 8 ++- README.md | 75 +++++++++++------------ TODO.md | 3 + dist/face-api.esm-nobundle.js | 2 +- dist/face-api.esm.js | 2 +- dist/face-api.js | 2 +- dist/face-api.node-cpu.js | 2 +- dist/face-api.node-cpu.js.map | 2 +- dist/face-api.node-gpu.js | 2 +- dist/face-api.node-gpu.js.map | 2 +- dist/face-api.node.js | 2 +- dist/face-api.node.js.map | 2 +- package-lock.json | 108 ++++++++++++++++++++++++---------- package.json | 8 +-- 14 files changed, 137 insertions(+), 83 deletions(-) create mode 100644 TODO.md diff --git a/CHANGELOG.md b/CHANGELOG.md index 3b7575d..97f7276 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/face-api -Version: **1.0.1** +Version: **1.0.2** Description: **FaceAPI: AI-powered Face Detection, Face Embedding & Recognition Using Tensorflow/JS** Author: **Vladimir Mandic ** @@ -12,6 +12,12 @@ Repository: **** ### **HEAD -> master** 2021/03/09 mandic00@live.com +### **1.0.2** 2021/03/09 mandic00@live.com + + +### **release: 1.0.1** 2021/03/09 mandic00@live.com + + ### **1.0.1** 2021/03/09 mandic00@live.com - add badges diff --git a/README.md b/README.md index efa44da..572c71f 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,6 @@ ## Note This is updated **face-api.js** with latest available TensorFlow/JS as the original face-api.js is not compatible with **tfjs 2.0+**. - Forked from [face-api.js](https://github.com/justadudewhohacks/face-api.js) version **0.22.2** which was released on March 22nd, 2020 Currently based on **`TensorFlow/JS` 3.3.0** @@ -27,36 +26,9 @@ Currently based on **`TensorFlow/JS` 3.3.0** Because I needed Face-API that does not cause version conflict with newer TFJS 2.0 that I use accross my projects And since original Face-API was open-source, I've released this version as well -Unfortunately, changes ended up being too large for a simple pull request on original Face-API and it ended up being a full-fledged version on its own +Changes ended up being too large for a simple pull request on original Face-API and it ended up being a full-fledged version on its own -### Differences - -- Compatible with `TensorFlow/JS 2.0+ & 3.0+` -- Compatible with `WebGL`, `CPU` and `WASM` TFJS Browser backends -- Compatible with both `tfjs-node` and `tfjs-node-gpu` TFJS NodeJS backends -- Updated all type castings for TypeScript type checking to `TypeScript 4.1` -- Switched bundling from `UMD` to `ESM` + `CommonJS` with fallback to `IIFE` - Resulting code is optimized per-platform instead of being universal - Fully tree shakable when imported as an `ESM` module - Browser bundle process uses `ESBuild` instead of `Rollup` -- Typescript build process now targets `ES2018` and instead of dual ES5/ES6 - Resulting code is clean ES2018 JavaScript without polyfills -- Removed old tests, docs, examples -- Removed old package dependencies (`karma`, `jasmine`, `babel`, etc.) -- Updated all package dependencies -- Updated TensorFlow/JS dependencies since backends were removed from `@tensorflow/tfjs-core` -- Updated mobileNetv1 model due to `batchNorm()` dependency -- Added `version` class that returns JSON object with version of FaceAPI as well as linked TFJS -- Added test/dev built-in HTTP & HTTPS Web server -- Removed `mtcnn` and `tinyYolov2` models as they were non-functional in latest public version of `Face-API` - *If there is a demand, I can re-implement them back.* -- Added `face angle` calculations that returns `roll`, `yaw` and `pitch` - -Which means valid models are **tinyFaceDetector** and **mobileNetv1** - -
-
-
+

## Examples @@ -64,11 +36,14 @@ Which means valid models are **tinyFaceDetector** and **mobileNetv1** ### Browser -Browser example that uses static images and showcases both models as well as all of the extensions is included in `/example/index.html` -Example can be accessed directly using Git pages using URL: +Browser example that uses static images and showcases both models +as well as all of the extensions is included in `/example/index.html` +Example can be accessed directly using Git pages using URL: + Browser example that uses live webcam is included in `/example/webcam.html` -Example can be accessed directly using Git pages using URL: +Example can be accessed directly using Git pages using URL: +
@@ -121,9 +96,7 @@ Two NodeJS examples are: Note that `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu` must be installed before using NodeJS example -
-
-
+

## Installation @@ -286,7 +259,10 @@ And then use with: const faceapi = require('@vladmandic/face-api/dist/face-api.node-cpu.js'); ``` -If you want to use graphical functions inside NodeJS, you must provide appropriate graphical library as NodeJS does not include implementation for DOM elements such as HTMLImageElement or HTMLCanvasElement: +If you want to use graphical functions inside NodeJS, +you must provide appropriate graphical library as +NodeJS does not include implementation for DOM elements +such as HTMLImageElement or HTMLCanvasElement: Install `Canvas` for NodeJS: @@ -410,6 +386,31 @@ npm run build - [**Tutorial**](TUTORIAL.md) - [**API Documentation**](https://justadudewhohacks.github.io/face-api.js/docs/globals.html) +## Differences + +- Compatible with `TensorFlow/JS 2.0+ & 3.0+` +- Compatible with `WebGL`, `CPU` and `WASM` TFJS Browser backends +- Compatible with both `tfjs-node` and `tfjs-node-gpu` TFJS NodeJS backends +- Updated all type castings for TypeScript type checking to `TypeScript 4.1` +- Switched bundling from `UMD` to `ESM` + `CommonJS` with fallback to `IIFE` + Resulting code is optimized per-platform instead of being universal + Fully tree shakable when imported as an `ESM` module + Browser bundle process uses `ESBuild` instead of `Rollup` +- Typescript build process now targets `ES2018` and instead of dual ES5/ES6 + Resulting code is clean ES2018 JavaScript without polyfills +- Removed old tests, docs, examples +- Removed old package dependencies (`karma`, `jasmine`, `babel`, etc.) +- Updated all package dependencies +- Updated TensorFlow/JS dependencies since backends were removed from `@tensorflow/tfjs-core` +- Updated mobileNetv1 model due to `batchNorm()` dependency +- Added `version` class that returns JSON object with version of FaceAPI as well as linked TFJS +- Added test/dev built-in HTTP & HTTPS Web server +- Removed `mtcnn` and `tinyYolov2` models as they were non-functional in latest public version of `Face-API` + *If there is a demand, I can re-implement them back.* +- Added `face angle` calculations that returns `roll`, `yaw` and `pitch` + +Which means valid models are **tinyFaceDetector** and **mobileNetv1** + ## Credits - Original project: [Face-API](https://github.com/justadudewhohacks/face-api.js) diff --git a/TODO.md b/TODO.md new file mode 100644 index 0000000..eff6fcb --- /dev/null +++ b/TODO.md @@ -0,0 +1,3 @@ +# To-do List for FaceAPI + +- Improve examples and reference them in readme diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index 50fb1b0..9b3beab 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -5,5 +5,5 @@ author: ' */ -var pn=Object.create,Ye=Object.defineProperty,dn=Object.getPrototypeOf,un=Object.prototype.hasOwnProperty,ln=Object.getOwnPropertyNames,fn=Object.getOwnPropertyDescriptor;var Dr=o=>Ye(o,"__esModule",{value:!0});var uo=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Er=(o,t)=>{for(var e in t)Ye(o,e,{get:t[e],enumerable:!0})},lt=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of ln(t))!un.call(o,r)&&r!=="default"&&Ye(o,r,{get:()=>t[r],enumerable:!(e=fn(t,r))||e.enumerable});return o},hn=o=>lt(Dr(Ye(o!=null?pn(dn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);import*as Ca from"@tensorflow/tfjs/dist/index.js";import*as Na from"@tensorflow/tfjs-backend-wasm";var b=uo(xn=>{Dr(xn);lt(xn,Ca);lt(xn,Na)});var go=uo((_n,bo)=>{Dr(_n);Er(_n,{isNodejs:()=>wn});function wn(){return typeof global=="object"&&!0&&typeof bo!="undefined"&&typeof process!="undefined"&&!!process.version}});var ya=b();var Po={};Er(Po,{AnchorPosition:()=>pt,DrawBox:()=>Ue,DrawBoxOptions:()=>jr,DrawFaceLandmarks:()=>Zr,DrawFaceLandmarksOptions:()=>qr,DrawTextField:()=>Mt,DrawTextFieldOptions:()=>Ce,drawContour:()=>ft,drawDetections:()=>En,drawFaceExpressions:()=>kn,drawFaceLandmarks:()=>Ln});function ft(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var lo={};Er(lo,{computeReshapedDimensions:()=>Nr,getCenterPoint:()=>$t,isDimensions:()=>ze,isEven:()=>Ge,isFloat:()=>Cr,isTensor:()=>Bt,isTensor1D:()=>bn,isTensor2D:()=>Mr,isTensor3D:()=>ht,isTensor4D:()=>G,isValidNumber:()=>et,isValidProbablitiy:()=>ee,range:()=>it,round:()=>Rt});var fo=b();var S=class{constructor(t,e){if(!et(t)||!et(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new S(1/this.width,1/this.height)}};function Bt(o,t){return o instanceof fo.Tensor&&o.shape.length===t}function bn(o){return Bt(o,1)}function Mr(o){return Bt(o,2)}function ht(o){return Bt(o,3)}function G(o){return Bt(o,4)}function Cr(o){return o%1!=0}function Ge(o){return o%2==0}function Rt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function ze(o){return o&&o.width&&o.height}function Nr({width:o,height:t},e){let r=e/Math.max(t,o);return new S(Math.round(o*r),Math.round(t*r))}function $t(o){return o.reduce((t,e)=>t.add(e),new x(0,0)).div(new x(o.length,o.length))}function it(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function et(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function ee(o){return et(o)&&o>=0&&o<=1}var x=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new x(this.x+t.x,this.y+t.y)}sub(t){return new x(this.x-t.x,this.y-t.y)}mul(t){return new x(this.x*t.x,this.y*t.y)}div(t){return new x(this.x/t.x,this.y/t.y)}abs(){return new x(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new x(Math.floor(this.x),Math.floor(this.y))}};var P=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(et)}static assertIsValidBox(t,e,r=!1){if(!P.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(et),a=[r.x,r.y,r.width,r.height].every(et);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];P.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new x(this.left,this.top)}get topRight(){return new x(this.right,this.top)}get bottomLeft(){return new x(this.left,this.bottom)}get bottomRight(){return new x(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new P({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new P({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new P({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var re=class extends P{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var Dt=class{constructor(t,e,r,n,a){this._imageDims=new S(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new P(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new P(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new Dt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var E=class extends Dt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new E(r,n,a)}};function kr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function Ir(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var ct=b();function rt(o,t){return ct.tidy(()=>{let[e,r,n]=t,a=ct.fill([...o.shape.slice(0,3),1],e,"float32"),s=ct.fill([...o.shape.slice(0,3),1],r,"float32"),i=ct.fill([...o.shape.slice(0,3),1],n,"float32"),c=ct.concat([a,s,i],3);return ct.sub(o,c)})}var Et=b();function Sr(o,t=!1){return Et.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Et.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Et.cast(u,"float32"));return Et.concat(d,s)})}function gn(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function De(o){return 1/(1+Math.exp(-o))}function vn(o){return Math.log(o/(1-o))}var oe=class extends P{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var yn=.5,Fn=.43,Tn=.45,z=class{constructor(t,e,r=new x(0,0)){let{width:n,height:a}=e;this._imgDims=new S(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new x(n,a)).add(r))}get shift(){return new x(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new x(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new x(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof E?t.box.floor():new P(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/Tn),c=$t(t),m=Math.floor(Math.max(0,c.x-yn*i)),p=Math.floor(Math.max(0,c.y-Fn*i));return new oe(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=Ir(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var ho=class extends z{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],$t([t[3],t[4]])]}};var ne=class extends z{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map($t)}};var Ee=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Rt(this.distance)})`:""}`}};var Me=class extends P{static assertIsValidLabeledBox(t,e){if(P.assertIsValidBox(t,e),!et(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var xt=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new xt(t.label,e)}};var xo=class extends Me{static assertIsValidPredictedBox(t,e){if(Me.assertIsValidLabeledBox(t,e),!ee(t.score)||!ee(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function mt(o){return o.detection instanceof E}function Ot(o,t){return{...o,...{detection:t}}}function Ar(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function Ve(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Wr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=Ve();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function Br(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Rr=hn(go()),I;function Pn(){if(!I)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return I}function $r(o){I=o}function Or(){return Br()?$r(Ar()):(0,Rr.isNodejs)()?$r(Wr()):null}function Dn(o){if(I||Or(),!I)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=I.Canvas,Image:e=I.Image}=o;I.Canvas=t,I.Image=e,I.createCanvasElement=o.createCanvasElement||(()=>new t),I.createImageElement=o.createImageElement||(()=>new e),I.ImageData=o.ImageData||I.ImageData,I.Video=o.Video||I.Video,I.fetch=o.fetch||I.fetch,I.readFile=o.readFile||I.readFile}var w={getEnv:Pn,setEnv:$r,initialize:Or,createBrowserEnv:Ar,createFileSystem:Ve,createNodejsEnv:Wr,monkeyPatch:Dn,isBrowser:Br,isNodejs:Rr.isNodejs};Or();function jt(o){return!w.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function R(o){let{Canvas:t,CanvasRenderingContext2D:e}=w.getEnv();if(o instanceof e)return o;let r=jt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var pt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(pt||(pt={}));var Ce=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||pt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},Mt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof Mt?t.text:t,this.anchor=e,this.options=new Ce(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let g=c+d.x,_=c+d.y+(l+1)*s;r.fillText(u,g,_)})}};var jr=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:pt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ce({...s,...a})}},Ue=class{constructor(t,e={}){this.box=new P(t),this.options=new jr(e)}draw(t){let e=R(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new Mt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function En(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof E?r.score:mt(r)?r.detection.score:void 0,a=r instanceof E?r.box:mt(r)?r.detection.box:new P(r),s=n?`${Rt(n)}`:void 0;new Ue(a,{label:s}).draw(o)})}var ue=b();function Ne(o){let{Image:t,Video:e}=w.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function Hr(o){return new Promise((t,e)=>{if(o instanceof w.getEnv().Canvas||Ne(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function Yr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=w.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function Ht(o){let{Image:t,Video:e}=w.getEnv();return o instanceof t?new S(o.naturalWidth,o.naturalHeight):o instanceof e?new S(o.videoWidth,o.videoHeight):new S(o.width,o.height)}function Yt({width:o,height:t}){let{createCanvasElement:e}=w.getEnv(),r=e();return r.width=o,r.height=t,r}function ke(o,t){let{ImageData:e}=w.getEnv();if(!(o instanceof e)&&!Ne(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||Ht(o),a=Yt({width:r,height:n});return o instanceof e?R(a).putImageData(o,0,0):R(a).drawImage(o,0,0,r,n),a}var Xe=b();async function Gr(o,t){let e=t||w.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(G(o)?1:0),s=Xe.tidy(()=>o.as3D(r,n,a).toInt());return await Xe.browser.toPixels(s,e),s.dispose(),e}function Je(o){let{Image:t,Canvas:e,Video:r}=w.getEnv();return o instanceof t||o instanceof e||o instanceof r}var X=b();function zr(o,t,e=!1){let{Image:r,Canvas:n}=w.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Yt({width:1,height:1});let a=Ht(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=Yt({width:t,height:t}),p=o instanceof n?o:ke(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&R(m).drawImage(p,u,l,i,c),m}var bt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(ht(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(G(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof w.getEnv().Canvas?r:ke(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return it(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Nr({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,X.tidy(()=>{let r=it(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof X.Tensor){let i=G(s)?s:s.expandDims();return i=Sr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=X.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof w.getEnv().Canvas)return X.browser.fromPixels(zr(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return X.stack(r.map(a=>X.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function D(o){if(o instanceof bt)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(jt);return r.forEach((n,a)=>{if(!Je(n)&&!ht(n)&&!G(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(G(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>Je(n)&&Hr(n))),new bt(r,Array.isArray(o))}async function ae(o,t){let{Canvas:e}=w.getEnv(),r=o;if(!(o instanceof e)){let s=await D(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await Gr(i)}let n=R(r);return t.map(s=>s instanceof E?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=Yt({width:c,height:m});return c>0&&m>0&&R(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var qe=b();async function se(o,t){if(!ht(o)&&!G(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(G(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return qe.tidy(()=>{let[e,r,n]=o.shape.slice(G(o)?1:0);return t.map(i=>i instanceof E?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>qe.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Gt(o,t){let{fetch:e}=w.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Mn(o){let t=await Gt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return Yr(e)}async function Vr(o){return(await Gt(o)).json()}async function Cn(o){return new Float32Array(await(await Gt(o)).arrayBuffer())}var vo=b();function Ze(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function Ur(o,t){let{manifestUri:e,modelBaseUri:r}=Ze(o,t),n=await Vr(e);return vo.io.loadWeights(n,r)}function Nn(o,t,e=!1){let{width:r,height:n}=e?Ht(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Nt=b();var gt=b();var L=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof gt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof gt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=gt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await Ur(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=w.getEnv(),{manifestUri:r,modelBaseUri:n}=Ze(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=gt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof gt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var M=b();var ie=b();function $(o,t,e){return ie.tidy(()=>{let r=ie.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=ie.add(r,t.bias),r})}function Ke(o,t,e=!1){return M.tidy(()=>{let r=M.relu(e?M.add(M.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):$(o,t.conv0,[2,2])),n=$(r,t.conv1,[1,1]),a=M.relu(M.add(r,n)),s=$(a,t.conv2,[1,1]);return M.relu(M.add(r,M.add(n,s)))})}function Ie(o,t,e=!1,r=!0){return M.tidy(()=>{let n=M.relu(e?M.add(M.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):$(o,t.conv0,r?[2,2]:[1,1])),a=$(n,t.conv1,[1,1]),s=M.relu(M.add(n,a)),i=$(s,t.conv2,[1,1]),c=M.relu(M.add(n,M.add(a,i))),m=$(c,t.conv3,[1,1]);return M.relu(M.add(n,M.add(a,M.add(i,m))))})}var Ct=b();function zt(o,t,e="same",r=!1){return Ct.tidy(()=>{let n=Ct.add(Ct.conv2d(o,t.filters,[1,1],e),t.bias);return r?Ct.relu(n):n})}function A(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var Qe=b();function ce(o,t){return(e,r,n,a)=>{let s=Qe.tensor4d(o(e*r*n*n),[n,n,e,r]),i=Qe.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var tr=b();function er(o,t){return(e,r,n)=>{let a=tr.tensor2d(o(e*r),[e,r]),s=tr.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var Le=b();var rr=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=Le.tensor4d(o(3*3*e),[3,3,e,1]),s=Le.tensor4d(o(e*r),[1,1,e,r]),i=Le.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new rr(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new rr(e,r,n)}}function O(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function W(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function or(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function yo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),{extractDenseBlock4Params:n}=or(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function nr(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function ar(o,t){let e=O(o,t),r=nr(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Fo(o){let t=[],{extractDenseBlock4Params:e}=ar(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return A(o,t),{params:r,paramMappings:t}}var Se=class extends L{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Ie(a,e.dense0,!0);return s=Ie(s,e.dense1),s=Ie(s,e.dense2),s=Ie(s,e.dense3),s=Nt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Fo(t)}extractParams(t){return yo(t)}};var wo=b();var de=b();function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function To(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=W(o),i=er(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function _o(o){let t=[],e=O(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return A(o,t),{params:n,paramMappings:t}}function sr(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends L{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return wo.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return To(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=sr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),_o(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Xr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],kt=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Xr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Xr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var ir=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await D(t))}async predictExpressions(t){let e=await D(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new kt(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Jr(o){return o.expressions instanceof kt}function cr(o,t){return{...o,...{expressions:t}}}function kn(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof kt?a:Jr(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=mt(a)?a.detection.box.bottomLeft:r||new x(0,0);new Mt(c.map(d=>`${d.expression} (${Rt(d.probability)})`),m).draw(o)})}function Vt(o){return mt(o)&&o.landmarks instanceof z&&o.unshiftedLandmarks instanceof z&&o.alignedRect instanceof E}function In(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function le(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new E(o.detection.score,n.rescale(a.reverse()),a),i=In(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var qr=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},Zr=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new qr(e)}draw(t){let e=R(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof ne&&(e.strokeStyle=s,e.lineWidth=a,ft(e,this.faceLandmarks.getJawOutline()),ft(e,this.faceLandmarks.getLeftEyeBrow()),ft(e,this.faceLandmarks.getRightEyeBrow()),ft(e,this.faceLandmarks.getNose()),ft(e,this.faceLandmarks.getLeftEye(),!0),ft(e,this.faceLandmarks.getRightEye(),!0),ft(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function Ln(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof z?r:Vt(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Zr(n).draw(o)})}var Do="1.0.1";var dt=b();var N=b();function Sn(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Eo(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=W(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=Sn(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};it(t,0,1).forEach(v=>{l[`main_block_${v}`]=c(128,`middle_flow/main_block_${v}`)});let g=i(128,256,"exit_flow/reduction_block"),_=s(256,512,"exit_flow/separable_conv"),h={reduction_block:g,separable_conv:_};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:h}}}function An(o,t){let e=O(o,t),r=nr(e),n=pe(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function Mo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=An(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};it(t,0,1).forEach(_=>{d[`main_block_${_}`]=s(`middle_flow/main_block_${_}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),g={reduction_block:u,separable_conv:l};return A(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:g},paramMappings:e}}function Co(o,t,e){return N.add(N.conv2d(o,t.filters,e,"same"),t.bias)}function Kr(o,t,e=!0){let r=e?N.relu(o):o;return r=$(r,t.separable_conv0,[1,1]),r=$(N.relu(r),t.separable_conv1,[1,1]),r=N.maxPool(r,[3,3],[2,2],"same"),r=N.add(r,Co(o,t.expansion_conv,[2,2])),r}function Wn(o,t){let e=$(N.relu(o),t.separable_conv0,[1,1]);return e=$(N.relu(e),t.separable_conv1,[1,1]),e=$(N.relu(e),t.separable_conv2,[1,1]),e=N.add(e,o),e}var Qr=class extends L{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return N.tidy(()=>{let r=N.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(N.scalar(256)),s=N.relu(Co(a,e.entry_flow.conv_in,[2,2]));return s=Kr(s,e.entry_flow.reduction_block_0,!1),s=Kr(s,e.entry_flow.reduction_block_1),it(this._numMainBlocks,0,1).forEach(i=>{s=Wn(s,e.middle_flow[`main_block_${i}`])}),s=Kr(s,e.exit_flow.reduction_block),s=N.relu($(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Mo(t,this._numMainBlocks)}extractParams(t){return Eo(t,this._numMainBlocks)}};function No(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),n=er(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function ko(o){let t=[],e=O(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return A(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var mr=class extends L{constructor(t=new Qr(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return dt.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=dt.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return dt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:dt.softmax(r)}})}async forward(t){return this.forwardInput(await D(t))}async predictAgeAndGender(t){let e=await D(t),r=await this.forwardInput(e),n=dt.unstack(r.age),a=dt.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,g=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:g}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return No(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=sr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),ko(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var j=b();var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return j.tidy(()=>{let s=(d,u)=>j.stack([j.fill([68],d,"float32"),j.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:g}=n[d];return u(l,g)?Math.abs(l-g)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(j.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return j.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await D(t))}async detectLandmarks(t){let e=await D(t),r=j.tidy(()=>j.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>Ge(d)),m=i.filter((p,d)=>!Ge(d));return new ne(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var fe=class extends Be{constructor(t=new Se){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var It=b();function Io(o){let t=[],{extractDenseBlock3Params:e}=ar(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return A(o,t),{params:r,paramMappings:t}}function Lo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),{extractDenseBlock3Params:n}=or(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var to=class extends L{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return It.tidy(()=>{let r=It.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(It.scalar(255)),s=Ke(a,e.dense0,!0);return s=Ke(s,e.dense1),s=Ke(s,e.dense2),s=It.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Io(t)}extractParams(t){return Lo(t)}};var pr=class extends Be{constructor(t=new to){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var So=class extends fe{};var V=b();var he=b();var dr=b();function Ao(o,t){return dr.add(dr.mul(o,t.weights),t.biases)}function eo(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=he.conv2d(o,a,e,n);return i=he.add(i,s),i=Ao(i,t.scale),r?he.relu(i):i}function Wo(o,t){return eo(o,t,[1,1],!0)}function ro(o,t){return eo(o,t,[1,1],!1)}function ur(o,t){return eo(o,t,[2,2],!0,"valid")}var H=b();function Bn(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Cr(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return H.tidy(()=>H.transpose(H.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=H.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=H.tensor1d(o(i)),p=H.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function Bo(o){let{extractWeights:t,getRemainingWeights:e}=W(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Bn(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),g=a(147456,128,3,"conv128_down",!0),_=a(147456,128,3,"conv128_1"),h=a(147456,128,3,"conv128_2"),v=a(589824,256,3,"conv256_down",!0),F=a(589824,256,3,"conv256_1"),y=a(589824,256,3,"conv256_2"),k=a(589824,256,3,"conv256_down_out"),Y=H.tidy(()=>H.transpose(H.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:g,conv128_1:_,conv128_2:h,conv256_down:v,conv256_1:F,conv256_2:y,conv256_down_out:k,fc:Y},paramMappings:r}}function Rn(o,t){let e=O(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function Ro(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=Rn(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),g=r("conv128_2"),_=r("conv256_down"),h=r("conv256_1"),v=r("conv256_2"),F=r("conv256_down_out"),{fc:y}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!Mr(y))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${y}`);let k={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:g,conv256_down:_,conv256_1:h,conv256_2:v,conv256_down_out:F,fc:y};return A(o,t),{params:k,paramMappings:t}}var B=b();function ot(o,t){let e=Wo(o,t.conv1);return e=ro(e,t.conv2),e=B.add(e,o),e=B.relu(e),e}function Re(o,t){let e=ur(o,t.conv1);e=ro(e,t.conv2);let r=B.avgPool(o,2,2,"valid"),n=B.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=B.zeros(i);e=B.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=B.zeros(m);e=B.concat([e,p],2)}return r=a?B.concat([r,n],3):r,e=B.add(r,e),e=B.relu(e),e}var xe=class extends L{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return V.tidy(()=>{let r=V.cast(t.toBatchTensor(150,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(V.scalar(256)),s=ur(a,e.conv32_down);s=V.maxPool(s,3,2,"valid"),s=ot(s,e.conv32_1),s=ot(s,e.conv32_2),s=ot(s,e.conv32_3),s=Re(s,e.conv64_down),s=ot(s,e.conv64_1),s=ot(s,e.conv64_2),s=ot(s,e.conv64_3),s=Re(s,e.conv128_down),s=ot(s,e.conv128_1),s=ot(s,e.conv128_2),s=Re(s,e.conv256_down),s=ot(s,e.conv256_1),s=ot(s,e.conv256_2),s=Re(s,e.conv256_down_out);let i=s.mean([1,2]);return V.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await D(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await D(t),r=V.tidy(()=>V.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return Ro(t)}extractParams(t){return Bo(t)}};function $n(o){let t=new xe;return t.extractWeights(o),t}function lr(o,t){return{...o,...{descriptor:t}}}function On(o){return typeof o.age=="number"}function fr(o,t){return{...o,...{age:t}}}function jn(o){return(o.gender===vt.MALE||o.gender===vt.FEMALE)&&ee(o.genderProbability)}function hr(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var at=b();var nt=b();function Hn(o,t){function e(c,m){let p=nt.tensor4d(o(3*3*c),[3,3,c,1]),d=nt.tensor1d(o(c)),u=nt.tensor1d(o(c)),l=nt.tensor1d(o(c)),g=nt.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:g}}function r(c,m,p,d,u){let l=nt.tensor4d(o(c*m*p*p),[p,p,c,m]),g=nt.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:g}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),g=a(256,512,"mobilenetv1/conv_6"),_=a(512,512,"mobilenetv1/conv_7"),h=a(512,512,"mobilenetv1/conv_8"),v=a(512,512,"mobilenetv1/conv_9"),F=a(512,512,"mobilenetv1/conv_10"),y=a(512,512,"mobilenetv1/conv_11"),k=a(512,1024,"mobilenetv1/conv_12"),Y=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:g,conv_7:_,conv_8:h,conv_9:v,conv_10:F,conv_11:y,conv_12:k,conv_13:Y}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),g=n(256,64,1,"prediction_layer/conv_6"),_=n(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),v=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),F=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),y=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),k=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Y=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),tt=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),st=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),U=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),_t=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),wt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Pt=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:g,conv_7:_,box_predictor_0:{box_encoding_predictor:h,class_predictor:v},box_predictor_1:{box_encoding_predictor:F,class_predictor:y},box_predictor_2:{box_encoding_predictor:k,class_predictor:Y},box_predictor_3:{box_encoding_predictor:tt,class_predictor:st},box_predictor_4:{box_encoding_predictor:U,class_predictor:_t},box_predictor_5:{box_encoding_predictor:wt,class_predictor:Pt}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function $o(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Hn(e,t),s=n(),i=a(),m={extra_dim:nt.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Yn(o,t){let e=O(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,g=e(`${d}/depthwise_weights`,4,`${u}/filters`),_=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),h=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),v=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),F=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:g,batch_norm_scale:_,batch_norm_offset:h,batch_norm_mean:v,batch_norm_variance:F},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function Oo(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Yn(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!ht(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return A(o,t),{params:a,paramMappings:t}}var yt=b();var Lt=b();function J(o,t,e){return Lt.tidy(()=>{let r=Lt.conv2d(o,t.filters,e,"same");return r=Lt.add(r,t.batch_norm_offset),Lt.clipByValue(r,0,6)})}var Gn=.0010000000474974513;function zn(o,t,e){return yt.tidy(()=>{let r=yt.depthwiseConv2d(o,t.filters,e,"same");return r=yt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Gn),yt.clipByValue(r,0,6)})}function Vn(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function jo(o,t){return yt.tidy(()=>{let e,r=J(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=Vn(i);r=zn(r,a.depthwise_conv,c),r=J(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function Un(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let g=Math.max(n,c),_=Math.max(a,m),h=Math.min(s,p),v=Math.min(i,d),F=Math.max(h-g,0)*Math.max(v-_,0);return F/(u+l-F)}function Ho(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=Un(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b();function Xn(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function Jn(o,t){let{sizes:e,centers:r}=Xn(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function Yo(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=Jn(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Oe=b();var $e=b();function Ut(o,t){return $e.tidy(()=>{let e=o.shape[0],r=$e.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=$e.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Go(o,t,e){return Oe.tidy(()=>{let r=J(o,e.conv_0,[1,1]),n=J(r,e.conv_1,[2,2]),a=J(n,e.conv_2,[1,1]),s=J(a,e.conv_3,[2,2]),i=J(s,e.conv_4,[1,1]),c=J(i,e.conv_5,[2,2]),m=J(c,e.conv_6,[1,1]),p=J(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),g=Ut(s,e.box_predictor_3),_=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),v=Oe.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,g.boxPredictionEncoding,_.boxPredictionEncoding,h.boxPredictionEncoding],1),F=Oe.concat([d.classPrediction,u.classPrediction,l.classPrediction,g.classPrediction,_.classPrediction,h.classPrediction],1);return{boxPredictions:v,classPredictions:F}})}var q=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends L{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return at.tidy(()=>{let r=at.cast(t.toBatchTensor(512,!1),"float32"),n=at.sub(at.mul(r,at.scalar(.007843137718737125)),at.scalar(1)),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Go(a.out,a.conv11,e.prediction_layer);return Yo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await D(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new q(e),a=await D(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let y=1;y{let[k,Y]=[Math.max(0,v[y][0]),Math.min(1,v[y][2])].map(U=>U*h),[tt,st]=[Math.max(0,v[y][1]),Math.min(1,v[y][3])].map(U=>U*_);return new E(p[y],new oe(tt,k,st-tt,Y-k),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),F}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Oo(t)}extractParams(t){return $o(t)}};function zo(o){let t=new Xt;return t.extractWeights(o),t}function qn(o){return zo(o)}var Vo=class extends Xt{};var Uo=.4,Xo=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Jo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],qo=[117.001,114.697,97.404],Zo="tiny_yolov2_model",Ko="tiny_yolov2_separable_conv_model";var C=b();var xr=o=>typeof o=="number";function oo(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!xr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>xr(t.x)&&xr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(xr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var K=b();var Z=b();function be(o){return Z.tidy(()=>{let t=Z.mul(o,Z.scalar(.10000000149011612));return Z.add(Z.relu(Z.sub(o,t)),t)})}function Ft(o,t){return K.tidy(()=>{let e=K.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=K.conv2d(e,t.conv.filters,[1,1],"valid"),e=K.sub(e,t.bn.sub),e=K.mul(e,t.bn.truediv),e=K.add(e,t.conv.bias),be(e)})}var St=b();function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var no=b();function Zn(o,t){let e=ce(o,t);function r(s,i){let c=no.tensor1d(o(s)),m=no.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function Qo(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=W(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Zn(n,s),p;if(t.withSeparableConvs){let[d,u,l,g,_,h,v,F,y]=r,k=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),Y=m(u,l,"conv1"),tt=m(l,g,"conv2"),st=m(g,_,"conv3"),U=m(_,h,"conv4"),_t=m(h,v,"conv5"),wt=F?m(v,F,"conv6"):void 0,Pt=y?m(F,y,"conv7"):void 0,te=i(y||F||v,5*e,1,"conv8");p={conv0:k,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}else{let[d,u,l,g,_,h,v,F,y]=r,k=c(d,u,"conv0"),Y=c(u,l,"conv1"),tt=c(l,g,"conv2"),st=c(g,_,"conv3"),U=c(_,h,"conv4"),_t=c(h,v,"conv5"),wt=c(v,F,"conv6"),Pt=c(F,y,"conv7"),te=i(y,5*e,1,"conv8");p={conv0:k,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Kn(o,t){let e=O(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function tn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Kn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return A(o,e),{params:s,paramMappings:e}}var ut=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var ao=class extends L{constructor(t){super("TinyYolov2");oo(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(C.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ut(e),a=await D(t),s=await this.forwardInput(a,r),i=C.tidy(()=>C.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Lr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return tn(t,this.config)}extractParams(t){let e=this.config.filterSizes||ao.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return Qo(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=C.tidy(()=>{let v=t.reshape([m,m,p,this.boxEncodingSize]),F=v.slice([0,0,0,0],[m,m,p,4]),y=v.slice([0,0,0,4],[m,m,p,1]),k=this.withClassScores?C.softmax(v.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[F,y,k]}),g=[],_=await u.array(),h=await d.array();for(let v=0;vr){let Y=(F+De(h[v][F][y][0]))/m*i,tt=(v+De(h[v][F][y][1]))/m*c,st=Math.exp(h[v][F][y][2])*this.config.anchors[y].x/m*i,U=Math.exp(h[v][F][y][3])*this.config.anchors[y].y/m*c,_t=Y-st/2,wt=tt-U/2,Pt={row:v,col:F,anchor:y},{classScore:te,label:po}=this.withClassScores?await this.extractPredictedClass(l,Pt):{classScore:1,label:0};g.push({box:new re(_t,wt,_t+st,wt+U),score:k,classScore:k*te,label:po,...Pt})}}return d.dispose(),u.dispose(),l.dispose(),g}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=ao;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Uo,classes:["face"],...t?{anchors:Jo,meanRgb:qo}:{anchors:Xo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Ko:Zo}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Qn(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var br=class extends ut{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Q=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b();var so=b();async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof so.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof so.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var en=.4,rn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],on=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:en,classes:["face"],anchors:rn,meanRgb:on,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new pr,faceRecognitionNet:new xe,faceExpressionNet:new ir,ageGenderNet:new mr},nn=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),ta=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),ea=(o,t)=>T.tinyYolov2.locateFaces(o,t),an=o=>T.faceLandmark68Net.detectLandmarks(o),ra=o=>T.faceLandmark68TinyNet.detectLandmarks(o),oa=o=>T.faceRecognitionNet.computeFaceDescriptor(o),na=o=>T.faceExpressionNet.predictExpressions(o),aa=o=>T.ageGenderNet.predictAgeAndGender(o),sn=o=>T.ssdMobilenetv1.load(o),sa=o=>T.tinyFaceDetector.load(o),ia=o=>T.tinyYolov2.load(o),ca=o=>T.faceLandmark68Net.load(o),ma=o=>T.faceLandmark68TinyNet.load(o),pa=o=>T.faceRecognitionNet.load(o),da=o=>T.faceExpressionNet.load(o),ua=o=>T.ageGenderNet.load(o),la=sn,fa=nn,ha=an;var io=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends io{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>cr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},Pe=class extends io{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return cr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends Pe{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var co=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends co{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return fr(hr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends co{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>T.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return fr(hr(t,r,n),e)}withFaceExpressions(){return new Pe(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var gr=class extends Q{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends gr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>T.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends gr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var vr=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},yr=class extends vr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Fr=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Tr=class extends Q{constructor(t,e=new q){super();this.input=t;this.options=e}},He=class extends Tr{async run(){let{input:t,options:e}=this,r;if(e instanceof br)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof q)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ut)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Ot({},r)))})}withFaceLandmarks(t=!1){return new yr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Tr{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function xa(o,t=new q){return new _r(o,t)}function wr(o,t=new q){return new He(o,t)}async function cn(o,t){return wr(o,new q(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ba(o,t={}){return wr(o,new ut(t)).withFaceLandmarks().withFaceDescriptors()}var ga=cn;function mo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Pr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>mo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Pr(e,t.distanceThreshold)}};function va(o){let t=new Fe;return t.extractWeights(o),t}function mn(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>mn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(Ot(o,n),a)}return mt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof z||o instanceof E?o.forSize(e,r):o}var Fa=typeof process!="undefined",Ta=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",_a={faceapi:Do,node:Fa,browser:Ta};export{mr as AgeGenderNet,re as BoundingBox,P as Box,Q as ComposableTask,At as ComputeAllFaceDescriptorsTask,gr as ComputeFaceDescriptorsTaskBase,Wt as ComputeSingleFaceDescriptorTask,yr as DetectAllFaceLandmarksTask,He as DetectAllFacesTask,vr as DetectFaceLandmarksTaskBase,Tr as DetectFacesTaskBase,Fr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,S as Dimensions,Xr as FACE_EXPRESSION_LABELS,E as FaceDetection,Vo as FaceDetectionNet,ir as FaceExpressionNet,kt as FaceExpressions,fe as FaceLandmark68Net,pr as FaceLandmark68TinyNet,So as FaceLandmarkNet,z as FaceLandmarks,ho as FaceLandmarks5,ne as FaceLandmarks68,Ee as FaceMatch,Pr as FaceMatcher,xe as FaceRecognitionNet,vt as Gender,Me as LabeledBox,xt as LabeledFaceDescriptors,bt as NetInput,L as NeuralNetwork,Dt as ObjectDetection,x as Point,xo as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,q as SsdMobilenetv1Options,Fe as TinyFaceDetector,br as TinyFaceDetectorOptions,ve as TinyYolov2,ut as TinyYolov2Options,ga as allFaces,cn as allFacesSsdMobilenetv1,ba as allFacesTinyYolov2,Hr as awaitMediaLoaded,Yr as bufferToImage,oa as computeFaceDescriptor,Yt as createCanvas,ke as createCanvasFromMedia,qn as createFaceDetectionNet,$n as createFaceRecognitionNet,zo as createSsdMobilenetv1,va as createTinyFaceDetector,Qn as createTinyYolov2,wr as detectAllFaces,an as detectFaceLandmarks,ra as detectFaceLandmarksTiny,ha as detectLandmarks,xa as detectSingleFace,Po as draw,w as env,mo as euclideanDistance,fr as extendWithAge,lr as extendWithFaceDescriptor,Ot as extendWithFaceDetection,cr as extendWithFaceExpressions,le as extendWithFaceLandmarks,hr as extendWithGender,se as extractFaceTensors,ae as extractFaces,Mn as fetchImage,Vr as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,R as getContext2dOrThrow,Ht as getMediaDimensions,Gr as imageTensorToCanvas,zr as imageToSquare,vn as inverseSigmoid,kr as iou,Je as isMediaElement,Ne as isMediaLoaded,On as isWithAge,mt as isWithFaceDetection,Jr as isWithFaceExpressions,Vt as isWithFaceLandmarks,jn as isWithGender,ua as loadAgeGenderModel,la as loadFaceDetectionModel,da as loadFaceExpressionModel,ca as loadFaceLandmarkModel,ma as loadFaceLandmarkTinyModel,pa as loadFaceRecognitionModel,sn as loadSsdMobilenetv1Model,sa as loadTinyFaceDetectorModel,ia as loadTinyYolov2Model,Ur as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Ir as minBbox,T as nets,Lr as nonMaxSuppression,rt as normalize,Sr as padToSquare,aa as predictAgeAndGender,na as recognizeFaceExpressions,mn as resizeResults,jt as resolveInput,gn as shuffleArray,De as sigmoid,nn as ssdMobilenetv1,ya as tf,ta as tinyFaceDetector,ea as tinyYolov2,D as toNetInput,lo as utils,oo as validateConfig,_a as version}; +var pn=Object.create,Ye=Object.defineProperty,dn=Object.getPrototypeOf,un=Object.prototype.hasOwnProperty,ln=Object.getOwnPropertyNames,fn=Object.getOwnPropertyDescriptor;var Dr=o=>Ye(o,"__esModule",{value:!0});var uo=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Er=(o,t)=>{for(var e in t)Ye(o,e,{get:t[e],enumerable:!0})},lt=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of ln(t))!un.call(o,r)&&r!=="default"&&Ye(o,r,{get:()=>t[r],enumerable:!(e=fn(t,r))||e.enumerable});return o},hn=o=>lt(Dr(Ye(o!=null?pn(dn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);import*as Ca from"@tensorflow/tfjs/dist/index.js";import*as Na from"@tensorflow/tfjs-backend-wasm";var b=uo(xn=>{Dr(xn);lt(xn,Ca);lt(xn,Na)});var go=uo((_n,bo)=>{Dr(_n);Er(_n,{isNodejs:()=>wn});function wn(){return typeof global=="object"&&!0&&typeof bo!="undefined"&&typeof process!="undefined"&&!!process.version}});var ya=b();var Po={};Er(Po,{AnchorPosition:()=>pt,DrawBox:()=>Ue,DrawBoxOptions:()=>jr,DrawFaceLandmarks:()=>Zr,DrawFaceLandmarksOptions:()=>qr,DrawTextField:()=>Mt,DrawTextFieldOptions:()=>Ce,drawContour:()=>ft,drawDetections:()=>En,drawFaceExpressions:()=>kn,drawFaceLandmarks:()=>Ln});function ft(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var lo={};Er(lo,{computeReshapedDimensions:()=>Nr,getCenterPoint:()=>$t,isDimensions:()=>ze,isEven:()=>Ge,isFloat:()=>Cr,isTensor:()=>Bt,isTensor1D:()=>bn,isTensor2D:()=>Mr,isTensor3D:()=>ht,isTensor4D:()=>G,isValidNumber:()=>et,isValidProbablitiy:()=>ee,range:()=>it,round:()=>Rt});var fo=b();var S=class{constructor(t,e){if(!et(t)||!et(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new S(1/this.width,1/this.height)}};function Bt(o,t){return o instanceof fo.Tensor&&o.shape.length===t}function bn(o){return Bt(o,1)}function Mr(o){return Bt(o,2)}function ht(o){return Bt(o,3)}function G(o){return Bt(o,4)}function Cr(o){return o%1!=0}function Ge(o){return o%2==0}function Rt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function ze(o){return o&&o.width&&o.height}function Nr({width:o,height:t},e){let r=e/Math.max(t,o);return new S(Math.round(o*r),Math.round(t*r))}function $t(o){return o.reduce((t,e)=>t.add(e),new x(0,0)).div(new x(o.length,o.length))}function it(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function et(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function ee(o){return et(o)&&o>=0&&o<=1}var x=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new x(this.x+t.x,this.y+t.y)}sub(t){return new x(this.x-t.x,this.y-t.y)}mul(t){return new x(this.x*t.x,this.y*t.y)}div(t){return new x(this.x/t.x,this.y/t.y)}abs(){return new x(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new x(Math.floor(this.x),Math.floor(this.y))}};var P=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(et)}static assertIsValidBox(t,e,r=!1){if(!P.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(et),a=[r.x,r.y,r.width,r.height].every(et);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];P.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new x(this.left,this.top)}get topRight(){return new x(this.right,this.top)}get bottomLeft(){return new x(this.left,this.bottom)}get bottomRight(){return new x(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new P({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new P({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new P({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var re=class extends P{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var Dt=class{constructor(t,e,r,n,a){this._imageDims=new S(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new P(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new P(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new Dt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var E=class extends Dt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new E(r,n,a)}};function kr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function Ir(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var ct=b();function rt(o,t){return ct.tidy(()=>{let[e,r,n]=t,a=ct.fill([...o.shape.slice(0,3),1],e,"float32"),s=ct.fill([...o.shape.slice(0,3),1],r,"float32"),i=ct.fill([...o.shape.slice(0,3),1],n,"float32"),c=ct.concat([a,s,i],3);return ct.sub(o,c)})}var Et=b();function Sr(o,t=!1){return Et.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Et.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Et.cast(u,"float32"));return Et.concat(d,s)})}function gn(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function De(o){return 1/(1+Math.exp(-o))}function vn(o){return Math.log(o/(1-o))}var oe=class extends P{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var yn=.5,Fn=.43,Tn=.45,z=class{constructor(t,e,r=new x(0,0)){let{width:n,height:a}=e;this._imgDims=new S(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new x(n,a)).add(r))}get shift(){return new x(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new x(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new x(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof E?t.box.floor():new P(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/Tn),c=$t(t),m=Math.floor(Math.max(0,c.x-yn*i)),p=Math.floor(Math.max(0,c.y-Fn*i));return new oe(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=Ir(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var ho=class extends z{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],$t([t[3],t[4]])]}};var ne=class extends z{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map($t)}};var Ee=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Rt(this.distance)})`:""}`}};var Me=class extends P{static assertIsValidLabeledBox(t,e){if(P.assertIsValidBox(t,e),!et(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var xt=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new xt(t.label,e)}};var xo=class extends Me{static assertIsValidPredictedBox(t,e){if(Me.assertIsValidLabeledBox(t,e),!ee(t.score)||!ee(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function mt(o){return o.detection instanceof E}function Ot(o,t){return{...o,...{detection:t}}}function Ar(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function Ve(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Wr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=Ve();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function Br(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Rr=hn(go()),I;function Pn(){if(!I)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return I}function $r(o){I=o}function Or(){return Br()?$r(Ar()):(0,Rr.isNodejs)()?$r(Wr()):null}function Dn(o){if(I||Or(),!I)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=I.Canvas,Image:e=I.Image}=o;I.Canvas=t,I.Image=e,I.createCanvasElement=o.createCanvasElement||(()=>new t),I.createImageElement=o.createImageElement||(()=>new e),I.ImageData=o.ImageData||I.ImageData,I.Video=o.Video||I.Video,I.fetch=o.fetch||I.fetch,I.readFile=o.readFile||I.readFile}var w={getEnv:Pn,setEnv:$r,initialize:Or,createBrowserEnv:Ar,createFileSystem:Ve,createNodejsEnv:Wr,monkeyPatch:Dn,isBrowser:Br,isNodejs:Rr.isNodejs};Or();function jt(o){return!w.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function R(o){let{Canvas:t,CanvasRenderingContext2D:e}=w.getEnv();if(o instanceof e)return o;let r=jt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var pt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(pt||(pt={}));var Ce=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||pt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},Mt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof Mt?t.text:t,this.anchor=e,this.options=new Ce(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let g=c+d.x,_=c+d.y+(l+1)*s;r.fillText(u,g,_)})}};var jr=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:pt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ce({...s,...a})}},Ue=class{constructor(t,e={}){this.box=new P(t),this.options=new jr(e)}draw(t){let e=R(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new Mt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function En(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof E?r.score:mt(r)?r.detection.score:void 0,a=r instanceof E?r.box:mt(r)?r.detection.box:new P(r),s=n?`${Rt(n)}`:void 0;new Ue(a,{label:s}).draw(o)})}var ue=b();function Ne(o){let{Image:t,Video:e}=w.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function Hr(o){return new Promise((t,e)=>{if(o instanceof w.getEnv().Canvas||Ne(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function Yr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=w.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function Ht(o){let{Image:t,Video:e}=w.getEnv();return o instanceof t?new S(o.naturalWidth,o.naturalHeight):o instanceof e?new S(o.videoWidth,o.videoHeight):new S(o.width,o.height)}function Yt({width:o,height:t}){let{createCanvasElement:e}=w.getEnv(),r=e();return r.width=o,r.height=t,r}function ke(o,t){let{ImageData:e}=w.getEnv();if(!(o instanceof e)&&!Ne(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||Ht(o),a=Yt({width:r,height:n});return o instanceof e?R(a).putImageData(o,0,0):R(a).drawImage(o,0,0,r,n),a}var Xe=b();async function Gr(o,t){let e=t||w.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(G(o)?1:0),s=Xe.tidy(()=>o.as3D(r,n,a).toInt());return await Xe.browser.toPixels(s,e),s.dispose(),e}function Je(o){let{Image:t,Canvas:e,Video:r}=w.getEnv();return o instanceof t||o instanceof e||o instanceof r}var X=b();function zr(o,t,e=!1){let{Image:r,Canvas:n}=w.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Yt({width:1,height:1});let a=Ht(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=Yt({width:t,height:t}),p=o instanceof n?o:ke(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&R(m).drawImage(p,u,l,i,c),m}var bt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(ht(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(G(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof w.getEnv().Canvas?r:ke(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return it(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Nr({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,X.tidy(()=>{let r=it(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof X.Tensor){let i=G(s)?s:s.expandDims();return i=Sr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=X.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof w.getEnv().Canvas)return X.browser.fromPixels(zr(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return X.stack(r.map(a=>X.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function D(o){if(o instanceof bt)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(jt);return r.forEach((n,a)=>{if(!Je(n)&&!ht(n)&&!G(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(G(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>Je(n)&&Hr(n))),new bt(r,Array.isArray(o))}async function ae(o,t){let{Canvas:e}=w.getEnv(),r=o;if(!(o instanceof e)){let s=await D(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await Gr(i)}let n=R(r);return t.map(s=>s instanceof E?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=Yt({width:c,height:m});return c>0&&m>0&&R(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var qe=b();async function se(o,t){if(!ht(o)&&!G(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(G(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return qe.tidy(()=>{let[e,r,n]=o.shape.slice(G(o)?1:0);return t.map(i=>i instanceof E?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>qe.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Gt(o,t){let{fetch:e}=w.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Mn(o){let t=await Gt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return Yr(e)}async function Vr(o){return(await Gt(o)).json()}async function Cn(o){return new Float32Array(await(await Gt(o)).arrayBuffer())}var vo=b();function Ze(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function Ur(o,t){let{manifestUri:e,modelBaseUri:r}=Ze(o,t),n=await Vr(e);return vo.io.loadWeights(n,r)}function Nn(o,t,e=!1){let{width:r,height:n}=e?Ht(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Nt=b();var gt=b();var L=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof gt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof gt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=gt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await Ur(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=w.getEnv(),{manifestUri:r,modelBaseUri:n}=Ze(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=gt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof gt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var M=b();var ie=b();function $(o,t,e){return ie.tidy(()=>{let r=ie.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=ie.add(r,t.bias),r})}function Ke(o,t,e=!1){return M.tidy(()=>{let r=M.relu(e?M.add(M.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):$(o,t.conv0,[2,2])),n=$(r,t.conv1,[1,1]),a=M.relu(M.add(r,n)),s=$(a,t.conv2,[1,1]);return M.relu(M.add(r,M.add(n,s)))})}function Ie(o,t,e=!1,r=!0){return M.tidy(()=>{let n=M.relu(e?M.add(M.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):$(o,t.conv0,r?[2,2]:[1,1])),a=$(n,t.conv1,[1,1]),s=M.relu(M.add(n,a)),i=$(s,t.conv2,[1,1]),c=M.relu(M.add(n,M.add(a,i))),m=$(c,t.conv3,[1,1]);return M.relu(M.add(n,M.add(a,M.add(i,m))))})}var Ct=b();function zt(o,t,e="same",r=!1){return Ct.tidy(()=>{let n=Ct.add(Ct.conv2d(o,t.filters,[1,1],e),t.bias);return r?Ct.relu(n):n})}function A(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var Qe=b();function ce(o,t){return(e,r,n,a)=>{let s=Qe.tensor4d(o(e*r*n*n),[n,n,e,r]),i=Qe.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var tr=b();function er(o,t){return(e,r,n)=>{let a=tr.tensor2d(o(e*r),[e,r]),s=tr.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var Le=b();var rr=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=Le.tensor4d(o(3*3*e),[3,3,e,1]),s=Le.tensor4d(o(e*r),[1,1,e,r]),i=Le.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new rr(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new rr(e,r,n)}}function O(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function W(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function or(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function yo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),{extractDenseBlock4Params:n}=or(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function nr(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function ar(o,t){let e=O(o,t),r=nr(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Fo(o){let t=[],{extractDenseBlock4Params:e}=ar(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return A(o,t),{params:r,paramMappings:t}}var Se=class extends L{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Ie(a,e.dense0,!0);return s=Ie(s,e.dense1),s=Ie(s,e.dense2),s=Ie(s,e.dense3),s=Nt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Fo(t)}extractParams(t){return yo(t)}};var wo=b();var de=b();function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function To(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=W(o),i=er(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function _o(o){let t=[],e=O(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return A(o,t),{params:n,paramMappings:t}}function sr(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends L{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return wo.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return To(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=sr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),_o(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Xr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],kt=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Xr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Xr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var ir=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await D(t))}async predictExpressions(t){let e=await D(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new kt(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Jr(o){return o.expressions instanceof kt}function cr(o,t){return{...o,...{expressions:t}}}function kn(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof kt?a:Jr(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=mt(a)?a.detection.box.bottomLeft:r||new x(0,0);new Mt(c.map(d=>`${d.expression} (${Rt(d.probability)})`),m).draw(o)})}function Vt(o){return mt(o)&&o.landmarks instanceof z&&o.unshiftedLandmarks instanceof z&&o.alignedRect instanceof E}function In(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function le(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new E(o.detection.score,n.rescale(a.reverse()),a),i=In(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var qr=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},Zr=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new qr(e)}draw(t){let e=R(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof ne&&(e.strokeStyle=s,e.lineWidth=a,ft(e,this.faceLandmarks.getJawOutline()),ft(e,this.faceLandmarks.getLeftEyeBrow()),ft(e,this.faceLandmarks.getRightEyeBrow()),ft(e,this.faceLandmarks.getNose()),ft(e,this.faceLandmarks.getLeftEye(),!0),ft(e,this.faceLandmarks.getRightEye(),!0),ft(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function Ln(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof z?r:Vt(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Zr(n).draw(o)})}var Do="1.0.2";var dt=b();var N=b();function Sn(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Eo(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=W(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=Sn(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};it(t,0,1).forEach(v=>{l[`main_block_${v}`]=c(128,`middle_flow/main_block_${v}`)});let g=i(128,256,"exit_flow/reduction_block"),_=s(256,512,"exit_flow/separable_conv"),h={reduction_block:g,separable_conv:_};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:h}}}function An(o,t){let e=O(o,t),r=nr(e),n=pe(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function Mo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=An(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};it(t,0,1).forEach(_=>{d[`main_block_${_}`]=s(`middle_flow/main_block_${_}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),g={reduction_block:u,separable_conv:l};return A(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:g},paramMappings:e}}function Co(o,t,e){return N.add(N.conv2d(o,t.filters,e,"same"),t.bias)}function Kr(o,t,e=!0){let r=e?N.relu(o):o;return r=$(r,t.separable_conv0,[1,1]),r=$(N.relu(r),t.separable_conv1,[1,1]),r=N.maxPool(r,[3,3],[2,2],"same"),r=N.add(r,Co(o,t.expansion_conv,[2,2])),r}function Wn(o,t){let e=$(N.relu(o),t.separable_conv0,[1,1]);return e=$(N.relu(e),t.separable_conv1,[1,1]),e=$(N.relu(e),t.separable_conv2,[1,1]),e=N.add(e,o),e}var Qr=class extends L{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return N.tidy(()=>{let r=N.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(N.scalar(256)),s=N.relu(Co(a,e.entry_flow.conv_in,[2,2]));return s=Kr(s,e.entry_flow.reduction_block_0,!1),s=Kr(s,e.entry_flow.reduction_block_1),it(this._numMainBlocks,0,1).forEach(i=>{s=Wn(s,e.middle_flow[`main_block_${i}`])}),s=Kr(s,e.exit_flow.reduction_block),s=N.relu($(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Mo(t,this._numMainBlocks)}extractParams(t){return Eo(t,this._numMainBlocks)}};function No(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),n=er(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function ko(o){let t=[],e=O(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return A(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var mr=class extends L{constructor(t=new Qr(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return dt.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=dt.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return dt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:dt.softmax(r)}})}async forward(t){return this.forwardInput(await D(t))}async predictAgeAndGender(t){let e=await D(t),r=await this.forwardInput(e),n=dt.unstack(r.age),a=dt.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,g=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:g}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return No(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=sr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),ko(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var j=b();var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return j.tidy(()=>{let s=(d,u)=>j.stack([j.fill([68],d,"float32"),j.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:g}=n[d];return u(l,g)?Math.abs(l-g)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(j.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return j.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await D(t))}async detectLandmarks(t){let e=await D(t),r=j.tidy(()=>j.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>Ge(d)),m=i.filter((p,d)=>!Ge(d));return new ne(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var fe=class extends Be{constructor(t=new Se){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var It=b();function Io(o){let t=[],{extractDenseBlock3Params:e}=ar(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return A(o,t),{params:r,paramMappings:t}}function Lo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),{extractDenseBlock3Params:n}=or(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var to=class extends L{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return It.tidy(()=>{let r=It.cast(t.toBatchTensor(112,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(It.scalar(255)),s=Ke(a,e.dense0,!0);return s=Ke(s,e.dense1),s=Ke(s,e.dense2),s=It.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await D(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Io(t)}extractParams(t){return Lo(t)}};var pr=class extends Be{constructor(t=new to){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var So=class extends fe{};var V=b();var he=b();var dr=b();function Ao(o,t){return dr.add(dr.mul(o,t.weights),t.biases)}function eo(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=he.conv2d(o,a,e,n);return i=he.add(i,s),i=Ao(i,t.scale),r?he.relu(i):i}function Wo(o,t){return eo(o,t,[1,1],!0)}function ro(o,t){return eo(o,t,[1,1],!1)}function ur(o,t){return eo(o,t,[2,2],!0,"valid")}var H=b();function Bn(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Cr(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return H.tidy(()=>H.transpose(H.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=H.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=H.tensor1d(o(i)),p=H.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function Bo(o){let{extractWeights:t,getRemainingWeights:e}=W(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Bn(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),g=a(147456,128,3,"conv128_down",!0),_=a(147456,128,3,"conv128_1"),h=a(147456,128,3,"conv128_2"),v=a(589824,256,3,"conv256_down",!0),F=a(589824,256,3,"conv256_1"),y=a(589824,256,3,"conv256_2"),k=a(589824,256,3,"conv256_down_out"),Y=H.tidy(()=>H.transpose(H.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:g,conv128_1:_,conv128_2:h,conv256_down:v,conv256_1:F,conv256_2:y,conv256_down_out:k,fc:Y},paramMappings:r}}function Rn(o,t){let e=O(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function Ro(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=Rn(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),g=r("conv128_2"),_=r("conv256_down"),h=r("conv256_1"),v=r("conv256_2"),F=r("conv256_down_out"),{fc:y}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!Mr(y))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${y}`);let k={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:g,conv256_down:_,conv256_1:h,conv256_2:v,conv256_down_out:F,fc:y};return A(o,t),{params:k,paramMappings:t}}var B=b();function ot(o,t){let e=Wo(o,t.conv1);return e=ro(e,t.conv2),e=B.add(e,o),e=B.relu(e),e}function Re(o,t){let e=ur(o,t.conv1);e=ro(e,t.conv2);let r=B.avgPool(o,2,2,"valid"),n=B.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=B.zeros(i);e=B.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=B.zeros(m);e=B.concat([e,p],2)}return r=a?B.concat([r,n],3):r,e=B.add(r,e),e=B.relu(e),e}var xe=class extends L{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return V.tidy(()=>{let r=V.cast(t.toBatchTensor(150,!0),"float32"),a=rt(r,[122.782,117.001,104.298]).div(V.scalar(256)),s=ur(a,e.conv32_down);s=V.maxPool(s,3,2,"valid"),s=ot(s,e.conv32_1),s=ot(s,e.conv32_2),s=ot(s,e.conv32_3),s=Re(s,e.conv64_down),s=ot(s,e.conv64_1),s=ot(s,e.conv64_2),s=ot(s,e.conv64_3),s=Re(s,e.conv128_down),s=ot(s,e.conv128_1),s=ot(s,e.conv128_2),s=Re(s,e.conv256_down),s=ot(s,e.conv256_1),s=ot(s,e.conv256_2),s=Re(s,e.conv256_down_out);let i=s.mean([1,2]);return V.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await D(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await D(t),r=V.tidy(()=>V.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return Ro(t)}extractParams(t){return Bo(t)}};function $n(o){let t=new xe;return t.extractWeights(o),t}function lr(o,t){return{...o,...{descriptor:t}}}function On(o){return typeof o.age=="number"}function fr(o,t){return{...o,...{age:t}}}function jn(o){return(o.gender===vt.MALE||o.gender===vt.FEMALE)&&ee(o.genderProbability)}function hr(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var at=b();var nt=b();function Hn(o,t){function e(c,m){let p=nt.tensor4d(o(3*3*c),[3,3,c,1]),d=nt.tensor1d(o(c)),u=nt.tensor1d(o(c)),l=nt.tensor1d(o(c)),g=nt.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:g}}function r(c,m,p,d,u){let l=nt.tensor4d(o(c*m*p*p),[p,p,c,m]),g=nt.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:g}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),g=a(256,512,"mobilenetv1/conv_6"),_=a(512,512,"mobilenetv1/conv_7"),h=a(512,512,"mobilenetv1/conv_8"),v=a(512,512,"mobilenetv1/conv_9"),F=a(512,512,"mobilenetv1/conv_10"),y=a(512,512,"mobilenetv1/conv_11"),k=a(512,1024,"mobilenetv1/conv_12"),Y=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:g,conv_7:_,conv_8:h,conv_9:v,conv_10:F,conv_11:y,conv_12:k,conv_13:Y}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),g=n(256,64,1,"prediction_layer/conv_6"),_=n(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),v=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),F=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),y=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),k=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Y=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),tt=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),st=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),U=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),_t=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),wt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Pt=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:g,conv_7:_,box_predictor_0:{box_encoding_predictor:h,class_predictor:v},box_predictor_1:{box_encoding_predictor:F,class_predictor:y},box_predictor_2:{box_encoding_predictor:k,class_predictor:Y},box_predictor_3:{box_encoding_predictor:tt,class_predictor:st},box_predictor_4:{box_encoding_predictor:U,class_predictor:_t},box_predictor_5:{box_encoding_predictor:wt,class_predictor:Pt}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function $o(o){let t=[],{extractWeights:e,getRemainingWeights:r}=W(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Hn(e,t),s=n(),i=a(),m={extra_dim:nt.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Yn(o,t){let e=O(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,g=e(`${d}/depthwise_weights`,4,`${u}/filters`),_=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),h=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),v=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),F=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:g,batch_norm_scale:_,batch_norm_offset:h,batch_norm_mean:v,batch_norm_variance:F},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function Oo(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Yn(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!ht(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return A(o,t),{params:a,paramMappings:t}}var yt=b();var Lt=b();function J(o,t,e){return Lt.tidy(()=>{let r=Lt.conv2d(o,t.filters,e,"same");return r=Lt.add(r,t.batch_norm_offset),Lt.clipByValue(r,0,6)})}var Gn=.0010000000474974513;function zn(o,t,e){return yt.tidy(()=>{let r=yt.depthwiseConv2d(o,t.filters,e,"same");return r=yt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Gn),yt.clipByValue(r,0,6)})}function Vn(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function jo(o,t){return yt.tidy(()=>{let e,r=J(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=Vn(i);r=zn(r,a.depthwise_conv,c),r=J(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function Un(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let g=Math.max(n,c),_=Math.max(a,m),h=Math.min(s,p),v=Math.min(i,d),F=Math.max(h-g,0)*Math.max(v-_,0);return F/(u+l-F)}function Ho(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=Un(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b();function Xn(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function Jn(o,t){let{sizes:e,centers:r}=Xn(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function Yo(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=Jn(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Oe=b();var $e=b();function Ut(o,t){return $e.tidy(()=>{let e=o.shape[0],r=$e.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=$e.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Go(o,t,e){return Oe.tidy(()=>{let r=J(o,e.conv_0,[1,1]),n=J(r,e.conv_1,[2,2]),a=J(n,e.conv_2,[1,1]),s=J(a,e.conv_3,[2,2]),i=J(s,e.conv_4,[1,1]),c=J(i,e.conv_5,[2,2]),m=J(c,e.conv_6,[1,1]),p=J(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),g=Ut(s,e.box_predictor_3),_=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),v=Oe.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,g.boxPredictionEncoding,_.boxPredictionEncoding,h.boxPredictionEncoding],1),F=Oe.concat([d.classPrediction,u.classPrediction,l.classPrediction,g.classPrediction,_.classPrediction,h.classPrediction],1);return{boxPredictions:v,classPredictions:F}})}var q=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends L{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return at.tidy(()=>{let r=at.cast(t.toBatchTensor(512,!1),"float32"),n=at.sub(at.mul(r,at.scalar(.007843137718737125)),at.scalar(1)),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Go(a.out,a.conv11,e.prediction_layer);return Yo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await D(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new q(e),a=await D(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let y=1;y{let[k,Y]=[Math.max(0,v[y][0]),Math.min(1,v[y][2])].map(U=>U*h),[tt,st]=[Math.max(0,v[y][1]),Math.min(1,v[y][3])].map(U=>U*_);return new E(p[y],new oe(tt,k,st-tt,Y-k),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),F}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Oo(t)}extractParams(t){return $o(t)}};function zo(o){let t=new Xt;return t.extractWeights(o),t}function qn(o){return zo(o)}var Vo=class extends Xt{};var Uo=.4,Xo=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Jo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],qo=[117.001,114.697,97.404],Zo="tiny_yolov2_model",Ko="tiny_yolov2_separable_conv_model";var C=b();var xr=o=>typeof o=="number";function oo(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!xr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>xr(t.x)&&xr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(xr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var K=b();var Z=b();function be(o){return Z.tidy(()=>{let t=Z.mul(o,Z.scalar(.10000000149011612));return Z.add(Z.relu(Z.sub(o,t)),t)})}function Ft(o,t){return K.tidy(()=>{let e=K.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=K.conv2d(e,t.conv.filters,[1,1],"valid"),e=K.sub(e,t.bn.sub),e=K.mul(e,t.bn.truediv),e=K.add(e,t.conv.bias),be(e)})}var St=b();function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var no=b();function Zn(o,t){let e=ce(o,t);function r(s,i){let c=no.tensor1d(o(s)),m=no.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function Qo(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=W(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Zn(n,s),p;if(t.withSeparableConvs){let[d,u,l,g,_,h,v,F,y]=r,k=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),Y=m(u,l,"conv1"),tt=m(l,g,"conv2"),st=m(g,_,"conv3"),U=m(_,h,"conv4"),_t=m(h,v,"conv5"),wt=F?m(v,F,"conv6"):void 0,Pt=y?m(F,y,"conv7"):void 0,te=i(y||F||v,5*e,1,"conv8");p={conv0:k,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}else{let[d,u,l,g,_,h,v,F,y]=r,k=c(d,u,"conv0"),Y=c(u,l,"conv1"),tt=c(l,g,"conv2"),st=c(g,_,"conv3"),U=c(_,h,"conv4"),_t=c(h,v,"conv5"),wt=c(v,F,"conv6"),Pt=c(F,y,"conv7"),te=i(y,5*e,1,"conv8");p={conv0:k,conv1:Y,conv2:tt,conv3:st,conv4:U,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Kn(o,t){let e=O(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function tn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Kn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return A(o,e),{params:s,paramMappings:e}}var ut=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var ao=class extends L{constructor(t){super("TinyYolov2");oo(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(C.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ut(e),a=await D(t),s=await this.forwardInput(a,r),i=C.tidy(()=>C.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Lr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return tn(t,this.config)}extractParams(t){let e=this.config.filterSizes||ao.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return Qo(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=C.tidy(()=>{let v=t.reshape([m,m,p,this.boxEncodingSize]),F=v.slice([0,0,0,0],[m,m,p,4]),y=v.slice([0,0,0,4],[m,m,p,1]),k=this.withClassScores?C.softmax(v.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[F,y,k]}),g=[],_=await u.array(),h=await d.array();for(let v=0;vr){let Y=(F+De(h[v][F][y][0]))/m*i,tt=(v+De(h[v][F][y][1]))/m*c,st=Math.exp(h[v][F][y][2])*this.config.anchors[y].x/m*i,U=Math.exp(h[v][F][y][3])*this.config.anchors[y].y/m*c,_t=Y-st/2,wt=tt-U/2,Pt={row:v,col:F,anchor:y},{classScore:te,label:po}=this.withClassScores?await this.extractPredictedClass(l,Pt):{classScore:1,label:0};g.push({box:new re(_t,wt,_t+st,wt+U),score:k,classScore:k*te,label:po,...Pt})}}return d.dispose(),u.dispose(),l.dispose(),g}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=ao;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Uo,classes:["face"],...t?{anchors:Jo,meanRgb:qo}:{anchors:Xo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Ko:Zo}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Qn(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var br=class extends ut{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Q=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b();var so=b();async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof so.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof so.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var en=.4,rn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],on=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:en,classes:["face"],anchors:rn,meanRgb:on,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new E(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new pr,faceRecognitionNet:new xe,faceExpressionNet:new ir,ageGenderNet:new mr},nn=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),ta=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),ea=(o,t)=>T.tinyYolov2.locateFaces(o,t),an=o=>T.faceLandmark68Net.detectLandmarks(o),ra=o=>T.faceLandmark68TinyNet.detectLandmarks(o),oa=o=>T.faceRecognitionNet.computeFaceDescriptor(o),na=o=>T.faceExpressionNet.predictExpressions(o),aa=o=>T.ageGenderNet.predictAgeAndGender(o),sn=o=>T.ssdMobilenetv1.load(o),sa=o=>T.tinyFaceDetector.load(o),ia=o=>T.tinyYolov2.load(o),ca=o=>T.faceLandmark68Net.load(o),ma=o=>T.faceLandmark68TinyNet.load(o),pa=o=>T.faceRecognitionNet.load(o),da=o=>T.faceExpressionNet.load(o),ua=o=>T.ageGenderNet.load(o),la=sn,fa=nn,ha=an;var io=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends io{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>cr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},Pe=class extends io{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return cr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends Pe{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var co=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends co{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>T.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return fr(hr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends co{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>T.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return fr(hr(t,r,n),e)}withFaceExpressions(){return new Pe(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var gr=class extends Q{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends gr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>T.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends gr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var vr=class extends Q{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},yr=class extends vr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Fr=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Tr=class extends Q{constructor(t,e=new q){super();this.input=t;this.options=e}},He=class extends Tr{async run(){let{input:t,options:e}=this,r;if(e instanceof br)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof q)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ut)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Ot({},r)))})}withFaceLandmarks(t=!1){return new yr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Tr{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function xa(o,t=new q){return new _r(o,t)}function wr(o,t=new q){return new He(o,t)}async function cn(o,t){return wr(o,new q(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ba(o,t={}){return wr(o,new ut(t)).withFaceLandmarks().withFaceDescriptors()}var ga=cn;function mo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Pr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>mo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Pr(e,t.distanceThreshold)}};function va(o){let t=new Fe;return t.extractWeights(o),t}function mn(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>mn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(Ot(o,n),a)}return mt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof z||o instanceof E?o.forSize(e,r):o}var Fa=typeof process!="undefined",Ta=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",_a={faceapi:Do,node:Fa,browser:Ta};export{mr as AgeGenderNet,re as BoundingBox,P as Box,Q as ComposableTask,At as ComputeAllFaceDescriptorsTask,gr as ComputeFaceDescriptorsTaskBase,Wt as ComputeSingleFaceDescriptorTask,yr as DetectAllFaceLandmarksTask,He as DetectAllFacesTask,vr as DetectFaceLandmarksTaskBase,Tr as DetectFacesTaskBase,Fr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,S as Dimensions,Xr as FACE_EXPRESSION_LABELS,E as FaceDetection,Vo as FaceDetectionNet,ir as FaceExpressionNet,kt as FaceExpressions,fe as FaceLandmark68Net,pr as FaceLandmark68TinyNet,So as FaceLandmarkNet,z as FaceLandmarks,ho as FaceLandmarks5,ne as FaceLandmarks68,Ee as FaceMatch,Pr as FaceMatcher,xe as FaceRecognitionNet,vt as Gender,Me as LabeledBox,xt as LabeledFaceDescriptors,bt as NetInput,L as NeuralNetwork,Dt as ObjectDetection,x as Point,xo as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,q as SsdMobilenetv1Options,Fe as TinyFaceDetector,br as TinyFaceDetectorOptions,ve as TinyYolov2,ut as TinyYolov2Options,ga as allFaces,cn as allFacesSsdMobilenetv1,ba as allFacesTinyYolov2,Hr as awaitMediaLoaded,Yr as bufferToImage,oa as computeFaceDescriptor,Yt as createCanvas,ke as createCanvasFromMedia,qn as createFaceDetectionNet,$n as createFaceRecognitionNet,zo as createSsdMobilenetv1,va as createTinyFaceDetector,Qn as createTinyYolov2,wr as detectAllFaces,an as detectFaceLandmarks,ra as detectFaceLandmarksTiny,ha as detectLandmarks,xa as detectSingleFace,Po as draw,w as env,mo as euclideanDistance,fr as extendWithAge,lr as extendWithFaceDescriptor,Ot as extendWithFaceDetection,cr as extendWithFaceExpressions,le as extendWithFaceLandmarks,hr as extendWithGender,se as extractFaceTensors,ae as extractFaces,Mn as fetchImage,Vr as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,R as getContext2dOrThrow,Ht as getMediaDimensions,Gr as imageTensorToCanvas,zr as imageToSquare,vn as inverseSigmoid,kr as iou,Je as isMediaElement,Ne as isMediaLoaded,On as isWithAge,mt as isWithFaceDetection,Jr as isWithFaceExpressions,Vt as isWithFaceLandmarks,jn as isWithGender,ua as loadAgeGenderModel,la as loadFaceDetectionModel,da as loadFaceExpressionModel,ca as loadFaceLandmarkModel,ma as loadFaceLandmarkTinyModel,pa as loadFaceRecognitionModel,sn as loadSsdMobilenetv1Model,sa as loadTinyFaceDetectorModel,ia as loadTinyYolov2Model,Ur as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Ir as minBbox,T as nets,Lr as nonMaxSuppression,rt as normalize,Sr as padToSquare,aa as predictAgeAndGender,na as recognizeFaceExpressions,mn as resizeResults,jt as resolveInput,gn as shuffleArray,De as sigmoid,nn as ssdMobilenetv1,ya as tf,ta as tinyFaceDetector,ea as tinyYolov2,D as toNetInput,lo as utils,oo as validateConfig,_a as version}; //# sourceMappingURL=face-api.esm-nobundle.js.map diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index 1d37d3c..e1a325e 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4163,7 +4163,7 @@ return a / b;`,XJ=` } setOutput(${l}); } - `}};function yee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,c=E.getAxesPermutation([u],o),p=r;c!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(p),u=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,u,i),h=I.sizeFromShape([p.shape[u]]),m=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=jd(r.dtype),g=(v,k,w,S,A)=>{let F=v.shape[0],D=v.shape[1],R=E.segment_util.segOpComputeOptimalWindowSize(D,A),L={windowSize:R,inSize:D,batchSize:F,numSegments:A},W=new gee(L,k),U=n.compileAndRun(W,[v,w],S);if(l.push(U),U.shape[1]===A)return U;let G=qT({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),X=YT({inputs:{x:G},backend:n,attrs:{reps:[D/R]}});return l.push(G),l.push(X),g(U,k,X,S,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=ye({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(c!=null){l.push(b);let v=E.getUndoAxesPermutation(c);x=_n({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var bee={kernelName:pc,backendName:"webgl",kernelFunc:yee},xee=[lJ,pJ,YX,QX,tK,rK,iK,uK,pK,hK,yK,xK,kK,TK,$K,AK,RK,zK,OK,UK,HK,qK,JK,r7,i7,d7,m7,b7,w7,EX,T7,R7,O7,F7,B7,U7,z7,j7,K7,Q7,eY,nY,sY,pY,hY,oY,gY,xY,IY,CY,EY,MY,RY,PY,LY,WY,VY,GY,jY,YY,e9,n9,r9,o9,p9,f9,x9,_X,w9,N7,N9,C9,_9,DX,M9,L9,W9,q9,G9,J9,eJ,rJ,hJ,wJ,xJ,TJ,CJ,FJ,yJ,EJ,DJ,OJ,BJ,HJ,ZJ,LX,tQ,rQ,oQ,cQ,l7,hQ,fQ,yQ,vQ,NQ,RX,SQ,CQ,u7,KJ,_Q,LQ,MQ,WX,VQ,HQ,XQ,JQ,tZ,aZ,iZ,uZ,pZ,mZ,yZ,vZ,IZ,SZ,FZ,n7,JJ,$Z,MZ,PZ,LZ,BZ,GZ,jZ,XZ,JZ,YJ,qX,eee,aee,iee,lee,pee,XX,hee,fee,bee,mQ];for(let e of xee)hc(e);var vee="3.3.0",wee={"tfjs-core":Q0,"tfjs-backend-cpu":BU,"tfjs-backend-webgl":FX,"tfjs-data":TN,"tfjs-layers":im,"tfjs-converter":xN,tfjs:vee},Hn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Hn||(Hn={}));var gp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(gp||(gp={}));var JT;function kee(e){JT=e.wasm.cwrap(gi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Iee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let A=n.dataIdMap.get(i.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=gp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),k=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return JT(d,w,r.shape.length,h,S,s.shape.length,l,u,g,m,f,p||0,k),v}var Nee={kernelName:gi,backendName:"wasm",setupFunc:kee,kernelFunc:Iee};function En(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return I.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Tee=En(So);function gn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:c}=l,p=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,m=E.assertAndGetBroadcastShape(u.shape,c.shape),f=o.makeOutput(m,h);if(I.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,u.shape.length,d,y,c.shape.length,Hn[u.dtype],b);if(t&&u.dtype==="float32")return x(),f;let v=E.getBroadcastDims(u.shape,m),k=E.getBroadcastDims(c.shape,m),w=v.every((A,F)=>A===F),S=k.every((A,F)=>A===F);if(w&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var See=!0,Cee=gn(Br,See),QT;function Aee(e){QT=e.wasm.cwrap(Ns,null,["array","number","number","number"])}function Fee(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(I.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return QT(s,r.length,Hn[a.dtype],i),a}var _ee={kernelName:Ns,backendName:"wasm",setupFunc:Aee,kernelFunc:Fee};function Wm(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Eee={kernelName:Bs,backendName:"wasm",kernelFunc:Wm},ZT;function $ee(e){ZT=e.wasm.cwrap(fi,null,["number","array","number","number","number","array","number"])}function Bm(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Mee(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Ree={kernelName:fi,backendName:"wasm",kernelFunc:Bm,setupFunc:$ee};function mu(e,t,n){let a=e.shape,r=e.shape.length,s=I.parseAxisParam(t,a),i=s,o=E.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let c=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var Vee={kernelName:cl,backendName:"wasm",kernelFunc:Pa},nS;function Uee(e){nS=e.wasm.cwrap(Cs,null,["number","array","number","number","array","number","number","number","number"])}function Gee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,c=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=I.sizeFromShape(m),y=I.sizeFromShape(f),b=g===y||g===1||y===1;I.assert(l>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);I.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,c,d]:[g,d,c],k=o?[y,h,p]:[y,p,h],w=Pa({inputs:{x:r},backend:n,attrs:{shape:v}}),S=Pa({inputs:{x:s},backend:n,attrs:{shape:k}}),A=n.dataIdMap.get(w.dataId).id,F=n.dataIdMap.get(S.dataId).id,D=i?w.shape[2]:w.shape[1],R=o?S.shape[1]:S.shape[2],L=Math.max(g,y),W=n.makeOutput([L,D,R],w.dtype),U=n.dataIdMap.get(W.dataId).id,G=new Uint8Array(new Int32Array(w.shape).buffer),X=new Uint8Array(new Int32Array(S.shape).buffer);return nS(A,G,w.shape.length,F,X,S.shape.length,i,o,U),n.disposeData(w.dataId),n.disposeData(S.dataId),W.shape=x,W}var Hee={kernelName:Cs,backendName:"wasm",setupFunc:Uee,kernelFunc:Gee};function Vm(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var jee={kernelName:As,backendName:"wasm",kernelFunc:Vm},qee=En(Fs),aS;function Xee(e){aS=e.wasm.cwrap(Vr,null,["number","number","number","number"])}function Kee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return aS(o,s,i,u),l}var Yee={kernelName:Vr,backendName:"wasm",setupFunc:Xee,kernelFunc:Kee};function rS(e){let{inputs:t,backend:n}=e,a=I.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>I.sizeFromShape(h.shape)>0);if(s.length===1)return Wm({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(I.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=I.sizeFromShape(x.shape.slice(a));return Pa({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=fv(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=I.sizeFromShape(s[0].shape.slice(0,a)),u=0,c=s.map(h=>{let m=I.sizeFromShape(h.shape.slice(a));return u+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=E.getAxesPermutation([s],l),c=r;u!==null&&(c=Bm({inputs:{x:r},attrs:{perm:u},backend:n}));let p=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],m=n.dataIdMap.get(c.dataId).id,f=n.dataIdMap.get(d.dataId).id;lS(m,i?1:0,o?1:0,h,f,Hn[r.dtype]);let g=d;if(u!==null){let y=E.getUndoAxesPermutation(u);g=Bm({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var cte={kernelName:Ds,backendName:"wasm",setupFunc:lte,kernelFunc:ute},uS;function pte(e){uS=e.wasm.cwrap(Oo,null,["number","number","number","array","number","array","array","number","number"])}function dte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;I.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=u*s,h=c/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(I.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(I.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return uS(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var hte={kernelName:Oo,backendName:"wasm",setupFunc:pte,kernelFunc:dte},cS;function mte(e){cS=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=E.computeConv2DInfo(r.shape,s.shape,l,d,c,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,k=h.dilationWidth,w=h.strideHeight,S=h.strideWidth,A=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=a.makeOutput(h.outShape,"float32"),L=a.dataIdMap.get(R.dataId).id;return cS(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,D,v,k,w,S,A,F,L),R}var gte={kernelName:Ms,backendName:"wasm",setupFunc:mte,kernelFunc:fte},yte=!1,bte=gn(Wo,yte,"bool"),xte=En(Ps);function qv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(I.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var vte={kernelName:Bo,backendName:"wasm",kernelFunc:qv};function wte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var kte={kernelName:tc,backendName:"wasm",kernelFunc:wte},pS;function Ite(e){pS=e.wasm.cwrap(Uo,null,["number","number","number","number","number","number"])}function Nte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,c]=a.shape;return pS(s,o,l,u,c,i),r}var Tte={kernelName:Uo,backendName:"wasm",kernelFunc:Nte,setupFunc:Ite},Ste=En(Os),Cte=!1,Ate=gn(Ls,Cte),dS;function Fte(e){dS=e.wasm.cwrap(zs,null,["number","number","number","number","number","number","number"])}function _te(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(I.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return dS(c,p,d,h,m,r,g),f}var Ete={kernelName:zs,backendName:"wasm",setupFunc:Fte,kernelFunc:_te},hS;function $te(e){hS=e.wasm.cwrap(yi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d),g=gp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return hS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var Mte={kernelName:yi,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},mS;function Rte(e){mS=e.wasm.cwrap(bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!0),g=gp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return mS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var Ote={kernelName:bi,backendName:"wasm",setupFunc:Rte,kernelFunc:Pte},fS;function Lte(e){fS=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number","array","number"])}function zte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=hy.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let c=r.shape,p=c[c.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return fS(d,Hn[a.dtype],h,i,p,o,m,f),u}var Wte={kernelName:Ho,backendName:"wasm",setupFunc:Lte,kernelFunc:zte},gS;function Bte(e){gS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Vte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=I.parseAxisParam(i,r.shape)[0],u=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Pa({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),p=I.sizeFromShape(s.shape),d=Pa({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],m=t.makeOutput(h,r.dtype);if(I.sizeFromShape(r.shape)===0)return m;let f=c.shape.length-1,g=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(I.computeStrides(c.shape)).buffer),v=new Uint8Array(new Int32Array(I.computeStrides(h)).buffer);return gS(g,Hn[r.dtype],x,f,y,u.batchSize,v,b),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var Ute={kernelName:Go,backendName:"wasm",setupFunc:Bte,kernelFunc:Vte},Gte=!1,Hte=gn(jo,Gte,"bool"),jte=!1,qte=gn(Ws,jte,"bool"),yS;function Xte(e){yS=e.wasm.cwrap(Vs,null,["number","number","number"])}function Kte(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(I.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;yS(r,n,i)}return s}var Yte={kernelName:Vs,backendName:"wasm",setupFunc:Xte,kernelFunc:Kte},Jte=!1,Qte=gn(Yo,Jte,"bool"),Zte=!1,ene=gn(Jo,Zte,"bool"),tne=En(Us),nne=!1,ane=gn(Zo,nne,"bool"),bS;function rne(e){bS=e.wasm.cwrap(Gs,null,["number, number, number"])}function sne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:p,inputWasTransposed:d}=mu(i,r,t);if(d){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",c,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,c),g=I.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(I.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;bS(o,g,b)}if(d&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var ine={kernelName:Gs,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=!1,lne=gn(Hs,one),xS;function une(e){xS=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=E.computePool2DInfo(r.shape,i,o,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,b=c.dilationWidth,x=c.strideHeight,v=c.strideWidth,k=c.inChannels,w=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(c.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return xS(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,k,w,A),S}var pne={kernelName:js,backendName:"wasm",setupFunc:une,kernelFunc:cne},vS;function dne(e){vS=e.wasm.cwrap(qs,null,["number, number, number"])}function hne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(u=c,l=v,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Vm({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(I.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;vS(l,y,v)}if(h&&t.disposeData(c.dataId),s){let v=E.expandShapeToKeepDim(x.shape,d);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var mne={kernelName:qs,backendName:"wasm",setupFunc:dne,kernelFunc:hne},wS;function fne(e){wS=e.wasm.cwrap(Xs,null,["number, number, number"])}function gne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x)}let m=u.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(u.shape,p),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;wS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var yne={kernelName:Xs,backendName:"wasm",setupFunc:fne,kernelFunc:gne},bne=!1,xne=gn(Ks,bne),vne=!0,wne=gn(Ys,vne),kne=En(tl);function Xv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var kS;function Ine(e){kS=e.wasm.cwrap(al,"number",["number","number","number","number","number"])}function Nne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=kS(u,c,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Xv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Tne={kernelName:al,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne},IS;function Sne(e){IS=e.wasm.cwrap(rl,"number",["number","number","number","number","number","bool"])}function Cne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=IS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Xv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Ane={kernelName:rl,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},NS;function Fne(e){NS=e.wasm.cwrap(sl,"number",["number","number","number","number","number","number"])}function _ne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=NS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Xv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Ene={kernelName:sl,backendName:"wasm",setupFunc:Fne,kernelFunc:_ne},$ne=!1,Dne=gn(nl,$ne,"bool"),TS;function Mne(e){TS=e.wasm.cwrap(Js,null,["number","number","number","number","number"])}function Rne(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(r.dataId).id;return TS(c,s,i,o,u),l}var Pne={kernelName:Js,backendName:"wasm",setupFunc:Mne,kernelFunc:Rne};function One(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Lne={kernelName:il,backendName:"wasm",kernelFunc:One};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return qv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let p=qv({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),u=rS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),u}var Wne={kernelName:ol,backendName:"wasm",kernelFunc:zne},SS;function Bne(e){SS=e.wasm.cwrap(Qs,null,["number","array","number","number","array","array","number","number"])}function Vne(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return SS(i,u,t.shape.length,Hn[t.dtype],d,h,r,l),o}var Une={kernelName:Qs,backendName:"wasm",kernelFunc:Vne,setupFunc:Bne},Gne=!1,Hne=gn(Zs,Gne),CS;function jne(e){CS=e.wasm.cwrap(ei,null,["number","number","number"])}function qne(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return CS(s,i,l),o}var Xne={kernelName:ei,backendName:"wasm",setupFunc:jne,kernelFunc:qne},AS;function Kne(e){AS=e.wasm.cwrap(ll,null,["number","number","number","number"])}function Yne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;AS(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Jne={kernelName:ll,backendName:"wasm",setupFunc:Kne,kernelFunc:Yne},Qne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=bv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Zne={kernelName:oc,backendName:"wasm",kernelFunc:Qne},eae=!0,tae=gn(Rs,eae),nae=En(ti),aae=En(ai),FS;function rae(e){FS=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number"])}function sae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[c,p,d,h]=r.shape,m=[c,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Vm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(I.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return FS(y,c,p,d,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var iae={kernelName:ni,backendName:"wasm",setupFunc:rae,kernelFunc:sae},_S;function oae(e){_S=e.wasm.cwrap(ri,null,["number","array","number","array","number","number"])}function lae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=I.parseAxisParam(s,r.shape);if(r.shape.length===0)return Wm({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);_S(l,c,i.length,p,r.shape.length,u);let d=Pa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var uae={kernelName:ri,backendName:"wasm",kernelFunc:lae,setupFunc:oae},ES;function cae(e){ES=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function pae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=E.getImageCenter(o,d,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return ES(u,p,d,h,m,s,f,g,v,x.length,c),l}var dae={kernelName:Nl,backendName:"wasm",kernelFunc:pae,setupFunc:cae},hae=En(si),mae=En(ii),$S;function fae(e){$S=e.wasm.cwrap(pl,null,["number","number","number","number","number","number","array","number","number"])}function gae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(I.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=my.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return $S(h,m,Hn[s.dtype],l,u,c,f,d,g),o}var yae={kernelName:pl,backendName:"wasm",setupFunc:fae,kernelFunc:gae},DS;function bae(e){DS=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function xae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(u.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:I.sizeFromShape(r.shape.slice(1));return DS(i,o,l,h,c),u}var vae={kernelName:dl,backendName:"wasm",kernelFunc:xae,setupFunc:bae},MS;function wae(e){MS=e.wasm.cwrap(li,null,["number","number"])}function kae(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return I.sizeFromShape(r.shape)===0||MS(a,s),r}var Iae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:wae,kernelFunc:kae},Nae=En(oi);function Um(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=pn.parseSliceParams(t,n,a),o=pn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),c=I.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(o){let m=pn.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=l.slice(m,m+I.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+I.sizeFromShape(i))),u}if(t.dtype==="string"){let m=km(l,s,i,t.shape,t.dtype);return p.stringBytes=m,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)Tae(l,c[0],d,s,i);else if(h===3)Sae(l,c[0],c[1],d,s,i);else if(h===4)Cae(l,c[0],c[1],c[2],d,s,i);else{let m=km(l,s,i,t.shape,t.dtype);d.set(m)}return u}function Tae(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u{let d=[...c];d[o]=p;let h=Um({inputs:{x:r},attrs:{begin:u,size:d},backend:a});return u[o]+=p,h})}var Dae={kernelName:bl,backendName:"wasm",kernelFunc:$ae},Mae=En(ui),Rae=En(cc),Pae=!0,Oae=gn(di,Pae),PS;function Lae(e){PS=e.wasm.cwrap(Gr,null,["number","number","number"])}function zae(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return PS(i,r,l),o}var Wae={kernelName:Gr,backendName:"wasm",setupFunc:Lae,kernelFunc:zae},OS;function Bae(e){OS=e.wasm.cwrap(xl,null,["number","array","number","array","array","array","array","array","number","number"])}function Vae(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=a,h=E.slice_util.maskToAxes(c);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let m=r.shape.length-s.length,f=E.slice_util.maskToAxes(p),g=r.shape.slice();f.forEach(D=>{s[D]=0,i[D]=1,g.splice(D,0,1)});let y=Pa({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:b,end:x,strides:v}=E.slice_util.getNormalizedAxes(y.shape,h,m,s,i,o,l,u,c);s=b,i=x,o=v;let k=E.slice_util.maskToAxes(d);k.forEach(D=>{i[D]=s[D]+1,o[D]=1});let w=E.slice_util.computeOutShape(s,i,o),S=w.filter((D,R)=>k.indexOf(R)===-1);if(o.every(D=>D===1)){let D=Um({inputs:{x:y},attrs:{begin:s,size:w},backend:t});t.disposeData(y.dataId);let R=Pa({inputs:{x:D},attrs:{shape:S},backend:t});return t.disposeData(D.dataId),R}let A=t.makeOutput(S,"float32");if(!S.some(D=>D===0)){let D=t.dataIdMap.get(y.dataId).id,R=new Uint8Array(new Int32Array(I.computeStrides(y.shape)).buffer),L=new Uint8Array(new Int32Array(s).buffer),W=new Uint8Array(new Int32Array(i).buffer),U=new Uint8Array(new Int32Array(o).buffer),G=new Uint8Array(new Int32Array(S).buffer),X=new Uint8Array(new Int32Array(I.computeStrides(S)).buffer),j=t.dataIdMap.get(A.dataId).id;OS(D,R,y.shape.length,L,W,U,G,X,S.length,j)}t.disposeData(y.dataId);let F=Pa({inputs:{x:A},attrs:{shape:S},backend:t});return t.disposeData(A.dataId),F}var Uae={kernelName:xl,backendName:"wasm",setupFunc:Bae,kernelFunc:Vae},Gae=!0,Hae=gn(hi,Gae),LS;function jae(e){LS=e.wasm.cwrap(ci,null,["number, number, number"])}function qae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;LS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Xae={kernelName:ci,backendName:"wasm",setupFunc:jae,kernelFunc:qae},Kae=En(mi),zS;function Yae(e){zS=e.wasm.cwrap(Ur,null,["number","array","number","array","number","number"])}function Jae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let d=0;d{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),c=t.dataIdMap.get(u.dataId).id,p=t.makeOutput(l,"int32"),d=t.dataIdMap.get(p.dataId).id;return WS(i,o,a.shape.length,Hn[a.dtype],r,s,c,d),[u,p]},tre={kernelName:wl,backendName:"wasm",setupFunc:Zae,kernelFunc:ere};function nre(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h({dataId:h,dtype:m,shape:l}))}var are={kernelName:kl,backendName:"wasm",kernelFunc:nre};function rre(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var sre={kernelName:Il,backendName:"wasm",kernelFunc:rre},ire=[Tee,Cee,_ee,Lee,Bee,Hee,jee,qee,Yee,Jee,ete,ate,rte,ote,cte,hte,gte,bte,xte,vte,kte,Tte,Ste,Ate,Nee,Ete,Mte,Ote,Wte,Ute,Hte,qte,Eee,Yte,Qte,ene,tne,ane,ine,lne,pne,mne,yne,xne,wne,kne,Tne,Ane,Ene,Dne,Pne,Lne,Wne,Une,Hne,Xne,Jne,Zne,tae,nae,aae,Vee,iae,uae,dae,mae,hae,yae,vae,Iae,Nae,Aae,Eae,Dae,Mae,Rae,Oae,Wae,Uae,Hae,Xae,Kae,Qae,tre,Ree,are,sre];for(let e of ire)hc(e);var Kv=Z();Kv.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));Kv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Kv.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var BS=Io(yF()),ore='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',lre=Io(bF()),VS=class extends ju{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new sd(this,Ua())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=I.now();return e(),{kernelMs:I.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=I.sizeFromShape(n),o=i*I.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:a,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let s=this.wasm.HEAPU8.slice(t,t+I.sizeFromShape(a)*I.bytesPerElement(n));return ure(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=I.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=I.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function cre(e){return(t,n)=>(I.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance)})})}),{})}function US(e,t,n){if(Gm!=null)return Gm;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),yp!=null&&yp[a]!=null?yp[a]:n+a}async function pre(){let[e,t]=await Promise.all([Z().getAsync("WASM_HAS_SIMD_SUPPORT"),Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=ore,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return o.endsWith(".wasm")?US(e,t,bp!=null?bp:l):l+o},Yv&&(r.instantiateWasm=cre(US(e,t,bp!=null?bp:"")));let s=!1;r.onAbort=()=>{s||xp||(xp=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&Gm==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+BS.default.toString()],{type:"text/javascript"}),i=(0,BS.default)(r)):i=(0,lre.default)(r),i.then(o=>{s=!0,xp=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function ure(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var dre=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Gm=null,bp=null,yp={},xp=!1,Yv=!1;function hre(e,t=!1){if(vy("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),xp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Gm=e,Yv=t}function mre(e,t=!1){if(xp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")bp=e;else{yp=e;let n=dre.filter(a=>yp[a]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Yv=t}var fre="3.3.0",gre=2;Qd("wasm",async()=>{let{wasm:e}=await pre();return new VS(e)},gre);var ZS={};ad(ZS,{AnchorPosition:()=>ir,DrawBox:()=>Xm,DrawBoxOptions:()=>cw,DrawFaceLandmarks:()=>vw,DrawFaceLandmarksOptions:()=>xw,DrawTextField:()=>fs,DrawTextFieldOptions:()=>Ip,drawContour:()=>Tr,drawDetections:()=>Cre,drawFaceExpressions:()=>Ere,drawFaceLandmarks:()=>Dre});function Tr(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:a,y:r},s)=>{let i=t[s];e.moveTo(i.x,i.y),e.lineTo(a,r)}),n){let a=t[t.length-1],r=t[0];if(!a||!r)return;e.moveTo(a.x,a.y),e.lineTo(r.x,r.y)}e.stroke()}var GS={};ad(GS,{computeReshapedDimensions:()=>Zv,getCenterPoint:()=>Ki,isDimensions:()=>jm,isEven:()=>Hm,isFloat:()=>Qv,isTensor:()=>qi,isTensor1D:()=>yre,isTensor2D:()=>Jv,isTensor3D:()=>Sr,isTensor4D:()=>aa,isValidNumber:()=>Oa,isValidProbablitiy:()=>fu,range:()=>rr,round:()=>Xi});var yn=class{constructor(t,n){if(!Oa(t)||!Oa(n))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new yn(1/this.width,1/this.height)}};function qi(e,t){return e instanceof Fe&&e.shape.length===t}function yre(e){return qi(e,1)}function Jv(e){return qi(e,2)}function Sr(e){return qi(e,3)}function aa(e){return qi(e,4)}function Qv(e){return e%1!=0}function Hm(e){return e%2==0}function Xi(e,t=2){let n=10**t;return Math.floor(e*n)/n}function jm(e){return e&&e.width&&e.height}function Zv({width:e,height:t},n){let a=n/Math.max(t,e);return new yn(Math.round(e*a),Math.round(t*a))}function Ki(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function rr(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function Oa(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function fu(e){return Oa(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var ut=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(Oa)}static assertIsValidBox(t,n,a=!1){if(!ut.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!a&&(t.width<0||t.height<0))throw new Error(`${n} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,n=!0){let a=t||{},r=[a.left,a.top,a.right,a.bottom].every(Oa),s=[a.x,a.y,a.width,a.height].every(Oa);if(!s&&!r)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(a)}`);let[i,o,l,u]=s?[a.x,a.y,a.width,a.height]:[a.left,a.top,a.right-a.left,a.bottom-a.top];ut.assertIsValidBox({x:i,y:o,width:l,height:u},"Box.constructor",n),this._x=i,this._y=o,this._width=l,this._height=u}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new De(this.left,this.top)}get topRight(){return new De(this.right,this.top)}get bottomLeft(){return new De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ut({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ut({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return an&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),u<1&&(l=2-u,u=1),c<1&&(l=2-c,c=1),{dy:i,edy:l,dx:s,edx:o,y:c,ey:d,x:u,ex:p,w:a,h:r}}calibrate(t){return new ut({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var gu=class extends ut{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var ms=class{constructor(t,n,a,r,s){this._imageDims=new yn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ut(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ut(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new ms(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var yt=class extends ms{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new yt(a,r,s)}};function ew(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function tw(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;ul[c]<=n)}return s}function La(e,t){return M(()=>{let[n,a,r]=t,s=Sn([...e.shape.slice(0,3),1],n,"float32"),i=Sn([...e.shape.slice(0,3),1],a,"float32"),o=Sn([...e.shape.slice(0,3),1],r,"float32"),l=Je([s,i,o],3);return me(e,l)})}function aw(e,t=!1){return M(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,Sn(h,0,"float32")},l=o(s),u=r-l.shape[i],p=[t&&u?o(u):null,e,l].filter(d=>!!d).map(d=>ue(d,"float32"));return Je(p,i)})}function bre(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function vp(e){return 1/(1+Math.exp(-e))}function xre(e){return Math.log(e/(1-e))}var yu=class extends ut{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var vre=.5,wre=.43,kre=.45,ra=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new yn(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new De(r,s)).add(a))}get shift(){return new De(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new De(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new De(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof yt?t.box.floor():new ut(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/kre),l=Ki(t),u=Math.floor(Math.max(0,l.x-vre*o)),c=Math.floor(Math.max(0,l.y-wre*o));return new yu(u,c,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+c))}alignMinBbox(t){let n=tw(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var HS=class extends ra{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Ki([t[3],t[4]])]}};var bu=class extends ra{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Ki)}};var wp=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Xi(this.distance)})`:""}`}};var kp=class extends ut{static assertIsValidLabeledBox(t,n){if(ut.assertIsValidBox(t,n),!Oa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var Cr=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new Cr(t.label,n)}};var jS=class extends kp{static assertIsValidPredictedBox(t,n){if(kp.assertIsValidLabeledBox(t,n),!fu(t.score)||!fu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function sr(e){return e.detection instanceof yt}function Yi(e,t){return{...e,...{detection:t}}}function rw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function qm(e){let t="";if(!e)try{e=require("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function sw(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},r=global.fetch,s=qm();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:a,fetch:r,...s}}function iw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var ow=PA(XS()),Zt;function Tre(){if(!Zt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Zt}function lw(e){Zt=e}function uw(){return iw()?lw(rw()):(0,ow.isNodejs)()?lw(sw()):null}function Sre(e){if(Zt||uw(),!Zt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=Zt.Canvas,Image:n=Zt.Image}=e;Zt.Canvas=t,Zt.Image=n,Zt.createCanvasElement=e.createCanvasElement||(()=>new t),Zt.createImageElement=e.createImageElement||(()=>new n),Zt.ImageData=e.ImageData||Zt.ImageData,Zt.Video=e.Video||Zt.Video,Zt.fetch=e.fetch||Zt.fetch,Zt.readFile=e.readFile||Zt.readFile}var st={getEnv:Tre,setEnv:lw,initialize:uw,createBrowserEnv:rw,createFileSystem:qm,createNodejsEnv:sw,monkeyPatch:Sre,isBrowser:iw,isNodejs:ow.isNodejs};uw();function Ji(e){return!st.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function $n(e){let{Canvas:t,CanvasRenderingContext2D:n}=st.getEnv();if(e instanceof n)return e;let a=Ji(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var ir;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(ir||(ir={}));var Ip=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||ir.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},fs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof fs?t.text:t,this.anchor=n,this.options=new Ip(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var cw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:ir.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ip({...i,...s})}},Xm=class{constructor(t,n={}){this.box=new ut(t),this.options=new cw(n)}draw(t){let n=$n(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new fs([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Cre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof yt?a.score:sr(a)?a.detection.score:void 0,s=a instanceof yt?a.box:sr(a)?a.detection.box:new ut(a),i=r?`${Xi(r)}`:void 0;new Xm(s,{label:i}).draw(e)})}function Np(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function pw(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||Np(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function dw(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=st.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function Qi(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new yn(e.naturalWidth,e.naturalHeight):e instanceof n?new yn(e.videoWidth,e.videoHeight):new yn(e.width,e.height)}function Zi({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Tp(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!Np(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||Qi(e),s=Zi({width:a,height:r});return e instanceof n?$n(s).putImageData(e,0,0):$n(s).drawImage(e,0,0,a,r),s}async function hw(e,t){let n=t||st.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(aa(e)?1:0),i=M(()=>e.as3D(a,r,s).toInt());return await ki.toPixels(i,n),i.dispose(),n}function Km(e){let{Image:t,Canvas:n,Video:a}=st.getEnv();return e instanceof t||e instanceof n||e instanceof a}function mw(e,t,n=!1){let{Image:a,Canvas:r}=st.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Zi({width:1,height:1});let s=Qi(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=Zi({width:t,height:t}),c=e instanceof r?e:Tp(e),p=Math.abs(o-l)/2,d=n&&o0&&c.height>0&&$n(u).drawImage(c,d,h,o,l),u}var Ar=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Sr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(aa(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof st.getEnv().Canvas?a:Tp(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return rr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Zv({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,M(()=>{let a=rr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Fe){let o=aa(i)?i:i.expandDims();return o=aw(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ka.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof st.getEnv().Canvas)return ki.fromPixels(mw(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>ue(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function mt(e){if(e instanceof Ar)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(Ji);return a.forEach((r,s)=>{if(!Km(r)&&!Sr(r)&&!aa(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(aa(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>Km(r)&&pw(r))),new Ar(a,Array.isArray(e))}async function xu(e,t){let{Canvas:n}=st.getEnv(),a=e;if(!(e instanceof n)){let i=await mt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await hw(o)}let r=$n(a);return t.map(i=>i instanceof yt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let c=Zi({width:l,height:u});return l>0&&u>0&&$n(c).putImageData(r.getImageData(i,o,l,u),0,0),c})}async function vu(e,t){if(!Sr(e)&&!aa(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(aa(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return M(()=>{let[n,a,r]=e.shape.slice(aa(e)?1:0);return t.map(o=>o instanceof yt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:u,height:c})=>Wl(e.as3D(n,a,r),[l,o,0],[c,u,r]))})}async function eo(e,t){let{fetch:n}=st.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function Are(e){let t=await eo(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return dw(n)}async function fw(e){return(await eo(e)).json()}async function Fre(e){return new Float32Array(await(await eo(e)).arrayBuffer())}function Ym(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function gw(e,t){let{manifestUri:n,modelBaseUri:a}=Ym(e,t),r=await fw(n);return jt.loadWeights(r,a)}function _re(e,t,n=!1){let{width:a,height:r}=n?Qi(t):t;return e.width=a,e.height=r,{width:a,height:r}}var on=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof Hr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof Hr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Yn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await gw(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=st.getEnv(),{manifestUri:a,modelBaseUri:r}=Ym(t,this.getDefaultModelName()),s=u=>Promise.all(u.map(c=>n(c).then(p=>p.buffer))),i=jt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof Fe))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function Dn(e,t,n){return M(()=>{let a=Fi(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function Jm(e,t,n=!1){return M(()=>{let a=qe(n?J(Et(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Sp(e,t,n=!1,a=!0){return M(()=>{let r=qe(n?J(Et(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),u=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,u))))})}function to(e,t,n="same",a=!1){return M(()=>{let r=J(Et(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function bn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function wu(e,t){return(n,a,r,s)=>{let i=Ca(e(n*a*r*r),[r,r,n,a]),o=Qe(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Qm(e,t){return(n,a,r)=>{let s=Sa(e(n*a),[n,a]),i=Qe(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Zm=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function ku(e,t){return(n,a,r)=>{let s=Ca(e(3*3*n),[3,3,n,1]),i=Ca(e(n*a),[1,1,n,a]),o=Qe(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Zm(s,i,o)}}function Iu(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Zm(n,a,r)}}function jn(e,t){return(n,a,r)=>{let s=e[n];if(!qi(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function xn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function ef(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l,u=!1){let c=u?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:c,conv1:p,conv2:d}}function s(i,o,l,u=!1){let{conv0:c,conv1:p,conv2:d}=r(i,o,l,u),h=a(o,o,`${l}/conv3`);return{conv0:c,conv1:p,conv2:d,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function KS(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock4Params:r}=ef(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function tf(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function nf(e,t){let n=jn(e,t),a=tf(n),r=Iu(n);function s(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`);return{conv0:u,conv1:c,conv2:p}}function i(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`),d=r(`${o}/conv3`);return{conv0:u,conv1:c,conv2:p,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function YS(e){let t=[],{extractDenseBlock4Params:n}=nf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return bn(e,t),{params:a,paramMappings:t}}var Cp=class extends on{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(255)),i=Sp(s,n.dense0,!0);return i=Sp(i,n.dense1),i=Sp(i,n.dense2),i=Sp(i,n.dense3),i=Qn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return YS(t)}extractParams(t){return KS(t)}};function Ap(e,t){return M(()=>J(ze(e,t.weights),t.bias))}function JS(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=xn(e),o=Qm(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function QS(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return bn(e,t),{params:r,paramMappings:t}}function af(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Fp=class extends on{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof Ar?this.faceFeatureExtractor.forwardInput(t):t;return Ap(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return JS(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),QS(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var yw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],gs=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);yw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return yw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var rf=class extends Fp{constructor(t=new Cp){super("FaceExpressionNet",t)}forwardInput(t){return M(()=>Ta(this.runNet(t)))}async forward(t){return this.forwardInput(await mt(t))}async predictExpressions(t){let n=await mt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new gs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function bw(e){return e.expressions instanceof gs}function sf(e,t){return{...e,...{expressions:t}}}function Ere(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof gs?s:bw(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),u=sr(s)?s.detection.box.bottomLeft:a||new De(0,0);new fs(l.map(p=>`${p.expression} (${Xi(p.probability)})`),u).draw(e)})}function no(e){return sr(e)&&e.landmarks instanceof ra&&e.unshiftedLandmarks instanceof ra&&e.alignedRect instanceof yt}function $re(e){let t=(o,l,u,c)=>Math.atan2(c-l,u-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function Nu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new yt(e.detection.score,r.rescale(s.reverse()),s),o=$re(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var xw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},vw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new xw(n)}draw(t){let n=$n(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof bu&&(n.strokeStyle=i,n.lineWidth=s,Tr(n,this.faceLandmarks.getJawOutline()),Tr(n,this.faceLandmarks.getLeftEyeBrow()),Tr(n,this.faceLandmarks.getRightEyeBrow()),Tr(n,this.faceLandmarks.getNose()),Tr(n,this.faceLandmarks.getLeftEye(),!0),Tr(n,this.faceLandmarks.getRightEye(),!0),Tr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=c=>{n.beginPath(),n.arc(c.x,c.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function Dre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ra?a:no(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new vw(r).draw(e)})}var eC="1.0.1";function Mre(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l){let u=a(i,o,`${l}/separable_conv0`),c=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:c,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),c=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function tC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=xn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Mre(a,n),u=s(3,32,3,"entry_flow/conv_in"),c=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:u,reduction_block_0:c,reduction_block_1:p},h={};rr(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Rre(e,t){let n=jn(e,t),a=tf(n),r=Iu(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:c}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function nC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Rre(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),c={conv_in:o,reduction_block_0:l,reduction_block_1:u},p={};rr(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return bn(e,n),{params:{entry_flow:c,middle_flow:p,exit_flow:m},paramMappings:n}}function aC(e,t,n){return J(Et(e,t.filters,n,"same"),t.bias)}function ww(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=$t(a,[3,3],[2,2],"same"),a=J(a,aC(e,t.expansion_conv,[2,2])),a}function Pre(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var kw=class extends on{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(256)),i=qe(aC(s,n.entry_flow.conv_in,[2,2]));return i=ww(i,n.entry_flow.reduction_block_0,!1),i=ww(i,n.entry_flow.reduction_block_1),rr(this._numMainBlocks,0,1).forEach(o=>{i=Pre(i,n.middle_flow[`main_block_${o}`])}),i=ww(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return nC(t,this._numMainBlocks)}extractParams(t){return tC(t,this._numMainBlocks)}};function rC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),r=Qm(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function sC(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return bn(e,t),{params:r,paramMappings:t}}var Fr;(function(e){e.FEMALE="female",e.MALE="male"})(Fr||(Fr={}));var of=class extends on{constructor(t=new kw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof Ar?this.faceFeatureExtractor.forwardInput(t):t,r=Qn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Ap(r,n.fc.age).as1D(),i=Ap(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return M(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Ta(a)}})}async forward(t){return this.forwardInput(await mt(t))}async predictAgeAndGender(t){let n=await mt(t),a=await this.forwardInput(n),r=ct(a.age),s=ct(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let c=(await l.data())[0],p=(await u.data())[0],d=p>.5,h=d?Fr.MALE:Fr.FEMALE,m=d?p:1-p;return l.dispose(),u.dispose(),{age:c,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return rC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),sC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var _p=class extends Fp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return M(()=>{let i=(p,d)=>Dt([Sn([68],p,"float32"),Sn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return d(h,m)?Math.abs(h-m)/2:0},l=p=>o(p,(d,h)=>do(p,(d,h)=>hi(l(d),u(d))))).div(Dt(Array.from(Array(s),(p,d)=>i(r[d].width,r[d].height))))})}forwardInput(t){return M(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await mt(t))}async detectLandmarks(t){let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(await s.data()),l=o.filter((c,p)=>Hm(p)),u=o.filter((c,p)=>!Hm(p));return new bu(Array(68).fill(0).map((c,p)=>new De(l[p],u[p])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var Tu=class extends _p{constructor(t=new Cp){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function iC(e){let t=[],{extractDenseBlock3Params:n}=nf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return bn(e,t),{params:a,paramMappings:t}}function oC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock3Params:r}=ef(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Iw=class extends on{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(255)),i=Jm(s,n.dense0,!0);return i=Jm(i,n.dense1),i=Jm(i,n.dense2),i=Qn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return iC(t)}extractParams(t){return oC(t)}};var lf=class extends _p{constructor(t=new Iw){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var lC=class extends Tu{};function uC(e,t){return J(B(e,t.weights),t.biases)}function Nw(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Et(e,s,n,r);return o=J(o,i),o=uC(o,t.scale),a?qe(o):o}function cC(e,t){return Nw(e,t,[1,1],!0)}function Tw(e,t){return Nw(e,t,[1,1],!1)}function uf(e,t){return Nw(e,t,[2,2],!0,"valid")}function Ore(e,t){function n(o,l,u){let c=e(o),p=c.length/(l*u*u);if(Qv(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${c.length}, numFilters: ${l}, filterSize: ${u}`);return M(()=>Ve(Ca(c,[l,p,u,u]),[2,3,1,0]))}function a(o,l,u,c){let p=n(o,l,u),d=Qe(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/bias`}),{filters:p,bias:d}}function r(o,l){let u=Qe(e(o)),c=Qe(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:u,biases:c}}function s(o,l,u,c){let p=a(o,l,u,`${c}/conv`),d=r(l,`${c}/scale`);return{conv:p,scale:d}}function i(o,l,u,c,p=!1){let d=s((p?.5:1)*o,l,u,`${c}/conv1`),h=s(o,l,u,`${c}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function pC(e){let{extractWeights:t,getRemainingWeights:n}=xn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=Ore(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),u=s(9216,32,3,"conv32_3"),c=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),k=M(()=>Ve(Sa(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:c,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:k},paramMappings:a}}function Lre(e,t){let n=jn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),u=a(i);return{conv:{filters:o,bias:l},scale:u}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function dC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Lre(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),u=a("conv64_1"),c=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!Jv(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:u,conv64_2:c,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return bn(e,t),{params:v,paramMappings:t}}function za(e,t){let n=cC(e,t.conv1);return n=Tw(n,t.conv2),n=J(n,e),n=qe(n),n}function Ep(e,t){let n=uf(e,t.conv1);n=Tw(n,t.conv2);let a=Qn(e,2,2,"valid"),r=vt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=vt(o);n=Je([n,l],1);let u=[...n.shape];u[2]=1;let c=vt(u);n=Je([n,c],2)}return a=s?Je([a,r],3):a,n=J(a,n),n=qe(n),n}var Su=class extends on{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(150,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(256)),i=uf(s,n.conv32_down);i=$t(i,3,2,"valid"),i=za(i,n.conv32_1),i=za(i,n.conv32_2),i=za(i,n.conv32_3),i=Ep(i,n.conv64_down),i=za(i,n.conv64_1),i=za(i,n.conv64_2),i=za(i,n.conv64_3),i=Ep(i,n.conv128_down),i=za(i,n.conv128_1),i=za(i,n.conv128_2),i=Ep(i,n.conv256_down),i=za(i,n.conv256_1),i=za(i,n.conv256_2),i=Ep(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await mt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)==null?void 0:s.some(i=>i<=0))return new Float32Array(128);let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return dC(t)}extractParams(t){return pC(t)}};function zre(e){let t=new Su;return t.extractWeights(e),t}function cf(e,t){return{...e,...{descriptor:t}}}function Wre(e){return typeof e.age=="number"}function pf(e,t){return{...e,...{age:t}}}function Bre(e){return(e.gender===Fr.MALE||e.gender===Fr.FEMALE)&&fu(e.genderProbability)}function df(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function Vre(e,t){function n(l,u){let c=Ca(e(3*3*l),[3,3,l,1]),p=Qe(e(l)),d=Qe(e(l)),h=Qe(e(l)),m=Qe(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:c,batch_norm_scale:p,batch_norm_offset:d,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,c,p,d){let h=Ca(e(l*u*c*c),[c,c,l,u]),m=Qe(e(u));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/${d?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,c,p){let{filters:d,bias:h}=a(l,u,c,p,!0);return{filters:d,batch_norm_offset:h}}function s(l,u,c){let p=n(l,`${c}/depthwise_conv`),d=r(l,u,1,`${c}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),c=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),k=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:k}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),u=r(256,512,3,"prediction_layer/conv_1"),c=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),k=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),w=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),S=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),A=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),F=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),D=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),R=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:k},box_predictor_3:{box_encoding_predictor:w,class_predictor:S},box_predictor_4:{box_encoding_predictor:A,class_predictor:F},box_predictor_5:{box_encoding_predictor:D,class_predictor:R}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function hC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=Vre(n,t),i=r(),o=s(),u={extra_dim:Kd(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:u},paramMappings:t}}function Ure(e,t){let n=jn(e,t);function a(u,c,p){let d=n(`${u}/Conv2d_${c}_pointwise/weights`,4,`${p}/filters`),h=n(`${u}/Conv2d_${c}_pointwise/convolution_bn_offset`,1,`${p}/batch_norm_offset`);return{filters:d,batch_norm_offset:h}}function r(u){let c=`mobilenetv1/conv_${u}`,p=`MobilenetV1/Conv2d_${u}_depthwise`,d=`${c}/depthwise_conv`,h=`${c}/pointwise_conv`,m=n(`${p}/depthwise_weights`,4,`${d}/filters`),f=n(`${p}/BatchNorm/gamma`,1,`${d}/batch_norm_scale`),g=n(`${p}/BatchNorm/beta`,1,`${d}/batch_norm_offset`),y=n(`${p}/BatchNorm/moving_mean`,1,`${d}/batch_norm_mean`),b=n(`${p}/BatchNorm/moving_variance`,1,`${d}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",u,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(u,c){let p=n(`${u}/weights`,4,`${c}/filters`),d=n(`${u}/biases`,1,`${c}/bias`);return{filters:p,bias:d}}function o(u){let c=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),p=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:c,class_predictor:p}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function mC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Ure(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Sr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return bn(e,t),{params:s,paramMappings:t}}function xa(e,t,n){return M(()=>{let a=Et(e,t.filters,n,"same");return a=J(a,t.batch_norm_offset),Xt(a,0,6)})}var Gre=.0010000000474974513;function Hre(e,t,n){return M(()=>{let a=Qr(e,t.filters,n,"same");return a=fr(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Gre),Xt(a,0,6)})}function jre(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function fC(e,t){return M(()=>{let n,a=xa(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=jre(o);a=Hre(a,s.depthwise_conv,l),a=xa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function qre(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),c=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(c-l)*(p-u);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,c),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function gC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((c,p)=>({score:c,boxIndex:p})).filter(c=>c.score>r).sort((c,p)=>p.score-c.score),l=c=>c<=a?1:0,u=[];return o.forEach(c=>{if(u.length>=i)return;let p=c.score;for(let d=u.length-1;d>=0;--d){let h=qre(e,c.boxIndex,u[d]);if(h!==0&&(c.score*=l(h),c.score<=r))break}p===c.score&&u.push(c.boxIndex)}),u}function Xre(e){let t=ct(Ve(e,[1,0])),n=[me(t[2],t[0]),me(t[3],t[1])],a=[J(t[0],xe(n[0],ce(2))),J(t[1],xe(n[1],ce(2)))];return{sizes:n,centers:a}}function Kre(e,t){let{sizes:n,centers:a}=Xre(e),r=ct(Ve(t,[1,0])),s=xe(B(dn(xe(r[2],ce(5))),n[0]),ce(2)),i=J(B(xe(r[0],ce(10)),n[0]),a[0]),o=xe(B(dn(xe(r[3],ce(5))),n[1]),ce(2)),l=J(B(xe(r[1],ce(10)),n[1]),a[1]);return Ve(Dt([me(i,s),me(l,o),J(i,s),J(l,o)]),[1,0])}function yC(e,t,n){return M(()=>{let a=e.shape[0],r=Kre(H(Ha(n.extra_dim,[a,1,1]),[-1,4]),H(e,[-1,4]));r=H(r,[a,r.shape[0]/a,4]);let s=ca(Be(t,[0,0,1],[-1,-1,-1])),i=Be(s,[0,0,0],[-1,-1,1]);i=H(i,[a,i.shape[1]]);let o=ct(r),l=ct(i);return{boxes:o,scores:l}})}function ao(e,t){return M(()=>{let n=e.shape[0],a=H(to(e,t.box_encoding_predictor),[n,-1,1,4]),r=H(to(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function bC(e,t,n){return M(()=>{let a=xa(e,n.conv_0,[1,1]),r=xa(a,n.conv_1,[2,2]),s=xa(r,n.conv_2,[1,1]),i=xa(s,n.conv_3,[2,2]),o=xa(i,n.conv_4,[1,1]),l=xa(o,n.conv_5,[2,2]),u=xa(l,n.conv_6,[1,1]),c=xa(u,n.conv_7,[2,2]),p=ao(t,n.box_predictor_0),d=ao(e,n.box_predictor_1),h=ao(r,n.box_predictor_2),m=ao(i,n.box_predictor_3),f=ao(l,n.box_predictor_4),g=ao(c,n.box_predictor_5),y=Je([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Je([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var va=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ro=class extends on{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(512,!1),"float32"),r=me(B(a,ce(.007843137718737125)),ce(1)),s=fC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=bC(s.out,s.conv11,n.prediction_layer);return yC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await mt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new va(n),s=await mt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,k]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(A=>A*g),[w,S]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(A=>A*f);return new yt(c[x],new yu(w,v,S-w,k-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return mC(t)}extractParams(t){return hC(t)}};function xC(e){let t=new ro;return t.extractWeights(e),t}function Yre(e){return xC(e)}var vC=class extends ro{};var wC=.4,kC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],IC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],NC=[117.001,114.697,97.404],TC="tiny_yolov2_model",SC="tiny_yolov2_separable_conv_model";var hf=e=>typeof e=="number";function Sw(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!hf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>hf(t.x)&&hf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(hf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Cu(e){return M(()=>{let t=B(e,ce(.10000000149011612));return J(qe(me(e,t)),t)})}function _r(e,t){return M(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Et(n,t.conv.filters,[1,1],"valid"),n=me(n,t.bn.sub),n=B(n,t.bn.truediv),n=J(n,t.conv.bias),Cu(n)})}function Er(e,t){return M(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Fi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Cu(n)})}function Jre(e,t){let n=wu(e,t);function a(i,o){let l=Qe(e(i)),u=Qe(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),c=a(o,`${l}/bn`);return{conv:u,bn:c}}let s=ku(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function CC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Jre(r,i),c;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):u(p,d,"conv0"),k=u(d,h,"conv1"),w=u(h,m,"conv2"),S=u(m,f,"conv3"),A=u(f,g,"conv4"),F=u(g,y,"conv5"),D=b?u(y,b,"conv6"):void 0,R=x?u(b,x,"conv7"):void 0,L=o(x||b||y,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),k=l(d,h,"conv1"),w=l(h,m,"conv2"),S=l(m,f,"conv3"),A=l(f,g,"conv4"),F=l(g,y,"conv5"),D=l(y,b,"conv6"),R=l(b,x,"conv7"),L=o(x,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:c,paramMappings:i}}function Qre(e,t){let n=jn(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Iu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function AC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Qre(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var or=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cw=class extends on{constructor(t){super("TinyYolov2");Sw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=_r(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=_r(a,n.conv6),a=_r(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Cu(to(t,n.conv0,"valid",!1)):Er(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Er(a,n.conv6):a,a=n.conv7?Er(a,n.conv7):a,to(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let r=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(ce(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await mt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new or(n),s=await mt(t),i=await this.forwardInput(s,a),o=M(()=>ct(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let c=u.map(g=>g.box),p=u.map(g=>g.score),d=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return nw(c.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],c[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return AC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Cw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return CC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],c=this.config.anchors.length,[p,d,h]=M(()=>{let y=t.reshape([u,u,c,this.boxEncodingSize]),b=y.slice([0,0,0,0],[u,u,c,4]),x=y.slice([0,0,0,4],[u,u,c,1]),v=this.withClassScores?Ta(y.slice([0,0,0,5],[u,u,c,this.config.classes.length]),3):ce(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let k=(b+vp(g[y][b][x][0]))/u*o,w=(y+vp(g[y][b][x][1]))/u*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/u*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/u*l,F=k-S/2,D=w-A/2,R={row:y,col:b,anchor:x},{classScore:L,label:W}=this.withClassScores?await this.extractPredictedClass(h,R):{classScore:1,label:0};m.push({box:new gu(F,D,F+S,D+A),score:v,classScore:v*L,label:W,...R})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Au=Cw;Au.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Fu=class extends Au{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:wC,classes:["face"],...t?{anchors:IC,meanRgb:NC}:{anchors:kC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?SC:TC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Zre(e,t=!0){let n=new Fu(t);return n.extractWeights(e),n}var mf=class extends or{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var wa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof Fe?await vu(t,s):await xu(t,s)),o=await n(i);return i.forEach(l=>l instanceof Fe&&l.dispose()),o}async function _u(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var FC=.4,_C=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],EC=[117.001,114.697,97.404];var Eu=class extends Au{constructor(){let t={withSeparableConvs:!0,iouThreshold:FC,classes:["face"],anchors:_C,meanRgb:EC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Ze={ssdMobilenetv1:new ro,tinyFaceDetector:new Eu,tinyYolov2:new Fu,faceLandmark68Net:new Tu,faceLandmark68TinyNet:new lf,faceRecognitionNet:new Su,faceExpressionNet:new rf,ageGenderNet:new of},$C=(e,t)=>Ze.ssdMobilenetv1.locateFaces(e,t),ese=(e,t)=>Ze.tinyFaceDetector.locateFaces(e,t),tse=(e,t)=>Ze.tinyYolov2.locateFaces(e,t),DC=e=>Ze.faceLandmark68Net.detectLandmarks(e),nse=e=>Ze.faceLandmark68TinyNet.detectLandmarks(e),ase=e=>Ze.faceRecognitionNet.computeFaceDescriptor(e),rse=e=>Ze.faceExpressionNet.predictExpressions(e),sse=e=>Ze.ageGenderNet.predictAgeAndGender(e),MC=e=>Ze.ssdMobilenetv1.load(e),ise=e=>Ze.tinyFaceDetector.load(e),ose=e=>Ze.tinyYolov2.load(e),lse=e=>Ze.faceLandmark68Net.load(e),use=e=>Ze.faceLandmark68TinyNet.load(e),cse=e=>Ze.faceRecognitionNet.load(e),pse=e=>Ze.faceExpressionNet.load(e),dse=e=>Ze.ageGenderNet.load(e),hse=MC,mse=$C,fse=DC;var Aw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Aw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>sf(a,n[r]))}withAgeAndGender(){return new $u(this,this.input)}},Ru=class extends Aw{async run(){let t=await this.parentTask;if(!t)return;let n=await _u(t,this.input,a=>Ze.faceExpressionNet.predictExpressions(a),this.extractedFaces);return sf(t,n)}withAgeAndGender(){return new Du(this,this.input)}},lo=class extends Mu{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Ru{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Fw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},$u=class extends Fw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return pf(df(a,i,o),s)})}withFaceExpressions(){return new Mu(this,this.input)}},Du=class extends Fw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await _u(t,this.input,s=>Ze.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return pf(df(t,a,r),n)}withFaceExpressions(){return new Ru(this,this.input)}},io=class extends $u{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Du{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var ff=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends ff{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>Ze.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>cf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends ff{async run(){let t=await this.parentTask;if(!t)return;let n=await _u(t,this.input,a=>Ze.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return cf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var gf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Ze.faceLandmark68TinyNet:Ze.faceLandmark68Net}},yf=class extends gf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Fe?await vu(this.input,n):await xu(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Fe&&s.dispose()),t.map((s,i)=>Nu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},bf=class extends gf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Fe?await vu(this.input,[n]):await xu(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Fe&&s.dispose()),Nu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var xf=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},$p=class extends xf{async run(){let{input:t,options:n}=this,a;if(n instanceof mf)a=Ze.tinyFaceDetector.locateFaces(t,n);else if(n instanceof va)a=Ze.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof or)a=Ze.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Yi({},a)))})}withFaceLandmarks(t=!1){return new yf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $u(this.runAndExtendWithFaceDetections(),this.input)}},vf=class extends xf{async run(){let t=await new $p(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Yi({},n):void 0)})}withFaceLandmarks(t=!1){return new bf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ru(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetection(),this.input)}};function gse(e,t=new va){return new vf(e,t)}function wf(e,t=new va){return new $p(e,t)}async function RC(e,t){return wf(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function yse(e,t={}){return wf(e,new or(t)).withFaceLandmarks().withFaceDescriptors()}var bse=RC;function _w(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var kf=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Cr)return i;if(i instanceof Float32Array)return new Cr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Cr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>_w(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new wp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Cr.fromJSON(a));return new kf(n,t.distanceThreshold)}};function xse(e){let t=new Eu;return t.extractWeights(e),t}function PC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>PC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Nu(Yi(e,r),s)}return sr(e)?Yi(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof yt?e.forSize(n,a):e}var vse=typeof process!="undefined",wse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",kse={faceapi:eC,node:vse,browser:wse};export{of as AgeGenderNet,gu as BoundingBox,ut as Box,wa as ComposableTask,ys as ComputeAllFaceDescriptorsTask,ff as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,yf as DetectAllFaceLandmarksTask,$p as DetectAllFacesTask,gf as DetectFaceLandmarksTaskBase,xf as DetectFacesTaskBase,bf as DetectSingleFaceLandmarksTask,vf as DetectSingleFaceTask,yn as Dimensions,yw as FACE_EXPRESSION_LABELS,yt as FaceDetection,vC as FaceDetectionNet,rf as FaceExpressionNet,gs as FaceExpressions,Tu as FaceLandmark68Net,lf as FaceLandmark68TinyNet,lC as FaceLandmarkNet,ra as FaceLandmarks,HS as FaceLandmarks5,bu as FaceLandmarks68,wp as FaceMatch,kf as FaceMatcher,Su as FaceRecognitionNet,Fr as Gender,kp as LabeledBox,Cr as LabeledFaceDescriptors,Ar as NetInput,on as NeuralNetwork,ms as ObjectDetection,De as Point,jS as PredictedBox,yu as Rect,ro as SsdMobilenetv1,va as SsdMobilenetv1Options,Eu as TinyFaceDetector,mf as TinyFaceDetectorOptions,Fu as TinyYolov2,or as TinyYolov2Options,bse as allFaces,RC as allFacesSsdMobilenetv1,yse as allFacesTinyYolov2,pw as awaitMediaLoaded,dw as bufferToImage,ase as computeFaceDescriptor,Zi as createCanvas,Tp as createCanvasFromMedia,Yre as createFaceDetectionNet,zre as createFaceRecognitionNet,xC as createSsdMobilenetv1,xse as createTinyFaceDetector,Zre as createTinyYolov2,wf as detectAllFaces,DC as detectFaceLandmarks,nse as detectFaceLandmarksTiny,fse as detectLandmarks,gse as detectSingleFace,ZS as draw,st as env,_w as euclideanDistance,pf as extendWithAge,cf as extendWithFaceDescriptor,Yi as extendWithFaceDetection,sf as extendWithFaceExpressions,Nu as extendWithFaceLandmarks,df as extendWithGender,vu as extractFaceTensors,xu as extractFaces,Are as fetchImage,fw as fetchJson,Fre as fetchNetWeights,eo as fetchOrThrow,$n as getContext2dOrThrow,Qi as getMediaDimensions,hw as imageTensorToCanvas,mw as imageToSquare,xre as inverseSigmoid,ew as iou,Km as isMediaElement,Np as isMediaLoaded,Wre as isWithAge,sr as isWithFaceDetection,bw as isWithFaceExpressions,no as isWithFaceLandmarks,Bre as isWithGender,dse as loadAgeGenderModel,hse as loadFaceDetectionModel,pse as loadFaceExpressionModel,lse as loadFaceLandmarkModel,use as loadFaceLandmarkTinyModel,cse as loadFaceRecognitionModel,MC as loadSsdMobilenetv1Model,ise as loadTinyFaceDetectorModel,ose as loadTinyYolov2Model,gw as loadWeightMap,mse as locateFaces,_re as matchDimensions,tw as minBbox,Ze as nets,nw as nonMaxSuppression,La as normalize,aw as padToSquare,sse as predictAgeAndGender,rse as recognizeFaceExpressions,PC as resizeResults,Ji as resolveInput,bre as shuffleArray,vp as sigmoid,$C as ssdMobilenetv1,Gw as tf,ese as tinyFaceDetector,tse as tinyYolov2,mt as toNetInput,GS as utils,Sw as validateConfig,kse as version}; + `}};function yee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,c=E.getAxesPermutation([u],o),p=r;c!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(p),u=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,u,i),h=I.sizeFromShape([p.shape[u]]),m=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=jd(r.dtype),g=(v,k,w,S,A)=>{let F=v.shape[0],D=v.shape[1],R=E.segment_util.segOpComputeOptimalWindowSize(D,A),L={windowSize:R,inSize:D,batchSize:F,numSegments:A},W=new gee(L,k),U=n.compileAndRun(W,[v,w],S);if(l.push(U),U.shape[1]===A)return U;let G=qT({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),X=YT({inputs:{x:G},backend:n,attrs:{reps:[D/R]}});return l.push(G),l.push(X),g(U,k,X,S,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=ye({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(c!=null){l.push(b);let v=E.getUndoAxesPermutation(c);x=_n({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var bee={kernelName:pc,backendName:"webgl",kernelFunc:yee},xee=[lJ,pJ,YX,QX,tK,rK,iK,uK,pK,hK,yK,xK,kK,TK,$K,AK,RK,zK,OK,UK,HK,qK,JK,r7,i7,d7,m7,b7,w7,EX,T7,R7,O7,F7,B7,U7,z7,j7,K7,Q7,eY,nY,sY,pY,hY,oY,gY,xY,IY,CY,EY,MY,RY,PY,LY,WY,VY,GY,jY,YY,e9,n9,r9,o9,p9,f9,x9,_X,w9,N7,N9,C9,_9,DX,M9,L9,W9,q9,G9,J9,eJ,rJ,hJ,wJ,xJ,TJ,CJ,FJ,yJ,EJ,DJ,OJ,BJ,HJ,ZJ,LX,tQ,rQ,oQ,cQ,l7,hQ,fQ,yQ,vQ,NQ,RX,SQ,CQ,u7,KJ,_Q,LQ,MQ,WX,VQ,HQ,XQ,JQ,tZ,aZ,iZ,uZ,pZ,mZ,yZ,vZ,IZ,SZ,FZ,n7,JJ,$Z,MZ,PZ,LZ,BZ,GZ,jZ,XZ,JZ,YJ,qX,eee,aee,iee,lee,pee,XX,hee,fee,bee,mQ];for(let e of xee)hc(e);var vee="3.3.0",wee={"tfjs-core":Q0,"tfjs-backend-cpu":BU,"tfjs-backend-webgl":FX,"tfjs-data":TN,"tfjs-layers":im,"tfjs-converter":xN,tfjs:vee},Hn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Hn||(Hn={}));var gp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(gp||(gp={}));var JT;function kee(e){JT=e.wasm.cwrap(gi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Iee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let A=n.dataIdMap.get(i.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=gp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),k=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return JT(d,w,r.shape.length,h,S,s.shape.length,l,u,g,m,f,p||0,k),v}var Nee={kernelName:gi,backendName:"wasm",setupFunc:kee,kernelFunc:Iee};function En(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return I.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Tee=En(So);function gn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:c}=l,p=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,m=E.assertAndGetBroadcastShape(u.shape,c.shape),f=o.makeOutput(m,h);if(I.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,u.shape.length,d,y,c.shape.length,Hn[u.dtype],b);if(t&&u.dtype==="float32")return x(),f;let v=E.getBroadcastDims(u.shape,m),k=E.getBroadcastDims(c.shape,m),w=v.every((A,F)=>A===F),S=k.every((A,F)=>A===F);if(w&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var See=!0,Cee=gn(Br,See),QT;function Aee(e){QT=e.wasm.cwrap(Ns,null,["array","number","number","number"])}function Fee(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(I.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return QT(s,r.length,Hn[a.dtype],i),a}var _ee={kernelName:Ns,backendName:"wasm",setupFunc:Aee,kernelFunc:Fee};function Wm(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Eee={kernelName:Bs,backendName:"wasm",kernelFunc:Wm},ZT;function $ee(e){ZT=e.wasm.cwrap(fi,null,["number","array","number","number","number","array","number"])}function Bm(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Mee(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Ree={kernelName:fi,backendName:"wasm",kernelFunc:Bm,setupFunc:$ee};function mu(e,t,n){let a=e.shape,r=e.shape.length,s=I.parseAxisParam(t,a),i=s,o=E.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let c=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var Vee={kernelName:cl,backendName:"wasm",kernelFunc:Pa},nS;function Uee(e){nS=e.wasm.cwrap(Cs,null,["number","array","number","number","array","number","number","number","number"])}function Gee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,c=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=I.sizeFromShape(m),y=I.sizeFromShape(f),b=g===y||g===1||y===1;I.assert(l>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);I.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,c,d]:[g,d,c],k=o?[y,h,p]:[y,p,h],w=Pa({inputs:{x:r},backend:n,attrs:{shape:v}}),S=Pa({inputs:{x:s},backend:n,attrs:{shape:k}}),A=n.dataIdMap.get(w.dataId).id,F=n.dataIdMap.get(S.dataId).id,D=i?w.shape[2]:w.shape[1],R=o?S.shape[1]:S.shape[2],L=Math.max(g,y),W=n.makeOutput([L,D,R],w.dtype),U=n.dataIdMap.get(W.dataId).id,G=new Uint8Array(new Int32Array(w.shape).buffer),X=new Uint8Array(new Int32Array(S.shape).buffer);return nS(A,G,w.shape.length,F,X,S.shape.length,i,o,U),n.disposeData(w.dataId),n.disposeData(S.dataId),W.shape=x,W}var Hee={kernelName:Cs,backendName:"wasm",setupFunc:Uee,kernelFunc:Gee};function Vm(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var jee={kernelName:As,backendName:"wasm",kernelFunc:Vm},qee=En(Fs),aS;function Xee(e){aS=e.wasm.cwrap(Vr,null,["number","number","number","number"])}function Kee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return aS(o,s,i,u),l}var Yee={kernelName:Vr,backendName:"wasm",setupFunc:Xee,kernelFunc:Kee};function rS(e){let{inputs:t,backend:n}=e,a=I.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>I.sizeFromShape(h.shape)>0);if(s.length===1)return Wm({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(I.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=I.sizeFromShape(x.shape.slice(a));return Pa({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=fv(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=I.sizeFromShape(s[0].shape.slice(0,a)),u=0,c=s.map(h=>{let m=I.sizeFromShape(h.shape.slice(a));return u+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=E.getAxesPermutation([s],l),c=r;u!==null&&(c=Bm({inputs:{x:r},attrs:{perm:u},backend:n}));let p=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],m=n.dataIdMap.get(c.dataId).id,f=n.dataIdMap.get(d.dataId).id;lS(m,i?1:0,o?1:0,h,f,Hn[r.dtype]);let g=d;if(u!==null){let y=E.getUndoAxesPermutation(u);g=Bm({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var cte={kernelName:Ds,backendName:"wasm",setupFunc:lte,kernelFunc:ute},uS;function pte(e){uS=e.wasm.cwrap(Oo,null,["number","number","number","array","number","array","array","number","number"])}function dte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;I.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=u*s,h=c/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(I.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(I.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return uS(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var hte={kernelName:Oo,backendName:"wasm",setupFunc:pte,kernelFunc:dte},cS;function mte(e){cS=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=E.computeConv2DInfo(r.shape,s.shape,l,d,c,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,k=h.dilationWidth,w=h.strideHeight,S=h.strideWidth,A=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=a.makeOutput(h.outShape,"float32"),L=a.dataIdMap.get(R.dataId).id;return cS(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,D,v,k,w,S,A,F,L),R}var gte={kernelName:Ms,backendName:"wasm",setupFunc:mte,kernelFunc:fte},yte=!1,bte=gn(Wo,yte,"bool"),xte=En(Ps);function qv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(I.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var vte={kernelName:Bo,backendName:"wasm",kernelFunc:qv};function wte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var kte={kernelName:tc,backendName:"wasm",kernelFunc:wte},pS;function Ite(e){pS=e.wasm.cwrap(Uo,null,["number","number","number","number","number","number"])}function Nte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,c]=a.shape;return pS(s,o,l,u,c,i),r}var Tte={kernelName:Uo,backendName:"wasm",kernelFunc:Nte,setupFunc:Ite},Ste=En(Os),Cte=!1,Ate=gn(Ls,Cte),dS;function Fte(e){dS=e.wasm.cwrap(zs,null,["number","number","number","number","number","number","number"])}function _te(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(I.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return dS(c,p,d,h,m,r,g),f}var Ete={kernelName:zs,backendName:"wasm",setupFunc:Fte,kernelFunc:_te},hS;function $te(e){hS=e.wasm.cwrap(yi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d),g=gp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return hS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var Mte={kernelName:yi,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},mS;function Rte(e){mS=e.wasm.cwrap(bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!0),g=gp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return mS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var Ote={kernelName:bi,backendName:"wasm",setupFunc:Rte,kernelFunc:Pte},fS;function Lte(e){fS=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number","array","number"])}function zte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=hy.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let c=r.shape,p=c[c.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return fS(d,Hn[a.dtype],h,i,p,o,m,f),u}var Wte={kernelName:Ho,backendName:"wasm",setupFunc:Lte,kernelFunc:zte},gS;function Bte(e){gS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Vte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=I.parseAxisParam(i,r.shape)[0],u=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Pa({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),p=I.sizeFromShape(s.shape),d=Pa({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],m=t.makeOutput(h,r.dtype);if(I.sizeFromShape(r.shape)===0)return m;let f=c.shape.length-1,g=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(I.computeStrides(c.shape)).buffer),v=new Uint8Array(new Int32Array(I.computeStrides(h)).buffer);return gS(g,Hn[r.dtype],x,f,y,u.batchSize,v,b),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var Ute={kernelName:Go,backendName:"wasm",setupFunc:Bte,kernelFunc:Vte},Gte=!1,Hte=gn(jo,Gte,"bool"),jte=!1,qte=gn(Ws,jte,"bool"),yS;function Xte(e){yS=e.wasm.cwrap(Vs,null,["number","number","number"])}function Kte(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(I.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;yS(r,n,i)}return s}var Yte={kernelName:Vs,backendName:"wasm",setupFunc:Xte,kernelFunc:Kte},Jte=!1,Qte=gn(Yo,Jte,"bool"),Zte=!1,ene=gn(Jo,Zte,"bool"),tne=En(Us),nne=!1,ane=gn(Zo,nne,"bool"),bS;function rne(e){bS=e.wasm.cwrap(Gs,null,["number, number, number"])}function sne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:p,inputWasTransposed:d}=mu(i,r,t);if(d){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",c,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,c),g=I.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(I.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;bS(o,g,b)}if(d&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var ine={kernelName:Gs,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=!1,lne=gn(Hs,one),xS;function une(e){xS=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=E.computePool2DInfo(r.shape,i,o,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,b=c.dilationWidth,x=c.strideHeight,v=c.strideWidth,k=c.inChannels,w=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(c.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return xS(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,k,w,A),S}var pne={kernelName:js,backendName:"wasm",setupFunc:une,kernelFunc:cne},vS;function dne(e){vS=e.wasm.cwrap(qs,null,["number, number, number"])}function hne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(u=c,l=v,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Vm({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(I.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;vS(l,y,v)}if(h&&t.disposeData(c.dataId),s){let v=E.expandShapeToKeepDim(x.shape,d);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var mne={kernelName:qs,backendName:"wasm",setupFunc:dne,kernelFunc:hne},wS;function fne(e){wS=e.wasm.cwrap(Xs,null,["number, number, number"])}function gne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x)}let m=u.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(u.shape,p),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;wS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var yne={kernelName:Xs,backendName:"wasm",setupFunc:fne,kernelFunc:gne},bne=!1,xne=gn(Ks,bne),vne=!0,wne=gn(Ys,vne),kne=En(tl);function Xv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var kS;function Ine(e){kS=e.wasm.cwrap(al,"number",["number","number","number","number","number"])}function Nne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=kS(u,c,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Xv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Tne={kernelName:al,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne},IS;function Sne(e){IS=e.wasm.cwrap(rl,"number",["number","number","number","number","number","bool"])}function Cne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=IS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Xv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Ane={kernelName:rl,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},NS;function Fne(e){NS=e.wasm.cwrap(sl,"number",["number","number","number","number","number","number"])}function _ne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=NS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Xv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Ene={kernelName:sl,backendName:"wasm",setupFunc:Fne,kernelFunc:_ne},$ne=!1,Dne=gn(nl,$ne,"bool"),TS;function Mne(e){TS=e.wasm.cwrap(Js,null,["number","number","number","number","number"])}function Rne(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(r.dataId).id;return TS(c,s,i,o,u),l}var Pne={kernelName:Js,backendName:"wasm",setupFunc:Mne,kernelFunc:Rne};function One(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Lne={kernelName:il,backendName:"wasm",kernelFunc:One};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return qv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let p=qv({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),u=rS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),u}var Wne={kernelName:ol,backendName:"wasm",kernelFunc:zne},SS;function Bne(e){SS=e.wasm.cwrap(Qs,null,["number","array","number","number","array","array","number","number"])}function Vne(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return SS(i,u,t.shape.length,Hn[t.dtype],d,h,r,l),o}var Une={kernelName:Qs,backendName:"wasm",kernelFunc:Vne,setupFunc:Bne},Gne=!1,Hne=gn(Zs,Gne),CS;function jne(e){CS=e.wasm.cwrap(ei,null,["number","number","number"])}function qne(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return CS(s,i,l),o}var Xne={kernelName:ei,backendName:"wasm",setupFunc:jne,kernelFunc:qne},AS;function Kne(e){AS=e.wasm.cwrap(ll,null,["number","number","number","number"])}function Yne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;AS(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Jne={kernelName:ll,backendName:"wasm",setupFunc:Kne,kernelFunc:Yne},Qne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=bv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Zne={kernelName:oc,backendName:"wasm",kernelFunc:Qne},eae=!0,tae=gn(Rs,eae),nae=En(ti),aae=En(ai),FS;function rae(e){FS=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number"])}function sae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[c,p,d,h]=r.shape,m=[c,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Vm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(I.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return FS(y,c,p,d,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var iae={kernelName:ni,backendName:"wasm",setupFunc:rae,kernelFunc:sae},_S;function oae(e){_S=e.wasm.cwrap(ri,null,["number","array","number","array","number","number"])}function lae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=I.parseAxisParam(s,r.shape);if(r.shape.length===0)return Wm({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);_S(l,c,i.length,p,r.shape.length,u);let d=Pa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var uae={kernelName:ri,backendName:"wasm",kernelFunc:lae,setupFunc:oae},ES;function cae(e){ES=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function pae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=E.getImageCenter(o,d,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return ES(u,p,d,h,m,s,f,g,v,x.length,c),l}var dae={kernelName:Nl,backendName:"wasm",kernelFunc:pae,setupFunc:cae},hae=En(si),mae=En(ii),$S;function fae(e){$S=e.wasm.cwrap(pl,null,["number","number","number","number","number","number","array","number","number"])}function gae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(I.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=my.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return $S(h,m,Hn[s.dtype],l,u,c,f,d,g),o}var yae={kernelName:pl,backendName:"wasm",setupFunc:fae,kernelFunc:gae},DS;function bae(e){DS=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function xae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(u.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:I.sizeFromShape(r.shape.slice(1));return DS(i,o,l,h,c),u}var vae={kernelName:dl,backendName:"wasm",kernelFunc:xae,setupFunc:bae},MS;function wae(e){MS=e.wasm.cwrap(li,null,["number","number"])}function kae(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return I.sizeFromShape(r.shape)===0||MS(a,s),r}var Iae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:wae,kernelFunc:kae},Nae=En(oi);function Um(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=pn.parseSliceParams(t,n,a),o=pn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),c=I.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(o){let m=pn.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=l.slice(m,m+I.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+I.sizeFromShape(i))),u}if(t.dtype==="string"){let m=km(l,s,i,t.shape,t.dtype);return p.stringBytes=m,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)Tae(l,c[0],d,s,i);else if(h===3)Sae(l,c[0],c[1],d,s,i);else if(h===4)Cae(l,c[0],c[1],c[2],d,s,i);else{let m=km(l,s,i,t.shape,t.dtype);d.set(m)}return u}function Tae(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u{let d=[...c];d[o]=p;let h=Um({inputs:{x:r},attrs:{begin:u,size:d},backend:a});return u[o]+=p,h})}var Dae={kernelName:bl,backendName:"wasm",kernelFunc:$ae},Mae=En(ui),Rae=En(cc),Pae=!0,Oae=gn(di,Pae),PS;function Lae(e){PS=e.wasm.cwrap(Gr,null,["number","number","number"])}function zae(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return PS(i,r,l),o}var Wae={kernelName:Gr,backendName:"wasm",setupFunc:Lae,kernelFunc:zae},OS;function Bae(e){OS=e.wasm.cwrap(xl,null,["number","array","number","array","array","array","array","array","number","number"])}function Vae(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=a,h=E.slice_util.maskToAxes(c);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let m=r.shape.length-s.length,f=E.slice_util.maskToAxes(p),g=r.shape.slice();f.forEach(D=>{s[D]=0,i[D]=1,g.splice(D,0,1)});let y=Pa({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:b,end:x,strides:v}=E.slice_util.getNormalizedAxes(y.shape,h,m,s,i,o,l,u,c);s=b,i=x,o=v;let k=E.slice_util.maskToAxes(d);k.forEach(D=>{i[D]=s[D]+1,o[D]=1});let w=E.slice_util.computeOutShape(s,i,o),S=w.filter((D,R)=>k.indexOf(R)===-1);if(o.every(D=>D===1)){let D=Um({inputs:{x:y},attrs:{begin:s,size:w},backend:t});t.disposeData(y.dataId);let R=Pa({inputs:{x:D},attrs:{shape:S},backend:t});return t.disposeData(D.dataId),R}let A=t.makeOutput(S,"float32");if(!S.some(D=>D===0)){let D=t.dataIdMap.get(y.dataId).id,R=new Uint8Array(new Int32Array(I.computeStrides(y.shape)).buffer),L=new Uint8Array(new Int32Array(s).buffer),W=new Uint8Array(new Int32Array(i).buffer),U=new Uint8Array(new Int32Array(o).buffer),G=new Uint8Array(new Int32Array(S).buffer),X=new Uint8Array(new Int32Array(I.computeStrides(S)).buffer),j=t.dataIdMap.get(A.dataId).id;OS(D,R,y.shape.length,L,W,U,G,X,S.length,j)}t.disposeData(y.dataId);let F=Pa({inputs:{x:A},attrs:{shape:S},backend:t});return t.disposeData(A.dataId),F}var Uae={kernelName:xl,backendName:"wasm",setupFunc:Bae,kernelFunc:Vae},Gae=!0,Hae=gn(hi,Gae),LS;function jae(e){LS=e.wasm.cwrap(ci,null,["number, number, number"])}function qae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;LS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Xae={kernelName:ci,backendName:"wasm",setupFunc:jae,kernelFunc:qae},Kae=En(mi),zS;function Yae(e){zS=e.wasm.cwrap(Ur,null,["number","array","number","array","number","number"])}function Jae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let d=0;d{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),c=t.dataIdMap.get(u.dataId).id,p=t.makeOutput(l,"int32"),d=t.dataIdMap.get(p.dataId).id;return WS(i,o,a.shape.length,Hn[a.dtype],r,s,c,d),[u,p]},tre={kernelName:wl,backendName:"wasm",setupFunc:Zae,kernelFunc:ere};function nre(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h({dataId:h,dtype:m,shape:l}))}var are={kernelName:kl,backendName:"wasm",kernelFunc:nre};function rre(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var sre={kernelName:Il,backendName:"wasm",kernelFunc:rre},ire=[Tee,Cee,_ee,Lee,Bee,Hee,jee,qee,Yee,Jee,ete,ate,rte,ote,cte,hte,gte,bte,xte,vte,kte,Tte,Ste,Ate,Nee,Ete,Mte,Ote,Wte,Ute,Hte,qte,Eee,Yte,Qte,ene,tne,ane,ine,lne,pne,mne,yne,xne,wne,kne,Tne,Ane,Ene,Dne,Pne,Lne,Wne,Une,Hne,Xne,Jne,Zne,tae,nae,aae,Vee,iae,uae,dae,mae,hae,yae,vae,Iae,Nae,Aae,Eae,Dae,Mae,Rae,Oae,Wae,Uae,Hae,Xae,Kae,Qae,tre,Ree,are,sre];for(let e of ire)hc(e);var Kv=Z();Kv.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));Kv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Kv.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var BS=Io(yF()),ore='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',lre=Io(bF()),VS=class extends ju{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new sd(this,Ua())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=I.now();return e(),{kernelMs:I.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=I.sizeFromShape(n),o=i*I.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:a,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let s=this.wasm.HEAPU8.slice(t,t+I.sizeFromShape(a)*I.bytesPerElement(n));return ure(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=I.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=I.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function cre(e){return(t,n)=>(I.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance)})})}),{})}function US(e,t,n){if(Gm!=null)return Gm;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),yp!=null&&yp[a]!=null?yp[a]:n+a}async function pre(){let[e,t]=await Promise.all([Z().getAsync("WASM_HAS_SIMD_SUPPORT"),Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=ore,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return o.endsWith(".wasm")?US(e,t,bp!=null?bp:l):l+o},Yv&&(r.instantiateWasm=cre(US(e,t,bp!=null?bp:"")));let s=!1;r.onAbort=()=>{s||xp||(xp=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&Gm==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+BS.default.toString()],{type:"text/javascript"}),i=(0,BS.default)(r)):i=(0,lre.default)(r),i.then(o=>{s=!0,xp=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function ure(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var dre=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Gm=null,bp=null,yp={},xp=!1,Yv=!1;function hre(e,t=!1){if(vy("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),xp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Gm=e,Yv=t}function mre(e,t=!1){if(xp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")bp=e;else{yp=e;let n=dre.filter(a=>yp[a]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Yv=t}var fre="3.3.0",gre=2;Qd("wasm",async()=>{let{wasm:e}=await pre();return new VS(e)},gre);var ZS={};ad(ZS,{AnchorPosition:()=>ir,DrawBox:()=>Xm,DrawBoxOptions:()=>cw,DrawFaceLandmarks:()=>vw,DrawFaceLandmarksOptions:()=>xw,DrawTextField:()=>fs,DrawTextFieldOptions:()=>Ip,drawContour:()=>Tr,drawDetections:()=>Cre,drawFaceExpressions:()=>Ere,drawFaceLandmarks:()=>Dre});function Tr(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:a,y:r},s)=>{let i=t[s];e.moveTo(i.x,i.y),e.lineTo(a,r)}),n){let a=t[t.length-1],r=t[0];if(!a||!r)return;e.moveTo(a.x,a.y),e.lineTo(r.x,r.y)}e.stroke()}var GS={};ad(GS,{computeReshapedDimensions:()=>Zv,getCenterPoint:()=>Ki,isDimensions:()=>jm,isEven:()=>Hm,isFloat:()=>Qv,isTensor:()=>qi,isTensor1D:()=>yre,isTensor2D:()=>Jv,isTensor3D:()=>Sr,isTensor4D:()=>aa,isValidNumber:()=>Oa,isValidProbablitiy:()=>fu,range:()=>rr,round:()=>Xi});var yn=class{constructor(t,n){if(!Oa(t)||!Oa(n))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new yn(1/this.width,1/this.height)}};function qi(e,t){return e instanceof Fe&&e.shape.length===t}function yre(e){return qi(e,1)}function Jv(e){return qi(e,2)}function Sr(e){return qi(e,3)}function aa(e){return qi(e,4)}function Qv(e){return e%1!=0}function Hm(e){return e%2==0}function Xi(e,t=2){let n=10**t;return Math.floor(e*n)/n}function jm(e){return e&&e.width&&e.height}function Zv({width:e,height:t},n){let a=n/Math.max(t,e);return new yn(Math.round(e*a),Math.round(t*a))}function Ki(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function rr(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function Oa(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function fu(e){return Oa(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var ut=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(Oa)}static assertIsValidBox(t,n,a=!1){if(!ut.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!a&&(t.width<0||t.height<0))throw new Error(`${n} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,n=!0){let a=t||{},r=[a.left,a.top,a.right,a.bottom].every(Oa),s=[a.x,a.y,a.width,a.height].every(Oa);if(!s&&!r)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(a)}`);let[i,o,l,u]=s?[a.x,a.y,a.width,a.height]:[a.left,a.top,a.right-a.left,a.bottom-a.top];ut.assertIsValidBox({x:i,y:o,width:l,height:u},"Box.constructor",n),this._x=i,this._y=o,this._width=l,this._height=u}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new De(this.left,this.top)}get topRight(){return new De(this.right,this.top)}get bottomLeft(){return new De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ut({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ut({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return an&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),u<1&&(l=2-u,u=1),c<1&&(l=2-c,c=1),{dy:i,edy:l,dx:s,edx:o,y:c,ey:d,x:u,ex:p,w:a,h:r}}calibrate(t){return new ut({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var gu=class extends ut{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var ms=class{constructor(t,n,a,r,s){this._imageDims=new yn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ut(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ut(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new ms(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var yt=class extends ms{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new yt(a,r,s)}};function ew(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function tw(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;ul[c]<=n)}return s}function La(e,t){return M(()=>{let[n,a,r]=t,s=Sn([...e.shape.slice(0,3),1],n,"float32"),i=Sn([...e.shape.slice(0,3),1],a,"float32"),o=Sn([...e.shape.slice(0,3),1],r,"float32"),l=Je([s,i,o],3);return me(e,l)})}function aw(e,t=!1){return M(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,Sn(h,0,"float32")},l=o(s),u=r-l.shape[i],p=[t&&u?o(u):null,e,l].filter(d=>!!d).map(d=>ue(d,"float32"));return Je(p,i)})}function bre(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function vp(e){return 1/(1+Math.exp(-e))}function xre(e){return Math.log(e/(1-e))}var yu=class extends ut{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var vre=.5,wre=.43,kre=.45,ra=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new yn(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new De(r,s)).add(a))}get shift(){return new De(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new De(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new De(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof yt?t.box.floor():new ut(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/kre),l=Ki(t),u=Math.floor(Math.max(0,l.x-vre*o)),c=Math.floor(Math.max(0,l.y-wre*o));return new yu(u,c,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+c))}alignMinBbox(t){let n=tw(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var HS=class extends ra{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Ki([t[3],t[4]])]}};var bu=class extends ra{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Ki)}};var wp=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Xi(this.distance)})`:""}`}};var kp=class extends ut{static assertIsValidLabeledBox(t,n){if(ut.assertIsValidBox(t,n),!Oa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var Cr=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new Cr(t.label,n)}};var jS=class extends kp{static assertIsValidPredictedBox(t,n){if(kp.assertIsValidLabeledBox(t,n),!fu(t.score)||!fu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function sr(e){return e.detection instanceof yt}function Yi(e,t){return{...e,...{detection:t}}}function rw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function qm(e){let t="";if(!e)try{e=require("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function sw(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},r=global.fetch,s=qm();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:a,fetch:r,...s}}function iw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var ow=PA(XS()),Zt;function Tre(){if(!Zt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Zt}function lw(e){Zt=e}function uw(){return iw()?lw(rw()):(0,ow.isNodejs)()?lw(sw()):null}function Sre(e){if(Zt||uw(),!Zt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=Zt.Canvas,Image:n=Zt.Image}=e;Zt.Canvas=t,Zt.Image=n,Zt.createCanvasElement=e.createCanvasElement||(()=>new t),Zt.createImageElement=e.createImageElement||(()=>new n),Zt.ImageData=e.ImageData||Zt.ImageData,Zt.Video=e.Video||Zt.Video,Zt.fetch=e.fetch||Zt.fetch,Zt.readFile=e.readFile||Zt.readFile}var st={getEnv:Tre,setEnv:lw,initialize:uw,createBrowserEnv:rw,createFileSystem:qm,createNodejsEnv:sw,monkeyPatch:Sre,isBrowser:iw,isNodejs:ow.isNodejs};uw();function Ji(e){return!st.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function $n(e){let{Canvas:t,CanvasRenderingContext2D:n}=st.getEnv();if(e instanceof n)return e;let a=Ji(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var ir;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(ir||(ir={}));var Ip=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||ir.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},fs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof fs?t.text:t,this.anchor=n,this.options=new Ip(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var cw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:ir.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ip({...i,...s})}},Xm=class{constructor(t,n={}){this.box=new ut(t),this.options=new cw(n)}draw(t){let n=$n(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new fs([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Cre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof yt?a.score:sr(a)?a.detection.score:void 0,s=a instanceof yt?a.box:sr(a)?a.detection.box:new ut(a),i=r?`${Xi(r)}`:void 0;new Xm(s,{label:i}).draw(e)})}function Np(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function pw(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||Np(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function dw(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=st.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function Qi(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new yn(e.naturalWidth,e.naturalHeight):e instanceof n?new yn(e.videoWidth,e.videoHeight):new yn(e.width,e.height)}function Zi({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Tp(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!Np(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||Qi(e),s=Zi({width:a,height:r});return e instanceof n?$n(s).putImageData(e,0,0):$n(s).drawImage(e,0,0,a,r),s}async function hw(e,t){let n=t||st.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(aa(e)?1:0),i=M(()=>e.as3D(a,r,s).toInt());return await ki.toPixels(i,n),i.dispose(),n}function Km(e){let{Image:t,Canvas:n,Video:a}=st.getEnv();return e instanceof t||e instanceof n||e instanceof a}function mw(e,t,n=!1){let{Image:a,Canvas:r}=st.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Zi({width:1,height:1});let s=Qi(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=Zi({width:t,height:t}),c=e instanceof r?e:Tp(e),p=Math.abs(o-l)/2,d=n&&o0&&c.height>0&&$n(u).drawImage(c,d,h,o,l),u}var Ar=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Sr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(aa(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof st.getEnv().Canvas?a:Tp(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return rr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Zv({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,M(()=>{let a=rr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Fe){let o=aa(i)?i:i.expandDims();return o=aw(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ka.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof st.getEnv().Canvas)return ki.fromPixels(mw(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>ue(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function mt(e){if(e instanceof Ar)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(Ji);return a.forEach((r,s)=>{if(!Km(r)&&!Sr(r)&&!aa(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(aa(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>Km(r)&&pw(r))),new Ar(a,Array.isArray(e))}async function xu(e,t){let{Canvas:n}=st.getEnv(),a=e;if(!(e instanceof n)){let i=await mt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await hw(o)}let r=$n(a);return t.map(i=>i instanceof yt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let c=Zi({width:l,height:u});return l>0&&u>0&&$n(c).putImageData(r.getImageData(i,o,l,u),0,0),c})}async function vu(e,t){if(!Sr(e)&&!aa(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(aa(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return M(()=>{let[n,a,r]=e.shape.slice(aa(e)?1:0);return t.map(o=>o instanceof yt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:u,height:c})=>Wl(e.as3D(n,a,r),[l,o,0],[c,u,r]))})}async function eo(e,t){let{fetch:n}=st.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function Are(e){let t=await eo(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return dw(n)}async function fw(e){return(await eo(e)).json()}async function Fre(e){return new Float32Array(await(await eo(e)).arrayBuffer())}function Ym(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function gw(e,t){let{manifestUri:n,modelBaseUri:a}=Ym(e,t),r=await fw(n);return jt.loadWeights(r,a)}function _re(e,t,n=!1){let{width:a,height:r}=n?Qi(t):t;return e.width=a,e.height=r,{width:a,height:r}}var on=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof Hr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof Hr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Yn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await gw(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=st.getEnv(),{manifestUri:a,modelBaseUri:r}=Ym(t,this.getDefaultModelName()),s=u=>Promise.all(u.map(c=>n(c).then(p=>p.buffer))),i=jt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof Fe))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function Dn(e,t,n){return M(()=>{let a=Fi(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function Jm(e,t,n=!1){return M(()=>{let a=qe(n?J(Et(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Sp(e,t,n=!1,a=!0){return M(()=>{let r=qe(n?J(Et(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),u=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,u))))})}function to(e,t,n="same",a=!1){return M(()=>{let r=J(Et(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function bn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function wu(e,t){return(n,a,r,s)=>{let i=Ca(e(n*a*r*r),[r,r,n,a]),o=Qe(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Qm(e,t){return(n,a,r)=>{let s=Sa(e(n*a),[n,a]),i=Qe(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Zm=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function ku(e,t){return(n,a,r)=>{let s=Ca(e(3*3*n),[3,3,n,1]),i=Ca(e(n*a),[1,1,n,a]),o=Qe(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Zm(s,i,o)}}function Iu(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Zm(n,a,r)}}function jn(e,t){return(n,a,r)=>{let s=e[n];if(!qi(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function xn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function ef(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l,u=!1){let c=u?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:c,conv1:p,conv2:d}}function s(i,o,l,u=!1){let{conv0:c,conv1:p,conv2:d}=r(i,o,l,u),h=a(o,o,`${l}/conv3`);return{conv0:c,conv1:p,conv2:d,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function KS(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock4Params:r}=ef(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function tf(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function nf(e,t){let n=jn(e,t),a=tf(n),r=Iu(n);function s(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`);return{conv0:u,conv1:c,conv2:p}}function i(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`),d=r(`${o}/conv3`);return{conv0:u,conv1:c,conv2:p,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function YS(e){let t=[],{extractDenseBlock4Params:n}=nf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return bn(e,t),{params:a,paramMappings:t}}var Cp=class extends on{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(255)),i=Sp(s,n.dense0,!0);return i=Sp(i,n.dense1),i=Sp(i,n.dense2),i=Sp(i,n.dense3),i=Qn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return YS(t)}extractParams(t){return KS(t)}};function Ap(e,t){return M(()=>J(ze(e,t.weights),t.bias))}function JS(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=xn(e),o=Qm(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function QS(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return bn(e,t),{params:r,paramMappings:t}}function af(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Fp=class extends on{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof Ar?this.faceFeatureExtractor.forwardInput(t):t;return Ap(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return JS(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),QS(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var yw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],gs=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);yw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return yw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var rf=class extends Fp{constructor(t=new Cp){super("FaceExpressionNet",t)}forwardInput(t){return M(()=>Ta(this.runNet(t)))}async forward(t){return this.forwardInput(await mt(t))}async predictExpressions(t){let n=await mt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new gs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function bw(e){return e.expressions instanceof gs}function sf(e,t){return{...e,...{expressions:t}}}function Ere(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof gs?s:bw(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),u=sr(s)?s.detection.box.bottomLeft:a||new De(0,0);new fs(l.map(p=>`${p.expression} (${Xi(p.probability)})`),u).draw(e)})}function no(e){return sr(e)&&e.landmarks instanceof ra&&e.unshiftedLandmarks instanceof ra&&e.alignedRect instanceof yt}function $re(e){let t=(o,l,u,c)=>Math.atan2(c-l,u-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function Nu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new yt(e.detection.score,r.rescale(s.reverse()),s),o=$re(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var xw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},vw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new xw(n)}draw(t){let n=$n(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof bu&&(n.strokeStyle=i,n.lineWidth=s,Tr(n,this.faceLandmarks.getJawOutline()),Tr(n,this.faceLandmarks.getLeftEyeBrow()),Tr(n,this.faceLandmarks.getRightEyeBrow()),Tr(n,this.faceLandmarks.getNose()),Tr(n,this.faceLandmarks.getLeftEye(),!0),Tr(n,this.faceLandmarks.getRightEye(),!0),Tr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=c=>{n.beginPath(),n.arc(c.x,c.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function Dre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ra?a:no(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new vw(r).draw(e)})}var eC="1.0.2";function Mre(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l){let u=a(i,o,`${l}/separable_conv0`),c=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:c,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),c=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function tC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=xn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Mre(a,n),u=s(3,32,3,"entry_flow/conv_in"),c=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:u,reduction_block_0:c,reduction_block_1:p},h={};rr(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function Rre(e,t){let n=jn(e,t),a=tf(n),r=Iu(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:c}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function nC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Rre(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),c={conv_in:o,reduction_block_0:l,reduction_block_1:u},p={};rr(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return bn(e,n),{params:{entry_flow:c,middle_flow:p,exit_flow:m},paramMappings:n}}function aC(e,t,n){return J(Et(e,t.filters,n,"same"),t.bias)}function ww(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=$t(a,[3,3],[2,2],"same"),a=J(a,aC(e,t.expansion_conv,[2,2])),a}function Pre(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var kw=class extends on{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(256)),i=qe(aC(s,n.entry_flow.conv_in,[2,2]));return i=ww(i,n.entry_flow.reduction_block_0,!1),i=ww(i,n.entry_flow.reduction_block_1),rr(this._numMainBlocks,0,1).forEach(o=>{i=Pre(i,n.middle_flow[`main_block_${o}`])}),i=ww(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return nC(t,this._numMainBlocks)}extractParams(t){return tC(t,this._numMainBlocks)}};function rC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),r=Qm(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function sC(e){let t=[],n=jn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return bn(e,t),{params:r,paramMappings:t}}var Fr;(function(e){e.FEMALE="female",e.MALE="male"})(Fr||(Fr={}));var of=class extends on{constructor(t=new kw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof Ar?this.faceFeatureExtractor.forwardInput(t):t,r=Qn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Ap(r,n.fc.age).as1D(),i=Ap(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return M(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Ta(a)}})}async forward(t){return this.forwardInput(await mt(t))}async predictAgeAndGender(t){let n=await mt(t),a=await this.forwardInput(n),r=ct(a.age),s=ct(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let c=(await l.data())[0],p=(await u.data())[0],d=p>.5,h=d?Fr.MALE:Fr.FEMALE,m=d?p:1-p;return l.dispose(),u.dispose(),{age:c,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return rC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),sC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var _p=class extends Fp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return M(()=>{let i=(p,d)=>Dt([Sn([68],p,"float32"),Sn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return d(h,m)?Math.abs(h-m)/2:0},l=p=>o(p,(d,h)=>do(p,(d,h)=>hi(l(d),u(d))))).div(Dt(Array.from(Array(s),(p,d)=>i(r[d].width,r[d].height))))})}forwardInput(t){return M(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await mt(t))}async detectLandmarks(t){let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(await s.data()),l=o.filter((c,p)=>Hm(p)),u=o.filter((c,p)=>!Hm(p));return new bu(Array(68).fill(0).map((c,p)=>new De(l[p],u[p])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var Tu=class extends _p{constructor(t=new Cp){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function iC(e){let t=[],{extractDenseBlock3Params:n}=nf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return bn(e,t),{params:a,paramMappings:t}}function oC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock3Params:r}=ef(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Iw=class extends on{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(255)),i=Jm(s,n.dense0,!0);return i=Jm(i,n.dense1),i=Jm(i,n.dense2),i=Qn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return iC(t)}extractParams(t){return oC(t)}};var lf=class extends _p{constructor(t=new Iw){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var lC=class extends Tu{};function uC(e,t){return J(B(e,t.weights),t.biases)}function Nw(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Et(e,s,n,r);return o=J(o,i),o=uC(o,t.scale),a?qe(o):o}function cC(e,t){return Nw(e,t,[1,1],!0)}function Tw(e,t){return Nw(e,t,[1,1],!1)}function uf(e,t){return Nw(e,t,[2,2],!0,"valid")}function Ore(e,t){function n(o,l,u){let c=e(o),p=c.length/(l*u*u);if(Qv(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${c.length}, numFilters: ${l}, filterSize: ${u}`);return M(()=>Ve(Ca(c,[l,p,u,u]),[2,3,1,0]))}function a(o,l,u,c){let p=n(o,l,u),d=Qe(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/bias`}),{filters:p,bias:d}}function r(o,l){let u=Qe(e(o)),c=Qe(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:u,biases:c}}function s(o,l,u,c){let p=a(o,l,u,`${c}/conv`),d=r(l,`${c}/scale`);return{conv:p,scale:d}}function i(o,l,u,c,p=!1){let d=s((p?.5:1)*o,l,u,`${c}/conv1`),h=s(o,l,u,`${c}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function pC(e){let{extractWeights:t,getRemainingWeights:n}=xn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=Ore(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),u=s(9216,32,3,"conv32_3"),c=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),k=M(()=>Ve(Sa(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:c,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:k},paramMappings:a}}function Lre(e,t){let n=jn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),u=a(i);return{conv:{filters:o,bias:l},scale:u}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function dC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Lre(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),u=a("conv64_1"),c=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!Jv(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:u,conv64_2:c,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return bn(e,t),{params:v,paramMappings:t}}function za(e,t){let n=cC(e,t.conv1);return n=Tw(n,t.conv2),n=J(n,e),n=qe(n),n}function Ep(e,t){let n=uf(e,t.conv1);n=Tw(n,t.conv2);let a=Qn(e,2,2,"valid"),r=vt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=vt(o);n=Je([n,l],1);let u=[...n.shape];u[2]=1;let c=vt(u);n=Je([n,c],2)}return a=s?Je([a,r],3):a,n=J(a,n),n=qe(n),n}var Su=class extends on{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(150,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(256)),i=uf(s,n.conv32_down);i=$t(i,3,2,"valid"),i=za(i,n.conv32_1),i=za(i,n.conv32_2),i=za(i,n.conv32_3),i=Ep(i,n.conv64_down),i=za(i,n.conv64_1),i=za(i,n.conv64_2),i=za(i,n.conv64_3),i=Ep(i,n.conv128_down),i=za(i,n.conv128_1),i=za(i,n.conv128_2),i=Ep(i,n.conv256_down),i=za(i,n.conv256_1),i=za(i,n.conv256_2),i=Ep(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await mt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)==null?void 0:s.some(i=>i<=0))return new Float32Array(128);let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return dC(t)}extractParams(t){return pC(t)}};function zre(e){let t=new Su;return t.extractWeights(e),t}function cf(e,t){return{...e,...{descriptor:t}}}function Wre(e){return typeof e.age=="number"}function pf(e,t){return{...e,...{age:t}}}function Bre(e){return(e.gender===Fr.MALE||e.gender===Fr.FEMALE)&&fu(e.genderProbability)}function df(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function Vre(e,t){function n(l,u){let c=Ca(e(3*3*l),[3,3,l,1]),p=Qe(e(l)),d=Qe(e(l)),h=Qe(e(l)),m=Qe(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:c,batch_norm_scale:p,batch_norm_offset:d,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,c,p,d){let h=Ca(e(l*u*c*c),[c,c,l,u]),m=Qe(e(u));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/${d?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,c,p){let{filters:d,bias:h}=a(l,u,c,p,!0);return{filters:d,batch_norm_offset:h}}function s(l,u,c){let p=n(l,`${c}/depthwise_conv`),d=r(l,u,1,`${c}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),c=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),k=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:k}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),u=r(256,512,3,"prediction_layer/conv_1"),c=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),k=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),w=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),S=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),A=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),F=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),D=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),R=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:k},box_predictor_3:{box_encoding_predictor:w,class_predictor:S},box_predictor_4:{box_encoding_predictor:A,class_predictor:F},box_predictor_5:{box_encoding_predictor:D,class_predictor:R}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function hC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=Vre(n,t),i=r(),o=s(),u={extra_dim:Kd(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:u},paramMappings:t}}function Ure(e,t){let n=jn(e,t);function a(u,c,p){let d=n(`${u}/Conv2d_${c}_pointwise/weights`,4,`${p}/filters`),h=n(`${u}/Conv2d_${c}_pointwise/convolution_bn_offset`,1,`${p}/batch_norm_offset`);return{filters:d,batch_norm_offset:h}}function r(u){let c=`mobilenetv1/conv_${u}`,p=`MobilenetV1/Conv2d_${u}_depthwise`,d=`${c}/depthwise_conv`,h=`${c}/pointwise_conv`,m=n(`${p}/depthwise_weights`,4,`${d}/filters`),f=n(`${p}/BatchNorm/gamma`,1,`${d}/batch_norm_scale`),g=n(`${p}/BatchNorm/beta`,1,`${d}/batch_norm_offset`),y=n(`${p}/BatchNorm/moving_mean`,1,`${d}/batch_norm_mean`),b=n(`${p}/BatchNorm/moving_variance`,1,`${d}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",u,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(u,c){let p=n(`${u}/weights`,4,`${c}/filters`),d=n(`${u}/biases`,1,`${c}/bias`);return{filters:p,bias:d}}function o(u){let c=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),p=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:c,class_predictor:p}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function mC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Ure(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Sr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return bn(e,t),{params:s,paramMappings:t}}function xa(e,t,n){return M(()=>{let a=Et(e,t.filters,n,"same");return a=J(a,t.batch_norm_offset),Xt(a,0,6)})}var Gre=.0010000000474974513;function Hre(e,t,n){return M(()=>{let a=Qr(e,t.filters,n,"same");return a=fr(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Gre),Xt(a,0,6)})}function jre(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function fC(e,t){return M(()=>{let n,a=xa(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=jre(o);a=Hre(a,s.depthwise_conv,l),a=xa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function qre(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),c=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(c-l)*(p-u);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,c),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function gC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((c,p)=>({score:c,boxIndex:p})).filter(c=>c.score>r).sort((c,p)=>p.score-c.score),l=c=>c<=a?1:0,u=[];return o.forEach(c=>{if(u.length>=i)return;let p=c.score;for(let d=u.length-1;d>=0;--d){let h=qre(e,c.boxIndex,u[d]);if(h!==0&&(c.score*=l(h),c.score<=r))break}p===c.score&&u.push(c.boxIndex)}),u}function Xre(e){let t=ct(Ve(e,[1,0])),n=[me(t[2],t[0]),me(t[3],t[1])],a=[J(t[0],xe(n[0],ce(2))),J(t[1],xe(n[1],ce(2)))];return{sizes:n,centers:a}}function Kre(e,t){let{sizes:n,centers:a}=Xre(e),r=ct(Ve(t,[1,0])),s=xe(B(dn(xe(r[2],ce(5))),n[0]),ce(2)),i=J(B(xe(r[0],ce(10)),n[0]),a[0]),o=xe(B(dn(xe(r[3],ce(5))),n[1]),ce(2)),l=J(B(xe(r[1],ce(10)),n[1]),a[1]);return Ve(Dt([me(i,s),me(l,o),J(i,s),J(l,o)]),[1,0])}function yC(e,t,n){return M(()=>{let a=e.shape[0],r=Kre(H(Ha(n.extra_dim,[a,1,1]),[-1,4]),H(e,[-1,4]));r=H(r,[a,r.shape[0]/a,4]);let s=ca(Be(t,[0,0,1],[-1,-1,-1])),i=Be(s,[0,0,0],[-1,-1,1]);i=H(i,[a,i.shape[1]]);let o=ct(r),l=ct(i);return{boxes:o,scores:l}})}function ao(e,t){return M(()=>{let n=e.shape[0],a=H(to(e,t.box_encoding_predictor),[n,-1,1,4]),r=H(to(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function bC(e,t,n){return M(()=>{let a=xa(e,n.conv_0,[1,1]),r=xa(a,n.conv_1,[2,2]),s=xa(r,n.conv_2,[1,1]),i=xa(s,n.conv_3,[2,2]),o=xa(i,n.conv_4,[1,1]),l=xa(o,n.conv_5,[2,2]),u=xa(l,n.conv_6,[1,1]),c=xa(u,n.conv_7,[2,2]),p=ao(t,n.box_predictor_0),d=ao(e,n.box_predictor_1),h=ao(r,n.box_predictor_2),m=ao(i,n.box_predictor_3),f=ao(l,n.box_predictor_4),g=ao(c,n.box_predictor_5),y=Je([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Je([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var va=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ro=class extends on{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(512,!1),"float32"),r=me(B(a,ce(.007843137718737125)),ce(1)),s=fC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=bC(s.out,s.conv11,n.prediction_layer);return yC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await mt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new va(n),s=await mt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,k]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(A=>A*g),[w,S]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(A=>A*f);return new yt(c[x],new yu(w,v,S-w,k-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return mC(t)}extractParams(t){return hC(t)}};function xC(e){let t=new ro;return t.extractWeights(e),t}function Yre(e){return xC(e)}var vC=class extends ro{};var wC=.4,kC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],IC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],NC=[117.001,114.697,97.404],TC="tiny_yolov2_model",SC="tiny_yolov2_separable_conv_model";var hf=e=>typeof e=="number";function Sw(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!hf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>hf(t.x)&&hf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(hf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Cu(e){return M(()=>{let t=B(e,ce(.10000000149011612));return J(qe(me(e,t)),t)})}function _r(e,t){return M(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Et(n,t.conv.filters,[1,1],"valid"),n=me(n,t.bn.sub),n=B(n,t.bn.truediv),n=J(n,t.conv.bias),Cu(n)})}function Er(e,t){return M(()=>{let n=ea(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Fi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Cu(n)})}function Jre(e,t){let n=wu(e,t);function a(i,o){let l=Qe(e(i)),u=Qe(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),c=a(o,`${l}/bn`);return{conv:u,bn:c}}let s=ku(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function CC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Jre(r,i),c;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):u(p,d,"conv0"),k=u(d,h,"conv1"),w=u(h,m,"conv2"),S=u(m,f,"conv3"),A=u(f,g,"conv4"),F=u(g,y,"conv5"),D=b?u(y,b,"conv6"):void 0,R=x?u(b,x,"conv7"):void 0,L=o(x||b||y,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),k=l(d,h,"conv1"),w=l(h,m,"conv2"),S=l(m,f,"conv3"),A=l(f,g,"conv4"),F=l(g,y,"conv5"),D=l(y,b,"conv6"),R=l(b,x,"conv7"),L=o(x,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:c,paramMappings:i}}function Qre(e,t){let n=jn(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Iu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function AC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Qre(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var or=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cw=class extends on{constructor(t){super("TinyYolov2");Sw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=_r(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=_r(a,n.conv6),a=_r(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Cu(to(t,n.conv0,"valid",!1)):Er(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Er(a,n.conv6):a,a=n.conv7?Er(a,n.conv7):a,to(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let r=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(ce(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await mt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new or(n),s=await mt(t),i=await this.forwardInput(s,a),o=M(()=>ct(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let c=u.map(g=>g.box),p=u.map(g=>g.score),d=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return nw(c.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],c[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return AC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Cw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return CC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],c=this.config.anchors.length,[p,d,h]=M(()=>{let y=t.reshape([u,u,c,this.boxEncodingSize]),b=y.slice([0,0,0,0],[u,u,c,4]),x=y.slice([0,0,0,4],[u,u,c,1]),v=this.withClassScores?Ta(y.slice([0,0,0,5],[u,u,c,this.config.classes.length]),3):ce(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let k=(b+vp(g[y][b][x][0]))/u*o,w=(y+vp(g[y][b][x][1]))/u*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/u*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/u*l,F=k-S/2,D=w-A/2,R={row:y,col:b,anchor:x},{classScore:L,label:W}=this.withClassScores?await this.extractPredictedClass(h,R):{classScore:1,label:0};m.push({box:new gu(F,D,F+S,D+A),score:v,classScore:v*L,label:W,...R})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Au=Cw;Au.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Fu=class extends Au{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:wC,classes:["face"],...t?{anchors:IC,meanRgb:NC}:{anchors:kC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?SC:TC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Zre(e,t=!0){let n=new Fu(t);return n.extractWeights(e),n}var mf=class extends or{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var wa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof Fe?await vu(t,s):await xu(t,s)),o=await n(i);return i.forEach(l=>l instanceof Fe&&l.dispose()),o}async function _u(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var FC=.4,_C=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],EC=[117.001,114.697,97.404];var Eu=class extends Au{constructor(){let t={withSeparableConvs:!0,iouThreshold:FC,classes:["face"],anchors:_C,meanRgb:EC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Ze={ssdMobilenetv1:new ro,tinyFaceDetector:new Eu,tinyYolov2:new Fu,faceLandmark68Net:new Tu,faceLandmark68TinyNet:new lf,faceRecognitionNet:new Su,faceExpressionNet:new rf,ageGenderNet:new of},$C=(e,t)=>Ze.ssdMobilenetv1.locateFaces(e,t),ese=(e,t)=>Ze.tinyFaceDetector.locateFaces(e,t),tse=(e,t)=>Ze.tinyYolov2.locateFaces(e,t),DC=e=>Ze.faceLandmark68Net.detectLandmarks(e),nse=e=>Ze.faceLandmark68TinyNet.detectLandmarks(e),ase=e=>Ze.faceRecognitionNet.computeFaceDescriptor(e),rse=e=>Ze.faceExpressionNet.predictExpressions(e),sse=e=>Ze.ageGenderNet.predictAgeAndGender(e),MC=e=>Ze.ssdMobilenetv1.load(e),ise=e=>Ze.tinyFaceDetector.load(e),ose=e=>Ze.tinyYolov2.load(e),lse=e=>Ze.faceLandmark68Net.load(e),use=e=>Ze.faceLandmark68TinyNet.load(e),cse=e=>Ze.faceRecognitionNet.load(e),pse=e=>Ze.faceExpressionNet.load(e),dse=e=>Ze.ageGenderNet.load(e),hse=MC,mse=$C,fse=DC;var Aw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Aw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>sf(a,n[r]))}withAgeAndGender(){return new $u(this,this.input)}},Ru=class extends Aw{async run(){let t=await this.parentTask;if(!t)return;let n=await _u(t,this.input,a=>Ze.faceExpressionNet.predictExpressions(a),this.extractedFaces);return sf(t,n)}withAgeAndGender(){return new Du(this,this.input)}},lo=class extends Mu{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Ru{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Fw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},$u=class extends Fw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return pf(df(a,i,o),s)})}withFaceExpressions(){return new Mu(this,this.input)}},Du=class extends Fw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await _u(t,this.input,s=>Ze.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return pf(df(t,a,r),n)}withFaceExpressions(){return new Ru(this,this.input)}},io=class extends $u{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Du{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var ff=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends ff{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>Ze.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>cf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends ff{async run(){let t=await this.parentTask;if(!t)return;let n=await _u(t,this.input,a=>Ze.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return cf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var gf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Ze.faceLandmark68TinyNet:Ze.faceLandmark68Net}},yf=class extends gf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Fe?await vu(this.input,n):await xu(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Fe&&s.dispose()),t.map((s,i)=>Nu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},bf=class extends gf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Fe?await vu(this.input,[n]):await xu(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Fe&&s.dispose()),Nu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var xf=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},$p=class extends xf{async run(){let{input:t,options:n}=this,a;if(n instanceof mf)a=Ze.tinyFaceDetector.locateFaces(t,n);else if(n instanceof va)a=Ze.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof or)a=Ze.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Yi({},a)))})}withFaceLandmarks(t=!1){return new yf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $u(this.runAndExtendWithFaceDetections(),this.input)}},vf=class extends xf{async run(){let t=await new $p(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Yi({},n):void 0)})}withFaceLandmarks(t=!1){return new bf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ru(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetection(),this.input)}};function gse(e,t=new va){return new vf(e,t)}function wf(e,t=new va){return new $p(e,t)}async function RC(e,t){return wf(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function yse(e,t={}){return wf(e,new or(t)).withFaceLandmarks().withFaceDescriptors()}var bse=RC;function _w(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var kf=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Cr)return i;if(i instanceof Float32Array)return new Cr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Cr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>_w(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new wp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Cr.fromJSON(a));return new kf(n,t.distanceThreshold)}};function xse(e){let t=new Eu;return t.extractWeights(e),t}function PC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>PC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Nu(Yi(e,r),s)}return sr(e)?Yi(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof yt?e.forSize(n,a):e}var vse=typeof process!="undefined",wse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",kse={faceapi:eC,node:vse,browser:wse};export{of as AgeGenderNet,gu as BoundingBox,ut as Box,wa as ComposableTask,ys as ComputeAllFaceDescriptorsTask,ff as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,yf as DetectAllFaceLandmarksTask,$p as DetectAllFacesTask,gf as DetectFaceLandmarksTaskBase,xf as DetectFacesTaskBase,bf as DetectSingleFaceLandmarksTask,vf as DetectSingleFaceTask,yn as Dimensions,yw as FACE_EXPRESSION_LABELS,yt as FaceDetection,vC as FaceDetectionNet,rf as FaceExpressionNet,gs as FaceExpressions,Tu as FaceLandmark68Net,lf as FaceLandmark68TinyNet,lC as FaceLandmarkNet,ra as FaceLandmarks,HS as FaceLandmarks5,bu as FaceLandmarks68,wp as FaceMatch,kf as FaceMatcher,Su as FaceRecognitionNet,Fr as Gender,kp as LabeledBox,Cr as LabeledFaceDescriptors,Ar as NetInput,on as NeuralNetwork,ms as ObjectDetection,De as Point,jS as PredictedBox,yu as Rect,ro as SsdMobilenetv1,va as SsdMobilenetv1Options,Eu as TinyFaceDetector,mf as TinyFaceDetectorOptions,Fu as TinyYolov2,or as TinyYolov2Options,bse as allFaces,RC as allFacesSsdMobilenetv1,yse as allFacesTinyYolov2,pw as awaitMediaLoaded,dw as bufferToImage,ase as computeFaceDescriptor,Zi as createCanvas,Tp as createCanvasFromMedia,Yre as createFaceDetectionNet,zre as createFaceRecognitionNet,xC as createSsdMobilenetv1,xse as createTinyFaceDetector,Zre as createTinyYolov2,wf as detectAllFaces,DC as detectFaceLandmarks,nse as detectFaceLandmarksTiny,fse as detectLandmarks,gse as detectSingleFace,ZS as draw,st as env,_w as euclideanDistance,pf as extendWithAge,cf as extendWithFaceDescriptor,Yi as extendWithFaceDetection,sf as extendWithFaceExpressions,Nu as extendWithFaceLandmarks,df as extendWithGender,vu as extractFaceTensors,xu as extractFaces,Are as fetchImage,fw as fetchJson,Fre as fetchNetWeights,eo as fetchOrThrow,$n as getContext2dOrThrow,Qi as getMediaDimensions,hw as imageTensorToCanvas,mw as imageToSquare,xre as inverseSigmoid,ew as iou,Km as isMediaElement,Np as isMediaLoaded,Wre as isWithAge,sr as isWithFaceDetection,bw as isWithFaceExpressions,no as isWithFaceLandmarks,Bre as isWithGender,dse as loadAgeGenderModel,hse as loadFaceDetectionModel,pse as loadFaceExpressionModel,lse as loadFaceLandmarkModel,use as loadFaceLandmarkTinyModel,cse as loadFaceRecognitionModel,MC as loadSsdMobilenetv1Model,ise as loadTinyFaceDetectorModel,ose as loadTinyYolov2Model,gw as loadWeightMap,mse as locateFaces,_re as matchDimensions,tw as minBbox,Ze as nets,nw as nonMaxSuppression,La as normalize,aw as padToSquare,sse as predictAgeAndGender,rse as recognizeFaceExpressions,PC as resizeResults,Ji as resolveInput,bre as shuffleArray,vp as sigmoid,$C as ssdMobilenetv1,Gw as tf,ese as tinyFaceDetector,tse as tinyYolov2,mt as toNetInput,GS as utils,Sw as validateConfig,kse as version}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/face-api.js b/dist/face-api.js index b94fe8b..5cff4c7 100644 --- a/dist/face-api.js +++ b/dist/face-api.js @@ -4163,7 +4163,7 @@ return a / b;`,TQ=` } setOutput(${l}); } - `}};function Xee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,c=E.getAxesPermutation([u],o),p=r;c!=null&&(p=En({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(p),u=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,u,i),h=I.sizeFromShape([p.shape[u]]),m=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=uh(r.dtype),g=(v,k,w,S,A)=>{let F=v.shape[0],D=v.shape[1],R=E.segment_util.segOpComputeOptimalWindowSize(D,A),L={windowSize:R,inSize:D,batchSize:F,numSegments:A},W=new qee(L,k),U=n.compileAndRun(W,[v,w],S);if(l.push(U),U.shape[1]===A)return U;let G=sS({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),X=lS({inputs:{x:G},backend:n,attrs:{reps:[D/R]}});return l.push(G),l.push(X),g(U,k,X,S,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=ye({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(c!=null){l.push(b);let v=E.getUndoAxesPermutation(c);x=En({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Kee={kernelName:bc,backendName:"webgl",kernelFunc:Xee},Yee=[zJ,VJ,CK,FK,$K,RK,OK,WK,VK,GK,XK,YK,ZK,n7,u7,s7,d7,g7,m7,v7,k7,N7,A7,R7,O7,U7,H7,K7,Q7,lK,nY,dY,mY,iY,bY,vY,gY,IY,SY,FY,EY,DY,PY,VY,GY,LY,qY,YY,e9,r9,l9,p9,d9,h9,f9,y9,x9,w9,I9,C9,E9,D9,R9,L9,V9,j9,Y9,oK,Q9,tY,tJ,rJ,oJ,cK,pJ,fJ,yJ,NJ,wJ,AJ,EJ,RJ,GJ,QJ,YJ,nQ,rQ,iQ,XJ,lQ,cQ,mQ,bQ,kQ,_Q,fK,$Q,RQ,LQ,BQ,z7,GQ,jQ,XQ,JQ,tZ,dK,aZ,rZ,W7,SQ,oZ,fZ,pZ,yK,xZ,kZ,TZ,AZ,$Z,MZ,OZ,WZ,VZ,HZ,XZ,JZ,eee,aee,iee,D7,AQ,uee,pee,hee,fee,bee,wee,Iee,Tee,Aee,CQ,NK,Eee,Mee,Oee,zee,Vee,TK,Gee,jee,Kee,HQ];for(let e of Yee)vc(e);var Jee="3.3.0",Qee={"tfjs-core":c1,"tfjs-backend-cpu":bG,"tfjs-backend-webgl":iK,"tfjs-data":ON,"tfjs-layers":Im,"tfjs-converter":EN,tfjs:Jee},Hn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Hn||(Hn={}));var Ip;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(Ip||(Ip={}));var uS;function Zee(e){uS=e.wasm.cwrap(Ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function ete(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let A=n.dataIdMap.get(i.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Ip[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),k=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return uS(d,w,r.shape.length,h,S,s.shape.length,l,u,g,m,f,p||0,k),v}var tte={kernelName:Ii,backendName:"wasm",setupFunc:Zee,kernelFunc:ete};function $n(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return I.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var nte=$n(Po);function yn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:c}=l,p=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,m=E.assertAndGetBroadcastShape(u.shape,c.shape),f=o.makeOutput(m,h);if(I.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,u.shape.length,d,y,c.shape.length,Hn[u.dtype],b);if(t&&u.dtype==="float32")return x(),f;let v=E.getBroadcastDims(u.shape,m),k=E.getBroadcastDims(c.shape,m),w=v.every((A,F)=>A===F),S=k.every((A,F)=>A===F);if(w&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var ate=!0,rte=yn(Hr,ate),cS;function ste(e){cS=e.wasm.cwrap(Es,null,["array","number","number","number"])}function ite(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(I.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return cS(s,r.length,Hn[a.dtype],i),a}var ote={kernelName:Es,backendName:"wasm",setupFunc:ste,kernelFunc:ite};function af(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var lte={kernelName:Xs,backendName:"wasm",kernelFunc:af},pS;function ute(e){pS=e.wasm.cwrap(ki,null,["number","array","number","number","number","array","number"])}function rf(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=pte(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var dte={kernelName:ki,backendName:"wasm",kernelFunc:rf,setupFunc:ute};function Nu(e,t,n){let a=e.shape,r=e.shape.length,s=I.parseAxisParam(t,a),i=s,o=E.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let c=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var xte={kernelName:vl,backendName:"wasm",kernelFunc:Oa},mS;function vte(e){mS=e.wasm.cwrap(Ms,null,["number","array","number","number","array","number","number","number","number"])}function wte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,c=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=I.sizeFromShape(m),y=I.sizeFromShape(f),b=g===y||g===1||y===1;I.assert(l>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);I.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,c,d]:[g,d,c],k=o?[y,h,p]:[y,p,h],w=Oa({inputs:{x:r},backend:n,attrs:{shape:v}}),S=Oa({inputs:{x:s},backend:n,attrs:{shape:k}}),A=n.dataIdMap.get(w.dataId).id,F=n.dataIdMap.get(S.dataId).id,D=i?w.shape[2]:w.shape[1],R=o?S.shape[1]:S.shape[2],L=Math.max(g,y),W=n.makeOutput([L,D,R],w.dtype),U=n.dataIdMap.get(W.dataId).id,G=new Uint8Array(new Int32Array(w.shape).buffer),X=new Uint8Array(new Int32Array(S.shape).buffer);return mS(A,G,w.shape.length,F,X,S.shape.length,i,o,U),n.disposeData(w.dataId),n.disposeData(S.dataId),W.shape=x,W}var kte={kernelName:Ms,backendName:"wasm",setupFunc:vte,kernelFunc:wte};function sf(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var Ite={kernelName:Rs,backendName:"wasm",kernelFunc:sf},Nte=$n(Ps),fS;function Tte(e){fS=e.wasm.cwrap(jr,null,["number","number","number","number"])}function Ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return fS(o,s,i,u),l}var Cte={kernelName:jr,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste};function gS(e){let{inputs:t,backend:n}=e,a=I.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>I.sizeFromShape(h.shape)>0);if(s.length===1)return af({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(I.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=I.sizeFromShape(x.shape.slice(a));return Oa({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=_v(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=I.sizeFromShape(s[0].shape.slice(0,a)),u=0,c=s.map(h=>{let m=I.sizeFromShape(h.shape.slice(a));return u+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=E.getAxesPermutation([s],l),c=r;u!==null&&(c=rf({inputs:{x:r},attrs:{perm:u},backend:n}));let p=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],m=n.dataIdMap.get(c.dataId).id,f=n.dataIdMap.get(d.dataId).id;vS(m,i?1:0,o?1:0,h,f,Hn[r.dtype]);let g=d;if(u!==null){let y=E.getUndoAxesPermutation(u);g=rf({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Bte={kernelName:Ws,backendName:"wasm",setupFunc:zte,kernelFunc:Wte},wS;function Vte(e){wS=e.wasm.cwrap(qo,null,["number","number","number","array","number","array","array","number","number"])}function Ute(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;I.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=u*s,h=c/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(I.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(I.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return wS(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var Gte={kernelName:qo,backendName:"wasm",setupFunc:Vte,kernelFunc:Ute},kS;function Hte(e){kS=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=E.computeConv2DInfo(r.shape,s.shape,l,d,c,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,k=h.dilationWidth,w=h.strideHeight,S=h.strideWidth,A=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=a.makeOutput(h.outShape,"float32"),L=a.dataIdMap.get(R.dataId).id;return kS(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,D,v,k,w,S,A,F,L),R}var qte={kernelName:Bs,backendName:"wasm",setupFunc:Hte,kernelFunc:jte},Xte=!1,Kte=yn(Yo,Xte,"bool"),Yte=$n(Us);function lw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(I.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Jte={kernelName:Jo,backendName:"wasm",kernelFunc:lw};function Qte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Zte={kernelName:lc,backendName:"wasm",kernelFunc:Qte},IS;function ene(e){IS=e.wasm.cwrap(Zo,null,["number","number","number","number","number","number"])}function tne(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,c]=a.shape;return IS(s,o,l,u,c,i),r}var nne={kernelName:Zo,backendName:"wasm",kernelFunc:tne,setupFunc:ene},ane=$n(Gs),rne=!1,sne=yn(Hs,rne),NS;function ine(e){NS=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number"])}function one(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(I.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return NS(c,p,d,h,m,r,g),f}var lne={kernelName:js,backendName:"wasm",setupFunc:ine,kernelFunc:one},TS;function une(e){TS=e.wasm.cwrap(Ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d),g=Ip[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return TS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var pne={kernelName:Ni,backendName:"wasm",setupFunc:une,kernelFunc:cne},SS;function dne(e){SS=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!0),g=Ip[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return SS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var mne={kernelName:Ti,backendName:"wasm",setupFunc:dne,kernelFunc:hne},CS;function fne(e){CS=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number"])}function gne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ay.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let c=r.shape,p=c[c.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return CS(d,Hn[a.dtype],h,i,p,o,m,f),u}var yne={kernelName:tl,backendName:"wasm",setupFunc:fne,kernelFunc:gne},AS;function bne(e){AS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function xne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=I.parseAxisParam(i,r.shape)[0],u=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Oa({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),p=I.sizeFromShape(s.shape),d=Oa({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],m=t.makeOutput(h,r.dtype);if(I.sizeFromShape(r.shape)===0)return m;let f=c.shape.length-1,g=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(I.computeStrides(c.shape)).buffer),v=new Uint8Array(new Int32Array(I.computeStrides(h)).buffer);return AS(g,Hn[r.dtype],x,f,y,u.batchSize,v,b),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var vne={kernelName:el,backendName:"wasm",setupFunc:bne,kernelFunc:xne},wne=!1,kne=yn(nl,wne,"bool"),Ine=!1,Nne=yn(qs,Ine,"bool"),FS;function Tne(e){FS=e.wasm.cwrap(Ks,null,["number","number","number"])}function Sne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(I.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;FS(r,n,i)}return s}var Cne={kernelName:Ks,backendName:"wasm",setupFunc:Tne,kernelFunc:Sne},Ane=!1,Fne=yn(il,Ane,"bool"),_ne=!1,Ene=yn(ol,_ne,"bool"),$ne=$n(Ys),Dne=!1,Mne=yn(ul,Dne,"bool"),_S;function Rne(e){_S=e.wasm.cwrap(Js,null,["number, number, number"])}function Pne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:p,inputWasTransposed:d}=Nu(i,r,t);if(d){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",c,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,c),g=I.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(I.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;_S(o,g,b)}if(d&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var One={kernelName:Js,backendName:"wasm",setupFunc:Rne,kernelFunc:Pne},Lne=!1,zne=yn(Qs,Lne),ES;function Wne(e){ES=e.wasm.cwrap(Zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=E.computePool2DInfo(r.shape,i,o,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,b=c.dilationWidth,x=c.strideHeight,v=c.strideWidth,k=c.inChannels,w=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(c.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return ES(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,k,w,A),S}var Vne={kernelName:Zs,backendName:"wasm",setupFunc:Wne,kernelFunc:Bne},$S;function Une(e){$S=e.wasm.cwrap(ei,null,["number, number, number"])}function Gne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(u=c,l=v,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=sf({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(I.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;$S(l,y,v)}if(h&&t.disposeData(c.dataId),s){let v=E.expandShapeToKeepDim(x.shape,d);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var Hne={kernelName:ei,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},DS;function jne(e){DS=e.wasm.cwrap(ti,null,["number, number, number"])}function qne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x)}let m=u.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(u.shape,p),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;DS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Xne={kernelName:ti,backendName:"wasm",setupFunc:jne,kernelFunc:qne},Kne=!1,Yne=yn(ni,Kne),Jne=!0,Qne=yn(ai,Jne),Zne=$n(pl);function uw(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var MS;function eae(e){MS=e.wasm.cwrap(hl,"number",["number","number","number","number","number"])}function tae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=MS(u,c,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=uw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var nae={kernelName:hl,backendName:"wasm",setupFunc:eae,kernelFunc:tae},RS;function aae(e){RS=e.wasm.cwrap(ml,"number",["number","number","number","number","number","bool"])}function rae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=RS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var sae={kernelName:ml,backendName:"wasm",setupFunc:aae,kernelFunc:rae},PS;function iae(e){PS=e.wasm.cwrap(fl,"number",["number","number","number","number","number","number"])}function oae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=PS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var lae={kernelName:fl,backendName:"wasm",setupFunc:iae,kernelFunc:oae},uae=!1,cae=yn(dl,uae,"bool"),OS;function pae(e){OS=e.wasm.cwrap(ri,null,["number","number","number","number","number"])}function dae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(r.dataId).id;return OS(c,s,i,o,u),l}var hae={kernelName:ri,backendName:"wasm",setupFunc:pae,kernelFunc:dae};function mae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var fae={kernelName:gl,backendName:"wasm",kernelFunc:mae};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return lw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let p=lw({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),u=gS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),u}var yae={kernelName:yl,backendName:"wasm",kernelFunc:gae},LS;function bae(e){LS=e.wasm.cwrap(si,null,["number","array","number","number","array","array","number","number"])}function xae(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return LS(i,u,t.shape.length,Hn[t.dtype],d,h,r,l),o}var vae={kernelName:si,backendName:"wasm",kernelFunc:xae,setupFunc:bae},wae=!1,kae=yn(ii,wae),zS;function Iae(e){zS=e.wasm.cwrap(oi,null,["number","number","number"])}function Nae(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return zS(s,i,l),o}var Tae={kernelName:oi,backendName:"wasm",setupFunc:Iae,kernelFunc:Nae},WS;function Sae(e){WS=e.wasm.cwrap(bl,null,["number","number","number","number"])}function Cae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;WS(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Aae={kernelName:bl,backendName:"wasm",setupFunc:Sae,kernelFunc:Cae},Fae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Dv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},_ae={kernelName:mc,backendName:"wasm",kernelFunc:Fae},Eae=!0,$ae=yn(Vs,Eae),Dae=$n(li),Mae=$n(ci),BS;function Rae(e){BS=e.wasm.cwrap(ui,null,["number","number","number","number","number","number","number","number","number","number"])}function Pae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[c,p,d,h]=r.shape,m=[c,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=sf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(I.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return BS(y,c,p,d,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var Oae={kernelName:ui,backendName:"wasm",setupFunc:Rae,kernelFunc:Pae},VS;function Lae(e){VS=e.wasm.cwrap(pi,null,["number","array","number","array","number","number"])}function zae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=I.parseAxisParam(s,r.shape);if(r.shape.length===0)return af({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);VS(l,c,i.length,p,r.shape.length,u);let d=Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Wae={kernelName:pi,backendName:"wasm",kernelFunc:zae,setupFunc:Lae},US;function Bae(e){US=e.wasm.cwrap(Ml,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Vae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=E.getImageCenter(o,d,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return US(u,p,d,h,m,s,f,g,v,x.length,c),l}var Uae={kernelName:Ml,backendName:"wasm",kernelFunc:Vae,setupFunc:Bae},Gae=$n(di),Hae=$n(hi),GS;function jae(e){GS=e.wasm.cwrap(wl,null,["number","number","number","number","number","number","array","number","number"])}function qae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(I.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=Fy.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return GS(h,m,Hn[s.dtype],l,u,c,f,d,g),o}var Xae={kernelName:wl,backendName:"wasm",setupFunc:jae,kernelFunc:qae},HS;function Kae(e){HS=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Yae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(u.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:I.sizeFromShape(r.shape.slice(1));return HS(i,o,l,h,c),u}var Jae={kernelName:kl,backendName:"wasm",kernelFunc:Yae,setupFunc:Kae},jS;function Qae(e){jS=e.wasm.cwrap(fi,null,["number","number"])}function Zae(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return I.sizeFromShape(r.shape)===0||jS(a,s),r}var ere={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Qae,kernelFunc:Zae},tre=$n(mi);function of(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=dn.parseSliceParams(t,n,a),o=dn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),c=I.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(o){let m=dn.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=l.slice(m,m+I.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+I.sizeFromShape(i))),u}if(t.dtype==="string"){let m=zm(l,s,i,t.shape,t.dtype);return p.stringBytes=m,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)nre(l,c[0],d,s,i);else if(h===3)are(l,c[0],c[1],d,s,i);else if(h===4)rre(l,c[0],c[1],c[2],d,s,i);else{let m=zm(l,s,i,t.shape,t.dtype);d.set(m)}return u}function nre(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u{let d=[...c];d[o]=p;let h=of({inputs:{x:r},attrs:{begin:u,size:d},backend:a});return u[o]+=p,h})}var cre={kernelName:Al,backendName:"wasm",kernelFunc:ure},pre=$n(gi),dre=$n(yc),hre=!0,mre=yn(xi,hre),XS;function fre(e){XS=e.wasm.cwrap(Xr,null,["number","number","number"])}function gre(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return XS(i,r,l),o}var yre={kernelName:Xr,backendName:"wasm",setupFunc:fre,kernelFunc:gre},KS;function bre(e){KS=e.wasm.cwrap(Fl,null,["number","array","number","array","array","array","array","array","number","number"])}function xre(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=a,h=E.slice_util.maskToAxes(c);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let m=r.shape.length-s.length,f=E.slice_util.maskToAxes(p),g=r.shape.slice();f.forEach(D=>{s[D]=0,i[D]=1,g.splice(D,0,1)});let y=Oa({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:b,end:x,strides:v}=E.slice_util.getNormalizedAxes(y.shape,h,m,s,i,o,l,u,c);s=b,i=x,o=v;let k=E.slice_util.maskToAxes(d);k.forEach(D=>{i[D]=s[D]+1,o[D]=1});let w=E.slice_util.computeOutShape(s,i,o),S=w.filter((D,R)=>k.indexOf(R)===-1);if(o.every(D=>D===1)){let D=of({inputs:{x:y},attrs:{begin:s,size:w},backend:t});t.disposeData(y.dataId);let R=Oa({inputs:{x:D},attrs:{shape:S},backend:t});return t.disposeData(D.dataId),R}let A=t.makeOutput(S,"float32");if(!S.some(D=>D===0)){let D=t.dataIdMap.get(y.dataId).id,R=new Uint8Array(new Int32Array(I.computeStrides(y.shape)).buffer),L=new Uint8Array(new Int32Array(s).buffer),W=new Uint8Array(new Int32Array(i).buffer),U=new Uint8Array(new Int32Array(o).buffer),G=new Uint8Array(new Int32Array(S).buffer),X=new Uint8Array(new Int32Array(I.computeStrides(S)).buffer),j=t.dataIdMap.get(A.dataId).id;KS(D,R,y.shape.length,L,W,U,G,X,S.length,j)}t.disposeData(y.dataId);let F=Oa({inputs:{x:A},attrs:{shape:S},backend:t});return t.disposeData(A.dataId),F}var vre={kernelName:Fl,backendName:"wasm",setupFunc:bre,kernelFunc:xre},wre=!0,kre=yn(vi,wre),YS;function Ire(e){YS=e.wasm.cwrap(yi,null,["number, number, number"])}function Nre(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;YS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Tre={kernelName:yi,backendName:"wasm",setupFunc:Ire,kernelFunc:Nre},Sre=$n(wi),JS;function Cre(e){JS=e.wasm.cwrap(qr,null,["number","array","number","array","number","number"])}function Are(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let d=0;d{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),c=t.dataIdMap.get(u.dataId).id,p=t.makeOutput(l,"int32"),d=t.dataIdMap.get(p.dataId).id;return QS(i,o,a.shape.length,Hn[a.dtype],r,s,c,d),[u,p]},$re={kernelName:El,backendName:"wasm",setupFunc:_re,kernelFunc:Ere};function Dre(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h({dataId:h,dtype:m,shape:l}))}var Mre={kernelName:$l,backendName:"wasm",kernelFunc:Dre};function Rre(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var Pre={kernelName:Dl,backendName:"wasm",kernelFunc:Rre},Ore=[nte,rte,ote,fte,bte,kte,Ite,Nte,Cte,Ate,Ete,Mte,Rte,Lte,Bte,Gte,qte,Kte,Yte,Jte,Zte,nne,ane,sne,tte,lne,pne,mne,yne,vne,kne,Nne,lte,Cne,Fne,Ene,$ne,Mne,One,zne,Vne,Hne,Xne,Yne,Qne,Zne,nae,sae,lae,cae,hae,fae,yae,vae,kae,Tae,Aae,_ae,$ae,Dae,Mae,xte,Oae,Wae,Uae,Hae,Gae,Xae,Jae,ere,tre,sre,lre,cre,pre,dre,mre,yre,vre,kre,Tre,Sre,Fre,$re,dte,Mre,Pre];for(let e of Ore)vc(e);var cw=Z();cw.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));cw.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(cw.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var ZS=Do(XF()),Lre='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',zre=Do(KF()),eC=class extends Zu{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new kd(this,Ha())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=I.now();return e(),{kernelMs:I.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=I.sizeFromShape(n),o=i*I.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:a,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let s=this.wasm.HEAPU8.slice(t,t+I.sizeFromShape(a)*I.bytesPerElement(n));return Wre(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=I.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=I.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function Bre(e){return(t,n)=>(I.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance)})})}),{})}function tC(e,t,n){if(lf!=null)return lf;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),Np!=null&&Np[a]!=null?Np[a]:n+a}async function Vre(){let[e,t]=await Promise.all([Z().getAsync("WASM_HAS_SIMD_SUPPORT"),Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=Lre,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return o.endsWith(".wasm")?tC(e,t,Tp!=null?Tp:l):l+o},pw&&(r.instantiateWasm=Bre(tC(e,t,Tp!=null?Tp:"")));let s=!1;r.onAbort=()=>{s||Sp||(Sp=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&lf==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+ZS.default.toString()],{type:"text/javascript"}),i=(0,ZS.default)(r)):i=(0,zre.default)(r),i.then(o=>{s=!0,Sp=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function Wre(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var Ure=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],lf=null,Tp=null,Np={},Sp=!1,pw=!1;function Gre(e,t=!1){if(Ry("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Sp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");lf=e,pw=t}function Hre(e,t=!1){if(Sp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Tp=e;else{Np=e;let n=Ure.filter(a=>Np[a]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}pw=t}var jre="3.3.0",qre=2;fh("wasm",async()=>{let{wasm:e}=await Vre();return new eC(e)},qre);var Cw={};Ju(Cw,{AnchorPosition:()=>lr,DrawBox:()=>gf,DrawBoxOptions:()=>Nw,DrawFaceLandmarks:()=>Sw,DrawFaceLandmarksOptions:()=>Tw,DrawTextField:()=>vs,DrawTextFieldOptions:()=>Cp,drawContour:()=>Ar,drawDetections:()=>nse,drawFaceExpressions:()=>ase,drawFaceLandmarks:()=>sse});function Ar(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:a,y:r},s)=>{let i=t[s];e.moveTo(i.x,i.y),e.lineTo(a,r)}),n){let a=t[t.length-1],r=t[0];if(!a||!r)return;e.moveTo(a.x,a.y),e.lineTo(r.x,r.y)}e.stroke()}var dw={};Ju(dw,{computeReshapedDimensions:()=>fw,getCenterPoint:()=>no,isDimensions:()=>cf,isEven:()=>uf,isFloat:()=>mw,isTensor:()=>eo,isTensor1D:()=>Xre,isTensor2D:()=>hw,isTensor3D:()=>Fr,isTensor4D:()=>ra,isValidNumber:()=>La,isValidProbablitiy:()=>Tu,range:()=>ir,round:()=>to});var cn=class{constructor(t,n){if(!La(t)||!La(n))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new cn(1/this.width,1/this.height)}};function eo(e,t){return e instanceof Fe&&e.shape.length===t}function Xre(e){return eo(e,1)}function hw(e){return eo(e,2)}function Fr(e){return eo(e,3)}function ra(e){return eo(e,4)}function mw(e){return e%1!=0}function uf(e){return e%2==0}function to(e,t=2){let n=10**t;return Math.floor(e*n)/n}function cf(e){return e&&e.width&&e.height}function fw({width:e,height:t},n){let a=n/Math.max(t,e);return new cn(Math.round(e*a),Math.round(t*a))}function no(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function ir(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function La(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function Tu(e){return La(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var ot=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(La)}static assertIsValidBox(t,n,a=!1){if(!ot.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!a&&(t.width<0||t.height<0))throw new Error(`${n} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,n=!0){let a=t||{},r=[a.left,a.top,a.right,a.bottom].every(La),s=[a.x,a.y,a.width,a.height].every(La);if(!s&&!r)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(a)}`);let[i,o,l,u]=s?[a.x,a.y,a.width,a.height]:[a.left,a.top,a.right-a.left,a.bottom-a.top];ot.assertIsValidBox({x:i,y:o,width:l,height:u},"Box.constructor",n),this._x=i,this._y=o,this._width=l,this._height=u}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new De(this.left,this.top)}get topRight(){return new De(this.right,this.top)}get bottomLeft(){return new De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ot({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ot({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return an&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),u<1&&(l=2-u,u=1),c<1&&(l=2-c,c=1),{dy:i,edy:l,dx:s,edx:o,y:c,ey:d,x:u,ex:p,w:a,h:r}}calibrate(t){return new ot({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var ao=class extends ot{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var _r=class{constructor(t,n,a,r,s){this._imageDims=new cn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ot(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ot(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new _r(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var ft=class extends _r{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new ft(a,r,s)}};function pf(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function df(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;ul[c]<=n)}return s}function wa(e,t){return M(()=>{let[n,a,r]=t,s=Cn([...e.shape.slice(0,3),1],n,"float32"),i=Cn([...e.shape.slice(0,3),1],a,"float32"),o=Cn([...e.shape.slice(0,3),1],r,"float32"),l=Je([s,i,o],3);return me(e,l)})}function mf(e,t=!1){return M(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,Cn(h,0,"float32")},l=o(s),u=r-l.shape[i],p=[t&&u?o(u):null,e,l].filter(d=>!!d).map(d=>ue(d,"float32"));return Je(p,i)})}function nC(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Su(e){return 1/(1+Math.exp(-e))}function aC(e){return Math.log(e/(1-e))}var ro=class extends ot{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Kre=.5,Yre=.43,Jre=.45,jn=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new cn(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new De(r,s)).add(a))}get shift(){return new De(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new De(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new De(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof ft?t.box.floor():new ot(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/Jre),l=no(t),u=Math.floor(Math.max(0,l.x-Kre*o)),c=Math.floor(Math.max(0,l.y-Yre*o));return new ro(u,c,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+c))}alignMinBbox(t){let n=df(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var gw=class extends jn{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],no([t[3],t[4]])]}};var so=class extends jn{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(no)}};var Cu=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${to(this.distance)})`:""}`}};var Au=class extends ot{static assertIsValidLabeledBox(t,n){if(ot.assertIsValidBox(t,n),!La(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var or=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new or(t.label,n)}};var yw=class extends Au{static assertIsValidPredictedBox(t,n){if(Au.assertIsValidLabeledBox(t,n),!Tu(t.score)||!Tu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function za(e){return e.detection instanceof ft}function bs(e,t){return{...e,...{detection:t}}}function bw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function ff(e){let t="";if(!e)try{e=require("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function xw(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},r=global.fetch,s=ff();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:a,fetch:r,...s}}function vw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var ww=hF(sC()),Zt;function ese(){if(!Zt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Zt}function kw(e){Zt=e}function Iw(){return vw()?kw(bw()):(0,ww.isNodejs)()?kw(xw()):null}function tse(e){if(Zt||Iw(),!Zt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=Zt.Canvas,Image:n=Zt.Image}=e;Zt.Canvas=t,Zt.Image=n,Zt.createCanvasElement=e.createCanvasElement||(()=>new t),Zt.createImageElement=e.createImageElement||(()=>new n),Zt.ImageData=e.ImageData||Zt.ImageData,Zt.Video=e.Video||Zt.Video,Zt.fetch=e.fetch||Zt.fetch,Zt.readFile=e.readFile||Zt.readFile}var nt={getEnv:ese,setEnv:kw,initialize:Iw,createBrowserEnv:bw,createFileSystem:ff,createNodejsEnv:xw,monkeyPatch:tse,isBrowser:vw,isNodejs:ww.isNodejs};Iw();function xs(e){return!nt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function bn(e){let{Canvas:t,CanvasRenderingContext2D:n}=nt.getEnv();if(e instanceof n)return e;let a=xs(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var lr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lr||(lr={}));var Cp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||lr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},vs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof vs?t.text:t,this.anchor=n,this.options=new Cp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var Nw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:lr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Cp({...i,...s})}},gf=class{constructor(t,n={}){this.box=new ot(t),this.options=new Nw(n)}draw(t){let n=bn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new vs([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function nse(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ft?a.score:za(a)?a.detection.score:void 0,s=a instanceof ft?a.box:za(a)?a.detection.box:new ot(a),i=r?`${to(r)}`:void 0;new gf(s,{label:i}).draw(e)})}function Fu(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function yf(e){return new Promise((t,n)=>{if(e instanceof nt.getEnv().Canvas||Fu(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function bf(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=nt.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function ws(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t?new cn(e.naturalWidth,e.naturalHeight):e instanceof n?new cn(e.videoWidth,e.videoHeight):new cn(e.width,e.height)}function ks({width:e,height:t}){let{createCanvasElement:n}=nt.getEnv(),a=n();return a.width=e,a.height=t,a}function _u(e,t){let{ImageData:n}=nt.getEnv();if(!(e instanceof n)&&!Fu(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||ws(e),s=ks({width:a,height:r});return e instanceof n?bn(s).putImageData(e,0,0):bn(s).drawImage(e,0,0,a,r),s}async function xf(e,t){let n=t||nt.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ra(e)?1:0),i=M(()=>e.as3D(a,r,s).toInt());return await Fi.toPixels(i,n),i.dispose(),n}function Ap(e){let{Image:t,Canvas:n,Video:a}=nt.getEnv();return e instanceof t||e instanceof n||e instanceof a}function vf(e,t,n=!1){let{Image:a,Canvas:r}=nt.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return ks({width:1,height:1});let s=ws(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=ks({width:t,height:t}),c=e instanceof r?e:_u(e),p=Math.abs(o-l)/2,d=n&&o0&&c.height>0&&bn(u).drawImage(c,d,h,o,l),u}var ur=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Fr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ra(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof nt.getEnv().Canvas?a:_u(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ir(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return fw({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,M(()=>{let a=ir(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Fe){let o=ra(i)?i:i.expandDims();return o=mf(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ja.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof nt.getEnv().Canvas)return Fi.fromPixels(vf(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>ue(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function mt(e){if(e instanceof ur)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(xs);return a.forEach((r,s)=>{if(!Ap(r)&&!Fr(r)&&!ra(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(ra(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>Ap(r)&&yf(r))),new ur(a,Array.isArray(e))}async function io(e,t){let{Canvas:n}=nt.getEnv(),a=e;if(!(e instanceof n)){let i=await mt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await xf(o)}let r=bn(a);return t.map(i=>i instanceof ft?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let c=ks({width:l,height:u});return l>0&&u>0&&bn(c).putImageData(r.getImageData(i,o,l,u),0,0),c})}async function oo(e,t){if(!Fr(e)&&!ra(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ra(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return M(()=>{let[n,a,r]=e.shape.slice(ra(e)?1:0);return t.map(o=>o instanceof ft?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:u,height:c})=>Yl(e.as3D(n,a,r),[l,o,0],[c,u,r]))})}async function Is(e,t){let{fetch:n}=nt.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function iC(e){let t=await Is(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return bf(n)}async function wf(e){return(await Is(e)).json()}async function oC(e){return new Float32Array(await(await Is(e)).arrayBuffer())}function kf(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function If(e,t){let{manifestUri:n,modelBaseUri:a}=kf(e,t),r=await wf(n);return jt.loadWeights(r,a)}function lC(e,t,n=!1){let{width:a,height:r}=n?ws(t):t;return e.width=a,e.height=r,{width:a,height:r}}var en=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof Kr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof Kr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Jn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await If(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=nt.getEnv(),{manifestUri:a,modelBaseUri:r}=kf(t,this.getDefaultModelName()),s=u=>Promise.all(u.map(c=>n(c).then(p=>p.buffer))),i=jt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof Fe))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function Dn(e,t,n){return M(()=>{let a=Pi(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function Nf(e,t,n=!1){return M(()=>{let a=qe(n?J(Et(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Fp(e,t,n=!1,a=!0){return M(()=>{let r=qe(n?J(Et(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),u=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,u))))})}function lo(e,t,n="same",a=!1){return M(()=>{let r=J(Et(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function xn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Eu(e,t){return(n,a,r,s)=>{let i=Aa(e(n*a*r*r),[r,r,n,a]),o=Ze(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Tf(e,t){return(n,a,r)=>{let s=Ca(e(n*a),[n,a]),i=Ze(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Sf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function $u(e,t){return(n,a,r)=>{let s=Aa(e(3*3*n),[3,3,n,1]),i=Aa(e(n*a),[1,1,n,a]),o=Ze(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Sf(s,i,o)}}function Du(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Sf(n,a,r)}}function qn(e,t){return(n,a,r)=>{let s=e[n];if(!eo(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function vn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function Cf(e,t){let n=Eu(e,t),a=$u(e,t);function r(i,o,l,u=!1){let c=u?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:c,conv1:p,conv2:d}}function s(i,o,l,u=!1){let{conv0:c,conv1:p,conv2:d}=r(i,o,l,u),h=a(o,o,`${l}/conv3`);return{conv0:c,conv1:p,conv2:d,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function uC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock4Params:r}=Cf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function Af(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function Ff(e,t){let n=qn(e,t),a=Af(n),r=Du(n);function s(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`);return{conv0:u,conv1:c,conv2:p}}function i(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`),d=r(`${o}/conv3`);return{conv0:u,conv1:c,conv2:p,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function cC(e){let t=[],{extractDenseBlock4Params:n}=Ff(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return xn(e,t),{params:a,paramMappings:t}}var _p=class extends en{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(255)),i=Fp(s,n.dense0,!0);return i=Fp(i,n.dense1),i=Fp(i,n.dense2),i=Fp(i,n.dense3),i=Zn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return cC(t)}extractParams(t){return uC(t)}};function Ep(e,t){return M(()=>J(ze(e,t.weights),t.bias))}function pC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=vn(e),o=Tf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function dC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return xn(e,t),{params:r,paramMappings:t}}function _f(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var $p=class extends en{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t;return Ep(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return pC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=_f(t);return this.faceFeatureExtractor.loadFromWeightMap(n),dC(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var Ef=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Er=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Ef.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Ef.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Dp=class extends $p{constructor(t=new _p){super("FaceExpressionNet",t)}forwardInput(t){return M(()=>Sa(this.runNet(t)))}async forward(t){return this.forwardInput(await mt(t))}async predictExpressions(t){let n=await mt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Er(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function $f(e){return e.expressions instanceof Er}function Mp(e,t){return{...e,...{expressions:t}}}function ase(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Er?s:$f(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),u=za(s)?s.detection.box.bottomLeft:a||new De(0,0);new vs(l.map(p=>`${p.expression} (${to(p.probability)})`),u).draw(e)})}function Ns(e){return za(e)&&e.landmarks instanceof jn&&e.unshiftedLandmarks instanceof jn&&e.alignedRect instanceof ft}function rse(e){let t=(o,l,u,c)=>Math.atan2(c-l,u-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function uo(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new ft(e.detection.score,r.rescale(s.reverse()),s),o=rse(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var Tw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},Sw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new Tw(n)}draw(t){let n=bn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof so&&(n.strokeStyle=i,n.lineWidth=s,Ar(n,this.faceLandmarks.getJawOutline()),Ar(n,this.faceLandmarks.getLeftEyeBrow()),Ar(n,this.faceLandmarks.getRightEyeBrow()),Ar(n,this.faceLandmarks.getNose()),Ar(n,this.faceLandmarks.getLeftEye(),!0),Ar(n,this.faceLandmarks.getRightEye(),!0),Ar(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=c=>{n.beginPath(),n.arc(c.x,c.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function sse(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof jn?a:Ns(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Sw(r).draw(e)})}var hC="1.0.1";function ise(e,t){let n=Eu(e,t),a=$u(e,t);function r(i,o,l){let u=a(i,o,`${l}/separable_conv0`),c=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:c,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),c=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function mC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=vn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=ise(a,n),u=s(3,32,3,"entry_flow/conv_in"),c=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:u,reduction_block_0:c,reduction_block_1:p},h={};ir(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function ose(e,t){let n=qn(e,t),a=Af(n),r=Du(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:c}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function fC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=ose(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),c={conv_in:o,reduction_block_0:l,reduction_block_1:u},p={};ir(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return xn(e,n),{params:{entry_flow:c,middle_flow:p,exit_flow:m},paramMappings:n}}function gC(e,t,n){return J(Et(e,t.filters,n,"same"),t.bias)}function Aw(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=$t(a,[3,3],[2,2],"same"),a=J(a,gC(e,t.expansion_conv,[2,2])),a}function lse(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var Fw=class extends en{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(256)),i=qe(gC(s,n.entry_flow.conv_in,[2,2]));return i=Aw(i,n.entry_flow.reduction_block_0,!1),i=Aw(i,n.entry_flow.reduction_block_1),ir(this._numMainBlocks,0,1).forEach(o=>{i=lse(i,n.middle_flow[`main_block_${o}`])}),i=Aw(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return fC(t,this._numMainBlocks)}extractParams(t){return mC(t,this._numMainBlocks)}};function yC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),r=Tf(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function bC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return xn(e,t),{params:r,paramMappings:t}}var cr;(function(e){e.FEMALE="female",e.MALE="male"})(cr||(cr={}));var Rp=class extends en{constructor(t=new Fw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t,r=Zn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Ep(r,n.fc.age).as1D(),i=Ep(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return M(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Sa(a)}})}async forward(t){return this.forwardInput(await mt(t))}async predictAgeAndGender(t){let n=await mt(t),a=await this.forwardInput(n),r=ct(a.age),s=ct(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let c=(await l.data())[0],p=(await u.data())[0],d=p>.5,h=d?cr.MALE:cr.FEMALE,m=d?p:1-p;return l.dispose(),u.dispose(),{age:c,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return yC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=_f(t);return this.faceFeatureExtractor.loadFromWeightMap(n),bC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var Pp=class extends $p{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return M(()=>{let i=(p,d)=>Dt([Cn([68],p,"float32"),Cn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return d(h,m)?Math.abs(h-m)/2:0},l=p=>o(p,(d,h)=>do(p,(d,h)=>hi(l(d),u(d))))).div(Dt(Array.from(Array(s),(p,d)=>i(r[d].width,r[d].height))))})}forwardInput(t){return M(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await mt(t))}async detectLandmarks(t){let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(await s.data()),l=o.filter((c,p)=>uf(p)),u=o.filter((c,p)=>!uf(p));return new so(Array(68).fill(0).map((c,p)=>new De(l[p],u[p])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var co=class extends Pp{constructor(t=new _p){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function xC(e){let t=[],{extractDenseBlock3Params:n}=Ff(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return xn(e,t),{params:a,paramMappings:t}}function vC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock3Params:r}=Cf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var _w=class extends en{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(255)),i=Nf(s,n.dense0,!0);return i=Nf(i,n.dense1),i=Nf(i,n.dense2),i=Zn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return xC(t)}extractParams(t){return vC(t)}};var Op=class extends Pp{constructor(t=new _w){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Ew=class extends co{};function wC(e,t){return J(B(e,t.weights),t.biases)}function $w(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Et(e,s,n,r);return o=J(o,i),o=wC(o,t.scale),a?qe(o):o}function kC(e,t){return $w(e,t,[1,1],!0)}function Dw(e,t){return $w(e,t,[1,1],!1)}function Df(e,t){return $w(e,t,[2,2],!0,"valid")}function use(e,t){function n(o,l,u){let c=e(o),p=c.length/(l*u*u);if(mw(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${c.length}, numFilters: ${l}, filterSize: ${u}`);return M(()=>Ve(Aa(c,[l,p,u,u]),[2,3,1,0]))}function a(o,l,u,c){let p=n(o,l,u),d=Ze(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/bias`}),{filters:p,bias:d}}function r(o,l){let u=Ze(e(o)),c=Ze(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:u,biases:c}}function s(o,l,u,c){let p=a(o,l,u,`${c}/conv`),d=r(l,`${c}/scale`);return{conv:p,scale:d}}function i(o,l,u,c,p=!1){let d=s((p?.5:1)*o,l,u,`${c}/conv1`),h=s(o,l,u,`${c}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function IC(e){let{extractWeights:t,getRemainingWeights:n}=vn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=use(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),u=s(9216,32,3,"conv32_3"),c=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),k=M(()=>Ve(Ca(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:c,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:k},paramMappings:a}}function cse(e,t){let n=qn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),u=a(i);return{conv:{filters:o,bias:l},scale:u}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function NC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=cse(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),u=a("conv64_1"),c=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!hw(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:u,conv64_2:c,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return xn(e,t),{params:v,paramMappings:t}}function Wa(e,t){let n=kC(e,t.conv1);return n=Dw(n,t.conv2),n=J(n,e),n=qe(n),n}function Lp(e,t){let n=Df(e,t.conv1);n=Dw(n,t.conv2);let a=Zn(e,2,2,"valid"),r=vt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=vt(o);n=Je([n,l],1);let u=[...n.shape];u[2]=1;let c=vt(u);n=Je([n,c],2)}return a=s?Je([a,r],3):a,n=J(a,n),n=qe(n),n}var po=class extends en{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(150,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(256)),i=Df(s,n.conv32_down);i=$t(i,3,2,"valid"),i=Wa(i,n.conv32_1),i=Wa(i,n.conv32_2),i=Wa(i,n.conv32_3),i=Lp(i,n.conv64_down),i=Wa(i,n.conv64_1),i=Wa(i,n.conv64_2),i=Wa(i,n.conv64_3),i=Lp(i,n.conv128_down),i=Wa(i,n.conv128_1),i=Wa(i,n.conv128_2),i=Lp(i,n.conv256_down),i=Wa(i,n.conv256_1),i=Wa(i,n.conv256_2),i=Lp(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await mt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)==null?void 0:s.some(i=>i<=0))return new Float32Array(128);let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return NC(t)}extractParams(t){return IC(t)}};function TC(e){let t=new po;return t.extractWeights(e),t}function zp(e,t){return{...e,...{descriptor:t}}}function SC(e){return typeof e.age=="number"}function Wp(e,t){return{...e,...{age:t}}}function CC(e){return(e.gender===cr.MALE||e.gender===cr.FEMALE)&&Tu(e.genderProbability)}function Bp(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function pse(e,t){function n(l,u){let c=Aa(e(3*3*l),[3,3,l,1]),p=Ze(e(l)),d=Ze(e(l)),h=Ze(e(l)),m=Ze(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:c,batch_norm_scale:p,batch_norm_offset:d,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,c,p,d){let h=Aa(e(l*u*c*c),[c,c,l,u]),m=Ze(e(u));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/${d?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,c,p){let{filters:d,bias:h}=a(l,u,c,p,!0);return{filters:d,batch_norm_offset:h}}function s(l,u,c){let p=n(l,`${c}/depthwise_conv`),d=r(l,u,1,`${c}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),c=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),k=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:k}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),u=r(256,512,3,"prediction_layer/conv_1"),c=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),k=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),w=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),S=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),A=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),F=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),D=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),R=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:k},box_predictor_3:{box_encoding_predictor:w,class_predictor:S},box_predictor_4:{box_encoding_predictor:A,class_predictor:F},box_predictor_5:{box_encoding_predictor:D,class_predictor:R}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function AC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=pse(n,t),i=r(),o=s(),u={extra_dim:dh(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:u},paramMappings:t}}function dse(e,t){let n=qn(e,t);function a(u,c,p){let d=n(`${u}/Conv2d_${c}_pointwise/weights`,4,`${p}/filters`),h=n(`${u}/Conv2d_${c}_pointwise/convolution_bn_offset`,1,`${p}/batch_norm_offset`);return{filters:d,batch_norm_offset:h}}function r(u){let c=`mobilenetv1/conv_${u}`,p=`MobilenetV1/Conv2d_${u}_depthwise`,d=`${c}/depthwise_conv`,h=`${c}/pointwise_conv`,m=n(`${p}/depthwise_weights`,4,`${d}/filters`),f=n(`${p}/BatchNorm/gamma`,1,`${d}/batch_norm_scale`),g=n(`${p}/BatchNorm/beta`,1,`${d}/batch_norm_offset`),y=n(`${p}/BatchNorm/moving_mean`,1,`${d}/batch_norm_mean`),b=n(`${p}/BatchNorm/moving_variance`,1,`${d}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",u,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(u,c){let p=n(`${u}/weights`,4,`${c}/filters`),d=n(`${u}/biases`,1,`${c}/bias`);return{filters:p,bias:d}}function o(u){let c=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),p=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:c,class_predictor:p}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function FC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=dse(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Fr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return xn(e,t),{params:s,paramMappings:t}}function ka(e,t,n){return M(()=>{let a=Et(e,t.filters,n,"same");return a=J(a,t.batch_norm_offset),Xt(a,0,6)})}var hse=.0010000000474974513;function mse(e,t,n){return M(()=>{let a=ns(e,t.filters,n,"same");return a=br(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,hse),Xt(a,0,6)})}function fse(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function _C(e,t){return M(()=>{let n,a=ka(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=fse(o);a=mse(a,s.depthwise_conv,l),a=ka(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function gse(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),c=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(c-l)*(p-u);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,c),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function EC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((c,p)=>({score:c,boxIndex:p})).filter(c=>c.score>r).sort((c,p)=>p.score-c.score),l=c=>c<=a?1:0,u=[];return o.forEach(c=>{if(u.length>=i)return;let p=c.score;for(let d=u.length-1;d>=0;--d){let h=gse(e,c.boxIndex,u[d]);if(h!==0&&(c.score*=l(h),c.score<=r))break}p===c.score&&u.push(c.boxIndex)}),u}function yse(e){let t=ct(Ve(e,[1,0])),n=[me(t[2],t[0]),me(t[3],t[1])],a=[J(t[0],xe(n[0],ce(2))),J(t[1],xe(n[1],ce(2)))];return{sizes:n,centers:a}}function bse(e,t){let{sizes:n,centers:a}=yse(e),r=ct(Ve(t,[1,0])),s=xe(B(hn(xe(r[2],ce(5))),n[0]),ce(2)),i=J(B(xe(r[0],ce(10)),n[0]),a[0]),o=xe(B(hn(xe(r[3],ce(5))),n[1]),ce(2)),l=J(B(xe(r[1],ce(10)),n[1]),a[1]);return Ve(Dt([me(i,s),me(l,o),J(i,s),J(l,o)]),[1,0])}function $C(e,t,n){return M(()=>{let a=e.shape[0],r=bse(H(qa(n.extra_dim,[a,1,1]),[-1,4]),H(e,[-1,4]));r=H(r,[a,r.shape[0]/a,4]);let s=da(Be(t,[0,0,1],[-1,-1,-1])),i=Be(s,[0,0,0],[-1,-1,1]);i=H(i,[a,i.shape[1]]);let o=ct(r),l=ct(i);return{boxes:o,scores:l}})}function ho(e,t){return M(()=>{let n=e.shape[0],a=H(lo(e,t.box_encoding_predictor),[n,-1,1,4]),r=H(lo(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function DC(e,t,n){return M(()=>{let a=ka(e,n.conv_0,[1,1]),r=ka(a,n.conv_1,[2,2]),s=ka(r,n.conv_2,[1,1]),i=ka(s,n.conv_3,[2,2]),o=ka(i,n.conv_4,[1,1]),l=ka(o,n.conv_5,[2,2]),u=ka(l,n.conv_6,[1,1]),c=ka(u,n.conv_7,[2,2]),p=ho(t,n.box_predictor_0),d=ho(e,n.box_predictor_1),h=ho(r,n.box_predictor_2),m=ho(i,n.box_predictor_3),f=ho(l,n.box_predictor_4),g=ho(c,n.box_predictor_5),y=Je([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Je([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var sa=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ts=class extends en{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(512,!1),"float32"),r=me(B(a,ce(.007843137718737125)),ce(1)),s=_C(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=DC(s.out,s.conv11,n.prediction_layer);return $C(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await mt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new sa(n),s=await mt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,k]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(A=>A*g),[w,S]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(A=>A*f);return new ft(c[x],new ro(w,v,S-w,k-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return FC(t)}extractParams(t){return AC(t)}};function Mw(e){let t=new Ts;return t.extractWeights(e),t}function MC(e){return Mw(e)}var Rw=class extends Ts{};var RC=.4,PC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],OC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],LC=[117.001,114.697,97.404],zC="tiny_yolov2_model",WC="tiny_yolov2_separable_conv_model";var Mf=e=>typeof e=="number";function Rf(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Mf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Mf(t.x)&&Mf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Mf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Mu(e){return M(()=>{let t=B(e,ce(.10000000149011612));return J(qe(me(e,t)),t)})}function $r(e,t){return M(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Et(n,t.conv.filters,[1,1],"valid"),n=me(n,t.bn.sub),n=B(n,t.bn.truediv),n=J(n,t.conv.bias),Mu(n)})}function Dr(e,t){return M(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Pi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Mu(n)})}function xse(e,t){let n=Eu(e,t);function a(i,o){let l=Ze(e(i)),u=Ze(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),c=a(o,`${l}/bn`);return{conv:u,bn:c}}let s=$u(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function BC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=vn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=xse(r,i),c;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):u(p,d,"conv0"),k=u(d,h,"conv1"),w=u(h,m,"conv2"),S=u(m,f,"conv3"),A=u(f,g,"conv4"),F=u(g,y,"conv5"),D=b?u(y,b,"conv6"):void 0,R=x?u(b,x,"conv7"):void 0,L=o(x||b||y,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),k=l(d,h,"conv1"),w=l(h,m,"conv2"),S=l(m,f,"conv3"),A=l(f,g,"conv4"),F=l(g,y,"conv5"),D=l(y,b,"conv6"),R=l(b,x,"conv7"),L=o(x,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:c,paramMappings:i}}function vse(e,t){let n=qn(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Du(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function VC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=vse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var Ba=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Pw=class extends en{constructor(t){super("TinyYolov2");Rf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=$r(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=$r(a,n.conv6),a=$r(a,n.conv7),lo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Mu(lo(t,n.conv0,"valid",!1)):Dr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Dr(a,n.conv6):a,a=n.conv7?Dr(a,n.conv7):a,lo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let r=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(ce(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await mt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ba(n),s=await mt(t),i=await this.forwardInput(s,a),o=M(()=>ct(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let c=u.map(g=>g.box),p=u.map(g=>g.score),d=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return hf(c.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new _r(p[g],d[g],h[g],c[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return VC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Pw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return BC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],c=this.config.anchors.length,[p,d,h]=M(()=>{let y=t.reshape([u,u,c,this.boxEncodingSize]),b=y.slice([0,0,0,0],[u,u,c,4]),x=y.slice([0,0,0,4],[u,u,c,1]),v=this.withClassScores?Sa(y.slice([0,0,0,5],[u,u,c,this.config.classes.length]),3):ce(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let k=(b+Su(g[y][b][x][0]))/u*o,w=(y+Su(g[y][b][x][1]))/u*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/u*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/u*l,F=k-S/2,D=w-A/2,R={row:y,col:b,anchor:x},{classScore:L,label:W}=this.withClassScores?await this.extractPredictedClass(h,R):{classScore:1,label:0};m.push({box:new ao(F,D,F+S,D+A),score:v,classScore:v*L,label:W,...R})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Ru=Pw;Ru.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var mo=class extends Ru{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:RC,classes:["face"],...t?{anchors:OC,meanRgb:LC}:{anchors:PC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?WC:zC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function UC(e,t=!0){let n=new mo(t);return n.extractWeights(e),n}var Vp=class extends Ba{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function fo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Ns(l)?r(l):l.detection),i=a||(t instanceof Fe?await oo(t,s):await io(t,s)),o=await n(i);return i.forEach(l=>l instanceof Fe&&l.dispose()),o}async function Pu(e,t,n,a,r){return fo([e],t,async s=>n(s[0]),a,r)}var GC=.4,HC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],jC=[117.001,114.697,97.404];var go=class extends Ru{constructor(){let t={withSeparableConvs:!0,iouThreshold:GC,classes:["face"],anchors:HC,meanRgb:jC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Qe={ssdMobilenetv1:new Ts,tinyFaceDetector:new go,tinyYolov2:new mo,faceLandmark68Net:new co,faceLandmark68TinyNet:new Op,faceRecognitionNet:new po,faceExpressionNet:new Dp,ageGenderNet:new Rp},Ow=(e,t)=>Qe.ssdMobilenetv1.locateFaces(e,t),qC=(e,t)=>Qe.tinyFaceDetector.locateFaces(e,t),XC=(e,t)=>Qe.tinyYolov2.locateFaces(e,t),Lw=e=>Qe.faceLandmark68Net.detectLandmarks(e),KC=e=>Qe.faceLandmark68TinyNet.detectLandmarks(e),YC=e=>Qe.faceRecognitionNet.computeFaceDescriptor(e),JC=e=>Qe.faceExpressionNet.predictExpressions(e),QC=e=>Qe.ageGenderNet.predictAgeAndGender(e),zw=e=>Qe.ssdMobilenetv1.load(e),ZC=e=>Qe.tinyFaceDetector.load(e),eA=e=>Qe.tinyYolov2.load(e),tA=e=>Qe.faceLandmark68Net.load(e),nA=e=>Qe.faceLandmark68TinyNet.load(e),aA=e=>Qe.faceRecognitionNet.load(e),rA=e=>Qe.faceExpressionNet.load(e),sA=e=>Qe.ageGenderNet.load(e),iA=zw,oA=Ow,lA=Lw;var Ww=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},zu=class extends Ww{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Mp(a,n[r]))}withAgeAndGender(){return new Ou(this,this.input)}},Wu=class extends Ww{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Mp(t,n)}withAgeAndGender(){return new Lu(this,this.input)}},xo=class extends zu{withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},vo=class extends Wu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Rr(this,this.input)}};var Bw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Bw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Wp(Bp(a,i,o),s)})}withFaceExpressions(){return new zu(this,this.input)}},Lu=class extends Bw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Pu(t,this.input,s=>Qe.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Wp(Bp(t,a,r),n)}withFaceExpressions(){return new Wu(this,this.input)}},yo=class extends Ou{withFaceExpressions(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},bo=class extends Lu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptor(){return new Rr(this,this.input)}};var Up=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Mr=class extends Up{async run(){let t=await this.parentTask;return(await fo(t,this.input,a=>Promise.all(a.map(r=>Qe.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>zp(t[r],a))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}},Rr=class extends Up{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return zp(t,n)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}};var Gp=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Qe.faceLandmark68TinyNet:Qe.faceLandmark68Net}},Hp=class extends Gp{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Fe?await oo(this.input,n):await io(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Fe&&s.dispose()),t.map((s,i)=>uo(s,r[i]))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},jp=class extends Gp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Fe?await oo(this.input,[n]):await io(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Fe&&s.dispose()),uo(t,r)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Rr(this,this.input)}};var qp=class extends ia{constructor(t,n=new sa){super();this.input=t;this.options=n}},Bu=class extends qp{async run(){let{input:t,options:n}=this,a;if(n instanceof Vp)a=Qe.tinyFaceDetector.locateFaces(t,n);else if(n instanceof sa)a=Qe.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ba)a=Qe.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>bs({},a)))})}withFaceLandmarks(t=!1){return new Hp(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new zu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}},Xp=class extends qp{async run(){let t=await new Bu(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?bs({},n):void 0)})}withFaceLandmarks(t=!1){return new jp(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}};function uA(e,t=new sa){return new Xp(e,t)}function Kp(e,t=new sa){return new Bu(e,t)}async function Vw(e,t){return Kp(e,new sa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function cA(e,t={}){return Kp(e,new Ba(t)).withFaceLandmarks().withFaceDescriptors()}var pA=Vw;function Pf(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Yp=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof or)return i;if(i instanceof Float32Array)return new or(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new or(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Pf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Cu(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>or.fromJSON(a));return new Yp(n,t.distanceThreshold)}};function dA(e){let t=new go;return t.extractWeights(e),t}function Uw(e,t){let{width:n,height:a}=new cn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Uw(r,{width:n,height:a}));if(Ns(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return uo(bs(e,r),s)}return za(e)?bs(e,e.detection.forSize(n,a)):e instanceof jn||e instanceof ft?e.forSize(n,a):e}var kse=typeof process!="undefined",Ise=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",hA={faceapi:hC,node:kse,browser:Ise};return wse;})(); + `}};function Xee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,c=E.getAxesPermutation([u],o),p=r;c!=null&&(p=En({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(p),u=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,u,i),h=I.sizeFromShape([p.shape[u]]),m=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=uh(r.dtype),g=(v,k,w,S,A)=>{let F=v.shape[0],D=v.shape[1],R=E.segment_util.segOpComputeOptimalWindowSize(D,A),L={windowSize:R,inSize:D,batchSize:F,numSegments:A},W=new qee(L,k),U=n.compileAndRun(W,[v,w],S);if(l.push(U),U.shape[1]===A)return U;let G=sS({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),X=lS({inputs:{x:G},backend:n,attrs:{reps:[D/R]}});return l.push(G),l.push(X),g(U,k,X,S,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=ye({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(c!=null){l.push(b);let v=E.getUndoAxesPermutation(c);x=En({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Kee={kernelName:bc,backendName:"webgl",kernelFunc:Xee},Yee=[zJ,VJ,CK,FK,$K,RK,OK,WK,VK,GK,XK,YK,ZK,n7,u7,s7,d7,g7,m7,v7,k7,N7,A7,R7,O7,U7,H7,K7,Q7,lK,nY,dY,mY,iY,bY,vY,gY,IY,SY,FY,EY,DY,PY,VY,GY,LY,qY,YY,e9,r9,l9,p9,d9,h9,f9,y9,x9,w9,I9,C9,E9,D9,R9,L9,V9,j9,Y9,oK,Q9,tY,tJ,rJ,oJ,cK,pJ,fJ,yJ,NJ,wJ,AJ,EJ,RJ,GJ,QJ,YJ,nQ,rQ,iQ,XJ,lQ,cQ,mQ,bQ,kQ,_Q,fK,$Q,RQ,LQ,BQ,z7,GQ,jQ,XQ,JQ,tZ,dK,aZ,rZ,W7,SQ,oZ,fZ,pZ,yK,xZ,kZ,TZ,AZ,$Z,MZ,OZ,WZ,VZ,HZ,XZ,JZ,eee,aee,iee,D7,AQ,uee,pee,hee,fee,bee,wee,Iee,Tee,Aee,CQ,NK,Eee,Mee,Oee,zee,Vee,TK,Gee,jee,Kee,HQ];for(let e of Yee)vc(e);var Jee="3.3.0",Qee={"tfjs-core":c1,"tfjs-backend-cpu":bG,"tfjs-backend-webgl":iK,"tfjs-data":ON,"tfjs-layers":Im,"tfjs-converter":EN,tfjs:Jee},Hn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Hn||(Hn={}));var Ip;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(Ip||(Ip={}));var uS;function Zee(e){uS=e.wasm.cwrap(Ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function ete(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let A=n.dataIdMap.get(i.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Ip[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),k=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return uS(d,w,r.shape.length,h,S,s.shape.length,l,u,g,m,f,p||0,k),v}var tte={kernelName:Ii,backendName:"wasm",setupFunc:Zee,kernelFunc:ete};function $n(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return I.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var nte=$n(Po);function yn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:c}=l,p=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,m=E.assertAndGetBroadcastShape(u.shape,c.shape),f=o.makeOutput(m,h);if(I.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,u.shape.length,d,y,c.shape.length,Hn[u.dtype],b);if(t&&u.dtype==="float32")return x(),f;let v=E.getBroadcastDims(u.shape,m),k=E.getBroadcastDims(c.shape,m),w=v.every((A,F)=>A===F),S=k.every((A,F)=>A===F);if(w&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var ate=!0,rte=yn(Hr,ate),cS;function ste(e){cS=e.wasm.cwrap(Es,null,["array","number","number","number"])}function ite(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(I.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return cS(s,r.length,Hn[a.dtype],i),a}var ote={kernelName:Es,backendName:"wasm",setupFunc:ste,kernelFunc:ite};function af(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var lte={kernelName:Xs,backendName:"wasm",kernelFunc:af},pS;function ute(e){pS=e.wasm.cwrap(ki,null,["number","array","number","number","number","array","number"])}function rf(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=pte(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var dte={kernelName:ki,backendName:"wasm",kernelFunc:rf,setupFunc:ute};function Nu(e,t,n){let a=e.shape,r=e.shape.length,s=I.parseAxisParam(t,a),i=s,o=E.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let c=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var xte={kernelName:vl,backendName:"wasm",kernelFunc:Oa},mS;function vte(e){mS=e.wasm.cwrap(Ms,null,["number","array","number","number","array","number","number","number","number"])}function wte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,c=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=I.sizeFromShape(m),y=I.sizeFromShape(f),b=g===y||g===1||y===1;I.assert(l>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);I.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,c,d]:[g,d,c],k=o?[y,h,p]:[y,p,h],w=Oa({inputs:{x:r},backend:n,attrs:{shape:v}}),S=Oa({inputs:{x:s},backend:n,attrs:{shape:k}}),A=n.dataIdMap.get(w.dataId).id,F=n.dataIdMap.get(S.dataId).id,D=i?w.shape[2]:w.shape[1],R=o?S.shape[1]:S.shape[2],L=Math.max(g,y),W=n.makeOutput([L,D,R],w.dtype),U=n.dataIdMap.get(W.dataId).id,G=new Uint8Array(new Int32Array(w.shape).buffer),X=new Uint8Array(new Int32Array(S.shape).buffer);return mS(A,G,w.shape.length,F,X,S.shape.length,i,o,U),n.disposeData(w.dataId),n.disposeData(S.dataId),W.shape=x,W}var kte={kernelName:Ms,backendName:"wasm",setupFunc:vte,kernelFunc:wte};function sf(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var Ite={kernelName:Rs,backendName:"wasm",kernelFunc:sf},Nte=$n(Ps),fS;function Tte(e){fS=e.wasm.cwrap(jr,null,["number","number","number","number"])}function Ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return fS(o,s,i,u),l}var Cte={kernelName:jr,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste};function gS(e){let{inputs:t,backend:n}=e,a=I.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>I.sizeFromShape(h.shape)>0);if(s.length===1)return af({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(I.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=I.sizeFromShape(x.shape.slice(a));return Oa({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=_v(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=I.sizeFromShape(s[0].shape.slice(0,a)),u=0,c=s.map(h=>{let m=I.sizeFromShape(h.shape.slice(a));return u+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=E.getAxesPermutation([s],l),c=r;u!==null&&(c=rf({inputs:{x:r},attrs:{perm:u},backend:n}));let p=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],m=n.dataIdMap.get(c.dataId).id,f=n.dataIdMap.get(d.dataId).id;vS(m,i?1:0,o?1:0,h,f,Hn[r.dtype]);let g=d;if(u!==null){let y=E.getUndoAxesPermutation(u);g=rf({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Bte={kernelName:Ws,backendName:"wasm",setupFunc:zte,kernelFunc:Wte},wS;function Vte(e){wS=e.wasm.cwrap(qo,null,["number","number","number","array","number","array","array","number","number"])}function Ute(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;I.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=u*s,h=c/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(I.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(I.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return wS(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var Gte={kernelName:qo,backendName:"wasm",setupFunc:Vte,kernelFunc:Ute},kS;function Hte(e){kS=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=E.computeConv2DInfo(r.shape,s.shape,l,d,c,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,k=h.dilationWidth,w=h.strideHeight,S=h.strideWidth,A=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=a.makeOutput(h.outShape,"float32"),L=a.dataIdMap.get(R.dataId).id;return kS(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,D,v,k,w,S,A,F,L),R}var qte={kernelName:Bs,backendName:"wasm",setupFunc:Hte,kernelFunc:jte},Xte=!1,Kte=yn(Yo,Xte,"bool"),Yte=$n(Us);function lw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(I.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Jte={kernelName:Jo,backendName:"wasm",kernelFunc:lw};function Qte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Zte={kernelName:lc,backendName:"wasm",kernelFunc:Qte},IS;function ene(e){IS=e.wasm.cwrap(Zo,null,["number","number","number","number","number","number"])}function tne(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,c]=a.shape;return IS(s,o,l,u,c,i),r}var nne={kernelName:Zo,backendName:"wasm",kernelFunc:tne,setupFunc:ene},ane=$n(Gs),rne=!1,sne=yn(Hs,rne),NS;function ine(e){NS=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number"])}function one(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(I.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return NS(c,p,d,h,m,r,g),f}var lne={kernelName:js,backendName:"wasm",setupFunc:ine,kernelFunc:one},TS;function une(e){TS=e.wasm.cwrap(Ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d),g=Ip[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return TS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var pne={kernelName:Ni,backendName:"wasm",setupFunc:une,kernelFunc:cne},SS;function dne(e){SS=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!0),g=Ip[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return SS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var mne={kernelName:Ti,backendName:"wasm",setupFunc:dne,kernelFunc:hne},CS;function fne(e){CS=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number"])}function gne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ay.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let c=r.shape,p=c[c.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return CS(d,Hn[a.dtype],h,i,p,o,m,f),u}var yne={kernelName:tl,backendName:"wasm",setupFunc:fne,kernelFunc:gne},AS;function bne(e){AS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function xne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=I.parseAxisParam(i,r.shape)[0],u=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Oa({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),p=I.sizeFromShape(s.shape),d=Oa({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],m=t.makeOutput(h,r.dtype);if(I.sizeFromShape(r.shape)===0)return m;let f=c.shape.length-1,g=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(I.computeStrides(c.shape)).buffer),v=new Uint8Array(new Int32Array(I.computeStrides(h)).buffer);return AS(g,Hn[r.dtype],x,f,y,u.batchSize,v,b),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var vne={kernelName:el,backendName:"wasm",setupFunc:bne,kernelFunc:xne},wne=!1,kne=yn(nl,wne,"bool"),Ine=!1,Nne=yn(qs,Ine,"bool"),FS;function Tne(e){FS=e.wasm.cwrap(Ks,null,["number","number","number"])}function Sne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(I.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;FS(r,n,i)}return s}var Cne={kernelName:Ks,backendName:"wasm",setupFunc:Tne,kernelFunc:Sne},Ane=!1,Fne=yn(il,Ane,"bool"),_ne=!1,Ene=yn(ol,_ne,"bool"),$ne=$n(Ys),Dne=!1,Mne=yn(ul,Dne,"bool"),_S;function Rne(e){_S=e.wasm.cwrap(Js,null,["number, number, number"])}function Pne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:p,inputWasTransposed:d}=Nu(i,r,t);if(d){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",c,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,c),g=I.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(I.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;_S(o,g,b)}if(d&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var One={kernelName:Js,backendName:"wasm",setupFunc:Rne,kernelFunc:Pne},Lne=!1,zne=yn(Qs,Lne),ES;function Wne(e){ES=e.wasm.cwrap(Zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=E.computePool2DInfo(r.shape,i,o,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,b=c.dilationWidth,x=c.strideHeight,v=c.strideWidth,k=c.inChannels,w=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(c.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return ES(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,k,w,A),S}var Vne={kernelName:Zs,backendName:"wasm",setupFunc:Wne,kernelFunc:Bne},$S;function Une(e){$S=e.wasm.cwrap(ei,null,["number, number, number"])}function Gne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(u=c,l=v,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=sf({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(I.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;$S(l,y,v)}if(h&&t.disposeData(c.dataId),s){let v=E.expandShapeToKeepDim(x.shape,d);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var Hne={kernelName:ei,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},DS;function jne(e){DS=e.wasm.cwrap(ti,null,["number, number, number"])}function qne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x)}let m=u.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(u.shape,p),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;DS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Xne={kernelName:ti,backendName:"wasm",setupFunc:jne,kernelFunc:qne},Kne=!1,Yne=yn(ni,Kne),Jne=!0,Qne=yn(ai,Jne),Zne=$n(pl);function uw(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var MS;function eae(e){MS=e.wasm.cwrap(hl,"number",["number","number","number","number","number"])}function tae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=MS(u,c,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=uw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var nae={kernelName:hl,backendName:"wasm",setupFunc:eae,kernelFunc:tae},RS;function aae(e){RS=e.wasm.cwrap(ml,"number",["number","number","number","number","number","bool"])}function rae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=RS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var sae={kernelName:ml,backendName:"wasm",setupFunc:aae,kernelFunc:rae},PS;function iae(e){PS=e.wasm.cwrap(fl,"number",["number","number","number","number","number","number"])}function oae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=PS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var lae={kernelName:fl,backendName:"wasm",setupFunc:iae,kernelFunc:oae},uae=!1,cae=yn(dl,uae,"bool"),OS;function pae(e){OS=e.wasm.cwrap(ri,null,["number","number","number","number","number"])}function dae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(r.dataId).id;return OS(c,s,i,o,u),l}var hae={kernelName:ri,backendName:"wasm",setupFunc:pae,kernelFunc:dae};function mae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var fae={kernelName:gl,backendName:"wasm",kernelFunc:mae};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return lw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let p=lw({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),u=gS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),u}var yae={kernelName:yl,backendName:"wasm",kernelFunc:gae},LS;function bae(e){LS=e.wasm.cwrap(si,null,["number","array","number","number","array","array","number","number"])}function xae(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return LS(i,u,t.shape.length,Hn[t.dtype],d,h,r,l),o}var vae={kernelName:si,backendName:"wasm",kernelFunc:xae,setupFunc:bae},wae=!1,kae=yn(ii,wae),zS;function Iae(e){zS=e.wasm.cwrap(oi,null,["number","number","number"])}function Nae(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return zS(s,i,l),o}var Tae={kernelName:oi,backendName:"wasm",setupFunc:Iae,kernelFunc:Nae},WS;function Sae(e){WS=e.wasm.cwrap(bl,null,["number","number","number","number"])}function Cae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;WS(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Aae={kernelName:bl,backendName:"wasm",setupFunc:Sae,kernelFunc:Cae},Fae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Dv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},_ae={kernelName:mc,backendName:"wasm",kernelFunc:Fae},Eae=!0,$ae=yn(Vs,Eae),Dae=$n(li),Mae=$n(ci),BS;function Rae(e){BS=e.wasm.cwrap(ui,null,["number","number","number","number","number","number","number","number","number","number"])}function Pae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[c,p,d,h]=r.shape,m=[c,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=sf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(I.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return BS(y,c,p,d,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var Oae={kernelName:ui,backendName:"wasm",setupFunc:Rae,kernelFunc:Pae},VS;function Lae(e){VS=e.wasm.cwrap(pi,null,["number","array","number","array","number","number"])}function zae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=I.parseAxisParam(s,r.shape);if(r.shape.length===0)return af({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);VS(l,c,i.length,p,r.shape.length,u);let d=Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Wae={kernelName:pi,backendName:"wasm",kernelFunc:zae,setupFunc:Lae},US;function Bae(e){US=e.wasm.cwrap(Ml,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Vae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=E.getImageCenter(o,d,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return US(u,p,d,h,m,s,f,g,v,x.length,c),l}var Uae={kernelName:Ml,backendName:"wasm",kernelFunc:Vae,setupFunc:Bae},Gae=$n(di),Hae=$n(hi),GS;function jae(e){GS=e.wasm.cwrap(wl,null,["number","number","number","number","number","number","array","number","number"])}function qae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(I.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=Fy.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return GS(h,m,Hn[s.dtype],l,u,c,f,d,g),o}var Xae={kernelName:wl,backendName:"wasm",setupFunc:jae,kernelFunc:qae},HS;function Kae(e){HS=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Yae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(u.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:I.sizeFromShape(r.shape.slice(1));return HS(i,o,l,h,c),u}var Jae={kernelName:kl,backendName:"wasm",kernelFunc:Yae,setupFunc:Kae},jS;function Qae(e){jS=e.wasm.cwrap(fi,null,["number","number"])}function Zae(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return I.sizeFromShape(r.shape)===0||jS(a,s),r}var ere={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Qae,kernelFunc:Zae},tre=$n(mi);function of(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=dn.parseSliceParams(t,n,a),o=dn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),c=I.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(o){let m=dn.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=l.slice(m,m+I.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+I.sizeFromShape(i))),u}if(t.dtype==="string"){let m=zm(l,s,i,t.shape,t.dtype);return p.stringBytes=m,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)nre(l,c[0],d,s,i);else if(h===3)are(l,c[0],c[1],d,s,i);else if(h===4)rre(l,c[0],c[1],c[2],d,s,i);else{let m=zm(l,s,i,t.shape,t.dtype);d.set(m)}return u}function nre(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u{let d=[...c];d[o]=p;let h=of({inputs:{x:r},attrs:{begin:u,size:d},backend:a});return u[o]+=p,h})}var cre={kernelName:Al,backendName:"wasm",kernelFunc:ure},pre=$n(gi),dre=$n(yc),hre=!0,mre=yn(xi,hre),XS;function fre(e){XS=e.wasm.cwrap(Xr,null,["number","number","number"])}function gre(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return XS(i,r,l),o}var yre={kernelName:Xr,backendName:"wasm",setupFunc:fre,kernelFunc:gre},KS;function bre(e){KS=e.wasm.cwrap(Fl,null,["number","array","number","array","array","array","array","array","number","number"])}function xre(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=a,h=E.slice_util.maskToAxes(c);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let m=r.shape.length-s.length,f=E.slice_util.maskToAxes(p),g=r.shape.slice();f.forEach(D=>{s[D]=0,i[D]=1,g.splice(D,0,1)});let y=Oa({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:b,end:x,strides:v}=E.slice_util.getNormalizedAxes(y.shape,h,m,s,i,o,l,u,c);s=b,i=x,o=v;let k=E.slice_util.maskToAxes(d);k.forEach(D=>{i[D]=s[D]+1,o[D]=1});let w=E.slice_util.computeOutShape(s,i,o),S=w.filter((D,R)=>k.indexOf(R)===-1);if(o.every(D=>D===1)){let D=of({inputs:{x:y},attrs:{begin:s,size:w},backend:t});t.disposeData(y.dataId);let R=Oa({inputs:{x:D},attrs:{shape:S},backend:t});return t.disposeData(D.dataId),R}let A=t.makeOutput(S,"float32");if(!S.some(D=>D===0)){let D=t.dataIdMap.get(y.dataId).id,R=new Uint8Array(new Int32Array(I.computeStrides(y.shape)).buffer),L=new Uint8Array(new Int32Array(s).buffer),W=new Uint8Array(new Int32Array(i).buffer),U=new Uint8Array(new Int32Array(o).buffer),G=new Uint8Array(new Int32Array(S).buffer),X=new Uint8Array(new Int32Array(I.computeStrides(S)).buffer),j=t.dataIdMap.get(A.dataId).id;KS(D,R,y.shape.length,L,W,U,G,X,S.length,j)}t.disposeData(y.dataId);let F=Oa({inputs:{x:A},attrs:{shape:S},backend:t});return t.disposeData(A.dataId),F}var vre={kernelName:Fl,backendName:"wasm",setupFunc:bre,kernelFunc:xre},wre=!0,kre=yn(vi,wre),YS;function Ire(e){YS=e.wasm.cwrap(yi,null,["number, number, number"])}function Nre(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Nu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;YS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Tre={kernelName:yi,backendName:"wasm",setupFunc:Ire,kernelFunc:Nre},Sre=$n(wi),JS;function Cre(e){JS=e.wasm.cwrap(qr,null,["number","array","number","array","number","number"])}function Are(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let d=0;d{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,a.dtype),c=t.dataIdMap.get(u.dataId).id,p=t.makeOutput(l,"int32"),d=t.dataIdMap.get(p.dataId).id;return QS(i,o,a.shape.length,Hn[a.dtype],r,s,c,d),[u,p]},$re={kernelName:El,backendName:"wasm",setupFunc:_re,kernelFunc:Ere};function Dre(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h({dataId:h,dtype:m,shape:l}))}var Mre={kernelName:$l,backendName:"wasm",kernelFunc:Dre};function Rre(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var Pre={kernelName:Dl,backendName:"wasm",kernelFunc:Rre},Ore=[nte,rte,ote,fte,bte,kte,Ite,Nte,Cte,Ate,Ete,Mte,Rte,Lte,Bte,Gte,qte,Kte,Yte,Jte,Zte,nne,ane,sne,tte,lne,pne,mne,yne,vne,kne,Nne,lte,Cne,Fne,Ene,$ne,Mne,One,zne,Vne,Hne,Xne,Yne,Qne,Zne,nae,sae,lae,cae,hae,fae,yae,vae,kae,Tae,Aae,_ae,$ae,Dae,Mae,xte,Oae,Wae,Uae,Hae,Gae,Xae,Jae,ere,tre,sre,lre,cre,pre,dre,mre,yre,vre,kre,Tre,Sre,Fre,$re,dte,Mre,Pre];for(let e of Ore)vc(e);var cw=Z();cw.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));cw.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(cw.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var ZS=Do(XF()),Lre='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',zre=Do(KF()),eC=class extends Zu{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new kd(this,Ha())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=I.now();return e(),{kernelMs:I.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=I.sizeFromShape(n),o=i*I.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:a,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let s=this.wasm.HEAPU8.slice(t,t+I.sizeFromShape(a)*I.bytesPerElement(n));return Wre(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=I.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=I.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function Bre(e){return(t,n)=>(I.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance)})})}),{})}function tC(e,t,n){if(lf!=null)return lf;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),Np!=null&&Np[a]!=null?Np[a]:n+a}async function Vre(){let[e,t]=await Promise.all([Z().getAsync("WASM_HAS_SIMD_SUPPORT"),Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=Lre,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return o.endsWith(".wasm")?tC(e,t,Tp!=null?Tp:l):l+o},pw&&(r.instantiateWasm=Bre(tC(e,t,Tp!=null?Tp:"")));let s=!1;r.onAbort=()=>{s||Sp||(Sp=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&lf==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+ZS.default.toString()],{type:"text/javascript"}),i=(0,ZS.default)(r)):i=(0,zre.default)(r),i.then(o=>{s=!0,Sp=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function Wre(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var Ure=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],lf=null,Tp=null,Np={},Sp=!1,pw=!1;function Gre(e,t=!1){if(Ry("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Sp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");lf=e,pw=t}function Hre(e,t=!1){if(Sp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Tp=e;else{Np=e;let n=Ure.filter(a=>Np[a]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}pw=t}var jre="3.3.0",qre=2;fh("wasm",async()=>{let{wasm:e}=await Vre();return new eC(e)},qre);var Cw={};Ju(Cw,{AnchorPosition:()=>lr,DrawBox:()=>gf,DrawBoxOptions:()=>Nw,DrawFaceLandmarks:()=>Sw,DrawFaceLandmarksOptions:()=>Tw,DrawTextField:()=>vs,DrawTextFieldOptions:()=>Cp,drawContour:()=>Ar,drawDetections:()=>nse,drawFaceExpressions:()=>ase,drawFaceLandmarks:()=>sse});function Ar(e,t,n=!1){if(e.beginPath(),t.slice(1).forEach(({x:a,y:r},s)=>{let i=t[s];e.moveTo(i.x,i.y),e.lineTo(a,r)}),n){let a=t[t.length-1],r=t[0];if(!a||!r)return;e.moveTo(a.x,a.y),e.lineTo(r.x,r.y)}e.stroke()}var dw={};Ju(dw,{computeReshapedDimensions:()=>fw,getCenterPoint:()=>no,isDimensions:()=>cf,isEven:()=>uf,isFloat:()=>mw,isTensor:()=>eo,isTensor1D:()=>Xre,isTensor2D:()=>hw,isTensor3D:()=>Fr,isTensor4D:()=>ra,isValidNumber:()=>La,isValidProbablitiy:()=>Tu,range:()=>ir,round:()=>to});var cn=class{constructor(t,n){if(!La(t)||!La(n))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:n})}`);this._width=t,this._height=n}get width(){return this._width}get height(){return this._height}reverse(){return new cn(1/this.width,1/this.height)}};function eo(e,t){return e instanceof Fe&&e.shape.length===t}function Xre(e){return eo(e,1)}function hw(e){return eo(e,2)}function Fr(e){return eo(e,3)}function ra(e){return eo(e,4)}function mw(e){return e%1!=0}function uf(e){return e%2==0}function to(e,t=2){let n=10**t;return Math.floor(e*n)/n}function cf(e){return e&&e.width&&e.height}function fw({width:e,height:t},n){let a=n/Math.max(t,e);return new cn(Math.round(e*a),Math.round(t*a))}function no(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function ir(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function La(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function Tu(e){return La(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var ot=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(La)}static assertIsValidBox(t,n,a=!1){if(!ot.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!a&&(t.width<0||t.height<0))throw new Error(`${n} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,n=!0){let a=t||{},r=[a.left,a.top,a.right,a.bottom].every(La),s=[a.x,a.y,a.width,a.height].every(La);if(!s&&!r)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(a)}`);let[i,o,l,u]=s?[a.x,a.y,a.width,a.height]:[a.left,a.top,a.right-a.left,a.bottom-a.top];ot.assertIsValidBox({x:i,y:o,width:l,height:u},"Box.constructor",n),this._x=i,this._y=o,this._width=l,this._height=u}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new De(this.left,this.top)}get topRight(){return new De(this.right,this.top)}get bottomLeft(){return new De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ot({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ot({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return an&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),u<1&&(l=2-u,u=1),c<1&&(l=2-c,c=1),{dy:i,edy:l,dx:s,edx:o,y:c,ey:d,x:u,ex:p,w:a,h:r}}calibrate(t){return new ot({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var ao=class extends ot{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var _r=class{constructor(t,n,a,r,s){this._imageDims=new cn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ot(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ot(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new _r(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var ft=class extends _r{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new ft(a,r,s)}};function pf(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function df(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;ul[c]<=n)}return s}function wa(e,t){return M(()=>{let[n,a,r]=t,s=Cn([...e.shape.slice(0,3),1],n,"float32"),i=Cn([...e.shape.slice(0,3),1],a,"float32"),o=Cn([...e.shape.slice(0,3),1],r,"float32"),l=Je([s,i,o],3);return me(e,l)})}function mf(e,t=!1){return M(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,Cn(h,0,"float32")},l=o(s),u=r-l.shape[i],p=[t&&u?o(u):null,e,l].filter(d=>!!d).map(d=>ue(d,"float32"));return Je(p,i)})}function nC(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Su(e){return 1/(1+Math.exp(-e))}function aC(e){return Math.log(e/(1-e))}var ro=class extends ot{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Kre=.5,Yre=.43,Jre=.45,jn=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new cn(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new De(r,s)).add(a))}get shift(){return new De(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new De(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new De(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof ft?t.box.floor():new ot(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/Jre),l=no(t),u=Math.floor(Math.max(0,l.x-Kre*o)),c=Math.floor(Math.max(0,l.y-Yre*o));return new ro(u,c,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+c))}alignMinBbox(t){let n=df(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var gw=class extends jn{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],no([t[3],t[4]])]}};var so=class extends jn{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(no)}};var Cu=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${to(this.distance)})`:""}`}};var Au=class extends ot{static assertIsValidLabeledBox(t,n){if(ot.assertIsValidBox(t,n),!La(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var or=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new or(t.label,n)}};var yw=class extends Au{static assertIsValidPredictedBox(t,n){if(Au.assertIsValidLabeledBox(t,n),!Tu(t.score)||!Tu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function za(e){return e.detection instanceof ft}function bs(e,t){return{...e,...{detection:t}}}function bw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function ff(e){let t="";if(!e)try{e=require("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function xw(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},a=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},r=global.fetch,s=ff();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:a,fetch:r,...s}}function vw(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var ww=hF(sC()),Zt;function ese(){if(!Zt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Zt}function kw(e){Zt=e}function Iw(){return vw()?kw(bw()):(0,ww.isNodejs)()?kw(xw()):null}function tse(e){if(Zt||Iw(),!Zt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=Zt.Canvas,Image:n=Zt.Image}=e;Zt.Canvas=t,Zt.Image=n,Zt.createCanvasElement=e.createCanvasElement||(()=>new t),Zt.createImageElement=e.createImageElement||(()=>new n),Zt.ImageData=e.ImageData||Zt.ImageData,Zt.Video=e.Video||Zt.Video,Zt.fetch=e.fetch||Zt.fetch,Zt.readFile=e.readFile||Zt.readFile}var nt={getEnv:ese,setEnv:kw,initialize:Iw,createBrowserEnv:bw,createFileSystem:ff,createNodejsEnv:xw,monkeyPatch:tse,isBrowser:vw,isNodejs:ww.isNodejs};Iw();function xs(e){return!nt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function bn(e){let{Canvas:t,CanvasRenderingContext2D:n}=nt.getEnv();if(e instanceof n)return e;let a=xs(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var lr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lr||(lr={}));var Cp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||lr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},vs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof vs?t.text:t,this.anchor=n,this.options=new Cp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var Nw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:lr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Cp({...i,...s})}},gf=class{constructor(t,n={}){this.box=new ot(t),this.options=new Nw(n)}draw(t){let n=bn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new vs([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function nse(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ft?a.score:za(a)?a.detection.score:void 0,s=a instanceof ft?a.box:za(a)?a.detection.box:new ot(a),i=r?`${to(r)}`:void 0;new gf(s,{label:i}).draw(e)})}function Fu(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function yf(e){return new Promise((t,n)=>{if(e instanceof nt.getEnv().Canvas||Fu(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function bf(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=nt.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function ws(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t?new cn(e.naturalWidth,e.naturalHeight):e instanceof n?new cn(e.videoWidth,e.videoHeight):new cn(e.width,e.height)}function ks({width:e,height:t}){let{createCanvasElement:n}=nt.getEnv(),a=n();return a.width=e,a.height=t,a}function _u(e,t){let{ImageData:n}=nt.getEnv();if(!(e instanceof n)&&!Fu(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||ws(e),s=ks({width:a,height:r});return e instanceof n?bn(s).putImageData(e,0,0):bn(s).drawImage(e,0,0,a,r),s}async function xf(e,t){let n=t||nt.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ra(e)?1:0),i=M(()=>e.as3D(a,r,s).toInt());return await Fi.toPixels(i,n),i.dispose(),n}function Ap(e){let{Image:t,Canvas:n,Video:a}=nt.getEnv();return e instanceof t||e instanceof n||e instanceof a}function vf(e,t,n=!1){let{Image:a,Canvas:r}=nt.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return ks({width:1,height:1});let s=ws(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=ks({width:t,height:t}),c=e instanceof r?e:_u(e),p=Math.abs(o-l)/2,d=n&&o0&&c.height>0&&bn(u).drawImage(c,d,h,o,l),u}var ur=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Fr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ra(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof nt.getEnv().Canvas?a:_u(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ir(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return fw({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,M(()=>{let a=ir(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Fe){let o=ra(i)?i:i.expandDims();return o=mf(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ja.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof nt.getEnv().Canvas)return Fi.fromPixels(vf(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>ue(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function mt(e){if(e instanceof ur)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(xs);return a.forEach((r,s)=>{if(!Ap(r)&&!Fr(r)&&!ra(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(ra(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>Ap(r)&&yf(r))),new ur(a,Array.isArray(e))}async function io(e,t){let{Canvas:n}=nt.getEnv(),a=e;if(!(e instanceof n)){let i=await mt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await xf(o)}let r=bn(a);return t.map(i=>i instanceof ft?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let c=ks({width:l,height:u});return l>0&&u>0&&bn(c).putImageData(r.getImageData(i,o,l,u),0,0),c})}async function oo(e,t){if(!Fr(e)&&!ra(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ra(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return M(()=>{let[n,a,r]=e.shape.slice(ra(e)?1:0);return t.map(o=>o instanceof ft?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).map(({x:o,y:l,width:u,height:c})=>Yl(e.as3D(n,a,r),[l,o,0],[c,u,r]))})}async function Is(e,t){let{fetch:n}=nt.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function iC(e){let t=await Is(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return bf(n)}async function wf(e){return(await Is(e)).json()}async function oC(e){return new Float32Array(await(await Is(e)).arrayBuffer())}function kf(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function If(e,t){let{manifestUri:n,modelBaseUri:a}=kf(e,t),r=await wf(n);return jt.loadWeights(r,a)}function lC(e,t,n=!1){let{width:a,height:r}=n?ws(t):t;return e.width=a,e.height=r,{width:a,height:r}}var en=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return n[a]}reassignParamFromPath(t,n){let{obj:a,objProp:r}=this.traversePropertyPath(t);a[r].dispose(),a[r]=n}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof Kr)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof Kr))}variable(){this.getFrozenParams().forEach(({path:t,tensor:n})=>{this.reassignParamFromPath(t,n.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:n})=>{let a=Jn(n.dataSync());n.dispose(),this.reassignParamFromPath(t,a)})}dispose(t=!0){this.getParamList().forEach(n=>{if(t&&n.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${n.path}`);n.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,n)=>t.concat(n)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await If(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=nt.getEnv(),{manifestUri:a,modelBaseUri:r}=kf(t,this.getDefaultModelName()),s=u=>Promise.all(u.map(c=>n(c).then(p=>p.buffer))),i=jt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof Fe))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function Dn(e,t,n){return M(()=>{let a=Pi(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=J(a,t.bias),a})}function Nf(e,t,n=!1){return M(()=>{let a=qe(n?J(Et(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Dn(e,t.conv0,[2,2])),r=Dn(a,t.conv1,[1,1]),s=qe(J(a,r)),i=Dn(s,t.conv2,[1,1]);return qe(J(a,J(r,i)))})}function Fp(e,t,n=!1,a=!0){return M(()=>{let r=qe(n?J(Et(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):Dn(e,t.conv0,a?[2,2]:[1,1])),s=Dn(r,t.conv1,[1,1]),i=qe(J(r,s)),o=Dn(i,t.conv2,[1,1]),l=qe(J(r,J(s,o))),u=Dn(l,t.conv3,[1,1]);return qe(J(r,J(s,J(o,u))))})}function lo(e,t,n="same",a=!1){return M(()=>{let r=J(Et(e,t.filters,[1,1],n),t.bias);return a?qe(r):r})}function xn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Eu(e,t){return(n,a,r,s)=>{let i=Aa(e(n*a*r*r),[r,r,n,a]),o=Ze(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Tf(e,t){return(n,a,r)=>{let s=Ca(e(n*a),[n,a]),i=Ze(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Sf=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function $u(e,t){return(n,a,r)=>{let s=Aa(e(3*3*n),[3,3,n,1]),i=Aa(e(n*a),[1,1,n,a]),o=Ze(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Sf(s,i,o)}}function Du(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Sf(n,a,r)}}function qn(e,t){return(n,a,r)=>{let s=e[n];if(!eo(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function vn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function Cf(e,t){let n=Eu(e,t),a=$u(e,t);function r(i,o,l,u=!1){let c=u?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:c,conv1:p,conv2:d}}function s(i,o,l,u=!1){let{conv0:c,conv1:p,conv2:d}=r(i,o,l,u),h=a(o,o,`${l}/conv3`);return{conv0:c,conv1:p,conv2:d,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function uC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock4Params:r}=Cf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function Af(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function Ff(e,t){let n=qn(e,t),a=Af(n),r=Du(n);function s(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`);return{conv0:u,conv1:c,conv2:p}}function i(o,l=!1){let u=l?a(`${o}/conv0`):r(`${o}/conv0`),c=r(`${o}/conv1`),p=r(`${o}/conv2`),d=r(`${o}/conv3`);return{conv0:u,conv1:c,conv2:p,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function cC(e){let t=[],{extractDenseBlock4Params:n}=Ff(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return xn(e,t),{params:a,paramMappings:t}}var _p=class extends en{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(255)),i=Fp(s,n.dense0,!0);return i=Fp(i,n.dense1),i=Fp(i,n.dense2),i=Fp(i,n.dense3),i=Zn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return cC(t)}extractParams(t){return uC(t)}};function Ep(e,t){return M(()=>J(ze(e,t.weights),t.bias))}function pC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=vn(e),o=Tf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function dC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return xn(e,t),{params:r,paramMappings:t}}function _f(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var $p=class extends en{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t;return Ep(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return pC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=_f(t);return this.faceFeatureExtractor.loadFromWeightMap(n),dC(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var Ef=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Er=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Ef.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Ef.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Dp=class extends $p{constructor(t=new _p){super("FaceExpressionNet",t)}forwardInput(t){return M(()=>Sa(this.runNet(t)))}async forward(t){return this.forwardInput(await mt(t))}async predictExpressions(t){let n=await mt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Er(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function $f(e){return e.expressions instanceof Er}function Mp(e,t){return{...e,...{expressions:t}}}function ase(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Er?s:$f(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),u=za(s)?s.detection.box.bottomLeft:a||new De(0,0);new vs(l.map(p=>`${p.expression} (${to(p.probability)})`),u).draw(e)})}function Ns(e){return za(e)&&e.landmarks instanceof jn&&e.unshiftedLandmarks instanceof jn&&e.alignedRect instanceof ft}function rse(e){let t=(o,l,u,c)=>Math.atan2(c-l,u-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>oo>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function uo(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new ft(e.detection.score,r.rescale(s.reverse()),s),o=rse(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var Tw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},Sw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new Tw(n)}draw(t){let n=bn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof so&&(n.strokeStyle=i,n.lineWidth=s,Ar(n,this.faceLandmarks.getJawOutline()),Ar(n,this.faceLandmarks.getLeftEyeBrow()),Ar(n,this.faceLandmarks.getRightEyeBrow()),Ar(n,this.faceLandmarks.getNose()),Ar(n,this.faceLandmarks.getLeftEye(),!0),Ar(n,this.faceLandmarks.getRightEye(),!0),Ar(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=c=>{n.beginPath(),n.arc(c.x,c.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function sse(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof jn?a:Ns(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Sw(r).draw(e)})}var hC="1.0.2";function ise(e,t){let n=Eu(e,t),a=$u(e,t);function r(i,o,l){let u=a(i,o,`${l}/separable_conv0`),c=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:c,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),c=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function mC(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=vn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=ise(a,n),u=s(3,32,3,"entry_flow/conv_in"),c=o(32,64,"entry_flow/reduction_block_0"),p=o(64,128,"entry_flow/reduction_block_1"),d={conv_in:u,reduction_block_0:c,reduction_block_1:p},h={};ir(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:h,exit_flow:g}}}function ose(e,t){let n=qn(e,t),a=Af(n),r=Du(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:c}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),c=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:c}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function fC(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=ose(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),c={conv_in:o,reduction_block_0:l,reduction_block_1:u},p={};ir(t,0,1).forEach(f=>{p[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return xn(e,n),{params:{entry_flow:c,middle_flow:p,exit_flow:m},paramMappings:n}}function gC(e,t,n){return J(Et(e,t.filters,n,"same"),t.bias)}function Aw(e,t,n=!0){let a=n?qe(e):e;return a=Dn(a,t.separable_conv0,[1,1]),a=Dn(qe(a),t.separable_conv1,[1,1]),a=$t(a,[3,3],[2,2],"same"),a=J(a,gC(e,t.expansion_conv,[2,2])),a}function lse(e,t){let n=Dn(qe(e),t.separable_conv0,[1,1]);return n=Dn(qe(n),t.separable_conv1,[1,1]),n=Dn(qe(n),t.separable_conv2,[1,1]),n=J(n,e),n}var Fw=class extends en{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(256)),i=qe(gC(s,n.entry_flow.conv_in,[2,2]));return i=Aw(i,n.entry_flow.reduction_block_0,!1),i=Aw(i,n.entry_flow.reduction_block_1),ir(this._numMainBlocks,0,1).forEach(o=>{i=lse(i,n.middle_flow[`main_block_${o}`])}),i=Aw(i,n.exit_flow.reduction_block),i=qe(Dn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return fC(t,this._numMainBlocks)}extractParams(t){return mC(t,this._numMainBlocks)}};function yC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),r=Tf(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function bC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return xn(e,t),{params:r,paramMappings:t}}var cr;(function(e){e.FEMALE="female",e.MALE="male"})(cr||(cr={}));var Rp=class extends en{constructor(t=new Fw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t,r=Zn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Ep(r,n.fc.age).as1D(),i=Ep(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return M(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Sa(a)}})}async forward(t){return this.forwardInput(await mt(t))}async predictAgeAndGender(t){let n=await mt(t),a=await this.forwardInput(n),r=ct(a.age),s=ct(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let c=(await l.data())[0],p=(await u.data())[0],d=p>.5,h=d?cr.MALE:cr.FEMALE,m=d?p:1-p;return l.dispose(),u.dispose(),{age:c,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return yC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=_f(t);return this.faceFeatureExtractor.loadFromWeightMap(n),bC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var Pp=class extends $p{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return M(()=>{let i=(p,d)=>Dt([Cn([68],p,"float32"),Cn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return d(h,m)?Math.abs(h-m)/2:0},l=p=>o(p,(d,h)=>do(p,(d,h)=>hi(l(d),u(d))))).div(Dt(Array.from(Array(s),(p,d)=>i(r[d].width,r[d].height))))})}forwardInput(t){return M(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await mt(t))}async detectLandmarks(t){let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(await s.data()),l=o.filter((c,p)=>uf(p)),u=o.filter((c,p)=>!uf(p));return new so(Array(68).fill(0).map((c,p)=>new De(l[p],u[p])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var co=class extends Pp{constructor(t=new _p){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function xC(e){let t=[],{extractDenseBlock3Params:n}=Ff(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return xn(e,t),{params:a,paramMappings:t}}function vC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractDenseBlock3Params:r}=Cf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var _w=class extends en{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(255)),i=Nf(s,n.dense0,!0);return i=Nf(i,n.dense1),i=Nf(i,n.dense2),i=Zn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await mt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return xC(t)}extractParams(t){return vC(t)}};var Op=class extends Pp{constructor(t=new _w){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Ew=class extends co{};function wC(e,t){return J(B(e,t.weights),t.biases)}function $w(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Et(e,s,n,r);return o=J(o,i),o=wC(o,t.scale),a?qe(o):o}function kC(e,t){return $w(e,t,[1,1],!0)}function Dw(e,t){return $w(e,t,[1,1],!1)}function Df(e,t){return $w(e,t,[2,2],!0,"valid")}function use(e,t){function n(o,l,u){let c=e(o),p=c.length/(l*u*u);if(mw(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${c.length}, numFilters: ${l}, filterSize: ${u}`);return M(()=>Ve(Aa(c,[l,p,u,u]),[2,3,1,0]))}function a(o,l,u,c){let p=n(o,l,u),d=Ze(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/bias`}),{filters:p,bias:d}}function r(o,l){let u=Ze(e(o)),c=Ze(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:u,biases:c}}function s(o,l,u,c){let p=a(o,l,u,`${c}/conv`),d=r(l,`${c}/scale`);return{conv:p,scale:d}}function i(o,l,u,c,p=!1){let d=s((p?.5:1)*o,l,u,`${c}/conv1`),h=s(o,l,u,`${c}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function IC(e){let{extractWeights:t,getRemainingWeights:n}=vn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=use(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),u=s(9216,32,3,"conv32_3"),c=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),k=M(()=>Ve(Ca(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:c,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:k},paramMappings:a}}function cse(e,t){let n=qn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),u=a(i);return{conv:{filters:o,bias:l},scale:u}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function NC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=cse(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),u=a("conv64_1"),c=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!hw(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:u,conv64_2:c,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return xn(e,t),{params:v,paramMappings:t}}function Wa(e,t){let n=kC(e,t.conv1);return n=Dw(n,t.conv2),n=J(n,e),n=qe(n),n}function Lp(e,t){let n=Df(e,t.conv1);n=Dw(n,t.conv2);let a=Zn(e,2,2,"valid"),r=vt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=vt(o);n=Je([n,l],1);let u=[...n.shape];u[2]=1;let c=vt(u);n=Je([n,c],2)}return a=s?Je([a,r],3):a,n=J(a,n),n=qe(n),n}var po=class extends en{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(150,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(ce(256)),i=Df(s,n.conv32_down);i=$t(i,3,2,"valid"),i=Wa(i,n.conv32_1),i=Wa(i,n.conv32_2),i=Wa(i,n.conv32_3),i=Lp(i,n.conv64_down),i=Wa(i,n.conv64_1),i=Wa(i,n.conv64_2),i=Wa(i,n.conv64_3),i=Lp(i,n.conv128_down),i=Wa(i,n.conv128_1),i=Wa(i,n.conv128_2),i=Lp(i,n.conv256_down),i=Wa(i,n.conv256_1),i=Wa(i,n.conv256_2),i=Lp(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await mt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)==null?void 0:s.some(i=>i<=0))return new Float32Array(128);let n=await mt(t),a=M(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return NC(t)}extractParams(t){return IC(t)}};function TC(e){let t=new po;return t.extractWeights(e),t}function zp(e,t){return{...e,...{descriptor:t}}}function SC(e){return typeof e.age=="number"}function Wp(e,t){return{...e,...{age:t}}}function CC(e){return(e.gender===cr.MALE||e.gender===cr.FEMALE)&&Tu(e.genderProbability)}function Bp(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function pse(e,t){function n(l,u){let c=Aa(e(3*3*l),[3,3,l,1]),p=Ze(e(l)),d=Ze(e(l)),h=Ze(e(l)),m=Ze(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:c,batch_norm_scale:p,batch_norm_offset:d,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,c,p,d){let h=Aa(e(l*u*c*c),[c,c,l,u]),m=Ze(e(u));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/${d?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,c,p){let{filters:d,bias:h}=a(l,u,c,p,!0);return{filters:d,batch_norm_offset:h}}function s(l,u,c){let p=n(l,`${c}/depthwise_conv`),d=r(l,u,1,`${c}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),c=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),k=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:k}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),u=r(256,512,3,"prediction_layer/conv_1"),c=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),k=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),w=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),S=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),A=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),F=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),D=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),R=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:u,conv_2:c,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:k},box_predictor_3:{box_encoding_predictor:w,class_predictor:S},box_predictor_4:{box_encoding_predictor:A,class_predictor:F},box_predictor_5:{box_encoding_predictor:D,class_predictor:R}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function AC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=vn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=pse(n,t),i=r(),o=s(),u={extra_dim:dh(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:u},paramMappings:t}}function dse(e,t){let n=qn(e,t);function a(u,c,p){let d=n(`${u}/Conv2d_${c}_pointwise/weights`,4,`${p}/filters`),h=n(`${u}/Conv2d_${c}_pointwise/convolution_bn_offset`,1,`${p}/batch_norm_offset`);return{filters:d,batch_norm_offset:h}}function r(u){let c=`mobilenetv1/conv_${u}`,p=`MobilenetV1/Conv2d_${u}_depthwise`,d=`${c}/depthwise_conv`,h=`${c}/pointwise_conv`,m=n(`${p}/depthwise_weights`,4,`${d}/filters`),f=n(`${p}/BatchNorm/gamma`,1,`${d}/batch_norm_scale`),g=n(`${p}/BatchNorm/beta`,1,`${d}/batch_norm_offset`),y=n(`${p}/BatchNorm/moving_mean`,1,`${d}/batch_norm_mean`),b=n(`${p}/BatchNorm/moving_variance`,1,`${d}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",u,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(u,c){let p=n(`${u}/weights`,4,`${c}/filters`),d=n(`${u}/biases`,1,`${c}/bias`);return{filters:p,bias:d}}function o(u){let c=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),p=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:c,class_predictor:p}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function FC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=dse(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Fr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return xn(e,t),{params:s,paramMappings:t}}function ka(e,t,n){return M(()=>{let a=Et(e,t.filters,n,"same");return a=J(a,t.batch_norm_offset),Xt(a,0,6)})}var hse=.0010000000474974513;function mse(e,t,n){return M(()=>{let a=ns(e,t.filters,n,"same");return a=br(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,hse),Xt(a,0,6)})}function fse(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function _C(e,t){return M(()=>{let n,a=ka(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=fse(o);a=mse(a,s.depthwise_conv,l),a=ka(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function gse(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),c=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(c-l)*(p-u);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,c),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function EC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((c,p)=>({score:c,boxIndex:p})).filter(c=>c.score>r).sort((c,p)=>p.score-c.score),l=c=>c<=a?1:0,u=[];return o.forEach(c=>{if(u.length>=i)return;let p=c.score;for(let d=u.length-1;d>=0;--d){let h=gse(e,c.boxIndex,u[d]);if(h!==0&&(c.score*=l(h),c.score<=r))break}p===c.score&&u.push(c.boxIndex)}),u}function yse(e){let t=ct(Ve(e,[1,0])),n=[me(t[2],t[0]),me(t[3],t[1])],a=[J(t[0],xe(n[0],ce(2))),J(t[1],xe(n[1],ce(2)))];return{sizes:n,centers:a}}function bse(e,t){let{sizes:n,centers:a}=yse(e),r=ct(Ve(t,[1,0])),s=xe(B(hn(xe(r[2],ce(5))),n[0]),ce(2)),i=J(B(xe(r[0],ce(10)),n[0]),a[0]),o=xe(B(hn(xe(r[3],ce(5))),n[1]),ce(2)),l=J(B(xe(r[1],ce(10)),n[1]),a[1]);return Ve(Dt([me(i,s),me(l,o),J(i,s),J(l,o)]),[1,0])}function $C(e,t,n){return M(()=>{let a=e.shape[0],r=bse(H(qa(n.extra_dim,[a,1,1]),[-1,4]),H(e,[-1,4]));r=H(r,[a,r.shape[0]/a,4]);let s=da(Be(t,[0,0,1],[-1,-1,-1])),i=Be(s,[0,0,0],[-1,-1,1]);i=H(i,[a,i.shape[1]]);let o=ct(r),l=ct(i);return{boxes:o,scores:l}})}function ho(e,t){return M(()=>{let n=e.shape[0],a=H(lo(e,t.box_encoding_predictor),[n,-1,1,4]),r=H(lo(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function DC(e,t,n){return M(()=>{let a=ka(e,n.conv_0,[1,1]),r=ka(a,n.conv_1,[2,2]),s=ka(r,n.conv_2,[1,1]),i=ka(s,n.conv_3,[2,2]),o=ka(i,n.conv_4,[1,1]),l=ka(o,n.conv_5,[2,2]),u=ka(l,n.conv_6,[1,1]),c=ka(u,n.conv_7,[2,2]),p=ho(t,n.box_predictor_0),d=ho(e,n.box_predictor_1),h=ho(r,n.box_predictor_2),m=ho(i,n.box_predictor_3),f=ho(l,n.box_predictor_4),g=ho(c,n.box_predictor_5),y=Je([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Je([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var sa=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ts=class extends en{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(512,!1),"float32"),r=me(B(a,ce(.007843137718737125)),ce(1)),s=_C(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=DC(s.out,s.conv11,n.prediction_layer);return $C(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await mt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new sa(n),s=await mt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,k]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(A=>A*g),[w,S]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(A=>A*f);return new ft(c[x],new ro(w,v,S-w,k-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return FC(t)}extractParams(t){return AC(t)}};function Mw(e){let t=new Ts;return t.extractWeights(e),t}function MC(e){return Mw(e)}var Rw=class extends Ts{};var RC=.4,PC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],OC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],LC=[117.001,114.697,97.404],zC="tiny_yolov2_model",WC="tiny_yolov2_separable_conv_model";var Mf=e=>typeof e=="number";function Rf(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Mf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Mf(t.x)&&Mf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Mf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Mu(e){return M(()=>{let t=B(e,ce(.10000000149011612));return J(qe(me(e,t)),t)})}function $r(e,t){return M(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Et(n,t.conv.filters,[1,1],"valid"),n=me(n,t.bn.sub),n=B(n,t.bn.truediv),n=J(n,t.conv.bias),Mu(n)})}function Dr(e,t){return M(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Pi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Mu(n)})}function xse(e,t){let n=Eu(e,t);function a(i,o){let l=Ze(e(i)),u=Ze(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),c=a(o,`${l}/bn`);return{conv:u,bn:c}}let s=$u(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function BC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=vn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=xse(r,i),c;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):u(p,d,"conv0"),k=u(d,h,"conv1"),w=u(h,m,"conv2"),S=u(m,f,"conv3"),A=u(f,g,"conv4"),F=u(g,y,"conv5"),D=b?u(y,b,"conv6"):void 0,R=x?u(b,x,"conv7"):void 0,L=o(x||b||y,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),k=l(d,h,"conv1"),w=l(h,m,"conv2"),S=l(m,f,"conv3"),A=l(f,g,"conv4"),F=l(g,y,"conv5"),D=l(y,b,"conv6"),R=l(b,x,"conv7"),L=o(x,5*n,1,"conv8");c={conv0:v,conv1:k,conv2:w,conv3:S,conv4:A,conv5:F,conv6:D,conv7:R,conv8:L}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:c,paramMappings:i}}function vse(e,t){let n=qn(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Du(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function VC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=vse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var Ba=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Pw=class extends en{constructor(t){super("TinyYolov2");Rf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=$r(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=$r(a,n.conv6),a=$r(a,n.conv7),lo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Mu(lo(t,n.conv0,"valid",!1)):Dr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Dr(a,n.conv6):a,a=n.conv7?Dr(a,n.conv7):a,lo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let r=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(ce(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await mt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ba(n),s=await mt(t),i=await this.forwardInput(s,a),o=M(()=>ct(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let c=u.map(g=>g.box),p=u.map(g=>g.score),d=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return hf(c.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new _r(p[g],d[g],h[g],c[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return VC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Pw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return BC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],c=this.config.anchors.length,[p,d,h]=M(()=>{let y=t.reshape([u,u,c,this.boxEncodingSize]),b=y.slice([0,0,0,0],[u,u,c,4]),x=y.slice([0,0,0,4],[u,u,c,1]),v=this.withClassScores?Sa(y.slice([0,0,0,5],[u,u,c,this.config.classes.length]),3):ce(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;ya){let k=(b+Su(g[y][b][x][0]))/u*o,w=(y+Su(g[y][b][x][1]))/u*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/u*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/u*l,F=k-S/2,D=w-A/2,R={row:y,col:b,anchor:x},{classScore:L,label:W}=this.withClassScores?await this.extractPredictedClass(h,R):{classScore:1,label:0};m.push({box:new ao(F,D,F+S,D+A),score:v,classScore:v*L,label:W,...R})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Ru=Pw;Ru.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var mo=class extends Ru{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:RC,classes:["face"],...t?{anchors:OC,meanRgb:LC}:{anchors:PC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?WC:zC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function UC(e,t=!0){let n=new mo(t);return n.extractWeights(e),n}var Vp=class extends Ba{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function fo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Ns(l)?r(l):l.detection),i=a||(t instanceof Fe?await oo(t,s):await io(t,s)),o=await n(i);return i.forEach(l=>l instanceof Fe&&l.dispose()),o}async function Pu(e,t,n,a,r){return fo([e],t,async s=>n(s[0]),a,r)}var GC=.4,HC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],jC=[117.001,114.697,97.404];var go=class extends Ru{constructor(){let t={withSeparableConvs:!0,iouThreshold:GC,classes:["face"],anchors:HC,meanRgb:jC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Qe={ssdMobilenetv1:new Ts,tinyFaceDetector:new go,tinyYolov2:new mo,faceLandmark68Net:new co,faceLandmark68TinyNet:new Op,faceRecognitionNet:new po,faceExpressionNet:new Dp,ageGenderNet:new Rp},Ow=(e,t)=>Qe.ssdMobilenetv1.locateFaces(e,t),qC=(e,t)=>Qe.tinyFaceDetector.locateFaces(e,t),XC=(e,t)=>Qe.tinyYolov2.locateFaces(e,t),Lw=e=>Qe.faceLandmark68Net.detectLandmarks(e),KC=e=>Qe.faceLandmark68TinyNet.detectLandmarks(e),YC=e=>Qe.faceRecognitionNet.computeFaceDescriptor(e),JC=e=>Qe.faceExpressionNet.predictExpressions(e),QC=e=>Qe.ageGenderNet.predictAgeAndGender(e),zw=e=>Qe.ssdMobilenetv1.load(e),ZC=e=>Qe.tinyFaceDetector.load(e),eA=e=>Qe.tinyYolov2.load(e),tA=e=>Qe.faceLandmark68Net.load(e),nA=e=>Qe.faceLandmark68TinyNet.load(e),aA=e=>Qe.faceRecognitionNet.load(e),rA=e=>Qe.faceExpressionNet.load(e),sA=e=>Qe.ageGenderNet.load(e),iA=zw,oA=Ow,lA=Lw;var Ww=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},zu=class extends Ww{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Mp(a,n[r]))}withAgeAndGender(){return new Ou(this,this.input)}},Wu=class extends Ww{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Mp(t,n)}withAgeAndGender(){return new Lu(this,this.input)}},xo=class extends zu{withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},vo=class extends Wu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Rr(this,this.input)}};var Bw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Bw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Wp(Bp(a,i,o),s)})}withFaceExpressions(){return new zu(this,this.input)}},Lu=class extends Bw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Pu(t,this.input,s=>Qe.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Wp(Bp(t,a,r),n)}withFaceExpressions(){return new Wu(this,this.input)}},yo=class extends Ou{withFaceExpressions(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},bo=class extends Lu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptor(){return new Rr(this,this.input)}};var Up=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Mr=class extends Up{async run(){let t=await this.parentTask;return(await fo(t,this.input,a=>Promise.all(a.map(r=>Qe.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>zp(t[r],a))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}},Rr=class extends Up{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return zp(t,n)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}};var Gp=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Qe.faceLandmark68TinyNet:Qe.faceLandmark68Net}},Hp=class extends Gp{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Fe?await oo(this.input,n):await io(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Fe&&s.dispose()),t.map((s,i)=>uo(s,r[i]))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},jp=class extends Gp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Fe?await oo(this.input,[n]):await io(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Fe&&s.dispose()),uo(t,r)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Rr(this,this.input)}};var qp=class extends ia{constructor(t,n=new sa){super();this.input=t;this.options=n}},Bu=class extends qp{async run(){let{input:t,options:n}=this,a;if(n instanceof Vp)a=Qe.tinyFaceDetector.locateFaces(t,n);else if(n instanceof sa)a=Qe.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ba)a=Qe.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>bs({},a)))})}withFaceLandmarks(t=!1){return new Hp(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new zu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}},Xp=class extends qp{async run(){let t=await new Bu(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?bs({},n):void 0)})}withFaceLandmarks(t=!1){return new jp(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}};function uA(e,t=new sa){return new Xp(e,t)}function Kp(e,t=new sa){return new Bu(e,t)}async function Vw(e,t){return Kp(e,new sa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function cA(e,t={}){return Kp(e,new Ba(t)).withFaceLandmarks().withFaceDescriptors()}var pA=Vw;function Pf(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Yp=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof or)return i;if(i instanceof Float32Array)return new or(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new or(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Pf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Cu(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>or.fromJSON(a));return new Yp(n,t.distanceThreshold)}};function dA(e){let t=new go;return t.extractWeights(e),t}function Uw(e,t){let{width:n,height:a}=new cn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Uw(r,{width:n,height:a}));if(Ns(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return uo(bs(e,r),s)}return za(e)?bs(e,e.detection.forSize(n,a)):e instanceof jn||e instanceof ft?e.forSize(n,a):e}var kse=typeof process!="undefined",Ise=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",hA={faceapi:hC,node:kse,browser:Ise};return wse;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/face-api.node-cpu.js b/dist/face-api.node-cpu.js index ed98b04..636db74 100644 --- a/dist/face-api.node-cpu.js +++ b/dist/face-api.node-cpu.js @@ -5,5 +5,5 @@ author: ' */ -var fn=Object.create,Ye=Object.defineProperty,hn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty,bn=Object.getOwnPropertyNames,gn=Object.getOwnPropertyDescriptor;var Er=o=>Ye(o,"__esModule",{value:!0});var fo=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Ge=(o,t)=>{for(var e in t)Ye(o,e,{get:t[e],enumerable:!0})},vn=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of bn(t))!xn.call(o,r)&&r!=="default"&&Ye(o,r,{get:()=>t[r],enumerable:!(e=gn(t,r))||e.enumerable});return o},b=o=>vn(Er(Ye(o!=null?fn(hn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);var g=fo(Mr=>{var yn=Object.create,Cr=Object.defineProperty,Fn=Object.getPrototypeOf,Tn=Object.prototype.hasOwnProperty,_n=Object.getOwnPropertyNames,wn=Object.getOwnPropertyDescriptor,ho=o=>Cr(o,"__esModule",{value:!0}),xo=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of _n(t))!Tn.call(o,r)&&r!=="default"&&Cr(o,r,{get:()=>t[r],enumerable:!(e=wn(t,r))||e.enumerable});return o},Pn=o=>xo(ho(Cr(o!=null?yn(Fn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);ho(Mr);xo(Mr,Pn(require("@tensorflow/tfjs")))});var To=fo((Ln,Fo)=>{Er(Ln);Ge(Ln,{isNodejs:()=>kn});function kn(){return typeof global=="object"&&!0&&typeof Fo!="undefined"&&typeof process!="undefined"&&!!process.version}});Er(exports);Ge(exports,{AgeGenderNet:()=>pr,BoundingBox:()=>re,Box:()=>D,ComposableTask:()=>tt,ComputeAllFaceDescriptorsTask:()=>At,ComputeFaceDescriptorsTaskBase:()=>vr,ComputeSingleFaceDescriptorTask:()=>Wt,DetectAllFaceLandmarksTask:()=>Fr,DetectAllFacesTask:()=>He,DetectFaceLandmarksTaskBase:()=>yr,DetectFacesTaskBase:()=>_r,DetectSingleFaceLandmarksTask:()=>Tr,DetectSingleFaceTask:()=>wr,Dimensions:()=>A,FACE_EXPRESSION_LABELS:()=>qr,FaceDetection:()=>M,FaceDetectionNet:()=>qo,FaceExpressionNet:()=>cr,FaceExpressions:()=>It,FaceLandmark68Net:()=>fe,FaceLandmark68TinyNet:()=>dr,FaceLandmarkNet:()=>Ro,FaceLandmarks:()=>V,FaceLandmarks5:()=>vo,FaceLandmarks68:()=>ne,FaceMatch:()=>Ee,FaceMatcher:()=>Dr,FaceRecognitionNet:()=>xe,Gender:()=>vt,LabeledBox:()=>Me,LabeledFaceDescriptors:()=>xt,NetInput:()=>bt,NeuralNetwork:()=>S,ObjectDetection:()=>Dt,Point:()=>x,PredictedBox:()=>yo,Rect:()=>oe,SsdMobilenetv1:()=>Xt,SsdMobilenetv1Options:()=>Z,TinyFaceDetector:()=>Fe,TinyFaceDetectorOptions:()=>gr,TinyYolov2:()=>ve,TinyYolov2Options:()=>lt,allFaces:()=>Ea,allFacesSsdMobilenetv1:()=>un,allFacesTinyYolov2:()=>Da,awaitMediaLoaded:()=>Gr,bufferToImage:()=>zr,computeFaceDescriptor:()=>ua,createCanvas:()=>Yt,createCanvasFromMedia:()=>Ie,createFaceDetectionNet:()=>aa,createFaceRecognitionNet:()=>Xn,createSsdMobilenetv1:()=>Jo,createTinyFaceDetector:()=>Ma,createTinyYolov2:()=>ca,detectAllFaces:()=>Pr,detectFaceLandmarks:()=>pn,detectFaceLandmarksTiny:()=>da,detectLandmarks:()=>wa,detectSingleFace:()=>Pa,draw:()=>Co,env:()=>P,euclideanDistance:()=>uo,extendWithAge:()=>hr,extendWithFaceDescriptor:()=>fr,extendWithFaceDetection:()=>Ot,extendWithFaceExpressions:()=>mr,extendWithFaceLandmarks:()=>le,extendWithGender:()=>xr,extractFaceTensors:()=>se,extractFaces:()=>ae,fetchImage:()=>Bn,fetchJson:()=>Xr,fetchNetWeights:()=>Rn,fetchOrThrow:()=>Gt,getContext2dOrThrow:()=>$,getMediaDimensions:()=>Ht,imageTensorToCanvas:()=>Vr,imageToSquare:()=>Ur,inverseSigmoid:()=>Mn,iou:()=>kr,isMediaElement:()=>qe,isMediaLoaded:()=>Ne,isWithAge:()=>Jn,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>Zr,isWithFaceLandmarks:()=>Vt,isWithGender:()=>qn,loadAgeGenderModel:()=>Fa,loadFaceDetectionModel:()=>Ta,loadFaceExpressionModel:()=>ya,loadFaceLandmarkModel:()=>ba,loadFaceLandmarkTinyModel:()=>ga,loadFaceRecognitionModel:()=>va,loadSsdMobilenetv1Model:()=>dn,loadTinyFaceDetectorModel:()=>ha,loadTinyYolov2Model:()=>xa,loadWeightMap:()=>Jr,locateFaces:()=>_a,matchDimensions:()=>$n,minBbox:()=>Sr,nets:()=>_,nonMaxSuppression:()=>Ar,normalize:()=>ot,padToSquare:()=>Wr,predictAgeAndGender:()=>fa,recognizeFaceExpressions:()=>la,resizeResults:()=>ln,resolveInput:()=>jt,shuffleArray:()=>En,sigmoid:()=>De,ssdMobilenetv1:()=>mn,tf:()=>Ca,tinyFaceDetector:()=>ma,tinyYolov2:()=>pa,toNetInput:()=>E,utils:()=>bo,validateConfig:()=>ao,version:()=>La});var Ca=b(g());var Co={};Ge(Co,{AnchorPosition:()=>dt,DrawBox:()=>Xe,DrawBoxOptions:()=>Yr,DrawFaceLandmarks:()=>Qr,DrawFaceLandmarksOptions:()=>Kr,DrawTextField:()=>Mt,DrawTextFieldOptions:()=>Ce,drawContour:()=>ft,drawDetections:()=>Wn,drawFaceExpressions:()=>On,drawFaceLandmarks:()=>Hn});function ft(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var bo={};Ge(bo,{computeReshapedDimensions:()=>Lr,getCenterPoint:()=>$t,isDimensions:()=>Ve,isEven:()=>ze,isFloat:()=>Ir,isTensor:()=>Bt,isTensor1D:()=>Dn,isTensor2D:()=>Nr,isTensor3D:()=>ht,isTensor4D:()=>z,isValidNumber:()=>rt,isValidProbablitiy:()=>ee,range:()=>ct,round:()=>Rt});var go=b(g());var A=class{constructor(t,e){if(!rt(t)||!rt(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new A(1/this.width,1/this.height)}};function Bt(o,t){return o instanceof go.Tensor&&o.shape.length===t}function Dn(o){return Bt(o,1)}function Nr(o){return Bt(o,2)}function ht(o){return Bt(o,3)}function z(o){return Bt(o,4)}function Ir(o){return o%1!=0}function ze(o){return o%2==0}function Rt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function Ve(o){return o&&o.width&&o.height}function Lr({width:o,height:t},e){let r=e/Math.max(t,o);return new A(Math.round(o*r),Math.round(t*r))}function $t(o){return o.reduce((t,e)=>t.add(e),new x(0,0)).div(new x(o.length,o.length))}function ct(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function rt(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function ee(o){return rt(o)&&o>=0&&o<=1}var x=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new x(this.x+t.x,this.y+t.y)}sub(t){return new x(this.x-t.x,this.y-t.y)}mul(t){return new x(this.x*t.x,this.y*t.y)}div(t){return new x(this.x/t.x,this.y/t.y)}abs(){return new x(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new x(Math.floor(this.x),Math.floor(this.y))}};var D=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(rt)}static assertIsValidBox(t,e,r=!1){if(!D.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(rt),a=[r.x,r.y,r.width,r.height].every(rt);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];D.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new x(this.left,this.top)}get topRight(){return new x(this.right,this.top)}get bottomLeft(){return new x(this.left,this.bottom)}get bottomRight(){return new x(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new D({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new D({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new D({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var re=class extends D{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var Dt=class{constructor(t,e,r,n,a){this._imageDims=new A(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new D(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new Dt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends Dt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function kr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function Sr(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var mt=b(g());function ot(o,t){return mt.tidy(()=>{let[e,r,n]=t,a=mt.fill([...o.shape.slice(0,3),1],e,"float32"),s=mt.fill([...o.shape.slice(0,3),1],r,"float32"),i=mt.fill([...o.shape.slice(0,3),1],n,"float32"),c=mt.concat([a,s,i],3);return mt.sub(o,c)})}var Et=b(g());function Wr(o,t=!1){return Et.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Et.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Et.cast(u,"float32"));return Et.concat(d,s)})}function En(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function De(o){return 1/(1+Math.exp(-o))}function Mn(o){return Math.log(o/(1-o))}var oe=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var Cn=.5,Nn=.43,In=.45,V=class{constructor(t,e,r=new x(0,0)){let{width:n,height:a}=e;this._imgDims=new A(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new x(n,a)).add(r))}get shift(){return new x(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new x(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new x(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof M?t.box.floor():new D(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/In),c=$t(t),m=Math.floor(Math.max(0,c.x-Cn*i)),p=Math.floor(Math.max(0,c.y-Nn*i));return new oe(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=Sr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var vo=class extends V{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],$t([t[3],t[4]])]}};var ne=class extends V{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map($t)}};var Ee=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Rt(this.distance)})`:""}`}};var Me=class extends D{static assertIsValidLabeledBox(t,e){if(D.assertIsValidBox(t,e),!rt(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var xt=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new xt(t.label,e)}};var yo=class extends Me{static assertIsValidPredictedBox(t,e){if(Me.assertIsValidLabeledBox(t,e),!ee(t.score)||!ee(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function pt(o){return o.detection instanceof M}function Ot(o,t){return{...o,...{detection:t}}}function Br(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function Ue(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Rr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=Ue();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function $r(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Or=b(To()),k;function Sn(){if(!k)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return k}function jr(o){k=o}function Hr(){return $r()?jr(Br()):(0,Or.isNodejs)()?jr(Rr()):null}function An(o){if(k||Hr(),!k)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=k.Canvas,Image:e=k.Image}=o;k.Canvas=t,k.Image=e,k.createCanvasElement=o.createCanvasElement||(()=>new t),k.createImageElement=o.createImageElement||(()=>new e),k.ImageData=o.ImageData||k.ImageData,k.Video=o.Video||k.Video,k.fetch=o.fetch||k.fetch,k.readFile=o.readFile||k.readFile}var P={getEnv:Sn,setEnv:jr,initialize:Hr,createBrowserEnv:Br,createFileSystem:Ue,createNodejsEnv:Rr,monkeyPatch:An,isBrowser:$r,isNodejs:Or.isNodejs};Hr();function jt(o){return!P.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function $(o){let{Canvas:t,CanvasRenderingContext2D:e}=P.getEnv();if(o instanceof e)return o;let r=jt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var dt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(dt||(dt={}));var Ce=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||dt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},Mt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof Mt?t.text:t,this.anchor=e,this.options=new Ce(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let v=c+d.x,w=c+d.y+(l+1)*s;r.fillText(u,v,w)})}};var Yr=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:dt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ce({...s,...a})}},Xe=class{constructor(t,e={}){this.box=new D(t),this.options=new Yr(e)}draw(t){let e=$(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new Mt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function Wn(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof M?r.score:pt(r)?r.detection.score:void 0,a=r instanceof M?r.box:pt(r)?r.detection.box:new D(r),s=n?`${Rt(n)}`:void 0;new Xe(a,{label:s}).draw(o)})}var ue=b(g());function Ne(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function Gr(o){return new Promise((t,e)=>{if(o instanceof P.getEnv().Canvas||Ne(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function zr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=P.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function Ht(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t?new A(o.naturalWidth,o.naturalHeight):o instanceof e?new A(o.videoWidth,o.videoHeight):new A(o.width,o.height)}function Yt({width:o,height:t}){let{createCanvasElement:e}=P.getEnv(),r=e();return r.width=o,r.height=t,r}function Ie(o,t){let{ImageData:e}=P.getEnv();if(!(o instanceof e)&&!Ne(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||Ht(o),a=Yt({width:r,height:n});return o instanceof e?$(a).putImageData(o,0,0):$(a).drawImage(o,0,0,r,n),a}var Je=b(g());async function Vr(o,t){let e=t||P.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(z(o)?1:0),s=Je.tidy(()=>o.as3D(r,n,a).toInt());return await Je.browser.toPixels(s,e),s.dispose(),e}function qe(o){let{Image:t,Canvas:e,Video:r}=P.getEnv();return o instanceof t||o instanceof e||o instanceof r}var J=b(g());function Ur(o,t,e=!1){let{Image:r,Canvas:n}=P.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Yt({width:1,height:1});let a=Ht(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=Yt({width:t,height:t}),p=o instanceof n?o:Ie(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&$(m).drawImage(p,u,l,i,c),m}var bt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(ht(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(z(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof P.getEnv().Canvas?r:Ie(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ct(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Lr({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,J.tidy(()=>{let r=ct(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof J.Tensor){let i=z(s)?s:s.expandDims();return i=Wr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=J.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof P.getEnv().Canvas)return J.browser.fromPixels(Ur(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return J.stack(r.map(a=>J.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function E(o){if(o instanceof bt)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(jt);return r.forEach((n,a)=>{if(!qe(n)&&!ht(n)&&!z(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(z(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>qe(n)&&Gr(n))),new bt(r,Array.isArray(o))}async function ae(o,t){let{Canvas:e}=P.getEnv(),r=o;if(!(o instanceof e)){let s=await E(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await Vr(i)}let n=$(r);return t.map(s=>s instanceof M?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=Yt({width:c,height:m});return c>0&&m>0&&$(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var Ze=b(g());async function se(o,t){if(!ht(o)&&!z(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(z(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Ze.tidy(()=>{let[e,r,n]=o.shape.slice(z(o)?1:0);return t.map(i=>i instanceof M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>Ze.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Gt(o,t){let{fetch:e}=P.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Bn(o){let t=await Gt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return zr(e)}async function Xr(o){return(await Gt(o)).json()}async function Rn(o){return new Float32Array(await(await Gt(o)).arrayBuffer())}var _o=b(g());function Ke(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function Jr(o,t){let{manifestUri:e,modelBaseUri:r}=Ke(o,t),n=await Xr(e);return _o.io.loadWeights(n,r)}function $n(o,t,e=!1){let{width:r,height:n}=e?Ht(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Nt=b(g());var gt=b(g());var S=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof gt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof gt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=gt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await Jr(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=P.getEnv(),{manifestUri:r,modelBaseUri:n}=Ke(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=gt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof gt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var C=b(g());var ie=b(g());function O(o,t,e){return ie.tidy(()=>{let r=ie.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=ie.add(r,t.bias),r})}function Qe(o,t,e=!1){return C.tidy(()=>{let r=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):O(o,t.conv0,[2,2])),n=O(r,t.conv1,[1,1]),a=C.relu(C.add(r,n)),s=O(a,t.conv2,[1,1]);return C.relu(C.add(r,C.add(n,s)))})}function Le(o,t,e=!1,r=!0){return C.tidy(()=>{let n=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):O(o,t.conv0,r?[2,2]:[1,1])),a=O(n,t.conv1,[1,1]),s=C.relu(C.add(n,a)),i=O(s,t.conv2,[1,1]),c=C.relu(C.add(n,C.add(a,i))),m=O(c,t.conv3,[1,1]);return C.relu(C.add(n,C.add(a,C.add(i,m))))})}var Ct=b(g());function zt(o,t,e="same",r=!1){return Ct.tidy(()=>{let n=Ct.add(Ct.conv2d(o,t.filters,[1,1],e),t.bias);return r?Ct.relu(n):n})}function W(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var tr=b(g());function ce(o,t){return(e,r,n,a)=>{let s=tr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=tr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var er=b(g());function rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function B(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function nr(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function wo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock4Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function ar(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Po(o){let t=[],{extractDenseBlock4Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return W(o,t),{params:r,paramMappings:t}}var Se=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Le(a,e.dense0,!0);return s=Le(s,e.dense1),s=Le(s,e.dense2),s=Le(s,e.dense3),s=Nt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Po(t)}extractParams(t){return wo(t)}};var Mo=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Do(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Eo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Mo.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Do(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Eo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var qr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);qr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return qr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var cr=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new It(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Zr(o){return o.expressions instanceof It}function mr(o,t){return{...o,...{expressions:t}}}function On(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof It?a:Zr(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=pt(a)?a.detection.box.bottomLeft:r||new x(0,0);new Mt(c.map(d=>`${d.expression} (${Rt(d.probability)})`),m).draw(o)})}function Vt(o){return pt(o)&&o.landmarks instanceof V&&o.unshiftedLandmarks instanceof V&&o.alignedRect instanceof M}function jn(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function le(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new M(o.detection.score,n.rescale(a.reverse()),a),i=jn(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var Kr=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},Qr=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new Kr(e)}draw(t){let e=$(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof ne&&(e.strokeStyle=s,e.lineWidth=a,ft(e,this.faceLandmarks.getJawOutline()),ft(e,this.faceLandmarks.getLeftEyeBrow()),ft(e,this.faceLandmarks.getRightEyeBrow()),ft(e,this.faceLandmarks.getNose()),ft(e,this.faceLandmarks.getLeftEye(),!0),ft(e,this.faceLandmarks.getRightEye(),!0),ft(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function Hn(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof V?r:Vt(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Qr(n).draw(o)})}var No="1.0.1";var ut=b(g());var I=b(g());function Yn(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Io(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=B(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=Yn(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};ct(t,0,1).forEach(y=>{l[`main_block_${y}`]=c(128,`middle_flow/main_block_${y}`)});let v=i(128,256,"exit_flow/reduction_block"),w=s(256,512,"exit_flow/separable_conv"),h={reduction_block:v,separable_conv:w};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:h}}}function Gn(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function Lo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=Gn(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};ct(t,0,1).forEach(w=>{d[`main_block_${w}`]=s(`middle_flow/main_block_${w}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return W(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function ko(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function to(o,t,e=!0){let r=e?I.relu(o):o;return r=O(r,t.separable_conv0,[1,1]),r=O(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,ko(o,t.expansion_conv,[2,2])),r}function zn(o,t){let e=O(I.relu(o),t.separable_conv0,[1,1]);return e=O(I.relu(e),t.separable_conv1,[1,1]),e=O(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var eo=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(ko(a,e.entry_flow.conv_in,[2,2]));return s=to(s,e.entry_flow.reduction_block_0,!1),s=to(s,e.entry_flow.reduction_block_1),ct(this._numMainBlocks,0,1).forEach(i=>{s=zn(s,e.middle_flow[`main_block_${i}`])}),s=to(s,e.exit_flow.reduction_block),s=I.relu(O(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Lo(t,this._numMainBlocks)}extractParams(t){return Io(t,this._numMainBlocks)}};function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ao(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new eo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return So(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ao(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var H=b(g());var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return H.tidy(()=>{let s=(d,u)=>H.stack([H.fill([68],d,"float32"),H.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return u(l,v)?Math.abs(l-v)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(H.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return H.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await E(t))}async detectLandmarks(t){let e=await E(t),r=H.tidy(()=>H.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>ze(d)),m=i.filter((p,d)=>!ze(d));return new ne(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var fe=class extends Be{constructor(t=new Se){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Lt=b(g());function Wo(o){let t=[],{extractDenseBlock3Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return W(o,t),{params:r,paramMappings:t}}function Bo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock3Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var ro=class extends S{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Lt.scalar(255)),s=Qe(a,e.dense0,!0);return s=Qe(s,e.dense1),s=Qe(s,e.dense2),s=Lt.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Wo(t)}extractParams(t){return Bo(t)}};var dr=class extends Be{constructor(t=new ro){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Ro=class extends fe{};var U=b(g());var he=b(g());var ur=b(g());function $o(o,t){return ur.add(ur.mul(o,t.weights),t.biases)}function oo(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=he.conv2d(o,a,e,n);return i=he.add(i,s),i=$o(i,t.scale),r?he.relu(i):i}function Oo(o,t){return oo(o,t,[1,1],!0)}function no(o,t){return oo(o,t,[1,1],!1)}function lr(o,t){return oo(o,t,[2,2],!0,"valid")}var Y=b(g());function Vn(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Ir(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return Y.tidy(()=>Y.transpose(Y.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=Y.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=Y.tensor1d(o(i)),p=Y.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function jo(o){let{extractWeights:t,getRemainingWeights:e}=B(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Vn(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),v=a(147456,128,3,"conv128_down",!0),w=a(147456,128,3,"conv128_1"),h=a(147456,128,3,"conv128_2"),y=a(589824,256,3,"conv256_down",!0),T=a(589824,256,3,"conv256_1"),F=a(589824,256,3,"conv256_2"),L=a(589824,256,3,"conv256_down_out"),G=Y.tidy(()=>Y.transpose(Y.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:v,conv128_1:w,conv128_2:h,conv256_down:y,conv256_1:T,conv256_2:F,conv256_down_out:L,fc:G},paramMappings:r}}function Un(o,t){let e=j(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function Ho(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=Un(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),v=r("conv128_2"),w=r("conv256_down"),h=r("conv256_1"),y=r("conv256_2"),T=r("conv256_down_out"),{fc:F}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!Nr(F))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${F}`);let L={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:v,conv256_down:w,conv256_1:h,conv256_2:y,conv256_down_out:T,fc:F};return W(o,t),{params:L,paramMappings:t}}var R=b(g());function nt(o,t){let e=Oo(o,t.conv1);return e=no(e,t.conv2),e=R.add(e,o),e=R.relu(e),e}function Re(o,t){let e=lr(o,t.conv1);e=no(e,t.conv2);let r=R.avgPool(o,2,2,"valid"),n=R.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=R.zeros(i);e=R.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=R.zeros(m);e=R.concat([e,p],2)}return r=a?R.concat([r,n],3):r,e=R.add(r,e),e=R.relu(e),e}var xe=class extends S{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return U.tidy(()=>{let r=U.cast(t.toBatchTensor(150,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(U.scalar(256)),s=lr(a,e.conv32_down);s=U.maxPool(s,3,2,"valid"),s=nt(s,e.conv32_1),s=nt(s,e.conv32_2),s=nt(s,e.conv32_3),s=Re(s,e.conv64_down),s=nt(s,e.conv64_1),s=nt(s,e.conv64_2),s=nt(s,e.conv64_3),s=Re(s,e.conv128_down),s=nt(s,e.conv128_1),s=nt(s,e.conv128_2),s=Re(s,e.conv256_down),s=nt(s,e.conv256_1),s=nt(s,e.conv256_2),s=Re(s,e.conv256_down_out);let i=s.mean([1,2]);return U.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=U.tidy(()=>U.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return Ho(t)}extractParams(t){return jo(t)}};function Xn(o){let t=new xe;return t.extractWeights(o),t}function fr(o,t){return{...o,...{descriptor:t}}}function Jn(o){return typeof o.age=="number"}function hr(o,t){return{...o,...{age:t}}}function qn(o){return(o.gender===vt.MALE||o.gender===vt.FEMALE)&&ee(o.genderProbability)}function xr(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var st=b(g());var at=b(g());function Zn(o,t){function e(c,m){let p=at.tensor4d(o(3*3*c),[3,3,c,1]),d=at.tensor1d(o(c)),u=at.tensor1d(o(c)),l=at.tensor1d(o(c)),v=at.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),v=a(256,512,"mobilenetv1/conv_6"),w=a(512,512,"mobilenetv1/conv_7"),h=a(512,512,"mobilenetv1/conv_8"),y=a(512,512,"mobilenetv1/conv_9"),T=a(512,512,"mobilenetv1/conv_10"),F=a(512,512,"mobilenetv1/conv_11"),L=a(512,1024,"mobilenetv1/conv_12"),G=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,conv_8:h,conv_9:y,conv_10:T,conv_11:F,conv_12:L,conv_13:G}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),v=n(256,64,1,"prediction_layer/conv_6"),w=n(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),T=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),F=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),L=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),G=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),et=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),it=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),X=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),_t=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),wt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Pt=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,box_predictor_0:{box_encoding_predictor:h,class_predictor:y},box_predictor_1:{box_encoding_predictor:T,class_predictor:F},box_predictor_2:{box_encoding_predictor:L,class_predictor:G},box_predictor_3:{box_encoding_predictor:et,class_predictor:it},box_predictor_4:{box_encoding_predictor:X,class_predictor:_t},box_predictor_5:{box_encoding_predictor:wt,class_predictor:Pt}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function Yo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Zn(e,t),s=n(),i=a(),m={extra_dim:at.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Kn(o,t){let e=j(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,v=e(`${d}/depthwise_weights`,4,`${u}/filters`),w=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),h=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),y=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),T=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:v,batch_norm_scale:w,batch_norm_offset:h,batch_norm_mean:y,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function Go(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Kn(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!ht(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return W(o,t),{params:a,paramMappings:t}}var yt=b(g());var kt=b(g());function q(o,t,e){return kt.tidy(()=>{let r=kt.conv2d(o,t.filters,e,"same");return r=kt.add(r,t.batch_norm_offset),kt.clipByValue(r,0,6)})}var Qn=.0010000000474974513;function ta(o,t,e){return yt.tidy(()=>{let r=yt.depthwiseConv2d(o,t.filters,e,"same");return r=yt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Qn),yt.clipByValue(r,0,6)})}function ea(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function zo(o,t){return yt.tidy(()=>{let e,r=q(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=ea(i);r=ta(r,a.depthwise_conv,c),r=q(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function ra(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let v=Math.max(n,c),w=Math.max(a,m),h=Math.min(s,p),y=Math.min(i,d),T=Math.max(h-v,0)*Math.max(y-w,0);return T/(u+l-T)}function Vo(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=ra(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function oa(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function na(o,t){let{sizes:e,centers:r}=oa(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function Uo(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=na(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Oe=b(g());var $e=b(g());function Ut(o,t){return $e.tidy(()=>{let e=o.shape[0],r=$e.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=$e.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Xo(o,t,e){return Oe.tidy(()=>{let r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),v=Ut(s,e.box_predictor_3),w=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),y=Oe.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,v.boxPredictionEncoding,w.boxPredictionEncoding,h.boxPredictionEncoding],1),T=Oe.concat([d.classPrediction,u.classPrediction,l.classPrediction,v.classPrediction,w.classPrediction,h.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var Z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=zo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Xo(a.out,a.conv11,e.prediction_layer);return Uo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*w);return new M(p[F],new oe(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Xt;return t.extractWeights(o),t}function aa(o){return Jo(o)}var qo=class extends Xt{};var Zo=.4,Ko=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Qo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],tn=[117.001,114.697,97.404],en="tiny_yolov2_model",rn="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function ao(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!br(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var so=b(g());function sa(o,t){let e=ce(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=sa(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,w,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,l,"conv1"),et=m(l,v,"conv2"),it=m(v,w,"conv3"),X=m(w,h,"conv4"),_t=m(h,y,"conv5"),wt=T?m(y,T,"conv6"):void 0,Pt=F?m(T,F,"conv7"):void 0,te=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}else{let[d,u,l,v,w,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,l,"conv1"),et=c(l,v,"conv2"),it=c(v,w,"conv3"),X=c(w,h,"conv4"),_t=c(h,y,"conv5"),wt=c(y,T,"conv6"),Pt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var io=class extends S{constructor(t){super("TinyYolov2");ao(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],w=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,_t=G-it/2,wt=et-X/2,Pt={row:y,col:T,anchor:F},{classScore:te,label:lo}=this.withClassScores?await this.extractPredictedClass(l,Pt):{classScore:1,label:0};v.push({box:new re(_t,wt,_t+it,wt+X),score:L,classScore:L*te,label:lo,...Pt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=io;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var co=b(g());async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var _={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>_.tinyYolov2.locateFaces(o,t),pn=o=>_.faceLandmark68Net.detectLandmarks(o),da=o=>_.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>_.faceRecognitionNet.computeFaceDescriptor(o),la=o=>_.faceExpressionNet.predictExpressions(o),fa=o=>_.ageGenderNet.predictAgeAndGender(o),dn=o=>_.ssdMobilenetv1.load(o),ha=o=>_.tinyFaceDetector.load(o),xa=o=>_.tinyYolov2.load(o),ba=o=>_.faceLandmark68Net.load(o),ga=o=>_.faceLandmark68TinyNet.load(o),va=o=>_.faceRecognitionNet.load(o),ya=o=>_.faceExpressionNet.load(o),Fa=o=>_.ageGenderNet.load(o),Ta=dn,_a=mn,wa=pn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends mo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},Pe=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends Pe{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new Pe(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>_.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var _r=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends _r{async run(){let{input:t,options:e}=this,r;if(e instanceof gr)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof Z)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof lt)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Ot({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},wr=class extends _r{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function Pa(o,t=new Z){return new wr(o,t)}function Pr(o,t=new Z){return new He(o,t)}async function un(o,t){return Pr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Pr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=un;function uo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Dr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>uo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ma(o){let t=new Fe;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(Ot(o,n),a)}return pt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Na=typeof process!="undefined",Ia=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",La={faceapi:No,node:Na,browser:Ia}; +var Vn=Object.create,sr=Object.defineProperty,Un=Object.getPrototypeOf,Xn=Object.prototype.hasOwnProperty,Jn=Object.getOwnPropertyNames,qn=Object.getOwnPropertyDescriptor;var jr=o=>sr(o,"__esModule",{value:!0});var Do=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),ir=(o,t)=>{for(var e in t)sr(o,e,{get:t[e],enumerable:!0})},Zn=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Jn(t))!Xn.call(o,r)&&r!=="default"&&sr(o,r,{get:()=>t[r],enumerable:!(e=qn(t,r))||e.enumerable});return o},b=o=>Zn(jr(sr(o!=null?Vn(Un(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);var g=Do(Hr=>{var Kn=Object.create,Yr=Object.defineProperty,Qn=Object.getPrototypeOf,ta=Object.prototype.hasOwnProperty,ea=Object.getOwnPropertyNames,ra=Object.getOwnPropertyDescriptor,Eo=o=>Yr(o,"__esModule",{value:!0}),Mo=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of ea(t))!ta.call(o,r)&&r!=="default"&&Yr(o,r,{get:()=>t[r],enumerable:!(e=ra(t,r))||e.enumerable});return o},oa=o=>Mo(Eo(Yr(o!=null?Kn(Qn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);Eo(Hr);Mo(Hr,oa(require("@tensorflow/tfjs")))});var ko=Do((ca,Lo)=>{jr(ca);ir(ca,{isNodejs:()=>ma});function ma(){return typeof global=="object"&&!0&&typeof Lo!="undefined"&&typeof process!="undefined"&&!!process.version}});jr(exports);ir(exports,{AgeGenderNet:()=>je,BoundingBox:()=>Ut,Box:()=>D,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>Pt,ComputeFaceDescriptorsTaskBase:()=>Ze,ComputeSingleFaceDescriptorTask:()=>Dt,DetectAllFaceLandmarksTask:()=>tr,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Qe,DetectFacesTaskBase:()=>rr,DetectSingleFaceLandmarksTask:()=>er,DetectSingleFaceTask:()=>or,Dimensions:()=>A,FACE_EXPRESSION_LABELS:()=>Sr,FaceDetection:()=>M,FaceDetectionNet:()=>fo,FaceExpressionNet:()=>$e,FaceExpressions:()=>Ft,FaceLandmark68Net:()=>te,FaceLandmark68TinyNet:()=>Ye,FaceLandmarkNet:()=>mo,FaceLandmarks:()=>j,FaceLandmarks5:()=>Xr,FaceLandmarks68:()=>Jt,FaceMatch:()=>le,FaceMatcher:()=>ar,FaceRecognitionNet:()=>ee,Gender:()=>ht,LabeledBox:()=>fe,LabeledFaceDescriptors:()=>ut,NetInput:()=>ft,NeuralNetwork:()=>S,ObjectDetection:()=>vt,Point:()=>h,PredictedBox:()=>Jr,Rect:()=>Xt,SsdMobilenetv1:()=>Ht,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ae,TinyFaceDetectorOptions:()=>qe,TinyYolov2:()=>oe,TinyYolov2Options:()=>ct,allFaces:()=>Hn,allFacesSsdMobilenetv1:()=>_o,allFacesTinyYolov2:()=>jn,awaitMediaLoaded:()=>xr,bufferToImage:()=>br,computeFaceDescriptor:()=>En,createCanvas:()=>At,createCanvasFromMedia:()=>xe,createFaceDetectionNet:()=>dn,createFaceRecognitionNet:()=>en,createSsdMobilenetv1:()=>lo,createTinyFaceDetector:()=>Yn,createTinyYolov2:()=>yn,detectAllFaces:()=>nr,detectFaceLandmarks:()=>vo,detectFaceLandmarksTiny:()=>Dn,detectLandmarks:()=>$n,detectSingleFace:()=>On,draw:()=>ao,env:()=>P,euclideanDistance:()=>Or,extendWithAge:()=>Ve,extendWithFaceDescriptor:()=>ze,extendWithFaceDetection:()=>It,extendWithFaceExpressions:()=>Oe,extendWithFaceLandmarks:()=>Qt,extendWithGender:()=>Ue,extractFaceTensors:()=>Zt,extractFaces:()=>qt,fetchImage:()=>So,fetchJson:()=>Tr,fetchNetWeights:()=>Ao,fetchOrThrow:()=>Wt,getContext2dOrThrow:()=>W,getMediaDimensions:()=>St,imageTensorToCanvas:()=>vr,imageToSquare:()=>yr,inverseSigmoid:()=>Io,iou:()=>pr,isMediaElement:()=>ke,isMediaLoaded:()=>he,isWithAge:()=>rn,isWithFaceDetection:()=>nt,isWithFaceExpressions:()=>Ar,isWithFaceLandmarks:()=>$t,isWithGender:()=>on,loadAgeGenderModel:()=>Wn,loadFaceDetectionModel:()=>Bn,loadFaceExpressionModel:()=>An,loadFaceLandmarkModel:()=>Ln,loadFaceLandmarkTinyModel:()=>kn,loadFaceRecognitionModel:()=>Sn,loadSsdMobilenetv1Model:()=>yo,loadTinyFaceDetectorModel:()=>Nn,loadTinyYolov2Model:()=>In,loadWeightMap:()=>wr,locateFaces:()=>Rn,matchDimensions:()=>Bo,minBbox:()=>dr,nets:()=>_,nonMaxSuppression:()=>ur,normalize:()=>Z,padToSquare:()=>lr,predictAgeAndGender:()=>Cn,recognizeFaceExpressions:()=>Mn,resizeResults:()=>wo,resolveInput:()=>Lt,shuffleArray:()=>No,sigmoid:()=>ue,ssdMobilenetv1:()=>go,tf:()=>Gn,tinyFaceDetector:()=>wn,tinyYolov2:()=>Pn,toNetInput:()=>E,utils:()=>Gr,validateConfig:()=>$r,version:()=>zn});var Gn=b(g());var ao={};ir(ao,{AnchorPosition:()=>lt,DrawBox:()=>hr,DrawBoxOptions:()=>ro,DrawFaceLandmarks:()=>no,DrawFaceLandmarksOptions:()=>oo,DrawTextField:()=>kt,DrawTextFieldOptions:()=>Le,drawContour:()=>bt,drawDetections:()=>ua,drawFaceExpressions:()=>la,drawFaceLandmarks:()=>ha});function bt(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var Gr={};ir(Gr,{computeReshapedDimensions:()=>Ur,getCenterPoint:()=>Vt,isDimensions:()=>mr,isEven:()=>cr,isFloat:()=>Vr,isTensor:()=>Gt,isTensor1D:()=>na,isTensor2D:()=>zr,isTensor3D:()=>gt,isTensor4D:()=>V,isValidNumber:()=>ot,isValidProbablitiy:()=>de,range:()=>pt,round:()=>zt});var Co=b(g());var A=class{constructor(t,e){if(!ot(t)||!ot(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new A(1/this.width,1/this.height)}};function Gt(o,t){return o instanceof Co.Tensor&&o.shape.length===t}function na(o){return Gt(o,1)}function zr(o){return Gt(o,2)}function gt(o){return Gt(o,3)}function V(o){return Gt(o,4)}function Vr(o){return o%1!=0}function cr(o){return o%2==0}function zt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function mr(o){return o&&o.width&&o.height}function Ur({width:o,height:t},e){let r=e/Math.max(t,o);return new A(Math.round(o*r),Math.round(t*r))}function Vt(o){return o.reduce((t,e)=>t.add(e),new h(0,0)).div(new h(o.length,o.length))}function pt(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function ot(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function de(o){return ot(o)&&o>=0&&o<=1}var h=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new h(this.x+t.x,this.y+t.y)}sub(t){return new h(this.x-t.x,this.y-t.y)}mul(t){return new h(this.x*t.x,this.y*t.y)}div(t){return new h(this.x/t.x,this.y/t.y)}abs(){return new h(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new h(Math.floor(this.x),Math.floor(this.y))}};var D=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(ot)}static assertIsValidBox(t,e,r=!1){if(!D.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(ot),a=[r.x,r.y,r.width,r.height].every(ot);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];D.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new h(this.left,this.top)}get topRight(){return new h(this.right,this.top)}get bottomLeft(){return new h(this.left,this.bottom)}get bottomRight(){return new h(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new D({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new D({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new D({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var Ut=class extends D{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var vt=class{constructor(t,e,r,n,a){this._imageDims=new A(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new D(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new vt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends vt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function pr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function dr(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var dt=b(g());function Z(o,t){return dt.tidy(()=>{let[e,r,n]=t,a=dt.fill([...o.shape.slice(0,3),1],e,"float32"),s=dt.fill([...o.shape.slice(0,3),1],r,"float32"),i=dt.fill([...o.shape.slice(0,3),1],n,"float32"),c=dt.concat([a,s,i],3);return dt.sub(o,c)})}var Nt=b(g());function lr(o,t=!1){return Nt.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Nt.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Nt.cast(u,"float32"));return Nt.concat(d,s)})}function No(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function ue(o){return 1/(1+Math.exp(-o))}function Io(o){return Math.log(o/(1-o))}var Xt=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var aa=.5,sa=.43,ia=.45,j=class{constructor(t,e,r=new h(0,0)){let{width:n,height:a}=e;this._imgDims=new A(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new h(n,a)).add(r))}get shift(){return new h(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new h(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new h(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof M?t.box.floor():new D(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/ia),c=Vt(t),m=Math.floor(Math.max(0,c.x-aa*i)),p=Math.floor(Math.max(0,c.y-sa*i));return new Xt(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=dr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Xr=class extends j{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Vt([t[3],t[4]])]}};var Jt=class extends j{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Vt)}};var le=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${zt(this.distance)})`:""}`}};var fe=class extends D{static assertIsValidLabeledBox(t,e){if(D.assertIsValidBox(t,e),!ot(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var ut=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new ut(t.label,e)}};var Jr=class extends fe{static assertIsValidPredictedBox(t,e){if(fe.assertIsValidLabeledBox(t,e),!de(t.score)||!de(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function nt(o){return o.detection instanceof M}function It(o,t){return{...o,...{detection:t}}}function qr(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function fr(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Zr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=fr();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function Kr(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Qr=b(ko()),k;function pa(){if(!k)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return k}function to(o){k=o}function eo(){return Kr()?to(qr()):(0,Qr.isNodejs)()?to(Zr()):null}function da(o){if(k||eo(),!k)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=k.Canvas,Image:e=k.Image}=o;k.Canvas=t,k.Image=e,k.createCanvasElement=o.createCanvasElement||(()=>new t),k.createImageElement=o.createImageElement||(()=>new e),k.ImageData=o.ImageData||k.ImageData,k.Video=o.Video||k.Video,k.fetch=o.fetch||k.fetch,k.readFile=o.readFile||k.readFile}var P={getEnv:pa,setEnv:to,initialize:eo,createBrowserEnv:qr,createFileSystem:fr,createNodejsEnv:Zr,monkeyPatch:da,isBrowser:Kr,isNodejs:Qr.isNodejs};eo();function Lt(o){return!P.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function W(o){let{Canvas:t,CanvasRenderingContext2D:e}=P.getEnv();if(o instanceof e)return o;let r=Lt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var lt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lt||(lt={}));var Le=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||lt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},kt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof kt?t.text:t,this.anchor=e,this.options=new Le(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let v=c+d.x,w=c+d.y+(l+1)*s;r.fillText(u,v,w)})}};var ro=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:lt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Le({...s,...a})}},hr=class{constructor(t,e={}){this.box=new D(t),this.options=new ro(e)}draw(t){let e=W(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new kt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function ua(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof M?r.score:nt(r)?r.detection.score:void 0,a=r instanceof M?r.box:nt(r)?r.detection.box:new D(r),s=n?`${zt(n)}`:void 0;new hr(a,{label:s}).draw(o)})}var Te=b(g());function he(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function xr(o){return new Promise((t,e)=>{if(o instanceof P.getEnv().Canvas||he(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function br(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=P.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function St(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t?new A(o.naturalWidth,o.naturalHeight):o instanceof e?new A(o.videoWidth,o.videoHeight):new A(o.width,o.height)}function At({width:o,height:t}){let{createCanvasElement:e}=P.getEnv(),r=e();return r.width=o,r.height=t,r}function xe(o,t){let{ImageData:e}=P.getEnv();if(!(o instanceof e)&&!he(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||St(o),a=At({width:r,height:n});return o instanceof e?W(a).putImageData(o,0,0):W(a).drawImage(o,0,0,r,n),a}var gr=b(g());async function vr(o,t){let e=t||P.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(V(o)?1:0),s=gr.tidy(()=>o.as3D(r,n,a).toInt());return await gr.browser.toPixels(s,e),s.dispose(),e}function ke(o){let{Image:t,Canvas:e,Video:r}=P.getEnv();return o instanceof t||o instanceof e||o instanceof r}var K=b(g());function yr(o,t,e=!1){let{Image:r,Canvas:n}=P.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return At({width:1,height:1});let a=St(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=At({width:t,height:t}),p=o instanceof n?o:xe(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&W(m).drawImage(p,u,l,i,c),m}var ft=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(gt(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(V(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof P.getEnv().Canvas?r:xe(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return pt(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Ur({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,K.tidy(()=>{let r=pt(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof K.Tensor){let i=V(s)?s:s.expandDims();return i=lr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=K.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof P.getEnv().Canvas)return K.browser.fromPixels(yr(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return K.stack(r.map(a=>K.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function E(o){if(o instanceof ft)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(Lt);return r.forEach((n,a)=>{if(!ke(n)&&!gt(n)&&!V(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(V(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>ke(n)&&xr(n))),new ft(r,Array.isArray(o))}async function qt(o,t){let{Canvas:e}=P.getEnv(),r=o;if(!(o instanceof e)){let s=await E(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await vr(i)}let n=W(r);return t.map(s=>s instanceof M?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=At({width:c,height:m});return c>0&&m>0&&W(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var Fr=b(g());async function Zt(o,t){if(!gt(o)&&!V(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(V(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Fr.tidy(()=>{let[e,r,n]=o.shape.slice(V(o)?1:0);return t.map(i=>i instanceof M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>Fr.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Wt(o,t){let{fetch:e}=P.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function So(o){let t=await Wt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return br(e)}async function Tr(o){return(await Wt(o)).json()}async function Ao(o){return new Float32Array(await(await Wt(o)).arrayBuffer())}var Wo=b(g());function _r(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function wr(o,t){let{manifestUri:e,modelBaseUri:r}=_r(o,t),n=await Tr(e);return Wo.io.loadWeights(n,r)}function Bo(o,t,e=!1){let{width:r,height:n}=e?St(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Rt=b(g());var yt=b(g());var S=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof yt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof yt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=yt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await wr(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=P.getEnv(),{manifestUri:r,modelBaseUri:n}=_r(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=yt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof yt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var C=b(g());var be=b(g());function O(o,t,e){return be.tidy(()=>{let r=be.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=be.add(r,t.bias),r})}function Pr(o,t,e=!1){return C.tidy(()=>{let r=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):O(o,t.conv0,[2,2])),n=O(r,t.conv1,[1,1]),a=C.relu(C.add(r,n)),s=O(a,t.conv2,[1,1]);return C.relu(C.add(r,C.add(n,s)))})}function Se(o,t,e=!1,r=!0){return C.tidy(()=>{let n=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):O(o,t.conv0,r?[2,2]:[1,1])),a=O(n,t.conv1,[1,1]),s=C.relu(C.add(n,a)),i=O(s,t.conv2,[1,1]),c=C.relu(C.add(n,C.add(a,i))),m=O(c,t.conv3,[1,1]);return C.relu(C.add(n,C.add(a,C.add(i,m))))})}var Bt=b(g());function Kt(o,t,e="same",r=!1){return Bt.tidy(()=>{let n=Bt.add(Bt.conv2d(o,t.filters,[1,1],e),t.bias);return r?Bt.relu(n):n})}function B(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var Dr=b(g());function ge(o,t){return(e,r,n,a)=>{let s=Dr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=Dr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var Er=b(g());function Mr(o,t){return(e,r,n)=>{let a=Er.tensor2d(o(e*r),[e,r]),s=Er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var Ae=b(g());var Cr=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function ve(o,t){return(e,r,n)=>{let a=Ae.tensor4d(o(3*3*e),[3,3,e,1]),s=Ae.tensor4d(o(e*r),[1,1,e,r]),i=Ae.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new Cr(a,s,i)}}function ye(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new Cr(e,r,n)}}function H(o,t){return(e,r,n)=>{let a=o[e];if(!Gt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function R(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function Nr(o,t){let e=ge(o,t),r=ve(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function Ro(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractDenseBlock4Params:n}=Nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function Ir(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function Lr(o,t){let e=H(o,t),r=Ir(e),n=ye(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function $o(o){let t=[],{extractDenseBlock4Params:e}=Lr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return B(o,t),{params:r,paramMappings:t}}var We=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Rt.tidy(()=>{let r=Rt.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(Rt.scalar(255)),s=Se(a,e.dense0,!0);return s=Se(s,e.dense1),s=Se(s,e.dense2),s=Se(s,e.dense3),s=Rt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return $o(t)}extractParams(t){return Ro(t)}};var Ho=b(g());var Fe=b(g());function Be(o,t){return Fe.tidy(()=>Fe.add(Fe.matMul(o,t.weights),t.bias))}function Oo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=R(o),i=Mr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function jo(o){let t=[],e=H(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return B(o,t),{params:n,paramMappings:t}}function kr(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var Re=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Ho.tidy(()=>{let r=t instanceof ft?this.faceFeatureExtractor.forwardInput(t):t;return Be(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Oo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=kr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),jo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Sr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ft=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Sr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Sr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var $e=class extends Re{constructor(t=new We){super("FaceExpressionNet",t)}forwardInput(t){return Te.tidy(()=>Te.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(Te.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new Ft(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ar(o){return o.expressions instanceof Ft}function Oe(o,t){return{...o,...{expressions:t}}}function la(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof Ft?a:Ar(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=nt(a)?a.detection.box.bottomLeft:r||new h(0,0);new kt(c.map(d=>`${d.expression} (${zt(d.probability)})`),m).draw(o)})}function $t(o){return nt(o)&&o.landmarks instanceof j&&o.unshiftedLandmarks instanceof j&&o.alignedRect instanceof M}function fa(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function Qt(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new M(o.detection.score,n.rescale(a.reverse()),a),i=fa(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var oo=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},no=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new oo(e)}draw(t){let e=W(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof Jt&&(e.strokeStyle=s,e.lineWidth=a,bt(e,this.faceLandmarks.getJawOutline()),bt(e,this.faceLandmarks.getLeftEyeBrow()),bt(e,this.faceLandmarks.getRightEyeBrow()),bt(e,this.faceLandmarks.getNose()),bt(e,this.faceLandmarks.getLeftEye(),!0),bt(e,this.faceLandmarks.getRightEye(),!0),bt(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function ha(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof j?r:$t(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new no(n).draw(o)})}var Yo="1.0.2";var xt=b(g());var I=b(g());function xa(o,t){let e=ge(o,t),r=ve(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Go(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=R(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=xa(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};pt(t,0,1).forEach(y=>{l[`main_block_${y}`]=c(128,`middle_flow/main_block_${y}`)});let v=i(128,256,"exit_flow/reduction_block"),w=s(256,512,"exit_flow/separable_conv"),x={reduction_block:v,separable_conv:w};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:x}}}function ba(o,t){let e=H(o,t),r=Ir(e),n=ye(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function zo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=ba(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};pt(t,0,1).forEach(w=>{d[`main_block_${w}`]=s(`middle_flow/main_block_${w}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return B(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function Vo(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function so(o,t,e=!0){let r=e?I.relu(o):o;return r=O(r,t.separable_conv0,[1,1]),r=O(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,Vo(o,t.expansion_conv,[2,2])),r}function ga(o,t){let e=O(I.relu(o),t.separable_conv0,[1,1]);return e=O(I.relu(e),t.separable_conv1,[1,1]),e=O(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var io=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(Vo(a,e.entry_flow.conv_in,[2,2]));return s=so(s,e.entry_flow.reduction_block_0,!1),s=so(s,e.entry_flow.reduction_block_1),pt(this._numMainBlocks,0,1).forEach(i=>{s=ga(s,e.middle_flow[`main_block_${i}`])}),s=so(s,e.exit_flow.reduction_block),s=I.relu(O(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return zo(t,this._numMainBlocks)}extractParams(t){return Go(t,this._numMainBlocks)}};function Uo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),n=Mr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Xo(o){let t=[],e=H(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return B(o,t),{params:n,paramMappings:t}}var ht;(function(o){o.FEMALE="female",o.MALE="male"})(ht||(ht={}));var je=class extends S{constructor(t=new io(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return xt.tidy(()=>{let r=t instanceof ft?this.faceFeatureExtractor.forwardInput(t):t,n=xt.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Be(n,e.fc.age).as1D(),s=Be(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return xt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:xt.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=xt.unstack(r.age),a=xt.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?ht.MALE:ht.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Uo(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=kr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Xo(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var Y=b(g());var He=class extends Re{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return Y.tidy(()=>{let s=(d,u)=>Y.stack([Y.fill([68],d,"float32"),Y.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return u(l,v)?Math.abs(l-v)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(Y.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return Y.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await E(t))}async detectLandmarks(t){let e=await E(t),r=Y.tidy(()=>Y.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>cr(d)),m=i.filter((p,d)=>!cr(d));return new Jt(Array(68).fill(0).map((p,d)=>new h(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var te=class extends He{constructor(t=new We){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Ot=b(g());function Jo(o){let t=[],{extractDenseBlock3Params:e}=Lr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return B(o,t),{params:r,paramMappings:t}}function qo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractDenseBlock3Params:n}=Nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var co=class extends S{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Ot.tidy(()=>{let r=Ot.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(Ot.scalar(255)),s=Pr(a,e.dense0,!0);return s=Pr(s,e.dense1),s=Pr(s,e.dense2),s=Ot.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Jo(t)}extractParams(t){return qo(t)}};var Ye=class extends He{constructor(t=new co){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var mo=class extends te{};var U=b(g());var _e=b(g());var Wr=b(g());function Zo(o,t){return Wr.add(Wr.mul(o,t.weights),t.biases)}function po(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=_e.conv2d(o,a,e,n);return i=_e.add(i,s),i=Zo(i,t.scale),r?_e.relu(i):i}function Ko(o,t){return po(o,t,[1,1],!0)}function uo(o,t){return po(o,t,[1,1],!1)}function Br(o,t){return po(o,t,[2,2],!0,"valid")}var G=b(g());function va(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Vr(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return G.tidy(()=>G.transpose(G.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=G.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=G.tensor1d(o(i)),p=G.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function Qo(o){let{extractWeights:t,getRemainingWeights:e}=R(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=va(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),v=a(147456,128,3,"conv128_down",!0),w=a(147456,128,3,"conv128_1"),x=a(147456,128,3,"conv128_2"),y=a(589824,256,3,"conv256_down",!0),T=a(589824,256,3,"conv256_1"),F=a(589824,256,3,"conv256_2"),L=a(589824,256,3,"conv256_down_out"),z=G.tidy(()=>G.transpose(G.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:v,conv128_1:w,conv128_2:x,conv256_down:y,conv256_1:T,conv256_2:F,conv256_down_out:L,fc:z},paramMappings:r}}function ya(o,t){let e=H(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function tn(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=ya(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),v=r("conv128_2"),w=r("conv256_down"),x=r("conv256_1"),y=r("conv256_2"),T=r("conv256_down_out"),{fc:F}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!zr(F))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${F}`);let L={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:v,conv256_down:w,conv256_1:x,conv256_2:y,conv256_down_out:T,fc:F};return B(o,t),{params:L,paramMappings:t}}var $=b(g());function at(o,t){let e=Ko(o,t.conv1);return e=uo(e,t.conv2),e=$.add(e,o),e=$.relu(e),e}function Ge(o,t){let e=Br(o,t.conv1);e=uo(e,t.conv2);let r=$.avgPool(o,2,2,"valid"),n=$.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=$.zeros(i);e=$.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=$.zeros(m);e=$.concat([e,p],2)}return r=a?$.concat([r,n],3):r,e=$.add(r,e),e=$.relu(e),e}var ee=class extends S{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return U.tidy(()=>{let r=U.cast(t.toBatchTensor(150,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(U.scalar(256)),s=Br(a,e.conv32_down);s=U.maxPool(s,3,2,"valid"),s=at(s,e.conv32_1),s=at(s,e.conv32_2),s=at(s,e.conv32_3),s=Ge(s,e.conv64_down),s=at(s,e.conv64_1),s=at(s,e.conv64_2),s=at(s,e.conv64_3),s=Ge(s,e.conv128_down),s=at(s,e.conv128_1),s=at(s,e.conv128_2),s=Ge(s,e.conv256_down),s=at(s,e.conv256_1),s=at(s,e.conv256_2),s=Ge(s,e.conv256_down_out);let i=s.mean([1,2]);return U.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=U.tidy(()=>U.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return tn(t)}extractParams(t){return Qo(t)}};function en(o){let t=new ee;return t.extractWeights(o),t}function ze(o,t){return{...o,...{descriptor:t}}}function rn(o){return typeof o.age=="number"}function Ve(o,t){return{...o,...{age:t}}}function on(o){return(o.gender===ht.MALE||o.gender===ht.FEMALE)&&de(o.genderProbability)}function Ue(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var it=b(g());var st=b(g());function Fa(o,t){function e(c,m){let p=st.tensor4d(o(3*3*c),[3,3,c,1]),d=st.tensor1d(o(c)),u=st.tensor1d(o(c)),l=st.tensor1d(o(c)),v=st.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=st.tensor4d(o(c*m*p*p),[p,p,c,m]),v=st.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),v=a(256,512,"mobilenetv1/conv_6"),w=a(512,512,"mobilenetv1/conv_7"),x=a(512,512,"mobilenetv1/conv_8"),y=a(512,512,"mobilenetv1/conv_9"),T=a(512,512,"mobilenetv1/conv_10"),F=a(512,512,"mobilenetv1/conv_11"),L=a(512,1024,"mobilenetv1/conv_12"),z=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,conv_8:x,conv_9:y,conv_10:T,conv_11:F,conv_12:L,conv_13:z}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),v=n(256,64,1,"prediction_layer/conv_6"),w=n(64,128,3,"prediction_layer/conv_7"),x=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),T=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),F=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),L=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),z=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),mt=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),q=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Et=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Mt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Ct=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,box_predictor_0:{box_encoding_predictor:x,class_predictor:y},box_predictor_1:{box_encoding_predictor:T,class_predictor:F},box_predictor_2:{box_encoding_predictor:L,class_predictor:z},box_predictor_3:{box_encoding_predictor:rt,class_predictor:mt},box_predictor_4:{box_encoding_predictor:q,class_predictor:Et},box_predictor_5:{box_encoding_predictor:Mt,class_predictor:Ct}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function nn(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Fa(e,t),s=n(),i=a(),m={extra_dim:st.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Ta(o,t){let e=H(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,v=e(`${d}/depthwise_weights`,4,`${u}/filters`),w=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),x=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),y=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),T=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:v,batch_norm_scale:w,batch_norm_offset:x,batch_norm_mean:y,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function an(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Ta(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!gt(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return B(o,t),{params:a,paramMappings:t}}var Tt=b(g());var jt=b(g());function Q(o,t,e){return jt.tidy(()=>{let r=jt.conv2d(o,t.filters,e,"same");return r=jt.add(r,t.batch_norm_offset),jt.clipByValue(r,0,6)})}var _a=.0010000000474974513;function wa(o,t,e){return Tt.tidy(()=>{let r=Tt.depthwiseConv2d(o,t.filters,e,"same");return r=Tt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,_a),Tt.clipByValue(r,0,6)})}function Pa(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function sn(o,t){return Tt.tidy(()=>{let e,r=Q(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=Pa(i);r=wa(r,a.depthwise_conv,c),r=Q(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function Da(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let v=Math.max(n,c),w=Math.max(a,m),x=Math.min(s,p),y=Math.min(i,d),T=Math.max(x-v,0)*Math.max(y-w,0);return T/(u+l-T)}function cn(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=Da(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function Ea(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function Ma(o,t){let{sizes:e,centers:r}=Ea(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function mn(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=Ma(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Je=b(g());var Xe=b(g());function re(o,t){return Xe.tidy(()=>{let e=o.shape[0],r=Xe.reshape(Kt(o,t.box_encoding_predictor),[e,-1,1,4]),n=Xe.reshape(Kt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function pn(o,t,e){return Je.tidy(()=>{let r=Q(o,e.conv_0,[1,1]),n=Q(r,e.conv_1,[2,2]),a=Q(n,e.conv_2,[1,1]),s=Q(a,e.conv_3,[2,2]),i=Q(s,e.conv_4,[1,1]),c=Q(i,e.conv_5,[2,2]),m=Q(c,e.conv_6,[1,1]),p=Q(m,e.conv_7,[2,2]),d=re(t,e.box_predictor_0),u=re(o,e.box_predictor_1),l=re(n,e.box_predictor_2),v=re(s,e.box_predictor_3),w=re(c,e.box_predictor_4),x=re(p,e.box_predictor_5),y=Je.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,v.boxPredictionEncoding,w.boxPredictionEncoding,x.boxPredictionEncoding],1),T=Je.concat([d.classPrediction,u.classPrediction,l.classPrediction,v.classPrediction,w.classPrediction,x.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ht=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return it.tidy(()=>{let r=it.cast(t.toBatchTensor(512,!1),"float32"),n=it.sub(it.mul(r,it.scalar(.007843137718737125)),it.scalar(1)),a=sn(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=pn(a.out,a.conv11,e.prediction_layer);return mn(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new X(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,z]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(q=>q*x),[rt,mt]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(q=>q*w);return new M(p[F],new Xt(rt,L,mt-rt,z-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return an(t)}extractParams(t){return nn(t)}};function lo(o){let t=new Ht;return t.extractWeights(o),t}function dn(o){return lo(o)}var fo=class extends Ht{};var un=.4,ln=[new h(.738768,.874946),new h(2.42204,2.65704),new h(4.30971,7.04493),new h(10.246,4.59428),new h(12.6868,11.8741)],fn=[new h(1.603231,2.094468),new h(6.041143,7.080126),new h(2.882459,3.518061),new h(4.266906,5.178857),new h(9.041765,10.66308)],hn=[117.001,114.697,97.404],xn="tiny_yolov2_model",bn="tiny_yolov2_separable_conv_model";var N=b(g());var Rr=o=>typeof o=="number";function $r(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Rr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Rr(t.x)&&Rr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Rr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var et=b(g());var tt=b(g());function we(o){return tt.tidy(()=>{let t=tt.mul(o,tt.scalar(.10000000149011612));return tt.add(tt.relu(tt.sub(o,t)),t)})}function _t(o,t){return et.tidy(()=>{let e=et.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=et.conv2d(e,t.conv.filters,[1,1],"valid"),e=et.sub(e,t.bn.sub),e=et.mul(e,t.bn.truediv),e=et.add(e,t.conv.bias),we(e)})}var Yt=b(g());function wt(o,t){return Yt.tidy(()=>{let e=Yt.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Yt.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=Yt.add(e,t.bias),we(e)})}var ho=b(g());function Ca(o,t){let e=ge(o,t);function r(s,i){let c=ho.tensor1d(o(s)),m=ho.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=ve(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function gn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Ca(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,w,x,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),z=m(u,l,"conv1"),rt=m(l,v,"conv2"),mt=m(v,w,"conv3"),q=m(w,x,"conv4"),Et=m(x,y,"conv5"),Mt=T?m(y,T,"conv6"):void 0,Ct=F?m(T,F,"conv7"):void 0,pe=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}else{let[d,u,l,v,w,x,y,T,F]=r,L=c(d,u,"conv0"),z=c(u,l,"conv1"),rt=c(l,v,"conv2"),mt=c(v,w,"conv3"),q=c(w,x,"conv4"),Et=c(x,y,"conv5"),Mt=c(y,T,"conv6"),Ct=c(T,F,"conv7"),pe=i(F,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Na(o,t){let e=H(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=ye(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function vn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Na(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var ct=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var xo=class extends S{constructor(t){super("TinyYolov2");$r(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=_t(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=_t(r,e.conv6),r=_t(r,e.conv7),Kt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?we(Kt(t,e.conv0,"valid",!1)):wt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?wt(r,e.conv6):r,r=e.conv7?wt(r,e.conv7):r,Kt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?Z(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ct(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(x=>x.box),d=m.map(x=>x.score),u=m.map(x=>x.classScore),l=m.map(x=>this.config.classes[x.label]);return ur(p.map(x=>x.rescale(r)),d,this.config.iouThreshold,!0).map(x=>new vt(d[x],u[x],l[x],p[x],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return vn(t,this.config)}extractParams(t){let e=this.config.filterSizes||xo.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return gn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],w=await u.array(),x=await d.array();for(let y=0;yr){let z=(T+ue(x[y][T][F][0]))/m*i,rt=(y+ue(x[y][T][F][1]))/m*c,mt=Math.exp(x[y][T][F][2])*this.config.anchors[F].x/m*i,q=Math.exp(x[y][T][F][3])*this.config.anchors[F].y/m*c,Et=z-mt/2,Mt=rt-q/2,Ct={row:y,col:T,anchor:F},{classScore:pe,label:Po}=this.withClassScores?await this.extractPredictedClass(l,Ct):{classScore:1,label:0};v.push({box:new Ut(Et,Mt,Et+mt,Mt+q),score:L,classScore:L*pe,label:Po,...Ct})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},Pe=xo;Pe.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var oe=class extends Pe{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:un,classes:["face"],...t?{anchors:fn,meanRgb:hn}:{anchors:ln,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?bn:xn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function yn(o,t=!0){let e=new oe(t);return e.extractWeights(o),e}var qe=class extends ct{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Ke=b(g());var bo=b(g());async function ne(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>$t(c)?n(c):c.detection),s=r||(t instanceof bo.Tensor?await Zt(t,a):await qt(t,a)),i=await e(s);return s.forEach(c=>c instanceof bo.Tensor&&c.dispose()),i}async function De(o,t,e,r,n){return ne([o],t,async a=>e(a[0]),r,n)}var Fn=.4,Tn=[new h(1.603231,2.094468),new h(6.041143,7.080126),new h(2.882459,3.518061),new h(4.266906,5.178857),new h(9.041765,10.66308)],_n=[117.001,114.697,97.404];var ae=class extends Pe{constructor(){let t={withSeparableConvs:!0,iouThreshold:Fn,classes:["face"],anchors:Tn,meanRgb:_n,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var _={ssdMobilenetv1:new Ht,tinyFaceDetector:new ae,tinyYolov2:new oe,faceLandmark68Net:new te,faceLandmark68TinyNet:new Ye,faceRecognitionNet:new ee,faceExpressionNet:new $e,ageGenderNet:new je},go=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),wn=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),Pn=(o,t)=>_.tinyYolov2.locateFaces(o,t),vo=o=>_.faceLandmark68Net.detectLandmarks(o),Dn=o=>_.faceLandmark68TinyNet.detectLandmarks(o),En=o=>_.faceRecognitionNet.computeFaceDescriptor(o),Mn=o=>_.faceExpressionNet.predictExpressions(o),Cn=o=>_.ageGenderNet.predictAgeAndGender(o),yo=o=>_.ssdMobilenetv1.load(o),Nn=o=>_.tinyFaceDetector.load(o),In=o=>_.tinyYolov2.load(o),Ln=o=>_.faceLandmark68Net.load(o),kn=o=>_.faceLandmark68TinyNet.load(o),Sn=o=>_.faceRecognitionNet.load(o),An=o=>_.faceExpressionNet.load(o),Wn=o=>_.ageGenderNet.load(o),Bn=yo,Rn=go,$n=vo;var Fo=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ce=class extends Fo{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>Oe(r,e[n]))}withAgeAndGender(){return new Ee(this,this.input)}},Ne=class extends Fo{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Oe(t,e)}withAgeAndGender(){return new Me(this,this.input)}},ce=class extends Ce{withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},me=class extends Ne{withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var To=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ee=class extends To{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Ve(Ue(r,s,i),a)})}withFaceExpressions(){return new Ce(this,this.input)}},Me=class extends To{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await De(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Ve(Ue(t,r,n),e)}withFaceExpressions(){return new Ne(this,this.input)}},se=class extends Ee{withFaceExpressions(){return new ce(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},ie=class extends Me{withFaceExpressions(){return new me(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var Ze=class extends J{constructor(t,e){super();this.parentTask=t;this.input=e}},Pt=class extends Ze{async run(){let t=await this.parentTask;return(await ne(t,this.input,r=>Promise.all(r.map(n=>_.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>ze(t[n],r))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}},Dt=class extends Ze{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ze(t,e)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}};var Qe=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},tr=class extends Qe{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Ke.Tensor?await Zt(this.input,e):await qt(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),t.map((a,s)=>Qt(a,n[s]))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},er=class extends Qe{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Ke.Tensor?await Zt(this.input,[e]):await qt(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),Qt(t,n)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var rr=class extends J{constructor(t,e=new X){super();this.input=t;this.options=e}},Ie=class extends rr{async run(){let{input:t,options:e}=this,r;if(e instanceof qe)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ct)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>It({},r)))})}withFaceLandmarks(t=!1){return new tr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ce(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ee(this.runAndExtendWithFaceDetections(),this.input)}},or=class extends rr{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?It({},e):void 0)})}withFaceLandmarks(t=!1){return new er(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Me(this.runAndExtendWithFaceDetection(),this.input)}};function On(o,t=new X){return new or(o,t)}function nr(o,t=new X){return new Ie(o,t)}async function _o(o,t){return nr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function jn(o,t={}){return nr(o,new ct(t)).withFaceLandmarks().withFaceDescriptors()}var Hn=_o;function Or(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var ar=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof ut)return s;if(s instanceof Float32Array)return new ut(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new ut(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>Or(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new le(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>ut.fromJSON(r));return new ar(e,t.distanceThreshold)}};function Yn(o){let t=new ae;return t.extractWeights(o),t}function wo(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>wo(n,{width:e,height:r}));if($t(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Qt(It(o,n),a)}return nt(o)?It(o,o.detection.forSize(e,r)):o instanceof j||o instanceof M?o.forSize(e,r):o}var Ia=typeof process!="undefined",La=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",zn={faceapi:Yo,node:Ia,browser:La};0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); //# sourceMappingURL=face-api.node-cpu.js.map diff --git a/dist/face-api.node-cpu.js.map b/dist/face-api.node-cpu.js.map index ce5f3d6..884b856 100644 --- a/dist/face-api.node-cpu.js.map +++ b/dist/face-api.node-cpu.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["../src/tfjs/tf-node-cpu.ts", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"], "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\nexport * from '@tensorflow/tfjs';\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof require === 'function'\n && typeof module !== 'undefined'\n // issues with gatsby.js: module.exports is undefined\n // && !!module.exports\n && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed: boolean = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number\n\n private _height: number\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number\n\n private _y: number\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number\n\n private _y: number\n\n private _width: number\n\n private _height: number\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({\n x, y, width, height,\n });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({\n x, y, width, height,\n });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const {\n x, y, right, bottom,\n } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({\n x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({\n x, y, width, height,\n });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return {\n dy, edy, dx, edx, y, ey, x, ex, w, h,\n };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n super({\n left, top, right, bottom,\n }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number\n\n private _classScore: number\n\n private _className: string\n\n private _box: Rect\n\n private _imageDims: Dimensions\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU: boolean = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n imgTensor: tf.Tensor4D,\n isCenterImage: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) {\n return imgTensor;\n }\n\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n ? createPaddingTensor(remainingPaddingAmount)\n : null;\n\n const tensorsToStack = [\n paddingTensorPrepend,\n imgTensor,\n paddingTensorAppend,\n ]\n .filter((t) => !!t)\n // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n super({\n x, y, width, height,\n }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point\n\n protected _positions: Point[]\n\n protected _imgDims: Dimensions\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string\n\n private _distance: number\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance: boolean = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string\n\n private _descriptors: Float32Array[]\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number\n\n private _classScore: number\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n TSource\n>(\n sourceObj: TSource,\n detection: FaceDetection,\n): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n fetch,\n readFile,\n };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n\n if (!fs) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = err.toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => {\n fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n })\n : () => {\n throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n };\n\n return {\n readFile,\n };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition\n\n public backgroundColor: string\n\n public fontColor: string\n\n public fontSize: number\n\n public fontStyle: string\n\n public padding: number\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[]\n\n public anchor : IPoint\n\n public options: DrawTextFieldOptions\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string\n\n public lineWidth: number\n\n public drawLabelOptions: DrawTextFieldOptions\n\n public label?: string\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box\n\n public options: DrawBoxOptions\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = await t.data();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n return resolve(null);\n }\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = []\n\n private _canvases: HTMLCanvasElement[] = []\n\n private _batchSize: number\n\n private _treatAsBatchInput: boolean = false\n\n private _inputDimensions: number[][] = []\n\n private _inputSize: number\n\n constructor(\n inputs: Array,\n treatAsBatchInput: boolean = false,\n ) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor'.\n let imgTensor = isTensor4D(input) ? input : input.expandDims();\n // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor'.\n imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n return batchTensor;\n });\n }\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) {\n return inputs;\n }\n\n const inputArgArray = Array.isArray(inputs)\n ? inputs\n : [inputs];\n\n if (!inputArgArray.length) {\n throw new Error('toNetInput - empty array passed as input');\n }\n\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n const inputArray = inputArgArray.map(resolveInput);\n\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') {\n throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n }\n\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) {\n throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n }\n });\n\n // wait for all media elements being loaded\n await Promise.all(\n inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n );\n\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n input: TNetInput,\n detections: Array,\n): Promise {\n const { Canvas } = env.getEnv();\n\n let canvas = input as HTMLCanvasElement;\n\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n\n if (netInput.batchSize > 1) {\n throw new Error('extractFaces - batchSize > 1 not supported');\n }\n\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas\n ? tensorOrCanvas\n : await imageTensorToCanvas(tensorOrCanvas);\n }\n\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections.map(\n (det) => (det instanceof FaceDetection\n ? det.forSize(canvas.width, canvas.height).box.floor()\n : det),\n )\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n return boxes.map(({\n x, y, width, height,\n }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n imageTensor: tf.Tensor3D | tf.Tensor4D,\n detections: Array,\n): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n const boxes = detections.map(\n (det) => (det instanceof FaceDetection\n ? det.forSize(imgWidth, imgHeight).box\n : det),\n )\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n const faceTensors = boxes.map(({\n x, y, width, height,\n }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined\n\n protected _paramMappings: ParamMapping[] = []\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer: boolean = false,\n isScaleDown: boolean = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import {\n extractConvParamsFactory,\n extractSeparableConvParamsFactory,\n ExtractWeightsFunction,\n ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n FaceFeatureExtractorParams,\n IFaceFeatureExtractor,\n TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n public neutral: number\n\n public happy: number\n\n public sad: number\n\n public angry: number\n\n public fearful: number\n\n public disgusted: number\n\n public surprised: number\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & {\n expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n TSource\n>(\n sourceObj: TSource,\n expressions: FaceExpressions,\n): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n canvasArg: string | HTMLCanvasElement,\n faceExpressions: DrawFaceExpressionsInput | Array,\n minConfidence = 0.1,\n textFieldAnchor?: IPoint,\n) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n landmarks: TFaceLandmarks,\n unshiftedLandmarks: TFaceLandmarks,\n alignedRect: FaceDetection,\n angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks, FaceLandmarks> {\n return isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n // convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (theta) => (theta * 180) / Math.PI;\n\n const angle = { roll: undefined, pitch: undefined, yaw: undefined };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n // value of 0 means center\n\n // roll is face lean from left to right\n // comparing x,y of outside corners of leftEye and rightEye\n angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n // pitch is face turn from left right\n // comparing x distance of top of nose to left and right edge of face\n // precision is lacking since coordinates are not precise enough\n angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n // yaw is face move from up to down\n // comparing size of the box around the face with top and bottom of detected landmarks\n // silly hack, but this gives us face compression on y-axis\n // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n return angle;\n}\n\nexport function extendWithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n const angle = calculateFaceAngle(unshiftedLandmarks);\n\n const extension = {\n landmarks,\n unshiftedLandmarks,\n alignedRect,\n angle,\n };\n\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean\n\n public drawPoints: boolean\n\n public lineWidth: number\n\n public pointSize: number\n\n public lineColor: string\n\n public pointColor: string\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks\n\n public options: DrawFaceLandmarksOptions\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (await ageTensor.data())[0];\n const probMale = (await genderTensor.data())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors,\n extractWeightEntryFactory,\n loadSeparableConvParamsFactory,\n ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(await landmarkTensor.data());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown: boolean = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n\n if (!params) {\n throw new Error('SsdMobilenetv1 - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n const features = mobileNetV1(x, params.mobilenetv1);\n\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(\n input: TNetInput,\n options: ISsdMobilenetv1Options = {},\n ): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n const netInput = await toNetInput(input);\n\n const {\n boxes: _boxes,\n scores: _scores,\n } = this.forwardInput(netInput);\n\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n\n const scoresData = Array.from(await scores.data());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(\n boxes,\n scoresData as number[],\n maxResults,\n iouThreshold,\n minConfidence,\n );\n\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(\n left,\n top,\n right - left,\n bottom - top,\n ),\n {\n height: netInput.getInputHeight(0),\n width: netInput.getInputWidth(0),\n },\n );\n });\n\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n x: tf.Tensor4D,\n params: MobileNetV1.DepthwiseConvParams,\n strides: [number, number],\n) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) {\n conv11 = out;\n }\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n x: tf.Tensor4D,\n params: PointwiseConvParams,\n strides: [number, number],\n) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) {\n return 0.0;\n }\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(\n maxOutputSize,\n numBoxes,\n );\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n ];\n return {\n sizes,\n centers,\n };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const {\n sizes,\n centers,\n } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(\n boxPredictions: tf.Tensor4D,\n classPredictions: tf.Tensor4D,\n params: OutputLayerParams,\n) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(\n boxes,\n [batchSize, (boxes.shape[0] / batchSize), 4],\n );\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(\n scores,\n [batchSize, scores.shape[1] as number],\n );\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return {\n boxes: boxesByBatch,\n scores: scoresByBatch,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name: string = 'SsdMobilenetv1Options'\n\n private _minConfidence: number\n\n private _maxResults: number\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name: string = 'TinyYolov2Options'\n\n private _inputSize: number\n\n private _scoreThreshold: number\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs: boolean = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask {\n public async then(\n // eslint-disable-next-line no-unused-vars\n onfulfilled: (value: T) => T | PromiseLike,\n ): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask<\n TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n\n const faceLandmarksByFace = await Promise.all(faces.map(\n (face) => this.landmarkNet.detectLandmarks(face),\n )) as FaceLandmarks68[];\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n\n const results = await computeResults(faces);\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderTask,\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map(\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n )),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictAllFaceExpressionsTask {\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleFaceExpressionsTask {\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllFaceExpressionsTask,\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map(\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n )),\n this.extractedFaces,\n );\n\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictAllAgeAndGenderTask {\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleAgeAndGenderTask {\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n ) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public async run(): Promise {\n const { input, options } = this;\n\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise[]>(async (resolve) => {\n const detections = await this.run();\n resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n input: TNetInput,\n minConfidence?: number,\n): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n input: TNetInput,\n forwardParams: ITinyYolov2Options = {},\n): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff ** 2), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[]\n\n private _distanceThreshold: number\n\n constructor(\n inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>,\n distanceThreshold: number = 0.6,\n ) {\n this._distanceThreshold = distanceThreshold;\n\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n if (!inputArray.length) {\n throw new Error('FaceRecognizer.constructor - expected atleast one input');\n }\n\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) {\n return desc;\n }\n\n if (desc instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n }\n\n if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n }\n\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0)\n / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(\n label,\n this.computeMeanDistance(queryDescriptor, descriptors),\n ))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return bestMatch.distance < this.distanceThreshold\n ? bestMatch\n : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this.distanceThreshold,\n labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors\n .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n"], - "mappings": ";;;;;;;4qCAAA,GAAA,IAGA,GAAA,GAAc,GAAA,gCCHd,oDAAO,aAA6B,CAClC,MAAO,OAAO,SAAW,UACpB,IACA,MAAO,KAAW,aAGlB,MAAO,UAAY,aAAe,CAAC,CAAC,QAAQ,WCNnD,yyFAAA,OAAoB,OCApB,qRCEO,YACL,EACA,EACA,EAAoB,GACpB,CASA,GARA,EAAI,YAEJ,EAAO,MAAM,GAAG,QAAQ,CAAC,CAAE,IAAG,KAAK,IAAY,CAC7C,GAAM,GAAO,EAAO,GACpB,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,KAGZ,EAAU,CACZ,GAAM,GAAO,EAAO,EAAO,OAAS,GAC9B,EAAK,EAAO,GAClB,GAAI,CAAC,GAAQ,CAAC,EACZ,OAGF,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,EAAG,EAAG,GAGtB,EAAI,SC1BN,2RAAA,OAAoB,OCOb,WAAwC,CAK7C,YAAY,EAAe,EAAgB,CACzC,GAAI,CAAC,GAAc,IAAU,CAAC,GAAc,GAC1C,KAAM,IAAI,OAAM,wFAAwF,KAAK,UAAU,CAAE,QAAO,cAGlI,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,QAEnC,SAAsB,CAC3B,MAAO,IAAI,GAAW,EAAI,KAAK,MAAO,EAAI,KAAK,UDrB5C,YAAkB,EAAa,EAAa,CACjD,MAAO,aAAqB,YAAU,EAAO,MAAM,SAAW,EAGzD,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,WAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAiB,EAAa,CACnC,MAAO,GAAM,GAAM,EAGd,YAAgB,EAAa,CAClC,MAAO,GAAM,GAAM,EAGd,YAAe,EAAa,EAAe,EAAG,CACnD,GAAM,GAAI,IAAM,EAChB,MAAO,MAAK,MAAM,EAAM,GAAK,EAGxB,YAAsB,EAAmB,CAC9C,MAAO,IAAO,EAAI,OAAS,EAAI,OAG1B,YAAmC,CAAE,QAAO,UAAuB,EAAmB,CAC3F,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,IAAI,GAAW,KAAK,MAAM,EAAQ,GAAQ,KAAK,MAAM,EAAS,IAGhE,YAAwB,EAAqB,CAClD,MAAO,GAAI,OAAO,CAAC,EAAK,IAAO,EAAI,IAAI,GAAK,GAAI,GAAM,EAAG,IACtD,IAAI,GAAI,GAAM,EAAI,OAAQ,EAAI,SAG5B,YAAe,EAAa,EAAe,EAAwB,CACxE,MAAO,OAAM,GAAK,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,EAAS,EAAI,GAGhD,YAAuB,EAAU,CACtC,MAAO,CAAC,CAAC,GAAQ,IAAQ,UAAc,IAAQ,WAAc,CAAC,OAAO,MAAM,IAAQ,IAAQ,EAGtF,YAA4B,EAAU,CAC3C,MAAO,IAAc,IAAQ,GAAO,GAAK,GAAO,EExD3C,WAA8B,CAKnC,YAAY,EAAW,EAAW,CAChC,KAAK,GAAK,EACV,KAAK,GAAK,KAGR,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,GAEvB,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,KAAa,CAClB,MAAO,IAAI,GAAM,KAAK,IAAI,KAAK,GAAI,KAAK,IAAI,KAAK,IAG5C,WAAoB,CACzB,MAAO,MAAK,KAAM,KAAK,GAAK,EAAM,KAAK,GAAK,GAGvC,OAAe,CACpB,MAAO,IAAI,GAAM,KAAK,MAAM,KAAK,GAAI,KAAK,MAAM,KAAK,MCtClD,WAAwD,OAC/C,QAAO,EAAoB,CACvC,MAAO,CAAC,CAAC,GAAQ,CAAC,EAAK,EAAG,EAAK,EAAG,EAAK,MAAO,EAAK,QAAQ,MAAM,UAGrD,kBAAiB,EAAU,EAAgB,EAAmC,GAAO,CACjG,GAAI,CAAC,EAAI,OAAO,GACd,KAAM,IAAI,OAAM,GAAG,oBAAyB,KAAK,UAAU,2DAG7D,GAAI,CAAC,GAA4B,GAAI,MAAQ,GAAK,EAAI,OAAS,GAC7D,KAAM,IAAI,OAAM,GAAG,cAAmB,EAAI,sBAAsB,EAAI,oCAYxE,YAAY,EAA4B,EAAmC,GAAM,CAC/E,GAAM,GAAO,GAAQ,GAEf,EAAS,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAO,EAAI,QAAQ,MAAM,IAC1D,EAAS,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAAQ,MAAM,IAE3D,GAAI,CAAC,GAAU,CAAC,EACd,KAAM,IAAI,OAAM,2EAA2E,KAAK,UAAU,MAG5G,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,EAC1B,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAC9B,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAQ,EAAI,KAAM,EAAI,OAAS,EAAI,KAE/D,EAAI,iBAAiB,CACnB,IAAG,IAAG,QAAO,UACZ,kBAAmB,GAEtB,KAAK,GAAK,EACV,KAAK,GAAK,EACV,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,MAE1B,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,WAE/B,OAAe,CAAE,MAAO,MAAK,KAE7B,MAAc,CAAE,MAAO,MAAK,KAE5B,QAAgB,CAAE,MAAO,MAAK,EAAI,KAAK,SAEvC,SAAiB,CAAE,MAAO,MAAK,EAAI,KAAK,UAExC,OAAe,CAAE,MAAO,MAAK,MAAQ,KAAK,UAE1C,UAAiB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,QAEpD,WAAkB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAEtD,aAAoB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,WAEvD,cAAqB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAE7D,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,UAAyB,CAC9B,GAAI,CACF,IAAG,IAAG,QAAO,UACX,KACE,EAAO,KAAK,IAAI,EAAQ,GAC9B,MAAI,GAAQ,GACV,IAAM,EAAO,EACb,GAAS,GAEP,EAAS,GACX,IAAM,EAAO,EACb,GAAU,GAGL,GAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,QAAQ,EAAuC,CACpD,GAAM,GAAS,GAAa,GAAM,EAAkB,MAAQ,EACtD,EAAS,GAAa,GAAM,EAAkB,OAAS,EAC7D,MAAO,IAAI,GAAI,CACb,EAAG,KAAK,EAAI,EACZ,EAAG,KAAK,EAAI,EACZ,MAAO,KAAK,MAAQ,EACpB,OAAQ,KAAK,OAAS,IAInB,IAAI,EAAc,EAA4B,CACnD,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAC5B,KAAK,EAAK,EAAO,EACjB,KAAK,EAAK,EAAO,EACjB,KAAK,MAAQ,EACb,KAAK,OAAS,GAEhB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,mBAAmB,EAAkB,EAAiC,CAC3E,GAAM,CACJ,IAAG,IAAG,QAAO,UACX,KACE,EAAW,KAAK,IAAI,EAAG,GACvB,EAAW,KAAK,IAAI,EAAG,GAEvB,EAAW,EAAQ,EACnB,EAAY,EAAS,EACrB,EAAe,KAAK,IAAI,EAAU,EAAW,GAC7C,EAAgB,KAAK,IAAI,EAAW,EAAY,GAEtD,MAAQ,IAAI,GAAI,CACd,EAAG,EAAU,EAAG,EAAU,MAAO,EAAc,OAAQ,IACrD,QAGC,MAAM,EAAY,EAA0B,CACjD,GAAM,CAAE,QAAO,UAAW,KACpB,EAAI,KAAK,EAAI,EACb,EAAI,KAAK,EAAI,EAEnB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,aAAa,EAAqB,EAAoB,CAC3D,GAAM,GAAI,KAAK,MAAQ,EACjB,EAAI,KAAK,OAAS,EAElB,EAAK,EACL,EAAK,EACP,EAAM,EACN,EAAM,EAEN,EAAI,KAAK,KACT,EAAI,KAAK,IACT,EAAK,KAAK,MACV,EAAK,KAAK,OAEd,MAAI,GAAK,GACP,GAAM,CAAC,EAAK,EAAa,EACzB,EAAK,GAEH,EAAK,GACP,GAAM,CAAC,EAAK,EAAc,EAC1B,EAAK,GAEH,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAEF,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAGC,CACL,KAAI,MAAK,KAAI,MAAK,IAAG,KAAI,IAAG,KAAI,IAAG,KAIhC,UAAU,EAAa,CAC5B,MAAO,IAAI,GAAI,CACb,KAAM,KAAK,KAAQ,EAAO,KAAO,KAAK,MACtC,IAAK,KAAK,IAAO,EAAO,IAAM,KAAK,OACnC,MAAO,KAAK,MAAS,EAAO,MAAQ,KAAK,MACzC,OAAQ,KAAK,OAAU,EAAO,OAAS,KAAK,SAC3C,WAAW,UCrMX,oBAA0B,EAAyC,CACxE,YAAY,EAAc,EAAa,EAAe,EAAgB,EAAmC,GAAO,CAC9G,MAAM,CACJ,OAAM,MAAK,QAAO,UACjB,KCTA,YAAsB,CAW3B,YACE,EACA,EACA,EACA,EACA,EACA,CACA,KAAK,WAAa,GAAI,GAAW,EAAU,MAAO,EAAU,QAC5D,KAAK,OAAS,EACd,KAAK,YAAc,EACnB,KAAK,WAAa,EAClB,KAAK,KAAO,GAAI,GAAI,GAAa,QAAQ,KAAK,eAGrC,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,eAEnC,YAAoB,CAAE,MAAO,MAAK,cAElC,MAAW,CAAE,MAAO,MAAK,QAEzB,YAAwB,CAAE,MAAO,MAAK,cAEtC,aAAqB,CAAE,MAAO,MAAK,UAAU,SAE7C,cAAsB,CAAE,MAAO,MAAK,UAAU,UAE9C,cAAmB,CAAE,MAAO,IAAI,GAAI,KAAK,MAAM,QAAQ,KAAK,UAAU,WAE1E,QAAQ,EAAe,EAAiC,CAC7D,MAAO,IAAI,IACT,KAAK,MACL,KAAK,WACL,KAAK,UACL,KAAK,YACL,CAAE,QAAO,aCzCR,mBAA4B,GAAyC,CAC1E,YACE,EACA,EACA,EACA,CACA,MAAM,EAAO,EAAO,GAAI,EAAa,GAGhC,QAAQ,EAAe,EAA+B,CAC3D,GAAM,CAAE,QAAO,cAAa,aAAc,MAAM,QAAQ,EAAO,GAC/D,MAAO,IAAI,GAAc,EAAO,EAAa,KCnB1C,YAAa,EAAW,EAAW,EAAiB,GAAM,CAC/D,GAAM,GAAQ,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,MAAO,EAAK,OAAS,KAAK,IAAI,EAAK,KAAM,EAAK,OAClF,EAAS,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,OAAQ,EAAK,QAAU,KAAK,IAAI,EAAK,IAAK,EAAK,MACpF,EAAe,EAAQ,EAE7B,MAAO,GACH,EAAgB,GAAK,KAAO,EAAK,KAAO,GACxC,EAAe,KAAK,IAAI,EAAK,KAAM,EAAK,MCPvC,YAAiB,EAA4B,CAClD,GAAM,GAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAExD,MAAO,IAAI,IAAY,EAAM,EAAM,EAAM,GCPpC,YACL,EACA,EACA,EACA,EAAiB,GACP,CACV,GAAI,GAAuB,EACxB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAC/B,IAAI,AAAC,GAAM,EAAE,UAEV,EAAiB,GAEvB,KAAO,EAAqB,OAAS,GAAG,CACtC,GAAM,GAAO,EAAqB,MAClC,EAAK,KAAK,GAEV,GAAM,GAAU,EAEV,EAAoB,GAC1B,OAAS,GAAI,EAAG,EAAI,EAAQ,OAAQ,IAAK,CACvC,GAAM,GAAM,EAAQ,GAEd,EAAU,EAAM,GAChB,EAAS,EAAM,GAErB,EAAQ,KAAK,GAAI,EAAS,EAAQ,IAGpC,EAAuB,EAAqB,OAC1C,CAAC,EAAG,IAAM,EAAQ,IAAM,GAI5B,MAAO,GCrCT,OAAoB,OAEb,YAAmB,EAAgB,EAAgC,CACxE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAG,EAAG,GAAK,EACZ,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAU,AAAG,UAAO,CAAC,EAAO,EAAO,GAAQ,GAEjD,MAAO,AAAG,QAAI,EAAG,KCVrB,OAAoB,OAUb,YACL,EACA,EAAyB,GACZ,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAQ,GAAS,EAAU,MAAM,MAAM,GAC9C,GAAI,IAAW,EACb,MAAO,GAGT,GAAM,GAAU,KAAK,IAAI,EAAS,GAC5B,EAAgB,KAAK,MAAM,EAAW,GAAgB,GAAM,IAC5D,EAAc,EAAS,EAAQ,EAAI,EAEnC,EAAsB,AAAC,GAA0C,CACrE,GAAM,GAAqB,EAAU,MAAM,QAC3C,SAAmB,GAAe,EAC3B,AAAG,QAAK,EAAoB,EAAG,YAGlC,EAAsB,EAAoB,GAC1C,EAAyB,EAAW,EAAoB,MAAM,GAM9D,EAAiB,CAJM,GAAiB,EAC1C,EAAoB,GACpB,KAIF,EACA,GAEC,OAAO,AAAC,GAAM,CAAC,CAAC,GAEhB,IAAI,AAAC,GAAiB,AAAG,QAAK,EAAG,YACpC,MAAO,AAAG,WAAO,EAAgB,KC7C9B,YAAsB,EAAmB,CAC9C,GAAM,GAAQ,EAAW,QACzB,OAAS,GAAI,EAAM,OAAS,EAAG,EAAI,EAAG,IAAK,CACzC,GAAM,GAAI,KAAK,MAAM,KAAK,SAAY,GAAI,IACpC,EAAI,EAAM,GAChB,EAAM,GAAK,EAAM,GACjB,EAAM,GAAK,EAEb,MAAO,GCDF,YAAiB,EAAW,CACjC,MAAO,GAAK,GAAI,KAAK,IAAI,CAAC,IAGrB,YAAwB,EAAW,CACxC,MAAO,MAAK,IAAI,EAAK,GAAI,ICHpB,oBAAmB,EAA2B,CACnD,YAAY,EAAW,EAAW,EAAe,EAAgB,EAAmC,GAAO,CACzG,MAAM,CACJ,IAAG,IAAG,QAAO,UACZ,KCHP,GAAM,IAAO,GACP,GAAO,IACP,GAAW,IAOV,OAA8C,CAOnD,YACE,EACA,EACA,EAAe,GAAI,GAAM,EAAG,GAC5B,CACA,GAAM,CAAE,QAAO,UAAW,EAC1B,KAAK,SAAW,GAAI,GAAW,EAAO,GACtC,KAAK,OAAS,EACd,KAAK,WAAa,EAA8B,IAC9C,AAAC,GAAO,EAAG,IAAI,GAAI,GAAM,EAAO,IAAS,IAAI,OAItC,QAAe,CAAE,MAAO,IAAI,GAAM,KAAK,OAAO,EAAG,KAAK,OAAO,MAE7D,aAAqB,CAAE,MAAO,MAAK,SAAS,SAE5C,cAAsB,CAAE,MAAO,MAAK,SAAS,UAE7C,YAAqB,CAAE,MAAO,MAAK,cAEnC,oBAA6B,CACtC,MAAO,MAAK,WAAW,IACrB,AAAC,GAAO,EAAG,IAAI,KAAK,QAAQ,IAAI,GAAI,GAAM,KAAK,WAAY,KAAK,eAI7D,QAAiC,EAAe,EAAmB,CACxE,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,CAAE,QAAO,WAIN,QAAiC,EAAW,EAAc,CAC/D,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,KAAK,SACL,GAAI,GAAM,EAAG,IAIV,aAAsC,EAAc,CACzD,MAAO,MAAK,QAAQ,EAAG,EAAG,EAAG,GAcxB,MACL,EACA,EAAkE,GAC7D,CACL,GAAI,EAAW,CACb,GAAM,GAAM,YAAqB,GAC7B,EAAU,IAAI,QACd,GAAI,GAAI,GAEZ,MAAO,MAAK,QAAQ,EAAI,EAAG,EAAI,GAAG,MAAM,KAAM,GAGhD,GAAM,CAAE,mBAAkB,iBAAkB,CAAE,iBAAkB,GAAO,cAAe,MAAQ,GAE9F,MAAI,GACK,KAAK,YAGP,KAAK,aAAa,GAGnB,WAAiB,CACvB,GAAM,GAAU,KAAK,2BAEf,CAAC,EAAe,EAAgB,GAAe,EAC/C,EAAc,AAAC,GAAc,EAAY,IAAI,GAAI,YACjD,EAAkB,GAAY,GAAiB,EAAY,IAAmB,EAE9E,EAAO,KAAK,MAAM,EAAiB,IAEnC,EAAW,GAAe,GAE1B,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAChD,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAEtD,MAAO,IAAI,IAAK,EAAG,EAAG,KAAK,IAAI,EAAM,KAAK,WAAa,GAAI,KAAK,IAAI,EAAM,KAAK,YAAc,IAGvF,aAAa,EAAsB,CACzC,GAAM,GAAM,GAAQ,KAAK,WACzB,MAAO,GAAI,IAAI,EAAI,MAAQ,EAAS,EAAI,OAAS,GAGzC,0BAAoC,CAC5C,KAAM,IAAI,OAAM,4DC3Hb,oBAA6B,EAAc,CACtC,0BAAoC,CAC5C,GAAM,GAAM,KAAK,UACjB,MAAO,CACL,EAAI,GACJ,EAAI,GACJ,GAAe,CAAC,EAAI,GAAI,EAAI,QCN3B,oBAA8B,EAAc,CAC1C,eAAyB,CAC9B,MAAO,MAAK,UAAU,MAAM,EAAG,IAG1B,gBAA0B,CAC/B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,iBAA2B,CAChC,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,SAAmB,CACxB,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,YAAsB,CAC3B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,aAAuB,CAC5B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,UAAoB,CACzB,MAAO,MAAK,UAAU,MAAM,GAAI,IAGxB,0BAAoC,CAC5C,MAAO,CACL,KAAK,aACL,KAAK,cACL,KAAK,YACL,IAAI,MC/BH,YAAsC,CAK3C,YAAY,EAAe,EAAkB,CAC3C,KAAK,OAAS,EACd,KAAK,UAAY,KAGR,QAAgB,CAAE,MAAO,MAAK,UAE9B,WAAmB,CAAE,MAAO,MAAK,UAErC,SAAS,EAAwB,GAAc,CACpD,MAAO,GAAG,KAAK,QAAQ,EAAe,KAAK,GAAM,KAAK,aAAe,OCjBlE,oBAAyB,EAAgB,OAChC,yBAAwB,EAAU,EAAgB,CAG9D,GAFA,EAAI,iBAAiB,EAAK,GAEtB,CAAC,GAAc,EAAI,OACrB,KAAM,IAAI,OAAM,GAAG,gCAAqC,EAAI,yBAMhE,YAAY,EAAiC,EAAe,CAC1D,MAAM,GACN,KAAK,OAAS,KAGL,QAAgB,CAAE,MAAO,MAAK,SCrBpC,YAA6B,CAKlC,YAAY,EAAe,EAA6B,CACtD,GAAM,MAAO,IAAU,SACrB,KAAM,IAAI,OAAM,sEAGlB,GAAI,CAAC,MAAM,QAAQ,IAAgB,EAAY,KAAK,AAAC,GAAS,CAAE,aAAgB,gBAC9E,KAAM,IAAI,OAAM,4FAGlB,KAAK,OAAS,EACd,KAAK,aAAe,KAGX,QAAgB,CAAE,MAAO,MAAK,UAE9B,cAA8B,CAAE,MAAO,MAAK,aAEhD,QAAc,CACnB,MAAO,CACL,MAAO,KAAK,MACZ,YAAa,KAAK,YAAY,IAAI,AAAC,GAAM,MAAM,KAAK,WAI1C,UAAS,EAAmC,CACxD,GAAM,GAAc,EAAK,YAAY,IAAI,AAAC,GAAW,GAAI,cAAa,IACtE,MAAO,IAAI,IAAuB,EAAK,MAAO,KC1B3C,oBAA2B,GAAW,OAC7B,2BAA0B,EAAU,EAAgB,CAGhE,GAFA,GAAW,wBAAwB,EAAK,GAGtC,CAAC,GAAmB,EAAI,QACrB,CAAC,GAAmB,EAAI,YAE3B,KAAM,IAAI,OAAM,GAAG,kCAAuC,EAAI,eAAe,EAAI,6CAQrF,YAAY,EAAiC,EAAe,EAAe,EAAoB,CAC7F,MAAM,EAAK,GACX,KAAK,OAAS,EACd,KAAK,YAAc,KAGV,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,cCvBzC,YAA6B,EAAwC,CAC1E,MAAO,GAAI,oBAAqB,GAG3B,YAGL,EACA,EAC4B,CAE5B,MAAO,IAAK,KADM,CAAE,cCdf,aAAyC,CAC9C,GAAM,GAAQ,OAAO,MACrB,GAAI,CAAC,EAAO,KAAM,IAAI,OAAM,gEAM5B,MAAO,CACL,OAAQ,kBACR,yBACA,MAAO,iBACP,UACA,MAAO,iBACP,oBAAqB,IAAM,SAAS,cAAc,UAClD,mBAAoB,IAAM,SAAS,cAAc,OACjD,QACA,SAbe,IAAM,CACrB,KAAM,IAAI,OAAM,iECLb,YAA0B,EAAsB,CACrD,GAAI,GAAiB,GAErB,GAAI,CAAC,EACH,GAAI,CAEF,EAAa,oBACN,EAAP,CACA,EAAiB,EAAI,WAYzB,MAAO,CACL,SATe,EACb,AAAC,GAAqB,GAAI,SAAgB,CAAC,EAAS,IAAW,CAC/D,EAAG,SAAS,EAAU,CAAC,EAAU,IAAoB,EAAM,EAAO,GAAO,EAAQ,MAEjF,IAAM,CACN,KAAM,IAAI,OAAM,qEAAqE,OCfpF,aAAwC,CAE7C,GAAM,GAAS,OAAO,QAAa,OAAO,kBACpC,EAAQ,OAAO,OAAS,OAAO,iBAE/B,EAAsB,IAAM,CAChC,GAAI,EAAQ,MAAO,IAAI,GACvB,KAAM,IAAI,OAAM,+EAGZ,EAAqB,IAAM,CAC/B,GAAI,EAAO,MAAO,IAAI,GACtB,KAAM,IAAI,OAAM,6EAGZ,EAAQ,OAAO,MAGf,EAAa,KAEnB,MAAO,CACL,OAAQ,GAAU,KAAM,GACxB,yBAA0B,OAAO,0BAA4B,KAAM,GACnE,MAAO,GAAS,KAAM,GACtB,UAAW,OAAO,WAAa,KAAM,GACrC,MAAO,OAAO,kBAAoB,KAAM,GACxC,sBACA,qBACA,WACG,GCjCA,aAA8B,CACnC,MAAO,OAAO,SAAW,UACpB,MAAO,WAAa,aACpB,MAAO,mBAAqB,aAC5B,MAAO,oBAAsB,aAC7B,MAAO,mBAAqB,aAC5B,MAAO,YAAc,aACrB,MAAO,2BAA6B,YCH3C,OAAyB,QAGrB,EAEJ,aAA+B,CAC7B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,yEAElB,MAAO,GAGT,YAAgB,EAAkB,CAChC,EAAc,EAGhB,aAAsB,CAGpB,MAAI,MAAoB,GAAO,MAC3B,kBAAmB,GAAO,MACvB,KAGT,YAAqB,EAA2B,CAK9C,GAJK,GACH,KAGE,CAAC,EACH,KAAM,IAAI,OAAM,8EAGlB,GAAM,CAAE,SAAS,EAAY,OAAQ,QAAQ,EAAY,OAAU,EACnE,EAAY,OAAS,EACrB,EAAY,MAAQ,EACpB,EAAY,oBAAsB,EAAI,qBAAwB,KAAM,GAAI,IACxE,EAAY,mBAAqB,EAAI,oBAAuB,KAAM,GAAI,IAEtE,EAAY,UAAY,EAAI,WAAa,EAAY,UACrD,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,SAAW,EAAI,UAAY,EAAY,SAG9C,GAAM,GAAM,CACjB,UACA,UACA,cACA,oBACA,oBACA,mBACA,eACA,aACA,sBAGF,KC3DO,YAAsB,EAAmB,CAC9C,MAAI,CAAC,EAAI,YAAc,MAAO,IAAQ,SAC7B,SAAS,eAAe,GAE1B,ECHF,WAA6B,EAA4F,CAC9H,GAAM,CAAE,SAAQ,4BAA6B,EAAI,SAEjD,GAAI,YAAqB,GACvB,MAAO,GAGT,GAAM,GAAS,GAAa,GAE5B,GAAI,CAAE,aAAkB,IACtB,KAAM,IAAI,OAAM,kEAGlB,GAAM,GAAM,EAAO,WAAW,MAC9B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,GCfF,GAAK,IAAL,UAAK,EAAL,CAEL,WAAW,WAEX,YAAY,YAEZ,cAAc,cAEd,eAAe,iBARL,aAoBL,YAA4D,CAajE,YAAY,EAAiC,GAAI,CAC/C,GAAM,CACJ,iBAAgB,kBAAiB,YAAW,WAAU,YAAW,WAC/D,EACJ,KAAK,eAAiB,GAAkB,GAAe,SACvD,KAAK,gBAAkB,GAAmB,qBAC1C,KAAK,UAAY,GAAa,yBAC9B,KAAK,SAAW,GAAY,GAC5B,KAAK,UAAY,GAAa,UAC9B,KAAK,QAAU,GAAW,IAIvB,QAAoB,CAOzB,YACE,EACA,EACA,EAAiC,GACjC,CAEA,KAAK,KAAO,MAAO,IAAS,SACxB,CAAC,GACA,YAAgB,IAAgB,EAAK,KAAO,EACjD,KAAK,OAAS,EACd,KAAK,QAAU,GAAI,IAAqB,GAG1C,aAAa,EAAuC,CAClD,GAAM,CAAE,WAAY,KAAK,QACzB,MAAO,MAAK,KAAK,IAAI,AAAC,GAAM,EAAI,YAAY,GAAG,OAAO,OAAO,CAAC,EAAI,IAAQ,EAAK,EAAK,EAAK,EAAK,GAAM,EAAI,EAG1G,eAAwB,CACtB,GAAM,CAAE,WAAU,WAAY,KAAK,QACnC,MAAO,MAAK,KAAK,OAAS,EAAY,EAAI,EAG5C,aAAa,EAA+B,EAAkC,CAC5E,GAAM,CAAE,kBAAmB,KAAK,QAC1B,EAAc,IAAmB,GAAe,cAAgB,IAAmB,GAAe,UAClG,EAAa,IAAmB,GAAe,aAAe,IAAmB,GAAe,aAEhG,EAAiB,KAAK,aAAa,GACnC,EAAkB,KAAK,gBACvB,EAAK,EAAc,KAAK,OAAO,EAAI,EAAiB,KAAK,OAAO,EAChE,EAAI,EAAa,KAAK,OAAO,EAAI,EAAkB,KAAK,OAAO,EAGrE,GAAI,EAAY,CACd,GAAM,CAAE,QAAO,UAAW,EACpB,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAQ,GAAiB,GACrD,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAS,GAAkB,GAC7D,MAAO,CAAE,EAAG,EAAM,EAAG,GAEvB,MAAO,CAAE,IAAG,KAGd,KAAK,EAAkE,CACrE,GAAM,GAAS,GAAa,GACtB,EAAM,EAAoB,GAE1B,CACJ,kBAAiB,YAAW,WAAU,YAAW,WAC/C,KAAK,QAET,EAAI,KAAO,GAAG,OAAc,IAC5B,GAAM,GAAe,KAAK,aAAa,GACjC,EAAa,KAAK,gBAExB,EAAI,UAAY,EAChB,GAAM,GAAY,KAAK,aAAa,EAAK,GACzC,EAAI,SAAS,EAAU,EAAG,EAAU,EAAG,EAAc,GAErD,EAAI,UAAY,EAChB,KAAK,KAAK,QAAQ,CAAC,EAAU,IAAM,CACjC,GAAM,GAAI,EAAU,EAAU,EACxB,EAAI,EAAU,EAAU,EAAM,GAAI,GAAK,EAC7C,EAAI,SAAS,EAAU,EAAG,OC5GzB,YAAqB,CAS1B,YAAY,EAA2B,GAAI,CACzC,GAAM,CACJ,WAAU,YAAW,QAAO,oBAC1B,EACJ,KAAK,SAAW,GAAY,qBAC5B,KAAK,UAAY,GAAa,EAC9B,KAAK,MAAQ,EAEb,GAAM,GAA0B,CAC9B,eAAgB,GAAe,YAC/B,gBAAiB,KAAK,UAExB,KAAK,iBAAmB,GAAI,IAAqB,IAAK,KAA4B,MAI/E,QAAc,CAKnB,YACE,EACA,EAA2B,GAC3B,CACA,KAAK,IAAM,GAAI,GAAI,GACnB,KAAK,QAAU,GAAI,IAAe,GAGpC,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CAAE,WAAU,aAAc,KAAK,QAE/B,CACJ,IAAG,IAAG,QAAO,UACX,KAAK,IACT,EAAI,YAAc,EAClB,EAAI,UAAY,EAChB,EAAI,WAAW,EAAG,EAAG,EAAO,GAE5B,GAAM,CAAE,SAAU,KAAK,QACvB,AAAI,GACF,GAAI,IAAc,CAAC,GAAQ,CAAE,EAAG,EAAK,EAAY,EAAI,KAAK,KAAK,QAAQ,kBAAkB,KAAK,KC1D7F,YACL,EACA,EACA,CAGA,AAFwB,OAAM,QAAQ,GAAc,EAAa,CAAC,IAElD,QAAQ,AAAC,GAAQ,CAE/B,GAAM,GAAQ,YAAe,GACzB,EAAI,MACH,GAAoB,GAAO,EAAI,UAAU,MAAQ,OAGhD,EAAM,YAAe,GACvB,EAAI,IACH,GAAoB,GAAO,EAAI,UAAU,IAAM,GAAI,GAAI,GAEtD,EAAQ,EAAQ,GAAG,GAAM,KAAW,OAC1C,GAAI,IAAQ,EAAK,CAAE,UAAS,KAAK,KC1BrC,OAAoB,OCEb,YAAuB,EAAsD,CAClF,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAQ,aAAiB,IAAS,EAAM,UAClC,YAAiB,IAAS,EAAM,YAAc,ECH/C,YAA0B,EAAgE,CAE/F,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,GAAI,YAAiB,GAAI,SAAS,QAAU,GAAc,GACxD,MAAO,GAAQ,MAGjB,WAAiB,EAAU,CACzB,AAAI,CAAC,EAAE,eAEP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAO,IAGT,WAAgB,EAAU,CACxB,AAAI,CAAC,EAAE,eACP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAQ,IAGV,EAAM,iBAAiB,OAAQ,GAC/B,EAAM,iBAAiB,QAAS,KCxB7B,YAAuB,EAAsC,CAClE,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,AAAM,YAAe,OAAO,EAAO,GAAI,OAAM,qDAC7C,GAAM,GAAS,GAAI,YACnB,EAAO,OAAS,IAAM,CACpB,AAAI,MAAO,GAAO,QAAW,UAAU,EAAO,GAAI,OAAM,qEACxD,GAAM,GAAM,EAAI,SAAS,qBACzB,EAAI,OAAS,IAAM,EAAQ,GAC3B,EAAI,QAAU,EACd,EAAI,IAAM,EAAO,QAEnB,EAAO,QAAU,EACjB,EAAO,cAAc,KCXlB,YAA4B,EAA0F,CAC3H,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAI,aAAiB,GACZ,GAAI,GAAW,EAAM,aAAc,EAAM,eAE9C,YAAiB,GACZ,GAAI,GAAW,EAAM,WAAY,EAAM,aAEzC,GAAI,GAAW,EAAM,MAAO,EAAM,QCNpC,YAAsB,CAAE,QAAO,UAA0C,CAC9E,GAAM,CAAE,uBAAwB,EAAI,SAC9B,EAAS,IACf,SAAO,MAAQ,EACf,EAAO,OAAS,EACT,EAGF,YAA+B,EAAwD,EAAuC,CACnI,GAAM,CAAE,aAAc,EAAI,SAE1B,GAAI,CAAE,aAAiB,KAAc,CAAC,GAAc,GAClD,KAAM,IAAI,OAAM,8DAGlB,GAAM,CAAE,QAAO,UAAW,GAAQ,GAAmB,GAC/C,EAAS,GAAa,CAAE,QAAO,WAErC,MAAI,aAAiB,GACnB,EAAoB,GAAQ,aAAa,EAAO,EAAG,GAEnD,EAAoB,GAAQ,UAAU,EAAO,EAAG,EAAG,EAAO,GAErD,EC7BT,OAAoB,OAKpB,kBACE,EACA,EAC4B,CAC5B,GAAM,GAAe,GAAU,EAAI,SAAS,sBAEtC,CAAC,EAAQ,EAAO,GAAe,EAAU,MAAM,MAAM,EAAW,GAAa,EAAI,GACjF,EAAc,AAAG,QAAK,IAAM,EAAU,KAAK,EAAQ,EAAO,GAAa,SAC7E,YAAM,AAAG,YAAQ,SAAS,EAAa,GAEvC,EAAY,UAEL,ECfF,YAAwB,EAAY,CACzC,GAAM,CAAE,QAAO,SAAQ,SAAU,EAAI,SAErC,MAAO,aAAiB,IACnB,YAAiB,IACjB,YAAiB,GCPxB,MAAoB,OCKb,YAAuB,EAA6C,EAAmB,EAAuB,GAAO,CAC1H,GAAM,CAAE,QAAO,UAAW,EAAI,SAE9B,GAAI,CAAE,aAAiB,IAAS,YAAiB,IAC/C,KAAM,IAAI,OAAM,4EAGlB,GAAI,GAAa,EAAG,MAAO,IAAa,CAAE,MAAO,EAAG,OAAQ,IAC5D,GAAM,GAAO,GAAmB,GAC1B,EAAQ,EAAY,KAAK,IAAI,EAAK,OAAQ,EAAK,OAC/C,EAAQ,EAAQ,EAAK,MACrB,EAAS,EAAQ,EAAK,OAEtB,EAAe,GAAa,CAAE,MAAO,EAAW,OAAQ,IACxD,EAAc,YAAiB,GAAS,EAAQ,GAAsB,GAEtE,EAAS,KAAK,IAAI,EAAQ,GAAU,EACpC,EAAK,GAAe,EAAQ,EAAS,EAAS,EAC9C,EAAK,GAAe,EAAS,EAAQ,EAAS,EACpD,MAAI,GAAY,MAAQ,GAAK,EAAY,OAAS,GAAG,EAAoB,GAAc,UAAU,EAAa,EAAI,EAAI,EAAO,GAEtH,EDdF,YAAe,CAapB,YACE,EACA,EAA6B,GAC7B,CAfM,mBAAkD,GAElD,eAAiC,GAIjC,wBAA8B,GAE9B,sBAA+B,GAQrC,GAAI,CAAC,MAAM,QAAQ,GACjB,KAAM,IAAI,OAAM,4HAA4H,KAG9I,KAAK,mBAAqB,EAC1B,KAAK,WAAa,EAAO,OAEzB,EAAO,QAAQ,CAAC,EAAO,IAAQ,CAC7B,GAAI,GAAW,GAAQ,CACrB,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAO,EAAM,MACnC,OAGF,GAAI,EAAW,GAAQ,CACrB,GAAM,GAAa,EAAc,MAAM,GACvC,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,yCAAyC,8CAG3D,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAQ,EAAc,MAAM,MAAM,GACxD,OAGF,GAAM,GAAU,YAAyB,GAAI,SAAS,OAAS,EAAQ,GAAsB,GAC7F,KAAK,UAAU,GAAO,EACtB,KAAK,iBAAiB,GAAO,CAAC,EAAO,OAAQ,EAAO,MAAO,QAIpD,eAAiD,CAC1D,MAAO,MAAK,iBAGH,WAAgC,CACzC,MAAO,MAAK,aAGH,eAAwB,CACjC,MAAO,MAAK,UAAY,GAAK,KAAK,sBAGzB,YAAoB,CAC7B,MAAO,MAAK,cAGH,kBAA8B,CACvC,MAAO,MAAK,oBAGH,YAAgC,CACzC,MAAO,MAAK,cAGH,0BAAwC,CACjD,MAAO,IAAM,KAAK,UAAW,EAAG,GAAG,IACjC,CAAC,EAAG,IAAa,KAAK,2BAA2B,IAI9C,SAAS,EAAiE,CAC/E,MAAO,MAAK,SAAS,IAAa,KAAK,aAAa,GAG/C,mBAAmB,EAA4B,CACpD,MAAO,MAAK,iBAAiB,GAGxB,eAAe,EAA0B,CAC9C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,cAAc,EAA0B,CAC7C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,2BAA2B,EAA8B,CAC9D,GAAI,MAAO,MAAK,WAAc,SAC5B,KAAM,IAAI,OAAM,yFAGlB,GAAM,GAAQ,KAAK,cAAc,GAC3B,EAAS,KAAK,eAAe,GACnC,MAAO,IAA0B,CAAE,QAAO,UAAU,KAAK,WAYpD,cAAc,EAAmB,EAA0B,GAAmB,CACnF,YAAK,WAAa,EAEX,AAAG,OAAK,IAAM,CACnB,GAAM,GAAe,GAAM,KAAK,UAAW,EAAG,GAAG,IAAI,AAAC,GAAa,CACjE,GAAM,GAAQ,KAAK,SAAS,GAE5B,GAAI,YAAoB,UAAQ,CAE9B,GAAI,GAAY,EAAW,GAAS,EAAQ,EAAM,aAElD,SAAY,GAAY,EAAW,GAE/B,GAAU,MAAM,KAAO,GAAa,EAAU,MAAM,KAAO,IAC7D,GAAY,AAAG,QAAM,eAAe,EAAW,CAAC,EAAW,KAGtD,EAAU,KAAK,EAAW,EAAW,GAG9C,GAAI,YAAiB,GAAI,SAAS,OAChC,MAAO,AAAG,WAAQ,WAAW,GAAc,EAAO,EAAW,IAG/D,KAAM,IAAI,OAAM,+BAA+B,8FAAqG,OAOtJ,MAHoB,AAAG,SAAM,EAAa,IAAI,AAAC,GAAM,AAAG,OAAK,EAAG,aAAa,KAAK,KAAK,UAAW,EAAW,EAAW,OE3I9H,iBAAiC,EAAsC,CACrE,GAAI,YAAkB,IACpB,MAAO,GAGT,GAAM,GAAgB,MAAM,QAAQ,GAChC,EACA,CAAC,GAEL,GAAI,CAAC,EAAc,OACjB,KAAM,IAAI,OAAM,4CAGlB,GAAM,GAAa,AAAC,GAAiB,MAAM,QAAQ,GAAU,mBAAmB,KAAS,GAEnF,EAAa,EAAc,IAAI,IAErC,SAAW,QAAQ,CAAC,EAAO,IAAM,CAC/B,GAAI,CAAC,GAAe,IAAU,CAAC,GAAW,IAAU,CAAC,EAAW,GAC9D,KAAI,OAAO,GAAc,IAAO,SACxB,GAAI,OAAM,eAAe,EAAW,sEAAsE,EAAc,MAG1H,GAAI,OAAM,eAAe,EAAW,iIAG5C,GAAI,EAAW,GAAQ,CAErB,GAAM,GAAY,EAAM,MAAM,GAC9B,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,eAAe,EAAW,iCAAiC,iDAMjF,KAAM,SAAQ,IACZ,EAAW,IAAI,AAAC,GAAU,GAAe,IAAU,GAAiB,KAG/D,GAAI,IAAS,EAAY,MAAM,QAAQ,ICtChD,kBACE,EACA,EAC8B,CAC9B,GAAM,CAAE,UAAW,EAAI,SAEnB,EAAS,EAEb,GAAI,CAAE,aAAiB,IAAS,CAC9B,GAAM,GAAW,KAAM,GAAW,GAElC,GAAI,EAAS,UAAY,EACvB,KAAM,IAAI,OAAM,8CAGlB,GAAM,GAAiB,EAAS,SAAS,GACzC,EAAS,YAA0B,GAC/B,EACA,KAAM,IAAoB,GAGhC,GAAM,GAAM,EAAoB,GAQhC,MAAO,AAPO,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAO,MAAO,EAAO,QAAQ,IAAI,QAC7C,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAO,MAAO,EAAO,SAE/C,IAAI,CAAC,CAChB,IAAG,IAAG,QAAO,YACT,CACJ,GAAM,GAAU,GAAa,CAAE,QAAO,WACtC,MAAI,GAAQ,GAAK,EAAS,GAAG,EAAoB,GAAS,aAAa,EAAI,aAAa,EAAG,EAAG,EAAO,GAAS,EAAG,GAC1G,IClDX,OAAoB,OAgBpB,kBACE,EACA,EACwB,CACxB,GAAI,CAAC,GAAW,IAAgB,CAAC,EAAW,GAC1C,KAAM,IAAI,OAAM,6DAGlB,GAAI,EAAW,IAAgB,EAAY,MAAM,GAAK,EACpD,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAW,EAAU,GAAe,EAAY,MAAM,MAAM,EAAW,GAAe,EAAI,GAajG,MAJoB,AAPN,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAU,GAAW,IACjC,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAU,IAEvB,IAAI,CAAC,CAC7B,IAAG,IAAG,QAAO,YACT,AAAG,WAAQ,EAAY,KAAK,EAAW,EAAU,GAAc,CAAC,EAAG,EAAG,GAAI,CAAC,EAAQ,EAAO,OCtCpG,kBACE,EAEA,EACmB,CACnB,GAAM,CAAE,SAAU,EAAI,SAChB,EAAM,KAAM,GAAM,EAAK,GAC7B,GAAI,CAAE,GAAI,OAAS,KACjB,KAAM,IAAI,OAAM,qBAAqB,EAAI,WAAW,EAAI,yBAAyB,EAAI,OAEvF,MAAO,GCTT,kBAAiC,EAAwC,CACvE,GAAM,GAAM,KAAM,IAAa,GACzB,EAAO,KAAO,GAAK,OAEzB,GAAI,CAAC,EAAK,KAAK,WAAW,UACxB,KAAM,IAAI,OAAM,wEAAwE,EAAK,kBAAkB,EAAI,OAErH,MAAO,IAAc,GCRvB,kBAAmC,EAAyB,CAC1D,MAAQ,MAAM,IAAa,IAAM,OCDnC,kBAAsC,EAAoC,CACxE,MAAO,IAAI,cAAa,KAAO,MAAM,IAAa,IAAM,eCH1D,OAAoB,OCAb,YAAsB,EAAyB,EAA0B,CAC9E,GAAM,GAA0B,GAAG,0BAEnC,GAAI,CAAC,EACH,MAAO,CACL,aAAc,GACd,YAAa,GAIjB,GAAI,IAAQ,IACV,MAAO,CACL,aAAc,IACd,YAAa,IAAI,KAIrB,GAAM,GAAW,EAAI,WAAW,WAAa,UAAY,EAAI,WAAW,YAAc,WAAa,GACnG,EAAM,EAAI,QAAQ,EAAU,IAE5B,GAAM,GAAQ,EAAI,MAAM,KAAK,OAAO,AAAC,GAAM,GAErC,EAAe,EAAI,SAAS,SAC9B,EAAM,EAAM,OAAS,GACrB,EAEA,EAAe,EAAY,GAAI,SAAS,SAAW,EAAM,MAAM,EAAG,EAAM,OAAS,GAAK,GAAO,KAAK,KACtG,SAAe,EAAI,WAAW,KAAO,IAAI,IAAiB,EAEnD,CACL,eACA,YAAa,IAAiB,IAAM,IAAI,IAAiB,GAAG,KAAgB,KD1BhF,kBACE,EACA,EAC4B,CAC5B,GAAM,CAAE,cAAa,gBAAiB,GAAa,EAAK,GAClD,EAAW,KAAM,IAAuC,GAE9D,MAAO,AAAG,OAAG,YAAY,EAAU,GET9B,YAAyB,EAAoB,EAAwB,EAA8B,GAAO,CAC/G,GAAM,CAAE,QAAO,UAAW,EACtB,GAAmB,GACnB,EACJ,SAAM,MAAQ,EACd,EAAM,OAAS,EACR,CAAE,QAAO,UCTlB,OAAoB,OCApB,OAAoB,OAOb,WAAyC,CAC9C,YAAY,EAAc,CAIhB,aAAkC,OAElC,oBAAiC,GALzC,KAAK,MAAQ,KASJ,SAAiC,CAAE,MAAO,MAAK,WAE/C,gBAAgC,CAAE,MAAO,MAAK,kBAE9C,WAAoB,CAAE,MAAO,CAAC,CAAC,KAAK,OAExC,iBAAiB,EAA8B,CACpD,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,MAAO,GAAI,GAGN,sBAAsB,EAAmB,EAAmB,CACjE,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,EAAI,GAAS,UACb,EAAI,GAAW,EAGV,cAAe,CACpB,MAAO,MAAK,eAAe,IAAI,CAAC,CAAE,eAAiB,EACjD,KAAM,EACN,OAAQ,KAAK,iBAAiB,MAI3B,oBAAqB,CAC1B,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,EAAM,iBAAqB,cAGnE,iBAAkB,CACvB,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,CAAE,GAAM,iBAAqB,eAGrE,UAAW,CAChB,KAAK,kBAAkB,QAAQ,CAAC,CAAE,OAAM,YAAa,CACnD,KAAK,sBAAsB,EAAM,EAAO,cAIrC,QAAS,CACd,KAAK,qBAAqB,QAAQ,CAAC,CAAE,OAAM,OAAQ,KAAe,CAChE,GAAM,GAAS,AAAG,UAAO,EAAS,YAClC,EAAS,UACT,KAAK,sBAAsB,EAAM,KAI9B,QAAQ,EAA4B,GAAM,CAC/C,KAAK,eAAe,QAAQ,AAAC,GAAU,CACrC,GAAI,GAAoB,EAAM,OAAO,WACnC,KAAM,IAAI,OAAM,mDAAmD,EAAM,QAE3E,EAAM,OAAO,YAEf,KAAK,QAAU,OAGV,iBAAgC,CACrC,MAAO,IAAI,cACT,KAAK,eACF,IAAI,CAAC,CAAE,YAAa,MAAM,KAAK,EAAO,aACtC,OAAO,CAAC,EAAM,IAAQ,EAAK,OAAO,UAI5B,MAAK,EAAgE,CAChF,GAAI,YAAwB,cAAc,CACxC,KAAK,eAAe,GACpB,OAEF,KAAM,MAAK,YAAY,QAGZ,aAAY,EAAyB,CAChD,GAAI,GAAO,MAAO,IAAQ,SACxB,KAAM,IAAI,OAAM,GAAG,KAAK,0CAE1B,GAAM,GAAY,KAAM,IAAc,EAAK,KAAK,uBAChD,KAAK,kBAAkB,QAGZ,cAAa,EAA8B,CACtD,GAAI,GAAY,MAAO,IAAa,SAClC,KAAM,IAAI,OAAM,GAAG,KAAK,iDAE1B,GAAM,CAAE,YAAa,EAAI,SACnB,CAAE,cAAa,gBAAiB,GAAa,EAAU,KAAK,uBAC5D,EAAuB,AAAC,GAAwB,QAAQ,IAAI,EAAU,IAAI,AAAC,GAAO,EAAS,GAAI,KAAK,AAAC,GAAQ,EAAI,UACjH,EAAc,AAAG,MAAG,qBAAqB,GACzC,EAAW,KAAK,MAAO,MAAM,GAAS,IAAc,YACpD,EAAY,KAAM,GAAY,EAAU,GAC9C,KAAK,kBAAkB,GAGlB,kBAAkB,EAA8B,CACrD,GAAM,CAAE,gBAAe,UAAW,KAAK,2BAA2B,GAClE,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGV,eAAe,EAAuB,CAC3C,GAAM,CAAE,gBAAe,UAAW,KAAK,cAAc,GACrD,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGT,qBAAqB,EAAmB,CAC9C,GAAI,CAAC,KAAK,OACR,KAAM,IAAI,OAAM,qDAGlB,GAAM,GAAS,EAAU,MAAM,KAAK,OAAO,CAAC,EAAoD,IAAY,CAE1G,GAAI,CAAC,EAAI,QAAQ,eAAe,GAC9B,KAAM,IAAI,OAAM,wDAAwD,eAAqB,KAE/F,MAAO,CAAE,IAAK,EAAI,QAAS,UAAS,QAAS,EAAI,QAAQ,KACxD,CAAE,QAAS,KAAK,SAEb,CAAE,MAAK,WAAY,EACzB,GAAI,CAAC,GAAO,CAAC,GAAW,CAAE,GAAI,YAAuB,YACnD,KAAM,IAAI,OAAM,8DAA8D,KAGhF,MAAO,CAAE,MAAK,aC7IlB,MAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,iBAAkB,EAAO,iBAAkB,EAAQ,QAC1F,SAAM,AAAG,OAAI,EAAK,EAAO,MAClB,IDNJ,YACL,EACA,EACA,EAAwB,GACX,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,CAAC,EAAG,GAAI,QACrE,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,CAAC,EAAG,KAE7E,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,OAItC,YACL,EACA,EACA,EAAwB,GACxB,EAAuB,GACV,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,GAAI,QAC5F,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,KAEpG,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAE/D,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,KACxC,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,QEpD1D,OAAoB,OAIb,YACL,EACA,EACA,EAA4B,OAC5B,EAAoB,GACP,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OACb,AAAG,UAAO,EAAG,EAAO,QAAS,CAAC,EAAG,GAAI,GACrC,EAAO,MAGT,MAAO,GAAW,AAAG,QAAK,GAAO,ICd9B,WAAoC,EAAgB,EAA+B,CACxF,OAAO,KAAK,GAAW,QAAQ,AAAC,GAAS,CACvC,AAAK,EAAc,KAAK,AAAC,GAAO,EAAG,eAAiB,IAClD,EAAU,GAAM,YCLtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,EACA,IACe,CACf,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,SCzBtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,IACa,CACb,GAAM,GAAa,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAY,IAChF,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CACL,QAAS,EACT,KAAM,ICvBZ,OAAoB,OCoBb,YAA0B,CAE/B,YAES,EAEA,EAEA,EAEP,CANO,wBAEA,wBAEA,cDxBJ,YACL,EACA,EACA,CACA,MAAO,CAAC,EAAoB,EAAqB,IAA8C,CAC7F,GAAM,GAAmB,AAAG,YAAS,EAAe,EAAI,EAAI,GAAa,CAAC,EAAG,EAAG,EAAY,IACtF,EAAmB,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAG,EAAG,EAAY,IAC5F,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,WAGX,GAAI,IACT,EACA,EACA,IAKC,YAEL,EACA,CACA,MAAO,AAAC,IAAwC,CAC9C,GAAM,GAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,IAAI,IACT,EACA,EACA,IEpCC,WAAmC,EAAgB,EAA+B,CACvF,MAAO,CAAC,EAAsB,EAAmB,IAAwB,CACvE,GAAM,GAAS,EAAU,GAEzB,GAAI,CAAC,GAAS,EAAQ,GACpB,KAAM,IAAI,OAAM,sBAAsB,oBAA+B,oBAA4B,KAGnG,SAAc,KACZ,CAAE,eAAc,UAAW,GAAc,IAGpC,GCfJ,WAA+B,EAAuB,CAC3D,GAAI,GAAmB,EAEvB,WAAwB,EAAkC,CACxD,GAAM,GAAM,EAAiB,MAAM,EAAG,GACtC,SAAmB,EAAiB,MAAM,GACnC,EAGT,YAA6C,CAC3C,MAAO,GAGT,MAAO,CACL,iBACA,uBCPG,YAA2B,EAAwC,EAA+B,CACvG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,GAAQ,EACV,EAAkB,EAAY,EAAa,EAAG,GAAG,WACjD,EAA2B,EAAY,EAAa,GAAG,WACrD,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAChE,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,CAAE,QAAO,QAAO,SAAU,EAAyB,EAAY,EAAa,EAAc,GAC1F,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YAAuB,EAA8F,CAC1H,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAC3C,EAAS,EAAyB,IAAK,IAAK,UAElD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CACN,SAAQ,SAAQ,SAAQ,WCvBvB,YAA+B,EAAuE,CAC3G,MAAO,AAAC,IAA+B,CACrC,GAAM,GAAU,EAAgC,GAAG,YAAkB,GAC/D,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,CAAE,UAAS,SCNf,YAA2B,EAAgB,EAA+B,CAC/E,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YACL,EACuE,CACvE,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBhBdZ,oBAAmC,EAAuG,CAC/I,aAAc,CACZ,MAAM,wBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,sDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAE/B,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,+BAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KiBlDzB,OAAoB,OCApB,OAAoB,OAIb,YACL,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,AAAG,OACtB,AAAG,UAAO,EAAG,EAAO,SACpB,EAAO,OCPJ,YAAuB,EAAuB,EAAoB,EAA2E,CAClJ,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAIpB,EAAK,AAFa,GAAuB,EAAgB,GAEpC,EAAY,EAAa,MAEpD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,OCdP,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,EAAgB,OAGtB,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,YAA4B,EAA8B,CAC/D,GAAM,GAAyC,GACzC,EAAmC,GAEzC,cAAO,KAAK,GAAW,QAAQ,AAAC,GAAQ,CACtC,GAAM,GAAM,EAAI,WAAW,MAAQ,EAAgB,EACnD,EAAI,GAAO,EAAU,KAGhB,CAAE,sBAAqB,iBJIzB,oBAGG,EAAyB,CAGjC,YAAY,EAAe,EAA+D,CACxF,MAAM,GACN,KAAK,sBAAwB,KAGpB,uBAAgE,CACzE,MAAO,MAAK,sBASP,OAAO,EAA4C,CACxD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EACJ,MAAO,IAAoB,EAAmB,KAAK,EAAmB,MAAM,GAAI,IAAK,EAAO,MAIzF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,EAAS,KAAK,0BAA2B,KAAK,4BAG3D,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAM,KAAK,0BACX,EAAO,KAAK,2BACZ,EAAwB,EAAO,EAAO,EAEtC,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KKnFjC,GAAM,IAAyB,CAAC,UAAW,QAAS,MAAO,QAAS,UAAW,YAAa,aAE5F,QAAsB,CAe3B,YAAY,EAAwC,CAClD,GAAI,EAAc,SAAW,EAC3B,KAAM,IAAI,OAAM,8EAA8E,EAAc,UAG9G,GAAuB,QAAQ,CAAC,EAAY,IAAQ,CAClD,KAAK,GAAc,EAAc,KAIrC,eAAgB,CACd,MAAO,IACJ,IAAI,AAAC,GAAgB,EAAE,aAAY,YAAa,KAAK,MACrD,KAAK,CAAC,EAAI,IAAO,EAAG,YAAc,EAAG,e1CtBrC,oBAAgC,GAA0C,CAC/E,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGtB,aAAa,EAA4C,CAC9D,MAAO,AAAG,SAAK,IAAM,AAAG,WAAQ,KAAK,OAAO,UAGjC,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,oBAAmB,EAAkB,CAChD,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAC9B,EAAsB,KAAM,SAAQ,IAAI,AAAG,WAAQ,GAAK,IAAI,KAAO,IAAM,CAC7E,GAAM,GAAO,KAAM,GAAE,OACrB,SAAE,UACK,KAET,EAAI,UAEJ,GAAM,GAAqB,EACxB,IAAI,AAAC,GAAiB,GAAI,IAAgB,IAE7C,MAAO,GAAS,aACZ,EACA,EAAmB,GAGf,qBAA8B,CACtC,MAAO,wBAGC,yBAAkC,CAC1C,MAAO,KAGC,0BAAmC,CAC3C,MAAO,K2C1CJ,YAA+B,EAA0C,CAC9E,MAAO,GAAI,sBAAuB,IAG7B,YAGL,EACA,EAC8B,CAE9B,MAAO,IAAK,KADM,CAAE,gBCPf,YACL,EACA,EACA,EAAgB,GAChB,EACA,CAGA,AAF6B,OAAM,QAAQ,GAAmB,EAAkB,CAAC,IAE5D,QAAQ,AAAC,GAAM,CAElC,GAAM,GAAO,YAAa,IACtB,EACC,GAAsB,GAAK,EAAE,YAAc,OAChD,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mHAIlB,GAAM,GAAmB,AADV,EAAK,gBACY,OAAO,AAAC,GAAc,EAAU,YAAc,GAExE,EAAS,GAAoB,GAC/B,EAAE,UAAU,IAAI,WACf,GAAmB,GAAI,GAAM,EAAG,GAMrC,AAJsB,GAAI,IACxB,EAAiB,IAAI,AAAC,GAAc,GAAG,EAAU,eAAe,GAAM,EAAU,iBAChF,GAEY,KAAK,KCvBhB,YAA6B,EAA0E,CAC5G,MAAO,IAAoB,IAEtB,EAAI,oBAAwB,IAE5B,EAAI,6BAAiC,IAErC,EAAI,sBAA0B,GAGrC,YAA4B,EAAM,CAEhC,GAAM,GAAU,CAAC,EAAI,EAAI,EAAI,IAAQ,KAAK,MAAM,EAAK,EAAI,EAAK,GAAM,KAAK,GAGnE,EAAU,AAAC,GAAW,EAAQ,IAAO,KAAK,GAE1C,EAAQ,CAAE,KAA0B,OAAW,MAA2B,OAAW,IAAyB,QAEpH,GAAI,CAAC,GAAQ,CAAC,EAAK,YAAc,EAAK,WAAW,SAAW,GAAI,MAAO,GACvE,GAAM,GAAK,EAAK,WAOhB,EAAM,KAAO,CAAC,EAAQ,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,IAK9D,EAAM,MAAQ,EAAQ,EAAG,KAAK,IAAI,EAAG,GAAG,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,GAAI,KAAK,GAAI,KAAK,IAAI,EAAG,IAAI,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,IAMvH,GAAM,GAAS,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,UACnE,EAAM,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,WACtE,SAAM,IAAM,KAAK,GAAM,GAAK,SAAS,QAAW,GAAM,GAAU,IAAO,GAEhE,EAGF,YAEoD,EAAoB,EAAgF,CAC7J,GAAM,CAAE,IAAK,GAAU,EAAU,UAC3B,EAAY,EAAmB,QAAwB,EAAM,EAAG,EAAM,GAEtE,EAAO,EAAU,QACjB,CAAE,aAAc,EAAU,UAC1B,EAAc,GAAI,GAAc,EAAU,UAAU,MAAO,EAAK,QAAQ,EAAU,WAAY,GAC9F,EAAQ,GAAmB,GASjC,MAAO,IAAK,KAPM,CAChB,YACA,qBACA,cACA,UCxDG,YAA+B,CAapC,YAAY,EAAqC,GAAI,CACnD,GAAM,CACJ,YAAY,GAAM,aAAa,GAAM,YAAW,YAAW,YAAW,cACpE,EACJ,KAAK,UAAY,EACjB,KAAK,WAAa,EAClB,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,uBAC9B,KAAK,WAAa,GAAc,yBAI7B,QAAwB,CAK7B,YACE,EACA,EAAqC,GACrC,CACA,KAAK,cAAgB,EACrB,KAAK,QAAU,GAAI,IAAyB,GAG9C,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CACJ,YAAW,aAAY,YAAW,YAAW,YAAW,cACtD,KAAK,QAcT,GAZI,GAAa,KAAK,wBAAyB,KAC7C,GAAI,YAAc,EAClB,EAAI,UAAY,EAChB,GAAY,EAAK,KAAK,cAAc,iBACpC,GAAY,EAAK,KAAK,cAAc,kBACpC,GAAY,EAAK,KAAK,cAAc,mBACpC,GAAY,EAAK,KAAK,cAAc,WACpC,GAAY,EAAK,KAAK,cAAc,aAAc,IAClD,GAAY,EAAK,KAAK,cAAc,cAAe,IACnD,GAAY,EAAK,KAAK,cAAc,WAAY,KAG9C,EAAY,CACd,EAAI,YAAc,EAClB,EAAI,UAAY,EAEhB,GAAM,GAAY,AAAC,GAAe,CAChC,EAAI,YACJ,EAAI,IAAI,EAAG,EAAG,EAAG,EAAG,EAAW,EAAG,EAAI,KAAK,IAC3C,EAAI,QAEN,KAAK,cAAc,UAAU,QAAQ,MAOpC,YACL,EACA,EACA,CAEA,AAD2B,OAAM,QAAQ,GAAiB,EAAgB,CAAC,IACxD,QAAQ,AAAC,GAAM,CAEhC,GAAM,GAAY,YAAa,GAC3B,EACC,GAAoB,GAAK,EAAE,UAAY,OAC5C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gIAGlB,GAAI,IAAkB,GAAW,KAAK,oBC1G1C,OAAoB,OCApB,MAAoB,OCKpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAqC,EAAoB,EAAqB,EAA4C,CACxH,GAAM,GAAkB,EAA2B,EAAY,EAAa,GAAG,qBACzE,EAAkB,EAA2B,EAAa,EAAa,GAAG,qBAC1E,EAAiB,EAAkB,EAAY,EAAa,EAAG,GAAG,oBAExE,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAkB,EAAuC,CACvF,GAAM,GAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBAE1E,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YAAuB,EAAuB,EAAsF,CACzI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAgB,GAEhC,EAAqB,EAAkB,EAAG,GAAI,EAAG,sBACjD,EAA+B,EAA4B,GAAI,GAAI,gCACnE,EAA+B,EAA4B,GAAI,IAAK,gCAEpE,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,IAAK,0BAA0B,OAG3F,GAAM,GAA4B,EAA4B,IAAK,IAAK,6BAClE,EAA2B,EAA2B,IAAK,IAAK,4BAEhE,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,aAAY,cAAa,cCjEvC,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAqC,EAA4C,CAC/E,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAiB,EAAkB,GAAG,oBAE5C,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAuC,CACrE,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAEtD,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YACL,EACA,EAC+D,CAC/D,GAAM,GAAgC,GAEhC,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAW,GAE3B,EAAqB,EAAkB,sBACvC,EAA+B,EAA4B,gCAC3D,EAA+B,EAA4B,gCAE3D,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,0BAA0B,OAGtF,GAAM,GAA4B,EAA4B,6BACxD,EAA2B,EAA2B,4BAEtD,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,SAA2B,EAAW,GAE/B,CAAE,OAAQ,CAAE,aAAY,cAAa,aAAa,iBFrE3D,YAAc,EAAgB,EAAoB,EAAuC,CACvF,MAAO,AAAG,OAAI,AAAG,SAAO,EAAG,EAAO,QAAS,EAAQ,QAAS,EAAO,MAGrE,YAAwB,EAAgB,EAA8B,EAA2B,GAAmB,CAClH,GAAI,GAAM,EAAkB,AAAG,OAAK,GAAK,EACzC,SAAM,EAAuB,EAAK,EAAO,gBAAiB,CAAC,EAAG,IAC9D,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,AAAG,MAAI,EAAK,GAAK,EAAG,EAAO,eAAgB,CAAC,EAAG,KAC9C,EAGT,YAAmB,EAAgB,EAAsC,CACvE,GAAI,GAAM,EAAuB,AAAG,OAAK,GAAI,EAAO,gBAAiB,CAAC,EAAG,IACzE,SAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,MAAI,EAAK,GACX,EAGF,oBAA2B,EAAkC,CAGlE,YAAY,EAAuB,CACjC,MAAM,gBACN,KAAK,eAAiB,EAGjB,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KACnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,8CAElB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAC7D,EAAM,AAAG,OAAK,GAAK,EAAY,EAAO,WAAW,QAAS,CAAC,EAAG,KAClE,SAAM,GAAe,EAAK,EAAO,WAAW,kBAAmB,IAC/D,EAAM,GAAe,EAAK,EAAO,WAAW,mBAC5C,GAAM,KAAK,eAAgB,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAChD,EAAM,GAAU,EAAK,EAAO,YAAY,cAAc,QAExD,EAAM,GAAe,EAAK,EAAO,UAAU,iBAC3C,EAAM,AAAG,OAAK,EAAuB,EAAK,EAAO,UAAU,eAAgB,CAAC,EAAG,KACxE,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,sBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,gBAG1C,cAAc,EAAuB,CAC7C,MAAO,IAAc,EAAS,KAAK,kBGvEhC,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAkB,GAAuB,EAAgB,GAEzD,EAAM,EAAgB,IAAK,EAAG,UAC9B,EAAS,EAAgB,IAAK,EAAG,aAEvC,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,GAAI,CAAE,MAAK,YCflB,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,CACF,IAAK,EAAgB,UACrB,OAAQ,EAAgB,eAI5B,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,GAAK,IAAL,UAAK,EAAL,CAEL,SAAS,SAET,OAAO,SAJG,aNQL,oBAA2B,EAAyB,CAGzD,YAAY,EAAqC,GAAI,IAAa,GAAI,CACpE,MAAM,gBACN,KAAK,sBAAwB,KAGpB,uBAAqC,CAC9C,MAAO,MAAK,sBAGP,OAAO,EAA0C,CACtD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EAEE,EAAS,AAAG,WAAQ,EAAoB,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAAS,KAAK,EAAmB,MAAM,GAAI,IACnG,EAAM,GAAoB,EAAQ,EAAO,GAAG,KAAK,OACjD,EAAS,GAAoB,EAAQ,EAAO,GAAG,QACrD,MAAO,CAAE,MAAK,YAIX,aAAa,EAA0C,CAC5D,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAE,MAAK,UAAW,KAAK,OAAO,GACpC,MAAO,CAAE,MAAK,OAAQ,AAAG,WAAQ,WAIxB,SAAQ,EAAsC,CACzD,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,qBAAoB,EAA8E,CAC7G,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAE9B,EAAO,AAAG,WAAQ,EAAI,KACtB,EAAU,AAAG,WAAQ,EAAI,QACzB,EAAsB,EAAK,IAAI,CAAC,EAAW,IAAO,EACtD,YACA,aAAc,EAAQ,MAGlB,EAAqB,KAAM,SAAQ,IACvC,EAAoB,IAAI,MAAO,CAAE,YAAW,kBAAmB,CAC7D,GAAM,GAAO,MAAM,GAAU,QAAQ,GAC/B,EAAY,MAAM,GAAa,QAAQ,GACvC,EAAS,EAAW,GACpB,EAAS,EAAS,GAAO,KAAO,GAAO,OACvC,EAAoB,EAAS,EAAY,EAAI,EAEnD,SAAU,UACV,EAAa,UACN,CAAE,MAAK,SAAQ,wBAG1B,SAAI,IAAI,UACR,EAAI,OAAO,UAEJ,EAAS,aAAe,EAAiD,EAAmB,GAG3F,qBAA8B,CACtC,MAAO,mBAGF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,GAGb,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAwB,IAAM,EAAI,EAAM,KAAM,EAAI,GAElD,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KOvHxC,MAAoB,OASb,oBAGG,GAAgC,CACjC,YAAY,EAAqB,EAAmB,EAAgD,CACzG,GAAM,GAAkB,EAAmB,IAAI,CAAC,CAAE,QAAO,YAAa,CACpE,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,CACL,MAAO,EAAQ,EACf,OAAQ,EAAS,KAIf,EAAY,EAAgB,OAElC,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAA0B,CAAC,EAAe,IAAkB,AAAG,QAAM,CAAC,AAAG,OAAK,CAAC,IAAK,EAAO,WAAY,AAAG,OAAK,CAAC,IAAK,EAAO,YAAa,GAAG,KAAK,EAAG,KAAK,OAGzJ,EAAa,CAAC,EAAkB,IAAoD,CACxF,GAAM,CAAE,QAAO,UAAW,EAAgB,GAC1C,MAAO,GAAK,EAAO,GAAU,KAAK,IAAI,EAAQ,GAAU,EAAI,GAGxD,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GACvE,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GAa7E,MAXwB,GACrB,IAAI,AAAG,OAAK,CAAC,EAAW,KAAM,EAAW,YACzC,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAY,GACZ,EAAY,OAEb,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAgB,GAAU,MAC1B,EAAgB,GAAU,aAO3B,aAAa,EAA8B,CAChD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,KAAK,OAAO,GACxB,MAAO,MAAK,YACV,EACA,EAAM,UACN,EAAM,gBAAgB,IAAI,CAAC,CAAC,EAAQ,KAAY,EAAE,SAAQ,mBAKnD,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,iBAAgB,EAAgE,CAC3F,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAkB,AAAG,OACzB,IAAM,AAAG,UAAQ,KAAK,aAAa,KAG/B,EAAoB,KAAM,SAAQ,IAAI,EAAgB,IAC1D,MAAO,EAAgB,IAAa,CAClC,GAAM,GAAiB,MAAM,KAAK,KAAM,GAAe,QACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,GAAO,IACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,CAAC,GAAO,IAExD,MAAO,IAAI,IACT,MAAM,IAAI,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,GAAI,GAAM,EAAQ,GAAc,EAAQ,KACxE,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,QAMtC,SAAgB,QAAQ,AAAC,GAAM,EAAE,WAE1B,EAAS,aAAe,EAAyC,EAAkB,GAGlF,0BAAmC,CAC3C,MAAO,OC1FJ,oBAAgC,GAAkD,CACvF,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGnB,qBAA8B,CACtC,MAAO,yBAGC,yBAAkC,CAC1C,MAAO,OCdX,OAAoB,OCMb,YACL,EAC2E,CAC3E,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCnBZ,YAA2B,EAAkG,CAClI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAEjD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,SAAQ,SAAQ,WFhBvB,oBAAuC,EAA+G,CAC3J,aAAc,CACZ,MAAM,4BAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,0DAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,GAAI,IAAK,CAAC,EAAG,GAAI,SAEjC,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,oCAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA+B,GAG9B,cAAc,EAAuB,CAC7C,MAAO,IAAkB,KG7CtB,oBAAoC,GAAsD,CAC/F,YAAY,EAAiD,GAAI,IAA4B,CAC3F,MAAM,wBAAyB,GAGvB,qBAA8B,CACtC,MAAO,8BAGC,yBAAkC,CAC1C,MAAO,OCVJ,oBAA8B,GAAkB,GCJvD,MAAoB,OCApB,OAAoB,OCApB,OAAoB,OAIb,YAAe,EAAgB,EAAuC,CAC3E,MAAO,AAAG,QAAI,AAAG,OAAI,EAAG,EAAO,SAAU,EAAO,QDAlD,YACE,EACA,EACA,EACA,EACA,EAA4B,OACf,CACb,GAAM,CAAE,UAAS,QAAS,EAAO,KAE7B,EAAM,AAAG,UAAO,EAAG,EAAS,EAAS,GACzC,SAAM,AAAG,OAAI,EAAK,GAClB,EAAM,GAAM,EAAK,EAAO,OACjB,EAAW,AAAG,QAAK,GAAO,EAG5B,YAAc,EAAgB,EAAyB,CAC5D,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAoB,EAAgB,EAAyB,CAClE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAkB,EAAgB,EAAyB,CAChE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,GAAM,SE7B5C,MAAoB,OAUpB,YAA2B,EAAwC,EAA+B,CAChG,WAA6B,EAAyB,EAAoB,EAAiC,CACzG,GAAM,GAAU,EAAe,GACzB,EAAQ,EAAQ,OAAU,GAAa,EAAa,GAE1D,GAAI,GAAQ,GACV,KAAM,IAAI,OAAM,+BAA+B,sBAA0B,EAAQ,uBAAuB,kBAA2B,KAGrI,MAAO,AAAG,QACR,IAAM,AAAG,YACP,AAAG,WAAS,EAAS,CAAC,EAAY,EAAO,EAAY,IACrD,CAAC,EAAG,EAAG,EAAG,KAKhB,WACE,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,EAAoB,EAAiB,EAAY,GAC3D,EAAO,AAAG,WAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,QAGpB,WAAiC,EAAoB,EAAwC,CAC3F,GAAM,GAAU,AAAG,WAAS,EAAe,IACrC,EAAS,AAAG,WAAS,EAAe,IAE1C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,aAGX,CACL,UACA,UAIJ,WACE,EACA,EACA,EACA,EACiB,CACjB,GAAM,GAAO,EAAkB,EAAiB,EAAY,EAAY,GAAG,UACrE,EAAQ,EAAwB,EAAY,GAAG,WAErD,MAAO,CAAE,OAAM,SAGjB,WACE,EACA,EACA,EACA,EACA,EAAkB,GACG,CACrB,GAAM,GAAQ,EAAwB,GAAS,GAAM,GAAK,EAAiB,EAAY,EAAY,GAAG,WAChG,EAAQ,EAAuB,EAAiB,EAAY,EAAY,GAAG,WAEjF,MAAO,CAAE,QAAO,SAGlB,MAAO,CACL,yBACA,8BAIG,YAAuB,EAA6E,CACzG,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAgB,GAEhC,EAAc,EAAuB,KAAM,GAAI,EAAG,eAClD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YAEnD,EAAc,EAA2B,MAAO,GAAI,EAAG,cAAe,IACtE,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YAEpD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aAEvD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAmB,EAA2B,OAAQ,IAAK,EAAG,oBAE9D,EAAK,AAAG,OACZ,IAAM,AAAG,YAAU,AAAG,WAAS,EAAe,IAAM,KAAM,CAAC,IAAK,MAAO,CAAC,EAAG,KAI7E,GAFA,EAAc,KAAK,CAAE,UAAW,OAE5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAsB1E,MAAO,CAAE,OAnBM,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGe,iBChJnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAiC,EAAkC,CACjE,GAAM,GAAU,EAAmB,GAAG,kBAAwB,GACxD,EAAS,EAAmB,GAAG,iBAAuB,GAE5D,MAAO,CAAE,UAAS,UAGpB,WAAgC,EAAiC,CAC/D,GAAM,GAAU,EAAmB,GAAG,iBAAuB,GACvD,EAAO,EAAmB,GAAG,cAAoB,GACjD,EAAQ,EAAwB,GAEtC,MAAO,CAAE,KAAM,CAAE,UAAS,QAAQ,SAGpC,WAAoC,EAAqC,CACvE,MAAO,CACL,MAAO,EAAuB,GAAG,WACjC,MAAO,EAAuB,GAAG,YAIrC,MAAO,CACL,yBACA,8BAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAW,GAE3B,EAAc,EAAuB,eACrC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAc,EAA2B,eACzC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aAEvC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aACvC,EAAmB,EAA2B,oBAE9C,CAAE,MAAO,EAGf,GAFA,EAAc,KAAK,CAAE,aAAc,KAAM,UAAW,OAEhD,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yDAAyD,KAG3E,GAAM,GAAS,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGF,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBC9FnB,MAAoB,OAKb,YAAkB,EAAgB,EAA0C,CACjF,GAAI,GAAM,GAAK,EAAG,EAAO,OACzB,SAAM,GAAW,EAAK,EAAO,OAC7B,EAAM,AAAG,MAAI,EAAK,GAClB,EAAM,AAAG,OAAK,GACP,EAGF,YAAsB,EAAgB,EAA0C,CACrF,GAAI,GAAM,GAAS,EAAG,EAAO,OAC7B,EAAM,GAAW,EAAK,EAAO,OAE7B,GAAI,GAAS,AAAG,UAAQ,EAAG,EAAG,EAAG,SAC3B,EAAQ,AAAG,QAAkB,EAAO,OACpC,EAAQ,EAAO,MAAM,KAAO,EAAI,MAAM,GAG5C,GAFsB,EAAO,MAAM,KAAO,EAAI,MAAM,IAAM,EAAO,MAAM,KAAO,EAAI,MAAM,GAErE,CACjB,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAE/B,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAGjC,SAAS,EAAQ,AAAG,SAAO,CAAC,EAAQ,GAAQ,GAAK,EACjD,EAAM,AAAG,MAAI,EAAQ,GAErB,EAAM,AAAG,OAAK,GACP,EL3BF,oBAAiC,EAAyB,CAC/D,aAAc,CACZ,MAAM,sBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAGtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAE7D,EAAM,GAAS,EAAY,EAAO,aACtC,EAAM,AAAG,UAAQ,EAAK,EAAG,EAAG,SAE5B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,aAC/B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAa,EAAK,EAAO,kBAE/B,GAAM,GAAY,EAAI,KAAK,CAAC,EAAG,IAG/B,MAFuB,AAAG,UAAO,EAAW,EAAO,WAM1C,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,uBAAsB,EAAwD,CA7D7F,MA8DI,GAAI,oBAAO,QAAP,cAAc,KAAK,AAAC,GAAQ,GAAO,GAAI,MAAO,IAAI,cAAa,KACnE,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAwB,AAAG,OAC/B,IAAM,AAAG,UAAQ,KAAK,aAAa,KAE/B,EAA0B,KAAM,SAAQ,IAAI,EAAsB,IAAI,AAAC,GAAM,EAAE,SACrF,SAAsB,QAAQ,AAAC,GAAM,EAAE,WAChC,EAAS,aAAe,EAA0B,EAAwB,GAGzE,qBAA8B,CACtC,MAAO,yBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KM7ElB,YAAkC,EAAuB,CAC9D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECHF,YAGL,EACA,EAC6B,CAE7B,MAAO,IAAK,KADM,CAAE,eCNf,YAAmB,EAA8B,CACtD,MAAO,OAAO,GAAI,KAAQ,SAGrB,YAGL,EACA,EACkB,CAElB,MAAO,IAAK,KADM,CAAE,QCNf,YAAsB,EAAiC,CAC5D,MAAQ,GAAI,SAAW,GAAO,MAAQ,EAAI,SAAW,GAAO,SACvD,GAAmB,EAAI,mBAGvB,YAGL,EACA,EACA,EACqB,CAErB,MAAO,IAAK,KADM,CAAE,SAAQ,sBCpB9B,OAAoB,OCApB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,WAAoC,EAAqB,EAAuD,CAC9G,GAAM,GAAU,AAAG,YAAS,EAAe,EAAI,EAAI,GAAc,CAAC,EAAG,EAAG,EAAa,IAC/E,EAAmB,AAAG,YAAS,EAAe,IAC9C,EAAoB,AAAG,YAAS,EAAe,IAC/C,EAAkB,AAAG,YAAS,EAAe,IAC7C,EAAsB,AAAG,YAAS,EAAe,IAEvD,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,uBAChB,CAAE,UAAW,GAAG,qBAChB,CAAE,UAAW,GAAG,0BAGX,CACL,UACA,mBACA,oBACA,kBACA,uBAIJ,WACE,EACA,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,KAAgB,EAAkB,oBAAsB,WAGnE,CAAE,UAAS,QAGpB,WACE,EACA,EACA,EACA,EACqB,CACrB,GAAM,CACJ,UACA,QACE,EAAkB,EAAY,EAAa,EAAY,EAAc,IAEzE,MAAO,CACL,UACA,kBAAmB,GAIvB,WACE,EACA,EACA,EAC4B,CAC5B,GAAM,GAAiB,EAA2B,EAAY,GAAG,oBAC3D,EAAiB,EAA2B,EAAY,EAAa,EAAG,GAAG,oBAEjF,MAAO,CAAE,iBAAgB,kBAG3B,YAAwD,CACtD,GAAM,GAAS,EAA2B,EAAG,GAAI,EAAG,sBAC9C,EAAS,EAAsB,GAAI,GAAI,sBACvC,EAAS,EAAsB,GAAI,IAAK,sBACxC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,KAAM,uBAC3C,EAAU,EAAsB,KAAM,KAAM,uBAClD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,UACA,UACA,UACA,WAIJ,YAA+D,CAC7D,GAAM,GAAS,EAA2B,KAAM,IAAK,EAAG,2BAClD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,GAAI,EAAG,2BAChD,EAAS,EAA2B,GAAI,IAAK,EAAG,2BAChD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,EAAG,EAAG,oDACjD,EAA2B,EAAkB,KAAM,GAAI,EAAG,2DAC1D,EAAoB,EAAkB,KAAM,GAAI,EAAG,oDACnD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDA0BxD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,gBAjCsB,CACtB,uBAAwB,EACxB,gBAAiB,GAgCjB,gBA9BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA6BjB,gBA3BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA0BjB,gBAxBsB,CACtB,uBAAwB,GACxB,gBAAiB,IAuBjB,gBArBsB,CACtB,uBAAwB,EACxB,gBAAiB,IAoBjB,gBAlBsB,CACtB,uBAAwB,GACxB,gBAAiB,KAoBrB,MAAO,CACL,2BACA,gCAIG,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAChC,CACJ,iBACA,uBACE,EAAsB,GACpB,CACJ,2BACA,gCACE,GAAkB,EAAgB,GAChC,EAAc,IACd,EAAmB,IAKnB,EAAe,CACnB,UALgB,AAAG,YACnB,EAAe,KAAO,GACtB,CAAC,EAAG,KAAM,KAMZ,GADA,EAAc,KAAK,CAAE,UAAW,2BAC5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,OAAQ,CACN,cACA,mBACA,gBAEF,iBC9MJ,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAoC,EAAgB,EAAa,EAA2C,CAC1G,GAAM,GAAU,EAAmB,GAAG,YAAiB,sBAAyB,EAAG,GAAG,aAChF,EAAoB,EAAmB,GAAG,YAAiB,oCAAuC,EAAG,GAAG,uBAC9G,MAAO,CAAE,UAAS,qBAGpB,WAA+B,EAAyC,CACtE,GAAM,GAAe,oBAAoB,IACnC,EAAsB,sBAAsB,cAC5C,EAA4B,GAAG,mBAC/B,EAA4B,GAAG,mBAE/B,EAAU,EAAmB,GAAG,sBAAyC,EAAG,GAAG,aAC/E,EAAmB,EAAmB,GAAG,oBAAuC,EAAG,GAAG,sBACtF,EAAoB,EAAmB,GAAG,mBAAsC,EAAG,GAAG,uBACtF,EAAkB,EAAmB,GAAG,0BAA6C,EAAG,GAAG,qBAC3F,EAAsB,EAAmB,GAAG,8BAAiD,EAAG,GAAG,yBAEzG,MAAO,CACL,eAAgB,CACd,UACA,mBACA,oBACA,kBACA,uBAEF,eAAgB,EAA2B,cAAe,EAAK,IAInE,YAAwD,CACtD,MAAO,CACL,OAAQ,EAA2B,cAAe,EAAG,sBACrD,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,KAInC,WAA2B,EAAgB,EAAkC,CAC3E,GAAM,GAAU,EAAmB,GAAG,YAAkB,EAAG,GAAG,aACxD,EAAO,EAAmB,GAAG,WAAiB,EAAG,GAAG,UAC1D,MAAO,CAAE,UAAS,QAGpB,WAAmC,EAAkC,CACnE,GAAM,GAAyB,EAC7B,2BAA2B,yBAC3B,kCAAkC,4BAE9B,EAAkB,EACtB,2BAA2B,mBAC3B,kCAAkC,qBAEpC,MAAO,CAAE,yBAAwB,mBAGnC,YAA+D,CAC7D,MAAO,CACL,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,IAI/C,MAAO,CACL,2BACA,gCAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAChC,CACJ,2BACA,gCACE,GAAkB,EAAW,GAC3B,EAAY,EAAU,oBAE5B,GADA,EAAc,KAAK,CAAE,aAAc,mBAAoB,UAAW,2BAC9D,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yEAAyE,KAG3F,GAAM,GAAS,CACb,YAAa,IACb,iBAAkB,IAClB,aAAc,CACZ,cAIJ,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBChInB,OAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,UAAO,EAAG,EAAO,QAAS,EAAS,QAChD,SAAM,AAAG,OAAI,EAAK,EAAO,mBAClB,AAAG,eAAY,EAAK,EAAG,KDPlC,GAAM,IAAU,qBAEhB,YACE,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,QAAS,EAAS,QACzD,SAAM,AAAG,aACP,EACA,EAAO,gBACP,EAAO,oBACP,EAAO,kBACP,EAAO,iBACP,IAEK,AAAG,eAAY,EAAK,EAAG,KAIlC,YAA+B,EAAoC,CACjE,MAAO,CAAC,EAAG,EAAG,EAAG,IAAI,KAAK,AAAC,GAAQ,IAAQ,GAAY,CAAC,EAAG,GAAK,CAAC,EAAG,GAG/D,YAAqB,EAAgB,EAA4B,CACtE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GACA,EAAM,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IA4BnD,GAVA,AAhBuB,CACrB,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,QACP,EAAO,QACP,EAAO,QACP,EAAO,SAGM,QAAQ,CAAC,EAAO,IAAM,CACnC,GAAM,GAAW,EAAI,EACf,EAAuB,GAAsB,GACnD,EAAM,GAAmB,EAAK,EAAM,eAAgB,GACpD,EAAM,EAAmB,EAAK,EAAM,eAAgB,CAAC,EAAG,IACpD,IAAa,IACf,GAAS,KAIT,IAAW,KACb,KAAM,IAAI,OAAM,iDAGlB,MAAO,CACL,MACA,OAAQ,KEjEd,YAAa,EAAoB,EAAW,EAAW,CACrD,GAAM,GAAY,EAAM,YAClB,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAS,GAAQ,GAAU,GAAQ,GACnC,EAAS,GAAQ,GAAU,GAAQ,GACzC,GAAI,GAAS,GAAK,GAAS,EACzB,MAAO,GAET,GAAM,GAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAmB,EAAkB,GACjE,KAAK,IAAI,EAAmB,EAAkB,GACpD,MAAO,GAAoB,GAAQ,EAAQ,GAGtC,YACL,EACA,EACA,EACA,EACA,EACU,CACV,GAAM,GAAW,EAAM,MAAM,GACvB,EAAa,KAAK,IACtB,EACA,GAGI,EAAa,EAChB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,OAAO,AAAC,GAAM,EAAE,MAAQ,GACxB,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAE5B,EAAe,AAAC,GAAe,GAAK,EAAe,EAAI,EACvD,EAAqB,GAE3B,SAAW,QAAQ,AAAC,GAAM,CACxB,GAAI,EAAS,QAAU,EAAY,OACnC,GAAM,GAAgB,EAAE,MACxB,OAAS,GAAI,EAAS,OAAS,EAAG,GAAK,EAAG,EAAE,EAAG,CAC7C,GAAM,GAAM,GAAI,EAAO,EAAE,SAAU,EAAS,IAC5C,GAAI,IAAQ,GACZ,GAAE,OAAS,EAAa,GACpB,EAAE,OAAS,GAAgB,MAEjC,AAAI,IAAkB,EAAE,OACtB,EAAS,KAAK,EAAE,YAGb,EC5DT,MAAoB,OAIpB,YAA2C,EAAgB,CACzD,GAAM,GAAM,AAAG,UAAQ,AAAG,YAAU,EAAG,CAAC,EAAG,KAErC,EAAQ,CACZ,AAAG,MAAI,EAAI,GAAI,EAAI,IACnB,AAAG,MAAI,EAAI,GAAI,EAAI,KAEf,EAAU,CACd,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,KAC1C,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,MAE5C,MAAO,CACL,QACA,WAIJ,YAA0B,EAAiB,EAAiB,CAC1D,GAAM,CACJ,QACA,WACE,GAAkC,GAEhC,EAAM,AAAG,UAAQ,AAAG,YAAU,EAAI,CAAC,EAAG,KACtC,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAC3E,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAEjF,MAAO,AAAG,aACR,AAAG,QAAM,CACP,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,KAEnB,CAAC,EAAG,IAID,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAY,EAAe,MAAM,GAEnC,EAAQ,GACV,AAAG,UAAQ,AAAG,OAAK,EAAO,UAAW,CAAC,EAAW,EAAG,IAAK,CAAC,GAAI,IAC9D,AAAG,UAAQ,EAAgB,CAAC,GAAI,KAElC,EAAQ,AAAG,UACT,EACA,CAAC,EAAY,EAAM,MAAM,GAAK,EAAY,IAG5C,GAAM,GAAmB,AAAG,UAAQ,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,MAC/E,EAAS,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,IAE5D,EAAS,AAAG,UACV,EACA,CAAC,EAAW,EAAO,MAAM,KAG3B,GAAM,GAAe,AAAG,UAAQ,GAC1B,EAAgB,AAAG,UAAQ,GAEjC,MAAO,CACL,MAAO,EACP,OAAQ,KC1Ed,OAAoB,OCApB,OAAoB,OAKb,YACL,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAY,EAAE,MAAM,GACpB,EAAwB,AAAG,WAC/B,GAAU,EAAG,EAAO,wBACpB,CAAC,EAAW,GAAI,EAAG,IAEf,EAAkB,AAAG,WACzB,GAAU,EAAG,EAAO,iBACpB,CAAC,EAAW,GAAI,IAElB,MAAO,CAAE,wBAAuB,qBDb7B,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAQ,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IACjD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IAErD,EAAiB,GAAmB,EAAQ,EAAO,iBACnD,EAAiB,GAAmB,EAAG,EAAO,iBAC9C,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAElD,EAAiB,AAAG,UAAO,CAC/B,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,uBACd,GAEG,EAAmB,AAAG,UAAO,CACjC,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,iBACd,GAEH,MAAO,CACL,iBACA,sBE3CC,WAA4B,CAOjC,YAAY,CAAE,gBAAe,cAAuC,GAAI,CAN9D,WAAgB,wBAUxB,GAHA,KAAK,eAAiB,GAAiB,GACvC,KAAK,YAAc,GAAc,IAE7B,MAAO,MAAK,gBAAmB,UAAY,KAAK,gBAAkB,GAAK,KAAK,gBAAkB,EAChG,KAAM,IAAI,OAAM,GAAG,KAAK,iEAG1B,GAAI,MAAO,MAAK,aAAgB,SAC9B,KAAM,IAAI,OAAM,GAAG,KAAK,iDAIxB,gBAAwB,CAAE,MAAO,MAAK,kBAEtC,aAAqB,CAAE,MAAO,MAAK,cTZlC,oBAA6B,EAAyB,CAC3D,aAAc,CACZ,MAAM,kBAGD,aAAa,EAAiB,CACnC,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAQ,WACvD,EAAI,AAAG,OAAI,AAAG,OAAI,EAAa,AAAG,UAAO,sBAAwB,AAAG,UAAO,IAC3E,EAAW,GAAY,EAAG,EAAO,aAEjC,CAAE,iBAAgB,oBAAqB,GAAgB,EAAS,IAAK,EAAS,OAAQ,EAAO,kBAEnG,MAAO,IAAY,EAAgB,EAAkB,EAAO,qBAInD,SAAQ,EAAkB,CACrC,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,aACX,EACA,EAAkC,GACR,CAC1B,GAAM,CAAE,aAAY,iBAAkB,GAAI,GAAsB,GAE1D,EAAW,KAAM,GAAW,GAE5B,CACJ,MAAO,EACP,OAAQ,GACN,KAAK,aAAa,GAEhB,EAAQ,EAAO,GACf,EAAS,EAAQ,GACvB,OAAS,GAAI,EAAG,EAAI,EAAO,OAAQ,IACjC,EAAO,GAAG,UACV,EAAQ,GAAG,UAGb,GAAM,GAAa,MAAM,KAAK,KAAM,GAAO,QAErC,EAAU,GACd,EACA,EACA,EAJmB,GAMnB,GAGI,EAAe,EAAS,2BAA2B,GACnD,EAAY,EAAS,UACrB,EAAO,EAAY,EAAa,MAChC,EAAO,EAAY,EAAa,OAEhC,EAAY,EAAM,YAClB,EAAU,EACb,IAAI,AAAC,GAAQ,CACZ,GAAM,CAAC,EAAK,GAAU,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACf,CAAC,GAAM,IAAS,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACrB,MAAO,IAAI,GACT,EAAW,GACX,GAAI,IACF,GACA,EACA,GAAQ,GACR,EAAS,GAEX,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,OAKtC,SAAM,UACN,EAAO,UACA,EAGC,qBAA8B,CACtC,MAAO,wBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KUhHlB,YAA8B,EAAuB,CAC1D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,EAGF,YAAgC,EAAuB,CAC5D,MAAO,IAAqB,GAIvB,oBAA+B,GAAe,GCd9C,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,QAAU,SACpB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,OAAQ,SAClB,GAAI,GAAM,QAAS,UAGR,GAAwB,CACnC,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAA+C,CAAC,QAAS,QAAS,QAElE,GAAqB,oBACrB,GAAoC,mCCvBjD,MAAoB,OCapB,GAAM,IAAW,AAAC,GAAa,MAAO,IAAQ,SAEvC,YAAwB,EAAa,CAC1C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mBAAmB,KAGrC,GAAI,MAAO,GAAO,oBAAuB,UACvC,KAAM,IAAI,OAAM,wDAAwD,EAAO,sBAGjF,GAAI,CAAC,GAAS,EAAO,eAAiB,EAAO,aAAe,GAAK,EAAO,aAAe,EACrF,KAAM,IAAI,OAAM,gEAAgE,EAAO,gBAGzF,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,MAAM,AAAC,GAAW,MAAO,IAAM,UAElD,KAAM,IAAI,OAAM,kEAAkE,KAAK,UAAU,EAAO,YAG1G,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,IAAI,AAAC,GAAW,GAAK,IAAI,MAAM,AAAC,GAAW,GAAS,EAAE,IAAM,GAAS,EAAE,IAE1F,KAAM,IAAI,OAAM,wEAAwE,KAAK,UAAU,EAAO,YAGhH,GAAI,EAAO,SACT,EAAC,MAAM,QAAQ,EAAO,UACnB,EAAO,QAAQ,SAAW,GAC1B,CAAC,EAAO,QAAQ,MAAM,KAEzB,KAAM,IAAI,OAAM,8EAA8E,KAAK,UAAU,EAAO,YCjDxH,MAAoB,OCApB,MAAoB,OAEb,YAAe,EAA6B,CACjD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,AAAG,MAAI,EAAG,AAAG,SAAO,qBAChC,MAAO,AAAG,OAAI,AAAG,OAAK,AAAG,MAAI,EAAG,IAAO,KDApC,YAA2B,EAAgB,EAAwC,CACxF,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAM,AAAG,MAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,SAAO,EAAK,EAAO,KAAK,QAAS,CAAC,EAAG,GAAI,SAClD,EAAM,AAAG,MAAI,EAAK,EAAO,GAAG,KAC5B,EAAM,AAAG,MAAI,EAAK,EAAO,GAAG,SAC5B,EAAM,AAAG,MAAI,EAAK,EAAO,KAAK,MACvB,GAAM,KEZjB,OAAoB,OAKb,YAAgC,EAAgB,EAA0C,CAC/F,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,mBAAgB,EAAK,EAAO,iBAAkB,EAAO,iBAAkB,CAAC,EAAG,GAAI,SACxF,EAAM,AAAG,OAAI,EAAK,EAAO,MAClB,GAAM,KCVjB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAEnE,WAAgC,EAAc,EAAiC,CAC7E,GAAM,GAAM,AAAG,YAAS,EAAe,IACjC,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,SAChB,CAAE,UAAW,GAAG,cAEX,CAAE,MAAK,WAGhB,WAAwC,EAAoB,EAAqB,EAAyC,CACxH,GAAM,GAAO,EAAkB,EAAY,EAAa,EAAG,GAAG,UACxD,EAAK,EAAuB,EAAa,GAAG,QAClD,MAAO,CAAE,OAAM,MAEjB,GAAM,GAA6B,GAAkC,EAAgB,GAErF,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACA,EACA,EACgE,CAChE,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAChC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAgB,GAClC,EAEJ,GAAI,EAAO,mBAAoB,CAC7B,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAAO,mBACjB,EAAkB,EAAI,EAAI,EAAG,SAC7B,EAA2B,EAAI,EAAI,SACjC,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAkB,GAAM,GAAM,EAAI,EAAI,EAAiB,EAAG,SACxE,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,cAErD,CACL,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAAkB,EAAI,EAAI,EAAiB,EAAG,SAC5D,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,UAG5D,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAE1E,MAAO,CAAE,SAAQ,iBChFnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAgC,EAA2B,CACzD,GAAM,GAAM,EAAmB,GAAG,QAAc,GAC1C,EAAU,EAAmB,GAAG,YAAkB,GACxD,MAAO,CAAE,MAAK,WAGhB,WAA2B,EAA4B,CACrD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,WAAwC,EAAmC,CACzE,GAAM,GAAO,EAAkB,GAAG,UAC5B,EAAK,EAAuB,GAAG,QACrC,MAAO,CAAE,OAAM,MAGjB,GAAM,GAA6B,GAA+B,GAClE,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACgE,CAChE,GAAM,GAAgC,GAEhC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAW,GAE7B,EAEJ,GAAI,EAAO,mBAAoB,CAE7B,GAAM,GAAc,EAAO,aAAe,EAAO,YAAY,QAAU,EACvE,EAAS,CACP,MAAO,EAAO,mBAAqB,EAAkB,SAAW,EAA2B,SAC3F,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAkB,cAG3B,GAAS,CACP,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAAkB,UAI7B,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBC7EZ,YAAwB,CAO7B,YAAY,CAAE,YAAW,kBAAuC,GAAI,CAN1D,WAAgB,oBAUxB,GAHA,KAAK,WAAa,GAAa,IAC/B,KAAK,gBAAkB,GAAkB,GAErC,MAAO,MAAK,YAAe,UAAY,KAAK,WAAa,IAAO,EAClE,KAAM,IAAI,OAAM,GAAG,KAAK,6DAG1B,GAAI,MAAO,MAAK,iBAAoB,UAAY,KAAK,iBAAmB,GAAK,KAAK,iBAAmB,EACnG,KAAM,IAAI,OAAM,GAAG,KAAK,qEAIxB,YAAoB,CAAE,MAAO,MAAK,cAElC,iBAAyB,CAAE,MAAO,MAAK,kBPJtC,oBAA6B,EAAmC,CAKrE,YAAY,EAA0B,CACpC,MAAM,cACN,GAAe,GACf,KAAK,QAAU,KAGN,SAA2B,CACpC,MAAO,MAAK,WAGH,kBAA2B,CACpC,MAAO,MAAK,OAAO,iBAAmB,KAAK,OAAO,QAAQ,OAAS,KAG1D,kBAA0B,CACnC,MAAO,GAAK,MAAK,gBAAkB,KAAK,OAAO,QAAQ,OAAS,GAG3D,cAAc,EAAgB,EAAiD,CACpF,GAAI,GAAM,GAAkB,EAAG,EAAO,OACtC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,GAAkB,EAAK,EAAO,OAC7B,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAgB,EAAsC,CACxE,GAAI,GAAM,KAAK,OAAO,mBAClB,GAAM,GAAU,EAAG,EAAO,MAAqB,QAAS,KACxD,GAAuB,EAAG,EAAO,OACrC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EACjE,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EAC1D,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAiB,EAAgC,CACnE,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,4CAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAc,AAAG,OAAK,EAAM,cAAc,EAAW,IAAQ,WACjE,SAAc,KAAK,OAAO,QACtB,GAAU,EAAa,KAAK,OAAO,SACnC,EACJ,EAAc,EAAY,IAAI,AAAG,SAAO,MACjC,KAAK,OAAO,mBACf,KAAK,aAAa,EAAa,GAC/B,KAAK,cAAc,EAAa,UAI3B,SAAQ,EAAkB,EAAyC,CAC9E,MAAO,MAAK,aAAa,KAAM,GAAW,GAAQ,QAGvC,QAAO,EAAkB,EAAoC,GAAgC,CACxG,GAAM,CAAE,YAAW,kBAAmB,GAAI,IAAkB,GACtD,EAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,EAAU,GACxC,EAAO,AAAG,OAAK,IAAM,AAAG,UAAQ,GAAK,GAAG,cACxC,EAAkB,CACtB,MAAO,EAAS,cAAc,GAC9B,OAAQ,EAAS,eAAe,IAG5B,EAAU,KAAM,MAAK,aAAa,EAAM,EAAS,2BAA2B,GAAI,GACtF,EAAI,UACJ,EAAK,UAEL,GAAM,GAAQ,EAAQ,IAAI,AAAC,GAAQ,EAAI,KACjC,EAAS,EAAQ,IAAI,AAAC,GAAQ,EAAI,OAClC,EAAc,EAAQ,IAAI,AAAC,GAAQ,EAAI,YACvC,EAAa,EAAQ,IAAI,AAAC,GAAQ,KAAK,OAAO,QAAQ,EAAI,QAgBhE,MAPmB,AAPH,IACd,EAAM,IAAI,AAAC,GAAQ,EAAI,QAAQ,IAC/B,EACA,KAAK,OAAO,aACZ,IAGyB,IAAI,AAAC,GAAQ,GAAI,IAC1C,EAAO,GACP,EAAY,GACZ,EAAW,GACX,EAAM,GACN,IAKM,qBAA8B,CACtC,MAAO,GAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,QAG1C,cAAc,EAAuB,CAC7C,GAAM,GAAc,KAAK,OAAO,aAAe,GAAe,qBAExD,EAAa,EAAc,EAAY,OAAS,OACtD,GAAI,IAAe,GAAK,IAAe,GAAK,IAAe,EACzD,KAAM,IAAI,OAAM,oEAAoE,2BAEtF,MAAO,IAAc,EAAS,KAAK,OAAQ,KAAK,gBAAiB,QAGnD,cACd,EACA,EACA,EACA,CACA,GAAM,CAAE,QAAO,UAAW,EACpB,EAAY,KAAK,IAAI,EAAO,GAC5B,EAAoB,EAAY,EAChC,EAAoB,EAAY,EAEhC,EAAW,EAAa,MAAM,GAC9B,EAAW,KAAK,OAAO,QAAQ,OAE/B,CAAC,EAAa,EAAc,GAAqB,AAAG,OAAK,IAAM,CACnE,GAAM,GAAW,EAAa,QAAQ,CAAC,EAAU,EAAU,EAAU,KAAK,kBAEpE,EAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACpE,EAAS,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACrE,EAAc,KAAK,gBACrB,AAAG,UAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,KAAK,OAAO,QAAQ,SAAU,GACrG,AAAG,SAAO,GACd,MAAO,CAAC,EAAO,EAAQ,KAGnB,EAAU,GACV,EAAa,KAAM,GAAa,QAChC,EAAY,KAAM,GAAY,QACpC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAS,EAAG,EAAS,EAAU,IAAU,CAChD,GAAM,GAAQ,GAAQ,EAAW,GAAK,GAAK,GAAQ,IACnD,GAAI,CAAC,GAAkB,EAAQ,EAAgB,CAC7C,GAAM,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAe,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACvG,EAAgB,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACxG,GAAK,EAAO,GAAa,EACzB,GAAK,GAAO,EAAc,EAC1B,GAAM,CAAE,MAAK,MAAK,UAClB,CAAE,cAAY,UAAU,KAAK,gBAC/B,KAAM,MAAK,sBAAsB,EAAkC,IACnE,CAAE,WAAY,EAAG,MAAO,GAC5B,EAAQ,KAAK,CACX,IAAK,GAAI,IAAY,GAAG,GAAG,GAAI,GAAY,GAAI,GAC/C,QACA,WAAY,EAAQ,GACpB,YACG,MAOb,SAAY,UACZ,EAAa,UACb,EAAkB,UACX,OAGK,uBAAsB,EAA4B,EAAmD,CACjH,GAAM,CAAE,MAAK,MAAK,UAAW,EACvB,EAAc,KAAM,GAAc,QACxC,MAAO,OAAM,KAAK,OAAO,QAAQ,QAAQ,KAAK,GAC3C,IAAI,CAAC,EAAG,IAAM,EAAY,GAAK,GAAK,GAAQ,IAC5C,IAAI,CAAC,EAAY,IAAW,EAC3B,aACA,WAED,OAAO,CAAC,EAAK,IAAU,EAAI,WAAa,EAAK,WAAa,EAAM,KA/MhE,MACS,AADT,GACS,qBAAuB,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,IAAK,KAAM,MQPrE,oBAAyB,GAAe,CAC7C,YAAY,EAA8B,GAAM,CAC9C,GAAM,GAAS,CACb,qBACA,aAAc,GACd,QAAS,CAAC,WACN,EACA,CACA,QAAS,GACT,QAAS,IAET,CACA,QAAS,GACT,gBAAiB,KAIvB,MAAM,MAGG,qBAA8B,CACvC,MAAO,MAAK,OAAO,sBAGV,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,MAAK,mBAAqB,GAAoC,GAG7D,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KChDrC,YAA0B,EAAuB,EAA8B,GAAM,CAC1F,GAAM,GAAM,GAAI,IAAW,GAC3B,SAAI,eAAe,GACZ,ECNF,oBAAsC,GAAkB,CAAxD,aAJP,CAIO,oBACK,WAAgB,4BCLrB,YAAwB,MAChB,MAEX,EACY,CACZ,MAAO,GAAY,KAAM,MAAK,YAGnB,MAAkB,CAC7B,KAAM,IAAI,OAAM,6CCRpB,OAAoB,OCDpB,OAAoB,OAOpB,kBACE,EACA,EAEA,EACA,EAEA,EAAwF,CAAC,CAAE,iBAAkB,EAC7G,CACA,GAAM,GAAY,EAAc,IAAI,AAAC,GAAkB,GAAoB,GACvE,EAAoB,GACpB,EAAa,WACX,EAAgD,GACpD,aAAoB,WAChB,KAAM,IAAmB,EAAO,GAChC,KAAM,IAAa,EAAO,IAG1B,EAAU,KAAM,GAAe,GAErC,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAGT,kBACE,EACA,EAEA,EACA,EAEA,EACA,CACA,MAAO,IACL,CAAC,GACD,EACA,KAAO,IAAU,EAAc,EAAM,IACrC,EACA,GC5CG,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAAqC,CAAC,QAAS,QAAS,QCF9D,oBAA+B,GAAe,CACnD,aAAc,CACZ,GAAM,GAAS,CACb,mBAAoB,GACpB,aAAc,GACd,QAAS,CAAC,QACV,QAAS,GACT,QAAS,GACT,mBAAoB,GACpB,YAAa,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,MAGzC,MAAM,MAGG,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,2BAGC,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KCvBrC,GAAM,GAAO,CAClB,eAAgB,GAAI,IACpB,iBAAkB,GAAI,IACtB,WAAY,GAAI,IAChB,kBAAmB,GAAI,IACvB,sBAAuB,GAAI,IAC3B,mBAAoB,GAAI,IACxB,kBAAmB,GAAI,IACvB,aAAc,GAAI,KAUP,GAAiB,CAAC,EAAkB,IAA6D,EAAK,eAAe,YAAY,EAAO,GASxI,GAAmB,CAAC,EAAkB,IAA+D,EAAK,iBAAiB,YAAY,EAAO,GAS9I,GAAa,CAAC,EAAkB,IAA0D,EAAK,WAAW,YAAY,EAAO,GAS7H,GAAsB,AAAC,GAAmE,EAAK,kBAAkB,gBAAgB,GAWjI,GAA0B,AAAC,GAAmE,EAAK,sBAAsB,gBAAgB,GAYzI,GAAwB,AAAC,GAA6D,EAAK,mBAAmB,sBAAsB,GASpI,GAA2B,AAAC,GAAmE,EAAK,kBAAkB,mBAAmB,GASzI,GAAsB,AAAC,GAAiF,EAAK,aAAa,oBAAoB,GAE9I,GAA0B,AAAC,GAAgB,EAAK,eAAe,KAAK,GACpE,GAA4B,AAAC,GAAgB,EAAK,iBAAiB,KAAK,GACxE,GAAsB,AAAC,GAAgB,EAAK,WAAW,KAAK,GAC5D,GAAwB,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACrE,GAA4B,AAAC,GAAgB,EAAK,sBAAsB,KAAK,GAC7E,GAA2B,AAAC,GAAgB,EAAK,mBAAmB,KAAK,GACzE,GAA0B,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACvE,GAAqB,AAAC,GAAgB,EAAK,aAAa,KAAK,GAG7D,GAAyB,GACzB,GAAc,GACd,GAAkB,GCjGxB,oBAAqE,GAAwB,CAClG,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAA0E,MACrE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAwB,KAAM,IAClC,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,KAEtD,KAAK,gBAGP,MAAO,GAAc,IACnB,CAAC,EAAc,IAAM,GAAmC,EAAc,EAAsB,KAIhG,kBAAmB,CACjB,MAAO,IAAI,IAA2B,KAAM,KAAK,SAI9C,gBAEG,GAA8F,MACzF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,GAAkB,KAAM,IAC5B,EACA,KAAK,MACL,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,GACpD,KAAK,gBAGP,MAAO,IAA0B,EAAc,GAGjD,kBAAmB,CACjB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA0C,CAClD,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCjFnD,oBAAkE,GAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAAuE,MAClE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAqB,KAAM,IAC/B,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,aAAa,oBAAoB,KAElD,KAAK,gBAGP,MAAO,GAAc,IAAI,CAAC,EAAc,IAAM,CAC5C,GAAM,CAAE,MAAK,SAAQ,qBAAsB,EAAmB,GAC9D,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,KAIpF,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA2F,MACtF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,MAAK,SAAQ,qBAAsB,KAAM,IAC/C,EACA,KAAK,MACL,AAAC,GAAS,EAAK,aAAa,oBAAoB,GAChD,KAAK,gBAGP,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,GAGlF,qBAAsB,CACpB,MAAO,IAAI,IAAiC,KAAM,KAAK,SAIpD,gBAEG,GAAoC,CAC5C,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCtFnD,oBAAqE,GAAwB,CAClG,YAEY,EAEA,EACV,CACA,QAJU,kBAEA,eAMP,gBAEG,GAAyE,MACpE,MAA8C,CACzD,GAAM,GAAgB,KAAM,MAAK,WAUjC,MAAO,AARa,MAAM,IACxB,EACA,KAAK,MACL,AAAC,GAAU,QAAQ,IAAI,EAAM,IAAI,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,KACzF,KACA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,OAGxD,IAAI,CAAC,EAAY,IAAM,GAAkC,EAAc,GAAI,IAGhG,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,SAI/D,gBAEG,GAA6F,MACxF,MAAwD,CACnE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAEF,GAAM,GAAa,KAAM,IACvB,EACA,KAAK,MACL,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,GACxD,KAEA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,MAG3E,MAAO,IAAyB,EAAc,GAGhD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,SP1DlE,oBAAkE,GAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,6BAKE,cAAyD,CACrE,MAAO,MAAK,mBACR,EAAK,sBACL,EAAK,oBAIN,gBAEG,GAAqE,MAChE,MAA6C,CACxD,GAAM,GAAgB,KAAM,MAAK,WAC3B,EAAa,EAAc,IAAI,AAAC,GAAQ,EAAI,WAE5C,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,GACrC,KAAM,IAAa,KAAK,MAAO,GAE7B,EAAsB,KAAM,SAAQ,IAAI,EAAM,IAClD,AAAC,GAAS,KAAK,YAAY,gBAAgB,KAG7C,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAAc,IAAI,CAAC,EAAc,IAAM,GAAiC,EAAc,EAAoB,KAGnH,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAyF,MACpF,MAAuD,CAClE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,aAAc,EAChB,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,CAAC,IACtC,KAAM,IAAa,KAAK,MAAO,CAAC,IAE9B,EAAY,KAAM,MAAK,YAAY,gBAAgB,EAAM,IAE/D,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,GAAiC,EAAc,GAGxD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SQzFnD,oBAA2C,GAAwB,CACxE,YAEY,EAEA,EAAgC,GAAI,GAC9C,CACA,QAJU,aAEA,iBAMP,gBAAiC,GAAqC,MAC9D,MAAgC,CAC3C,GAAM,CAAE,QAAO,WAAY,KAEvB,EACJ,GAAI,YAAmB,IAAyB,EAAS,EAAK,iBAAiB,YAAY,EAAO,WACzF,YAAmB,GAAuB,EAAS,EAAK,eAAe,YAAY,EAAO,WAC1F,YAAmB,IAAmB,EAAS,EAAK,WAAW,YAAY,EAAO,OACtF,MAAM,IAAI,OAAM,wHAErB,MAAO,GAGD,gCAAmE,CAEzE,MAAO,IAAI,SAAiC,KAAO,IAAY,CAC7D,GAAM,GAAa,KAAM,MAAK,MAC9B,EAAQ,EAAW,IAAI,AAAC,GAAc,GAAwB,GAAI,OAItE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,SAKJ,gBAAmC,GAA+C,MAC1E,MAA0C,CACrD,GAAM,GAAiB,KAAM,IAAI,IAAmB,KAAK,MAAO,KAAK,SACjE,EAAgC,EAAe,GACnD,SAAe,QAAQ,AAAC,GAAkB,CACxC,AAAI,EAAc,MAAQ,EAA8B,OAAO,GAAgC,KAE1F,EAGD,+BAA4E,CAElF,MAAO,IAAI,SAA2C,KAAO,IAAY,CACvE,GAAM,GAAY,KAAM,MAAK,MAC7B,EAAQ,EAAY,GAA4B,GAAI,GAAa,UAIrE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,SCpGJ,YAA0B,EAAkB,EAAgC,GAAI,GAA+C,CACpI,MAAO,IAAI,IAAqB,EAAO,GAGlC,YAAwB,EAAkB,EAAgC,GAAI,GAA6C,CAChI,MAAO,IAAI,IAAmB,EAAO,GCFvC,kBACE,EACA,EACyE,CACzE,MAAO,IAAe,EAAO,GAAI,GAAsB,EAAgB,CAAE,iBAAkB,KACxF,oBACA,sBAGL,kBACE,EACA,EAAoC,GACqC,CACzE,MAAO,IAAe,EAAO,GAAI,IAAkB,IAChD,oBACA,sBAGE,GAAM,IAAW,GC1BjB,YAA2B,EAA+B,EAA+B,CAC9F,GAAI,EAAK,SAAW,EAAK,OAAQ,KAAM,IAAI,OAAM,kDAEjD,GAAM,GAAQ,MAAM,KAAK,GACnB,EAAQ,MAAM,KAAK,GAEzB,MAAO,MAAK,KACV,EACG,IAAI,CAAC,EAAK,IAAM,EAAM,EAAM,IAC5B,OAAO,CAAC,EAAK,IAAS,EAAO,GAAQ,EAAI,ICJzC,YAAkB,CAKvB,YACE,EACA,EAA4B,GAC5B,CACA,KAAK,mBAAqB,EAE1B,GAAM,GAAa,MAAM,QAAQ,GAAU,EAAS,CAAC,GAErD,GAAI,CAAC,EAAW,OACd,KAAM,IAAI,OAAM,2DAGlB,GAAI,GAAQ,EACN,EAAoB,IAAM,UAAU,MAE1C,KAAK,oBAAsB,EAAW,IAAI,AAAC,GAAS,CAClD,GAAI,YAAgB,IAClB,MAAO,GAGT,GAAI,YAAgB,cAClB,MAAO,IAAI,IAAuB,IAAqB,CAAC,IAG1D,GAAI,EAAK,YAAc,EAAK,qBAAsB,cAChD,MAAO,IAAI,IAAuB,IAAqB,CAAC,EAAK,aAG/D,KAAM,IAAI,OAAM,4MAIT,qBAA+C,CAAE,MAAO,MAAK,uBAE7D,oBAA4B,CAAE,MAAO,MAAK,mBAE9C,oBAAoB,EAA+B,EAAqC,CAC7F,MAAO,GACJ,IAAI,AAAC,GAAM,GAAkB,EAAG,IAChC,OAAO,CAAC,EAAI,IAAO,EAAK,EAAI,GACxB,GAAY,QAAU,GAGxB,gBAAgB,EAA0C,CAC/D,MAAO,MAAK,mBACT,IAAI,CAAC,CAAE,cAAa,WAAY,GAAI,IACnC,EACA,KAAK,oBAAoB,EAAiB,KAE3C,OAAO,CAAC,EAAM,IAAU,EAAK,SAAW,EAAK,SAAW,EAAO,GAG7D,cAAc,EAA0C,CAC7D,GAAM,GAAY,KAAK,gBAAgB,GACvC,MAAO,GAAU,SAAW,KAAK,kBAC7B,EACA,GAAI,IAAU,UAAW,EAAU,UAGlC,QAAc,CACnB,MAAO,CACL,kBAAmB,KAAK,kBACxB,mBAAoB,KAAK,mBAAmB,IAAI,AAAC,GAAO,EAAG,iBAIjD,UAAS,EAAwB,CAC7C,GAAM,GAAqB,EAAK,mBAC7B,IAAI,AAAC,GAAY,GAAuB,SAAS,IACpD,MAAO,IAAI,IAAY,EAAoB,EAAK,qBC1E7C,YAAgC,EAAuB,CAC5D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECFF,YAA0B,EAAY,EAA4B,CACvE,GAAM,CAAE,QAAO,UAAW,GAAI,GAAW,EAAW,MAAO,EAAW,QAEtE,GAAI,GAAS,GAAK,GAAU,EAC1B,KAAM,IAAI,OAAM,uCAAuC,KAAK,UAAU,CAAE,QAAO,cAGjF,GAAI,MAAM,QAAQ,GAEhB,MAAQ,GAAuB,IAAI,AAAC,GAAQ,GAAc,EAAK,CAAE,QAAO,YAG1E,GAAI,GAAoB,GAAU,CAChC,GAAM,GAAmB,EAAQ,UAAU,QAAQ,EAAO,GACpD,EAAmB,EAAQ,mBAAmB,QAAQ,EAAiB,IAAI,MAAO,EAAiB,IAAI,QAC7G,MAAO,IAAwB,GAAwB,EAAS,GAAmB,GAGrF,MAAI,IAAoB,GACf,GAAwB,EAAS,EAAQ,UAAU,QAAQ,EAAO,IAGvE,YAAmB,IAAiB,YAAmB,GACjD,EAAgB,QAAQ,EAAO,GAGlC,EjJRT,GAAM,IAAQ,MAAO,UAAY,YAC3B,GAAW,MAAO,YAAc,aAAiB,MAAO,WAAU,WAAc,YACzE,GAAU,CAAE,QAAa,GAAmB,QAAM", + "mappings": ";;;;;;;4qCAAA,GAAA,IAGA,GAAA,GAAc,GAAA,gCCHd,oDAAO,aAA6B,CAClC,MAAO,OAAO,SAAW,UACpB,IACA,MAAO,KAAW,aAGlB,MAAO,UAAY,aAAe,CAAC,CAAC,QAAQ,WCNnD,uyFAAA,OAAoB,OCApB,qRCEO,YACL,EACA,EACA,EAAoB,GACpB,CASA,GARA,EAAI,YAEJ,EAAO,MAAM,GAAG,QAAQ,CAAC,CAAE,IAAG,KAAK,IAAY,CAC7C,GAAM,GAAO,EAAO,GACpB,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,KAGZ,EAAU,CACZ,GAAM,GAAO,EAAO,EAAO,OAAS,GAC9B,EAAK,EAAO,GAClB,GAAI,CAAC,GAAQ,CAAC,EACZ,OAGF,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,EAAG,EAAG,GAGtB,EAAI,SC1BN,2RAAA,OAAoB,OCOb,WAAwC,CAK7C,YAAY,EAAe,EAAgB,CACzC,GAAI,CAAC,GAAc,IAAU,CAAC,GAAc,GAC1C,KAAM,IAAI,OAAM,wFAAwF,KAAK,UAAU,CAAE,QAAO,cAGlI,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,QAEnC,SAAsB,CAC3B,MAAO,IAAI,GAAW,EAAI,KAAK,MAAO,EAAI,KAAK,UDrB5C,YAAkB,EAAa,EAAa,CACjD,MAAO,aAAqB,YAAU,EAAO,MAAM,SAAW,EAGzD,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,WAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAiB,EAAa,CACnC,MAAO,GAAM,GAAM,EAGd,YAAgB,EAAa,CAClC,MAAO,GAAM,GAAM,EAGd,YAAe,EAAa,EAAe,EAAG,CACnD,GAAM,GAAI,IAAM,EAChB,MAAO,MAAK,MAAM,EAAM,GAAK,EAGxB,YAAsB,EAAmB,CAC9C,MAAO,IAAO,EAAI,OAAS,EAAI,OAG1B,YAAmC,CAAE,QAAO,UAAuB,EAAmB,CAC3F,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,IAAI,GAAW,KAAK,MAAM,EAAQ,GAAQ,KAAK,MAAM,EAAS,IAGhE,YAAwB,EAAqB,CAClD,MAAO,GAAI,OAAO,CAAC,EAAK,IAAO,EAAI,IAAI,GAAK,GAAI,GAAM,EAAG,IACtD,IAAI,GAAI,GAAM,EAAI,OAAQ,EAAI,SAG5B,YAAe,EAAa,EAAe,EAAwB,CACxE,MAAO,OAAM,GAAK,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,EAAS,EAAI,GAGhD,YAAuB,EAAU,CACtC,MAAO,CAAC,CAAC,GAAQ,IAAQ,UAAc,IAAQ,WAAc,CAAC,OAAO,MAAM,IAAQ,IAAQ,EAGtF,YAA4B,EAAU,CAC3C,MAAO,IAAc,IAAQ,GAAO,GAAK,GAAO,EExD3C,WAA8B,CAKnC,YAAY,EAAW,EAAW,CAChC,KAAK,GAAK,EACV,KAAK,GAAK,KAGR,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,GAEvB,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,KAAa,CAClB,MAAO,IAAI,GAAM,KAAK,IAAI,KAAK,GAAI,KAAK,IAAI,KAAK,IAG5C,WAAoB,CACzB,MAAO,MAAK,KAAM,KAAK,GAAK,EAAM,KAAK,GAAK,GAGvC,OAAe,CACpB,MAAO,IAAI,GAAM,KAAK,MAAM,KAAK,GAAI,KAAK,MAAM,KAAK,MCtClD,WAAwD,OAC/C,QAAO,EAAoB,CACvC,MAAO,CAAC,CAAC,GAAQ,CAAC,EAAK,EAAG,EAAK,EAAG,EAAK,MAAO,EAAK,QAAQ,MAAM,UAGrD,kBAAiB,EAAU,EAAgB,EAAmC,GAAO,CACjG,GAAI,CAAC,EAAI,OAAO,GACd,KAAM,IAAI,OAAM,GAAG,oBAAyB,KAAK,UAAU,2DAG7D,GAAI,CAAC,GAA4B,GAAI,MAAQ,GAAK,EAAI,OAAS,GAC7D,KAAM,IAAI,OAAM,GAAG,cAAmB,EAAI,sBAAsB,EAAI,oCAYxE,YAAY,EAA4B,EAAmC,GAAM,CAC/E,GAAM,GAAO,GAAQ,GAEf,EAAS,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAO,EAAI,QAAQ,MAAM,IAC1D,EAAS,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAAQ,MAAM,IAE3D,GAAI,CAAC,GAAU,CAAC,EACd,KAAM,IAAI,OAAM,2EAA2E,KAAK,UAAU,MAG5G,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,EAC1B,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAC9B,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAQ,EAAI,KAAM,EAAI,OAAS,EAAI,KAE/D,EAAI,iBAAiB,CACnB,IAAG,IAAG,QAAO,UACZ,kBAAmB,GAEtB,KAAK,GAAK,EACV,KAAK,GAAK,EACV,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,MAE1B,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,WAE/B,OAAe,CAAE,MAAO,MAAK,KAE7B,MAAc,CAAE,MAAO,MAAK,KAE5B,QAAgB,CAAE,MAAO,MAAK,EAAI,KAAK,SAEvC,SAAiB,CAAE,MAAO,MAAK,EAAI,KAAK,UAExC,OAAe,CAAE,MAAO,MAAK,MAAQ,KAAK,UAE1C,UAAiB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,QAEpD,WAAkB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAEtD,aAAoB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,WAEvD,cAAqB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAE7D,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,UAAyB,CAC9B,GAAI,CACF,IAAG,IAAG,QAAO,UACX,KACE,EAAO,KAAK,IAAI,EAAQ,GAC9B,MAAI,GAAQ,GACV,IAAM,EAAO,EACb,GAAS,GAEP,EAAS,GACX,IAAM,EAAO,EACb,GAAU,GAGL,GAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,QAAQ,EAAuC,CACpD,GAAM,GAAS,GAAa,GAAM,EAAkB,MAAQ,EACtD,EAAS,GAAa,GAAM,EAAkB,OAAS,EAC7D,MAAO,IAAI,GAAI,CACb,EAAG,KAAK,EAAI,EACZ,EAAG,KAAK,EAAI,EACZ,MAAO,KAAK,MAAQ,EACpB,OAAQ,KAAK,OAAS,IAInB,IAAI,EAAc,EAA4B,CACnD,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAC5B,KAAK,EAAK,EAAO,EACjB,KAAK,EAAK,EAAO,EACjB,KAAK,MAAQ,EACb,KAAK,OAAS,GAEhB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,mBAAmB,EAAkB,EAAiC,CAC3E,GAAM,CACJ,IAAG,IAAG,QAAO,UACX,KACE,EAAW,KAAK,IAAI,EAAG,GACvB,EAAW,KAAK,IAAI,EAAG,GAEvB,EAAW,EAAQ,EACnB,EAAY,EAAS,EACrB,EAAe,KAAK,IAAI,EAAU,EAAW,GAC7C,EAAgB,KAAK,IAAI,EAAW,EAAY,GAEtD,MAAQ,IAAI,GAAI,CACd,EAAG,EAAU,EAAG,EAAU,MAAO,EAAc,OAAQ,IACrD,QAGC,MAAM,EAAY,EAA0B,CACjD,GAAM,CAAE,QAAO,UAAW,KACpB,EAAI,KAAK,EAAI,EACb,EAAI,KAAK,EAAI,EAEnB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,aAAa,EAAqB,EAAoB,CAC3D,GAAM,GAAI,KAAK,MAAQ,EACjB,EAAI,KAAK,OAAS,EAElB,EAAK,EACL,EAAK,EACP,EAAM,EACN,EAAM,EAEN,EAAI,KAAK,KACT,EAAI,KAAK,IACT,EAAK,KAAK,MACV,EAAK,KAAK,OAEd,MAAI,GAAK,GACP,GAAM,CAAC,EAAK,EAAa,EACzB,EAAK,GAEH,EAAK,GACP,GAAM,CAAC,EAAK,EAAc,EAC1B,EAAK,GAEH,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAEF,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAGC,CACL,KAAI,MAAK,KAAI,MAAK,IAAG,KAAI,IAAG,KAAI,IAAG,KAIhC,UAAU,EAAa,CAC5B,MAAO,IAAI,GAAI,CACb,KAAM,KAAK,KAAQ,EAAO,KAAO,KAAK,MACtC,IAAK,KAAK,IAAO,EAAO,IAAM,KAAK,OACnC,MAAO,KAAK,MAAS,EAAO,MAAQ,KAAK,MACzC,OAAQ,KAAK,OAAU,EAAO,OAAS,KAAK,SAC3C,WAAW,UCrMX,oBAA0B,EAAyC,CACxE,YAAY,EAAc,EAAa,EAAe,EAAgB,EAAmC,GAAO,CAC9G,MAAM,CACJ,OAAM,MAAK,QAAO,UACjB,KCTA,YAAsB,CAW3B,YACE,EACA,EACA,EACA,EACA,EACA,CACA,KAAK,WAAa,GAAI,GAAW,EAAU,MAAO,EAAU,QAC5D,KAAK,OAAS,EACd,KAAK,YAAc,EACnB,KAAK,WAAa,EAClB,KAAK,KAAO,GAAI,GAAI,GAAa,QAAQ,KAAK,eAGrC,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,eAEnC,YAAoB,CAAE,MAAO,MAAK,cAElC,MAAW,CAAE,MAAO,MAAK,QAEzB,YAAwB,CAAE,MAAO,MAAK,cAEtC,aAAqB,CAAE,MAAO,MAAK,UAAU,SAE7C,cAAsB,CAAE,MAAO,MAAK,UAAU,UAE9C,cAAmB,CAAE,MAAO,IAAI,GAAI,KAAK,MAAM,QAAQ,KAAK,UAAU,WAE1E,QAAQ,EAAe,EAAiC,CAC7D,MAAO,IAAI,IACT,KAAK,MACL,KAAK,WACL,KAAK,UACL,KAAK,YACL,CAAE,QAAO,aCzCR,mBAA4B,GAAyC,CAC1E,YACE,EACA,EACA,EACA,CACA,MAAM,EAAO,EAAO,GAAI,EAAa,GAGhC,QAAQ,EAAe,EAA+B,CAC3D,GAAM,CAAE,QAAO,cAAa,aAAc,MAAM,QAAQ,EAAO,GAC/D,MAAO,IAAI,GAAc,EAAO,EAAa,KCnB1C,YAAa,EAAW,EAAW,EAAiB,GAAM,CAC/D,GAAM,GAAQ,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,MAAO,EAAK,OAAS,KAAK,IAAI,EAAK,KAAM,EAAK,OAClF,EAAS,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,OAAQ,EAAK,QAAU,KAAK,IAAI,EAAK,IAAK,EAAK,MACpF,EAAe,EAAQ,EAE7B,MAAO,GACH,EAAgB,GAAK,KAAO,EAAK,KAAO,GACxC,EAAe,KAAK,IAAI,EAAK,KAAM,EAAK,MCPvC,YAAiB,EAA4B,CAClD,GAAM,GAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAExD,MAAO,IAAI,IAAY,EAAM,EAAM,EAAM,GCPpC,YACL,EACA,EACA,EACA,EAAiB,GACP,CACV,GAAI,GAAuB,EACxB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAC/B,IAAI,AAAC,GAAM,EAAE,UAEV,EAAiB,GAEvB,KAAO,EAAqB,OAAS,GAAG,CACtC,GAAM,GAAO,EAAqB,MAClC,EAAK,KAAK,GAEV,GAAM,GAAU,EAEV,EAAoB,GAC1B,OAAS,GAAI,EAAG,EAAI,EAAQ,OAAQ,IAAK,CACvC,GAAM,GAAM,EAAQ,GAEd,EAAU,EAAM,GAChB,EAAS,EAAM,GAErB,EAAQ,KAAK,GAAI,EAAS,EAAQ,IAGpC,EAAuB,EAAqB,OAC1C,CAAC,EAAG,IAAM,EAAQ,IAAM,GAI5B,MAAO,GCrCT,OAAoB,OAEb,WAAmB,EAAgB,EAAgC,CACxE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAG,EAAG,GAAK,EACZ,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAU,AAAG,UAAO,CAAC,EAAO,EAAO,GAAQ,GAEjD,MAAO,AAAG,QAAI,EAAG,KCVrB,OAAoB,OAUb,YACL,EACA,EAAyB,GACZ,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAQ,GAAS,EAAU,MAAM,MAAM,GAC9C,GAAI,IAAW,EACb,MAAO,GAGT,GAAM,GAAU,KAAK,IAAI,EAAS,GAC5B,EAAgB,KAAK,MAAM,EAAW,GAAgB,GAAM,IAC5D,EAAc,EAAS,EAAQ,EAAI,EAEnC,EAAsB,AAAC,GAA0C,CACrE,GAAM,GAAqB,EAAU,MAAM,QAC3C,SAAmB,GAAe,EAC3B,AAAG,QAAK,EAAoB,EAAG,YAGlC,EAAsB,EAAoB,GAC1C,EAAyB,EAAW,EAAoB,MAAM,GAM9D,EAAiB,CAJM,GAAiB,EAC1C,EAAoB,GACpB,KAIF,EACA,GAEC,OAAO,AAAC,GAAM,CAAC,CAAC,GAEhB,IAAI,AAAC,GAAiB,AAAG,QAAK,EAAG,YACpC,MAAO,AAAG,WAAO,EAAgB,KC7C9B,YAAsB,EAAmB,CAC9C,GAAM,GAAQ,EAAW,QACzB,OAAS,GAAI,EAAM,OAAS,EAAG,EAAI,EAAG,IAAK,CACzC,GAAM,GAAI,KAAK,MAAM,KAAK,SAAY,GAAI,IACpC,EAAI,EAAM,GAChB,EAAM,GAAK,EAAM,GACjB,EAAM,GAAK,EAEb,MAAO,GCDF,YAAiB,EAAW,CACjC,MAAO,GAAK,GAAI,KAAK,IAAI,CAAC,IAGrB,YAAwB,EAAW,CACxC,MAAO,MAAK,IAAI,EAAK,GAAI,ICHpB,oBAAmB,EAA2B,CACnD,YAAY,EAAW,EAAW,EAAe,EAAgB,EAAmC,GAAO,CACzG,MAAM,CACJ,IAAG,IAAG,QAAO,UACZ,KCHP,GAAM,IAAO,GACP,GAAO,IACP,GAAW,IAOV,OAA8C,CAOnD,YACE,EACA,EACA,EAAe,GAAI,GAAM,EAAG,GAC5B,CACA,GAAM,CAAE,QAAO,UAAW,EAC1B,KAAK,SAAW,GAAI,GAAW,EAAO,GACtC,KAAK,OAAS,EACd,KAAK,WAAa,EAA8B,IAC9C,AAAC,GAAO,EAAG,IAAI,GAAI,GAAM,EAAO,IAAS,IAAI,OAItC,QAAe,CAAE,MAAO,IAAI,GAAM,KAAK,OAAO,EAAG,KAAK,OAAO,MAE7D,aAAqB,CAAE,MAAO,MAAK,SAAS,SAE5C,cAAsB,CAAE,MAAO,MAAK,SAAS,UAE7C,YAAqB,CAAE,MAAO,MAAK,cAEnC,oBAA6B,CACtC,MAAO,MAAK,WAAW,IACrB,AAAC,GAAO,EAAG,IAAI,KAAK,QAAQ,IAAI,GAAI,GAAM,KAAK,WAAY,KAAK,eAI7D,QAAiC,EAAe,EAAmB,CACxE,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,CAAE,QAAO,WAIN,QAAiC,EAAW,EAAc,CAC/D,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,KAAK,SACL,GAAI,GAAM,EAAG,IAIV,aAAsC,EAAc,CACzD,MAAO,MAAK,QAAQ,EAAG,EAAG,EAAG,GAcxB,MACL,EACA,EAAkE,GAC7D,CACL,GAAI,EAAW,CACb,GAAM,GAAM,YAAqB,GAC7B,EAAU,IAAI,QACd,GAAI,GAAI,GAEZ,MAAO,MAAK,QAAQ,EAAI,EAAG,EAAI,GAAG,MAAM,KAAM,GAGhD,GAAM,CAAE,mBAAkB,iBAAkB,CAAE,iBAAkB,GAAO,cAAe,MAAQ,GAE9F,MAAI,GACK,KAAK,YAGP,KAAK,aAAa,GAGnB,WAAiB,CACvB,GAAM,GAAU,KAAK,2BAEf,CAAC,EAAe,EAAgB,GAAe,EAC/C,EAAc,AAAC,GAAc,EAAY,IAAI,GAAI,YACjD,EAAkB,GAAY,GAAiB,EAAY,IAAmB,EAE9E,EAAO,KAAK,MAAM,EAAiB,IAEnC,EAAW,GAAe,GAE1B,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAChD,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAEtD,MAAO,IAAI,IAAK,EAAG,EAAG,KAAK,IAAI,EAAM,KAAK,WAAa,GAAI,KAAK,IAAI,EAAM,KAAK,YAAc,IAGvF,aAAa,EAAsB,CACzC,GAAM,GAAM,GAAQ,KAAK,WACzB,MAAO,GAAI,IAAI,EAAI,MAAQ,EAAS,EAAI,OAAS,GAGzC,0BAAoC,CAC5C,KAAM,IAAI,OAAM,4DC3Hb,oBAA6B,EAAc,CACtC,0BAAoC,CAC5C,GAAM,GAAM,KAAK,UACjB,MAAO,CACL,EAAI,GACJ,EAAI,GACJ,GAAe,CAAC,EAAI,GAAI,EAAI,QCN3B,oBAA8B,EAAc,CAC1C,eAAyB,CAC9B,MAAO,MAAK,UAAU,MAAM,EAAG,IAG1B,gBAA0B,CAC/B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,iBAA2B,CAChC,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,SAAmB,CACxB,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,YAAsB,CAC3B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,aAAuB,CAC5B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,UAAoB,CACzB,MAAO,MAAK,UAAU,MAAM,GAAI,IAGxB,0BAAoC,CAC5C,MAAO,CACL,KAAK,aACL,KAAK,cACL,KAAK,YACL,IAAI,MC/BH,YAAsC,CAK3C,YAAY,EAAe,EAAkB,CAC3C,KAAK,OAAS,EACd,KAAK,UAAY,KAGR,QAAgB,CAAE,MAAO,MAAK,UAE9B,WAAmB,CAAE,MAAO,MAAK,UAErC,SAAS,EAAwB,GAAc,CACpD,MAAO,GAAG,KAAK,QAAQ,EAAe,KAAK,GAAM,KAAK,aAAe,OCjBlE,oBAAyB,EAAgB,OAChC,yBAAwB,EAAU,EAAgB,CAG9D,GAFA,EAAI,iBAAiB,EAAK,GAEtB,CAAC,GAAc,EAAI,OACrB,KAAM,IAAI,OAAM,GAAG,gCAAqC,EAAI,yBAMhE,YAAY,EAAiC,EAAe,CAC1D,MAAM,GACN,KAAK,OAAS,KAGL,QAAgB,CAAE,MAAO,MAAK,SCrBpC,YAA6B,CAKlC,YAAY,EAAe,EAA6B,CACtD,GAAM,MAAO,IAAU,SACrB,KAAM,IAAI,OAAM,sEAGlB,GAAI,CAAC,MAAM,QAAQ,IAAgB,EAAY,KAAK,AAAC,GAAS,CAAE,aAAgB,gBAC9E,KAAM,IAAI,OAAM,4FAGlB,KAAK,OAAS,EACd,KAAK,aAAe,KAGX,QAAgB,CAAE,MAAO,MAAK,UAE9B,cAA8B,CAAE,MAAO,MAAK,aAEhD,QAAc,CACnB,MAAO,CACL,MAAO,KAAK,MACZ,YAAa,KAAK,YAAY,IAAI,AAAC,GAAM,MAAM,KAAK,WAI1C,UAAS,EAAmC,CACxD,GAAM,GAAc,EAAK,YAAY,IAAI,AAAC,GAAW,GAAI,cAAa,IACtE,MAAO,IAAI,IAAuB,EAAK,MAAO,KC1B3C,oBAA2B,GAAW,OAC7B,2BAA0B,EAAU,EAAgB,CAGhE,GAFA,GAAW,wBAAwB,EAAK,GAGtC,CAAC,GAAmB,EAAI,QACrB,CAAC,GAAmB,EAAI,YAE3B,KAAM,IAAI,OAAM,GAAG,kCAAuC,EAAI,eAAe,EAAI,6CAQrF,YAAY,EAAiC,EAAe,EAAe,EAAoB,CAC7F,MAAM,EAAK,GACX,KAAK,OAAS,EACd,KAAK,YAAc,KAGV,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,cCvBzC,YAA6B,EAAwC,CAC1E,MAAO,GAAI,oBAAqB,GAG3B,YAGL,EACA,EAC4B,CAE5B,MAAO,IAAK,KADM,CAAE,cCdf,aAAyC,CAC9C,GAAM,GAAQ,OAAO,MACrB,GAAI,CAAC,EAAO,KAAM,IAAI,OAAM,gEAM5B,MAAO,CACL,OAAQ,kBACR,yBACA,MAAO,iBACP,UACA,MAAO,iBACP,oBAAqB,IAAM,SAAS,cAAc,UAClD,mBAAoB,IAAM,SAAS,cAAc,OACjD,QACA,SAbe,IAAM,CACrB,KAAM,IAAI,OAAM,iECLb,YAA0B,EAAsB,CACrD,GAAI,GAAiB,GAErB,GAAI,CAAC,EACH,GAAI,CAEF,EAAa,oBACN,EAAP,CACA,EAAiB,EAAI,WAYzB,MAAO,CACL,SATe,EACb,AAAC,GAAqB,GAAI,SAAgB,CAAC,EAAS,IAAW,CAC/D,EAAG,SAAS,EAAU,CAAC,EAAU,IAAoB,EAAM,EAAO,GAAO,EAAQ,MAEjF,IAAM,CACN,KAAM,IAAI,OAAM,qEAAqE,OCfpF,aAAwC,CAE7C,GAAM,GAAS,OAAO,QAAa,OAAO,kBACpC,EAAQ,OAAO,OAAS,OAAO,iBAE/B,EAAsB,IAAM,CAChC,GAAI,EAAQ,MAAO,IAAI,GACvB,KAAM,IAAI,OAAM,+EAGZ,EAAqB,IAAM,CAC/B,GAAI,EAAO,MAAO,IAAI,GACtB,KAAM,IAAI,OAAM,6EAGZ,EAAQ,OAAO,MAGf,EAAa,KAEnB,MAAO,CACL,OAAQ,GAAU,KAAM,GACxB,yBAA0B,OAAO,0BAA4B,KAAM,GACnE,MAAO,GAAS,KAAM,GACtB,UAAW,OAAO,WAAa,KAAM,GACrC,MAAO,OAAO,kBAAoB,KAAM,GACxC,sBACA,qBACA,WACG,GCjCA,aAA8B,CACnC,MAAO,OAAO,SAAW,UACpB,MAAO,WAAa,aACpB,MAAO,mBAAqB,aAC5B,MAAO,oBAAsB,aAC7B,MAAO,mBAAqB,aAC5B,MAAO,YAAc,aACrB,MAAO,2BAA6B,YCH3C,OAAyB,QAGrB,EAEJ,aAA+B,CAC7B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,yEAElB,MAAO,GAGT,YAAgB,EAAkB,CAChC,EAAc,EAGhB,aAAsB,CAGpB,MAAI,MAAoB,GAAO,MAC3B,kBAAmB,GAAO,MACvB,KAGT,YAAqB,EAA2B,CAK9C,GAJK,GACH,KAGE,CAAC,EACH,KAAM,IAAI,OAAM,8EAGlB,GAAM,CAAE,SAAS,EAAY,OAAQ,QAAQ,EAAY,OAAU,EACnE,EAAY,OAAS,EACrB,EAAY,MAAQ,EACpB,EAAY,oBAAsB,EAAI,qBAAwB,KAAM,GAAI,IACxE,EAAY,mBAAqB,EAAI,oBAAuB,KAAM,GAAI,IAEtE,EAAY,UAAY,EAAI,WAAa,EAAY,UACrD,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,SAAW,EAAI,UAAY,EAAY,SAG9C,GAAM,GAAM,CACjB,UACA,UACA,cACA,oBACA,oBACA,mBACA,eACA,aACA,sBAGF,KC3DO,YAAsB,EAAmB,CAC9C,MAAI,CAAC,EAAI,YAAc,MAAO,IAAQ,SAC7B,SAAS,eAAe,GAE1B,ECHF,WAA6B,EAA4F,CAC9H,GAAM,CAAE,SAAQ,4BAA6B,EAAI,SAEjD,GAAI,YAAqB,GACvB,MAAO,GAGT,GAAM,GAAS,GAAa,GAE5B,GAAI,CAAE,aAAkB,IACtB,KAAM,IAAI,OAAM,kEAGlB,GAAM,GAAM,EAAO,WAAW,MAC9B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,GCfF,GAAK,IAAL,UAAK,EAAL,CAEL,WAAW,WAEX,YAAY,YAEZ,cAAc,cAEd,eAAe,iBARL,aAoBL,YAA4D,CAajE,YAAY,EAAiC,GAAI,CAC/C,GAAM,CACJ,iBAAgB,kBAAiB,YAAW,WAAU,YAAW,WAC/D,EACJ,KAAK,eAAiB,GAAkB,GAAe,SACvD,KAAK,gBAAkB,GAAmB,qBAC1C,KAAK,UAAY,GAAa,yBAC9B,KAAK,SAAW,GAAY,GAC5B,KAAK,UAAY,GAAa,UAC9B,KAAK,QAAU,GAAW,IAIvB,QAAoB,CAOzB,YACE,EACA,EACA,EAAiC,GACjC,CAEA,KAAK,KAAO,MAAO,IAAS,SACxB,CAAC,GACA,YAAgB,IAAgB,EAAK,KAAO,EACjD,KAAK,OAAS,EACd,KAAK,QAAU,GAAI,IAAqB,GAG1C,aAAa,EAAuC,CAClD,GAAM,CAAE,WAAY,KAAK,QACzB,MAAO,MAAK,KAAK,IAAI,AAAC,GAAM,EAAI,YAAY,GAAG,OAAO,OAAO,CAAC,EAAI,IAAQ,EAAK,EAAK,EAAK,EAAK,GAAM,EAAI,EAG1G,eAAwB,CACtB,GAAM,CAAE,WAAU,WAAY,KAAK,QACnC,MAAO,MAAK,KAAK,OAAS,EAAY,EAAI,EAG5C,aAAa,EAA+B,EAAkC,CAC5E,GAAM,CAAE,kBAAmB,KAAK,QAC1B,EAAc,IAAmB,GAAe,cAAgB,IAAmB,GAAe,UAClG,EAAa,IAAmB,GAAe,aAAe,IAAmB,GAAe,aAEhG,EAAiB,KAAK,aAAa,GACnC,EAAkB,KAAK,gBACvB,EAAK,EAAc,KAAK,OAAO,EAAI,EAAiB,KAAK,OAAO,EAChE,EAAI,EAAa,KAAK,OAAO,EAAI,EAAkB,KAAK,OAAO,EAGrE,GAAI,EAAY,CACd,GAAM,CAAE,QAAO,UAAW,EACpB,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAQ,GAAiB,GACrD,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAS,GAAkB,GAC7D,MAAO,CAAE,EAAG,EAAM,EAAG,GAEvB,MAAO,CAAE,IAAG,KAGd,KAAK,EAAkE,CACrE,GAAM,GAAS,GAAa,GACtB,EAAM,EAAoB,GAE1B,CACJ,kBAAiB,YAAW,WAAU,YAAW,WAC/C,KAAK,QAET,EAAI,KAAO,GAAG,OAAc,IAC5B,GAAM,GAAe,KAAK,aAAa,GACjC,EAAa,KAAK,gBAExB,EAAI,UAAY,EAChB,GAAM,GAAY,KAAK,aAAa,EAAK,GACzC,EAAI,SAAS,EAAU,EAAG,EAAU,EAAG,EAAc,GAErD,EAAI,UAAY,EAChB,KAAK,KAAK,QAAQ,CAAC,EAAU,IAAM,CACjC,GAAM,GAAI,EAAU,EAAU,EACxB,EAAI,EAAU,EAAU,EAAM,GAAI,GAAK,EAC7C,EAAI,SAAS,EAAU,EAAG,OC5GzB,YAAqB,CAS1B,YAAY,EAA2B,GAAI,CACzC,GAAM,CACJ,WAAU,YAAW,QAAO,oBAC1B,EACJ,KAAK,SAAW,GAAY,qBAC5B,KAAK,UAAY,GAAa,EAC9B,KAAK,MAAQ,EAEb,GAAM,GAA0B,CAC9B,eAAgB,GAAe,YAC/B,gBAAiB,KAAK,UAExB,KAAK,iBAAmB,GAAI,IAAqB,IAAK,KAA4B,MAI/E,QAAc,CAKnB,YACE,EACA,EAA2B,GAC3B,CACA,KAAK,IAAM,GAAI,GAAI,GACnB,KAAK,QAAU,GAAI,IAAe,GAGpC,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CAAE,WAAU,aAAc,KAAK,QAE/B,CACJ,IAAG,IAAG,QAAO,UACX,KAAK,IACT,EAAI,YAAc,EAClB,EAAI,UAAY,EAChB,EAAI,WAAW,EAAG,EAAG,EAAO,GAE5B,GAAM,CAAE,SAAU,KAAK,QACvB,AAAI,GACF,GAAI,IAAc,CAAC,GAAQ,CAAE,EAAG,EAAK,EAAY,EAAI,KAAK,KAAK,QAAQ,kBAAkB,KAAK,KC1D7F,YACL,EACA,EACA,CAGA,AAFwB,OAAM,QAAQ,GAAc,EAAa,CAAC,IAElD,QAAQ,AAAC,GAAQ,CAE/B,GAAM,GAAQ,YAAe,GACzB,EAAI,MACH,GAAoB,GAAO,EAAI,UAAU,MAAQ,OAGhD,EAAM,YAAe,GACvB,EAAI,IACH,GAAoB,GAAO,EAAI,UAAU,IAAM,GAAI,GAAI,GAEtD,EAAQ,EAAQ,GAAG,GAAM,KAAW,OAC1C,GAAI,IAAQ,EAAK,CAAE,UAAS,KAAK,KC1BrC,OAAoB,OCEb,YAAuB,EAAsD,CAClF,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAQ,aAAiB,IAAS,EAAM,UAClC,YAAiB,IAAS,EAAM,YAAc,ECH/C,YAA0B,EAAgE,CAE/F,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,GAAI,YAAiB,GAAI,SAAS,QAAU,GAAc,GACxD,MAAO,GAAQ,MAGjB,WAAiB,EAAU,CACzB,AAAI,CAAC,EAAE,eAEP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAO,IAGT,WAAgB,EAAU,CACxB,AAAI,CAAC,EAAE,eACP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAQ,IAGV,EAAM,iBAAiB,OAAQ,GAC/B,EAAM,iBAAiB,QAAS,KCxB7B,YAAuB,EAAsC,CAClE,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,AAAM,YAAe,OAAO,EAAO,GAAI,OAAM,qDAC7C,GAAM,GAAS,GAAI,YACnB,EAAO,OAAS,IAAM,CACpB,AAAI,MAAO,GAAO,QAAW,UAAU,EAAO,GAAI,OAAM,qEACxD,GAAM,GAAM,EAAI,SAAS,qBACzB,EAAI,OAAS,IAAM,EAAQ,GAC3B,EAAI,QAAU,EACd,EAAI,IAAM,EAAO,QAEnB,EAAO,QAAU,EACjB,EAAO,cAAc,KCXlB,YAA4B,EAA0F,CAC3H,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAI,aAAiB,GACZ,GAAI,GAAW,EAAM,aAAc,EAAM,eAE9C,YAAiB,GACZ,GAAI,GAAW,EAAM,WAAY,EAAM,aAEzC,GAAI,GAAW,EAAM,MAAO,EAAM,QCNpC,YAAsB,CAAE,QAAO,UAA0C,CAC9E,GAAM,CAAE,uBAAwB,EAAI,SAC9B,EAAS,IACf,SAAO,MAAQ,EACf,EAAO,OAAS,EACT,EAGF,YAA+B,EAAwD,EAAuC,CACnI,GAAM,CAAE,aAAc,EAAI,SAE1B,GAAI,CAAE,aAAiB,KAAc,CAAC,GAAc,GAClD,KAAM,IAAI,OAAM,8DAGlB,GAAM,CAAE,QAAO,UAAW,GAAQ,GAAmB,GAC/C,EAAS,GAAa,CAAE,QAAO,WAErC,MAAI,aAAiB,GACnB,EAAoB,GAAQ,aAAa,EAAO,EAAG,GAEnD,EAAoB,GAAQ,UAAU,EAAO,EAAG,EAAG,EAAO,GAErD,EC7BT,OAAoB,OAKpB,kBACE,EACA,EAC4B,CAC5B,GAAM,GAAe,GAAU,EAAI,SAAS,sBAEtC,CAAC,EAAQ,EAAO,GAAe,EAAU,MAAM,MAAM,EAAW,GAAa,EAAI,GACjF,EAAc,AAAG,QAAK,IAAM,EAAU,KAAK,EAAQ,EAAO,GAAa,SAC7E,YAAM,AAAG,YAAQ,SAAS,EAAa,GAEvC,EAAY,UAEL,ECfF,YAAwB,EAAY,CACzC,GAAM,CAAE,QAAO,SAAQ,SAAU,EAAI,SAErC,MAAO,aAAiB,IACnB,YAAiB,IACjB,YAAiB,GCPxB,MAAoB,OCKb,YAAuB,EAA6C,EAAmB,EAAuB,GAAO,CAC1H,GAAM,CAAE,QAAO,UAAW,EAAI,SAE9B,GAAI,CAAE,aAAiB,IAAS,YAAiB,IAC/C,KAAM,IAAI,OAAM,4EAGlB,GAAI,GAAa,EAAG,MAAO,IAAa,CAAE,MAAO,EAAG,OAAQ,IAC5D,GAAM,GAAO,GAAmB,GAC1B,EAAQ,EAAY,KAAK,IAAI,EAAK,OAAQ,EAAK,OAC/C,EAAQ,EAAQ,EAAK,MACrB,EAAS,EAAQ,EAAK,OAEtB,EAAe,GAAa,CAAE,MAAO,EAAW,OAAQ,IACxD,EAAc,YAAiB,GAAS,EAAQ,GAAsB,GAEtE,EAAS,KAAK,IAAI,EAAQ,GAAU,EACpC,EAAK,GAAe,EAAQ,EAAS,EAAS,EAC9C,EAAK,GAAe,EAAS,EAAQ,EAAS,EACpD,MAAI,GAAY,MAAQ,GAAK,EAAY,OAAS,GAAG,EAAoB,GAAc,UAAU,EAAa,EAAI,EAAI,EAAO,GAEtH,EDdF,YAAe,CAapB,YACE,EACA,EAA6B,GAC7B,CAfM,mBAAkD,GAElD,eAAiC,GAIjC,wBAA8B,GAE9B,sBAA+B,GAQrC,GAAI,CAAC,MAAM,QAAQ,GACjB,KAAM,IAAI,OAAM,4HAA4H,KAG9I,KAAK,mBAAqB,EAC1B,KAAK,WAAa,EAAO,OAEzB,EAAO,QAAQ,CAAC,EAAO,IAAQ,CAC7B,GAAI,GAAW,GAAQ,CACrB,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAO,EAAM,MACnC,OAGF,GAAI,EAAW,GAAQ,CACrB,GAAM,GAAa,EAAc,MAAM,GACvC,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,yCAAyC,8CAG3D,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAQ,EAAc,MAAM,MAAM,GACxD,OAGF,GAAM,GAAU,YAAyB,GAAI,SAAS,OAAS,EAAQ,GAAsB,GAC7F,KAAK,UAAU,GAAO,EACtB,KAAK,iBAAiB,GAAO,CAAC,EAAO,OAAQ,EAAO,MAAO,QAIpD,eAAiD,CAC1D,MAAO,MAAK,iBAGH,WAAgC,CACzC,MAAO,MAAK,aAGH,eAAwB,CACjC,MAAO,MAAK,UAAY,GAAK,KAAK,sBAGzB,YAAoB,CAC7B,MAAO,MAAK,cAGH,kBAA8B,CACvC,MAAO,MAAK,oBAGH,YAAgC,CACzC,MAAO,MAAK,cAGH,0BAAwC,CACjD,MAAO,IAAM,KAAK,UAAW,EAAG,GAAG,IACjC,CAAC,EAAG,IAAa,KAAK,2BAA2B,IAI9C,SAAS,EAAiE,CAC/E,MAAO,MAAK,SAAS,IAAa,KAAK,aAAa,GAG/C,mBAAmB,EAA4B,CACpD,MAAO,MAAK,iBAAiB,GAGxB,eAAe,EAA0B,CAC9C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,cAAc,EAA0B,CAC7C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,2BAA2B,EAA8B,CAC9D,GAAI,MAAO,MAAK,WAAc,SAC5B,KAAM,IAAI,OAAM,yFAGlB,GAAM,GAAQ,KAAK,cAAc,GAC3B,EAAS,KAAK,eAAe,GACnC,MAAO,IAA0B,CAAE,QAAO,UAAU,KAAK,WAYpD,cAAc,EAAmB,EAA0B,GAAmB,CACnF,YAAK,WAAa,EAEX,AAAG,OAAK,IAAM,CACnB,GAAM,GAAe,GAAM,KAAK,UAAW,EAAG,GAAG,IAAI,AAAC,GAAa,CACjE,GAAM,GAAQ,KAAK,SAAS,GAE5B,GAAI,YAAoB,UAAQ,CAE9B,GAAI,GAAY,EAAW,GAAS,EAAQ,EAAM,aAElD,SAAY,GAAY,EAAW,GAE/B,GAAU,MAAM,KAAO,GAAa,EAAU,MAAM,KAAO,IAC7D,GAAY,AAAG,QAAM,eAAe,EAAW,CAAC,EAAW,KAGtD,EAAU,KAAK,EAAW,EAAW,GAG9C,GAAI,YAAiB,GAAI,SAAS,OAChC,MAAO,AAAG,WAAQ,WAAW,GAAc,EAAO,EAAW,IAG/D,KAAM,IAAI,OAAM,+BAA+B,8FAAqG,OAOtJ,MAHoB,AAAG,SAAM,EAAa,IAAI,AAAC,GAAM,AAAG,OAAK,EAAG,aAAa,KAAK,KAAK,UAAW,EAAW,EAAW,OE3I9H,iBAAiC,EAAsC,CACrE,GAAI,YAAkB,IACpB,MAAO,GAGT,GAAM,GAAgB,MAAM,QAAQ,GAChC,EACA,CAAC,GAEL,GAAI,CAAC,EAAc,OACjB,KAAM,IAAI,OAAM,4CAGlB,GAAM,GAAa,AAAC,GAAiB,MAAM,QAAQ,GAAU,mBAAmB,KAAS,GAEnF,EAAa,EAAc,IAAI,IAErC,SAAW,QAAQ,CAAC,EAAO,IAAM,CAC/B,GAAI,CAAC,GAAe,IAAU,CAAC,GAAW,IAAU,CAAC,EAAW,GAC9D,KAAI,OAAO,GAAc,IAAO,SACxB,GAAI,OAAM,eAAe,EAAW,sEAAsE,EAAc,MAG1H,GAAI,OAAM,eAAe,EAAW,iIAG5C,GAAI,EAAW,GAAQ,CAErB,GAAM,GAAY,EAAM,MAAM,GAC9B,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,eAAe,EAAW,iCAAiC,iDAMjF,KAAM,SAAQ,IACZ,EAAW,IAAI,AAAC,GAAU,GAAe,IAAU,GAAiB,KAG/D,GAAI,IAAS,EAAY,MAAM,QAAQ,ICtChD,kBACE,EACA,EAC8B,CAC9B,GAAM,CAAE,UAAW,EAAI,SAEnB,EAAS,EAEb,GAAI,CAAE,aAAiB,IAAS,CAC9B,GAAM,GAAW,KAAM,GAAW,GAElC,GAAI,EAAS,UAAY,EACvB,KAAM,IAAI,OAAM,8CAGlB,GAAM,GAAiB,EAAS,SAAS,GACzC,EAAS,YAA0B,GAC/B,EACA,KAAM,IAAoB,GAGhC,GAAM,GAAM,EAAoB,GAQhC,MAAO,AAPO,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAO,MAAO,EAAO,QAAQ,IAAI,QAC7C,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAO,MAAO,EAAO,SAE/C,IAAI,CAAC,CAChB,IAAG,IAAG,QAAO,YACT,CACJ,GAAM,GAAU,GAAa,CAAE,QAAO,WACtC,MAAI,GAAQ,GAAK,EAAS,GAAG,EAAoB,GAAS,aAAa,EAAI,aAAa,EAAG,EAAG,EAAO,GAAS,EAAG,GAC1G,IClDX,OAAoB,OAgBpB,kBACE,EACA,EACwB,CACxB,GAAI,CAAC,GAAW,IAAgB,CAAC,EAAW,GAC1C,KAAM,IAAI,OAAM,6DAGlB,GAAI,EAAW,IAAgB,EAAY,MAAM,GAAK,EACpD,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAW,EAAU,GAAe,EAAY,MAAM,MAAM,EAAW,GAAe,EAAI,GAajG,MAJoB,AAPN,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAU,GAAW,IACjC,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAU,IAEvB,IAAI,CAAC,CAC7B,IAAG,IAAG,QAAO,YACT,AAAG,WAAQ,EAAY,KAAK,EAAW,EAAU,GAAc,CAAC,EAAG,EAAG,GAAI,CAAC,EAAQ,EAAO,OCtCpG,kBACE,EAEA,EACmB,CACnB,GAAM,CAAE,SAAU,EAAI,SAChB,EAAM,KAAM,GAAM,EAAK,GAC7B,GAAI,CAAE,GAAI,OAAS,KACjB,KAAM,IAAI,OAAM,qBAAqB,EAAI,WAAW,EAAI,yBAAyB,EAAI,OAEvF,MAAO,GCTT,kBAAiC,EAAwC,CACvE,GAAM,GAAM,KAAM,IAAa,GACzB,EAAO,KAAO,GAAK,OAEzB,GAAI,CAAC,EAAK,KAAK,WAAW,UACxB,KAAM,IAAI,OAAM,wEAAwE,EAAK,kBAAkB,EAAI,OAErH,MAAO,IAAc,GCRvB,kBAAmC,EAAyB,CAC1D,MAAQ,MAAM,IAAa,IAAM,OCDnC,kBAAsC,EAAoC,CACxE,MAAO,IAAI,cAAa,KAAO,MAAM,IAAa,IAAM,eCH1D,OAAoB,OCAb,YAAsB,EAAyB,EAA0B,CAC9E,GAAM,GAA0B,GAAG,0BAEnC,GAAI,CAAC,EACH,MAAO,CACL,aAAc,GACd,YAAa,GAIjB,GAAI,IAAQ,IACV,MAAO,CACL,aAAc,IACd,YAAa,IAAI,KAIrB,GAAM,GAAW,EAAI,WAAW,WAAa,UAAY,EAAI,WAAW,YAAc,WAAa,GACnG,EAAM,EAAI,QAAQ,EAAU,IAE5B,GAAM,GAAQ,EAAI,MAAM,KAAK,OAAO,AAAC,GAAM,GAErC,EAAe,EAAI,SAAS,SAC9B,EAAM,EAAM,OAAS,GACrB,EAEA,EAAe,EAAY,GAAI,SAAS,SAAW,EAAM,MAAM,EAAG,EAAM,OAAS,GAAK,GAAO,KAAK,KACtG,SAAe,EAAI,WAAW,KAAO,IAAI,IAAiB,EAEnD,CACL,eACA,YAAa,IAAiB,IAAM,IAAI,IAAiB,GAAG,KAAgB,KD1BhF,kBACE,EACA,EAC4B,CAC5B,GAAM,CAAE,cAAa,gBAAiB,GAAa,EAAK,GAClD,EAAW,KAAM,IAAuC,GAE9D,MAAO,AAAG,OAAG,YAAY,EAAU,GET9B,YAAyB,EAAoB,EAAwB,EAA8B,GAAO,CAC/G,GAAM,CAAE,QAAO,UAAW,EACtB,GAAmB,GACnB,EACJ,SAAM,MAAQ,EACd,EAAM,OAAS,EACR,CAAE,QAAO,UCTlB,OAAoB,OCApB,OAAoB,OAOb,WAAyC,CAC9C,YAAY,EAAc,CAIhB,aAAkC,OAElC,oBAAiC,GALzC,KAAK,MAAQ,KASJ,SAAiC,CAAE,MAAO,MAAK,WAE/C,gBAAgC,CAAE,MAAO,MAAK,kBAE9C,WAAoB,CAAE,MAAO,CAAC,CAAC,KAAK,OAExC,iBAAiB,EAA8B,CACpD,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,MAAO,GAAI,GAGN,sBAAsB,EAAmB,EAAmB,CACjE,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,EAAI,GAAS,UACb,EAAI,GAAW,EAGV,cAAe,CACpB,MAAO,MAAK,eAAe,IAAI,CAAC,CAAE,eAAiB,EACjD,KAAM,EACN,OAAQ,KAAK,iBAAiB,MAI3B,oBAAqB,CAC1B,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,EAAM,iBAAqB,cAGnE,iBAAkB,CACvB,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,CAAE,GAAM,iBAAqB,eAGrE,UAAW,CAChB,KAAK,kBAAkB,QAAQ,CAAC,CAAE,OAAM,YAAa,CACnD,KAAK,sBAAsB,EAAM,EAAO,cAIrC,QAAS,CACd,KAAK,qBAAqB,QAAQ,CAAC,CAAE,OAAM,OAAQ,KAAe,CAChE,GAAM,GAAS,AAAG,UAAO,EAAS,YAClC,EAAS,UACT,KAAK,sBAAsB,EAAM,KAI9B,QAAQ,EAA4B,GAAM,CAC/C,KAAK,eAAe,QAAQ,AAAC,GAAU,CACrC,GAAI,GAAoB,EAAM,OAAO,WACnC,KAAM,IAAI,OAAM,mDAAmD,EAAM,QAE3E,EAAM,OAAO,YAEf,KAAK,QAAU,OAGV,iBAAgC,CACrC,MAAO,IAAI,cACT,KAAK,eACF,IAAI,CAAC,CAAE,YAAa,MAAM,KAAK,EAAO,aACtC,OAAO,CAAC,EAAM,IAAQ,EAAK,OAAO,UAI5B,MAAK,EAAgE,CAChF,GAAI,YAAwB,cAAc,CACxC,KAAK,eAAe,GACpB,OAEF,KAAM,MAAK,YAAY,QAGZ,aAAY,EAAyB,CAChD,GAAI,GAAO,MAAO,IAAQ,SACxB,KAAM,IAAI,OAAM,GAAG,KAAK,0CAE1B,GAAM,GAAY,KAAM,IAAc,EAAK,KAAK,uBAChD,KAAK,kBAAkB,QAGZ,cAAa,EAA8B,CACtD,GAAI,GAAY,MAAO,IAAa,SAClC,KAAM,IAAI,OAAM,GAAG,KAAK,iDAE1B,GAAM,CAAE,YAAa,EAAI,SACnB,CAAE,cAAa,gBAAiB,GAAa,EAAU,KAAK,uBAC5D,EAAuB,AAAC,GAAwB,QAAQ,IAAI,EAAU,IAAI,AAAC,GAAO,EAAS,GAAI,KAAK,AAAC,GAAQ,EAAI,UACjH,EAAc,AAAG,MAAG,qBAAqB,GACzC,EAAW,KAAK,MAAO,MAAM,GAAS,IAAc,YACpD,EAAY,KAAM,GAAY,EAAU,GAC9C,KAAK,kBAAkB,GAGlB,kBAAkB,EAA8B,CACrD,GAAM,CAAE,gBAAe,UAAW,KAAK,2BAA2B,GAClE,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGV,eAAe,EAAuB,CAC3C,GAAM,CAAE,gBAAe,UAAW,KAAK,cAAc,GACrD,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGT,qBAAqB,EAAmB,CAC9C,GAAI,CAAC,KAAK,OACR,KAAM,IAAI,OAAM,qDAGlB,GAAM,GAAS,EAAU,MAAM,KAAK,OAAO,CAAC,EAAoD,IAAY,CAE1G,GAAI,CAAC,EAAI,QAAQ,eAAe,GAC9B,KAAM,IAAI,OAAM,wDAAwD,eAAqB,KAE/F,MAAO,CAAE,IAAK,EAAI,QAAS,UAAS,QAAS,EAAI,QAAQ,KACxD,CAAE,QAAS,KAAK,SAEb,CAAE,MAAK,WAAY,EACzB,GAAI,CAAC,GAAO,CAAC,GAAW,CAAE,GAAI,YAAuB,YACnD,KAAM,IAAI,OAAM,8DAA8D,KAGhF,MAAO,CAAE,MAAK,aC7IlB,MAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,iBAAkB,EAAO,iBAAkB,EAAQ,QAC1F,SAAM,AAAG,OAAI,EAAK,EAAO,MAClB,IDNJ,YACL,EACA,EACA,EAAwB,GACX,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,CAAC,EAAG,GAAI,QACrE,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,CAAC,EAAG,KAE7E,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,OAItC,YACL,EACA,EACA,EAAwB,GACxB,EAAuB,GACV,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,GAAI,QAC5F,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,KAEpG,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAE/D,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,KACxC,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,QEpD1D,OAAoB,OAIb,YACL,EACA,EACA,EAA4B,OAC5B,EAAoB,GACP,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OACb,AAAG,UAAO,EAAG,EAAO,QAAS,CAAC,EAAG,GAAI,GACrC,EAAO,MAGT,MAAO,GAAW,AAAG,QAAK,GAAO,ICd9B,WAAoC,EAAgB,EAA+B,CACxF,OAAO,KAAK,GAAW,QAAQ,AAAC,GAAS,CACvC,AAAK,EAAc,KAAK,AAAC,GAAO,EAAG,eAAiB,IAClD,EAAU,GAAM,YCLtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,EACA,IACe,CACf,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,SCzBtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,IACa,CACb,GAAM,GAAa,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAY,IAChF,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CACL,QAAS,EACT,KAAM,ICvBZ,OAAoB,OCoBb,YAA0B,CAE/B,YAES,EAEA,EAEA,EAEP,CANO,wBAEA,wBAEA,cDxBJ,YACL,EACA,EACA,CACA,MAAO,CAAC,EAAoB,EAAqB,IAA8C,CAC7F,GAAM,GAAmB,AAAG,YAAS,EAAe,EAAI,EAAI,GAAa,CAAC,EAAG,EAAG,EAAY,IACtF,EAAmB,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAG,EAAG,EAAY,IAC5F,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,WAGX,GAAI,IACT,EACA,EACA,IAKC,YAEL,EACA,CACA,MAAO,AAAC,IAAwC,CAC9C,GAAM,GAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,IAAI,IACT,EACA,EACA,IEpCC,WAAmC,EAAgB,EAA+B,CACvF,MAAO,CAAC,EAAsB,EAAmB,IAAwB,CACvE,GAAM,GAAS,EAAU,GAEzB,GAAI,CAAC,GAAS,EAAQ,GACpB,KAAM,IAAI,OAAM,sBAAsB,oBAA+B,oBAA4B,KAGnG,SAAc,KACZ,CAAE,eAAc,UAAW,GAAc,IAGpC,GCfJ,WAA+B,EAAuB,CAC3D,GAAI,GAAmB,EAEvB,WAAwB,EAAkC,CACxD,GAAM,GAAM,EAAiB,MAAM,EAAG,GACtC,SAAmB,EAAiB,MAAM,GACnC,EAGT,YAA6C,CAC3C,MAAO,GAGT,MAAO,CACL,iBACA,uBCPG,YAA2B,EAAwC,EAA+B,CACvG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,GAAQ,EACV,EAAkB,EAAY,EAAa,EAAG,GAAG,WACjD,EAA2B,EAAY,EAAa,GAAG,WACrD,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAChE,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,CAAE,QAAO,QAAO,SAAU,EAAyB,EAAY,EAAa,EAAc,GAC1F,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YAAuB,EAA8F,CAC1H,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAC3C,EAAS,EAAyB,IAAK,IAAK,UAElD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CACN,SAAQ,SAAQ,SAAQ,WCvBvB,YAA+B,EAAuE,CAC3G,MAAO,AAAC,IAA+B,CACrC,GAAM,GAAU,EAAgC,GAAG,YAAkB,GAC/D,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,CAAE,UAAS,SCNf,YAA2B,EAAgB,EAA+B,CAC/E,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YACL,EACuE,CACvE,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBhBdZ,oBAAmC,EAAuG,CAC/I,aAAc,CACZ,MAAM,wBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,sDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAE/B,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,+BAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KiBlDzB,OAAoB,OCApB,OAAoB,OAIb,YACL,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,AAAG,OACtB,AAAG,UAAO,EAAG,EAAO,SACpB,EAAO,OCPJ,YAAuB,EAAuB,EAAoB,EAA2E,CAClJ,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAIpB,EAAK,AAFa,GAAuB,EAAgB,GAEpC,EAAY,EAAa,MAEpD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,OCdP,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,EAAgB,OAGtB,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,YAA4B,EAA8B,CAC/D,GAAM,GAAyC,GACzC,EAAmC,GAEzC,cAAO,KAAK,GAAW,QAAQ,AAAC,GAAQ,CACtC,GAAM,GAAM,EAAI,WAAW,MAAQ,EAAgB,EACnD,EAAI,GAAO,EAAU,KAGhB,CAAE,sBAAqB,iBJIzB,oBAGG,EAAyB,CAGjC,YAAY,EAAe,EAA+D,CACxF,MAAM,GACN,KAAK,sBAAwB,KAGpB,uBAAgE,CACzE,MAAO,MAAK,sBASP,OAAO,EAA4C,CACxD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EACJ,MAAO,IAAoB,EAAmB,KAAK,EAAmB,MAAM,GAAI,IAAK,EAAO,MAIzF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,EAAS,KAAK,0BAA2B,KAAK,4BAG3D,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAM,KAAK,0BACX,EAAO,KAAK,2BACZ,EAAwB,EAAO,EAAO,EAEtC,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KKnFjC,GAAM,IAAyB,CAAC,UAAW,QAAS,MAAO,QAAS,UAAW,YAAa,aAE5F,QAAsB,CAe3B,YAAY,EAAwC,CAClD,GAAI,EAAc,SAAW,EAC3B,KAAM,IAAI,OAAM,8EAA8E,EAAc,UAG9G,GAAuB,QAAQ,CAAC,EAAY,IAAQ,CAClD,KAAK,GAAc,EAAc,KAIrC,eAAgB,CACd,MAAO,IACJ,IAAI,AAAC,GAAgB,EAAE,aAAY,YAAa,KAAK,MACrD,KAAK,CAAC,EAAI,IAAO,EAAG,YAAc,EAAG,e1CtBrC,oBAAgC,GAA0C,CAC/E,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGtB,aAAa,EAA4C,CAC9D,MAAO,AAAG,SAAK,IAAM,AAAG,WAAQ,KAAK,OAAO,UAGjC,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,oBAAmB,EAAkB,CAChD,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAC9B,EAAsB,KAAM,SAAQ,IAAI,AAAG,WAAQ,GAAK,IAAI,KAAO,IAAM,CAC7E,GAAM,GAAO,KAAM,GAAE,OACrB,SAAE,UACK,KAET,EAAI,UAEJ,GAAM,GAAqB,EACxB,IAAI,AAAC,GAAiB,GAAI,IAAgB,IAE7C,MAAO,GAAS,aACZ,EACA,EAAmB,GAGf,qBAA8B,CACtC,MAAO,wBAGC,yBAAkC,CAC1C,MAAO,KAGC,0BAAmC,CAC3C,MAAO,K2C1CJ,YAA+B,EAA0C,CAC9E,MAAO,GAAI,sBAAuB,IAG7B,YAGL,EACA,EAC8B,CAE9B,MAAO,IAAK,KADM,CAAE,gBCPf,YACL,EACA,EACA,EAAgB,GAChB,EACA,CAGA,AAF6B,OAAM,QAAQ,GAAmB,EAAkB,CAAC,IAE5D,QAAQ,AAAC,GAAM,CAElC,GAAM,GAAO,YAAa,IACtB,EACC,GAAsB,GAAK,EAAE,YAAc,OAChD,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mHAIlB,GAAM,GAAmB,AADV,EAAK,gBACY,OAAO,AAAC,GAAc,EAAU,YAAc,GAExE,EAAS,GAAoB,GAC/B,EAAE,UAAU,IAAI,WACf,GAAmB,GAAI,GAAM,EAAG,GAMrC,AAJsB,GAAI,IACxB,EAAiB,IAAI,AAAC,GAAc,GAAG,EAAU,eAAe,GAAM,EAAU,iBAChF,GAEY,KAAK,KCvBhB,YAA6B,EAA0E,CAC5G,MAAO,IAAoB,IAEtB,EAAI,oBAAwB,IAE5B,EAAI,6BAAiC,IAErC,EAAI,sBAA0B,GAGrC,YAA4B,EAAM,CAEhC,GAAM,GAAU,CAAC,EAAI,EAAI,EAAI,IAAQ,KAAK,MAAM,EAAK,EAAI,EAAK,GAAM,KAAK,GAGnE,EAAU,AAAC,GAAW,EAAQ,IAAO,KAAK,GAE1C,EAAQ,CAAE,KAA0B,OAAW,MAA2B,OAAW,IAAyB,QAEpH,GAAI,CAAC,GAAQ,CAAC,EAAK,YAAc,EAAK,WAAW,SAAW,GAAI,MAAO,GACvE,GAAM,GAAK,EAAK,WAOhB,EAAM,KAAO,CAAC,EAAQ,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,IAK9D,EAAM,MAAQ,EAAQ,EAAG,KAAK,IAAI,EAAG,GAAG,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,GAAI,KAAK,GAAI,KAAK,IAAI,EAAG,IAAI,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,IAMvH,GAAM,GAAS,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,UACnE,EAAM,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,WACtE,SAAM,IAAM,KAAK,GAAM,GAAK,SAAS,QAAW,GAAM,GAAU,IAAO,GAEhE,EAGF,YAEoD,EAAoB,EAAgF,CAC7J,GAAM,CAAE,IAAK,GAAU,EAAU,UAC3B,EAAY,EAAmB,QAAwB,EAAM,EAAG,EAAM,GAEtE,EAAO,EAAU,QACjB,CAAE,aAAc,EAAU,UAC1B,EAAc,GAAI,GAAc,EAAU,UAAU,MAAO,EAAK,QAAQ,EAAU,WAAY,GAC9F,EAAQ,GAAmB,GASjC,MAAO,IAAK,KAPM,CAChB,YACA,qBACA,cACA,UCxDG,YAA+B,CAapC,YAAY,EAAqC,GAAI,CACnD,GAAM,CACJ,YAAY,GAAM,aAAa,GAAM,YAAW,YAAW,YAAW,cACpE,EACJ,KAAK,UAAY,EACjB,KAAK,WAAa,EAClB,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,uBAC9B,KAAK,WAAa,GAAc,yBAI7B,QAAwB,CAK7B,YACE,EACA,EAAqC,GACrC,CACA,KAAK,cAAgB,EACrB,KAAK,QAAU,GAAI,IAAyB,GAG9C,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CACJ,YAAW,aAAY,YAAW,YAAW,YAAW,cACtD,KAAK,QAcT,GAZI,GAAa,KAAK,wBAAyB,KAC7C,GAAI,YAAc,EAClB,EAAI,UAAY,EAChB,GAAY,EAAK,KAAK,cAAc,iBACpC,GAAY,EAAK,KAAK,cAAc,kBACpC,GAAY,EAAK,KAAK,cAAc,mBACpC,GAAY,EAAK,KAAK,cAAc,WACpC,GAAY,EAAK,KAAK,cAAc,aAAc,IAClD,GAAY,EAAK,KAAK,cAAc,cAAe,IACnD,GAAY,EAAK,KAAK,cAAc,WAAY,KAG9C,EAAY,CACd,EAAI,YAAc,EAClB,EAAI,UAAY,EAEhB,GAAM,GAAY,AAAC,GAAe,CAChC,EAAI,YACJ,EAAI,IAAI,EAAG,EAAG,EAAG,EAAG,EAAW,EAAG,EAAI,KAAK,IAC3C,EAAI,QAEN,KAAK,cAAc,UAAU,QAAQ,MAOpC,YACL,EACA,EACA,CAEA,AAD2B,OAAM,QAAQ,GAAiB,EAAgB,CAAC,IACxD,QAAQ,AAAC,GAAM,CAEhC,GAAM,GAAY,YAAa,GAC3B,EACC,GAAoB,GAAK,EAAE,UAAY,OAC5C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gIAGlB,GAAI,IAAkB,GAAW,KAAK,oBC1G1C,OAAoB,OCApB,MAAoB,OCKpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAqC,EAAoB,EAAqB,EAA4C,CACxH,GAAM,GAAkB,EAA2B,EAAY,EAAa,GAAG,qBACzE,EAAkB,EAA2B,EAAa,EAAa,GAAG,qBAC1E,EAAiB,EAAkB,EAAY,EAAa,EAAG,GAAG,oBAExE,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAkB,EAAuC,CACvF,GAAM,GAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBAE1E,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YAAuB,EAAuB,EAAsF,CACzI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAgB,GAEhC,EAAqB,EAAkB,EAAG,GAAI,EAAG,sBACjD,EAA+B,EAA4B,GAAI,GAAI,gCACnE,EAA+B,EAA4B,GAAI,IAAK,gCAEpE,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,IAAK,0BAA0B,OAG3F,GAAM,GAA4B,EAA4B,IAAK,IAAK,6BAClE,EAA2B,EAA2B,IAAK,IAAK,4BAEhE,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,aAAY,cAAa,cCjEvC,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAqC,EAA4C,CAC/E,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAiB,EAAkB,GAAG,oBAE5C,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAuC,CACrE,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAEtD,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YACL,EACA,EAC+D,CAC/D,GAAM,GAAgC,GAEhC,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAW,GAE3B,EAAqB,EAAkB,sBACvC,EAA+B,EAA4B,gCAC3D,EAA+B,EAA4B,gCAE3D,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,0BAA0B,OAGtF,GAAM,GAA4B,EAA4B,6BACxD,EAA2B,EAA2B,4BAEtD,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,SAA2B,EAAW,GAE/B,CAAE,OAAQ,CAAE,aAAY,cAAa,aAAa,iBFrE3D,YAAc,EAAgB,EAAoB,EAAuC,CACvF,MAAO,AAAG,OAAI,AAAG,SAAO,EAAG,EAAO,QAAS,EAAQ,QAAS,EAAO,MAGrE,YAAwB,EAAgB,EAA8B,EAA2B,GAAmB,CAClH,GAAI,GAAM,EAAkB,AAAG,OAAK,GAAK,EACzC,SAAM,EAAuB,EAAK,EAAO,gBAAiB,CAAC,EAAG,IAC9D,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,AAAG,MAAI,EAAK,GAAK,EAAG,EAAO,eAAgB,CAAC,EAAG,KAC9C,EAGT,YAAmB,EAAgB,EAAsC,CACvE,GAAI,GAAM,EAAuB,AAAG,OAAK,GAAI,EAAO,gBAAiB,CAAC,EAAG,IACzE,SAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,MAAI,EAAK,GACX,EAGF,oBAA2B,EAAkC,CAGlE,YAAY,EAAuB,CACjC,MAAM,gBACN,KAAK,eAAiB,EAGjB,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KACnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,8CAElB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAC7D,EAAM,AAAG,OAAK,GAAK,EAAY,EAAO,WAAW,QAAS,CAAC,EAAG,KAClE,SAAM,GAAe,EAAK,EAAO,WAAW,kBAAmB,IAC/D,EAAM,GAAe,EAAK,EAAO,WAAW,mBAC5C,GAAM,KAAK,eAAgB,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAChD,EAAM,GAAU,EAAK,EAAO,YAAY,cAAc,QAExD,EAAM,GAAe,EAAK,EAAO,UAAU,iBAC3C,EAAM,AAAG,OAAK,EAAuB,EAAK,EAAO,UAAU,eAAgB,CAAC,EAAG,KACxE,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,sBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,gBAG1C,cAAc,EAAuB,CAC7C,MAAO,IAAc,EAAS,KAAK,kBGvEhC,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAkB,GAAuB,EAAgB,GAEzD,EAAM,EAAgB,IAAK,EAAG,UAC9B,EAAS,EAAgB,IAAK,EAAG,aAEvC,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,GAAI,CAAE,MAAK,YCflB,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,CACF,IAAK,EAAgB,UACrB,OAAQ,EAAgB,eAI5B,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,GAAK,IAAL,UAAK,EAAL,CAEL,SAAS,SAET,OAAO,SAJG,aNQL,oBAA2B,EAAyB,CAGzD,YAAY,EAAqC,GAAI,IAAa,GAAI,CACpE,MAAM,gBACN,KAAK,sBAAwB,KAGpB,uBAAqC,CAC9C,MAAO,MAAK,sBAGP,OAAO,EAA0C,CACtD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EAEE,EAAS,AAAG,WAAQ,EAAoB,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAAS,KAAK,EAAmB,MAAM,GAAI,IACnG,EAAM,GAAoB,EAAQ,EAAO,GAAG,KAAK,OACjD,EAAS,GAAoB,EAAQ,EAAO,GAAG,QACrD,MAAO,CAAE,MAAK,YAIX,aAAa,EAA0C,CAC5D,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAE,MAAK,UAAW,KAAK,OAAO,GACpC,MAAO,CAAE,MAAK,OAAQ,AAAG,WAAQ,WAIxB,SAAQ,EAAsC,CACzD,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,qBAAoB,EAA8E,CAC7G,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAE9B,EAAO,AAAG,WAAQ,EAAI,KACtB,EAAU,AAAG,WAAQ,EAAI,QACzB,EAAsB,EAAK,IAAI,CAAC,EAAW,IAAO,EACtD,YACA,aAAc,EAAQ,MAGlB,EAAqB,KAAM,SAAQ,IACvC,EAAoB,IAAI,MAAO,CAAE,YAAW,kBAAmB,CAC7D,GAAM,GAAO,MAAM,GAAU,QAAQ,GAC/B,EAAY,MAAM,GAAa,QAAQ,GACvC,EAAS,EAAW,GACpB,EAAS,EAAS,GAAO,KAAO,GAAO,OACvC,EAAoB,EAAS,EAAY,EAAI,EAEnD,SAAU,UACV,EAAa,UACN,CAAE,MAAK,SAAQ,wBAG1B,SAAI,IAAI,UACR,EAAI,OAAO,UAEJ,EAAS,aAAe,EAAiD,EAAmB,GAG3F,qBAA8B,CACtC,MAAO,mBAGF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,GAGb,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAwB,IAAM,EAAI,EAAM,KAAM,EAAI,GAElD,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KOvHxC,MAAoB,OASb,oBAGG,GAAgC,CACjC,YAAY,EAAqB,EAAmB,EAAgD,CACzG,GAAM,GAAkB,EAAmB,IAAI,CAAC,CAAE,QAAO,YAAa,CACpE,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,CACL,MAAO,EAAQ,EACf,OAAQ,EAAS,KAIf,EAAY,EAAgB,OAElC,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAA0B,CAAC,EAAe,IAAkB,AAAG,QAAM,CAAC,AAAG,OAAK,CAAC,IAAK,EAAO,WAAY,AAAG,OAAK,CAAC,IAAK,EAAO,YAAa,GAAG,KAAK,EAAG,KAAK,OAGzJ,EAAa,CAAC,EAAkB,IAAoD,CACxF,GAAM,CAAE,QAAO,UAAW,EAAgB,GAC1C,MAAO,GAAK,EAAO,GAAU,KAAK,IAAI,EAAQ,GAAU,EAAI,GAGxD,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GACvE,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GAa7E,MAXwB,GACrB,IAAI,AAAG,OAAK,CAAC,EAAW,KAAM,EAAW,YACzC,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAY,GACZ,EAAY,OAEb,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAgB,GAAU,MAC1B,EAAgB,GAAU,aAO3B,aAAa,EAA8B,CAChD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,KAAK,OAAO,GACxB,MAAO,MAAK,YACV,EACA,EAAM,UACN,EAAM,gBAAgB,IAAI,CAAC,CAAC,EAAQ,KAAY,EAAE,SAAQ,mBAKnD,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,iBAAgB,EAAgE,CAC3F,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAkB,AAAG,OACzB,IAAM,AAAG,UAAQ,KAAK,aAAa,KAG/B,EAAoB,KAAM,SAAQ,IAAI,EAAgB,IAC1D,MAAO,EAAgB,IAAa,CAClC,GAAM,GAAiB,MAAM,KAAK,KAAM,GAAe,QACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,GAAO,IACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,CAAC,GAAO,IAExD,MAAO,IAAI,IACT,MAAM,IAAI,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,GAAI,GAAM,EAAQ,GAAc,EAAQ,KACxE,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,QAMtC,SAAgB,QAAQ,AAAC,GAAM,EAAE,WAE1B,EAAS,aAAe,EAAyC,EAAkB,GAGlF,0BAAmC,CAC3C,MAAO,OC1FJ,oBAAgC,GAAkD,CACvF,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGnB,qBAA8B,CACtC,MAAO,yBAGC,yBAAkC,CAC1C,MAAO,OCdX,OAAoB,OCMb,YACL,EAC2E,CAC3E,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCnBZ,YAA2B,EAAkG,CAClI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAEjD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,SAAQ,SAAQ,WFhBvB,oBAAuC,EAA+G,CAC3J,aAAc,CACZ,MAAM,4BAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,0DAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,GAAI,IAAK,CAAC,EAAG,GAAI,SAEjC,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,oCAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA+B,GAG9B,cAAc,EAAuB,CAC7C,MAAO,IAAkB,KG7CtB,oBAAoC,GAAsD,CAC/F,YAAY,EAAiD,GAAI,IAA4B,CAC3F,MAAM,wBAAyB,GAGvB,qBAA8B,CACtC,MAAO,8BAGC,yBAAkC,CAC1C,MAAO,OCVJ,oBAA8B,GAAkB,GCJvD,MAAoB,OCApB,OAAoB,OCApB,OAAoB,OAIb,YAAe,EAAgB,EAAuC,CAC3E,MAAO,AAAG,QAAI,AAAG,OAAI,EAAG,EAAO,SAAU,EAAO,QDAlD,YACE,EACA,EACA,EACA,EACA,EAA4B,OACf,CACb,GAAM,CAAE,UAAS,QAAS,EAAO,KAE7B,EAAM,AAAG,UAAO,EAAG,EAAS,EAAS,GACzC,SAAM,AAAG,OAAI,EAAK,GAClB,EAAM,GAAM,EAAK,EAAO,OACjB,EAAW,AAAG,QAAK,GAAO,EAG5B,YAAc,EAAgB,EAAyB,CAC5D,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAoB,EAAgB,EAAyB,CAClE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAkB,EAAgB,EAAyB,CAChE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,GAAM,SE7B5C,MAAoB,OAUpB,YAA2B,EAAwC,EAA+B,CAChG,WAA6B,EAAyB,EAAoB,EAAiC,CACzG,GAAM,GAAU,EAAe,GACzB,EAAQ,EAAQ,OAAU,GAAa,EAAa,GAE1D,GAAI,GAAQ,GACV,KAAM,IAAI,OAAM,+BAA+B,sBAA0B,EAAQ,uBAAuB,kBAA2B,KAGrI,MAAO,AAAG,QACR,IAAM,AAAG,YACP,AAAG,WAAS,EAAS,CAAC,EAAY,EAAO,EAAY,IACrD,CAAC,EAAG,EAAG,EAAG,KAKhB,WACE,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,EAAoB,EAAiB,EAAY,GAC3D,EAAO,AAAG,WAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,QAGpB,WAAiC,EAAoB,EAAwC,CAC3F,GAAM,GAAU,AAAG,WAAS,EAAe,IACrC,EAAS,AAAG,WAAS,EAAe,IAE1C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,aAGX,CACL,UACA,UAIJ,WACE,EACA,EACA,EACA,EACiB,CACjB,GAAM,GAAO,EAAkB,EAAiB,EAAY,EAAY,GAAG,UACrE,EAAQ,EAAwB,EAAY,GAAG,WAErD,MAAO,CAAE,OAAM,SAGjB,WACE,EACA,EACA,EACA,EACA,EAAkB,GACG,CACrB,GAAM,GAAQ,EAAwB,GAAS,GAAM,GAAK,EAAiB,EAAY,EAAY,GAAG,WAChG,EAAQ,EAAuB,EAAiB,EAAY,EAAY,GAAG,WAEjF,MAAO,CAAE,QAAO,SAGlB,MAAO,CACL,yBACA,8BAIG,YAAuB,EAA6E,CACzG,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAgB,GAEhC,EAAc,EAAuB,KAAM,GAAI,EAAG,eAClD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YAEnD,EAAc,EAA2B,MAAO,GAAI,EAAG,cAAe,IACtE,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YAEpD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aAEvD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAmB,EAA2B,OAAQ,IAAK,EAAG,oBAE9D,EAAK,AAAG,OACZ,IAAM,AAAG,YAAU,AAAG,WAAS,EAAe,IAAM,KAAM,CAAC,IAAK,MAAO,CAAC,EAAG,KAI7E,GAFA,EAAc,KAAK,CAAE,UAAW,OAE5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAsB1E,MAAO,CAAE,OAnBM,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGe,iBChJnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAiC,EAAkC,CACjE,GAAM,GAAU,EAAmB,GAAG,kBAAwB,GACxD,EAAS,EAAmB,GAAG,iBAAuB,GAE5D,MAAO,CAAE,UAAS,UAGpB,WAAgC,EAAiC,CAC/D,GAAM,GAAU,EAAmB,GAAG,iBAAuB,GACvD,EAAO,EAAmB,GAAG,cAAoB,GACjD,EAAQ,EAAwB,GAEtC,MAAO,CAAE,KAAM,CAAE,UAAS,QAAQ,SAGpC,WAAoC,EAAqC,CACvE,MAAO,CACL,MAAO,EAAuB,GAAG,WACjC,MAAO,EAAuB,GAAG,YAIrC,MAAO,CACL,yBACA,8BAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAW,GAE3B,EAAc,EAAuB,eACrC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAc,EAA2B,eACzC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aAEvC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aACvC,EAAmB,EAA2B,oBAE9C,CAAE,MAAO,EAGf,GAFA,EAAc,KAAK,CAAE,aAAc,KAAM,UAAW,OAEhD,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yDAAyD,KAG3E,GAAM,GAAS,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGF,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBC9FnB,MAAoB,OAKb,YAAkB,EAAgB,EAA0C,CACjF,GAAI,GAAM,GAAK,EAAG,EAAO,OACzB,SAAM,GAAW,EAAK,EAAO,OAC7B,EAAM,AAAG,MAAI,EAAK,GAClB,EAAM,AAAG,OAAK,GACP,EAGF,YAAsB,EAAgB,EAA0C,CACrF,GAAI,GAAM,GAAS,EAAG,EAAO,OAC7B,EAAM,GAAW,EAAK,EAAO,OAE7B,GAAI,GAAS,AAAG,UAAQ,EAAG,EAAG,EAAG,SAC3B,EAAQ,AAAG,QAAkB,EAAO,OACpC,EAAQ,EAAO,MAAM,KAAO,EAAI,MAAM,GAG5C,GAFsB,EAAO,MAAM,KAAO,EAAI,MAAM,IAAM,EAAO,MAAM,KAAO,EAAI,MAAM,GAErE,CACjB,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAE/B,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAGjC,SAAS,EAAQ,AAAG,SAAO,CAAC,EAAQ,GAAQ,GAAK,EACjD,EAAM,AAAG,MAAI,EAAQ,GAErB,EAAM,AAAG,OAAK,GACP,EL3BF,oBAAiC,EAAyB,CAC/D,aAAc,CACZ,MAAM,sBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAGtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAE7D,EAAM,GAAS,EAAY,EAAO,aACtC,EAAM,AAAG,UAAQ,EAAK,EAAG,EAAG,SAE5B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,aAC/B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAa,EAAK,EAAO,kBAE/B,GAAM,GAAY,EAAI,KAAK,CAAC,EAAG,IAG/B,MAFuB,AAAG,UAAO,EAAW,EAAO,WAM1C,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,uBAAsB,EAAwD,CA7D7F,MA8DI,GAAI,oBAAO,QAAP,cAAc,KAAK,AAAC,GAAQ,GAAO,GAAI,MAAO,IAAI,cAAa,KACnE,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAwB,AAAG,OAC/B,IAAM,AAAG,UAAQ,KAAK,aAAa,KAE/B,EAA0B,KAAM,SAAQ,IAAI,EAAsB,IAAI,AAAC,GAAM,EAAE,SACrF,SAAsB,QAAQ,AAAC,GAAM,EAAE,WAChC,EAAS,aAAe,EAA0B,EAAwB,GAGzE,qBAA8B,CACtC,MAAO,yBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KM7ElB,YAAkC,EAAuB,CAC9D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECHF,YAGL,EACA,EAC6B,CAE7B,MAAO,IAAK,KADM,CAAE,eCNf,YAAmB,EAA8B,CACtD,MAAO,OAAO,GAAI,KAAQ,SAGrB,YAGL,EACA,EACkB,CAElB,MAAO,IAAK,KADM,CAAE,QCNf,YAAsB,EAAiC,CAC5D,MAAQ,GAAI,SAAW,GAAO,MAAQ,EAAI,SAAW,GAAO,SACvD,GAAmB,EAAI,mBAGvB,YAGL,EACA,EACA,EACqB,CAErB,MAAO,IAAK,KADM,CAAE,SAAQ,sBCpB9B,OAAoB,OCApB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,WAAoC,EAAqB,EAAuD,CAC9G,GAAM,GAAU,AAAG,YAAS,EAAe,EAAI,EAAI,GAAc,CAAC,EAAG,EAAG,EAAa,IAC/E,EAAmB,AAAG,YAAS,EAAe,IAC9C,EAAoB,AAAG,YAAS,EAAe,IAC/C,EAAkB,AAAG,YAAS,EAAe,IAC7C,EAAsB,AAAG,YAAS,EAAe,IAEvD,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,uBAChB,CAAE,UAAW,GAAG,qBAChB,CAAE,UAAW,GAAG,0BAGX,CACL,UACA,mBACA,oBACA,kBACA,uBAIJ,WACE,EACA,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,KAAgB,EAAkB,oBAAsB,WAGnE,CAAE,UAAS,QAGpB,WACE,EACA,EACA,EACA,EACqB,CACrB,GAAM,CACJ,UACA,QACE,EAAkB,EAAY,EAAa,EAAY,EAAc,IAEzE,MAAO,CACL,UACA,kBAAmB,GAIvB,WACE,EACA,EACA,EAC4B,CAC5B,GAAM,GAAiB,EAA2B,EAAY,GAAG,oBAC3D,EAAiB,EAA2B,EAAY,EAAa,EAAG,GAAG,oBAEjF,MAAO,CAAE,iBAAgB,kBAG3B,YAAwD,CACtD,GAAM,GAAS,EAA2B,EAAG,GAAI,EAAG,sBAC9C,EAAS,EAAsB,GAAI,GAAI,sBACvC,EAAS,EAAsB,GAAI,IAAK,sBACxC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,KAAM,uBAC3C,EAAU,EAAsB,KAAM,KAAM,uBAClD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,UACA,UACA,UACA,WAIJ,YAA+D,CAC7D,GAAM,GAAS,EAA2B,KAAM,IAAK,EAAG,2BAClD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,GAAI,EAAG,2BAChD,EAAS,EAA2B,GAAI,IAAK,EAAG,2BAChD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,EAAG,EAAG,oDACjD,EAA2B,EAAkB,KAAM,GAAI,EAAG,2DAC1D,EAAoB,EAAkB,KAAM,GAAI,EAAG,oDACnD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDA0BxD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,gBAjCsB,CACtB,uBAAwB,EACxB,gBAAiB,GAgCjB,gBA9BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA6BjB,gBA3BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA0BjB,gBAxBsB,CACtB,uBAAwB,GACxB,gBAAiB,IAuBjB,gBArBsB,CACtB,uBAAwB,EACxB,gBAAiB,IAoBjB,gBAlBsB,CACtB,uBAAwB,GACxB,gBAAiB,KAoBrB,MAAO,CACL,2BACA,gCAIG,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAChC,CACJ,iBACA,uBACE,EAAsB,GACpB,CACJ,2BACA,gCACE,GAAkB,EAAgB,GAChC,EAAc,IACd,EAAmB,IAKnB,EAAe,CACnB,UALgB,AAAG,YACnB,EAAe,KAAO,GACtB,CAAC,EAAG,KAAM,KAMZ,GADA,EAAc,KAAK,CAAE,UAAW,2BAC5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,OAAQ,CACN,cACA,mBACA,gBAEF,iBC9MJ,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAoC,EAAgB,EAAa,EAA2C,CAC1G,GAAM,GAAU,EAAmB,GAAG,YAAiB,sBAAyB,EAAG,GAAG,aAChF,EAAoB,EAAmB,GAAG,YAAiB,oCAAuC,EAAG,GAAG,uBAC9G,MAAO,CAAE,UAAS,qBAGpB,WAA+B,EAAyC,CACtE,GAAM,GAAe,oBAAoB,IACnC,EAAsB,sBAAsB,cAC5C,EAA4B,GAAG,mBAC/B,EAA4B,GAAG,mBAE/B,EAAU,EAAmB,GAAG,sBAAyC,EAAG,GAAG,aAC/E,EAAmB,EAAmB,GAAG,oBAAuC,EAAG,GAAG,sBACtF,EAAoB,EAAmB,GAAG,mBAAsC,EAAG,GAAG,uBACtF,EAAkB,EAAmB,GAAG,0BAA6C,EAAG,GAAG,qBAC3F,EAAsB,EAAmB,GAAG,8BAAiD,EAAG,GAAG,yBAEzG,MAAO,CACL,eAAgB,CACd,UACA,mBACA,oBACA,kBACA,uBAEF,eAAgB,EAA2B,cAAe,EAAK,IAInE,YAAwD,CACtD,MAAO,CACL,OAAQ,EAA2B,cAAe,EAAG,sBACrD,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,KAInC,WAA2B,EAAgB,EAAkC,CAC3E,GAAM,GAAU,EAAmB,GAAG,YAAkB,EAAG,GAAG,aACxD,EAAO,EAAmB,GAAG,WAAiB,EAAG,GAAG,UAC1D,MAAO,CAAE,UAAS,QAGpB,WAAmC,EAAkC,CACnE,GAAM,GAAyB,EAC7B,2BAA2B,yBAC3B,kCAAkC,4BAE9B,EAAkB,EACtB,2BAA2B,mBAC3B,kCAAkC,qBAEpC,MAAO,CAAE,yBAAwB,mBAGnC,YAA+D,CAC7D,MAAO,CACL,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,IAI/C,MAAO,CACL,2BACA,gCAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAChC,CACJ,2BACA,gCACE,GAAkB,EAAW,GAC3B,EAAY,EAAU,oBAE5B,GADA,EAAc,KAAK,CAAE,aAAc,mBAAoB,UAAW,2BAC9D,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yEAAyE,KAG3F,GAAM,GAAS,CACb,YAAa,IACb,iBAAkB,IAClB,aAAc,CACZ,cAIJ,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBChInB,OAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,UAAO,EAAG,EAAO,QAAS,EAAS,QAChD,SAAM,AAAG,OAAI,EAAK,EAAO,mBAClB,AAAG,eAAY,EAAK,EAAG,KDPlC,GAAM,IAAU,qBAEhB,YACE,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,QAAS,EAAS,QACzD,SAAM,AAAG,aACP,EACA,EAAO,gBACP,EAAO,oBACP,EAAO,kBACP,EAAO,iBACP,IAEK,AAAG,eAAY,EAAK,EAAG,KAIlC,YAA+B,EAAoC,CACjE,MAAO,CAAC,EAAG,EAAG,EAAG,IAAI,KAAK,AAAC,GAAQ,IAAQ,GAAY,CAAC,EAAG,GAAK,CAAC,EAAG,GAG/D,YAAqB,EAAgB,EAA4B,CACtE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GACA,EAAM,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IA4BnD,GAVA,AAhBuB,CACrB,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,QACP,EAAO,QACP,EAAO,QACP,EAAO,SAGM,QAAQ,CAAC,EAAO,IAAM,CACnC,GAAM,GAAW,EAAI,EACf,EAAuB,GAAsB,GACnD,EAAM,GAAmB,EAAK,EAAM,eAAgB,GACpD,EAAM,EAAmB,EAAK,EAAM,eAAgB,CAAC,EAAG,IACpD,IAAa,IACf,GAAS,KAIT,IAAW,KACb,KAAM,IAAI,OAAM,iDAGlB,MAAO,CACL,MACA,OAAQ,KEjEd,YAAa,EAAoB,EAAW,EAAW,CACrD,GAAM,GAAY,EAAM,YAClB,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAS,GAAQ,GAAU,GAAQ,GACnC,EAAS,GAAQ,GAAU,GAAQ,GACzC,GAAI,GAAS,GAAK,GAAS,EACzB,MAAO,GAET,GAAM,GAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAmB,EAAkB,GACjE,KAAK,IAAI,EAAmB,EAAkB,GACpD,MAAO,GAAoB,GAAQ,EAAQ,GAGtC,YACL,EACA,EACA,EACA,EACA,EACU,CACV,GAAM,GAAW,EAAM,MAAM,GACvB,EAAa,KAAK,IACtB,EACA,GAGI,EAAa,EAChB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,OAAO,AAAC,GAAM,EAAE,MAAQ,GACxB,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAE5B,EAAe,AAAC,GAAe,GAAK,EAAe,EAAI,EACvD,EAAqB,GAE3B,SAAW,QAAQ,AAAC,GAAM,CACxB,GAAI,EAAS,QAAU,EAAY,OACnC,GAAM,GAAgB,EAAE,MACxB,OAAS,GAAI,EAAS,OAAS,EAAG,GAAK,EAAG,EAAE,EAAG,CAC7C,GAAM,GAAM,GAAI,EAAO,EAAE,SAAU,EAAS,IAC5C,GAAI,IAAQ,GACZ,GAAE,OAAS,EAAa,GACpB,EAAE,OAAS,GAAgB,MAEjC,AAAI,IAAkB,EAAE,OACtB,EAAS,KAAK,EAAE,YAGb,EC5DT,MAAoB,OAIpB,YAA2C,EAAgB,CACzD,GAAM,GAAM,AAAG,UAAQ,AAAG,YAAU,EAAG,CAAC,EAAG,KAErC,EAAQ,CACZ,AAAG,MAAI,EAAI,GAAI,EAAI,IACnB,AAAG,MAAI,EAAI,GAAI,EAAI,KAEf,EAAU,CACd,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,KAC1C,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,MAE5C,MAAO,CACL,QACA,WAIJ,YAA0B,EAAiB,EAAiB,CAC1D,GAAM,CACJ,QACA,WACE,GAAkC,GAEhC,EAAM,AAAG,UAAQ,AAAG,YAAU,EAAI,CAAC,EAAG,KACtC,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAC3E,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAEjF,MAAO,AAAG,aACR,AAAG,QAAM,CACP,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,KAEnB,CAAC,EAAG,IAID,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAY,EAAe,MAAM,GAEnC,EAAQ,GACV,AAAG,UAAQ,AAAG,OAAK,EAAO,UAAW,CAAC,EAAW,EAAG,IAAK,CAAC,GAAI,IAC9D,AAAG,UAAQ,EAAgB,CAAC,GAAI,KAElC,EAAQ,AAAG,UACT,EACA,CAAC,EAAY,EAAM,MAAM,GAAK,EAAY,IAG5C,GAAM,GAAmB,AAAG,UAAQ,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,MAC/E,EAAS,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,IAE5D,EAAS,AAAG,UACV,EACA,CAAC,EAAW,EAAO,MAAM,KAG3B,GAAM,GAAe,AAAG,UAAQ,GAC1B,EAAgB,AAAG,UAAQ,GAEjC,MAAO,CACL,MAAO,EACP,OAAQ,KC1Ed,OAAoB,OCApB,OAAoB,OAKb,YACL,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAY,EAAE,MAAM,GACpB,EAAwB,AAAG,WAC/B,GAAU,EAAG,EAAO,wBACpB,CAAC,EAAW,GAAI,EAAG,IAEf,EAAkB,AAAG,WACzB,GAAU,EAAG,EAAO,iBACpB,CAAC,EAAW,GAAI,IAElB,MAAO,CAAE,wBAAuB,qBDb7B,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAQ,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IACjD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IAErD,EAAiB,GAAmB,EAAQ,EAAO,iBACnD,EAAiB,GAAmB,EAAG,EAAO,iBAC9C,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAElD,EAAiB,AAAG,UAAO,CAC/B,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,uBACd,GAEG,EAAmB,AAAG,UAAO,CACjC,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,iBACd,GAEH,MAAO,CACL,iBACA,sBE3CC,WAA4B,CAOjC,YAAY,CAAE,gBAAe,cAAuC,GAAI,CAN9D,WAAgB,wBAUxB,GAHA,KAAK,eAAiB,GAAiB,GACvC,KAAK,YAAc,GAAc,IAE7B,MAAO,MAAK,gBAAmB,UAAY,KAAK,gBAAkB,GAAK,KAAK,gBAAkB,EAChG,KAAM,IAAI,OAAM,GAAG,KAAK,iEAG1B,GAAI,MAAO,MAAK,aAAgB,SAC9B,KAAM,IAAI,OAAM,GAAG,KAAK,iDAIxB,gBAAwB,CAAE,MAAO,MAAK,kBAEtC,aAAqB,CAAE,MAAO,MAAK,cTZlC,oBAA6B,EAAyB,CAC3D,aAAc,CACZ,MAAM,kBAGD,aAAa,EAAiB,CACnC,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAQ,WACvD,EAAI,AAAG,OAAI,AAAG,OAAI,EAAa,AAAG,UAAO,sBAAwB,AAAG,UAAO,IAC3E,EAAW,GAAY,EAAG,EAAO,aAEjC,CAAE,iBAAgB,oBAAqB,GAAgB,EAAS,IAAK,EAAS,OAAQ,EAAO,kBAEnG,MAAO,IAAY,EAAgB,EAAkB,EAAO,qBAInD,SAAQ,EAAkB,CACrC,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,aACX,EACA,EAAkC,GACR,CAC1B,GAAM,CAAE,aAAY,iBAAkB,GAAI,GAAsB,GAE1D,EAAW,KAAM,GAAW,GAE5B,CACJ,MAAO,EACP,OAAQ,GACN,KAAK,aAAa,GAEhB,EAAQ,EAAO,GACf,EAAS,EAAQ,GACvB,OAAS,GAAI,EAAG,EAAI,EAAO,OAAQ,IACjC,EAAO,GAAG,UACV,EAAQ,GAAG,UAGb,GAAM,GAAa,MAAM,KAAK,KAAM,GAAO,QAErC,EAAU,GACd,EACA,EACA,EAJmB,GAMnB,GAGI,EAAe,EAAS,2BAA2B,GACnD,EAAY,EAAS,UACrB,EAAO,EAAY,EAAa,MAChC,EAAO,EAAY,EAAa,OAEhC,EAAY,EAAM,YAClB,EAAU,EACb,IAAI,AAAC,GAAQ,CACZ,GAAM,CAAC,EAAK,GAAU,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACf,CAAC,GAAM,IAAS,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACrB,MAAO,IAAI,GACT,EAAW,GACX,GAAI,IACF,GACA,EACA,GAAQ,GACR,EAAS,GAEX,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,OAKtC,SAAM,UACN,EAAO,UACA,EAGC,qBAA8B,CACtC,MAAO,wBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KUhHlB,YAA8B,EAAuB,CAC1D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,EAGF,YAAgC,EAAuB,CAC5D,MAAO,IAAqB,GAIvB,oBAA+B,GAAe,GCd9C,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,QAAU,SACpB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,OAAQ,SAClB,GAAI,GAAM,QAAS,UAGR,GAAwB,CACnC,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAA+C,CAAC,QAAS,QAAS,QAElE,GAAqB,oBACrB,GAAoC,mCCvBjD,MAAoB,OCapB,GAAM,IAAW,AAAC,GAAa,MAAO,IAAQ,SAEvC,YAAwB,EAAa,CAC1C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mBAAmB,KAGrC,GAAI,MAAO,GAAO,oBAAuB,UACvC,KAAM,IAAI,OAAM,wDAAwD,EAAO,sBAGjF,GAAI,CAAC,GAAS,EAAO,eAAiB,EAAO,aAAe,GAAK,EAAO,aAAe,EACrF,KAAM,IAAI,OAAM,gEAAgE,EAAO,gBAGzF,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,MAAM,AAAC,GAAW,MAAO,IAAM,UAElD,KAAM,IAAI,OAAM,kEAAkE,KAAK,UAAU,EAAO,YAG1G,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,IAAI,AAAC,GAAW,GAAK,IAAI,MAAM,AAAC,GAAW,GAAS,EAAE,IAAM,GAAS,EAAE,IAE1F,KAAM,IAAI,OAAM,wEAAwE,KAAK,UAAU,EAAO,YAGhH,GAAI,EAAO,SACT,EAAC,MAAM,QAAQ,EAAO,UACnB,EAAO,QAAQ,SAAW,GAC1B,CAAC,EAAO,QAAQ,MAAM,KAEzB,KAAM,IAAI,OAAM,8EAA8E,KAAK,UAAU,EAAO,YCjDxH,OAAoB,OCApB,OAAoB,OAEb,YAAe,EAA6B,CACjD,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OAAI,EAAG,AAAG,UAAO,qBAChC,MAAO,AAAG,QAAI,AAAG,QAAK,AAAG,OAAI,EAAG,IAAO,KDApC,YAA2B,EAAgB,EAAwC,CACxF,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,UAAO,EAAK,EAAO,KAAK,QAAS,CAAC,EAAG,GAAI,SAClD,EAAM,AAAG,OAAI,EAAK,EAAO,GAAG,KAC5B,EAAM,AAAG,OAAI,EAAK,EAAO,GAAG,SAC5B,EAAM,AAAG,OAAI,EAAK,EAAO,KAAK,MACvB,GAAM,KEZjB,OAAoB,OAKb,YAAgC,EAAgB,EAA0C,CAC/F,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,mBAAgB,EAAK,EAAO,iBAAkB,EAAO,iBAAkB,CAAC,EAAG,GAAI,SACxF,EAAM,AAAG,OAAI,EAAK,EAAO,MAClB,GAAM,KCVjB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAEnE,WAAgC,EAAc,EAAiC,CAC7E,GAAM,GAAM,AAAG,YAAS,EAAe,IACjC,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,SAChB,CAAE,UAAW,GAAG,cAEX,CAAE,MAAK,WAGhB,WAAwC,EAAoB,EAAqB,EAAyC,CACxH,GAAM,GAAO,EAAkB,EAAY,EAAa,EAAG,GAAG,UACxD,EAAK,EAAuB,EAAa,GAAG,QAClD,MAAO,CAAE,OAAM,MAEjB,GAAM,GAA6B,GAAkC,EAAgB,GAErF,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACA,EACA,EACgE,CAChE,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAChC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAgB,GAClC,EAEJ,GAAI,EAAO,mBAAoB,CAC7B,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAAO,mBACjB,EAAkB,EAAI,EAAI,EAAG,SAC7B,EAA2B,EAAI,EAAI,SACjC,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAkB,GAAM,GAAM,EAAI,EAAI,EAAiB,EAAG,SACxE,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,cAErD,CACL,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAAkB,EAAI,EAAI,EAAiB,EAAG,SAC5D,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,UAG5D,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAE1E,MAAO,CAAE,SAAQ,iBChFnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAgC,EAA2B,CACzD,GAAM,GAAM,EAAmB,GAAG,QAAc,GAC1C,EAAU,EAAmB,GAAG,YAAkB,GACxD,MAAO,CAAE,MAAK,WAGhB,WAA2B,EAA4B,CACrD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,WAAwC,EAAmC,CACzE,GAAM,GAAO,EAAkB,GAAG,UAC5B,EAAK,EAAuB,GAAG,QACrC,MAAO,CAAE,OAAM,MAGjB,GAAM,GAA6B,GAA+B,GAClE,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACgE,CAChE,GAAM,GAAgC,GAEhC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAW,GAE7B,EAEJ,GAAI,EAAO,mBAAoB,CAE7B,GAAM,GAAc,EAAO,aAAe,EAAO,YAAY,QAAU,EACvE,EAAS,CACP,MAAO,EAAO,mBAAqB,EAAkB,SAAW,EAA2B,SAC3F,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAkB,cAG3B,GAAS,CACP,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAAkB,UAI7B,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBC7EZ,YAAwB,CAO7B,YAAY,CAAE,YAAW,kBAAuC,GAAI,CAN1D,WAAgB,oBAUxB,GAHA,KAAK,WAAa,GAAa,IAC/B,KAAK,gBAAkB,GAAkB,GAErC,MAAO,MAAK,YAAe,UAAY,KAAK,WAAa,IAAO,EAClE,KAAM,IAAI,OAAM,GAAG,KAAK,6DAG1B,GAAI,MAAO,MAAK,iBAAoB,UAAY,KAAK,iBAAmB,GAAK,KAAK,iBAAmB,EACnG,KAAM,IAAI,OAAM,GAAG,KAAK,qEAIxB,YAAoB,CAAE,MAAO,MAAK,cAElC,iBAAyB,CAAE,MAAO,MAAK,kBPJtC,oBAA6B,EAAmC,CAKrE,YAAY,EAA0B,CACpC,MAAM,cACN,GAAe,GACf,KAAK,QAAU,KAGN,SAA2B,CACpC,MAAO,MAAK,WAGH,kBAA2B,CACpC,MAAO,MAAK,OAAO,iBAAmB,KAAK,OAAO,QAAQ,OAAS,KAG1D,kBAA0B,CACnC,MAAO,GAAK,MAAK,gBAAkB,KAAK,OAAO,QAAQ,OAAS,GAG3D,cAAc,EAAgB,EAAiD,CACpF,GAAI,GAAM,GAAkB,EAAG,EAAO,OACtC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,GAAkB,EAAK,EAAO,OAC7B,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAgB,EAAsC,CACxE,GAAI,GAAM,KAAK,OAAO,mBAClB,GAAM,GAAU,EAAG,EAAO,MAAqB,QAAS,KACxD,GAAuB,EAAG,EAAO,OACrC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EACjE,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EAC1D,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAiB,EAAgC,CACnE,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,4CAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAc,AAAG,OAAK,EAAM,cAAc,EAAW,IAAQ,WACjE,SAAc,KAAK,OAAO,QACtB,EAAU,EAAa,KAAK,OAAO,SACnC,EACJ,EAAc,EAAY,IAAI,AAAG,SAAO,MACjC,KAAK,OAAO,mBACf,KAAK,aAAa,EAAa,GAC/B,KAAK,cAAc,EAAa,UAI3B,SAAQ,EAAkB,EAAyC,CAC9E,MAAO,MAAK,aAAa,KAAM,GAAW,GAAQ,QAGvC,QAAO,EAAkB,EAAoC,GAAgC,CACxG,GAAM,CAAE,YAAW,kBAAmB,GAAI,IAAkB,GACtD,EAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,EAAU,GACxC,EAAO,AAAG,OAAK,IAAM,AAAG,UAAQ,GAAK,GAAG,cACxC,EAAkB,CACtB,MAAO,EAAS,cAAc,GAC9B,OAAQ,EAAS,eAAe,IAG5B,EAAU,KAAM,MAAK,aAAa,EAAM,EAAS,2BAA2B,GAAI,GACtF,EAAI,UACJ,EAAK,UAEL,GAAM,GAAQ,EAAQ,IAAI,AAAC,GAAQ,EAAI,KACjC,EAAS,EAAQ,IAAI,AAAC,GAAQ,EAAI,OAClC,EAAc,EAAQ,IAAI,AAAC,GAAQ,EAAI,YACvC,EAAa,EAAQ,IAAI,AAAC,GAAQ,KAAK,OAAO,QAAQ,EAAI,QAgBhE,MAPmB,AAPH,IACd,EAAM,IAAI,AAAC,GAAQ,EAAI,QAAQ,IAC/B,EACA,KAAK,OAAO,aACZ,IAGyB,IAAI,AAAC,GAAQ,GAAI,IAC1C,EAAO,GACP,EAAY,GACZ,EAAW,GACX,EAAM,GACN,IAKM,qBAA8B,CACtC,MAAO,GAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,QAG1C,cAAc,EAAuB,CAC7C,GAAM,GAAc,KAAK,OAAO,aAAe,GAAe,qBAExD,EAAa,EAAc,EAAY,OAAS,OACtD,GAAI,IAAe,GAAK,IAAe,GAAK,IAAe,EACzD,KAAM,IAAI,OAAM,oEAAoE,2BAEtF,MAAO,IAAc,EAAS,KAAK,OAAQ,KAAK,gBAAiB,QAGnD,cACd,EACA,EACA,EACA,CACA,GAAM,CAAE,QAAO,UAAW,EACpB,EAAY,KAAK,IAAI,EAAO,GAC5B,EAAoB,EAAY,EAChC,EAAoB,EAAY,EAEhC,EAAW,EAAa,MAAM,GAC9B,EAAW,KAAK,OAAO,QAAQ,OAE/B,CAAC,EAAa,EAAc,GAAqB,AAAG,OAAK,IAAM,CACnE,GAAM,GAAW,EAAa,QAAQ,CAAC,EAAU,EAAU,EAAU,KAAK,kBAEpE,EAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACpE,EAAS,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACrE,EAAc,KAAK,gBACrB,AAAG,UAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,KAAK,OAAO,QAAQ,SAAU,GACrG,AAAG,SAAO,GACd,MAAO,CAAC,EAAO,EAAQ,KAGnB,EAAU,GACV,EAAa,KAAM,GAAa,QAChC,EAAY,KAAM,GAAY,QACpC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAS,EAAG,EAAS,EAAU,IAAU,CAChD,GAAM,GAAQ,GAAQ,EAAW,GAAK,GAAK,GAAQ,IACnD,GAAI,CAAC,GAAkB,EAAQ,EAAgB,CAC7C,GAAM,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAe,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACvG,EAAgB,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACxG,GAAK,EAAO,GAAa,EACzB,GAAK,GAAO,EAAc,EAC1B,GAAM,CAAE,MAAK,MAAK,UAClB,CAAE,cAAY,UAAU,KAAK,gBAC/B,KAAM,MAAK,sBAAsB,EAAkC,IACnE,CAAE,WAAY,EAAG,MAAO,GAC5B,EAAQ,KAAK,CACX,IAAK,GAAI,IAAY,GAAG,GAAG,GAAI,GAAY,GAAI,GAC/C,QACA,WAAY,EAAQ,GACpB,YACG,MAOb,SAAY,UACZ,EAAa,UACb,EAAkB,UACX,OAGK,uBAAsB,EAA4B,EAAmD,CACjH,GAAM,CAAE,MAAK,MAAK,UAAW,EACvB,EAAc,KAAM,GAAc,QACxC,MAAO,OAAM,KAAK,OAAO,QAAQ,QAAQ,KAAK,GAC3C,IAAI,CAAC,EAAG,IAAM,EAAY,GAAK,GAAK,GAAQ,IAC5C,IAAI,CAAC,EAAY,IAAW,EAC3B,aACA,WAED,OAAO,CAAC,EAAK,IAAU,EAAI,WAAa,EAAK,WAAa,EAAM,KA/MhE,MACS,AADT,GACS,qBAAuB,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,IAAK,KAAM,MQPrE,oBAAyB,GAAe,CAC7C,YAAY,EAA8B,GAAM,CAC9C,GAAM,GAAS,CACb,qBACA,aAAc,GACd,QAAS,CAAC,WACN,EACA,CACA,QAAS,GACT,QAAS,IAET,CACA,QAAS,GACT,gBAAiB,KAIvB,MAAM,MAGG,qBAA8B,CACvC,MAAO,MAAK,OAAO,sBAGV,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,MAAK,mBAAqB,GAAoC,GAG7D,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KChDrC,YAA0B,EAAuB,EAA8B,GAAM,CAC1F,GAAM,GAAM,GAAI,IAAW,GAC3B,SAAI,eAAe,GACZ,ECNF,oBAAsC,GAAkB,CAAxD,aAJP,CAIO,oBACK,WAAgB,4BCLrB,WAAwB,MAChB,MAEX,EACY,CACZ,MAAO,GAAY,KAAM,MAAK,YAGnB,MAAkB,CAC7B,KAAM,IAAI,OAAM,6CCRpB,OAAoB,OCDpB,OAAoB,OAOpB,kBACE,EACA,EAEA,EACA,EAEA,EAAwF,CAAC,CAAE,iBAAkB,EAC7G,CACA,GAAM,GAAY,EAAc,IAAI,AAAC,GAAkB,GAAoB,GACvE,EAAoB,GACpB,EAAa,WACX,EAAgD,GACpD,aAAoB,WAChB,KAAM,IAAmB,EAAO,GAChC,KAAM,IAAa,EAAO,IAG1B,EAAU,KAAM,GAAe,GAErC,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAGT,kBACE,EACA,EAEA,EACA,EAEA,EACA,CACA,MAAO,IACL,CAAC,GACD,EACA,KAAO,IAAU,EAAc,EAAM,IACrC,EACA,GC5CG,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAAqC,CAAC,QAAS,QAAS,QCF9D,oBAA+B,GAAe,CACnD,aAAc,CACZ,GAAM,GAAS,CACb,mBAAoB,GACpB,aAAc,GACd,QAAS,CAAC,QACV,QAAS,GACT,QAAS,GACT,mBAAoB,GACpB,YAAa,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,MAGzC,MAAM,MAGG,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,2BAGC,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KCvBrC,GAAM,GAAO,CAClB,eAAgB,GAAI,IACpB,iBAAkB,GAAI,IACtB,WAAY,GAAI,IAChB,kBAAmB,GAAI,IACvB,sBAAuB,GAAI,IAC3B,mBAAoB,GAAI,IACxB,kBAAmB,GAAI,IACvB,aAAc,GAAI,KAUP,GAAiB,CAAC,EAAkB,IAA6D,EAAK,eAAe,YAAY,EAAO,GASxI,GAAmB,CAAC,EAAkB,IAA+D,EAAK,iBAAiB,YAAY,EAAO,GAS9I,GAAa,CAAC,EAAkB,IAA0D,EAAK,WAAW,YAAY,EAAO,GAS7H,GAAsB,AAAC,GAAmE,EAAK,kBAAkB,gBAAgB,GAWjI,GAA0B,AAAC,GAAmE,EAAK,sBAAsB,gBAAgB,GAYzI,GAAwB,AAAC,GAA6D,EAAK,mBAAmB,sBAAsB,GASpI,GAA2B,AAAC,GAAmE,EAAK,kBAAkB,mBAAmB,GASzI,GAAsB,AAAC,GAAiF,EAAK,aAAa,oBAAoB,GAE9I,GAA0B,AAAC,GAAgB,EAAK,eAAe,KAAK,GACpE,GAA4B,AAAC,GAAgB,EAAK,iBAAiB,KAAK,GACxE,GAAsB,AAAC,GAAgB,EAAK,WAAW,KAAK,GAC5D,GAAwB,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACrE,GAA4B,AAAC,GAAgB,EAAK,sBAAsB,KAAK,GAC7E,GAA2B,AAAC,GAAgB,EAAK,mBAAmB,KAAK,GACzE,GAA0B,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACvE,GAAqB,AAAC,GAAgB,EAAK,aAAa,KAAK,GAG7D,GAAyB,GACzB,GAAc,GACd,GAAkB,GCjGxB,oBAAqE,EAAwB,CAClG,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAA0E,MACrE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAwB,KAAM,IAClC,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,KAEtD,KAAK,gBAGP,MAAO,GAAc,IACnB,CAAC,EAAc,IAAM,GAAmC,EAAc,EAAsB,KAIhG,kBAAmB,CACjB,MAAO,IAAI,IAA2B,KAAM,KAAK,SAI9C,gBAEG,GAA8F,MACzF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,GAAkB,KAAM,IAC5B,EACA,KAAK,MACL,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,GACpD,KAAK,gBAGP,MAAO,IAA0B,EAAc,GAGjD,kBAAmB,CACjB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA0C,CAClD,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCjFnD,oBAAkE,EAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAAuE,MAClE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAqB,KAAM,IAC/B,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,aAAa,oBAAoB,KAElD,KAAK,gBAGP,MAAO,GAAc,IAAI,CAAC,EAAc,IAAM,CAC5C,GAAM,CAAE,MAAK,SAAQ,qBAAsB,EAAmB,GAC9D,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,KAIpF,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA2F,MACtF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,MAAK,SAAQ,qBAAsB,KAAM,IAC/C,EACA,KAAK,MACL,AAAC,GAAS,EAAK,aAAa,oBAAoB,GAChD,KAAK,gBAGP,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,GAGlF,qBAAsB,CACpB,MAAO,IAAI,IAAiC,KAAM,KAAK,SAIpD,gBAEG,GAAoC,CAC5C,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCtFnD,oBAAqE,EAAwB,CAClG,YAEY,EAEA,EACV,CACA,QAJU,kBAEA,eAMP,gBAEG,GAAyE,MACpE,MAA8C,CACzD,GAAM,GAAgB,KAAM,MAAK,WAUjC,MAAO,AARa,MAAM,IACxB,EACA,KAAK,MACL,AAAC,GAAU,QAAQ,IAAI,EAAM,IAAI,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,KACzF,KACA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,OAGxD,IAAI,CAAC,EAAY,IAAM,GAAkC,EAAc,GAAI,IAGhG,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,SAI/D,gBAEG,GAA6F,MACxF,MAAwD,CACnE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAEF,GAAM,GAAa,KAAM,IACvB,EACA,KAAK,MACL,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,GACxD,KAEA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,MAG3E,MAAO,IAAyB,EAAc,GAGhD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,SP1DlE,oBAAkE,EAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,6BAKE,cAAyD,CACrE,MAAO,MAAK,mBACR,EAAK,sBACL,EAAK,oBAIN,gBAEG,GAAqE,MAChE,MAA6C,CACxD,GAAM,GAAgB,KAAM,MAAK,WAC3B,EAAa,EAAc,IAAI,AAAC,GAAQ,EAAI,WAE5C,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,GACrC,KAAM,IAAa,KAAK,MAAO,GAE7B,EAAsB,KAAM,SAAQ,IAAI,EAAM,IAClD,AAAC,GAAS,KAAK,YAAY,gBAAgB,KAG7C,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAAc,IAAI,CAAC,EAAc,IAAM,GAAiC,EAAc,EAAoB,KAGnH,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAyF,MACpF,MAAuD,CAClE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,aAAc,EAChB,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,CAAC,IACtC,KAAM,IAAa,KAAK,MAAO,CAAC,IAE9B,EAAY,KAAM,MAAK,YAAY,gBAAgB,EAAM,IAE/D,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,GAAiC,EAAc,GAGxD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SQzFnD,oBAA2C,EAAwB,CACxE,YAEY,EAEA,EAAgC,GAAI,GAC9C,CACA,QAJU,aAEA,iBAMP,gBAAiC,GAAqC,MAC9D,MAAgC,CAC3C,GAAM,CAAE,QAAO,WAAY,KAEvB,EACJ,GAAI,YAAmB,IAAyB,EAAS,EAAK,iBAAiB,YAAY,EAAO,WACzF,YAAmB,GAAuB,EAAS,EAAK,eAAe,YAAY,EAAO,WAC1F,YAAmB,IAAmB,EAAS,EAAK,WAAW,YAAY,EAAO,OACtF,MAAM,IAAI,OAAM,wHAErB,MAAO,GAGD,gCAAmE,CAEzE,MAAO,IAAI,SAAiC,KAAO,IAAY,CAC7D,GAAM,GAAa,KAAM,MAAK,MAC9B,EAAQ,EAAW,IAAI,AAAC,GAAc,GAAwB,GAAI,OAItE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,SAKJ,gBAAmC,GAA+C,MAC1E,MAA0C,CACrD,GAAM,GAAiB,KAAM,IAAI,IAAmB,KAAK,MAAO,KAAK,SACjE,EAAgC,EAAe,GACnD,SAAe,QAAQ,AAAC,GAAkB,CACxC,AAAI,EAAc,MAAQ,EAA8B,OAAO,GAAgC,KAE1F,EAGD,+BAA4E,CAElF,MAAO,IAAI,SAA2C,KAAO,IAAY,CACvE,GAAM,GAAY,KAAM,MAAK,MAC7B,EAAQ,EAAY,GAA4B,GAAI,GAAa,UAIrE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,SCpGJ,YAA0B,EAAkB,EAAgC,GAAI,GAA+C,CACpI,MAAO,IAAI,IAAqB,EAAO,GAGlC,YAAwB,EAAkB,EAAgC,GAAI,GAA6C,CAChI,MAAO,IAAI,IAAmB,EAAO,GCFvC,kBACE,EACA,EACyE,CACzE,MAAO,IAAe,EAAO,GAAI,GAAsB,EAAgB,CAAE,iBAAkB,KACxF,oBACA,sBAGL,kBACE,EACA,EAAoC,GACqC,CACzE,MAAO,IAAe,EAAO,GAAI,IAAkB,IAChD,oBACA,sBAGE,GAAM,IAAW,GC1BjB,YAA2B,EAA+B,EAA+B,CAC9F,GAAI,EAAK,SAAW,EAAK,OAAQ,KAAM,IAAI,OAAM,kDAEjD,GAAM,GAAQ,MAAM,KAAK,GACnB,EAAQ,MAAM,KAAK,GAEzB,MAAO,MAAK,KACV,EACG,IAAI,CAAC,EAAK,IAAM,EAAM,EAAM,IAC5B,OAAO,CAAC,EAAK,IAAS,EAAO,GAAQ,EAAI,ICJzC,YAAkB,CAKvB,YACE,EACA,EAA4B,GAC5B,CACA,KAAK,mBAAqB,EAE1B,GAAM,GAAa,MAAM,QAAQ,GAAU,EAAS,CAAC,GAErD,GAAI,CAAC,EAAW,OACd,KAAM,IAAI,OAAM,2DAGlB,GAAI,GAAQ,EACN,EAAoB,IAAM,UAAU,MAE1C,KAAK,oBAAsB,EAAW,IAAI,AAAC,GAAS,CAClD,GAAI,YAAgB,IAClB,MAAO,GAGT,GAAI,YAAgB,cAClB,MAAO,IAAI,IAAuB,IAAqB,CAAC,IAG1D,GAAI,EAAK,YAAc,EAAK,qBAAsB,cAChD,MAAO,IAAI,IAAuB,IAAqB,CAAC,EAAK,aAG/D,KAAM,IAAI,OAAM,4MAIT,qBAA+C,CAAE,MAAO,MAAK,uBAE7D,oBAA4B,CAAE,MAAO,MAAK,mBAE9C,oBAAoB,EAA+B,EAAqC,CAC7F,MAAO,GACJ,IAAI,AAAC,GAAM,GAAkB,EAAG,IAChC,OAAO,CAAC,EAAI,IAAO,EAAK,EAAI,GACxB,GAAY,QAAU,GAGxB,gBAAgB,EAA0C,CAC/D,MAAO,MAAK,mBACT,IAAI,CAAC,CAAE,cAAa,WAAY,GAAI,IACnC,EACA,KAAK,oBAAoB,EAAiB,KAE3C,OAAO,CAAC,EAAM,IAAU,EAAK,SAAW,EAAK,SAAW,EAAO,GAG7D,cAAc,EAA0C,CAC7D,GAAM,GAAY,KAAK,gBAAgB,GACvC,MAAO,GAAU,SAAW,KAAK,kBAC7B,EACA,GAAI,IAAU,UAAW,EAAU,UAGlC,QAAc,CACnB,MAAO,CACL,kBAAmB,KAAK,kBACxB,mBAAoB,KAAK,mBAAmB,IAAI,AAAC,GAAO,EAAG,iBAIjD,UAAS,EAAwB,CAC7C,GAAM,GAAqB,EAAK,mBAC7B,IAAI,AAAC,GAAY,GAAuB,SAAS,IACpD,MAAO,IAAI,IAAY,EAAoB,EAAK,qBC1E7C,YAAgC,EAAuB,CAC5D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECFF,YAA0B,EAAY,EAA4B,CACvE,GAAM,CAAE,QAAO,UAAW,GAAI,GAAW,EAAW,MAAO,EAAW,QAEtE,GAAI,GAAS,GAAK,GAAU,EAC1B,KAAM,IAAI,OAAM,uCAAuC,KAAK,UAAU,CAAE,QAAO,cAGjF,GAAI,MAAM,QAAQ,GAEhB,MAAQ,GAAuB,IAAI,AAAC,GAAQ,GAAc,EAAK,CAAE,QAAO,YAG1E,GAAI,GAAoB,GAAU,CAChC,GAAM,GAAmB,EAAQ,UAAU,QAAQ,EAAO,GACpD,EAAmB,EAAQ,mBAAmB,QAAQ,EAAiB,IAAI,MAAO,EAAiB,IAAI,QAC7G,MAAO,IAAwB,GAAwB,EAAS,GAAmB,GAGrF,MAAI,IAAoB,GACf,GAAwB,EAAS,EAAQ,UAAU,QAAQ,EAAO,IAGvE,YAAmB,IAAiB,YAAmB,GACjD,EAAgB,QAAQ,EAAO,GAGlC,EjJRT,GAAM,IAAQ,MAAO,UAAY,YAC3B,GAAW,MAAO,YAAc,aAAiB,MAAO,WAAU,WAAc,YACzE,GAAU,CAAE,QAAa,GAAmB,QAAM", "names": [] } diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js index 3aec920..7e579ea 100644 --- a/dist/face-api.node-gpu.js +++ b/dist/face-api.node-gpu.js @@ -5,5 +5,5 @@ author: ' */ -var fn=Object.create,Ye=Object.defineProperty,hn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty,bn=Object.getOwnPropertyNames,gn=Object.getOwnPropertyDescriptor;var Er=o=>Ye(o,"__esModule",{value:!0});var fo=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Ge=(o,t)=>{for(var e in t)Ye(o,e,{get:t[e],enumerable:!0})},vn=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of bn(t))!xn.call(o,r)&&r!=="default"&&Ye(o,r,{get:()=>t[r],enumerable:!(e=gn(t,r))||e.enumerable});return o},b=o=>vn(Er(Ye(o!=null?fn(hn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);var g=fo(Mr=>{var yn=Object.create,Cr=Object.defineProperty,Fn=Object.getPrototypeOf,Tn=Object.prototype.hasOwnProperty,_n=Object.getOwnPropertyNames,wn=Object.getOwnPropertyDescriptor,ho=o=>Cr(o,"__esModule",{value:!0}),xo=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of _n(t))!Tn.call(o,r)&&r!=="default"&&Cr(o,r,{get:()=>t[r],enumerable:!(e=wn(t,r))||e.enumerable});return o},Pn=o=>xo(ho(Cr(o!=null?yn(Fn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);ho(Mr);xo(Mr,Pn(require("@tensorflow/tfjs-node-gpu")))});var To=fo((Ln,Fo)=>{Er(Ln);Ge(Ln,{isNodejs:()=>kn});function kn(){return typeof global=="object"&&!0&&typeof Fo!="undefined"&&typeof process!="undefined"&&!!process.version}});Er(exports);Ge(exports,{AgeGenderNet:()=>pr,BoundingBox:()=>re,Box:()=>D,ComposableTask:()=>tt,ComputeAllFaceDescriptorsTask:()=>At,ComputeFaceDescriptorsTaskBase:()=>vr,ComputeSingleFaceDescriptorTask:()=>Wt,DetectAllFaceLandmarksTask:()=>Fr,DetectAllFacesTask:()=>He,DetectFaceLandmarksTaskBase:()=>yr,DetectFacesTaskBase:()=>_r,DetectSingleFaceLandmarksTask:()=>Tr,DetectSingleFaceTask:()=>wr,Dimensions:()=>A,FACE_EXPRESSION_LABELS:()=>qr,FaceDetection:()=>M,FaceDetectionNet:()=>qo,FaceExpressionNet:()=>cr,FaceExpressions:()=>It,FaceLandmark68Net:()=>fe,FaceLandmark68TinyNet:()=>dr,FaceLandmarkNet:()=>Ro,FaceLandmarks:()=>V,FaceLandmarks5:()=>vo,FaceLandmarks68:()=>ne,FaceMatch:()=>Ee,FaceMatcher:()=>Dr,FaceRecognitionNet:()=>xe,Gender:()=>vt,LabeledBox:()=>Me,LabeledFaceDescriptors:()=>xt,NetInput:()=>bt,NeuralNetwork:()=>S,ObjectDetection:()=>Dt,Point:()=>x,PredictedBox:()=>yo,Rect:()=>oe,SsdMobilenetv1:()=>Xt,SsdMobilenetv1Options:()=>Z,TinyFaceDetector:()=>Fe,TinyFaceDetectorOptions:()=>gr,TinyYolov2:()=>ve,TinyYolov2Options:()=>lt,allFaces:()=>Ea,allFacesSsdMobilenetv1:()=>un,allFacesTinyYolov2:()=>Da,awaitMediaLoaded:()=>Gr,bufferToImage:()=>zr,computeFaceDescriptor:()=>ua,createCanvas:()=>Yt,createCanvasFromMedia:()=>Ie,createFaceDetectionNet:()=>aa,createFaceRecognitionNet:()=>Xn,createSsdMobilenetv1:()=>Jo,createTinyFaceDetector:()=>Ma,createTinyYolov2:()=>ca,detectAllFaces:()=>Pr,detectFaceLandmarks:()=>pn,detectFaceLandmarksTiny:()=>da,detectLandmarks:()=>wa,detectSingleFace:()=>Pa,draw:()=>Co,env:()=>P,euclideanDistance:()=>uo,extendWithAge:()=>hr,extendWithFaceDescriptor:()=>fr,extendWithFaceDetection:()=>Ot,extendWithFaceExpressions:()=>mr,extendWithFaceLandmarks:()=>le,extendWithGender:()=>xr,extractFaceTensors:()=>se,extractFaces:()=>ae,fetchImage:()=>Bn,fetchJson:()=>Xr,fetchNetWeights:()=>Rn,fetchOrThrow:()=>Gt,getContext2dOrThrow:()=>$,getMediaDimensions:()=>Ht,imageTensorToCanvas:()=>Vr,imageToSquare:()=>Ur,inverseSigmoid:()=>Mn,iou:()=>kr,isMediaElement:()=>qe,isMediaLoaded:()=>Ne,isWithAge:()=>Jn,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>Zr,isWithFaceLandmarks:()=>Vt,isWithGender:()=>qn,loadAgeGenderModel:()=>Fa,loadFaceDetectionModel:()=>Ta,loadFaceExpressionModel:()=>ya,loadFaceLandmarkModel:()=>ba,loadFaceLandmarkTinyModel:()=>ga,loadFaceRecognitionModel:()=>va,loadSsdMobilenetv1Model:()=>dn,loadTinyFaceDetectorModel:()=>ha,loadTinyYolov2Model:()=>xa,loadWeightMap:()=>Jr,locateFaces:()=>_a,matchDimensions:()=>$n,minBbox:()=>Sr,nets:()=>_,nonMaxSuppression:()=>Ar,normalize:()=>ot,padToSquare:()=>Wr,predictAgeAndGender:()=>fa,recognizeFaceExpressions:()=>la,resizeResults:()=>ln,resolveInput:()=>jt,shuffleArray:()=>En,sigmoid:()=>De,ssdMobilenetv1:()=>mn,tf:()=>Ca,tinyFaceDetector:()=>ma,tinyYolov2:()=>pa,toNetInput:()=>E,utils:()=>bo,validateConfig:()=>ao,version:()=>La});var Ca=b(g());var Co={};Ge(Co,{AnchorPosition:()=>dt,DrawBox:()=>Xe,DrawBoxOptions:()=>Yr,DrawFaceLandmarks:()=>Qr,DrawFaceLandmarksOptions:()=>Kr,DrawTextField:()=>Mt,DrawTextFieldOptions:()=>Ce,drawContour:()=>ft,drawDetections:()=>Wn,drawFaceExpressions:()=>On,drawFaceLandmarks:()=>Hn});function ft(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var bo={};Ge(bo,{computeReshapedDimensions:()=>Lr,getCenterPoint:()=>$t,isDimensions:()=>Ve,isEven:()=>ze,isFloat:()=>Ir,isTensor:()=>Bt,isTensor1D:()=>Dn,isTensor2D:()=>Nr,isTensor3D:()=>ht,isTensor4D:()=>z,isValidNumber:()=>rt,isValidProbablitiy:()=>ee,range:()=>ct,round:()=>Rt});var go=b(g());var A=class{constructor(t,e){if(!rt(t)||!rt(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new A(1/this.width,1/this.height)}};function Bt(o,t){return o instanceof go.Tensor&&o.shape.length===t}function Dn(o){return Bt(o,1)}function Nr(o){return Bt(o,2)}function ht(o){return Bt(o,3)}function z(o){return Bt(o,4)}function Ir(o){return o%1!=0}function ze(o){return o%2==0}function Rt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function Ve(o){return o&&o.width&&o.height}function Lr({width:o,height:t},e){let r=e/Math.max(t,o);return new A(Math.round(o*r),Math.round(t*r))}function $t(o){return o.reduce((t,e)=>t.add(e),new x(0,0)).div(new x(o.length,o.length))}function ct(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function rt(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function ee(o){return rt(o)&&o>=0&&o<=1}var x=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new x(this.x+t.x,this.y+t.y)}sub(t){return new x(this.x-t.x,this.y-t.y)}mul(t){return new x(this.x*t.x,this.y*t.y)}div(t){return new x(this.x/t.x,this.y/t.y)}abs(){return new x(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new x(Math.floor(this.x),Math.floor(this.y))}};var D=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(rt)}static assertIsValidBox(t,e,r=!1){if(!D.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(rt),a=[r.x,r.y,r.width,r.height].every(rt);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];D.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new x(this.left,this.top)}get topRight(){return new x(this.right,this.top)}get bottomLeft(){return new x(this.left,this.bottom)}get bottomRight(){return new x(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new D({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new D({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new D({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var re=class extends D{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var Dt=class{constructor(t,e,r,n,a){this._imageDims=new A(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new D(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new Dt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends Dt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function kr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function Sr(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var mt=b(g());function ot(o,t){return mt.tidy(()=>{let[e,r,n]=t,a=mt.fill([...o.shape.slice(0,3),1],e,"float32"),s=mt.fill([...o.shape.slice(0,3),1],r,"float32"),i=mt.fill([...o.shape.slice(0,3),1],n,"float32"),c=mt.concat([a,s,i],3);return mt.sub(o,c)})}var Et=b(g());function Wr(o,t=!1){return Et.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Et.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Et.cast(u,"float32"));return Et.concat(d,s)})}function En(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function De(o){return 1/(1+Math.exp(-o))}function Mn(o){return Math.log(o/(1-o))}var oe=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var Cn=.5,Nn=.43,In=.45,V=class{constructor(t,e,r=new x(0,0)){let{width:n,height:a}=e;this._imgDims=new A(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new x(n,a)).add(r))}get shift(){return new x(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new x(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new x(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof M?t.box.floor():new D(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/In),c=$t(t),m=Math.floor(Math.max(0,c.x-Cn*i)),p=Math.floor(Math.max(0,c.y-Nn*i));return new oe(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=Sr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var vo=class extends V{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],$t([t[3],t[4]])]}};var ne=class extends V{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map($t)}};var Ee=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Rt(this.distance)})`:""}`}};var Me=class extends D{static assertIsValidLabeledBox(t,e){if(D.assertIsValidBox(t,e),!rt(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var xt=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new xt(t.label,e)}};var yo=class extends Me{static assertIsValidPredictedBox(t,e){if(Me.assertIsValidLabeledBox(t,e),!ee(t.score)||!ee(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function pt(o){return o.detection instanceof M}function Ot(o,t){return{...o,...{detection:t}}}function Br(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function Ue(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Rr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=Ue();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function $r(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Or=b(To()),k;function Sn(){if(!k)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return k}function jr(o){k=o}function Hr(){return $r()?jr(Br()):(0,Or.isNodejs)()?jr(Rr()):null}function An(o){if(k||Hr(),!k)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=k.Canvas,Image:e=k.Image}=o;k.Canvas=t,k.Image=e,k.createCanvasElement=o.createCanvasElement||(()=>new t),k.createImageElement=o.createImageElement||(()=>new e),k.ImageData=o.ImageData||k.ImageData,k.Video=o.Video||k.Video,k.fetch=o.fetch||k.fetch,k.readFile=o.readFile||k.readFile}var P={getEnv:Sn,setEnv:jr,initialize:Hr,createBrowserEnv:Br,createFileSystem:Ue,createNodejsEnv:Rr,monkeyPatch:An,isBrowser:$r,isNodejs:Or.isNodejs};Hr();function jt(o){return!P.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function $(o){let{Canvas:t,CanvasRenderingContext2D:e}=P.getEnv();if(o instanceof e)return o;let r=jt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var dt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(dt||(dt={}));var Ce=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||dt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},Mt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof Mt?t.text:t,this.anchor=e,this.options=new Ce(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let v=c+d.x,w=c+d.y+(l+1)*s;r.fillText(u,v,w)})}};var Yr=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:dt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ce({...s,...a})}},Xe=class{constructor(t,e={}){this.box=new D(t),this.options=new Yr(e)}draw(t){let e=$(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new Mt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function Wn(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof M?r.score:pt(r)?r.detection.score:void 0,a=r instanceof M?r.box:pt(r)?r.detection.box:new D(r),s=n?`${Rt(n)}`:void 0;new Xe(a,{label:s}).draw(o)})}var ue=b(g());function Ne(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function Gr(o){return new Promise((t,e)=>{if(o instanceof P.getEnv().Canvas||Ne(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function zr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=P.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function Ht(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t?new A(o.naturalWidth,o.naturalHeight):o instanceof e?new A(o.videoWidth,o.videoHeight):new A(o.width,o.height)}function Yt({width:o,height:t}){let{createCanvasElement:e}=P.getEnv(),r=e();return r.width=o,r.height=t,r}function Ie(o,t){let{ImageData:e}=P.getEnv();if(!(o instanceof e)&&!Ne(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||Ht(o),a=Yt({width:r,height:n});return o instanceof e?$(a).putImageData(o,0,0):$(a).drawImage(o,0,0,r,n),a}var Je=b(g());async function Vr(o,t){let e=t||P.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(z(o)?1:0),s=Je.tidy(()=>o.as3D(r,n,a).toInt());return await Je.browser.toPixels(s,e),s.dispose(),e}function qe(o){let{Image:t,Canvas:e,Video:r}=P.getEnv();return o instanceof t||o instanceof e||o instanceof r}var J=b(g());function Ur(o,t,e=!1){let{Image:r,Canvas:n}=P.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Yt({width:1,height:1});let a=Ht(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=Yt({width:t,height:t}),p=o instanceof n?o:Ie(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&$(m).drawImage(p,u,l,i,c),m}var bt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(ht(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(z(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof P.getEnv().Canvas?r:Ie(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ct(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Lr({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,J.tidy(()=>{let r=ct(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof J.Tensor){let i=z(s)?s:s.expandDims();return i=Wr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=J.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof P.getEnv().Canvas)return J.browser.fromPixels(Ur(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return J.stack(r.map(a=>J.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function E(o){if(o instanceof bt)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(jt);return r.forEach((n,a)=>{if(!qe(n)&&!ht(n)&&!z(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(z(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>qe(n)&&Gr(n))),new bt(r,Array.isArray(o))}async function ae(o,t){let{Canvas:e}=P.getEnv(),r=o;if(!(o instanceof e)){let s=await E(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await Vr(i)}let n=$(r);return t.map(s=>s instanceof M?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=Yt({width:c,height:m});return c>0&&m>0&&$(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var Ze=b(g());async function se(o,t){if(!ht(o)&&!z(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(z(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Ze.tidy(()=>{let[e,r,n]=o.shape.slice(z(o)?1:0);return t.map(i=>i instanceof M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>Ze.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Gt(o,t){let{fetch:e}=P.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Bn(o){let t=await Gt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return zr(e)}async function Xr(o){return(await Gt(o)).json()}async function Rn(o){return new Float32Array(await(await Gt(o)).arrayBuffer())}var _o=b(g());function Ke(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function Jr(o,t){let{manifestUri:e,modelBaseUri:r}=Ke(o,t),n=await Xr(e);return _o.io.loadWeights(n,r)}function $n(o,t,e=!1){let{width:r,height:n}=e?Ht(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Nt=b(g());var gt=b(g());var S=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof gt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof gt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=gt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await Jr(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=P.getEnv(),{manifestUri:r,modelBaseUri:n}=Ke(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=gt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof gt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var C=b(g());var ie=b(g());function O(o,t,e){return ie.tidy(()=>{let r=ie.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=ie.add(r,t.bias),r})}function Qe(o,t,e=!1){return C.tidy(()=>{let r=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):O(o,t.conv0,[2,2])),n=O(r,t.conv1,[1,1]),a=C.relu(C.add(r,n)),s=O(a,t.conv2,[1,1]);return C.relu(C.add(r,C.add(n,s)))})}function Le(o,t,e=!1,r=!0){return C.tidy(()=>{let n=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):O(o,t.conv0,r?[2,2]:[1,1])),a=O(n,t.conv1,[1,1]),s=C.relu(C.add(n,a)),i=O(s,t.conv2,[1,1]),c=C.relu(C.add(n,C.add(a,i))),m=O(c,t.conv3,[1,1]);return C.relu(C.add(n,C.add(a,C.add(i,m))))})}var Ct=b(g());function zt(o,t,e="same",r=!1){return Ct.tidy(()=>{let n=Ct.add(Ct.conv2d(o,t.filters,[1,1],e),t.bias);return r?Ct.relu(n):n})}function W(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var tr=b(g());function ce(o,t){return(e,r,n,a)=>{let s=tr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=tr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var er=b(g());function rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function B(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function nr(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function wo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock4Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function ar(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Po(o){let t=[],{extractDenseBlock4Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return W(o,t),{params:r,paramMappings:t}}var Se=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Le(a,e.dense0,!0);return s=Le(s,e.dense1),s=Le(s,e.dense2),s=Le(s,e.dense3),s=Nt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Po(t)}extractParams(t){return wo(t)}};var Mo=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Do(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Eo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Mo.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Do(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Eo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var qr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);qr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return qr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var cr=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new It(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Zr(o){return o.expressions instanceof It}function mr(o,t){return{...o,...{expressions:t}}}function On(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof It?a:Zr(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=pt(a)?a.detection.box.bottomLeft:r||new x(0,0);new Mt(c.map(d=>`${d.expression} (${Rt(d.probability)})`),m).draw(o)})}function Vt(o){return pt(o)&&o.landmarks instanceof V&&o.unshiftedLandmarks instanceof V&&o.alignedRect instanceof M}function jn(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function le(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new M(o.detection.score,n.rescale(a.reverse()),a),i=jn(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var Kr=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},Qr=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new Kr(e)}draw(t){let e=$(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof ne&&(e.strokeStyle=s,e.lineWidth=a,ft(e,this.faceLandmarks.getJawOutline()),ft(e,this.faceLandmarks.getLeftEyeBrow()),ft(e,this.faceLandmarks.getRightEyeBrow()),ft(e,this.faceLandmarks.getNose()),ft(e,this.faceLandmarks.getLeftEye(),!0),ft(e,this.faceLandmarks.getRightEye(),!0),ft(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function Hn(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof V?r:Vt(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Qr(n).draw(o)})}var No="1.0.1";var ut=b(g());var I=b(g());function Yn(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Io(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=B(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=Yn(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};ct(t,0,1).forEach(y=>{l[`main_block_${y}`]=c(128,`middle_flow/main_block_${y}`)});let v=i(128,256,"exit_flow/reduction_block"),w=s(256,512,"exit_flow/separable_conv"),h={reduction_block:v,separable_conv:w};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:h}}}function Gn(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function Lo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=Gn(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};ct(t,0,1).forEach(w=>{d[`main_block_${w}`]=s(`middle_flow/main_block_${w}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return W(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function ko(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function to(o,t,e=!0){let r=e?I.relu(o):o;return r=O(r,t.separable_conv0,[1,1]),r=O(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,ko(o,t.expansion_conv,[2,2])),r}function zn(o,t){let e=O(I.relu(o),t.separable_conv0,[1,1]);return e=O(I.relu(e),t.separable_conv1,[1,1]),e=O(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var eo=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(ko(a,e.entry_flow.conv_in,[2,2]));return s=to(s,e.entry_flow.reduction_block_0,!1),s=to(s,e.entry_flow.reduction_block_1),ct(this._numMainBlocks,0,1).forEach(i=>{s=zn(s,e.middle_flow[`main_block_${i}`])}),s=to(s,e.exit_flow.reduction_block),s=I.relu(O(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Lo(t,this._numMainBlocks)}extractParams(t){return Io(t,this._numMainBlocks)}};function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ao(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new eo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return So(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ao(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var H=b(g());var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return H.tidy(()=>{let s=(d,u)=>H.stack([H.fill([68],d,"float32"),H.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return u(l,v)?Math.abs(l-v)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(H.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return H.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await E(t))}async detectLandmarks(t){let e=await E(t),r=H.tidy(()=>H.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>ze(d)),m=i.filter((p,d)=>!ze(d));return new ne(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var fe=class extends Be{constructor(t=new Se){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Lt=b(g());function Wo(o){let t=[],{extractDenseBlock3Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return W(o,t),{params:r,paramMappings:t}}function Bo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock3Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var ro=class extends S{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Lt.scalar(255)),s=Qe(a,e.dense0,!0);return s=Qe(s,e.dense1),s=Qe(s,e.dense2),s=Lt.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Wo(t)}extractParams(t){return Bo(t)}};var dr=class extends Be{constructor(t=new ro){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Ro=class extends fe{};var U=b(g());var he=b(g());var ur=b(g());function $o(o,t){return ur.add(ur.mul(o,t.weights),t.biases)}function oo(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=he.conv2d(o,a,e,n);return i=he.add(i,s),i=$o(i,t.scale),r?he.relu(i):i}function Oo(o,t){return oo(o,t,[1,1],!0)}function no(o,t){return oo(o,t,[1,1],!1)}function lr(o,t){return oo(o,t,[2,2],!0,"valid")}var Y=b(g());function Vn(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Ir(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return Y.tidy(()=>Y.transpose(Y.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=Y.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=Y.tensor1d(o(i)),p=Y.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function jo(o){let{extractWeights:t,getRemainingWeights:e}=B(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Vn(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),v=a(147456,128,3,"conv128_down",!0),w=a(147456,128,3,"conv128_1"),h=a(147456,128,3,"conv128_2"),y=a(589824,256,3,"conv256_down",!0),T=a(589824,256,3,"conv256_1"),F=a(589824,256,3,"conv256_2"),L=a(589824,256,3,"conv256_down_out"),G=Y.tidy(()=>Y.transpose(Y.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:v,conv128_1:w,conv128_2:h,conv256_down:y,conv256_1:T,conv256_2:F,conv256_down_out:L,fc:G},paramMappings:r}}function Un(o,t){let e=j(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function Ho(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=Un(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),v=r("conv128_2"),w=r("conv256_down"),h=r("conv256_1"),y=r("conv256_2"),T=r("conv256_down_out"),{fc:F}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!Nr(F))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${F}`);let L={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:v,conv256_down:w,conv256_1:h,conv256_2:y,conv256_down_out:T,fc:F};return W(o,t),{params:L,paramMappings:t}}var R=b(g());function nt(o,t){let e=Oo(o,t.conv1);return e=no(e,t.conv2),e=R.add(e,o),e=R.relu(e),e}function Re(o,t){let e=lr(o,t.conv1);e=no(e,t.conv2);let r=R.avgPool(o,2,2,"valid"),n=R.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=R.zeros(i);e=R.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=R.zeros(m);e=R.concat([e,p],2)}return r=a?R.concat([r,n],3):r,e=R.add(r,e),e=R.relu(e),e}var xe=class extends S{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return U.tidy(()=>{let r=U.cast(t.toBatchTensor(150,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(U.scalar(256)),s=lr(a,e.conv32_down);s=U.maxPool(s,3,2,"valid"),s=nt(s,e.conv32_1),s=nt(s,e.conv32_2),s=nt(s,e.conv32_3),s=Re(s,e.conv64_down),s=nt(s,e.conv64_1),s=nt(s,e.conv64_2),s=nt(s,e.conv64_3),s=Re(s,e.conv128_down),s=nt(s,e.conv128_1),s=nt(s,e.conv128_2),s=Re(s,e.conv256_down),s=nt(s,e.conv256_1),s=nt(s,e.conv256_2),s=Re(s,e.conv256_down_out);let i=s.mean([1,2]);return U.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=U.tidy(()=>U.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return Ho(t)}extractParams(t){return jo(t)}};function Xn(o){let t=new xe;return t.extractWeights(o),t}function fr(o,t){return{...o,...{descriptor:t}}}function Jn(o){return typeof o.age=="number"}function hr(o,t){return{...o,...{age:t}}}function qn(o){return(o.gender===vt.MALE||o.gender===vt.FEMALE)&&ee(o.genderProbability)}function xr(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var st=b(g());var at=b(g());function Zn(o,t){function e(c,m){let p=at.tensor4d(o(3*3*c),[3,3,c,1]),d=at.tensor1d(o(c)),u=at.tensor1d(o(c)),l=at.tensor1d(o(c)),v=at.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),v=a(256,512,"mobilenetv1/conv_6"),w=a(512,512,"mobilenetv1/conv_7"),h=a(512,512,"mobilenetv1/conv_8"),y=a(512,512,"mobilenetv1/conv_9"),T=a(512,512,"mobilenetv1/conv_10"),F=a(512,512,"mobilenetv1/conv_11"),L=a(512,1024,"mobilenetv1/conv_12"),G=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,conv_8:h,conv_9:y,conv_10:T,conv_11:F,conv_12:L,conv_13:G}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),v=n(256,64,1,"prediction_layer/conv_6"),w=n(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),T=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),F=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),L=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),G=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),et=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),it=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),X=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),_t=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),wt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Pt=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,box_predictor_0:{box_encoding_predictor:h,class_predictor:y},box_predictor_1:{box_encoding_predictor:T,class_predictor:F},box_predictor_2:{box_encoding_predictor:L,class_predictor:G},box_predictor_3:{box_encoding_predictor:et,class_predictor:it},box_predictor_4:{box_encoding_predictor:X,class_predictor:_t},box_predictor_5:{box_encoding_predictor:wt,class_predictor:Pt}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function Yo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Zn(e,t),s=n(),i=a(),m={extra_dim:at.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Kn(o,t){let e=j(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,v=e(`${d}/depthwise_weights`,4,`${u}/filters`),w=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),h=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),y=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),T=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:v,batch_norm_scale:w,batch_norm_offset:h,batch_norm_mean:y,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function Go(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Kn(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!ht(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return W(o,t),{params:a,paramMappings:t}}var yt=b(g());var kt=b(g());function q(o,t,e){return kt.tidy(()=>{let r=kt.conv2d(o,t.filters,e,"same");return r=kt.add(r,t.batch_norm_offset),kt.clipByValue(r,0,6)})}var Qn=.0010000000474974513;function ta(o,t,e){return yt.tidy(()=>{let r=yt.depthwiseConv2d(o,t.filters,e,"same");return r=yt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Qn),yt.clipByValue(r,0,6)})}function ea(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function zo(o,t){return yt.tidy(()=>{let e,r=q(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=ea(i);r=ta(r,a.depthwise_conv,c),r=q(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function ra(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let v=Math.max(n,c),w=Math.max(a,m),h=Math.min(s,p),y=Math.min(i,d),T=Math.max(h-v,0)*Math.max(y-w,0);return T/(u+l-T)}function Vo(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=ra(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function oa(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function na(o,t){let{sizes:e,centers:r}=oa(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function Uo(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=na(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Oe=b(g());var $e=b(g());function Ut(o,t){return $e.tidy(()=>{let e=o.shape[0],r=$e.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=$e.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Xo(o,t,e){return Oe.tidy(()=>{let r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),v=Ut(s,e.box_predictor_3),w=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),y=Oe.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,v.boxPredictionEncoding,w.boxPredictionEncoding,h.boxPredictionEncoding],1),T=Oe.concat([d.classPrediction,u.classPrediction,l.classPrediction,v.classPrediction,w.classPrediction,h.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var Z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=zo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Xo(a.out,a.conv11,e.prediction_layer);return Uo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*w);return new M(p[F],new oe(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Xt;return t.extractWeights(o),t}function aa(o){return Jo(o)}var qo=class extends Xt{};var Zo=.4,Ko=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Qo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],tn=[117.001,114.697,97.404],en="tiny_yolov2_model",rn="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function ao(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!br(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var so=b(g());function sa(o,t){let e=ce(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=sa(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,w,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,l,"conv1"),et=m(l,v,"conv2"),it=m(v,w,"conv3"),X=m(w,h,"conv4"),_t=m(h,y,"conv5"),wt=T?m(y,T,"conv6"):void 0,Pt=F?m(T,F,"conv7"):void 0,te=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}else{let[d,u,l,v,w,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,l,"conv1"),et=c(l,v,"conv2"),it=c(v,w,"conv3"),X=c(w,h,"conv4"),_t=c(h,y,"conv5"),wt=c(y,T,"conv6"),Pt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var io=class extends S{constructor(t){super("TinyYolov2");ao(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],w=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,_t=G-it/2,wt=et-X/2,Pt={row:y,col:T,anchor:F},{classScore:te,label:lo}=this.withClassScores?await this.extractPredictedClass(l,Pt):{classScore:1,label:0};v.push({box:new re(_t,wt,_t+it,wt+X),score:L,classScore:L*te,label:lo,...Pt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=io;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var co=b(g());async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var _={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>_.tinyYolov2.locateFaces(o,t),pn=o=>_.faceLandmark68Net.detectLandmarks(o),da=o=>_.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>_.faceRecognitionNet.computeFaceDescriptor(o),la=o=>_.faceExpressionNet.predictExpressions(o),fa=o=>_.ageGenderNet.predictAgeAndGender(o),dn=o=>_.ssdMobilenetv1.load(o),ha=o=>_.tinyFaceDetector.load(o),xa=o=>_.tinyYolov2.load(o),ba=o=>_.faceLandmark68Net.load(o),ga=o=>_.faceLandmark68TinyNet.load(o),va=o=>_.faceRecognitionNet.load(o),ya=o=>_.faceExpressionNet.load(o),Fa=o=>_.ageGenderNet.load(o),Ta=dn,_a=mn,wa=pn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends mo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},Pe=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends Pe{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new Pe(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>_.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var _r=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends _r{async run(){let{input:t,options:e}=this,r;if(e instanceof gr)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof Z)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof lt)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Ot({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},wr=class extends _r{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function Pa(o,t=new Z){return new wr(o,t)}function Pr(o,t=new Z){return new He(o,t)}async function un(o,t){return Pr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Pr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=un;function uo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Dr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>uo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ma(o){let t=new Fe;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(Ot(o,n),a)}return pt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Na=typeof process!="undefined",Ia=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",La={faceapi:No,node:Na,browser:Ia}; +var Vn=Object.create,sr=Object.defineProperty,Un=Object.getPrototypeOf,Xn=Object.prototype.hasOwnProperty,Jn=Object.getOwnPropertyNames,qn=Object.getOwnPropertyDescriptor;var jr=o=>sr(o,"__esModule",{value:!0});var Do=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),ir=(o,t)=>{for(var e in t)sr(o,e,{get:t[e],enumerable:!0})},Zn=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Jn(t))!Xn.call(o,r)&&r!=="default"&&sr(o,r,{get:()=>t[r],enumerable:!(e=qn(t,r))||e.enumerable});return o},b=o=>Zn(jr(sr(o!=null?Vn(Un(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);var g=Do(Hr=>{var Kn=Object.create,Yr=Object.defineProperty,Qn=Object.getPrototypeOf,ta=Object.prototype.hasOwnProperty,ea=Object.getOwnPropertyNames,ra=Object.getOwnPropertyDescriptor,Eo=o=>Yr(o,"__esModule",{value:!0}),Mo=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of ea(t))!ta.call(o,r)&&r!=="default"&&Yr(o,r,{get:()=>t[r],enumerable:!(e=ra(t,r))||e.enumerable});return o},oa=o=>Mo(Eo(Yr(o!=null?Kn(Qn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);Eo(Hr);Mo(Hr,oa(require("@tensorflow/tfjs-node-gpu")))});var ko=Do((ca,Lo)=>{jr(ca);ir(ca,{isNodejs:()=>ma});function ma(){return typeof global=="object"&&!0&&typeof Lo!="undefined"&&typeof process!="undefined"&&!!process.version}});jr(exports);ir(exports,{AgeGenderNet:()=>je,BoundingBox:()=>Ut,Box:()=>D,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>Pt,ComputeFaceDescriptorsTaskBase:()=>Ze,ComputeSingleFaceDescriptorTask:()=>Dt,DetectAllFaceLandmarksTask:()=>tr,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Qe,DetectFacesTaskBase:()=>rr,DetectSingleFaceLandmarksTask:()=>er,DetectSingleFaceTask:()=>or,Dimensions:()=>A,FACE_EXPRESSION_LABELS:()=>Sr,FaceDetection:()=>M,FaceDetectionNet:()=>fo,FaceExpressionNet:()=>$e,FaceExpressions:()=>Ft,FaceLandmark68Net:()=>te,FaceLandmark68TinyNet:()=>Ye,FaceLandmarkNet:()=>mo,FaceLandmarks:()=>j,FaceLandmarks5:()=>Xr,FaceLandmarks68:()=>Jt,FaceMatch:()=>le,FaceMatcher:()=>ar,FaceRecognitionNet:()=>ee,Gender:()=>ht,LabeledBox:()=>fe,LabeledFaceDescriptors:()=>ut,NetInput:()=>ft,NeuralNetwork:()=>S,ObjectDetection:()=>vt,Point:()=>h,PredictedBox:()=>Jr,Rect:()=>Xt,SsdMobilenetv1:()=>Ht,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ae,TinyFaceDetectorOptions:()=>qe,TinyYolov2:()=>oe,TinyYolov2Options:()=>ct,allFaces:()=>Hn,allFacesSsdMobilenetv1:()=>_o,allFacesTinyYolov2:()=>jn,awaitMediaLoaded:()=>xr,bufferToImage:()=>br,computeFaceDescriptor:()=>En,createCanvas:()=>At,createCanvasFromMedia:()=>xe,createFaceDetectionNet:()=>dn,createFaceRecognitionNet:()=>en,createSsdMobilenetv1:()=>lo,createTinyFaceDetector:()=>Yn,createTinyYolov2:()=>yn,detectAllFaces:()=>nr,detectFaceLandmarks:()=>vo,detectFaceLandmarksTiny:()=>Dn,detectLandmarks:()=>$n,detectSingleFace:()=>On,draw:()=>ao,env:()=>P,euclideanDistance:()=>Or,extendWithAge:()=>Ve,extendWithFaceDescriptor:()=>ze,extendWithFaceDetection:()=>It,extendWithFaceExpressions:()=>Oe,extendWithFaceLandmarks:()=>Qt,extendWithGender:()=>Ue,extractFaceTensors:()=>Zt,extractFaces:()=>qt,fetchImage:()=>So,fetchJson:()=>Tr,fetchNetWeights:()=>Ao,fetchOrThrow:()=>Wt,getContext2dOrThrow:()=>W,getMediaDimensions:()=>St,imageTensorToCanvas:()=>vr,imageToSquare:()=>yr,inverseSigmoid:()=>Io,iou:()=>pr,isMediaElement:()=>ke,isMediaLoaded:()=>he,isWithAge:()=>rn,isWithFaceDetection:()=>nt,isWithFaceExpressions:()=>Ar,isWithFaceLandmarks:()=>$t,isWithGender:()=>on,loadAgeGenderModel:()=>Wn,loadFaceDetectionModel:()=>Bn,loadFaceExpressionModel:()=>An,loadFaceLandmarkModel:()=>Ln,loadFaceLandmarkTinyModel:()=>kn,loadFaceRecognitionModel:()=>Sn,loadSsdMobilenetv1Model:()=>yo,loadTinyFaceDetectorModel:()=>Nn,loadTinyYolov2Model:()=>In,loadWeightMap:()=>wr,locateFaces:()=>Rn,matchDimensions:()=>Bo,minBbox:()=>dr,nets:()=>_,nonMaxSuppression:()=>ur,normalize:()=>Z,padToSquare:()=>lr,predictAgeAndGender:()=>Cn,recognizeFaceExpressions:()=>Mn,resizeResults:()=>wo,resolveInput:()=>Lt,shuffleArray:()=>No,sigmoid:()=>ue,ssdMobilenetv1:()=>go,tf:()=>Gn,tinyFaceDetector:()=>wn,tinyYolov2:()=>Pn,toNetInput:()=>E,utils:()=>Gr,validateConfig:()=>$r,version:()=>zn});var Gn=b(g());var ao={};ir(ao,{AnchorPosition:()=>lt,DrawBox:()=>hr,DrawBoxOptions:()=>ro,DrawFaceLandmarks:()=>no,DrawFaceLandmarksOptions:()=>oo,DrawTextField:()=>kt,DrawTextFieldOptions:()=>Le,drawContour:()=>bt,drawDetections:()=>ua,drawFaceExpressions:()=>la,drawFaceLandmarks:()=>ha});function bt(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var Gr={};ir(Gr,{computeReshapedDimensions:()=>Ur,getCenterPoint:()=>Vt,isDimensions:()=>mr,isEven:()=>cr,isFloat:()=>Vr,isTensor:()=>Gt,isTensor1D:()=>na,isTensor2D:()=>zr,isTensor3D:()=>gt,isTensor4D:()=>V,isValidNumber:()=>ot,isValidProbablitiy:()=>de,range:()=>pt,round:()=>zt});var Co=b(g());var A=class{constructor(t,e){if(!ot(t)||!ot(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new A(1/this.width,1/this.height)}};function Gt(o,t){return o instanceof Co.Tensor&&o.shape.length===t}function na(o){return Gt(o,1)}function zr(o){return Gt(o,2)}function gt(o){return Gt(o,3)}function V(o){return Gt(o,4)}function Vr(o){return o%1!=0}function cr(o){return o%2==0}function zt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function mr(o){return o&&o.width&&o.height}function Ur({width:o,height:t},e){let r=e/Math.max(t,o);return new A(Math.round(o*r),Math.round(t*r))}function Vt(o){return o.reduce((t,e)=>t.add(e),new h(0,0)).div(new h(o.length,o.length))}function pt(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function ot(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function de(o){return ot(o)&&o>=0&&o<=1}var h=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new h(this.x+t.x,this.y+t.y)}sub(t){return new h(this.x-t.x,this.y-t.y)}mul(t){return new h(this.x*t.x,this.y*t.y)}div(t){return new h(this.x/t.x,this.y/t.y)}abs(){return new h(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new h(Math.floor(this.x),Math.floor(this.y))}};var D=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(ot)}static assertIsValidBox(t,e,r=!1){if(!D.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(ot),a=[r.x,r.y,r.width,r.height].every(ot);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];D.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new h(this.left,this.top)}get topRight(){return new h(this.right,this.top)}get bottomLeft(){return new h(this.left,this.bottom)}get bottomRight(){return new h(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new D({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new D({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new D({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var Ut=class extends D{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var vt=class{constructor(t,e,r,n,a){this._imageDims=new A(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new D(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new vt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends vt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function pr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function dr(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var dt=b(g());function Z(o,t){return dt.tidy(()=>{let[e,r,n]=t,a=dt.fill([...o.shape.slice(0,3),1],e,"float32"),s=dt.fill([...o.shape.slice(0,3),1],r,"float32"),i=dt.fill([...o.shape.slice(0,3),1],n,"float32"),c=dt.concat([a,s,i],3);return dt.sub(o,c)})}var Nt=b(g());function lr(o,t=!1){return Nt.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Nt.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Nt.cast(u,"float32"));return Nt.concat(d,s)})}function No(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function ue(o){return 1/(1+Math.exp(-o))}function Io(o){return Math.log(o/(1-o))}var Xt=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var aa=.5,sa=.43,ia=.45,j=class{constructor(t,e,r=new h(0,0)){let{width:n,height:a}=e;this._imgDims=new A(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new h(n,a)).add(r))}get shift(){return new h(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new h(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new h(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof M?t.box.floor():new D(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/ia),c=Vt(t),m=Math.floor(Math.max(0,c.x-aa*i)),p=Math.floor(Math.max(0,c.y-sa*i));return new Xt(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=dr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Xr=class extends j{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Vt([t[3],t[4]])]}};var Jt=class extends j{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Vt)}};var le=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${zt(this.distance)})`:""}`}};var fe=class extends D{static assertIsValidLabeledBox(t,e){if(D.assertIsValidBox(t,e),!ot(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var ut=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new ut(t.label,e)}};var Jr=class extends fe{static assertIsValidPredictedBox(t,e){if(fe.assertIsValidLabeledBox(t,e),!de(t.score)||!de(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function nt(o){return o.detection instanceof M}function It(o,t){return{...o,...{detection:t}}}function qr(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function fr(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Zr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=fr();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function Kr(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Qr=b(ko()),k;function pa(){if(!k)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return k}function to(o){k=o}function eo(){return Kr()?to(qr()):(0,Qr.isNodejs)()?to(Zr()):null}function da(o){if(k||eo(),!k)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=k.Canvas,Image:e=k.Image}=o;k.Canvas=t,k.Image=e,k.createCanvasElement=o.createCanvasElement||(()=>new t),k.createImageElement=o.createImageElement||(()=>new e),k.ImageData=o.ImageData||k.ImageData,k.Video=o.Video||k.Video,k.fetch=o.fetch||k.fetch,k.readFile=o.readFile||k.readFile}var P={getEnv:pa,setEnv:to,initialize:eo,createBrowserEnv:qr,createFileSystem:fr,createNodejsEnv:Zr,monkeyPatch:da,isBrowser:Kr,isNodejs:Qr.isNodejs};eo();function Lt(o){return!P.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function W(o){let{Canvas:t,CanvasRenderingContext2D:e}=P.getEnv();if(o instanceof e)return o;let r=Lt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var lt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lt||(lt={}));var Le=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||lt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},kt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof kt?t.text:t,this.anchor=e,this.options=new Le(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let v=c+d.x,w=c+d.y+(l+1)*s;r.fillText(u,v,w)})}};var ro=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:lt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Le({...s,...a})}},hr=class{constructor(t,e={}){this.box=new D(t),this.options=new ro(e)}draw(t){let e=W(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new kt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function ua(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof M?r.score:nt(r)?r.detection.score:void 0,a=r instanceof M?r.box:nt(r)?r.detection.box:new D(r),s=n?`${zt(n)}`:void 0;new hr(a,{label:s}).draw(o)})}var Te=b(g());function he(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function xr(o){return new Promise((t,e)=>{if(o instanceof P.getEnv().Canvas||he(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function br(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=P.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function St(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t?new A(o.naturalWidth,o.naturalHeight):o instanceof e?new A(o.videoWidth,o.videoHeight):new A(o.width,o.height)}function At({width:o,height:t}){let{createCanvasElement:e}=P.getEnv(),r=e();return r.width=o,r.height=t,r}function xe(o,t){let{ImageData:e}=P.getEnv();if(!(o instanceof e)&&!he(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||St(o),a=At({width:r,height:n});return o instanceof e?W(a).putImageData(o,0,0):W(a).drawImage(o,0,0,r,n),a}var gr=b(g());async function vr(o,t){let e=t||P.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(V(o)?1:0),s=gr.tidy(()=>o.as3D(r,n,a).toInt());return await gr.browser.toPixels(s,e),s.dispose(),e}function ke(o){let{Image:t,Canvas:e,Video:r}=P.getEnv();return o instanceof t||o instanceof e||o instanceof r}var K=b(g());function yr(o,t,e=!1){let{Image:r,Canvas:n}=P.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return At({width:1,height:1});let a=St(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=At({width:t,height:t}),p=o instanceof n?o:xe(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&W(m).drawImage(p,u,l,i,c),m}var ft=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(gt(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(V(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof P.getEnv().Canvas?r:xe(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return pt(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Ur({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,K.tidy(()=>{let r=pt(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof K.Tensor){let i=V(s)?s:s.expandDims();return i=lr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=K.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof P.getEnv().Canvas)return K.browser.fromPixels(yr(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return K.stack(r.map(a=>K.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function E(o){if(o instanceof ft)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(Lt);return r.forEach((n,a)=>{if(!ke(n)&&!gt(n)&&!V(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(V(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>ke(n)&&xr(n))),new ft(r,Array.isArray(o))}async function qt(o,t){let{Canvas:e}=P.getEnv(),r=o;if(!(o instanceof e)){let s=await E(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await vr(i)}let n=W(r);return t.map(s=>s instanceof M?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=At({width:c,height:m});return c>0&&m>0&&W(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var Fr=b(g());async function Zt(o,t){if(!gt(o)&&!V(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(V(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Fr.tidy(()=>{let[e,r,n]=o.shape.slice(V(o)?1:0);return t.map(i=>i instanceof M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>Fr.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Wt(o,t){let{fetch:e}=P.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function So(o){let t=await Wt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return br(e)}async function Tr(o){return(await Wt(o)).json()}async function Ao(o){return new Float32Array(await(await Wt(o)).arrayBuffer())}var Wo=b(g());function _r(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function wr(o,t){let{manifestUri:e,modelBaseUri:r}=_r(o,t),n=await Tr(e);return Wo.io.loadWeights(n,r)}function Bo(o,t,e=!1){let{width:r,height:n}=e?St(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Rt=b(g());var yt=b(g());var S=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof yt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof yt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=yt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await wr(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=P.getEnv(),{manifestUri:r,modelBaseUri:n}=_r(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=yt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof yt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var C=b(g());var be=b(g());function O(o,t,e){return be.tidy(()=>{let r=be.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=be.add(r,t.bias),r})}function Pr(o,t,e=!1){return C.tidy(()=>{let r=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):O(o,t.conv0,[2,2])),n=O(r,t.conv1,[1,1]),a=C.relu(C.add(r,n)),s=O(a,t.conv2,[1,1]);return C.relu(C.add(r,C.add(n,s)))})}function Se(o,t,e=!1,r=!0){return C.tidy(()=>{let n=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):O(o,t.conv0,r?[2,2]:[1,1])),a=O(n,t.conv1,[1,1]),s=C.relu(C.add(n,a)),i=O(s,t.conv2,[1,1]),c=C.relu(C.add(n,C.add(a,i))),m=O(c,t.conv3,[1,1]);return C.relu(C.add(n,C.add(a,C.add(i,m))))})}var Bt=b(g());function Kt(o,t,e="same",r=!1){return Bt.tidy(()=>{let n=Bt.add(Bt.conv2d(o,t.filters,[1,1],e),t.bias);return r?Bt.relu(n):n})}function B(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var Dr=b(g());function ge(o,t){return(e,r,n,a)=>{let s=Dr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=Dr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var Er=b(g());function Mr(o,t){return(e,r,n)=>{let a=Er.tensor2d(o(e*r),[e,r]),s=Er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var Ae=b(g());var Cr=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function ve(o,t){return(e,r,n)=>{let a=Ae.tensor4d(o(3*3*e),[3,3,e,1]),s=Ae.tensor4d(o(e*r),[1,1,e,r]),i=Ae.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new Cr(a,s,i)}}function ye(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new Cr(e,r,n)}}function H(o,t){return(e,r,n)=>{let a=o[e];if(!Gt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function R(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function Nr(o,t){let e=ge(o,t),r=ve(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function Ro(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractDenseBlock4Params:n}=Nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function Ir(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function Lr(o,t){let e=H(o,t),r=Ir(e),n=ye(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function $o(o){let t=[],{extractDenseBlock4Params:e}=Lr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return B(o,t),{params:r,paramMappings:t}}var We=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Rt.tidy(()=>{let r=Rt.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(Rt.scalar(255)),s=Se(a,e.dense0,!0);return s=Se(s,e.dense1),s=Se(s,e.dense2),s=Se(s,e.dense3),s=Rt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return $o(t)}extractParams(t){return Ro(t)}};var Ho=b(g());var Fe=b(g());function Be(o,t){return Fe.tidy(()=>Fe.add(Fe.matMul(o,t.weights),t.bias))}function Oo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=R(o),i=Mr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function jo(o){let t=[],e=H(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return B(o,t),{params:n,paramMappings:t}}function kr(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var Re=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Ho.tidy(()=>{let r=t instanceof ft?this.faceFeatureExtractor.forwardInput(t):t;return Be(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Oo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=kr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),jo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Sr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ft=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Sr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Sr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var $e=class extends Re{constructor(t=new We){super("FaceExpressionNet",t)}forwardInput(t){return Te.tidy(()=>Te.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(Te.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new Ft(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ar(o){return o.expressions instanceof Ft}function Oe(o,t){return{...o,...{expressions:t}}}function la(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof Ft?a:Ar(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=nt(a)?a.detection.box.bottomLeft:r||new h(0,0);new kt(c.map(d=>`${d.expression} (${zt(d.probability)})`),m).draw(o)})}function $t(o){return nt(o)&&o.landmarks instanceof j&&o.unshiftedLandmarks instanceof j&&o.alignedRect instanceof M}function fa(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function Qt(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new M(o.detection.score,n.rescale(a.reverse()),a),i=fa(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var oo=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},no=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new oo(e)}draw(t){let e=W(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof Jt&&(e.strokeStyle=s,e.lineWidth=a,bt(e,this.faceLandmarks.getJawOutline()),bt(e,this.faceLandmarks.getLeftEyeBrow()),bt(e,this.faceLandmarks.getRightEyeBrow()),bt(e,this.faceLandmarks.getNose()),bt(e,this.faceLandmarks.getLeftEye(),!0),bt(e,this.faceLandmarks.getRightEye(),!0),bt(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function ha(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof j?r:$t(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new no(n).draw(o)})}var Yo="1.0.2";var xt=b(g());var I=b(g());function xa(o,t){let e=ge(o,t),r=ve(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Go(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=R(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=xa(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};pt(t,0,1).forEach(y=>{l[`main_block_${y}`]=c(128,`middle_flow/main_block_${y}`)});let v=i(128,256,"exit_flow/reduction_block"),w=s(256,512,"exit_flow/separable_conv"),x={reduction_block:v,separable_conv:w};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:x}}}function ba(o,t){let e=H(o,t),r=Ir(e),n=ye(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function zo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=ba(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};pt(t,0,1).forEach(w=>{d[`main_block_${w}`]=s(`middle_flow/main_block_${w}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return B(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function Vo(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function so(o,t,e=!0){let r=e?I.relu(o):o;return r=O(r,t.separable_conv0,[1,1]),r=O(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,Vo(o,t.expansion_conv,[2,2])),r}function ga(o,t){let e=O(I.relu(o),t.separable_conv0,[1,1]);return e=O(I.relu(e),t.separable_conv1,[1,1]),e=O(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var io=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(Vo(a,e.entry_flow.conv_in,[2,2]));return s=so(s,e.entry_flow.reduction_block_0,!1),s=so(s,e.entry_flow.reduction_block_1),pt(this._numMainBlocks,0,1).forEach(i=>{s=ga(s,e.middle_flow[`main_block_${i}`])}),s=so(s,e.exit_flow.reduction_block),s=I.relu(O(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return zo(t,this._numMainBlocks)}extractParams(t){return Go(t,this._numMainBlocks)}};function Uo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),n=Mr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Xo(o){let t=[],e=H(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return B(o,t),{params:n,paramMappings:t}}var ht;(function(o){o.FEMALE="female",o.MALE="male"})(ht||(ht={}));var je=class extends S{constructor(t=new io(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return xt.tidy(()=>{let r=t instanceof ft?this.faceFeatureExtractor.forwardInput(t):t,n=xt.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Be(n,e.fc.age).as1D(),s=Be(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return xt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:xt.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=xt.unstack(r.age),a=xt.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?ht.MALE:ht.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Uo(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=kr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Xo(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var Y=b(g());var He=class extends Re{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return Y.tidy(()=>{let s=(d,u)=>Y.stack([Y.fill([68],d,"float32"),Y.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return u(l,v)?Math.abs(l-v)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(Y.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return Y.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await E(t))}async detectLandmarks(t){let e=await E(t),r=Y.tidy(()=>Y.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>cr(d)),m=i.filter((p,d)=>!cr(d));return new Jt(Array(68).fill(0).map((p,d)=>new h(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var te=class extends He{constructor(t=new We){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Ot=b(g());function Jo(o){let t=[],{extractDenseBlock3Params:e}=Lr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return B(o,t),{params:r,paramMappings:t}}function qo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractDenseBlock3Params:n}=Nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var co=class extends S{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Ot.tidy(()=>{let r=Ot.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(Ot.scalar(255)),s=Pr(a,e.dense0,!0);return s=Pr(s,e.dense1),s=Pr(s,e.dense2),s=Ot.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Jo(t)}extractParams(t){return qo(t)}};var Ye=class extends He{constructor(t=new co){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var mo=class extends te{};var U=b(g());var _e=b(g());var Wr=b(g());function Zo(o,t){return Wr.add(Wr.mul(o,t.weights),t.biases)}function po(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=_e.conv2d(o,a,e,n);return i=_e.add(i,s),i=Zo(i,t.scale),r?_e.relu(i):i}function Ko(o,t){return po(o,t,[1,1],!0)}function uo(o,t){return po(o,t,[1,1],!1)}function Br(o,t){return po(o,t,[2,2],!0,"valid")}var G=b(g());function va(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Vr(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return G.tidy(()=>G.transpose(G.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=G.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=G.tensor1d(o(i)),p=G.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function Qo(o){let{extractWeights:t,getRemainingWeights:e}=R(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=va(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),v=a(147456,128,3,"conv128_down",!0),w=a(147456,128,3,"conv128_1"),x=a(147456,128,3,"conv128_2"),y=a(589824,256,3,"conv256_down",!0),T=a(589824,256,3,"conv256_1"),F=a(589824,256,3,"conv256_2"),L=a(589824,256,3,"conv256_down_out"),z=G.tidy(()=>G.transpose(G.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:v,conv128_1:w,conv128_2:x,conv256_down:y,conv256_1:T,conv256_2:F,conv256_down_out:L,fc:z},paramMappings:r}}function ya(o,t){let e=H(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function tn(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=ya(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),v=r("conv128_2"),w=r("conv256_down"),x=r("conv256_1"),y=r("conv256_2"),T=r("conv256_down_out"),{fc:F}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!zr(F))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${F}`);let L={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:v,conv256_down:w,conv256_1:x,conv256_2:y,conv256_down_out:T,fc:F};return B(o,t),{params:L,paramMappings:t}}var $=b(g());function at(o,t){let e=Ko(o,t.conv1);return e=uo(e,t.conv2),e=$.add(e,o),e=$.relu(e),e}function Ge(o,t){let e=Br(o,t.conv1);e=uo(e,t.conv2);let r=$.avgPool(o,2,2,"valid"),n=$.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=$.zeros(i);e=$.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=$.zeros(m);e=$.concat([e,p],2)}return r=a?$.concat([r,n],3):r,e=$.add(r,e),e=$.relu(e),e}var ee=class extends S{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return U.tidy(()=>{let r=U.cast(t.toBatchTensor(150,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(U.scalar(256)),s=Br(a,e.conv32_down);s=U.maxPool(s,3,2,"valid"),s=at(s,e.conv32_1),s=at(s,e.conv32_2),s=at(s,e.conv32_3),s=Ge(s,e.conv64_down),s=at(s,e.conv64_1),s=at(s,e.conv64_2),s=at(s,e.conv64_3),s=Ge(s,e.conv128_down),s=at(s,e.conv128_1),s=at(s,e.conv128_2),s=Ge(s,e.conv256_down),s=at(s,e.conv256_1),s=at(s,e.conv256_2),s=Ge(s,e.conv256_down_out);let i=s.mean([1,2]);return U.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=U.tidy(()=>U.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return tn(t)}extractParams(t){return Qo(t)}};function en(o){let t=new ee;return t.extractWeights(o),t}function ze(o,t){return{...o,...{descriptor:t}}}function rn(o){return typeof o.age=="number"}function Ve(o,t){return{...o,...{age:t}}}function on(o){return(o.gender===ht.MALE||o.gender===ht.FEMALE)&&de(o.genderProbability)}function Ue(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var it=b(g());var st=b(g());function Fa(o,t){function e(c,m){let p=st.tensor4d(o(3*3*c),[3,3,c,1]),d=st.tensor1d(o(c)),u=st.tensor1d(o(c)),l=st.tensor1d(o(c)),v=st.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=st.tensor4d(o(c*m*p*p),[p,p,c,m]),v=st.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),v=a(256,512,"mobilenetv1/conv_6"),w=a(512,512,"mobilenetv1/conv_7"),x=a(512,512,"mobilenetv1/conv_8"),y=a(512,512,"mobilenetv1/conv_9"),T=a(512,512,"mobilenetv1/conv_10"),F=a(512,512,"mobilenetv1/conv_11"),L=a(512,1024,"mobilenetv1/conv_12"),z=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,conv_8:x,conv_9:y,conv_10:T,conv_11:F,conv_12:L,conv_13:z}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),v=n(256,64,1,"prediction_layer/conv_6"),w=n(64,128,3,"prediction_layer/conv_7"),x=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),T=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),F=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),L=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),z=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),mt=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),q=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Et=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Mt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Ct=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,box_predictor_0:{box_encoding_predictor:x,class_predictor:y},box_predictor_1:{box_encoding_predictor:T,class_predictor:F},box_predictor_2:{box_encoding_predictor:L,class_predictor:z},box_predictor_3:{box_encoding_predictor:rt,class_predictor:mt},box_predictor_4:{box_encoding_predictor:q,class_predictor:Et},box_predictor_5:{box_encoding_predictor:Mt,class_predictor:Ct}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function nn(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Fa(e,t),s=n(),i=a(),m={extra_dim:st.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Ta(o,t){let e=H(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,v=e(`${d}/depthwise_weights`,4,`${u}/filters`),w=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),x=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),y=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),T=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:v,batch_norm_scale:w,batch_norm_offset:x,batch_norm_mean:y,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function an(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Ta(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!gt(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return B(o,t),{params:a,paramMappings:t}}var Tt=b(g());var jt=b(g());function Q(o,t,e){return jt.tidy(()=>{let r=jt.conv2d(o,t.filters,e,"same");return r=jt.add(r,t.batch_norm_offset),jt.clipByValue(r,0,6)})}var _a=.0010000000474974513;function wa(o,t,e){return Tt.tidy(()=>{let r=Tt.depthwiseConv2d(o,t.filters,e,"same");return r=Tt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,_a),Tt.clipByValue(r,0,6)})}function Pa(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function sn(o,t){return Tt.tidy(()=>{let e,r=Q(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=Pa(i);r=wa(r,a.depthwise_conv,c),r=Q(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function Da(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let v=Math.max(n,c),w=Math.max(a,m),x=Math.min(s,p),y=Math.min(i,d),T=Math.max(x-v,0)*Math.max(y-w,0);return T/(u+l-T)}function cn(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=Da(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function Ea(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function Ma(o,t){let{sizes:e,centers:r}=Ea(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function mn(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=Ma(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Je=b(g());var Xe=b(g());function re(o,t){return Xe.tidy(()=>{let e=o.shape[0],r=Xe.reshape(Kt(o,t.box_encoding_predictor),[e,-1,1,4]),n=Xe.reshape(Kt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function pn(o,t,e){return Je.tidy(()=>{let r=Q(o,e.conv_0,[1,1]),n=Q(r,e.conv_1,[2,2]),a=Q(n,e.conv_2,[1,1]),s=Q(a,e.conv_3,[2,2]),i=Q(s,e.conv_4,[1,1]),c=Q(i,e.conv_5,[2,2]),m=Q(c,e.conv_6,[1,1]),p=Q(m,e.conv_7,[2,2]),d=re(t,e.box_predictor_0),u=re(o,e.box_predictor_1),l=re(n,e.box_predictor_2),v=re(s,e.box_predictor_3),w=re(c,e.box_predictor_4),x=re(p,e.box_predictor_5),y=Je.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,v.boxPredictionEncoding,w.boxPredictionEncoding,x.boxPredictionEncoding],1),T=Je.concat([d.classPrediction,u.classPrediction,l.classPrediction,v.classPrediction,w.classPrediction,x.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ht=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return it.tidy(()=>{let r=it.cast(t.toBatchTensor(512,!1),"float32"),n=it.sub(it.mul(r,it.scalar(.007843137718737125)),it.scalar(1)),a=sn(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=pn(a.out,a.conv11,e.prediction_layer);return mn(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new X(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,z]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(q=>q*x),[rt,mt]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(q=>q*w);return new M(p[F],new Xt(rt,L,mt-rt,z-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return an(t)}extractParams(t){return nn(t)}};function lo(o){let t=new Ht;return t.extractWeights(o),t}function dn(o){return lo(o)}var fo=class extends Ht{};var un=.4,ln=[new h(.738768,.874946),new h(2.42204,2.65704),new h(4.30971,7.04493),new h(10.246,4.59428),new h(12.6868,11.8741)],fn=[new h(1.603231,2.094468),new h(6.041143,7.080126),new h(2.882459,3.518061),new h(4.266906,5.178857),new h(9.041765,10.66308)],hn=[117.001,114.697,97.404],xn="tiny_yolov2_model",bn="tiny_yolov2_separable_conv_model";var N=b(g());var Rr=o=>typeof o=="number";function $r(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Rr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Rr(t.x)&&Rr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Rr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var et=b(g());var tt=b(g());function we(o){return tt.tidy(()=>{let t=tt.mul(o,tt.scalar(.10000000149011612));return tt.add(tt.relu(tt.sub(o,t)),t)})}function _t(o,t){return et.tidy(()=>{let e=et.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=et.conv2d(e,t.conv.filters,[1,1],"valid"),e=et.sub(e,t.bn.sub),e=et.mul(e,t.bn.truediv),e=et.add(e,t.conv.bias),we(e)})}var Yt=b(g());function wt(o,t){return Yt.tidy(()=>{let e=Yt.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Yt.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=Yt.add(e,t.bias),we(e)})}var ho=b(g());function Ca(o,t){let e=ge(o,t);function r(s,i){let c=ho.tensor1d(o(s)),m=ho.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=ve(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function gn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Ca(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,w,x,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),z=m(u,l,"conv1"),rt=m(l,v,"conv2"),mt=m(v,w,"conv3"),q=m(w,x,"conv4"),Et=m(x,y,"conv5"),Mt=T?m(y,T,"conv6"):void 0,Ct=F?m(T,F,"conv7"):void 0,pe=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}else{let[d,u,l,v,w,x,y,T,F]=r,L=c(d,u,"conv0"),z=c(u,l,"conv1"),rt=c(l,v,"conv2"),mt=c(v,w,"conv3"),q=c(w,x,"conv4"),Et=c(x,y,"conv5"),Mt=c(y,T,"conv6"),Ct=c(T,F,"conv7"),pe=i(F,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Na(o,t){let e=H(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=ye(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function vn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Na(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var ct=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var xo=class extends S{constructor(t){super("TinyYolov2");$r(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=_t(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=_t(r,e.conv6),r=_t(r,e.conv7),Kt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?we(Kt(t,e.conv0,"valid",!1)):wt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?wt(r,e.conv6):r,r=e.conv7?wt(r,e.conv7):r,Kt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?Z(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ct(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(x=>x.box),d=m.map(x=>x.score),u=m.map(x=>x.classScore),l=m.map(x=>this.config.classes[x.label]);return ur(p.map(x=>x.rescale(r)),d,this.config.iouThreshold,!0).map(x=>new vt(d[x],u[x],l[x],p[x],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return vn(t,this.config)}extractParams(t){let e=this.config.filterSizes||xo.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return gn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],w=await u.array(),x=await d.array();for(let y=0;yr){let z=(T+ue(x[y][T][F][0]))/m*i,rt=(y+ue(x[y][T][F][1]))/m*c,mt=Math.exp(x[y][T][F][2])*this.config.anchors[F].x/m*i,q=Math.exp(x[y][T][F][3])*this.config.anchors[F].y/m*c,Et=z-mt/2,Mt=rt-q/2,Ct={row:y,col:T,anchor:F},{classScore:pe,label:Po}=this.withClassScores?await this.extractPredictedClass(l,Ct):{classScore:1,label:0};v.push({box:new Ut(Et,Mt,Et+mt,Mt+q),score:L,classScore:L*pe,label:Po,...Ct})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},Pe=xo;Pe.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var oe=class extends Pe{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:un,classes:["face"],...t?{anchors:fn,meanRgb:hn}:{anchors:ln,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?bn:xn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function yn(o,t=!0){let e=new oe(t);return e.extractWeights(o),e}var qe=class extends ct{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Ke=b(g());var bo=b(g());async function ne(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>$t(c)?n(c):c.detection),s=r||(t instanceof bo.Tensor?await Zt(t,a):await qt(t,a)),i=await e(s);return s.forEach(c=>c instanceof bo.Tensor&&c.dispose()),i}async function De(o,t,e,r,n){return ne([o],t,async a=>e(a[0]),r,n)}var Fn=.4,Tn=[new h(1.603231,2.094468),new h(6.041143,7.080126),new h(2.882459,3.518061),new h(4.266906,5.178857),new h(9.041765,10.66308)],_n=[117.001,114.697,97.404];var ae=class extends Pe{constructor(){let t={withSeparableConvs:!0,iouThreshold:Fn,classes:["face"],anchors:Tn,meanRgb:_n,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var _={ssdMobilenetv1:new Ht,tinyFaceDetector:new ae,tinyYolov2:new oe,faceLandmark68Net:new te,faceLandmark68TinyNet:new Ye,faceRecognitionNet:new ee,faceExpressionNet:new $e,ageGenderNet:new je},go=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),wn=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),Pn=(o,t)=>_.tinyYolov2.locateFaces(o,t),vo=o=>_.faceLandmark68Net.detectLandmarks(o),Dn=o=>_.faceLandmark68TinyNet.detectLandmarks(o),En=o=>_.faceRecognitionNet.computeFaceDescriptor(o),Mn=o=>_.faceExpressionNet.predictExpressions(o),Cn=o=>_.ageGenderNet.predictAgeAndGender(o),yo=o=>_.ssdMobilenetv1.load(o),Nn=o=>_.tinyFaceDetector.load(o),In=o=>_.tinyYolov2.load(o),Ln=o=>_.faceLandmark68Net.load(o),kn=o=>_.faceLandmark68TinyNet.load(o),Sn=o=>_.faceRecognitionNet.load(o),An=o=>_.faceExpressionNet.load(o),Wn=o=>_.ageGenderNet.load(o),Bn=yo,Rn=go,$n=vo;var Fo=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ce=class extends Fo{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>Oe(r,e[n]))}withAgeAndGender(){return new Ee(this,this.input)}},Ne=class extends Fo{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Oe(t,e)}withAgeAndGender(){return new Me(this,this.input)}},ce=class extends Ce{withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},me=class extends Ne{withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var To=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ee=class extends To{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Ve(Ue(r,s,i),a)})}withFaceExpressions(){return new Ce(this,this.input)}},Me=class extends To{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await De(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Ve(Ue(t,r,n),e)}withFaceExpressions(){return new Ne(this,this.input)}},se=class extends Ee{withFaceExpressions(){return new ce(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},ie=class extends Me{withFaceExpressions(){return new me(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var Ze=class extends J{constructor(t,e){super();this.parentTask=t;this.input=e}},Pt=class extends Ze{async run(){let t=await this.parentTask;return(await ne(t,this.input,r=>Promise.all(r.map(n=>_.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>ze(t[n],r))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}},Dt=class extends Ze{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ze(t,e)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}};var Qe=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},tr=class extends Qe{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Ke.Tensor?await Zt(this.input,e):await qt(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),t.map((a,s)=>Qt(a,n[s]))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},er=class extends Qe{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Ke.Tensor?await Zt(this.input,[e]):await qt(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),Qt(t,n)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var rr=class extends J{constructor(t,e=new X){super();this.input=t;this.options=e}},Ie=class extends rr{async run(){let{input:t,options:e}=this,r;if(e instanceof qe)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ct)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>It({},r)))})}withFaceLandmarks(t=!1){return new tr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ce(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ee(this.runAndExtendWithFaceDetections(),this.input)}},or=class extends rr{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?It({},e):void 0)})}withFaceLandmarks(t=!1){return new er(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Me(this.runAndExtendWithFaceDetection(),this.input)}};function On(o,t=new X){return new or(o,t)}function nr(o,t=new X){return new Ie(o,t)}async function _o(o,t){return nr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function jn(o,t={}){return nr(o,new ct(t)).withFaceLandmarks().withFaceDescriptors()}var Hn=_o;function Or(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var ar=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof ut)return s;if(s instanceof Float32Array)return new ut(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new ut(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>Or(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new le(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>ut.fromJSON(r));return new ar(e,t.distanceThreshold)}};function Yn(o){let t=new ae;return t.extractWeights(o),t}function wo(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>wo(n,{width:e,height:r}));if($t(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Qt(It(o,n),a)}return nt(o)?It(o,o.detection.forSize(e,r)):o instanceof j||o instanceof M?o.forSize(e,r):o}var Ia=typeof process!="undefined",La=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",zn={faceapi:Yo,node:Ia,browser:La};0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); //# sourceMappingURL=face-api.node-gpu.js.map diff --git a/dist/face-api.node-gpu.js.map b/dist/face-api.node-gpu.js.map index 50f5ae9..dbfcb90 100644 --- a/dist/face-api.node-gpu.js.map +++ b/dist/face-api.node-gpu.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["../src/tfjs/tf-node-gpu.ts", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"], "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\nexport * from '@tensorflow/tfjs-node-gpu';\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof require === 'function'\n && typeof module !== 'undefined'\n // issues with gatsby.js: module.exports is undefined\n // && !!module.exports\n && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed: boolean = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number\n\n private _height: number\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number\n\n private _y: number\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number\n\n private _y: number\n\n private _width: number\n\n private _height: number\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({\n x, y, width, height,\n });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({\n x, y, width, height,\n });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const {\n x, y, right, bottom,\n } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({\n x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({\n x, y, width, height,\n });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return {\n dy, edy, dx, edx, y, ey, x, ex, w, h,\n };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n super({\n left, top, right, bottom,\n }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number\n\n private _classScore: number\n\n private _className: string\n\n private _box: Rect\n\n private _imageDims: Dimensions\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU: boolean = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n imgTensor: tf.Tensor4D,\n isCenterImage: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) {\n return imgTensor;\n }\n\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n ? createPaddingTensor(remainingPaddingAmount)\n : null;\n\n const tensorsToStack = [\n paddingTensorPrepend,\n imgTensor,\n paddingTensorAppend,\n ]\n .filter((t) => !!t)\n // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n super({\n x, y, width, height,\n }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point\n\n protected _positions: Point[]\n\n protected _imgDims: Dimensions\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string\n\n private _distance: number\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance: boolean = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string\n\n private _descriptors: Float32Array[]\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number\n\n private _classScore: number\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n TSource\n>(\n sourceObj: TSource,\n detection: FaceDetection,\n): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n fetch,\n readFile,\n };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n\n if (!fs) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = err.toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => {\n fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n })\n : () => {\n throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n };\n\n return {\n readFile,\n };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition\n\n public backgroundColor: string\n\n public fontColor: string\n\n public fontSize: number\n\n public fontStyle: string\n\n public padding: number\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[]\n\n public anchor : IPoint\n\n public options: DrawTextFieldOptions\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string\n\n public lineWidth: number\n\n public drawLabelOptions: DrawTextFieldOptions\n\n public label?: string\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box\n\n public options: DrawBoxOptions\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = await t.data();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n return resolve(null);\n }\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = []\n\n private _canvases: HTMLCanvasElement[] = []\n\n private _batchSize: number\n\n private _treatAsBatchInput: boolean = false\n\n private _inputDimensions: number[][] = []\n\n private _inputSize: number\n\n constructor(\n inputs: Array,\n treatAsBatchInput: boolean = false,\n ) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor'.\n let imgTensor = isTensor4D(input) ? input : input.expandDims();\n // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor'.\n imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n return batchTensor;\n });\n }\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) {\n return inputs;\n }\n\n const inputArgArray = Array.isArray(inputs)\n ? inputs\n : [inputs];\n\n if (!inputArgArray.length) {\n throw new Error('toNetInput - empty array passed as input');\n }\n\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n const inputArray = inputArgArray.map(resolveInput);\n\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') {\n throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n }\n\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) {\n throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n }\n });\n\n // wait for all media elements being loaded\n await Promise.all(\n inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n );\n\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n input: TNetInput,\n detections: Array,\n): Promise {\n const { Canvas } = env.getEnv();\n\n let canvas = input as HTMLCanvasElement;\n\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n\n if (netInput.batchSize > 1) {\n throw new Error('extractFaces - batchSize > 1 not supported');\n }\n\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas\n ? tensorOrCanvas\n : await imageTensorToCanvas(tensorOrCanvas);\n }\n\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections.map(\n (det) => (det instanceof FaceDetection\n ? det.forSize(canvas.width, canvas.height).box.floor()\n : det),\n )\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n return boxes.map(({\n x, y, width, height,\n }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n imageTensor: tf.Tensor3D | tf.Tensor4D,\n detections: Array,\n): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n const boxes = detections.map(\n (det) => (det instanceof FaceDetection\n ? det.forSize(imgWidth, imgHeight).box\n : det),\n )\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n const faceTensors = boxes.map(({\n x, y, width, height,\n }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined\n\n protected _paramMappings: ParamMapping[] = []\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer: boolean = false,\n isScaleDown: boolean = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import {\n extractConvParamsFactory,\n extractSeparableConvParamsFactory,\n ExtractWeightsFunction,\n ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n FaceFeatureExtractorParams,\n IFaceFeatureExtractor,\n TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n public neutral: number\n\n public happy: number\n\n public sad: number\n\n public angry: number\n\n public fearful: number\n\n public disgusted: number\n\n public surprised: number\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & {\n expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n TSource\n>(\n sourceObj: TSource,\n expressions: FaceExpressions,\n): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n canvasArg: string | HTMLCanvasElement,\n faceExpressions: DrawFaceExpressionsInput | Array,\n minConfidence = 0.1,\n textFieldAnchor?: IPoint,\n) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n landmarks: TFaceLandmarks,\n unshiftedLandmarks: TFaceLandmarks,\n alignedRect: FaceDetection,\n angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks, FaceLandmarks> {\n return isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n // convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (theta) => (theta * 180) / Math.PI;\n\n const angle = { roll: undefined, pitch: undefined, yaw: undefined };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n // value of 0 means center\n\n // roll is face lean from left to right\n // comparing x,y of outside corners of leftEye and rightEye\n angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n // pitch is face turn from left right\n // comparing x distance of top of nose to left and right edge of face\n // precision is lacking since coordinates are not precise enough\n angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n // yaw is face move from up to down\n // comparing size of the box around the face with top and bottom of detected landmarks\n // silly hack, but this gives us face compression on y-axis\n // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n return angle;\n}\n\nexport function extendWithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n const angle = calculateFaceAngle(unshiftedLandmarks);\n\n const extension = {\n landmarks,\n unshiftedLandmarks,\n alignedRect,\n angle,\n };\n\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean\n\n public drawPoints: boolean\n\n public lineWidth: number\n\n public pointSize: number\n\n public lineColor: string\n\n public pointColor: string\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks\n\n public options: DrawFaceLandmarksOptions\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (await ageTensor.data())[0];\n const probMale = (await genderTensor.data())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors,\n extractWeightEntryFactory,\n loadSeparableConvParamsFactory,\n ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(await landmarkTensor.data());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown: boolean = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n\n if (!params) {\n throw new Error('SsdMobilenetv1 - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n const features = mobileNetV1(x, params.mobilenetv1);\n\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(\n input: TNetInput,\n options: ISsdMobilenetv1Options = {},\n ): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n const netInput = await toNetInput(input);\n\n const {\n boxes: _boxes,\n scores: _scores,\n } = this.forwardInput(netInput);\n\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n\n const scoresData = Array.from(await scores.data());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(\n boxes,\n scoresData as number[],\n maxResults,\n iouThreshold,\n minConfidence,\n );\n\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(\n left,\n top,\n right - left,\n bottom - top,\n ),\n {\n height: netInput.getInputHeight(0),\n width: netInput.getInputWidth(0),\n },\n );\n });\n\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n x: tf.Tensor4D,\n params: MobileNetV1.DepthwiseConvParams,\n strides: [number, number],\n) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) {\n conv11 = out;\n }\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n x: tf.Tensor4D,\n params: PointwiseConvParams,\n strides: [number, number],\n) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) {\n return 0.0;\n }\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(\n maxOutputSize,\n numBoxes,\n );\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n ];\n return {\n sizes,\n centers,\n };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const {\n sizes,\n centers,\n } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(\n boxPredictions: tf.Tensor4D,\n classPredictions: tf.Tensor4D,\n params: OutputLayerParams,\n) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(\n boxes,\n [batchSize, (boxes.shape[0] / batchSize), 4],\n );\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(\n scores,\n [batchSize, scores.shape[1] as number],\n );\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return {\n boxes: boxesByBatch,\n scores: scoresByBatch,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name: string = 'SsdMobilenetv1Options'\n\n private _minConfidence: number\n\n private _maxResults: number\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name: string = 'TinyYolov2Options'\n\n private _inputSize: number\n\n private _scoreThreshold: number\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs: boolean = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask {\n public async then(\n // eslint-disable-next-line no-unused-vars\n onfulfilled: (value: T) => T | PromiseLike,\n ): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask<\n TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n\n const faceLandmarksByFace = await Promise.all(faces.map(\n (face) => this.landmarkNet.detectLandmarks(face),\n )) as FaceLandmarks68[];\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n\n const results = await computeResults(faces);\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderTask,\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map(\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n )),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictAllFaceExpressionsTask {\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleFaceExpressionsTask {\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllFaceExpressionsTask,\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map(\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n )),\n this.extractedFaces,\n );\n\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictAllAgeAndGenderTask {\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleAgeAndGenderTask {\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n ) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public async run(): Promise {\n const { input, options } = this;\n\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise[]>(async (resolve) => {\n const detections = await this.run();\n resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n input: TNetInput,\n minConfidence?: number,\n): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n input: TNetInput,\n forwardParams: ITinyYolov2Options = {},\n): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff ** 2), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[]\n\n private _distanceThreshold: number\n\n constructor(\n inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>,\n distanceThreshold: number = 0.6,\n ) {\n this._distanceThreshold = distanceThreshold;\n\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n if (!inputArray.length) {\n throw new Error('FaceRecognizer.constructor - expected atleast one input');\n }\n\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) {\n return desc;\n }\n\n if (desc instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n }\n\n if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n }\n\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0)\n / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(\n label,\n this.computeMeanDistance(queryDescriptor, descriptors),\n ))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return bestMatch.distance < this.distanceThreshold\n ? bestMatch\n : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this.distanceThreshold,\n labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors\n .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n"], - "mappings": ";;;;;;;4qCAAA,GAAA,IAGA,GAAA,GAAc,GAAA,yCCHd,oDAAO,aAA6B,CAClC,MAAO,OAAO,SAAW,UACpB,IACA,MAAO,KAAW,aAGlB,MAAO,UAAY,aAAe,CAAC,CAAC,QAAQ,WCNnD,yyFAAA,OAAoB,OCApB,qRCEO,YACL,EACA,EACA,EAAoB,GACpB,CASA,GARA,EAAI,YAEJ,EAAO,MAAM,GAAG,QAAQ,CAAC,CAAE,IAAG,KAAK,IAAY,CAC7C,GAAM,GAAO,EAAO,GACpB,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,KAGZ,EAAU,CACZ,GAAM,GAAO,EAAO,EAAO,OAAS,GAC9B,EAAK,EAAO,GAClB,GAAI,CAAC,GAAQ,CAAC,EACZ,OAGF,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,EAAG,EAAG,GAGtB,EAAI,SC1BN,2RAAA,OAAoB,OCOb,WAAwC,CAK7C,YAAY,EAAe,EAAgB,CACzC,GAAI,CAAC,GAAc,IAAU,CAAC,GAAc,GAC1C,KAAM,IAAI,OAAM,wFAAwF,KAAK,UAAU,CAAE,QAAO,cAGlI,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,QAEnC,SAAsB,CAC3B,MAAO,IAAI,GAAW,EAAI,KAAK,MAAO,EAAI,KAAK,UDrB5C,YAAkB,EAAa,EAAa,CACjD,MAAO,aAAqB,YAAU,EAAO,MAAM,SAAW,EAGzD,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,WAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAiB,EAAa,CACnC,MAAO,GAAM,GAAM,EAGd,YAAgB,EAAa,CAClC,MAAO,GAAM,GAAM,EAGd,YAAe,EAAa,EAAe,EAAG,CACnD,GAAM,GAAI,IAAM,EAChB,MAAO,MAAK,MAAM,EAAM,GAAK,EAGxB,YAAsB,EAAmB,CAC9C,MAAO,IAAO,EAAI,OAAS,EAAI,OAG1B,YAAmC,CAAE,QAAO,UAAuB,EAAmB,CAC3F,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,IAAI,GAAW,KAAK,MAAM,EAAQ,GAAQ,KAAK,MAAM,EAAS,IAGhE,YAAwB,EAAqB,CAClD,MAAO,GAAI,OAAO,CAAC,EAAK,IAAO,EAAI,IAAI,GAAK,GAAI,GAAM,EAAG,IACtD,IAAI,GAAI,GAAM,EAAI,OAAQ,EAAI,SAG5B,YAAe,EAAa,EAAe,EAAwB,CACxE,MAAO,OAAM,GAAK,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,EAAS,EAAI,GAGhD,YAAuB,EAAU,CACtC,MAAO,CAAC,CAAC,GAAQ,IAAQ,UAAc,IAAQ,WAAc,CAAC,OAAO,MAAM,IAAQ,IAAQ,EAGtF,YAA4B,EAAU,CAC3C,MAAO,IAAc,IAAQ,GAAO,GAAK,GAAO,EExD3C,WAA8B,CAKnC,YAAY,EAAW,EAAW,CAChC,KAAK,GAAK,EACV,KAAK,GAAK,KAGR,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,GAEvB,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,KAAa,CAClB,MAAO,IAAI,GAAM,KAAK,IAAI,KAAK,GAAI,KAAK,IAAI,KAAK,IAG5C,WAAoB,CACzB,MAAO,MAAK,KAAM,KAAK,GAAK,EAAM,KAAK,GAAK,GAGvC,OAAe,CACpB,MAAO,IAAI,GAAM,KAAK,MAAM,KAAK,GAAI,KAAK,MAAM,KAAK,MCtClD,WAAwD,OAC/C,QAAO,EAAoB,CACvC,MAAO,CAAC,CAAC,GAAQ,CAAC,EAAK,EAAG,EAAK,EAAG,EAAK,MAAO,EAAK,QAAQ,MAAM,UAGrD,kBAAiB,EAAU,EAAgB,EAAmC,GAAO,CACjG,GAAI,CAAC,EAAI,OAAO,GACd,KAAM,IAAI,OAAM,GAAG,oBAAyB,KAAK,UAAU,2DAG7D,GAAI,CAAC,GAA4B,GAAI,MAAQ,GAAK,EAAI,OAAS,GAC7D,KAAM,IAAI,OAAM,GAAG,cAAmB,EAAI,sBAAsB,EAAI,oCAYxE,YAAY,EAA4B,EAAmC,GAAM,CAC/E,GAAM,GAAO,GAAQ,GAEf,EAAS,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAO,EAAI,QAAQ,MAAM,IAC1D,EAAS,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAAQ,MAAM,IAE3D,GAAI,CAAC,GAAU,CAAC,EACd,KAAM,IAAI,OAAM,2EAA2E,KAAK,UAAU,MAG5G,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,EAC1B,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAC9B,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAQ,EAAI,KAAM,EAAI,OAAS,EAAI,KAE/D,EAAI,iBAAiB,CACnB,IAAG,IAAG,QAAO,UACZ,kBAAmB,GAEtB,KAAK,GAAK,EACV,KAAK,GAAK,EACV,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,MAE1B,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,WAE/B,OAAe,CAAE,MAAO,MAAK,KAE7B,MAAc,CAAE,MAAO,MAAK,KAE5B,QAAgB,CAAE,MAAO,MAAK,EAAI,KAAK,SAEvC,SAAiB,CAAE,MAAO,MAAK,EAAI,KAAK,UAExC,OAAe,CAAE,MAAO,MAAK,MAAQ,KAAK,UAE1C,UAAiB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,QAEpD,WAAkB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAEtD,aAAoB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,WAEvD,cAAqB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAE7D,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,UAAyB,CAC9B,GAAI,CACF,IAAG,IAAG,QAAO,UACX,KACE,EAAO,KAAK,IAAI,EAAQ,GAC9B,MAAI,GAAQ,GACV,IAAM,EAAO,EACb,GAAS,GAEP,EAAS,GACX,IAAM,EAAO,EACb,GAAU,GAGL,GAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,QAAQ,EAAuC,CACpD,GAAM,GAAS,GAAa,GAAM,EAAkB,MAAQ,EACtD,EAAS,GAAa,GAAM,EAAkB,OAAS,EAC7D,MAAO,IAAI,GAAI,CACb,EAAG,KAAK,EAAI,EACZ,EAAG,KAAK,EAAI,EACZ,MAAO,KAAK,MAAQ,EACpB,OAAQ,KAAK,OAAS,IAInB,IAAI,EAAc,EAA4B,CACnD,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAC5B,KAAK,EAAK,EAAO,EACjB,KAAK,EAAK,EAAO,EACjB,KAAK,MAAQ,EACb,KAAK,OAAS,GAEhB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,mBAAmB,EAAkB,EAAiC,CAC3E,GAAM,CACJ,IAAG,IAAG,QAAO,UACX,KACE,EAAW,KAAK,IAAI,EAAG,GACvB,EAAW,KAAK,IAAI,EAAG,GAEvB,EAAW,EAAQ,EACnB,EAAY,EAAS,EACrB,EAAe,KAAK,IAAI,EAAU,EAAW,GAC7C,EAAgB,KAAK,IAAI,EAAW,EAAY,GAEtD,MAAQ,IAAI,GAAI,CACd,EAAG,EAAU,EAAG,EAAU,MAAO,EAAc,OAAQ,IACrD,QAGC,MAAM,EAAY,EAA0B,CACjD,GAAM,CAAE,QAAO,UAAW,KACpB,EAAI,KAAK,EAAI,EACb,EAAI,KAAK,EAAI,EAEnB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,aAAa,EAAqB,EAAoB,CAC3D,GAAM,GAAI,KAAK,MAAQ,EACjB,EAAI,KAAK,OAAS,EAElB,EAAK,EACL,EAAK,EACP,EAAM,EACN,EAAM,EAEN,EAAI,KAAK,KACT,EAAI,KAAK,IACT,EAAK,KAAK,MACV,EAAK,KAAK,OAEd,MAAI,GAAK,GACP,GAAM,CAAC,EAAK,EAAa,EACzB,EAAK,GAEH,EAAK,GACP,GAAM,CAAC,EAAK,EAAc,EAC1B,EAAK,GAEH,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAEF,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAGC,CACL,KAAI,MAAK,KAAI,MAAK,IAAG,KAAI,IAAG,KAAI,IAAG,KAIhC,UAAU,EAAa,CAC5B,MAAO,IAAI,GAAI,CACb,KAAM,KAAK,KAAQ,EAAO,KAAO,KAAK,MACtC,IAAK,KAAK,IAAO,EAAO,IAAM,KAAK,OACnC,MAAO,KAAK,MAAS,EAAO,MAAQ,KAAK,MACzC,OAAQ,KAAK,OAAU,EAAO,OAAS,KAAK,SAC3C,WAAW,UCrMX,oBAA0B,EAAyC,CACxE,YAAY,EAAc,EAAa,EAAe,EAAgB,EAAmC,GAAO,CAC9G,MAAM,CACJ,OAAM,MAAK,QAAO,UACjB,KCTA,YAAsB,CAW3B,YACE,EACA,EACA,EACA,EACA,EACA,CACA,KAAK,WAAa,GAAI,GAAW,EAAU,MAAO,EAAU,QAC5D,KAAK,OAAS,EACd,KAAK,YAAc,EACnB,KAAK,WAAa,EAClB,KAAK,KAAO,GAAI,GAAI,GAAa,QAAQ,KAAK,eAGrC,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,eAEnC,YAAoB,CAAE,MAAO,MAAK,cAElC,MAAW,CAAE,MAAO,MAAK,QAEzB,YAAwB,CAAE,MAAO,MAAK,cAEtC,aAAqB,CAAE,MAAO,MAAK,UAAU,SAE7C,cAAsB,CAAE,MAAO,MAAK,UAAU,UAE9C,cAAmB,CAAE,MAAO,IAAI,GAAI,KAAK,MAAM,QAAQ,KAAK,UAAU,WAE1E,QAAQ,EAAe,EAAiC,CAC7D,MAAO,IAAI,IACT,KAAK,MACL,KAAK,WACL,KAAK,UACL,KAAK,YACL,CAAE,QAAO,aCzCR,mBAA4B,GAAyC,CAC1E,YACE,EACA,EACA,EACA,CACA,MAAM,EAAO,EAAO,GAAI,EAAa,GAGhC,QAAQ,EAAe,EAA+B,CAC3D,GAAM,CAAE,QAAO,cAAa,aAAc,MAAM,QAAQ,EAAO,GAC/D,MAAO,IAAI,GAAc,EAAO,EAAa,KCnB1C,YAAa,EAAW,EAAW,EAAiB,GAAM,CAC/D,GAAM,GAAQ,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,MAAO,EAAK,OAAS,KAAK,IAAI,EAAK,KAAM,EAAK,OAClF,EAAS,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,OAAQ,EAAK,QAAU,KAAK,IAAI,EAAK,IAAK,EAAK,MACpF,EAAe,EAAQ,EAE7B,MAAO,GACH,EAAgB,GAAK,KAAO,EAAK,KAAO,GACxC,EAAe,KAAK,IAAI,EAAK,KAAM,EAAK,MCPvC,YAAiB,EAA4B,CAClD,GAAM,GAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAExD,MAAO,IAAI,IAAY,EAAM,EAAM,EAAM,GCPpC,YACL,EACA,EACA,EACA,EAAiB,GACP,CACV,GAAI,GAAuB,EACxB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAC/B,IAAI,AAAC,GAAM,EAAE,UAEV,EAAiB,GAEvB,KAAO,EAAqB,OAAS,GAAG,CACtC,GAAM,GAAO,EAAqB,MAClC,EAAK,KAAK,GAEV,GAAM,GAAU,EAEV,EAAoB,GAC1B,OAAS,GAAI,EAAG,EAAI,EAAQ,OAAQ,IAAK,CACvC,GAAM,GAAM,EAAQ,GAEd,EAAU,EAAM,GAChB,EAAS,EAAM,GAErB,EAAQ,KAAK,GAAI,EAAS,EAAQ,IAGpC,EAAuB,EAAqB,OAC1C,CAAC,EAAG,IAAM,EAAQ,IAAM,GAI5B,MAAO,GCrCT,OAAoB,OAEb,YAAmB,EAAgB,EAAgC,CACxE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAG,EAAG,GAAK,EACZ,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAU,AAAG,UAAO,CAAC,EAAO,EAAO,GAAQ,GAEjD,MAAO,AAAG,QAAI,EAAG,KCVrB,OAAoB,OAUb,YACL,EACA,EAAyB,GACZ,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAQ,GAAS,EAAU,MAAM,MAAM,GAC9C,GAAI,IAAW,EACb,MAAO,GAGT,GAAM,GAAU,KAAK,IAAI,EAAS,GAC5B,EAAgB,KAAK,MAAM,EAAW,GAAgB,GAAM,IAC5D,EAAc,EAAS,EAAQ,EAAI,EAEnC,EAAsB,AAAC,GAA0C,CACrE,GAAM,GAAqB,EAAU,MAAM,QAC3C,SAAmB,GAAe,EAC3B,AAAG,QAAK,EAAoB,EAAG,YAGlC,EAAsB,EAAoB,GAC1C,EAAyB,EAAW,EAAoB,MAAM,GAM9D,EAAiB,CAJM,GAAiB,EAC1C,EAAoB,GACpB,KAIF,EACA,GAEC,OAAO,AAAC,GAAM,CAAC,CAAC,GAEhB,IAAI,AAAC,GAAiB,AAAG,QAAK,EAAG,YACpC,MAAO,AAAG,WAAO,EAAgB,KC7C9B,YAAsB,EAAmB,CAC9C,GAAM,GAAQ,EAAW,QACzB,OAAS,GAAI,EAAM,OAAS,EAAG,EAAI,EAAG,IAAK,CACzC,GAAM,GAAI,KAAK,MAAM,KAAK,SAAY,GAAI,IACpC,EAAI,EAAM,GAChB,EAAM,GAAK,EAAM,GACjB,EAAM,GAAK,EAEb,MAAO,GCDF,YAAiB,EAAW,CACjC,MAAO,GAAK,GAAI,KAAK,IAAI,CAAC,IAGrB,YAAwB,EAAW,CACxC,MAAO,MAAK,IAAI,EAAK,GAAI,ICHpB,oBAAmB,EAA2B,CACnD,YAAY,EAAW,EAAW,EAAe,EAAgB,EAAmC,GAAO,CACzG,MAAM,CACJ,IAAG,IAAG,QAAO,UACZ,KCHP,GAAM,IAAO,GACP,GAAO,IACP,GAAW,IAOV,OAA8C,CAOnD,YACE,EACA,EACA,EAAe,GAAI,GAAM,EAAG,GAC5B,CACA,GAAM,CAAE,QAAO,UAAW,EAC1B,KAAK,SAAW,GAAI,GAAW,EAAO,GACtC,KAAK,OAAS,EACd,KAAK,WAAa,EAA8B,IAC9C,AAAC,GAAO,EAAG,IAAI,GAAI,GAAM,EAAO,IAAS,IAAI,OAItC,QAAe,CAAE,MAAO,IAAI,GAAM,KAAK,OAAO,EAAG,KAAK,OAAO,MAE7D,aAAqB,CAAE,MAAO,MAAK,SAAS,SAE5C,cAAsB,CAAE,MAAO,MAAK,SAAS,UAE7C,YAAqB,CAAE,MAAO,MAAK,cAEnC,oBAA6B,CACtC,MAAO,MAAK,WAAW,IACrB,AAAC,GAAO,EAAG,IAAI,KAAK,QAAQ,IAAI,GAAI,GAAM,KAAK,WAAY,KAAK,eAI7D,QAAiC,EAAe,EAAmB,CACxE,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,CAAE,QAAO,WAIN,QAAiC,EAAW,EAAc,CAC/D,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,KAAK,SACL,GAAI,GAAM,EAAG,IAIV,aAAsC,EAAc,CACzD,MAAO,MAAK,QAAQ,EAAG,EAAG,EAAG,GAcxB,MACL,EACA,EAAkE,GAC7D,CACL,GAAI,EAAW,CACb,GAAM,GAAM,YAAqB,GAC7B,EAAU,IAAI,QACd,GAAI,GAAI,GAEZ,MAAO,MAAK,QAAQ,EAAI,EAAG,EAAI,GAAG,MAAM,KAAM,GAGhD,GAAM,CAAE,mBAAkB,iBAAkB,CAAE,iBAAkB,GAAO,cAAe,MAAQ,GAE9F,MAAI,GACK,KAAK,YAGP,KAAK,aAAa,GAGnB,WAAiB,CACvB,GAAM,GAAU,KAAK,2BAEf,CAAC,EAAe,EAAgB,GAAe,EAC/C,EAAc,AAAC,GAAc,EAAY,IAAI,GAAI,YACjD,EAAkB,GAAY,GAAiB,EAAY,IAAmB,EAE9E,EAAO,KAAK,MAAM,EAAiB,IAEnC,EAAW,GAAe,GAE1B,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAChD,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAEtD,MAAO,IAAI,IAAK,EAAG,EAAG,KAAK,IAAI,EAAM,KAAK,WAAa,GAAI,KAAK,IAAI,EAAM,KAAK,YAAc,IAGvF,aAAa,EAAsB,CACzC,GAAM,GAAM,GAAQ,KAAK,WACzB,MAAO,GAAI,IAAI,EAAI,MAAQ,EAAS,EAAI,OAAS,GAGzC,0BAAoC,CAC5C,KAAM,IAAI,OAAM,4DC3Hb,oBAA6B,EAAc,CACtC,0BAAoC,CAC5C,GAAM,GAAM,KAAK,UACjB,MAAO,CACL,EAAI,GACJ,EAAI,GACJ,GAAe,CAAC,EAAI,GAAI,EAAI,QCN3B,oBAA8B,EAAc,CAC1C,eAAyB,CAC9B,MAAO,MAAK,UAAU,MAAM,EAAG,IAG1B,gBAA0B,CAC/B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,iBAA2B,CAChC,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,SAAmB,CACxB,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,YAAsB,CAC3B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,aAAuB,CAC5B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,UAAoB,CACzB,MAAO,MAAK,UAAU,MAAM,GAAI,IAGxB,0BAAoC,CAC5C,MAAO,CACL,KAAK,aACL,KAAK,cACL,KAAK,YACL,IAAI,MC/BH,YAAsC,CAK3C,YAAY,EAAe,EAAkB,CAC3C,KAAK,OAAS,EACd,KAAK,UAAY,KAGR,QAAgB,CAAE,MAAO,MAAK,UAE9B,WAAmB,CAAE,MAAO,MAAK,UAErC,SAAS,EAAwB,GAAc,CACpD,MAAO,GAAG,KAAK,QAAQ,EAAe,KAAK,GAAM,KAAK,aAAe,OCjBlE,oBAAyB,EAAgB,OAChC,yBAAwB,EAAU,EAAgB,CAG9D,GAFA,EAAI,iBAAiB,EAAK,GAEtB,CAAC,GAAc,EAAI,OACrB,KAAM,IAAI,OAAM,GAAG,gCAAqC,EAAI,yBAMhE,YAAY,EAAiC,EAAe,CAC1D,MAAM,GACN,KAAK,OAAS,KAGL,QAAgB,CAAE,MAAO,MAAK,SCrBpC,YAA6B,CAKlC,YAAY,EAAe,EAA6B,CACtD,GAAM,MAAO,IAAU,SACrB,KAAM,IAAI,OAAM,sEAGlB,GAAI,CAAC,MAAM,QAAQ,IAAgB,EAAY,KAAK,AAAC,GAAS,CAAE,aAAgB,gBAC9E,KAAM,IAAI,OAAM,4FAGlB,KAAK,OAAS,EACd,KAAK,aAAe,KAGX,QAAgB,CAAE,MAAO,MAAK,UAE9B,cAA8B,CAAE,MAAO,MAAK,aAEhD,QAAc,CACnB,MAAO,CACL,MAAO,KAAK,MACZ,YAAa,KAAK,YAAY,IAAI,AAAC,GAAM,MAAM,KAAK,WAI1C,UAAS,EAAmC,CACxD,GAAM,GAAc,EAAK,YAAY,IAAI,AAAC,GAAW,GAAI,cAAa,IACtE,MAAO,IAAI,IAAuB,EAAK,MAAO,KC1B3C,oBAA2B,GAAW,OAC7B,2BAA0B,EAAU,EAAgB,CAGhE,GAFA,GAAW,wBAAwB,EAAK,GAGtC,CAAC,GAAmB,EAAI,QACrB,CAAC,GAAmB,EAAI,YAE3B,KAAM,IAAI,OAAM,GAAG,kCAAuC,EAAI,eAAe,EAAI,6CAQrF,YAAY,EAAiC,EAAe,EAAe,EAAoB,CAC7F,MAAM,EAAK,GACX,KAAK,OAAS,EACd,KAAK,YAAc,KAGV,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,cCvBzC,YAA6B,EAAwC,CAC1E,MAAO,GAAI,oBAAqB,GAG3B,YAGL,EACA,EAC4B,CAE5B,MAAO,IAAK,KADM,CAAE,cCdf,aAAyC,CAC9C,GAAM,GAAQ,OAAO,MACrB,GAAI,CAAC,EAAO,KAAM,IAAI,OAAM,gEAM5B,MAAO,CACL,OAAQ,kBACR,yBACA,MAAO,iBACP,UACA,MAAO,iBACP,oBAAqB,IAAM,SAAS,cAAc,UAClD,mBAAoB,IAAM,SAAS,cAAc,OACjD,QACA,SAbe,IAAM,CACrB,KAAM,IAAI,OAAM,iECLb,YAA0B,EAAsB,CACrD,GAAI,GAAiB,GAErB,GAAI,CAAC,EACH,GAAI,CAEF,EAAa,oBACN,EAAP,CACA,EAAiB,EAAI,WAYzB,MAAO,CACL,SATe,EACb,AAAC,GAAqB,GAAI,SAAgB,CAAC,EAAS,IAAW,CAC/D,EAAG,SAAS,EAAU,CAAC,EAAU,IAAoB,EAAM,EAAO,GAAO,EAAQ,MAEjF,IAAM,CACN,KAAM,IAAI,OAAM,qEAAqE,OCfpF,aAAwC,CAE7C,GAAM,GAAS,OAAO,QAAa,OAAO,kBACpC,EAAQ,OAAO,OAAS,OAAO,iBAE/B,EAAsB,IAAM,CAChC,GAAI,EAAQ,MAAO,IAAI,GACvB,KAAM,IAAI,OAAM,+EAGZ,EAAqB,IAAM,CAC/B,GAAI,EAAO,MAAO,IAAI,GACtB,KAAM,IAAI,OAAM,6EAGZ,EAAQ,OAAO,MAGf,EAAa,KAEnB,MAAO,CACL,OAAQ,GAAU,KAAM,GACxB,yBAA0B,OAAO,0BAA4B,KAAM,GACnE,MAAO,GAAS,KAAM,GACtB,UAAW,OAAO,WAAa,KAAM,GACrC,MAAO,OAAO,kBAAoB,KAAM,GACxC,sBACA,qBACA,WACG,GCjCA,aAA8B,CACnC,MAAO,OAAO,SAAW,UACpB,MAAO,WAAa,aACpB,MAAO,mBAAqB,aAC5B,MAAO,oBAAsB,aAC7B,MAAO,mBAAqB,aAC5B,MAAO,YAAc,aACrB,MAAO,2BAA6B,YCH3C,OAAyB,QAGrB,EAEJ,aAA+B,CAC7B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,yEAElB,MAAO,GAGT,YAAgB,EAAkB,CAChC,EAAc,EAGhB,aAAsB,CAGpB,MAAI,MAAoB,GAAO,MAC3B,kBAAmB,GAAO,MACvB,KAGT,YAAqB,EAA2B,CAK9C,GAJK,GACH,KAGE,CAAC,EACH,KAAM,IAAI,OAAM,8EAGlB,GAAM,CAAE,SAAS,EAAY,OAAQ,QAAQ,EAAY,OAAU,EACnE,EAAY,OAAS,EACrB,EAAY,MAAQ,EACpB,EAAY,oBAAsB,EAAI,qBAAwB,KAAM,GAAI,IACxE,EAAY,mBAAqB,EAAI,oBAAuB,KAAM,GAAI,IAEtE,EAAY,UAAY,EAAI,WAAa,EAAY,UACrD,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,SAAW,EAAI,UAAY,EAAY,SAG9C,GAAM,GAAM,CACjB,UACA,UACA,cACA,oBACA,oBACA,mBACA,eACA,aACA,sBAGF,KC3DO,YAAsB,EAAmB,CAC9C,MAAI,CAAC,EAAI,YAAc,MAAO,IAAQ,SAC7B,SAAS,eAAe,GAE1B,ECHF,WAA6B,EAA4F,CAC9H,GAAM,CAAE,SAAQ,4BAA6B,EAAI,SAEjD,GAAI,YAAqB,GACvB,MAAO,GAGT,GAAM,GAAS,GAAa,GAE5B,GAAI,CAAE,aAAkB,IACtB,KAAM,IAAI,OAAM,kEAGlB,GAAM,GAAM,EAAO,WAAW,MAC9B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,GCfF,GAAK,IAAL,UAAK,EAAL,CAEL,WAAW,WAEX,YAAY,YAEZ,cAAc,cAEd,eAAe,iBARL,aAoBL,YAA4D,CAajE,YAAY,EAAiC,GAAI,CAC/C,GAAM,CACJ,iBAAgB,kBAAiB,YAAW,WAAU,YAAW,WAC/D,EACJ,KAAK,eAAiB,GAAkB,GAAe,SACvD,KAAK,gBAAkB,GAAmB,qBAC1C,KAAK,UAAY,GAAa,yBAC9B,KAAK,SAAW,GAAY,GAC5B,KAAK,UAAY,GAAa,UAC9B,KAAK,QAAU,GAAW,IAIvB,QAAoB,CAOzB,YACE,EACA,EACA,EAAiC,GACjC,CAEA,KAAK,KAAO,MAAO,IAAS,SACxB,CAAC,GACA,YAAgB,IAAgB,EAAK,KAAO,EACjD,KAAK,OAAS,EACd,KAAK,QAAU,GAAI,IAAqB,GAG1C,aAAa,EAAuC,CAClD,GAAM,CAAE,WAAY,KAAK,QACzB,MAAO,MAAK,KAAK,IAAI,AAAC,GAAM,EAAI,YAAY,GAAG,OAAO,OAAO,CAAC,EAAI,IAAQ,EAAK,EAAK,EAAK,EAAK,GAAM,EAAI,EAG1G,eAAwB,CACtB,GAAM,CAAE,WAAU,WAAY,KAAK,QACnC,MAAO,MAAK,KAAK,OAAS,EAAY,EAAI,EAG5C,aAAa,EAA+B,EAAkC,CAC5E,GAAM,CAAE,kBAAmB,KAAK,QAC1B,EAAc,IAAmB,GAAe,cAAgB,IAAmB,GAAe,UAClG,EAAa,IAAmB,GAAe,aAAe,IAAmB,GAAe,aAEhG,EAAiB,KAAK,aAAa,GACnC,EAAkB,KAAK,gBACvB,EAAK,EAAc,KAAK,OAAO,EAAI,EAAiB,KAAK,OAAO,EAChE,EAAI,EAAa,KAAK,OAAO,EAAI,EAAkB,KAAK,OAAO,EAGrE,GAAI,EAAY,CACd,GAAM,CAAE,QAAO,UAAW,EACpB,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAQ,GAAiB,GACrD,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAS,GAAkB,GAC7D,MAAO,CAAE,EAAG,EAAM,EAAG,GAEvB,MAAO,CAAE,IAAG,KAGd,KAAK,EAAkE,CACrE,GAAM,GAAS,GAAa,GACtB,EAAM,EAAoB,GAE1B,CACJ,kBAAiB,YAAW,WAAU,YAAW,WAC/C,KAAK,QAET,EAAI,KAAO,GAAG,OAAc,IAC5B,GAAM,GAAe,KAAK,aAAa,GACjC,EAAa,KAAK,gBAExB,EAAI,UAAY,EAChB,GAAM,GAAY,KAAK,aAAa,EAAK,GACzC,EAAI,SAAS,EAAU,EAAG,EAAU,EAAG,EAAc,GAErD,EAAI,UAAY,EAChB,KAAK,KAAK,QAAQ,CAAC,EAAU,IAAM,CACjC,GAAM,GAAI,EAAU,EAAU,EACxB,EAAI,EAAU,EAAU,EAAM,GAAI,GAAK,EAC7C,EAAI,SAAS,EAAU,EAAG,OC5GzB,YAAqB,CAS1B,YAAY,EAA2B,GAAI,CACzC,GAAM,CACJ,WAAU,YAAW,QAAO,oBAC1B,EACJ,KAAK,SAAW,GAAY,qBAC5B,KAAK,UAAY,GAAa,EAC9B,KAAK,MAAQ,EAEb,GAAM,GAA0B,CAC9B,eAAgB,GAAe,YAC/B,gBAAiB,KAAK,UAExB,KAAK,iBAAmB,GAAI,IAAqB,IAAK,KAA4B,MAI/E,QAAc,CAKnB,YACE,EACA,EAA2B,GAC3B,CACA,KAAK,IAAM,GAAI,GAAI,GACnB,KAAK,QAAU,GAAI,IAAe,GAGpC,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CAAE,WAAU,aAAc,KAAK,QAE/B,CACJ,IAAG,IAAG,QAAO,UACX,KAAK,IACT,EAAI,YAAc,EAClB,EAAI,UAAY,EAChB,EAAI,WAAW,EAAG,EAAG,EAAO,GAE5B,GAAM,CAAE,SAAU,KAAK,QACvB,AAAI,GACF,GAAI,IAAc,CAAC,GAAQ,CAAE,EAAG,EAAK,EAAY,EAAI,KAAK,KAAK,QAAQ,kBAAkB,KAAK,KC1D7F,YACL,EACA,EACA,CAGA,AAFwB,OAAM,QAAQ,GAAc,EAAa,CAAC,IAElD,QAAQ,AAAC,GAAQ,CAE/B,GAAM,GAAQ,YAAe,GACzB,EAAI,MACH,GAAoB,GAAO,EAAI,UAAU,MAAQ,OAGhD,EAAM,YAAe,GACvB,EAAI,IACH,GAAoB,GAAO,EAAI,UAAU,IAAM,GAAI,GAAI,GAEtD,EAAQ,EAAQ,GAAG,GAAM,KAAW,OAC1C,GAAI,IAAQ,EAAK,CAAE,UAAS,KAAK,KC1BrC,OAAoB,OCEb,YAAuB,EAAsD,CAClF,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAQ,aAAiB,IAAS,EAAM,UAClC,YAAiB,IAAS,EAAM,YAAc,ECH/C,YAA0B,EAAgE,CAE/F,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,GAAI,YAAiB,GAAI,SAAS,QAAU,GAAc,GACxD,MAAO,GAAQ,MAGjB,WAAiB,EAAU,CACzB,AAAI,CAAC,EAAE,eAEP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAO,IAGT,WAAgB,EAAU,CACxB,AAAI,CAAC,EAAE,eACP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAQ,IAGV,EAAM,iBAAiB,OAAQ,GAC/B,EAAM,iBAAiB,QAAS,KCxB7B,YAAuB,EAAsC,CAClE,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,AAAM,YAAe,OAAO,EAAO,GAAI,OAAM,qDAC7C,GAAM,GAAS,GAAI,YACnB,EAAO,OAAS,IAAM,CACpB,AAAI,MAAO,GAAO,QAAW,UAAU,EAAO,GAAI,OAAM,qEACxD,GAAM,GAAM,EAAI,SAAS,qBACzB,EAAI,OAAS,IAAM,EAAQ,GAC3B,EAAI,QAAU,EACd,EAAI,IAAM,EAAO,QAEnB,EAAO,QAAU,EACjB,EAAO,cAAc,KCXlB,YAA4B,EAA0F,CAC3H,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAI,aAAiB,GACZ,GAAI,GAAW,EAAM,aAAc,EAAM,eAE9C,YAAiB,GACZ,GAAI,GAAW,EAAM,WAAY,EAAM,aAEzC,GAAI,GAAW,EAAM,MAAO,EAAM,QCNpC,YAAsB,CAAE,QAAO,UAA0C,CAC9E,GAAM,CAAE,uBAAwB,EAAI,SAC9B,EAAS,IACf,SAAO,MAAQ,EACf,EAAO,OAAS,EACT,EAGF,YAA+B,EAAwD,EAAuC,CACnI,GAAM,CAAE,aAAc,EAAI,SAE1B,GAAI,CAAE,aAAiB,KAAc,CAAC,GAAc,GAClD,KAAM,IAAI,OAAM,8DAGlB,GAAM,CAAE,QAAO,UAAW,GAAQ,GAAmB,GAC/C,EAAS,GAAa,CAAE,QAAO,WAErC,MAAI,aAAiB,GACnB,EAAoB,GAAQ,aAAa,EAAO,EAAG,GAEnD,EAAoB,GAAQ,UAAU,EAAO,EAAG,EAAG,EAAO,GAErD,EC7BT,OAAoB,OAKpB,kBACE,EACA,EAC4B,CAC5B,GAAM,GAAe,GAAU,EAAI,SAAS,sBAEtC,CAAC,EAAQ,EAAO,GAAe,EAAU,MAAM,MAAM,EAAW,GAAa,EAAI,GACjF,EAAc,AAAG,QAAK,IAAM,EAAU,KAAK,EAAQ,EAAO,GAAa,SAC7E,YAAM,AAAG,YAAQ,SAAS,EAAa,GAEvC,EAAY,UAEL,ECfF,YAAwB,EAAY,CACzC,GAAM,CAAE,QAAO,SAAQ,SAAU,EAAI,SAErC,MAAO,aAAiB,IACnB,YAAiB,IACjB,YAAiB,GCPxB,MAAoB,OCKb,YAAuB,EAA6C,EAAmB,EAAuB,GAAO,CAC1H,GAAM,CAAE,QAAO,UAAW,EAAI,SAE9B,GAAI,CAAE,aAAiB,IAAS,YAAiB,IAC/C,KAAM,IAAI,OAAM,4EAGlB,GAAI,GAAa,EAAG,MAAO,IAAa,CAAE,MAAO,EAAG,OAAQ,IAC5D,GAAM,GAAO,GAAmB,GAC1B,EAAQ,EAAY,KAAK,IAAI,EAAK,OAAQ,EAAK,OAC/C,EAAQ,EAAQ,EAAK,MACrB,EAAS,EAAQ,EAAK,OAEtB,EAAe,GAAa,CAAE,MAAO,EAAW,OAAQ,IACxD,EAAc,YAAiB,GAAS,EAAQ,GAAsB,GAEtE,EAAS,KAAK,IAAI,EAAQ,GAAU,EACpC,EAAK,GAAe,EAAQ,EAAS,EAAS,EAC9C,EAAK,GAAe,EAAS,EAAQ,EAAS,EACpD,MAAI,GAAY,MAAQ,GAAK,EAAY,OAAS,GAAG,EAAoB,GAAc,UAAU,EAAa,EAAI,EAAI,EAAO,GAEtH,EDdF,YAAe,CAapB,YACE,EACA,EAA6B,GAC7B,CAfM,mBAAkD,GAElD,eAAiC,GAIjC,wBAA8B,GAE9B,sBAA+B,GAQrC,GAAI,CAAC,MAAM,QAAQ,GACjB,KAAM,IAAI,OAAM,4HAA4H,KAG9I,KAAK,mBAAqB,EAC1B,KAAK,WAAa,EAAO,OAEzB,EAAO,QAAQ,CAAC,EAAO,IAAQ,CAC7B,GAAI,GAAW,GAAQ,CACrB,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAO,EAAM,MACnC,OAGF,GAAI,EAAW,GAAQ,CACrB,GAAM,GAAa,EAAc,MAAM,GACvC,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,yCAAyC,8CAG3D,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAQ,EAAc,MAAM,MAAM,GACxD,OAGF,GAAM,GAAU,YAAyB,GAAI,SAAS,OAAS,EAAQ,GAAsB,GAC7F,KAAK,UAAU,GAAO,EACtB,KAAK,iBAAiB,GAAO,CAAC,EAAO,OAAQ,EAAO,MAAO,QAIpD,eAAiD,CAC1D,MAAO,MAAK,iBAGH,WAAgC,CACzC,MAAO,MAAK,aAGH,eAAwB,CACjC,MAAO,MAAK,UAAY,GAAK,KAAK,sBAGzB,YAAoB,CAC7B,MAAO,MAAK,cAGH,kBAA8B,CACvC,MAAO,MAAK,oBAGH,YAAgC,CACzC,MAAO,MAAK,cAGH,0BAAwC,CACjD,MAAO,IAAM,KAAK,UAAW,EAAG,GAAG,IACjC,CAAC,EAAG,IAAa,KAAK,2BAA2B,IAI9C,SAAS,EAAiE,CAC/E,MAAO,MAAK,SAAS,IAAa,KAAK,aAAa,GAG/C,mBAAmB,EAA4B,CACpD,MAAO,MAAK,iBAAiB,GAGxB,eAAe,EAA0B,CAC9C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,cAAc,EAA0B,CAC7C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,2BAA2B,EAA8B,CAC9D,GAAI,MAAO,MAAK,WAAc,SAC5B,KAAM,IAAI,OAAM,yFAGlB,GAAM,GAAQ,KAAK,cAAc,GAC3B,EAAS,KAAK,eAAe,GACnC,MAAO,IAA0B,CAAE,QAAO,UAAU,KAAK,WAYpD,cAAc,EAAmB,EAA0B,GAAmB,CACnF,YAAK,WAAa,EAEX,AAAG,OAAK,IAAM,CACnB,GAAM,GAAe,GAAM,KAAK,UAAW,EAAG,GAAG,IAAI,AAAC,GAAa,CACjE,GAAM,GAAQ,KAAK,SAAS,GAE5B,GAAI,YAAoB,UAAQ,CAE9B,GAAI,GAAY,EAAW,GAAS,EAAQ,EAAM,aAElD,SAAY,GAAY,EAAW,GAE/B,GAAU,MAAM,KAAO,GAAa,EAAU,MAAM,KAAO,IAC7D,GAAY,AAAG,QAAM,eAAe,EAAW,CAAC,EAAW,KAGtD,EAAU,KAAK,EAAW,EAAW,GAG9C,GAAI,YAAiB,GAAI,SAAS,OAChC,MAAO,AAAG,WAAQ,WAAW,GAAc,EAAO,EAAW,IAG/D,KAAM,IAAI,OAAM,+BAA+B,8FAAqG,OAOtJ,MAHoB,AAAG,SAAM,EAAa,IAAI,AAAC,GAAM,AAAG,OAAK,EAAG,aAAa,KAAK,KAAK,UAAW,EAAW,EAAW,OE3I9H,iBAAiC,EAAsC,CACrE,GAAI,YAAkB,IACpB,MAAO,GAGT,GAAM,GAAgB,MAAM,QAAQ,GAChC,EACA,CAAC,GAEL,GAAI,CAAC,EAAc,OACjB,KAAM,IAAI,OAAM,4CAGlB,GAAM,GAAa,AAAC,GAAiB,MAAM,QAAQ,GAAU,mBAAmB,KAAS,GAEnF,EAAa,EAAc,IAAI,IAErC,SAAW,QAAQ,CAAC,EAAO,IAAM,CAC/B,GAAI,CAAC,GAAe,IAAU,CAAC,GAAW,IAAU,CAAC,EAAW,GAC9D,KAAI,OAAO,GAAc,IAAO,SACxB,GAAI,OAAM,eAAe,EAAW,sEAAsE,EAAc,MAG1H,GAAI,OAAM,eAAe,EAAW,iIAG5C,GAAI,EAAW,GAAQ,CAErB,GAAM,GAAY,EAAM,MAAM,GAC9B,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,eAAe,EAAW,iCAAiC,iDAMjF,KAAM,SAAQ,IACZ,EAAW,IAAI,AAAC,GAAU,GAAe,IAAU,GAAiB,KAG/D,GAAI,IAAS,EAAY,MAAM,QAAQ,ICtChD,kBACE,EACA,EAC8B,CAC9B,GAAM,CAAE,UAAW,EAAI,SAEnB,EAAS,EAEb,GAAI,CAAE,aAAiB,IAAS,CAC9B,GAAM,GAAW,KAAM,GAAW,GAElC,GAAI,EAAS,UAAY,EACvB,KAAM,IAAI,OAAM,8CAGlB,GAAM,GAAiB,EAAS,SAAS,GACzC,EAAS,YAA0B,GAC/B,EACA,KAAM,IAAoB,GAGhC,GAAM,GAAM,EAAoB,GAQhC,MAAO,AAPO,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAO,MAAO,EAAO,QAAQ,IAAI,QAC7C,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAO,MAAO,EAAO,SAE/C,IAAI,CAAC,CAChB,IAAG,IAAG,QAAO,YACT,CACJ,GAAM,GAAU,GAAa,CAAE,QAAO,WACtC,MAAI,GAAQ,GAAK,EAAS,GAAG,EAAoB,GAAS,aAAa,EAAI,aAAa,EAAG,EAAG,EAAO,GAAS,EAAG,GAC1G,IClDX,OAAoB,OAgBpB,kBACE,EACA,EACwB,CACxB,GAAI,CAAC,GAAW,IAAgB,CAAC,EAAW,GAC1C,KAAM,IAAI,OAAM,6DAGlB,GAAI,EAAW,IAAgB,EAAY,MAAM,GAAK,EACpD,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAW,EAAU,GAAe,EAAY,MAAM,MAAM,EAAW,GAAe,EAAI,GAajG,MAJoB,AAPN,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAU,GAAW,IACjC,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAU,IAEvB,IAAI,CAAC,CAC7B,IAAG,IAAG,QAAO,YACT,AAAG,WAAQ,EAAY,KAAK,EAAW,EAAU,GAAc,CAAC,EAAG,EAAG,GAAI,CAAC,EAAQ,EAAO,OCtCpG,kBACE,EAEA,EACmB,CACnB,GAAM,CAAE,SAAU,EAAI,SAChB,EAAM,KAAM,GAAM,EAAK,GAC7B,GAAI,CAAE,GAAI,OAAS,KACjB,KAAM,IAAI,OAAM,qBAAqB,EAAI,WAAW,EAAI,yBAAyB,EAAI,OAEvF,MAAO,GCTT,kBAAiC,EAAwC,CACvE,GAAM,GAAM,KAAM,IAAa,GACzB,EAAO,KAAO,GAAK,OAEzB,GAAI,CAAC,EAAK,KAAK,WAAW,UACxB,KAAM,IAAI,OAAM,wEAAwE,EAAK,kBAAkB,EAAI,OAErH,MAAO,IAAc,GCRvB,kBAAmC,EAAyB,CAC1D,MAAQ,MAAM,IAAa,IAAM,OCDnC,kBAAsC,EAAoC,CACxE,MAAO,IAAI,cAAa,KAAO,MAAM,IAAa,IAAM,eCH1D,OAAoB,OCAb,YAAsB,EAAyB,EAA0B,CAC9E,GAAM,GAA0B,GAAG,0BAEnC,GAAI,CAAC,EACH,MAAO,CACL,aAAc,GACd,YAAa,GAIjB,GAAI,IAAQ,IACV,MAAO,CACL,aAAc,IACd,YAAa,IAAI,KAIrB,GAAM,GAAW,EAAI,WAAW,WAAa,UAAY,EAAI,WAAW,YAAc,WAAa,GACnG,EAAM,EAAI,QAAQ,EAAU,IAE5B,GAAM,GAAQ,EAAI,MAAM,KAAK,OAAO,AAAC,GAAM,GAErC,EAAe,EAAI,SAAS,SAC9B,EAAM,EAAM,OAAS,GACrB,EAEA,EAAe,EAAY,GAAI,SAAS,SAAW,EAAM,MAAM,EAAG,EAAM,OAAS,GAAK,GAAO,KAAK,KACtG,SAAe,EAAI,WAAW,KAAO,IAAI,IAAiB,EAEnD,CACL,eACA,YAAa,IAAiB,IAAM,IAAI,IAAiB,GAAG,KAAgB,KD1BhF,kBACE,EACA,EAC4B,CAC5B,GAAM,CAAE,cAAa,gBAAiB,GAAa,EAAK,GAClD,EAAW,KAAM,IAAuC,GAE9D,MAAO,AAAG,OAAG,YAAY,EAAU,GET9B,YAAyB,EAAoB,EAAwB,EAA8B,GAAO,CAC/G,GAAM,CAAE,QAAO,UAAW,EACtB,GAAmB,GACnB,EACJ,SAAM,MAAQ,EACd,EAAM,OAAS,EACR,CAAE,QAAO,UCTlB,OAAoB,OCApB,OAAoB,OAOb,WAAyC,CAC9C,YAAY,EAAc,CAIhB,aAAkC,OAElC,oBAAiC,GALzC,KAAK,MAAQ,KASJ,SAAiC,CAAE,MAAO,MAAK,WAE/C,gBAAgC,CAAE,MAAO,MAAK,kBAE9C,WAAoB,CAAE,MAAO,CAAC,CAAC,KAAK,OAExC,iBAAiB,EAA8B,CACpD,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,MAAO,GAAI,GAGN,sBAAsB,EAAmB,EAAmB,CACjE,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,EAAI,GAAS,UACb,EAAI,GAAW,EAGV,cAAe,CACpB,MAAO,MAAK,eAAe,IAAI,CAAC,CAAE,eAAiB,EACjD,KAAM,EACN,OAAQ,KAAK,iBAAiB,MAI3B,oBAAqB,CAC1B,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,EAAM,iBAAqB,cAGnE,iBAAkB,CACvB,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,CAAE,GAAM,iBAAqB,eAGrE,UAAW,CAChB,KAAK,kBAAkB,QAAQ,CAAC,CAAE,OAAM,YAAa,CACnD,KAAK,sBAAsB,EAAM,EAAO,cAIrC,QAAS,CACd,KAAK,qBAAqB,QAAQ,CAAC,CAAE,OAAM,OAAQ,KAAe,CAChE,GAAM,GAAS,AAAG,UAAO,EAAS,YAClC,EAAS,UACT,KAAK,sBAAsB,EAAM,KAI9B,QAAQ,EAA4B,GAAM,CAC/C,KAAK,eAAe,QAAQ,AAAC,GAAU,CACrC,GAAI,GAAoB,EAAM,OAAO,WACnC,KAAM,IAAI,OAAM,mDAAmD,EAAM,QAE3E,EAAM,OAAO,YAEf,KAAK,QAAU,OAGV,iBAAgC,CACrC,MAAO,IAAI,cACT,KAAK,eACF,IAAI,CAAC,CAAE,YAAa,MAAM,KAAK,EAAO,aACtC,OAAO,CAAC,EAAM,IAAQ,EAAK,OAAO,UAI5B,MAAK,EAAgE,CAChF,GAAI,YAAwB,cAAc,CACxC,KAAK,eAAe,GACpB,OAEF,KAAM,MAAK,YAAY,QAGZ,aAAY,EAAyB,CAChD,GAAI,GAAO,MAAO,IAAQ,SACxB,KAAM,IAAI,OAAM,GAAG,KAAK,0CAE1B,GAAM,GAAY,KAAM,IAAc,EAAK,KAAK,uBAChD,KAAK,kBAAkB,QAGZ,cAAa,EAA8B,CACtD,GAAI,GAAY,MAAO,IAAa,SAClC,KAAM,IAAI,OAAM,GAAG,KAAK,iDAE1B,GAAM,CAAE,YAAa,EAAI,SACnB,CAAE,cAAa,gBAAiB,GAAa,EAAU,KAAK,uBAC5D,EAAuB,AAAC,GAAwB,QAAQ,IAAI,EAAU,IAAI,AAAC,GAAO,EAAS,GAAI,KAAK,AAAC,GAAQ,EAAI,UACjH,EAAc,AAAG,MAAG,qBAAqB,GACzC,EAAW,KAAK,MAAO,MAAM,GAAS,IAAc,YACpD,EAAY,KAAM,GAAY,EAAU,GAC9C,KAAK,kBAAkB,GAGlB,kBAAkB,EAA8B,CACrD,GAAM,CAAE,gBAAe,UAAW,KAAK,2BAA2B,GAClE,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGV,eAAe,EAAuB,CAC3C,GAAM,CAAE,gBAAe,UAAW,KAAK,cAAc,GACrD,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGT,qBAAqB,EAAmB,CAC9C,GAAI,CAAC,KAAK,OACR,KAAM,IAAI,OAAM,qDAGlB,GAAM,GAAS,EAAU,MAAM,KAAK,OAAO,CAAC,EAAoD,IAAY,CAE1G,GAAI,CAAC,EAAI,QAAQ,eAAe,GAC9B,KAAM,IAAI,OAAM,wDAAwD,eAAqB,KAE/F,MAAO,CAAE,IAAK,EAAI,QAAS,UAAS,QAAS,EAAI,QAAQ,KACxD,CAAE,QAAS,KAAK,SAEb,CAAE,MAAK,WAAY,EACzB,GAAI,CAAC,GAAO,CAAC,GAAW,CAAE,GAAI,YAAuB,YACnD,KAAM,IAAI,OAAM,8DAA8D,KAGhF,MAAO,CAAE,MAAK,aC7IlB,MAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,iBAAkB,EAAO,iBAAkB,EAAQ,QAC1F,SAAM,AAAG,OAAI,EAAK,EAAO,MAClB,IDNJ,YACL,EACA,EACA,EAAwB,GACX,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,CAAC,EAAG,GAAI,QACrE,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,CAAC,EAAG,KAE7E,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,OAItC,YACL,EACA,EACA,EAAwB,GACxB,EAAuB,GACV,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,GAAI,QAC5F,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,KAEpG,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAE/D,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,KACxC,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,QEpD1D,OAAoB,OAIb,YACL,EACA,EACA,EAA4B,OAC5B,EAAoB,GACP,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OACb,AAAG,UAAO,EAAG,EAAO,QAAS,CAAC,EAAG,GAAI,GACrC,EAAO,MAGT,MAAO,GAAW,AAAG,QAAK,GAAO,ICd9B,WAAoC,EAAgB,EAA+B,CACxF,OAAO,KAAK,GAAW,QAAQ,AAAC,GAAS,CACvC,AAAK,EAAc,KAAK,AAAC,GAAO,EAAG,eAAiB,IAClD,EAAU,GAAM,YCLtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,EACA,IACe,CACf,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,SCzBtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,IACa,CACb,GAAM,GAAa,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAY,IAChF,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CACL,QAAS,EACT,KAAM,ICvBZ,OAAoB,OCoBb,YAA0B,CAE/B,YAES,EAEA,EAEA,EAEP,CANO,wBAEA,wBAEA,cDxBJ,YACL,EACA,EACA,CACA,MAAO,CAAC,EAAoB,EAAqB,IAA8C,CAC7F,GAAM,GAAmB,AAAG,YAAS,EAAe,EAAI,EAAI,GAAa,CAAC,EAAG,EAAG,EAAY,IACtF,EAAmB,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAG,EAAG,EAAY,IAC5F,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,WAGX,GAAI,IACT,EACA,EACA,IAKC,YAEL,EACA,CACA,MAAO,AAAC,IAAwC,CAC9C,GAAM,GAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,IAAI,IACT,EACA,EACA,IEpCC,WAAmC,EAAgB,EAA+B,CACvF,MAAO,CAAC,EAAsB,EAAmB,IAAwB,CACvE,GAAM,GAAS,EAAU,GAEzB,GAAI,CAAC,GAAS,EAAQ,GACpB,KAAM,IAAI,OAAM,sBAAsB,oBAA+B,oBAA4B,KAGnG,SAAc,KACZ,CAAE,eAAc,UAAW,GAAc,IAGpC,GCfJ,WAA+B,EAAuB,CAC3D,GAAI,GAAmB,EAEvB,WAAwB,EAAkC,CACxD,GAAM,GAAM,EAAiB,MAAM,EAAG,GACtC,SAAmB,EAAiB,MAAM,GACnC,EAGT,YAA6C,CAC3C,MAAO,GAGT,MAAO,CACL,iBACA,uBCPG,YAA2B,EAAwC,EAA+B,CACvG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,GAAQ,EACV,EAAkB,EAAY,EAAa,EAAG,GAAG,WACjD,EAA2B,EAAY,EAAa,GAAG,WACrD,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAChE,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,CAAE,QAAO,QAAO,SAAU,EAAyB,EAAY,EAAa,EAAc,GAC1F,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YAAuB,EAA8F,CAC1H,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAC3C,EAAS,EAAyB,IAAK,IAAK,UAElD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CACN,SAAQ,SAAQ,SAAQ,WCvBvB,YAA+B,EAAuE,CAC3G,MAAO,AAAC,IAA+B,CACrC,GAAM,GAAU,EAAgC,GAAG,YAAkB,GAC/D,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,CAAE,UAAS,SCNf,YAA2B,EAAgB,EAA+B,CAC/E,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YACL,EACuE,CACvE,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBhBdZ,oBAAmC,EAAuG,CAC/I,aAAc,CACZ,MAAM,wBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,sDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAE/B,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,+BAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KiBlDzB,OAAoB,OCApB,OAAoB,OAIb,YACL,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,AAAG,OACtB,AAAG,UAAO,EAAG,EAAO,SACpB,EAAO,OCPJ,YAAuB,EAAuB,EAAoB,EAA2E,CAClJ,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAIpB,EAAK,AAFa,GAAuB,EAAgB,GAEpC,EAAY,EAAa,MAEpD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,OCdP,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,EAAgB,OAGtB,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,YAA4B,EAA8B,CAC/D,GAAM,GAAyC,GACzC,EAAmC,GAEzC,cAAO,KAAK,GAAW,QAAQ,AAAC,GAAQ,CACtC,GAAM,GAAM,EAAI,WAAW,MAAQ,EAAgB,EACnD,EAAI,GAAO,EAAU,KAGhB,CAAE,sBAAqB,iBJIzB,oBAGG,EAAyB,CAGjC,YAAY,EAAe,EAA+D,CACxF,MAAM,GACN,KAAK,sBAAwB,KAGpB,uBAAgE,CACzE,MAAO,MAAK,sBASP,OAAO,EAA4C,CACxD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EACJ,MAAO,IAAoB,EAAmB,KAAK,EAAmB,MAAM,GAAI,IAAK,EAAO,MAIzF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,EAAS,KAAK,0BAA2B,KAAK,4BAG3D,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAM,KAAK,0BACX,EAAO,KAAK,2BACZ,EAAwB,EAAO,EAAO,EAEtC,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KKnFjC,GAAM,IAAyB,CAAC,UAAW,QAAS,MAAO,QAAS,UAAW,YAAa,aAE5F,QAAsB,CAe3B,YAAY,EAAwC,CAClD,GAAI,EAAc,SAAW,EAC3B,KAAM,IAAI,OAAM,8EAA8E,EAAc,UAG9G,GAAuB,QAAQ,CAAC,EAAY,IAAQ,CAClD,KAAK,GAAc,EAAc,KAIrC,eAAgB,CACd,MAAO,IACJ,IAAI,AAAC,GAAgB,EAAE,aAAY,YAAa,KAAK,MACrD,KAAK,CAAC,EAAI,IAAO,EAAG,YAAc,EAAG,e1CtBrC,oBAAgC,GAA0C,CAC/E,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGtB,aAAa,EAA4C,CAC9D,MAAO,AAAG,SAAK,IAAM,AAAG,WAAQ,KAAK,OAAO,UAGjC,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,oBAAmB,EAAkB,CAChD,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAC9B,EAAsB,KAAM,SAAQ,IAAI,AAAG,WAAQ,GAAK,IAAI,KAAO,IAAM,CAC7E,GAAM,GAAO,KAAM,GAAE,OACrB,SAAE,UACK,KAET,EAAI,UAEJ,GAAM,GAAqB,EACxB,IAAI,AAAC,GAAiB,GAAI,IAAgB,IAE7C,MAAO,GAAS,aACZ,EACA,EAAmB,GAGf,qBAA8B,CACtC,MAAO,wBAGC,yBAAkC,CAC1C,MAAO,KAGC,0BAAmC,CAC3C,MAAO,K2C1CJ,YAA+B,EAA0C,CAC9E,MAAO,GAAI,sBAAuB,IAG7B,YAGL,EACA,EAC8B,CAE9B,MAAO,IAAK,KADM,CAAE,gBCPf,YACL,EACA,EACA,EAAgB,GAChB,EACA,CAGA,AAF6B,OAAM,QAAQ,GAAmB,EAAkB,CAAC,IAE5D,QAAQ,AAAC,GAAM,CAElC,GAAM,GAAO,YAAa,IACtB,EACC,GAAsB,GAAK,EAAE,YAAc,OAChD,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mHAIlB,GAAM,GAAmB,AADV,EAAK,gBACY,OAAO,AAAC,GAAc,EAAU,YAAc,GAExE,EAAS,GAAoB,GAC/B,EAAE,UAAU,IAAI,WACf,GAAmB,GAAI,GAAM,EAAG,GAMrC,AAJsB,GAAI,IACxB,EAAiB,IAAI,AAAC,GAAc,GAAG,EAAU,eAAe,GAAM,EAAU,iBAChF,GAEY,KAAK,KCvBhB,YAA6B,EAA0E,CAC5G,MAAO,IAAoB,IAEtB,EAAI,oBAAwB,IAE5B,EAAI,6BAAiC,IAErC,EAAI,sBAA0B,GAGrC,YAA4B,EAAM,CAEhC,GAAM,GAAU,CAAC,EAAI,EAAI,EAAI,IAAQ,KAAK,MAAM,EAAK,EAAI,EAAK,GAAM,KAAK,GAGnE,EAAU,AAAC,GAAW,EAAQ,IAAO,KAAK,GAE1C,EAAQ,CAAE,KAA0B,OAAW,MAA2B,OAAW,IAAyB,QAEpH,GAAI,CAAC,GAAQ,CAAC,EAAK,YAAc,EAAK,WAAW,SAAW,GAAI,MAAO,GACvE,GAAM,GAAK,EAAK,WAOhB,EAAM,KAAO,CAAC,EAAQ,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,IAK9D,EAAM,MAAQ,EAAQ,EAAG,KAAK,IAAI,EAAG,GAAG,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,GAAI,KAAK,GAAI,KAAK,IAAI,EAAG,IAAI,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,IAMvH,GAAM,GAAS,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,UACnE,EAAM,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,WACtE,SAAM,IAAM,KAAK,GAAM,GAAK,SAAS,QAAW,GAAM,GAAU,IAAO,GAEhE,EAGF,YAEoD,EAAoB,EAAgF,CAC7J,GAAM,CAAE,IAAK,GAAU,EAAU,UAC3B,EAAY,EAAmB,QAAwB,EAAM,EAAG,EAAM,GAEtE,EAAO,EAAU,QACjB,CAAE,aAAc,EAAU,UAC1B,EAAc,GAAI,GAAc,EAAU,UAAU,MAAO,EAAK,QAAQ,EAAU,WAAY,GAC9F,EAAQ,GAAmB,GASjC,MAAO,IAAK,KAPM,CAChB,YACA,qBACA,cACA,UCxDG,YAA+B,CAapC,YAAY,EAAqC,GAAI,CACnD,GAAM,CACJ,YAAY,GAAM,aAAa,GAAM,YAAW,YAAW,YAAW,cACpE,EACJ,KAAK,UAAY,EACjB,KAAK,WAAa,EAClB,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,uBAC9B,KAAK,WAAa,GAAc,yBAI7B,QAAwB,CAK7B,YACE,EACA,EAAqC,GACrC,CACA,KAAK,cAAgB,EACrB,KAAK,QAAU,GAAI,IAAyB,GAG9C,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CACJ,YAAW,aAAY,YAAW,YAAW,YAAW,cACtD,KAAK,QAcT,GAZI,GAAa,KAAK,wBAAyB,KAC7C,GAAI,YAAc,EAClB,EAAI,UAAY,EAChB,GAAY,EAAK,KAAK,cAAc,iBACpC,GAAY,EAAK,KAAK,cAAc,kBACpC,GAAY,EAAK,KAAK,cAAc,mBACpC,GAAY,EAAK,KAAK,cAAc,WACpC,GAAY,EAAK,KAAK,cAAc,aAAc,IAClD,GAAY,EAAK,KAAK,cAAc,cAAe,IACnD,GAAY,EAAK,KAAK,cAAc,WAAY,KAG9C,EAAY,CACd,EAAI,YAAc,EAClB,EAAI,UAAY,EAEhB,GAAM,GAAY,AAAC,GAAe,CAChC,EAAI,YACJ,EAAI,IAAI,EAAG,EAAG,EAAG,EAAG,EAAW,EAAG,EAAI,KAAK,IAC3C,EAAI,QAEN,KAAK,cAAc,UAAU,QAAQ,MAOpC,YACL,EACA,EACA,CAEA,AAD2B,OAAM,QAAQ,GAAiB,EAAgB,CAAC,IACxD,QAAQ,AAAC,GAAM,CAEhC,GAAM,GAAY,YAAa,GAC3B,EACC,GAAoB,GAAK,EAAE,UAAY,OAC5C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gIAGlB,GAAI,IAAkB,GAAW,KAAK,oBC1G1C,OAAoB,OCApB,MAAoB,OCKpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAqC,EAAoB,EAAqB,EAA4C,CACxH,GAAM,GAAkB,EAA2B,EAAY,EAAa,GAAG,qBACzE,EAAkB,EAA2B,EAAa,EAAa,GAAG,qBAC1E,EAAiB,EAAkB,EAAY,EAAa,EAAG,GAAG,oBAExE,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAkB,EAAuC,CACvF,GAAM,GAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBAE1E,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YAAuB,EAAuB,EAAsF,CACzI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAgB,GAEhC,EAAqB,EAAkB,EAAG,GAAI,EAAG,sBACjD,EAA+B,EAA4B,GAAI,GAAI,gCACnE,EAA+B,EAA4B,GAAI,IAAK,gCAEpE,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,IAAK,0BAA0B,OAG3F,GAAM,GAA4B,EAA4B,IAAK,IAAK,6BAClE,EAA2B,EAA2B,IAAK,IAAK,4BAEhE,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,aAAY,cAAa,cCjEvC,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAqC,EAA4C,CAC/E,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAiB,EAAkB,GAAG,oBAE5C,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAuC,CACrE,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAEtD,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YACL,EACA,EAC+D,CAC/D,GAAM,GAAgC,GAEhC,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAW,GAE3B,EAAqB,EAAkB,sBACvC,EAA+B,EAA4B,gCAC3D,EAA+B,EAA4B,gCAE3D,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,0BAA0B,OAGtF,GAAM,GAA4B,EAA4B,6BACxD,EAA2B,EAA2B,4BAEtD,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,SAA2B,EAAW,GAE/B,CAAE,OAAQ,CAAE,aAAY,cAAa,aAAa,iBFrE3D,YAAc,EAAgB,EAAoB,EAAuC,CACvF,MAAO,AAAG,OAAI,AAAG,SAAO,EAAG,EAAO,QAAS,EAAQ,QAAS,EAAO,MAGrE,YAAwB,EAAgB,EAA8B,EAA2B,GAAmB,CAClH,GAAI,GAAM,EAAkB,AAAG,OAAK,GAAK,EACzC,SAAM,EAAuB,EAAK,EAAO,gBAAiB,CAAC,EAAG,IAC9D,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,AAAG,MAAI,EAAK,GAAK,EAAG,EAAO,eAAgB,CAAC,EAAG,KAC9C,EAGT,YAAmB,EAAgB,EAAsC,CACvE,GAAI,GAAM,EAAuB,AAAG,OAAK,GAAI,EAAO,gBAAiB,CAAC,EAAG,IACzE,SAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,MAAI,EAAK,GACX,EAGF,oBAA2B,EAAkC,CAGlE,YAAY,EAAuB,CACjC,MAAM,gBACN,KAAK,eAAiB,EAGjB,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KACnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,8CAElB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAC7D,EAAM,AAAG,OAAK,GAAK,EAAY,EAAO,WAAW,QAAS,CAAC,EAAG,KAClE,SAAM,GAAe,EAAK,EAAO,WAAW,kBAAmB,IAC/D,EAAM,GAAe,EAAK,EAAO,WAAW,mBAC5C,GAAM,KAAK,eAAgB,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAChD,EAAM,GAAU,EAAK,EAAO,YAAY,cAAc,QAExD,EAAM,GAAe,EAAK,EAAO,UAAU,iBAC3C,EAAM,AAAG,OAAK,EAAuB,EAAK,EAAO,UAAU,eAAgB,CAAC,EAAG,KACxE,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,sBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,gBAG1C,cAAc,EAAuB,CAC7C,MAAO,IAAc,EAAS,KAAK,kBGvEhC,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAkB,GAAuB,EAAgB,GAEzD,EAAM,EAAgB,IAAK,EAAG,UAC9B,EAAS,EAAgB,IAAK,EAAG,aAEvC,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,GAAI,CAAE,MAAK,YCflB,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,CACF,IAAK,EAAgB,UACrB,OAAQ,EAAgB,eAI5B,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,GAAK,IAAL,UAAK,EAAL,CAEL,SAAS,SAET,OAAO,SAJG,aNQL,oBAA2B,EAAyB,CAGzD,YAAY,EAAqC,GAAI,IAAa,GAAI,CACpE,MAAM,gBACN,KAAK,sBAAwB,KAGpB,uBAAqC,CAC9C,MAAO,MAAK,sBAGP,OAAO,EAA0C,CACtD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EAEE,EAAS,AAAG,WAAQ,EAAoB,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAAS,KAAK,EAAmB,MAAM,GAAI,IACnG,EAAM,GAAoB,EAAQ,EAAO,GAAG,KAAK,OACjD,EAAS,GAAoB,EAAQ,EAAO,GAAG,QACrD,MAAO,CAAE,MAAK,YAIX,aAAa,EAA0C,CAC5D,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAE,MAAK,UAAW,KAAK,OAAO,GACpC,MAAO,CAAE,MAAK,OAAQ,AAAG,WAAQ,WAIxB,SAAQ,EAAsC,CACzD,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,qBAAoB,EAA8E,CAC7G,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAE9B,EAAO,AAAG,WAAQ,EAAI,KACtB,EAAU,AAAG,WAAQ,EAAI,QACzB,EAAsB,EAAK,IAAI,CAAC,EAAW,IAAO,EACtD,YACA,aAAc,EAAQ,MAGlB,EAAqB,KAAM,SAAQ,IACvC,EAAoB,IAAI,MAAO,CAAE,YAAW,kBAAmB,CAC7D,GAAM,GAAO,MAAM,GAAU,QAAQ,GAC/B,EAAY,MAAM,GAAa,QAAQ,GACvC,EAAS,EAAW,GACpB,EAAS,EAAS,GAAO,KAAO,GAAO,OACvC,EAAoB,EAAS,EAAY,EAAI,EAEnD,SAAU,UACV,EAAa,UACN,CAAE,MAAK,SAAQ,wBAG1B,SAAI,IAAI,UACR,EAAI,OAAO,UAEJ,EAAS,aAAe,EAAiD,EAAmB,GAG3F,qBAA8B,CACtC,MAAO,mBAGF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,GAGb,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAwB,IAAM,EAAI,EAAM,KAAM,EAAI,GAElD,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KOvHxC,MAAoB,OASb,oBAGG,GAAgC,CACjC,YAAY,EAAqB,EAAmB,EAAgD,CACzG,GAAM,GAAkB,EAAmB,IAAI,CAAC,CAAE,QAAO,YAAa,CACpE,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,CACL,MAAO,EAAQ,EACf,OAAQ,EAAS,KAIf,EAAY,EAAgB,OAElC,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAA0B,CAAC,EAAe,IAAkB,AAAG,QAAM,CAAC,AAAG,OAAK,CAAC,IAAK,EAAO,WAAY,AAAG,OAAK,CAAC,IAAK,EAAO,YAAa,GAAG,KAAK,EAAG,KAAK,OAGzJ,EAAa,CAAC,EAAkB,IAAoD,CACxF,GAAM,CAAE,QAAO,UAAW,EAAgB,GAC1C,MAAO,GAAK,EAAO,GAAU,KAAK,IAAI,EAAQ,GAAU,EAAI,GAGxD,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GACvE,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GAa7E,MAXwB,GACrB,IAAI,AAAG,OAAK,CAAC,EAAW,KAAM,EAAW,YACzC,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAY,GACZ,EAAY,OAEb,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAgB,GAAU,MAC1B,EAAgB,GAAU,aAO3B,aAAa,EAA8B,CAChD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,KAAK,OAAO,GACxB,MAAO,MAAK,YACV,EACA,EAAM,UACN,EAAM,gBAAgB,IAAI,CAAC,CAAC,EAAQ,KAAY,EAAE,SAAQ,mBAKnD,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,iBAAgB,EAAgE,CAC3F,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAkB,AAAG,OACzB,IAAM,AAAG,UAAQ,KAAK,aAAa,KAG/B,EAAoB,KAAM,SAAQ,IAAI,EAAgB,IAC1D,MAAO,EAAgB,IAAa,CAClC,GAAM,GAAiB,MAAM,KAAK,KAAM,GAAe,QACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,GAAO,IACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,CAAC,GAAO,IAExD,MAAO,IAAI,IACT,MAAM,IAAI,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,GAAI,GAAM,EAAQ,GAAc,EAAQ,KACxE,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,QAMtC,SAAgB,QAAQ,AAAC,GAAM,EAAE,WAE1B,EAAS,aAAe,EAAyC,EAAkB,GAGlF,0BAAmC,CAC3C,MAAO,OC1FJ,oBAAgC,GAAkD,CACvF,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGnB,qBAA8B,CACtC,MAAO,yBAGC,yBAAkC,CAC1C,MAAO,OCdX,OAAoB,OCMb,YACL,EAC2E,CAC3E,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCnBZ,YAA2B,EAAkG,CAClI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAEjD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,SAAQ,SAAQ,WFhBvB,oBAAuC,EAA+G,CAC3J,aAAc,CACZ,MAAM,4BAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,0DAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,GAAI,IAAK,CAAC,EAAG,GAAI,SAEjC,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,oCAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA+B,GAG9B,cAAc,EAAuB,CAC7C,MAAO,IAAkB,KG7CtB,oBAAoC,GAAsD,CAC/F,YAAY,EAAiD,GAAI,IAA4B,CAC3F,MAAM,wBAAyB,GAGvB,qBAA8B,CACtC,MAAO,8BAGC,yBAAkC,CAC1C,MAAO,OCVJ,oBAA8B,GAAkB,GCJvD,MAAoB,OCApB,OAAoB,OCApB,OAAoB,OAIb,YAAe,EAAgB,EAAuC,CAC3E,MAAO,AAAG,QAAI,AAAG,OAAI,EAAG,EAAO,SAAU,EAAO,QDAlD,YACE,EACA,EACA,EACA,EACA,EAA4B,OACf,CACb,GAAM,CAAE,UAAS,QAAS,EAAO,KAE7B,EAAM,AAAG,UAAO,EAAG,EAAS,EAAS,GACzC,SAAM,AAAG,OAAI,EAAK,GAClB,EAAM,GAAM,EAAK,EAAO,OACjB,EAAW,AAAG,QAAK,GAAO,EAG5B,YAAc,EAAgB,EAAyB,CAC5D,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAoB,EAAgB,EAAyB,CAClE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAkB,EAAgB,EAAyB,CAChE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,GAAM,SE7B5C,MAAoB,OAUpB,YAA2B,EAAwC,EAA+B,CAChG,WAA6B,EAAyB,EAAoB,EAAiC,CACzG,GAAM,GAAU,EAAe,GACzB,EAAQ,EAAQ,OAAU,GAAa,EAAa,GAE1D,GAAI,GAAQ,GACV,KAAM,IAAI,OAAM,+BAA+B,sBAA0B,EAAQ,uBAAuB,kBAA2B,KAGrI,MAAO,AAAG,QACR,IAAM,AAAG,YACP,AAAG,WAAS,EAAS,CAAC,EAAY,EAAO,EAAY,IACrD,CAAC,EAAG,EAAG,EAAG,KAKhB,WACE,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,EAAoB,EAAiB,EAAY,GAC3D,EAAO,AAAG,WAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,QAGpB,WAAiC,EAAoB,EAAwC,CAC3F,GAAM,GAAU,AAAG,WAAS,EAAe,IACrC,EAAS,AAAG,WAAS,EAAe,IAE1C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,aAGX,CACL,UACA,UAIJ,WACE,EACA,EACA,EACA,EACiB,CACjB,GAAM,GAAO,EAAkB,EAAiB,EAAY,EAAY,GAAG,UACrE,EAAQ,EAAwB,EAAY,GAAG,WAErD,MAAO,CAAE,OAAM,SAGjB,WACE,EACA,EACA,EACA,EACA,EAAkB,GACG,CACrB,GAAM,GAAQ,EAAwB,GAAS,GAAM,GAAK,EAAiB,EAAY,EAAY,GAAG,WAChG,EAAQ,EAAuB,EAAiB,EAAY,EAAY,GAAG,WAEjF,MAAO,CAAE,QAAO,SAGlB,MAAO,CACL,yBACA,8BAIG,YAAuB,EAA6E,CACzG,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAgB,GAEhC,EAAc,EAAuB,KAAM,GAAI,EAAG,eAClD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YAEnD,EAAc,EAA2B,MAAO,GAAI,EAAG,cAAe,IACtE,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YAEpD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aAEvD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAmB,EAA2B,OAAQ,IAAK,EAAG,oBAE9D,EAAK,AAAG,OACZ,IAAM,AAAG,YAAU,AAAG,WAAS,EAAe,IAAM,KAAM,CAAC,IAAK,MAAO,CAAC,EAAG,KAI7E,GAFA,EAAc,KAAK,CAAE,UAAW,OAE5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAsB1E,MAAO,CAAE,OAnBM,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGe,iBChJnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAiC,EAAkC,CACjE,GAAM,GAAU,EAAmB,GAAG,kBAAwB,GACxD,EAAS,EAAmB,GAAG,iBAAuB,GAE5D,MAAO,CAAE,UAAS,UAGpB,WAAgC,EAAiC,CAC/D,GAAM,GAAU,EAAmB,GAAG,iBAAuB,GACvD,EAAO,EAAmB,GAAG,cAAoB,GACjD,EAAQ,EAAwB,GAEtC,MAAO,CAAE,KAAM,CAAE,UAAS,QAAQ,SAGpC,WAAoC,EAAqC,CACvE,MAAO,CACL,MAAO,EAAuB,GAAG,WACjC,MAAO,EAAuB,GAAG,YAIrC,MAAO,CACL,yBACA,8BAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAW,GAE3B,EAAc,EAAuB,eACrC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAc,EAA2B,eACzC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aAEvC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aACvC,EAAmB,EAA2B,oBAE9C,CAAE,MAAO,EAGf,GAFA,EAAc,KAAK,CAAE,aAAc,KAAM,UAAW,OAEhD,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yDAAyD,KAG3E,GAAM,GAAS,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGF,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBC9FnB,MAAoB,OAKb,YAAkB,EAAgB,EAA0C,CACjF,GAAI,GAAM,GAAK,EAAG,EAAO,OACzB,SAAM,GAAW,EAAK,EAAO,OAC7B,EAAM,AAAG,MAAI,EAAK,GAClB,EAAM,AAAG,OAAK,GACP,EAGF,YAAsB,EAAgB,EAA0C,CACrF,GAAI,GAAM,GAAS,EAAG,EAAO,OAC7B,EAAM,GAAW,EAAK,EAAO,OAE7B,GAAI,GAAS,AAAG,UAAQ,EAAG,EAAG,EAAG,SAC3B,EAAQ,AAAG,QAAkB,EAAO,OACpC,EAAQ,EAAO,MAAM,KAAO,EAAI,MAAM,GAG5C,GAFsB,EAAO,MAAM,KAAO,EAAI,MAAM,IAAM,EAAO,MAAM,KAAO,EAAI,MAAM,GAErE,CACjB,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAE/B,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAGjC,SAAS,EAAQ,AAAG,SAAO,CAAC,EAAQ,GAAQ,GAAK,EACjD,EAAM,AAAG,MAAI,EAAQ,GAErB,EAAM,AAAG,OAAK,GACP,EL3BF,oBAAiC,EAAyB,CAC/D,aAAc,CACZ,MAAM,sBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAGtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAE7D,EAAM,GAAS,EAAY,EAAO,aACtC,EAAM,AAAG,UAAQ,EAAK,EAAG,EAAG,SAE5B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,aAC/B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAa,EAAK,EAAO,kBAE/B,GAAM,GAAY,EAAI,KAAK,CAAC,EAAG,IAG/B,MAFuB,AAAG,UAAO,EAAW,EAAO,WAM1C,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,uBAAsB,EAAwD,CA7D7F,MA8DI,GAAI,oBAAO,QAAP,cAAc,KAAK,AAAC,GAAQ,GAAO,GAAI,MAAO,IAAI,cAAa,KACnE,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAwB,AAAG,OAC/B,IAAM,AAAG,UAAQ,KAAK,aAAa,KAE/B,EAA0B,KAAM,SAAQ,IAAI,EAAsB,IAAI,AAAC,GAAM,EAAE,SACrF,SAAsB,QAAQ,AAAC,GAAM,EAAE,WAChC,EAAS,aAAe,EAA0B,EAAwB,GAGzE,qBAA8B,CACtC,MAAO,yBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KM7ElB,YAAkC,EAAuB,CAC9D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECHF,YAGL,EACA,EAC6B,CAE7B,MAAO,IAAK,KADM,CAAE,eCNf,YAAmB,EAA8B,CACtD,MAAO,OAAO,GAAI,KAAQ,SAGrB,YAGL,EACA,EACkB,CAElB,MAAO,IAAK,KADM,CAAE,QCNf,YAAsB,EAAiC,CAC5D,MAAQ,GAAI,SAAW,GAAO,MAAQ,EAAI,SAAW,GAAO,SACvD,GAAmB,EAAI,mBAGvB,YAGL,EACA,EACA,EACqB,CAErB,MAAO,IAAK,KADM,CAAE,SAAQ,sBCpB9B,OAAoB,OCApB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,WAAoC,EAAqB,EAAuD,CAC9G,GAAM,GAAU,AAAG,YAAS,EAAe,EAAI,EAAI,GAAc,CAAC,EAAG,EAAG,EAAa,IAC/E,EAAmB,AAAG,YAAS,EAAe,IAC9C,EAAoB,AAAG,YAAS,EAAe,IAC/C,EAAkB,AAAG,YAAS,EAAe,IAC7C,EAAsB,AAAG,YAAS,EAAe,IAEvD,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,uBAChB,CAAE,UAAW,GAAG,qBAChB,CAAE,UAAW,GAAG,0BAGX,CACL,UACA,mBACA,oBACA,kBACA,uBAIJ,WACE,EACA,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,KAAgB,EAAkB,oBAAsB,WAGnE,CAAE,UAAS,QAGpB,WACE,EACA,EACA,EACA,EACqB,CACrB,GAAM,CACJ,UACA,QACE,EAAkB,EAAY,EAAa,EAAY,EAAc,IAEzE,MAAO,CACL,UACA,kBAAmB,GAIvB,WACE,EACA,EACA,EAC4B,CAC5B,GAAM,GAAiB,EAA2B,EAAY,GAAG,oBAC3D,EAAiB,EAA2B,EAAY,EAAa,EAAG,GAAG,oBAEjF,MAAO,CAAE,iBAAgB,kBAG3B,YAAwD,CACtD,GAAM,GAAS,EAA2B,EAAG,GAAI,EAAG,sBAC9C,EAAS,EAAsB,GAAI,GAAI,sBACvC,EAAS,EAAsB,GAAI,IAAK,sBACxC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,KAAM,uBAC3C,EAAU,EAAsB,KAAM,KAAM,uBAClD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,UACA,UACA,UACA,WAIJ,YAA+D,CAC7D,GAAM,GAAS,EAA2B,KAAM,IAAK,EAAG,2BAClD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,GAAI,EAAG,2BAChD,EAAS,EAA2B,GAAI,IAAK,EAAG,2BAChD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,EAAG,EAAG,oDACjD,EAA2B,EAAkB,KAAM,GAAI,EAAG,2DAC1D,EAAoB,EAAkB,KAAM,GAAI,EAAG,oDACnD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDA0BxD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,gBAjCsB,CACtB,uBAAwB,EACxB,gBAAiB,GAgCjB,gBA9BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA6BjB,gBA3BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA0BjB,gBAxBsB,CACtB,uBAAwB,GACxB,gBAAiB,IAuBjB,gBArBsB,CACtB,uBAAwB,EACxB,gBAAiB,IAoBjB,gBAlBsB,CACtB,uBAAwB,GACxB,gBAAiB,KAoBrB,MAAO,CACL,2BACA,gCAIG,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAChC,CACJ,iBACA,uBACE,EAAsB,GACpB,CACJ,2BACA,gCACE,GAAkB,EAAgB,GAChC,EAAc,IACd,EAAmB,IAKnB,EAAe,CACnB,UALgB,AAAG,YACnB,EAAe,KAAO,GACtB,CAAC,EAAG,KAAM,KAMZ,GADA,EAAc,KAAK,CAAE,UAAW,2BAC5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,OAAQ,CACN,cACA,mBACA,gBAEF,iBC9MJ,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAoC,EAAgB,EAAa,EAA2C,CAC1G,GAAM,GAAU,EAAmB,GAAG,YAAiB,sBAAyB,EAAG,GAAG,aAChF,EAAoB,EAAmB,GAAG,YAAiB,oCAAuC,EAAG,GAAG,uBAC9G,MAAO,CAAE,UAAS,qBAGpB,WAA+B,EAAyC,CACtE,GAAM,GAAe,oBAAoB,IACnC,EAAsB,sBAAsB,cAC5C,EAA4B,GAAG,mBAC/B,EAA4B,GAAG,mBAE/B,EAAU,EAAmB,GAAG,sBAAyC,EAAG,GAAG,aAC/E,EAAmB,EAAmB,GAAG,oBAAuC,EAAG,GAAG,sBACtF,EAAoB,EAAmB,GAAG,mBAAsC,EAAG,GAAG,uBACtF,EAAkB,EAAmB,GAAG,0BAA6C,EAAG,GAAG,qBAC3F,EAAsB,EAAmB,GAAG,8BAAiD,EAAG,GAAG,yBAEzG,MAAO,CACL,eAAgB,CACd,UACA,mBACA,oBACA,kBACA,uBAEF,eAAgB,EAA2B,cAAe,EAAK,IAInE,YAAwD,CACtD,MAAO,CACL,OAAQ,EAA2B,cAAe,EAAG,sBACrD,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,KAInC,WAA2B,EAAgB,EAAkC,CAC3E,GAAM,GAAU,EAAmB,GAAG,YAAkB,EAAG,GAAG,aACxD,EAAO,EAAmB,GAAG,WAAiB,EAAG,GAAG,UAC1D,MAAO,CAAE,UAAS,QAGpB,WAAmC,EAAkC,CACnE,GAAM,GAAyB,EAC7B,2BAA2B,yBAC3B,kCAAkC,4BAE9B,EAAkB,EACtB,2BAA2B,mBAC3B,kCAAkC,qBAEpC,MAAO,CAAE,yBAAwB,mBAGnC,YAA+D,CAC7D,MAAO,CACL,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,IAI/C,MAAO,CACL,2BACA,gCAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAChC,CACJ,2BACA,gCACE,GAAkB,EAAW,GAC3B,EAAY,EAAU,oBAE5B,GADA,EAAc,KAAK,CAAE,aAAc,mBAAoB,UAAW,2BAC9D,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yEAAyE,KAG3F,GAAM,GAAS,CACb,YAAa,IACb,iBAAkB,IAClB,aAAc,CACZ,cAIJ,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBChInB,OAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,UAAO,EAAG,EAAO,QAAS,EAAS,QAChD,SAAM,AAAG,OAAI,EAAK,EAAO,mBAClB,AAAG,eAAY,EAAK,EAAG,KDPlC,GAAM,IAAU,qBAEhB,YACE,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,QAAS,EAAS,QACzD,SAAM,AAAG,aACP,EACA,EAAO,gBACP,EAAO,oBACP,EAAO,kBACP,EAAO,iBACP,IAEK,AAAG,eAAY,EAAK,EAAG,KAIlC,YAA+B,EAAoC,CACjE,MAAO,CAAC,EAAG,EAAG,EAAG,IAAI,KAAK,AAAC,GAAQ,IAAQ,GAAY,CAAC,EAAG,GAAK,CAAC,EAAG,GAG/D,YAAqB,EAAgB,EAA4B,CACtE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GACA,EAAM,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IA4BnD,GAVA,AAhBuB,CACrB,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,QACP,EAAO,QACP,EAAO,QACP,EAAO,SAGM,QAAQ,CAAC,EAAO,IAAM,CACnC,GAAM,GAAW,EAAI,EACf,EAAuB,GAAsB,GACnD,EAAM,GAAmB,EAAK,EAAM,eAAgB,GACpD,EAAM,EAAmB,EAAK,EAAM,eAAgB,CAAC,EAAG,IACpD,IAAa,IACf,GAAS,KAIT,IAAW,KACb,KAAM,IAAI,OAAM,iDAGlB,MAAO,CACL,MACA,OAAQ,KEjEd,YAAa,EAAoB,EAAW,EAAW,CACrD,GAAM,GAAY,EAAM,YAClB,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAS,GAAQ,GAAU,GAAQ,GACnC,EAAS,GAAQ,GAAU,GAAQ,GACzC,GAAI,GAAS,GAAK,GAAS,EACzB,MAAO,GAET,GAAM,GAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAmB,EAAkB,GACjE,KAAK,IAAI,EAAmB,EAAkB,GACpD,MAAO,GAAoB,GAAQ,EAAQ,GAGtC,YACL,EACA,EACA,EACA,EACA,EACU,CACV,GAAM,GAAW,EAAM,MAAM,GACvB,EAAa,KAAK,IACtB,EACA,GAGI,EAAa,EAChB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,OAAO,AAAC,GAAM,EAAE,MAAQ,GACxB,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAE5B,EAAe,AAAC,GAAe,GAAK,EAAe,EAAI,EACvD,EAAqB,GAE3B,SAAW,QAAQ,AAAC,GAAM,CACxB,GAAI,EAAS,QAAU,EAAY,OACnC,GAAM,GAAgB,EAAE,MACxB,OAAS,GAAI,EAAS,OAAS,EAAG,GAAK,EAAG,EAAE,EAAG,CAC7C,GAAM,GAAM,GAAI,EAAO,EAAE,SAAU,EAAS,IAC5C,GAAI,IAAQ,GACZ,GAAE,OAAS,EAAa,GACpB,EAAE,OAAS,GAAgB,MAEjC,AAAI,IAAkB,EAAE,OACtB,EAAS,KAAK,EAAE,YAGb,EC5DT,MAAoB,OAIpB,YAA2C,EAAgB,CACzD,GAAM,GAAM,AAAG,UAAQ,AAAG,YAAU,EAAG,CAAC,EAAG,KAErC,EAAQ,CACZ,AAAG,MAAI,EAAI,GAAI,EAAI,IACnB,AAAG,MAAI,EAAI,GAAI,EAAI,KAEf,EAAU,CACd,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,KAC1C,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,MAE5C,MAAO,CACL,QACA,WAIJ,YAA0B,EAAiB,EAAiB,CAC1D,GAAM,CACJ,QACA,WACE,GAAkC,GAEhC,EAAM,AAAG,UAAQ,AAAG,YAAU,EAAI,CAAC,EAAG,KACtC,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAC3E,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAEjF,MAAO,AAAG,aACR,AAAG,QAAM,CACP,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,KAEnB,CAAC,EAAG,IAID,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAY,EAAe,MAAM,GAEnC,EAAQ,GACV,AAAG,UAAQ,AAAG,OAAK,EAAO,UAAW,CAAC,EAAW,EAAG,IAAK,CAAC,GAAI,IAC9D,AAAG,UAAQ,EAAgB,CAAC,GAAI,KAElC,EAAQ,AAAG,UACT,EACA,CAAC,EAAY,EAAM,MAAM,GAAK,EAAY,IAG5C,GAAM,GAAmB,AAAG,UAAQ,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,MAC/E,EAAS,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,IAE5D,EAAS,AAAG,UACV,EACA,CAAC,EAAW,EAAO,MAAM,KAG3B,GAAM,GAAe,AAAG,UAAQ,GAC1B,EAAgB,AAAG,UAAQ,GAEjC,MAAO,CACL,MAAO,EACP,OAAQ,KC1Ed,OAAoB,OCApB,OAAoB,OAKb,YACL,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAY,EAAE,MAAM,GACpB,EAAwB,AAAG,WAC/B,GAAU,EAAG,EAAO,wBACpB,CAAC,EAAW,GAAI,EAAG,IAEf,EAAkB,AAAG,WACzB,GAAU,EAAG,EAAO,iBACpB,CAAC,EAAW,GAAI,IAElB,MAAO,CAAE,wBAAuB,qBDb7B,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAQ,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IACjD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IAErD,EAAiB,GAAmB,EAAQ,EAAO,iBACnD,EAAiB,GAAmB,EAAG,EAAO,iBAC9C,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAElD,EAAiB,AAAG,UAAO,CAC/B,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,uBACd,GAEG,EAAmB,AAAG,UAAO,CACjC,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,iBACd,GAEH,MAAO,CACL,iBACA,sBE3CC,WAA4B,CAOjC,YAAY,CAAE,gBAAe,cAAuC,GAAI,CAN9D,WAAgB,wBAUxB,GAHA,KAAK,eAAiB,GAAiB,GACvC,KAAK,YAAc,GAAc,IAE7B,MAAO,MAAK,gBAAmB,UAAY,KAAK,gBAAkB,GAAK,KAAK,gBAAkB,EAChG,KAAM,IAAI,OAAM,GAAG,KAAK,iEAG1B,GAAI,MAAO,MAAK,aAAgB,SAC9B,KAAM,IAAI,OAAM,GAAG,KAAK,iDAIxB,gBAAwB,CAAE,MAAO,MAAK,kBAEtC,aAAqB,CAAE,MAAO,MAAK,cTZlC,oBAA6B,EAAyB,CAC3D,aAAc,CACZ,MAAM,kBAGD,aAAa,EAAiB,CACnC,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAQ,WACvD,EAAI,AAAG,OAAI,AAAG,OAAI,EAAa,AAAG,UAAO,sBAAwB,AAAG,UAAO,IAC3E,EAAW,GAAY,EAAG,EAAO,aAEjC,CAAE,iBAAgB,oBAAqB,GAAgB,EAAS,IAAK,EAAS,OAAQ,EAAO,kBAEnG,MAAO,IAAY,EAAgB,EAAkB,EAAO,qBAInD,SAAQ,EAAkB,CACrC,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,aACX,EACA,EAAkC,GACR,CAC1B,GAAM,CAAE,aAAY,iBAAkB,GAAI,GAAsB,GAE1D,EAAW,KAAM,GAAW,GAE5B,CACJ,MAAO,EACP,OAAQ,GACN,KAAK,aAAa,GAEhB,EAAQ,EAAO,GACf,EAAS,EAAQ,GACvB,OAAS,GAAI,EAAG,EAAI,EAAO,OAAQ,IACjC,EAAO,GAAG,UACV,EAAQ,GAAG,UAGb,GAAM,GAAa,MAAM,KAAK,KAAM,GAAO,QAErC,EAAU,GACd,EACA,EACA,EAJmB,GAMnB,GAGI,EAAe,EAAS,2BAA2B,GACnD,EAAY,EAAS,UACrB,EAAO,EAAY,EAAa,MAChC,EAAO,EAAY,EAAa,OAEhC,EAAY,EAAM,YAClB,EAAU,EACb,IAAI,AAAC,GAAQ,CACZ,GAAM,CAAC,EAAK,GAAU,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACf,CAAC,GAAM,IAAS,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACrB,MAAO,IAAI,GACT,EAAW,GACX,GAAI,IACF,GACA,EACA,GAAQ,GACR,EAAS,GAEX,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,OAKtC,SAAM,UACN,EAAO,UACA,EAGC,qBAA8B,CACtC,MAAO,wBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KUhHlB,YAA8B,EAAuB,CAC1D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,EAGF,YAAgC,EAAuB,CAC5D,MAAO,IAAqB,GAIvB,oBAA+B,GAAe,GCd9C,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,QAAU,SACpB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,OAAQ,SAClB,GAAI,GAAM,QAAS,UAGR,GAAwB,CACnC,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAA+C,CAAC,QAAS,QAAS,QAElE,GAAqB,oBACrB,GAAoC,mCCvBjD,MAAoB,OCapB,GAAM,IAAW,AAAC,GAAa,MAAO,IAAQ,SAEvC,YAAwB,EAAa,CAC1C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mBAAmB,KAGrC,GAAI,MAAO,GAAO,oBAAuB,UACvC,KAAM,IAAI,OAAM,wDAAwD,EAAO,sBAGjF,GAAI,CAAC,GAAS,EAAO,eAAiB,EAAO,aAAe,GAAK,EAAO,aAAe,EACrF,KAAM,IAAI,OAAM,gEAAgE,EAAO,gBAGzF,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,MAAM,AAAC,GAAW,MAAO,IAAM,UAElD,KAAM,IAAI,OAAM,kEAAkE,KAAK,UAAU,EAAO,YAG1G,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,IAAI,AAAC,GAAW,GAAK,IAAI,MAAM,AAAC,GAAW,GAAS,EAAE,IAAM,GAAS,EAAE,IAE1F,KAAM,IAAI,OAAM,wEAAwE,KAAK,UAAU,EAAO,YAGhH,GAAI,EAAO,SACT,EAAC,MAAM,QAAQ,EAAO,UACnB,EAAO,QAAQ,SAAW,GAC1B,CAAC,EAAO,QAAQ,MAAM,KAEzB,KAAM,IAAI,OAAM,8EAA8E,KAAK,UAAU,EAAO,YCjDxH,MAAoB,OCApB,MAAoB,OAEb,YAAe,EAA6B,CACjD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,AAAG,MAAI,EAAG,AAAG,SAAO,qBAChC,MAAO,AAAG,OAAI,AAAG,OAAK,AAAG,MAAI,EAAG,IAAO,KDApC,YAA2B,EAAgB,EAAwC,CACxF,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAM,AAAG,MAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,SAAO,EAAK,EAAO,KAAK,QAAS,CAAC,EAAG,GAAI,SAClD,EAAM,AAAG,MAAI,EAAK,EAAO,GAAG,KAC5B,EAAM,AAAG,MAAI,EAAK,EAAO,GAAG,SAC5B,EAAM,AAAG,MAAI,EAAK,EAAO,KAAK,MACvB,GAAM,KEZjB,OAAoB,OAKb,YAAgC,EAAgB,EAA0C,CAC/F,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,mBAAgB,EAAK,EAAO,iBAAkB,EAAO,iBAAkB,CAAC,EAAG,GAAI,SACxF,EAAM,AAAG,OAAI,EAAK,EAAO,MAClB,GAAM,KCVjB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAEnE,WAAgC,EAAc,EAAiC,CAC7E,GAAM,GAAM,AAAG,YAAS,EAAe,IACjC,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,SAChB,CAAE,UAAW,GAAG,cAEX,CAAE,MAAK,WAGhB,WAAwC,EAAoB,EAAqB,EAAyC,CACxH,GAAM,GAAO,EAAkB,EAAY,EAAa,EAAG,GAAG,UACxD,EAAK,EAAuB,EAAa,GAAG,QAClD,MAAO,CAAE,OAAM,MAEjB,GAAM,GAA6B,GAAkC,EAAgB,GAErF,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACA,EACA,EACgE,CAChE,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAChC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAgB,GAClC,EAEJ,GAAI,EAAO,mBAAoB,CAC7B,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAAO,mBACjB,EAAkB,EAAI,EAAI,EAAG,SAC7B,EAA2B,EAAI,EAAI,SACjC,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAkB,GAAM,GAAM,EAAI,EAAI,EAAiB,EAAG,SACxE,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,cAErD,CACL,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAAkB,EAAI,EAAI,EAAiB,EAAG,SAC5D,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,UAG5D,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAE1E,MAAO,CAAE,SAAQ,iBChFnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAgC,EAA2B,CACzD,GAAM,GAAM,EAAmB,GAAG,QAAc,GAC1C,EAAU,EAAmB,GAAG,YAAkB,GACxD,MAAO,CAAE,MAAK,WAGhB,WAA2B,EAA4B,CACrD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,WAAwC,EAAmC,CACzE,GAAM,GAAO,EAAkB,GAAG,UAC5B,EAAK,EAAuB,GAAG,QACrC,MAAO,CAAE,OAAM,MAGjB,GAAM,GAA6B,GAA+B,GAClE,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACgE,CAChE,GAAM,GAAgC,GAEhC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAW,GAE7B,EAEJ,GAAI,EAAO,mBAAoB,CAE7B,GAAM,GAAc,EAAO,aAAe,EAAO,YAAY,QAAU,EACvE,EAAS,CACP,MAAO,EAAO,mBAAqB,EAAkB,SAAW,EAA2B,SAC3F,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAkB,cAG3B,GAAS,CACP,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAAkB,UAI7B,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBC7EZ,YAAwB,CAO7B,YAAY,CAAE,YAAW,kBAAuC,GAAI,CAN1D,WAAgB,oBAUxB,GAHA,KAAK,WAAa,GAAa,IAC/B,KAAK,gBAAkB,GAAkB,GAErC,MAAO,MAAK,YAAe,UAAY,KAAK,WAAa,IAAO,EAClE,KAAM,IAAI,OAAM,GAAG,KAAK,6DAG1B,GAAI,MAAO,MAAK,iBAAoB,UAAY,KAAK,iBAAmB,GAAK,KAAK,iBAAmB,EACnG,KAAM,IAAI,OAAM,GAAG,KAAK,qEAIxB,YAAoB,CAAE,MAAO,MAAK,cAElC,iBAAyB,CAAE,MAAO,MAAK,kBPJtC,oBAA6B,EAAmC,CAKrE,YAAY,EAA0B,CACpC,MAAM,cACN,GAAe,GACf,KAAK,QAAU,KAGN,SAA2B,CACpC,MAAO,MAAK,WAGH,kBAA2B,CACpC,MAAO,MAAK,OAAO,iBAAmB,KAAK,OAAO,QAAQ,OAAS,KAG1D,kBAA0B,CACnC,MAAO,GAAK,MAAK,gBAAkB,KAAK,OAAO,QAAQ,OAAS,GAG3D,cAAc,EAAgB,EAAiD,CACpF,GAAI,GAAM,GAAkB,EAAG,EAAO,OACtC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,GAAkB,EAAK,EAAO,OAC7B,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAgB,EAAsC,CACxE,GAAI,GAAM,KAAK,OAAO,mBAClB,GAAM,GAAU,EAAG,EAAO,MAAqB,QAAS,KACxD,GAAuB,EAAG,EAAO,OACrC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EACjE,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EAC1D,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAiB,EAAgC,CACnE,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,4CAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAc,AAAG,OAAK,EAAM,cAAc,EAAW,IAAQ,WACjE,SAAc,KAAK,OAAO,QACtB,GAAU,EAAa,KAAK,OAAO,SACnC,EACJ,EAAc,EAAY,IAAI,AAAG,SAAO,MACjC,KAAK,OAAO,mBACf,KAAK,aAAa,EAAa,GAC/B,KAAK,cAAc,EAAa,UAI3B,SAAQ,EAAkB,EAAyC,CAC9E,MAAO,MAAK,aAAa,KAAM,GAAW,GAAQ,QAGvC,QAAO,EAAkB,EAAoC,GAAgC,CACxG,GAAM,CAAE,YAAW,kBAAmB,GAAI,IAAkB,GACtD,EAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,EAAU,GACxC,EAAO,AAAG,OAAK,IAAM,AAAG,UAAQ,GAAK,GAAG,cACxC,EAAkB,CACtB,MAAO,EAAS,cAAc,GAC9B,OAAQ,EAAS,eAAe,IAG5B,EAAU,KAAM,MAAK,aAAa,EAAM,EAAS,2BAA2B,GAAI,GACtF,EAAI,UACJ,EAAK,UAEL,GAAM,GAAQ,EAAQ,IAAI,AAAC,GAAQ,EAAI,KACjC,EAAS,EAAQ,IAAI,AAAC,GAAQ,EAAI,OAClC,EAAc,EAAQ,IAAI,AAAC,GAAQ,EAAI,YACvC,EAAa,EAAQ,IAAI,AAAC,GAAQ,KAAK,OAAO,QAAQ,EAAI,QAgBhE,MAPmB,AAPH,IACd,EAAM,IAAI,AAAC,GAAQ,EAAI,QAAQ,IAC/B,EACA,KAAK,OAAO,aACZ,IAGyB,IAAI,AAAC,GAAQ,GAAI,IAC1C,EAAO,GACP,EAAY,GACZ,EAAW,GACX,EAAM,GACN,IAKM,qBAA8B,CACtC,MAAO,GAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,QAG1C,cAAc,EAAuB,CAC7C,GAAM,GAAc,KAAK,OAAO,aAAe,GAAe,qBAExD,EAAa,EAAc,EAAY,OAAS,OACtD,GAAI,IAAe,GAAK,IAAe,GAAK,IAAe,EACzD,KAAM,IAAI,OAAM,oEAAoE,2BAEtF,MAAO,IAAc,EAAS,KAAK,OAAQ,KAAK,gBAAiB,QAGnD,cACd,EACA,EACA,EACA,CACA,GAAM,CAAE,QAAO,UAAW,EACpB,EAAY,KAAK,IAAI,EAAO,GAC5B,EAAoB,EAAY,EAChC,EAAoB,EAAY,EAEhC,EAAW,EAAa,MAAM,GAC9B,EAAW,KAAK,OAAO,QAAQ,OAE/B,CAAC,EAAa,EAAc,GAAqB,AAAG,OAAK,IAAM,CACnE,GAAM,GAAW,EAAa,QAAQ,CAAC,EAAU,EAAU,EAAU,KAAK,kBAEpE,EAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACpE,EAAS,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACrE,EAAc,KAAK,gBACrB,AAAG,UAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,KAAK,OAAO,QAAQ,SAAU,GACrG,AAAG,SAAO,GACd,MAAO,CAAC,EAAO,EAAQ,KAGnB,EAAU,GACV,EAAa,KAAM,GAAa,QAChC,EAAY,KAAM,GAAY,QACpC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAS,EAAG,EAAS,EAAU,IAAU,CAChD,GAAM,GAAQ,GAAQ,EAAW,GAAK,GAAK,GAAQ,IACnD,GAAI,CAAC,GAAkB,EAAQ,EAAgB,CAC7C,GAAM,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAe,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACvG,EAAgB,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACxG,GAAK,EAAO,GAAa,EACzB,GAAK,GAAO,EAAc,EAC1B,GAAM,CAAE,MAAK,MAAK,UAClB,CAAE,cAAY,UAAU,KAAK,gBAC/B,KAAM,MAAK,sBAAsB,EAAkC,IACnE,CAAE,WAAY,EAAG,MAAO,GAC5B,EAAQ,KAAK,CACX,IAAK,GAAI,IAAY,GAAG,GAAG,GAAI,GAAY,GAAI,GAC/C,QACA,WAAY,EAAQ,GACpB,YACG,MAOb,SAAY,UACZ,EAAa,UACb,EAAkB,UACX,OAGK,uBAAsB,EAA4B,EAAmD,CACjH,GAAM,CAAE,MAAK,MAAK,UAAW,EACvB,EAAc,KAAM,GAAc,QACxC,MAAO,OAAM,KAAK,OAAO,QAAQ,QAAQ,KAAK,GAC3C,IAAI,CAAC,EAAG,IAAM,EAAY,GAAK,GAAK,GAAQ,IAC5C,IAAI,CAAC,EAAY,IAAW,EAC3B,aACA,WAED,OAAO,CAAC,EAAK,IAAU,EAAI,WAAa,EAAK,WAAa,EAAM,KA/MhE,MACS,AADT,GACS,qBAAuB,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,IAAK,KAAM,MQPrE,oBAAyB,GAAe,CAC7C,YAAY,EAA8B,GAAM,CAC9C,GAAM,GAAS,CACb,qBACA,aAAc,GACd,QAAS,CAAC,WACN,EACA,CACA,QAAS,GACT,QAAS,IAET,CACA,QAAS,GACT,gBAAiB,KAIvB,MAAM,MAGG,qBAA8B,CACvC,MAAO,MAAK,OAAO,sBAGV,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,MAAK,mBAAqB,GAAoC,GAG7D,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KChDrC,YAA0B,EAAuB,EAA8B,GAAM,CAC1F,GAAM,GAAM,GAAI,IAAW,GAC3B,SAAI,eAAe,GACZ,ECNF,oBAAsC,GAAkB,CAAxD,aAJP,CAIO,oBACK,WAAgB,4BCLrB,YAAwB,MAChB,MAEX,EACY,CACZ,MAAO,GAAY,KAAM,MAAK,YAGnB,MAAkB,CAC7B,KAAM,IAAI,OAAM,6CCRpB,OAAoB,OCDpB,OAAoB,OAOpB,kBACE,EACA,EAEA,EACA,EAEA,EAAwF,CAAC,CAAE,iBAAkB,EAC7G,CACA,GAAM,GAAY,EAAc,IAAI,AAAC,GAAkB,GAAoB,GACvE,EAAoB,GACpB,EAAa,WACX,EAAgD,GACpD,aAAoB,WAChB,KAAM,IAAmB,EAAO,GAChC,KAAM,IAAa,EAAO,IAG1B,EAAU,KAAM,GAAe,GAErC,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAGT,kBACE,EACA,EAEA,EACA,EAEA,EACA,CACA,MAAO,IACL,CAAC,GACD,EACA,KAAO,IAAU,EAAc,EAAM,IACrC,EACA,GC5CG,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAAqC,CAAC,QAAS,QAAS,QCF9D,oBAA+B,GAAe,CACnD,aAAc,CACZ,GAAM,GAAS,CACb,mBAAoB,GACpB,aAAc,GACd,QAAS,CAAC,QACV,QAAS,GACT,QAAS,GACT,mBAAoB,GACpB,YAAa,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,MAGzC,MAAM,MAGG,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,2BAGC,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KCvBrC,GAAM,GAAO,CAClB,eAAgB,GAAI,IACpB,iBAAkB,GAAI,IACtB,WAAY,GAAI,IAChB,kBAAmB,GAAI,IACvB,sBAAuB,GAAI,IAC3B,mBAAoB,GAAI,IACxB,kBAAmB,GAAI,IACvB,aAAc,GAAI,KAUP,GAAiB,CAAC,EAAkB,IAA6D,EAAK,eAAe,YAAY,EAAO,GASxI,GAAmB,CAAC,EAAkB,IAA+D,EAAK,iBAAiB,YAAY,EAAO,GAS9I,GAAa,CAAC,EAAkB,IAA0D,EAAK,WAAW,YAAY,EAAO,GAS7H,GAAsB,AAAC,GAAmE,EAAK,kBAAkB,gBAAgB,GAWjI,GAA0B,AAAC,GAAmE,EAAK,sBAAsB,gBAAgB,GAYzI,GAAwB,AAAC,GAA6D,EAAK,mBAAmB,sBAAsB,GASpI,GAA2B,AAAC,GAAmE,EAAK,kBAAkB,mBAAmB,GASzI,GAAsB,AAAC,GAAiF,EAAK,aAAa,oBAAoB,GAE9I,GAA0B,AAAC,GAAgB,EAAK,eAAe,KAAK,GACpE,GAA4B,AAAC,GAAgB,EAAK,iBAAiB,KAAK,GACxE,GAAsB,AAAC,GAAgB,EAAK,WAAW,KAAK,GAC5D,GAAwB,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACrE,GAA4B,AAAC,GAAgB,EAAK,sBAAsB,KAAK,GAC7E,GAA2B,AAAC,GAAgB,EAAK,mBAAmB,KAAK,GACzE,GAA0B,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACvE,GAAqB,AAAC,GAAgB,EAAK,aAAa,KAAK,GAG7D,GAAyB,GACzB,GAAc,GACd,GAAkB,GCjGxB,oBAAqE,GAAwB,CAClG,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAA0E,MACrE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAwB,KAAM,IAClC,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,KAEtD,KAAK,gBAGP,MAAO,GAAc,IACnB,CAAC,EAAc,IAAM,GAAmC,EAAc,EAAsB,KAIhG,kBAAmB,CACjB,MAAO,IAAI,IAA2B,KAAM,KAAK,SAI9C,gBAEG,GAA8F,MACzF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,GAAkB,KAAM,IAC5B,EACA,KAAK,MACL,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,GACpD,KAAK,gBAGP,MAAO,IAA0B,EAAc,GAGjD,kBAAmB,CACjB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA0C,CAClD,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCjFnD,oBAAkE,GAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAAuE,MAClE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAqB,KAAM,IAC/B,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,aAAa,oBAAoB,KAElD,KAAK,gBAGP,MAAO,GAAc,IAAI,CAAC,EAAc,IAAM,CAC5C,GAAM,CAAE,MAAK,SAAQ,qBAAsB,EAAmB,GAC9D,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,KAIpF,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA2F,MACtF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,MAAK,SAAQ,qBAAsB,KAAM,IAC/C,EACA,KAAK,MACL,AAAC,GAAS,EAAK,aAAa,oBAAoB,GAChD,KAAK,gBAGP,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,GAGlF,qBAAsB,CACpB,MAAO,IAAI,IAAiC,KAAM,KAAK,SAIpD,gBAEG,GAAoC,CAC5C,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCtFnD,oBAAqE,GAAwB,CAClG,YAEY,EAEA,EACV,CACA,QAJU,kBAEA,eAMP,gBAEG,GAAyE,MACpE,MAA8C,CACzD,GAAM,GAAgB,KAAM,MAAK,WAUjC,MAAO,AARa,MAAM,IACxB,EACA,KAAK,MACL,AAAC,GAAU,QAAQ,IAAI,EAAM,IAAI,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,KACzF,KACA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,OAGxD,IAAI,CAAC,EAAY,IAAM,GAAkC,EAAc,GAAI,IAGhG,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,SAI/D,gBAEG,GAA6F,MACxF,MAAwD,CACnE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAEF,GAAM,GAAa,KAAM,IACvB,EACA,KAAK,MACL,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,GACxD,KAEA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,MAG3E,MAAO,IAAyB,EAAc,GAGhD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,SP1DlE,oBAAkE,GAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,6BAKE,cAAyD,CACrE,MAAO,MAAK,mBACR,EAAK,sBACL,EAAK,oBAIN,gBAEG,GAAqE,MAChE,MAA6C,CACxD,GAAM,GAAgB,KAAM,MAAK,WAC3B,EAAa,EAAc,IAAI,AAAC,GAAQ,EAAI,WAE5C,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,GACrC,KAAM,IAAa,KAAK,MAAO,GAE7B,EAAsB,KAAM,SAAQ,IAAI,EAAM,IAClD,AAAC,GAAS,KAAK,YAAY,gBAAgB,KAG7C,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAAc,IAAI,CAAC,EAAc,IAAM,GAAiC,EAAc,EAAoB,KAGnH,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAyF,MACpF,MAAuD,CAClE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,aAAc,EAChB,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,CAAC,IACtC,KAAM,IAAa,KAAK,MAAO,CAAC,IAE9B,EAAY,KAAM,MAAK,YAAY,gBAAgB,EAAM,IAE/D,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,GAAiC,EAAc,GAGxD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SQzFnD,oBAA2C,GAAwB,CACxE,YAEY,EAEA,EAAgC,GAAI,GAC9C,CACA,QAJU,aAEA,iBAMP,gBAAiC,GAAqC,MAC9D,MAAgC,CAC3C,GAAM,CAAE,QAAO,WAAY,KAEvB,EACJ,GAAI,YAAmB,IAAyB,EAAS,EAAK,iBAAiB,YAAY,EAAO,WACzF,YAAmB,GAAuB,EAAS,EAAK,eAAe,YAAY,EAAO,WAC1F,YAAmB,IAAmB,EAAS,EAAK,WAAW,YAAY,EAAO,OACtF,MAAM,IAAI,OAAM,wHAErB,MAAO,GAGD,gCAAmE,CAEzE,MAAO,IAAI,SAAiC,KAAO,IAAY,CAC7D,GAAM,GAAa,KAAM,MAAK,MAC9B,EAAQ,EAAW,IAAI,AAAC,GAAc,GAAwB,GAAI,OAItE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,SAKJ,gBAAmC,GAA+C,MAC1E,MAA0C,CACrD,GAAM,GAAiB,KAAM,IAAI,IAAmB,KAAK,MAAO,KAAK,SACjE,EAAgC,EAAe,GACnD,SAAe,QAAQ,AAAC,GAAkB,CACxC,AAAI,EAAc,MAAQ,EAA8B,OAAO,GAAgC,KAE1F,EAGD,+BAA4E,CAElF,MAAO,IAAI,SAA2C,KAAO,IAAY,CACvE,GAAM,GAAY,KAAM,MAAK,MAC7B,EAAQ,EAAY,GAA4B,GAAI,GAAa,UAIrE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,SCpGJ,YAA0B,EAAkB,EAAgC,GAAI,GAA+C,CACpI,MAAO,IAAI,IAAqB,EAAO,GAGlC,YAAwB,EAAkB,EAAgC,GAAI,GAA6C,CAChI,MAAO,IAAI,IAAmB,EAAO,GCFvC,kBACE,EACA,EACyE,CACzE,MAAO,IAAe,EAAO,GAAI,GAAsB,EAAgB,CAAE,iBAAkB,KACxF,oBACA,sBAGL,kBACE,EACA,EAAoC,GACqC,CACzE,MAAO,IAAe,EAAO,GAAI,IAAkB,IAChD,oBACA,sBAGE,GAAM,IAAW,GC1BjB,YAA2B,EAA+B,EAA+B,CAC9F,GAAI,EAAK,SAAW,EAAK,OAAQ,KAAM,IAAI,OAAM,kDAEjD,GAAM,GAAQ,MAAM,KAAK,GACnB,EAAQ,MAAM,KAAK,GAEzB,MAAO,MAAK,KACV,EACG,IAAI,CAAC,EAAK,IAAM,EAAM,EAAM,IAC5B,OAAO,CAAC,EAAK,IAAS,EAAO,GAAQ,EAAI,ICJzC,YAAkB,CAKvB,YACE,EACA,EAA4B,GAC5B,CACA,KAAK,mBAAqB,EAE1B,GAAM,GAAa,MAAM,QAAQ,GAAU,EAAS,CAAC,GAErD,GAAI,CAAC,EAAW,OACd,KAAM,IAAI,OAAM,2DAGlB,GAAI,GAAQ,EACN,EAAoB,IAAM,UAAU,MAE1C,KAAK,oBAAsB,EAAW,IAAI,AAAC,GAAS,CAClD,GAAI,YAAgB,IAClB,MAAO,GAGT,GAAI,YAAgB,cAClB,MAAO,IAAI,IAAuB,IAAqB,CAAC,IAG1D,GAAI,EAAK,YAAc,EAAK,qBAAsB,cAChD,MAAO,IAAI,IAAuB,IAAqB,CAAC,EAAK,aAG/D,KAAM,IAAI,OAAM,4MAIT,qBAA+C,CAAE,MAAO,MAAK,uBAE7D,oBAA4B,CAAE,MAAO,MAAK,mBAE9C,oBAAoB,EAA+B,EAAqC,CAC7F,MAAO,GACJ,IAAI,AAAC,GAAM,GAAkB,EAAG,IAChC,OAAO,CAAC,EAAI,IAAO,EAAK,EAAI,GACxB,GAAY,QAAU,GAGxB,gBAAgB,EAA0C,CAC/D,MAAO,MAAK,mBACT,IAAI,CAAC,CAAE,cAAa,WAAY,GAAI,IACnC,EACA,KAAK,oBAAoB,EAAiB,KAE3C,OAAO,CAAC,EAAM,IAAU,EAAK,SAAW,EAAK,SAAW,EAAO,GAG7D,cAAc,EAA0C,CAC7D,GAAM,GAAY,KAAK,gBAAgB,GACvC,MAAO,GAAU,SAAW,KAAK,kBAC7B,EACA,GAAI,IAAU,UAAW,EAAU,UAGlC,QAAc,CACnB,MAAO,CACL,kBAAmB,KAAK,kBACxB,mBAAoB,KAAK,mBAAmB,IAAI,AAAC,GAAO,EAAG,iBAIjD,UAAS,EAAwB,CAC7C,GAAM,GAAqB,EAAK,mBAC7B,IAAI,AAAC,GAAY,GAAuB,SAAS,IACpD,MAAO,IAAI,IAAY,EAAoB,EAAK,qBC1E7C,YAAgC,EAAuB,CAC5D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECFF,YAA0B,EAAY,EAA4B,CACvE,GAAM,CAAE,QAAO,UAAW,GAAI,GAAW,EAAW,MAAO,EAAW,QAEtE,GAAI,GAAS,GAAK,GAAU,EAC1B,KAAM,IAAI,OAAM,uCAAuC,KAAK,UAAU,CAAE,QAAO,cAGjF,GAAI,MAAM,QAAQ,GAEhB,MAAQ,GAAuB,IAAI,AAAC,GAAQ,GAAc,EAAK,CAAE,QAAO,YAG1E,GAAI,GAAoB,GAAU,CAChC,GAAM,GAAmB,EAAQ,UAAU,QAAQ,EAAO,GACpD,EAAmB,EAAQ,mBAAmB,QAAQ,EAAiB,IAAI,MAAO,EAAiB,IAAI,QAC7G,MAAO,IAAwB,GAAwB,EAAS,GAAmB,GAGrF,MAAI,IAAoB,GACf,GAAwB,EAAS,EAAQ,UAAU,QAAQ,EAAO,IAGvE,YAAmB,IAAiB,YAAmB,GACjD,EAAgB,QAAQ,EAAO,GAGlC,EjJRT,GAAM,IAAQ,MAAO,UAAY,YAC3B,GAAW,MAAO,YAAc,aAAiB,MAAO,WAAU,WAAc,YACzE,GAAU,CAAE,QAAa,GAAmB,QAAM", + "mappings": ";;;;;;;4qCAAA,GAAA,IAGA,GAAA,GAAc,GAAA,yCCHd,oDAAO,aAA6B,CAClC,MAAO,OAAO,SAAW,UACpB,IACA,MAAO,KAAW,aAGlB,MAAO,UAAY,aAAe,CAAC,CAAC,QAAQ,WCNnD,uyFAAA,OAAoB,OCApB,qRCEO,YACL,EACA,EACA,EAAoB,GACpB,CASA,GARA,EAAI,YAEJ,EAAO,MAAM,GAAG,QAAQ,CAAC,CAAE,IAAG,KAAK,IAAY,CAC7C,GAAM,GAAO,EAAO,GACpB,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,KAGZ,EAAU,CACZ,GAAM,GAAO,EAAO,EAAO,OAAS,GAC9B,EAAK,EAAO,GAClB,GAAI,CAAC,GAAQ,CAAC,EACZ,OAGF,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,EAAG,EAAG,GAGtB,EAAI,SC1BN,2RAAA,OAAoB,OCOb,WAAwC,CAK7C,YAAY,EAAe,EAAgB,CACzC,GAAI,CAAC,GAAc,IAAU,CAAC,GAAc,GAC1C,KAAM,IAAI,OAAM,wFAAwF,KAAK,UAAU,CAAE,QAAO,cAGlI,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,QAEnC,SAAsB,CAC3B,MAAO,IAAI,GAAW,EAAI,KAAK,MAAO,EAAI,KAAK,UDrB5C,YAAkB,EAAa,EAAa,CACjD,MAAO,aAAqB,YAAU,EAAO,MAAM,SAAW,EAGzD,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,WAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAiB,EAAa,CACnC,MAAO,GAAM,GAAM,EAGd,YAAgB,EAAa,CAClC,MAAO,GAAM,GAAM,EAGd,YAAe,EAAa,EAAe,EAAG,CACnD,GAAM,GAAI,IAAM,EAChB,MAAO,MAAK,MAAM,EAAM,GAAK,EAGxB,YAAsB,EAAmB,CAC9C,MAAO,IAAO,EAAI,OAAS,EAAI,OAG1B,YAAmC,CAAE,QAAO,UAAuB,EAAmB,CAC3F,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,IAAI,GAAW,KAAK,MAAM,EAAQ,GAAQ,KAAK,MAAM,EAAS,IAGhE,YAAwB,EAAqB,CAClD,MAAO,GAAI,OAAO,CAAC,EAAK,IAAO,EAAI,IAAI,GAAK,GAAI,GAAM,EAAG,IACtD,IAAI,GAAI,GAAM,EAAI,OAAQ,EAAI,SAG5B,YAAe,EAAa,EAAe,EAAwB,CACxE,MAAO,OAAM,GAAK,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,EAAS,EAAI,GAGhD,YAAuB,EAAU,CACtC,MAAO,CAAC,CAAC,GAAQ,IAAQ,UAAc,IAAQ,WAAc,CAAC,OAAO,MAAM,IAAQ,IAAQ,EAGtF,YAA4B,EAAU,CAC3C,MAAO,IAAc,IAAQ,GAAO,GAAK,GAAO,EExD3C,WAA8B,CAKnC,YAAY,EAAW,EAAW,CAChC,KAAK,GAAK,EACV,KAAK,GAAK,KAGR,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,GAEvB,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,KAAa,CAClB,MAAO,IAAI,GAAM,KAAK,IAAI,KAAK,GAAI,KAAK,IAAI,KAAK,IAG5C,WAAoB,CACzB,MAAO,MAAK,KAAM,KAAK,GAAK,EAAM,KAAK,GAAK,GAGvC,OAAe,CACpB,MAAO,IAAI,GAAM,KAAK,MAAM,KAAK,GAAI,KAAK,MAAM,KAAK,MCtClD,WAAwD,OAC/C,QAAO,EAAoB,CACvC,MAAO,CAAC,CAAC,GAAQ,CAAC,EAAK,EAAG,EAAK,EAAG,EAAK,MAAO,EAAK,QAAQ,MAAM,UAGrD,kBAAiB,EAAU,EAAgB,EAAmC,GAAO,CACjG,GAAI,CAAC,EAAI,OAAO,GACd,KAAM,IAAI,OAAM,GAAG,oBAAyB,KAAK,UAAU,2DAG7D,GAAI,CAAC,GAA4B,GAAI,MAAQ,GAAK,EAAI,OAAS,GAC7D,KAAM,IAAI,OAAM,GAAG,cAAmB,EAAI,sBAAsB,EAAI,oCAYxE,YAAY,EAA4B,EAAmC,GAAM,CAC/E,GAAM,GAAO,GAAQ,GAEf,EAAS,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAO,EAAI,QAAQ,MAAM,IAC1D,EAAS,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAAQ,MAAM,IAE3D,GAAI,CAAC,GAAU,CAAC,EACd,KAAM,IAAI,OAAM,2EAA2E,KAAK,UAAU,MAG5G,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,EAC1B,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAC9B,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAQ,EAAI,KAAM,EAAI,OAAS,EAAI,KAE/D,EAAI,iBAAiB,CACnB,IAAG,IAAG,QAAO,UACZ,kBAAmB,GAEtB,KAAK,GAAK,EACV,KAAK,GAAK,EACV,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,MAE1B,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,WAE/B,OAAe,CAAE,MAAO,MAAK,KAE7B,MAAc,CAAE,MAAO,MAAK,KAE5B,QAAgB,CAAE,MAAO,MAAK,EAAI,KAAK,SAEvC,SAAiB,CAAE,MAAO,MAAK,EAAI,KAAK,UAExC,OAAe,CAAE,MAAO,MAAK,MAAQ,KAAK,UAE1C,UAAiB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,QAEpD,WAAkB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAEtD,aAAoB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,WAEvD,cAAqB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAE7D,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,UAAyB,CAC9B,GAAI,CACF,IAAG,IAAG,QAAO,UACX,KACE,EAAO,KAAK,IAAI,EAAQ,GAC9B,MAAI,GAAQ,GACV,IAAM,EAAO,EACb,GAAS,GAEP,EAAS,GACX,IAAM,EAAO,EACb,GAAU,GAGL,GAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,QAAQ,EAAuC,CACpD,GAAM,GAAS,GAAa,GAAM,EAAkB,MAAQ,EACtD,EAAS,GAAa,GAAM,EAAkB,OAAS,EAC7D,MAAO,IAAI,GAAI,CACb,EAAG,KAAK,EAAI,EACZ,EAAG,KAAK,EAAI,EACZ,MAAO,KAAK,MAAQ,EACpB,OAAQ,KAAK,OAAS,IAInB,IAAI,EAAc,EAA4B,CACnD,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAC5B,KAAK,EAAK,EAAO,EACjB,KAAK,EAAK,EAAO,EACjB,KAAK,MAAQ,EACb,KAAK,OAAS,GAEhB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,mBAAmB,EAAkB,EAAiC,CAC3E,GAAM,CACJ,IAAG,IAAG,QAAO,UACX,KACE,EAAW,KAAK,IAAI,EAAG,GACvB,EAAW,KAAK,IAAI,EAAG,GAEvB,EAAW,EAAQ,EACnB,EAAY,EAAS,EACrB,EAAe,KAAK,IAAI,EAAU,EAAW,GAC7C,EAAgB,KAAK,IAAI,EAAW,EAAY,GAEtD,MAAQ,IAAI,GAAI,CACd,EAAG,EAAU,EAAG,EAAU,MAAO,EAAc,OAAQ,IACrD,QAGC,MAAM,EAAY,EAA0B,CACjD,GAAM,CAAE,QAAO,UAAW,KACpB,EAAI,KAAK,EAAI,EACb,EAAI,KAAK,EAAI,EAEnB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,aAAa,EAAqB,EAAoB,CAC3D,GAAM,GAAI,KAAK,MAAQ,EACjB,EAAI,KAAK,OAAS,EAElB,EAAK,EACL,EAAK,EACP,EAAM,EACN,EAAM,EAEN,EAAI,KAAK,KACT,EAAI,KAAK,IACT,EAAK,KAAK,MACV,EAAK,KAAK,OAEd,MAAI,GAAK,GACP,GAAM,CAAC,EAAK,EAAa,EACzB,EAAK,GAEH,EAAK,GACP,GAAM,CAAC,EAAK,EAAc,EAC1B,EAAK,GAEH,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAEF,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAGC,CACL,KAAI,MAAK,KAAI,MAAK,IAAG,KAAI,IAAG,KAAI,IAAG,KAIhC,UAAU,EAAa,CAC5B,MAAO,IAAI,GAAI,CACb,KAAM,KAAK,KAAQ,EAAO,KAAO,KAAK,MACtC,IAAK,KAAK,IAAO,EAAO,IAAM,KAAK,OACnC,MAAO,KAAK,MAAS,EAAO,MAAQ,KAAK,MACzC,OAAQ,KAAK,OAAU,EAAO,OAAS,KAAK,SAC3C,WAAW,UCrMX,oBAA0B,EAAyC,CACxE,YAAY,EAAc,EAAa,EAAe,EAAgB,EAAmC,GAAO,CAC9G,MAAM,CACJ,OAAM,MAAK,QAAO,UACjB,KCTA,YAAsB,CAW3B,YACE,EACA,EACA,EACA,EACA,EACA,CACA,KAAK,WAAa,GAAI,GAAW,EAAU,MAAO,EAAU,QAC5D,KAAK,OAAS,EACd,KAAK,YAAc,EACnB,KAAK,WAAa,EAClB,KAAK,KAAO,GAAI,GAAI,GAAa,QAAQ,KAAK,eAGrC,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,eAEnC,YAAoB,CAAE,MAAO,MAAK,cAElC,MAAW,CAAE,MAAO,MAAK,QAEzB,YAAwB,CAAE,MAAO,MAAK,cAEtC,aAAqB,CAAE,MAAO,MAAK,UAAU,SAE7C,cAAsB,CAAE,MAAO,MAAK,UAAU,UAE9C,cAAmB,CAAE,MAAO,IAAI,GAAI,KAAK,MAAM,QAAQ,KAAK,UAAU,WAE1E,QAAQ,EAAe,EAAiC,CAC7D,MAAO,IAAI,IACT,KAAK,MACL,KAAK,WACL,KAAK,UACL,KAAK,YACL,CAAE,QAAO,aCzCR,mBAA4B,GAAyC,CAC1E,YACE,EACA,EACA,EACA,CACA,MAAM,EAAO,EAAO,GAAI,EAAa,GAGhC,QAAQ,EAAe,EAA+B,CAC3D,GAAM,CAAE,QAAO,cAAa,aAAc,MAAM,QAAQ,EAAO,GAC/D,MAAO,IAAI,GAAc,EAAO,EAAa,KCnB1C,YAAa,EAAW,EAAW,EAAiB,GAAM,CAC/D,GAAM,GAAQ,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,MAAO,EAAK,OAAS,KAAK,IAAI,EAAK,KAAM,EAAK,OAClF,EAAS,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,OAAQ,EAAK,QAAU,KAAK,IAAI,EAAK,IAAK,EAAK,MACpF,EAAe,EAAQ,EAE7B,MAAO,GACH,EAAgB,GAAK,KAAO,EAAK,KAAO,GACxC,EAAe,KAAK,IAAI,EAAK,KAAM,EAAK,MCPvC,YAAiB,EAA4B,CAClD,GAAM,GAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAExD,MAAO,IAAI,IAAY,EAAM,EAAM,EAAM,GCPpC,YACL,EACA,EACA,EACA,EAAiB,GACP,CACV,GAAI,GAAuB,EACxB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAC/B,IAAI,AAAC,GAAM,EAAE,UAEV,EAAiB,GAEvB,KAAO,EAAqB,OAAS,GAAG,CACtC,GAAM,GAAO,EAAqB,MAClC,EAAK,KAAK,GAEV,GAAM,GAAU,EAEV,EAAoB,GAC1B,OAAS,GAAI,EAAG,EAAI,EAAQ,OAAQ,IAAK,CACvC,GAAM,GAAM,EAAQ,GAEd,EAAU,EAAM,GAChB,EAAS,EAAM,GAErB,EAAQ,KAAK,GAAI,EAAS,EAAQ,IAGpC,EAAuB,EAAqB,OAC1C,CAAC,EAAG,IAAM,EAAQ,IAAM,GAI5B,MAAO,GCrCT,OAAoB,OAEb,WAAmB,EAAgB,EAAgC,CACxE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAG,EAAG,GAAK,EACZ,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAU,AAAG,UAAO,CAAC,EAAO,EAAO,GAAQ,GAEjD,MAAO,AAAG,QAAI,EAAG,KCVrB,OAAoB,OAUb,YACL,EACA,EAAyB,GACZ,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAQ,GAAS,EAAU,MAAM,MAAM,GAC9C,GAAI,IAAW,EACb,MAAO,GAGT,GAAM,GAAU,KAAK,IAAI,EAAS,GAC5B,EAAgB,KAAK,MAAM,EAAW,GAAgB,GAAM,IAC5D,EAAc,EAAS,EAAQ,EAAI,EAEnC,EAAsB,AAAC,GAA0C,CACrE,GAAM,GAAqB,EAAU,MAAM,QAC3C,SAAmB,GAAe,EAC3B,AAAG,QAAK,EAAoB,EAAG,YAGlC,EAAsB,EAAoB,GAC1C,EAAyB,EAAW,EAAoB,MAAM,GAM9D,EAAiB,CAJM,GAAiB,EAC1C,EAAoB,GACpB,KAIF,EACA,GAEC,OAAO,AAAC,GAAM,CAAC,CAAC,GAEhB,IAAI,AAAC,GAAiB,AAAG,QAAK,EAAG,YACpC,MAAO,AAAG,WAAO,EAAgB,KC7C9B,YAAsB,EAAmB,CAC9C,GAAM,GAAQ,EAAW,QACzB,OAAS,GAAI,EAAM,OAAS,EAAG,EAAI,EAAG,IAAK,CACzC,GAAM,GAAI,KAAK,MAAM,KAAK,SAAY,GAAI,IACpC,EAAI,EAAM,GAChB,EAAM,GAAK,EAAM,GACjB,EAAM,GAAK,EAEb,MAAO,GCDF,YAAiB,EAAW,CACjC,MAAO,GAAK,GAAI,KAAK,IAAI,CAAC,IAGrB,YAAwB,EAAW,CACxC,MAAO,MAAK,IAAI,EAAK,GAAI,ICHpB,oBAAmB,EAA2B,CACnD,YAAY,EAAW,EAAW,EAAe,EAAgB,EAAmC,GAAO,CACzG,MAAM,CACJ,IAAG,IAAG,QAAO,UACZ,KCHP,GAAM,IAAO,GACP,GAAO,IACP,GAAW,IAOV,OAA8C,CAOnD,YACE,EACA,EACA,EAAe,GAAI,GAAM,EAAG,GAC5B,CACA,GAAM,CAAE,QAAO,UAAW,EAC1B,KAAK,SAAW,GAAI,GAAW,EAAO,GACtC,KAAK,OAAS,EACd,KAAK,WAAa,EAA8B,IAC9C,AAAC,GAAO,EAAG,IAAI,GAAI,GAAM,EAAO,IAAS,IAAI,OAItC,QAAe,CAAE,MAAO,IAAI,GAAM,KAAK,OAAO,EAAG,KAAK,OAAO,MAE7D,aAAqB,CAAE,MAAO,MAAK,SAAS,SAE5C,cAAsB,CAAE,MAAO,MAAK,SAAS,UAE7C,YAAqB,CAAE,MAAO,MAAK,cAEnC,oBAA6B,CACtC,MAAO,MAAK,WAAW,IACrB,AAAC,GAAO,EAAG,IAAI,KAAK,QAAQ,IAAI,GAAI,GAAM,KAAK,WAAY,KAAK,eAI7D,QAAiC,EAAe,EAAmB,CACxE,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,CAAE,QAAO,WAIN,QAAiC,EAAW,EAAc,CAC/D,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,KAAK,SACL,GAAI,GAAM,EAAG,IAIV,aAAsC,EAAc,CACzD,MAAO,MAAK,QAAQ,EAAG,EAAG,EAAG,GAcxB,MACL,EACA,EAAkE,GAC7D,CACL,GAAI,EAAW,CACb,GAAM,GAAM,YAAqB,GAC7B,EAAU,IAAI,QACd,GAAI,GAAI,GAEZ,MAAO,MAAK,QAAQ,EAAI,EAAG,EAAI,GAAG,MAAM,KAAM,GAGhD,GAAM,CAAE,mBAAkB,iBAAkB,CAAE,iBAAkB,GAAO,cAAe,MAAQ,GAE9F,MAAI,GACK,KAAK,YAGP,KAAK,aAAa,GAGnB,WAAiB,CACvB,GAAM,GAAU,KAAK,2BAEf,CAAC,EAAe,EAAgB,GAAe,EAC/C,EAAc,AAAC,GAAc,EAAY,IAAI,GAAI,YACjD,EAAkB,GAAY,GAAiB,EAAY,IAAmB,EAE9E,EAAO,KAAK,MAAM,EAAiB,IAEnC,EAAW,GAAe,GAE1B,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAChD,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAEtD,MAAO,IAAI,IAAK,EAAG,EAAG,KAAK,IAAI,EAAM,KAAK,WAAa,GAAI,KAAK,IAAI,EAAM,KAAK,YAAc,IAGvF,aAAa,EAAsB,CACzC,GAAM,GAAM,GAAQ,KAAK,WACzB,MAAO,GAAI,IAAI,EAAI,MAAQ,EAAS,EAAI,OAAS,GAGzC,0BAAoC,CAC5C,KAAM,IAAI,OAAM,4DC3Hb,oBAA6B,EAAc,CACtC,0BAAoC,CAC5C,GAAM,GAAM,KAAK,UACjB,MAAO,CACL,EAAI,GACJ,EAAI,GACJ,GAAe,CAAC,EAAI,GAAI,EAAI,QCN3B,oBAA8B,EAAc,CAC1C,eAAyB,CAC9B,MAAO,MAAK,UAAU,MAAM,EAAG,IAG1B,gBAA0B,CAC/B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,iBAA2B,CAChC,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,SAAmB,CACxB,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,YAAsB,CAC3B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,aAAuB,CAC5B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,UAAoB,CACzB,MAAO,MAAK,UAAU,MAAM,GAAI,IAGxB,0BAAoC,CAC5C,MAAO,CACL,KAAK,aACL,KAAK,cACL,KAAK,YACL,IAAI,MC/BH,YAAsC,CAK3C,YAAY,EAAe,EAAkB,CAC3C,KAAK,OAAS,EACd,KAAK,UAAY,KAGR,QAAgB,CAAE,MAAO,MAAK,UAE9B,WAAmB,CAAE,MAAO,MAAK,UAErC,SAAS,EAAwB,GAAc,CACpD,MAAO,GAAG,KAAK,QAAQ,EAAe,KAAK,GAAM,KAAK,aAAe,OCjBlE,oBAAyB,EAAgB,OAChC,yBAAwB,EAAU,EAAgB,CAG9D,GAFA,EAAI,iBAAiB,EAAK,GAEtB,CAAC,GAAc,EAAI,OACrB,KAAM,IAAI,OAAM,GAAG,gCAAqC,EAAI,yBAMhE,YAAY,EAAiC,EAAe,CAC1D,MAAM,GACN,KAAK,OAAS,KAGL,QAAgB,CAAE,MAAO,MAAK,SCrBpC,YAA6B,CAKlC,YAAY,EAAe,EAA6B,CACtD,GAAM,MAAO,IAAU,SACrB,KAAM,IAAI,OAAM,sEAGlB,GAAI,CAAC,MAAM,QAAQ,IAAgB,EAAY,KAAK,AAAC,GAAS,CAAE,aAAgB,gBAC9E,KAAM,IAAI,OAAM,4FAGlB,KAAK,OAAS,EACd,KAAK,aAAe,KAGX,QAAgB,CAAE,MAAO,MAAK,UAE9B,cAA8B,CAAE,MAAO,MAAK,aAEhD,QAAc,CACnB,MAAO,CACL,MAAO,KAAK,MACZ,YAAa,KAAK,YAAY,IAAI,AAAC,GAAM,MAAM,KAAK,WAI1C,UAAS,EAAmC,CACxD,GAAM,GAAc,EAAK,YAAY,IAAI,AAAC,GAAW,GAAI,cAAa,IACtE,MAAO,IAAI,IAAuB,EAAK,MAAO,KC1B3C,oBAA2B,GAAW,OAC7B,2BAA0B,EAAU,EAAgB,CAGhE,GAFA,GAAW,wBAAwB,EAAK,GAGtC,CAAC,GAAmB,EAAI,QACrB,CAAC,GAAmB,EAAI,YAE3B,KAAM,IAAI,OAAM,GAAG,kCAAuC,EAAI,eAAe,EAAI,6CAQrF,YAAY,EAAiC,EAAe,EAAe,EAAoB,CAC7F,MAAM,EAAK,GACX,KAAK,OAAS,EACd,KAAK,YAAc,KAGV,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,cCvBzC,YAA6B,EAAwC,CAC1E,MAAO,GAAI,oBAAqB,GAG3B,YAGL,EACA,EAC4B,CAE5B,MAAO,IAAK,KADM,CAAE,cCdf,aAAyC,CAC9C,GAAM,GAAQ,OAAO,MACrB,GAAI,CAAC,EAAO,KAAM,IAAI,OAAM,gEAM5B,MAAO,CACL,OAAQ,kBACR,yBACA,MAAO,iBACP,UACA,MAAO,iBACP,oBAAqB,IAAM,SAAS,cAAc,UAClD,mBAAoB,IAAM,SAAS,cAAc,OACjD,QACA,SAbe,IAAM,CACrB,KAAM,IAAI,OAAM,iECLb,YAA0B,EAAsB,CACrD,GAAI,GAAiB,GAErB,GAAI,CAAC,EACH,GAAI,CAEF,EAAa,oBACN,EAAP,CACA,EAAiB,EAAI,WAYzB,MAAO,CACL,SATe,EACb,AAAC,GAAqB,GAAI,SAAgB,CAAC,EAAS,IAAW,CAC/D,EAAG,SAAS,EAAU,CAAC,EAAU,IAAoB,EAAM,EAAO,GAAO,EAAQ,MAEjF,IAAM,CACN,KAAM,IAAI,OAAM,qEAAqE,OCfpF,aAAwC,CAE7C,GAAM,GAAS,OAAO,QAAa,OAAO,kBACpC,EAAQ,OAAO,OAAS,OAAO,iBAE/B,EAAsB,IAAM,CAChC,GAAI,EAAQ,MAAO,IAAI,GACvB,KAAM,IAAI,OAAM,+EAGZ,EAAqB,IAAM,CAC/B,GAAI,EAAO,MAAO,IAAI,GACtB,KAAM,IAAI,OAAM,6EAGZ,EAAQ,OAAO,MAGf,EAAa,KAEnB,MAAO,CACL,OAAQ,GAAU,KAAM,GACxB,yBAA0B,OAAO,0BAA4B,KAAM,GACnE,MAAO,GAAS,KAAM,GACtB,UAAW,OAAO,WAAa,KAAM,GACrC,MAAO,OAAO,kBAAoB,KAAM,GACxC,sBACA,qBACA,WACG,GCjCA,aAA8B,CACnC,MAAO,OAAO,SAAW,UACpB,MAAO,WAAa,aACpB,MAAO,mBAAqB,aAC5B,MAAO,oBAAsB,aAC7B,MAAO,mBAAqB,aAC5B,MAAO,YAAc,aACrB,MAAO,2BAA6B,YCH3C,OAAyB,QAGrB,EAEJ,aAA+B,CAC7B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,yEAElB,MAAO,GAGT,YAAgB,EAAkB,CAChC,EAAc,EAGhB,aAAsB,CAGpB,MAAI,MAAoB,GAAO,MAC3B,kBAAmB,GAAO,MACvB,KAGT,YAAqB,EAA2B,CAK9C,GAJK,GACH,KAGE,CAAC,EACH,KAAM,IAAI,OAAM,8EAGlB,GAAM,CAAE,SAAS,EAAY,OAAQ,QAAQ,EAAY,OAAU,EACnE,EAAY,OAAS,EACrB,EAAY,MAAQ,EACpB,EAAY,oBAAsB,EAAI,qBAAwB,KAAM,GAAI,IACxE,EAAY,mBAAqB,EAAI,oBAAuB,KAAM,GAAI,IAEtE,EAAY,UAAY,EAAI,WAAa,EAAY,UACrD,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,SAAW,EAAI,UAAY,EAAY,SAG9C,GAAM,GAAM,CACjB,UACA,UACA,cACA,oBACA,oBACA,mBACA,eACA,aACA,sBAGF,KC3DO,YAAsB,EAAmB,CAC9C,MAAI,CAAC,EAAI,YAAc,MAAO,IAAQ,SAC7B,SAAS,eAAe,GAE1B,ECHF,WAA6B,EAA4F,CAC9H,GAAM,CAAE,SAAQ,4BAA6B,EAAI,SAEjD,GAAI,YAAqB,GACvB,MAAO,GAGT,GAAM,GAAS,GAAa,GAE5B,GAAI,CAAE,aAAkB,IACtB,KAAM,IAAI,OAAM,kEAGlB,GAAM,GAAM,EAAO,WAAW,MAC9B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,GCfF,GAAK,IAAL,UAAK,EAAL,CAEL,WAAW,WAEX,YAAY,YAEZ,cAAc,cAEd,eAAe,iBARL,aAoBL,YAA4D,CAajE,YAAY,EAAiC,GAAI,CAC/C,GAAM,CACJ,iBAAgB,kBAAiB,YAAW,WAAU,YAAW,WAC/D,EACJ,KAAK,eAAiB,GAAkB,GAAe,SACvD,KAAK,gBAAkB,GAAmB,qBAC1C,KAAK,UAAY,GAAa,yBAC9B,KAAK,SAAW,GAAY,GAC5B,KAAK,UAAY,GAAa,UAC9B,KAAK,QAAU,GAAW,IAIvB,QAAoB,CAOzB,YACE,EACA,EACA,EAAiC,GACjC,CAEA,KAAK,KAAO,MAAO,IAAS,SACxB,CAAC,GACA,YAAgB,IAAgB,EAAK,KAAO,EACjD,KAAK,OAAS,EACd,KAAK,QAAU,GAAI,IAAqB,GAG1C,aAAa,EAAuC,CAClD,GAAM,CAAE,WAAY,KAAK,QACzB,MAAO,MAAK,KAAK,IAAI,AAAC,GAAM,EAAI,YAAY,GAAG,OAAO,OAAO,CAAC,EAAI,IAAQ,EAAK,EAAK,EAAK,EAAK,GAAM,EAAI,EAG1G,eAAwB,CACtB,GAAM,CAAE,WAAU,WAAY,KAAK,QACnC,MAAO,MAAK,KAAK,OAAS,EAAY,EAAI,EAG5C,aAAa,EAA+B,EAAkC,CAC5E,GAAM,CAAE,kBAAmB,KAAK,QAC1B,EAAc,IAAmB,GAAe,cAAgB,IAAmB,GAAe,UAClG,EAAa,IAAmB,GAAe,aAAe,IAAmB,GAAe,aAEhG,EAAiB,KAAK,aAAa,GACnC,EAAkB,KAAK,gBACvB,EAAK,EAAc,KAAK,OAAO,EAAI,EAAiB,KAAK,OAAO,EAChE,EAAI,EAAa,KAAK,OAAO,EAAI,EAAkB,KAAK,OAAO,EAGrE,GAAI,EAAY,CACd,GAAM,CAAE,QAAO,UAAW,EACpB,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAQ,GAAiB,GACrD,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAS,GAAkB,GAC7D,MAAO,CAAE,EAAG,EAAM,EAAG,GAEvB,MAAO,CAAE,IAAG,KAGd,KAAK,EAAkE,CACrE,GAAM,GAAS,GAAa,GACtB,EAAM,EAAoB,GAE1B,CACJ,kBAAiB,YAAW,WAAU,YAAW,WAC/C,KAAK,QAET,EAAI,KAAO,GAAG,OAAc,IAC5B,GAAM,GAAe,KAAK,aAAa,GACjC,EAAa,KAAK,gBAExB,EAAI,UAAY,EAChB,GAAM,GAAY,KAAK,aAAa,EAAK,GACzC,EAAI,SAAS,EAAU,EAAG,EAAU,EAAG,EAAc,GAErD,EAAI,UAAY,EAChB,KAAK,KAAK,QAAQ,CAAC,EAAU,IAAM,CACjC,GAAM,GAAI,EAAU,EAAU,EACxB,EAAI,EAAU,EAAU,EAAM,GAAI,GAAK,EAC7C,EAAI,SAAS,EAAU,EAAG,OC5GzB,YAAqB,CAS1B,YAAY,EAA2B,GAAI,CACzC,GAAM,CACJ,WAAU,YAAW,QAAO,oBAC1B,EACJ,KAAK,SAAW,GAAY,qBAC5B,KAAK,UAAY,GAAa,EAC9B,KAAK,MAAQ,EAEb,GAAM,GAA0B,CAC9B,eAAgB,GAAe,YAC/B,gBAAiB,KAAK,UAExB,KAAK,iBAAmB,GAAI,IAAqB,IAAK,KAA4B,MAI/E,QAAc,CAKnB,YACE,EACA,EAA2B,GAC3B,CACA,KAAK,IAAM,GAAI,GAAI,GACnB,KAAK,QAAU,GAAI,IAAe,GAGpC,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CAAE,WAAU,aAAc,KAAK,QAE/B,CACJ,IAAG,IAAG,QAAO,UACX,KAAK,IACT,EAAI,YAAc,EAClB,EAAI,UAAY,EAChB,EAAI,WAAW,EAAG,EAAG,EAAO,GAE5B,GAAM,CAAE,SAAU,KAAK,QACvB,AAAI,GACF,GAAI,IAAc,CAAC,GAAQ,CAAE,EAAG,EAAK,EAAY,EAAI,KAAK,KAAK,QAAQ,kBAAkB,KAAK,KC1D7F,YACL,EACA,EACA,CAGA,AAFwB,OAAM,QAAQ,GAAc,EAAa,CAAC,IAElD,QAAQ,AAAC,GAAQ,CAE/B,GAAM,GAAQ,YAAe,GACzB,EAAI,MACH,GAAoB,GAAO,EAAI,UAAU,MAAQ,OAGhD,EAAM,YAAe,GACvB,EAAI,IACH,GAAoB,GAAO,EAAI,UAAU,IAAM,GAAI,GAAI,GAEtD,EAAQ,EAAQ,GAAG,GAAM,KAAW,OAC1C,GAAI,IAAQ,EAAK,CAAE,UAAS,KAAK,KC1BrC,OAAoB,OCEb,YAAuB,EAAsD,CAClF,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAQ,aAAiB,IAAS,EAAM,UAClC,YAAiB,IAAS,EAAM,YAAc,ECH/C,YAA0B,EAAgE,CAE/F,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,GAAI,YAAiB,GAAI,SAAS,QAAU,GAAc,GACxD,MAAO,GAAQ,MAGjB,WAAiB,EAAU,CACzB,AAAI,CAAC,EAAE,eAEP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAO,IAGT,WAAgB,EAAU,CACxB,AAAI,CAAC,EAAE,eACP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAQ,IAGV,EAAM,iBAAiB,OAAQ,GAC/B,EAAM,iBAAiB,QAAS,KCxB7B,YAAuB,EAAsC,CAClE,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,AAAM,YAAe,OAAO,EAAO,GAAI,OAAM,qDAC7C,GAAM,GAAS,GAAI,YACnB,EAAO,OAAS,IAAM,CACpB,AAAI,MAAO,GAAO,QAAW,UAAU,EAAO,GAAI,OAAM,qEACxD,GAAM,GAAM,EAAI,SAAS,qBACzB,EAAI,OAAS,IAAM,EAAQ,GAC3B,EAAI,QAAU,EACd,EAAI,IAAM,EAAO,QAEnB,EAAO,QAAU,EACjB,EAAO,cAAc,KCXlB,YAA4B,EAA0F,CAC3H,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAI,aAAiB,GACZ,GAAI,GAAW,EAAM,aAAc,EAAM,eAE9C,YAAiB,GACZ,GAAI,GAAW,EAAM,WAAY,EAAM,aAEzC,GAAI,GAAW,EAAM,MAAO,EAAM,QCNpC,YAAsB,CAAE,QAAO,UAA0C,CAC9E,GAAM,CAAE,uBAAwB,EAAI,SAC9B,EAAS,IACf,SAAO,MAAQ,EACf,EAAO,OAAS,EACT,EAGF,YAA+B,EAAwD,EAAuC,CACnI,GAAM,CAAE,aAAc,EAAI,SAE1B,GAAI,CAAE,aAAiB,KAAc,CAAC,GAAc,GAClD,KAAM,IAAI,OAAM,8DAGlB,GAAM,CAAE,QAAO,UAAW,GAAQ,GAAmB,GAC/C,EAAS,GAAa,CAAE,QAAO,WAErC,MAAI,aAAiB,GACnB,EAAoB,GAAQ,aAAa,EAAO,EAAG,GAEnD,EAAoB,GAAQ,UAAU,EAAO,EAAG,EAAG,EAAO,GAErD,EC7BT,OAAoB,OAKpB,kBACE,EACA,EAC4B,CAC5B,GAAM,GAAe,GAAU,EAAI,SAAS,sBAEtC,CAAC,EAAQ,EAAO,GAAe,EAAU,MAAM,MAAM,EAAW,GAAa,EAAI,GACjF,EAAc,AAAG,QAAK,IAAM,EAAU,KAAK,EAAQ,EAAO,GAAa,SAC7E,YAAM,AAAG,YAAQ,SAAS,EAAa,GAEvC,EAAY,UAEL,ECfF,YAAwB,EAAY,CACzC,GAAM,CAAE,QAAO,SAAQ,SAAU,EAAI,SAErC,MAAO,aAAiB,IACnB,YAAiB,IACjB,YAAiB,GCPxB,MAAoB,OCKb,YAAuB,EAA6C,EAAmB,EAAuB,GAAO,CAC1H,GAAM,CAAE,QAAO,UAAW,EAAI,SAE9B,GAAI,CAAE,aAAiB,IAAS,YAAiB,IAC/C,KAAM,IAAI,OAAM,4EAGlB,GAAI,GAAa,EAAG,MAAO,IAAa,CAAE,MAAO,EAAG,OAAQ,IAC5D,GAAM,GAAO,GAAmB,GAC1B,EAAQ,EAAY,KAAK,IAAI,EAAK,OAAQ,EAAK,OAC/C,EAAQ,EAAQ,EAAK,MACrB,EAAS,EAAQ,EAAK,OAEtB,EAAe,GAAa,CAAE,MAAO,EAAW,OAAQ,IACxD,EAAc,YAAiB,GAAS,EAAQ,GAAsB,GAEtE,EAAS,KAAK,IAAI,EAAQ,GAAU,EACpC,EAAK,GAAe,EAAQ,EAAS,EAAS,EAC9C,EAAK,GAAe,EAAS,EAAQ,EAAS,EACpD,MAAI,GAAY,MAAQ,GAAK,EAAY,OAAS,GAAG,EAAoB,GAAc,UAAU,EAAa,EAAI,EAAI,EAAO,GAEtH,EDdF,YAAe,CAapB,YACE,EACA,EAA6B,GAC7B,CAfM,mBAAkD,GAElD,eAAiC,GAIjC,wBAA8B,GAE9B,sBAA+B,GAQrC,GAAI,CAAC,MAAM,QAAQ,GACjB,KAAM,IAAI,OAAM,4HAA4H,KAG9I,KAAK,mBAAqB,EAC1B,KAAK,WAAa,EAAO,OAEzB,EAAO,QAAQ,CAAC,EAAO,IAAQ,CAC7B,GAAI,GAAW,GAAQ,CACrB,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAO,EAAM,MACnC,OAGF,GAAI,EAAW,GAAQ,CACrB,GAAM,GAAa,EAAc,MAAM,GACvC,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,yCAAyC,8CAG3D,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAQ,EAAc,MAAM,MAAM,GACxD,OAGF,GAAM,GAAU,YAAyB,GAAI,SAAS,OAAS,EAAQ,GAAsB,GAC7F,KAAK,UAAU,GAAO,EACtB,KAAK,iBAAiB,GAAO,CAAC,EAAO,OAAQ,EAAO,MAAO,QAIpD,eAAiD,CAC1D,MAAO,MAAK,iBAGH,WAAgC,CACzC,MAAO,MAAK,aAGH,eAAwB,CACjC,MAAO,MAAK,UAAY,GAAK,KAAK,sBAGzB,YAAoB,CAC7B,MAAO,MAAK,cAGH,kBAA8B,CACvC,MAAO,MAAK,oBAGH,YAAgC,CACzC,MAAO,MAAK,cAGH,0BAAwC,CACjD,MAAO,IAAM,KAAK,UAAW,EAAG,GAAG,IACjC,CAAC,EAAG,IAAa,KAAK,2BAA2B,IAI9C,SAAS,EAAiE,CAC/E,MAAO,MAAK,SAAS,IAAa,KAAK,aAAa,GAG/C,mBAAmB,EAA4B,CACpD,MAAO,MAAK,iBAAiB,GAGxB,eAAe,EAA0B,CAC9C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,cAAc,EAA0B,CAC7C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,2BAA2B,EAA8B,CAC9D,GAAI,MAAO,MAAK,WAAc,SAC5B,KAAM,IAAI,OAAM,yFAGlB,GAAM,GAAQ,KAAK,cAAc,GAC3B,EAAS,KAAK,eAAe,GACnC,MAAO,IAA0B,CAAE,QAAO,UAAU,KAAK,WAYpD,cAAc,EAAmB,EAA0B,GAAmB,CACnF,YAAK,WAAa,EAEX,AAAG,OAAK,IAAM,CACnB,GAAM,GAAe,GAAM,KAAK,UAAW,EAAG,GAAG,IAAI,AAAC,GAAa,CACjE,GAAM,GAAQ,KAAK,SAAS,GAE5B,GAAI,YAAoB,UAAQ,CAE9B,GAAI,GAAY,EAAW,GAAS,EAAQ,EAAM,aAElD,SAAY,GAAY,EAAW,GAE/B,GAAU,MAAM,KAAO,GAAa,EAAU,MAAM,KAAO,IAC7D,GAAY,AAAG,QAAM,eAAe,EAAW,CAAC,EAAW,KAGtD,EAAU,KAAK,EAAW,EAAW,GAG9C,GAAI,YAAiB,GAAI,SAAS,OAChC,MAAO,AAAG,WAAQ,WAAW,GAAc,EAAO,EAAW,IAG/D,KAAM,IAAI,OAAM,+BAA+B,8FAAqG,OAOtJ,MAHoB,AAAG,SAAM,EAAa,IAAI,AAAC,GAAM,AAAG,OAAK,EAAG,aAAa,KAAK,KAAK,UAAW,EAAW,EAAW,OE3I9H,iBAAiC,EAAsC,CACrE,GAAI,YAAkB,IACpB,MAAO,GAGT,GAAM,GAAgB,MAAM,QAAQ,GAChC,EACA,CAAC,GAEL,GAAI,CAAC,EAAc,OACjB,KAAM,IAAI,OAAM,4CAGlB,GAAM,GAAa,AAAC,GAAiB,MAAM,QAAQ,GAAU,mBAAmB,KAAS,GAEnF,EAAa,EAAc,IAAI,IAErC,SAAW,QAAQ,CAAC,EAAO,IAAM,CAC/B,GAAI,CAAC,GAAe,IAAU,CAAC,GAAW,IAAU,CAAC,EAAW,GAC9D,KAAI,OAAO,GAAc,IAAO,SACxB,GAAI,OAAM,eAAe,EAAW,sEAAsE,EAAc,MAG1H,GAAI,OAAM,eAAe,EAAW,iIAG5C,GAAI,EAAW,GAAQ,CAErB,GAAM,GAAY,EAAM,MAAM,GAC9B,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,eAAe,EAAW,iCAAiC,iDAMjF,KAAM,SAAQ,IACZ,EAAW,IAAI,AAAC,GAAU,GAAe,IAAU,GAAiB,KAG/D,GAAI,IAAS,EAAY,MAAM,QAAQ,ICtChD,kBACE,EACA,EAC8B,CAC9B,GAAM,CAAE,UAAW,EAAI,SAEnB,EAAS,EAEb,GAAI,CAAE,aAAiB,IAAS,CAC9B,GAAM,GAAW,KAAM,GAAW,GAElC,GAAI,EAAS,UAAY,EACvB,KAAM,IAAI,OAAM,8CAGlB,GAAM,GAAiB,EAAS,SAAS,GACzC,EAAS,YAA0B,GAC/B,EACA,KAAM,IAAoB,GAGhC,GAAM,GAAM,EAAoB,GAQhC,MAAO,AAPO,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAO,MAAO,EAAO,QAAQ,IAAI,QAC7C,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAO,MAAO,EAAO,SAE/C,IAAI,CAAC,CAChB,IAAG,IAAG,QAAO,YACT,CACJ,GAAM,GAAU,GAAa,CAAE,QAAO,WACtC,MAAI,GAAQ,GAAK,EAAS,GAAG,EAAoB,GAAS,aAAa,EAAI,aAAa,EAAG,EAAG,EAAO,GAAS,EAAG,GAC1G,IClDX,OAAoB,OAgBpB,kBACE,EACA,EACwB,CACxB,GAAI,CAAC,GAAW,IAAgB,CAAC,EAAW,GAC1C,KAAM,IAAI,OAAM,6DAGlB,GAAI,EAAW,IAAgB,EAAY,MAAM,GAAK,EACpD,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAW,EAAU,GAAe,EAAY,MAAM,MAAM,EAAW,GAAe,EAAI,GAajG,MAJoB,AAPN,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAU,GAAW,IACjC,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAU,IAEvB,IAAI,CAAC,CAC7B,IAAG,IAAG,QAAO,YACT,AAAG,WAAQ,EAAY,KAAK,EAAW,EAAU,GAAc,CAAC,EAAG,EAAG,GAAI,CAAC,EAAQ,EAAO,OCtCpG,kBACE,EAEA,EACmB,CACnB,GAAM,CAAE,SAAU,EAAI,SAChB,EAAM,KAAM,GAAM,EAAK,GAC7B,GAAI,CAAE,GAAI,OAAS,KACjB,KAAM,IAAI,OAAM,qBAAqB,EAAI,WAAW,EAAI,yBAAyB,EAAI,OAEvF,MAAO,GCTT,kBAAiC,EAAwC,CACvE,GAAM,GAAM,KAAM,IAAa,GACzB,EAAO,KAAO,GAAK,OAEzB,GAAI,CAAC,EAAK,KAAK,WAAW,UACxB,KAAM,IAAI,OAAM,wEAAwE,EAAK,kBAAkB,EAAI,OAErH,MAAO,IAAc,GCRvB,kBAAmC,EAAyB,CAC1D,MAAQ,MAAM,IAAa,IAAM,OCDnC,kBAAsC,EAAoC,CACxE,MAAO,IAAI,cAAa,KAAO,MAAM,IAAa,IAAM,eCH1D,OAAoB,OCAb,YAAsB,EAAyB,EAA0B,CAC9E,GAAM,GAA0B,GAAG,0BAEnC,GAAI,CAAC,EACH,MAAO,CACL,aAAc,GACd,YAAa,GAIjB,GAAI,IAAQ,IACV,MAAO,CACL,aAAc,IACd,YAAa,IAAI,KAIrB,GAAM,GAAW,EAAI,WAAW,WAAa,UAAY,EAAI,WAAW,YAAc,WAAa,GACnG,EAAM,EAAI,QAAQ,EAAU,IAE5B,GAAM,GAAQ,EAAI,MAAM,KAAK,OAAO,AAAC,GAAM,GAErC,EAAe,EAAI,SAAS,SAC9B,EAAM,EAAM,OAAS,GACrB,EAEA,EAAe,EAAY,GAAI,SAAS,SAAW,EAAM,MAAM,EAAG,EAAM,OAAS,GAAK,GAAO,KAAK,KACtG,SAAe,EAAI,WAAW,KAAO,IAAI,IAAiB,EAEnD,CACL,eACA,YAAa,IAAiB,IAAM,IAAI,IAAiB,GAAG,KAAgB,KD1BhF,kBACE,EACA,EAC4B,CAC5B,GAAM,CAAE,cAAa,gBAAiB,GAAa,EAAK,GAClD,EAAW,KAAM,IAAuC,GAE9D,MAAO,AAAG,OAAG,YAAY,EAAU,GET9B,YAAyB,EAAoB,EAAwB,EAA8B,GAAO,CAC/G,GAAM,CAAE,QAAO,UAAW,EACtB,GAAmB,GACnB,EACJ,SAAM,MAAQ,EACd,EAAM,OAAS,EACR,CAAE,QAAO,UCTlB,OAAoB,OCApB,OAAoB,OAOb,WAAyC,CAC9C,YAAY,EAAc,CAIhB,aAAkC,OAElC,oBAAiC,GALzC,KAAK,MAAQ,KASJ,SAAiC,CAAE,MAAO,MAAK,WAE/C,gBAAgC,CAAE,MAAO,MAAK,kBAE9C,WAAoB,CAAE,MAAO,CAAC,CAAC,KAAK,OAExC,iBAAiB,EAA8B,CACpD,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,MAAO,GAAI,GAGN,sBAAsB,EAAmB,EAAmB,CACjE,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,EAAI,GAAS,UACb,EAAI,GAAW,EAGV,cAAe,CACpB,MAAO,MAAK,eAAe,IAAI,CAAC,CAAE,eAAiB,EACjD,KAAM,EACN,OAAQ,KAAK,iBAAiB,MAI3B,oBAAqB,CAC1B,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,EAAM,iBAAqB,cAGnE,iBAAkB,CACvB,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,CAAE,GAAM,iBAAqB,eAGrE,UAAW,CAChB,KAAK,kBAAkB,QAAQ,CAAC,CAAE,OAAM,YAAa,CACnD,KAAK,sBAAsB,EAAM,EAAO,cAIrC,QAAS,CACd,KAAK,qBAAqB,QAAQ,CAAC,CAAE,OAAM,OAAQ,KAAe,CAChE,GAAM,GAAS,AAAG,UAAO,EAAS,YAClC,EAAS,UACT,KAAK,sBAAsB,EAAM,KAI9B,QAAQ,EAA4B,GAAM,CAC/C,KAAK,eAAe,QAAQ,AAAC,GAAU,CACrC,GAAI,GAAoB,EAAM,OAAO,WACnC,KAAM,IAAI,OAAM,mDAAmD,EAAM,QAE3E,EAAM,OAAO,YAEf,KAAK,QAAU,OAGV,iBAAgC,CACrC,MAAO,IAAI,cACT,KAAK,eACF,IAAI,CAAC,CAAE,YAAa,MAAM,KAAK,EAAO,aACtC,OAAO,CAAC,EAAM,IAAQ,EAAK,OAAO,UAI5B,MAAK,EAAgE,CAChF,GAAI,YAAwB,cAAc,CACxC,KAAK,eAAe,GACpB,OAEF,KAAM,MAAK,YAAY,QAGZ,aAAY,EAAyB,CAChD,GAAI,GAAO,MAAO,IAAQ,SACxB,KAAM,IAAI,OAAM,GAAG,KAAK,0CAE1B,GAAM,GAAY,KAAM,IAAc,EAAK,KAAK,uBAChD,KAAK,kBAAkB,QAGZ,cAAa,EAA8B,CACtD,GAAI,GAAY,MAAO,IAAa,SAClC,KAAM,IAAI,OAAM,GAAG,KAAK,iDAE1B,GAAM,CAAE,YAAa,EAAI,SACnB,CAAE,cAAa,gBAAiB,GAAa,EAAU,KAAK,uBAC5D,EAAuB,AAAC,GAAwB,QAAQ,IAAI,EAAU,IAAI,AAAC,GAAO,EAAS,GAAI,KAAK,AAAC,GAAQ,EAAI,UACjH,EAAc,AAAG,MAAG,qBAAqB,GACzC,EAAW,KAAK,MAAO,MAAM,GAAS,IAAc,YACpD,EAAY,KAAM,GAAY,EAAU,GAC9C,KAAK,kBAAkB,GAGlB,kBAAkB,EAA8B,CACrD,GAAM,CAAE,gBAAe,UAAW,KAAK,2BAA2B,GAClE,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGV,eAAe,EAAuB,CAC3C,GAAM,CAAE,gBAAe,UAAW,KAAK,cAAc,GACrD,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGT,qBAAqB,EAAmB,CAC9C,GAAI,CAAC,KAAK,OACR,KAAM,IAAI,OAAM,qDAGlB,GAAM,GAAS,EAAU,MAAM,KAAK,OAAO,CAAC,EAAoD,IAAY,CAE1G,GAAI,CAAC,EAAI,QAAQ,eAAe,GAC9B,KAAM,IAAI,OAAM,wDAAwD,eAAqB,KAE/F,MAAO,CAAE,IAAK,EAAI,QAAS,UAAS,QAAS,EAAI,QAAQ,KACxD,CAAE,QAAS,KAAK,SAEb,CAAE,MAAK,WAAY,EACzB,GAAI,CAAC,GAAO,CAAC,GAAW,CAAE,GAAI,YAAuB,YACnD,KAAM,IAAI,OAAM,8DAA8D,KAGhF,MAAO,CAAE,MAAK,aC7IlB,MAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,iBAAkB,EAAO,iBAAkB,EAAQ,QAC1F,SAAM,AAAG,OAAI,EAAK,EAAO,MAClB,IDNJ,YACL,EACA,EACA,EAAwB,GACX,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,CAAC,EAAG,GAAI,QACrE,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,CAAC,EAAG,KAE7E,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,OAItC,YACL,EACA,EACA,EAAwB,GACxB,EAAuB,GACV,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,GAAI,QAC5F,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,KAEpG,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAE/D,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,KACxC,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,QEpD1D,OAAoB,OAIb,YACL,EACA,EACA,EAA4B,OAC5B,EAAoB,GACP,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OACb,AAAG,UAAO,EAAG,EAAO,QAAS,CAAC,EAAG,GAAI,GACrC,EAAO,MAGT,MAAO,GAAW,AAAG,QAAK,GAAO,ICd9B,WAAoC,EAAgB,EAA+B,CACxF,OAAO,KAAK,GAAW,QAAQ,AAAC,GAAS,CACvC,AAAK,EAAc,KAAK,AAAC,GAAO,EAAG,eAAiB,IAClD,EAAU,GAAM,YCLtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,EACA,IACe,CACf,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,SCzBtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,IACa,CACb,GAAM,GAAa,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAY,IAChF,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CACL,QAAS,EACT,KAAM,ICvBZ,OAAoB,OCoBb,YAA0B,CAE/B,YAES,EAEA,EAEA,EAEP,CANO,wBAEA,wBAEA,cDxBJ,YACL,EACA,EACA,CACA,MAAO,CAAC,EAAoB,EAAqB,IAA8C,CAC7F,GAAM,GAAmB,AAAG,YAAS,EAAe,EAAI,EAAI,GAAa,CAAC,EAAG,EAAG,EAAY,IACtF,EAAmB,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAG,EAAG,EAAY,IAC5F,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,WAGX,GAAI,IACT,EACA,EACA,IAKC,YAEL,EACA,CACA,MAAO,AAAC,IAAwC,CAC9C,GAAM,GAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,IAAI,IACT,EACA,EACA,IEpCC,WAAmC,EAAgB,EAA+B,CACvF,MAAO,CAAC,EAAsB,EAAmB,IAAwB,CACvE,GAAM,GAAS,EAAU,GAEzB,GAAI,CAAC,GAAS,EAAQ,GACpB,KAAM,IAAI,OAAM,sBAAsB,oBAA+B,oBAA4B,KAGnG,SAAc,KACZ,CAAE,eAAc,UAAW,GAAc,IAGpC,GCfJ,WAA+B,EAAuB,CAC3D,GAAI,GAAmB,EAEvB,WAAwB,EAAkC,CACxD,GAAM,GAAM,EAAiB,MAAM,EAAG,GACtC,SAAmB,EAAiB,MAAM,GACnC,EAGT,YAA6C,CAC3C,MAAO,GAGT,MAAO,CACL,iBACA,uBCPG,YAA2B,EAAwC,EAA+B,CACvG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,GAAQ,EACV,EAAkB,EAAY,EAAa,EAAG,GAAG,WACjD,EAA2B,EAAY,EAAa,GAAG,WACrD,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAChE,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,CAAE,QAAO,QAAO,SAAU,EAAyB,EAAY,EAAa,EAAc,GAC1F,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YAAuB,EAA8F,CAC1H,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAC3C,EAAS,EAAyB,IAAK,IAAK,UAElD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CACN,SAAQ,SAAQ,SAAQ,WCvBvB,YAA+B,EAAuE,CAC3G,MAAO,AAAC,IAA+B,CACrC,GAAM,GAAU,EAAgC,GAAG,YAAkB,GAC/D,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,CAAE,UAAS,SCNf,YAA2B,EAAgB,EAA+B,CAC/E,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YACL,EACuE,CACvE,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBhBdZ,oBAAmC,EAAuG,CAC/I,aAAc,CACZ,MAAM,wBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,sDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAE/B,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,+BAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KiBlDzB,OAAoB,OCApB,OAAoB,OAIb,YACL,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,AAAG,OACtB,AAAG,UAAO,EAAG,EAAO,SACpB,EAAO,OCPJ,YAAuB,EAAuB,EAAoB,EAA2E,CAClJ,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAIpB,EAAK,AAFa,GAAuB,EAAgB,GAEpC,EAAY,EAAa,MAEpD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,OCdP,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,EAAgB,OAGtB,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,YAA4B,EAA8B,CAC/D,GAAM,GAAyC,GACzC,EAAmC,GAEzC,cAAO,KAAK,GAAW,QAAQ,AAAC,GAAQ,CACtC,GAAM,GAAM,EAAI,WAAW,MAAQ,EAAgB,EACnD,EAAI,GAAO,EAAU,KAGhB,CAAE,sBAAqB,iBJIzB,oBAGG,EAAyB,CAGjC,YAAY,EAAe,EAA+D,CACxF,MAAM,GACN,KAAK,sBAAwB,KAGpB,uBAAgE,CACzE,MAAO,MAAK,sBASP,OAAO,EAA4C,CACxD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EACJ,MAAO,IAAoB,EAAmB,KAAK,EAAmB,MAAM,GAAI,IAAK,EAAO,MAIzF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,EAAS,KAAK,0BAA2B,KAAK,4BAG3D,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAM,KAAK,0BACX,EAAO,KAAK,2BACZ,EAAwB,EAAO,EAAO,EAEtC,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KKnFjC,GAAM,IAAyB,CAAC,UAAW,QAAS,MAAO,QAAS,UAAW,YAAa,aAE5F,QAAsB,CAe3B,YAAY,EAAwC,CAClD,GAAI,EAAc,SAAW,EAC3B,KAAM,IAAI,OAAM,8EAA8E,EAAc,UAG9G,GAAuB,QAAQ,CAAC,EAAY,IAAQ,CAClD,KAAK,GAAc,EAAc,KAIrC,eAAgB,CACd,MAAO,IACJ,IAAI,AAAC,GAAgB,EAAE,aAAY,YAAa,KAAK,MACrD,KAAK,CAAC,EAAI,IAAO,EAAG,YAAc,EAAG,e1CtBrC,oBAAgC,GAA0C,CAC/E,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGtB,aAAa,EAA4C,CAC9D,MAAO,AAAG,SAAK,IAAM,AAAG,WAAQ,KAAK,OAAO,UAGjC,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,oBAAmB,EAAkB,CAChD,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAC9B,EAAsB,KAAM,SAAQ,IAAI,AAAG,WAAQ,GAAK,IAAI,KAAO,IAAM,CAC7E,GAAM,GAAO,KAAM,GAAE,OACrB,SAAE,UACK,KAET,EAAI,UAEJ,GAAM,GAAqB,EACxB,IAAI,AAAC,GAAiB,GAAI,IAAgB,IAE7C,MAAO,GAAS,aACZ,EACA,EAAmB,GAGf,qBAA8B,CACtC,MAAO,wBAGC,yBAAkC,CAC1C,MAAO,KAGC,0BAAmC,CAC3C,MAAO,K2C1CJ,YAA+B,EAA0C,CAC9E,MAAO,GAAI,sBAAuB,IAG7B,YAGL,EACA,EAC8B,CAE9B,MAAO,IAAK,KADM,CAAE,gBCPf,YACL,EACA,EACA,EAAgB,GAChB,EACA,CAGA,AAF6B,OAAM,QAAQ,GAAmB,EAAkB,CAAC,IAE5D,QAAQ,AAAC,GAAM,CAElC,GAAM,GAAO,YAAa,IACtB,EACC,GAAsB,GAAK,EAAE,YAAc,OAChD,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mHAIlB,GAAM,GAAmB,AADV,EAAK,gBACY,OAAO,AAAC,GAAc,EAAU,YAAc,GAExE,EAAS,GAAoB,GAC/B,EAAE,UAAU,IAAI,WACf,GAAmB,GAAI,GAAM,EAAG,GAMrC,AAJsB,GAAI,IACxB,EAAiB,IAAI,AAAC,GAAc,GAAG,EAAU,eAAe,GAAM,EAAU,iBAChF,GAEY,KAAK,KCvBhB,YAA6B,EAA0E,CAC5G,MAAO,IAAoB,IAEtB,EAAI,oBAAwB,IAE5B,EAAI,6BAAiC,IAErC,EAAI,sBAA0B,GAGrC,YAA4B,EAAM,CAEhC,GAAM,GAAU,CAAC,EAAI,EAAI,EAAI,IAAQ,KAAK,MAAM,EAAK,EAAI,EAAK,GAAM,KAAK,GAGnE,EAAU,AAAC,GAAW,EAAQ,IAAO,KAAK,GAE1C,EAAQ,CAAE,KAA0B,OAAW,MAA2B,OAAW,IAAyB,QAEpH,GAAI,CAAC,GAAQ,CAAC,EAAK,YAAc,EAAK,WAAW,SAAW,GAAI,MAAO,GACvE,GAAM,GAAK,EAAK,WAOhB,EAAM,KAAO,CAAC,EAAQ,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,IAK9D,EAAM,MAAQ,EAAQ,EAAG,KAAK,IAAI,EAAG,GAAG,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,GAAI,KAAK,GAAI,KAAK,IAAI,EAAG,IAAI,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,IAMvH,GAAM,GAAS,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,UACnE,EAAM,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,WACtE,SAAM,IAAM,KAAK,GAAM,GAAK,SAAS,QAAW,GAAM,GAAU,IAAO,GAEhE,EAGF,YAEoD,EAAoB,EAAgF,CAC7J,GAAM,CAAE,IAAK,GAAU,EAAU,UAC3B,EAAY,EAAmB,QAAwB,EAAM,EAAG,EAAM,GAEtE,EAAO,EAAU,QACjB,CAAE,aAAc,EAAU,UAC1B,EAAc,GAAI,GAAc,EAAU,UAAU,MAAO,EAAK,QAAQ,EAAU,WAAY,GAC9F,EAAQ,GAAmB,GASjC,MAAO,IAAK,KAPM,CAChB,YACA,qBACA,cACA,UCxDG,YAA+B,CAapC,YAAY,EAAqC,GAAI,CACnD,GAAM,CACJ,YAAY,GAAM,aAAa,GAAM,YAAW,YAAW,YAAW,cACpE,EACJ,KAAK,UAAY,EACjB,KAAK,WAAa,EAClB,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,uBAC9B,KAAK,WAAa,GAAc,yBAI7B,QAAwB,CAK7B,YACE,EACA,EAAqC,GACrC,CACA,KAAK,cAAgB,EACrB,KAAK,QAAU,GAAI,IAAyB,GAG9C,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CACJ,YAAW,aAAY,YAAW,YAAW,YAAW,cACtD,KAAK,QAcT,GAZI,GAAa,KAAK,wBAAyB,KAC7C,GAAI,YAAc,EAClB,EAAI,UAAY,EAChB,GAAY,EAAK,KAAK,cAAc,iBACpC,GAAY,EAAK,KAAK,cAAc,kBACpC,GAAY,EAAK,KAAK,cAAc,mBACpC,GAAY,EAAK,KAAK,cAAc,WACpC,GAAY,EAAK,KAAK,cAAc,aAAc,IAClD,GAAY,EAAK,KAAK,cAAc,cAAe,IACnD,GAAY,EAAK,KAAK,cAAc,WAAY,KAG9C,EAAY,CACd,EAAI,YAAc,EAClB,EAAI,UAAY,EAEhB,GAAM,GAAY,AAAC,GAAe,CAChC,EAAI,YACJ,EAAI,IAAI,EAAG,EAAG,EAAG,EAAG,EAAW,EAAG,EAAI,KAAK,IAC3C,EAAI,QAEN,KAAK,cAAc,UAAU,QAAQ,MAOpC,YACL,EACA,EACA,CAEA,AAD2B,OAAM,QAAQ,GAAiB,EAAgB,CAAC,IACxD,QAAQ,AAAC,GAAM,CAEhC,GAAM,GAAY,YAAa,GAC3B,EACC,GAAoB,GAAK,EAAE,UAAY,OAC5C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gIAGlB,GAAI,IAAkB,GAAW,KAAK,oBC1G1C,OAAoB,OCApB,MAAoB,OCKpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAqC,EAAoB,EAAqB,EAA4C,CACxH,GAAM,GAAkB,EAA2B,EAAY,EAAa,GAAG,qBACzE,EAAkB,EAA2B,EAAa,EAAa,GAAG,qBAC1E,EAAiB,EAAkB,EAAY,EAAa,EAAG,GAAG,oBAExE,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAkB,EAAuC,CACvF,GAAM,GAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBAE1E,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YAAuB,EAAuB,EAAsF,CACzI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAgB,GAEhC,EAAqB,EAAkB,EAAG,GAAI,EAAG,sBACjD,EAA+B,EAA4B,GAAI,GAAI,gCACnE,EAA+B,EAA4B,GAAI,IAAK,gCAEpE,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,IAAK,0BAA0B,OAG3F,GAAM,GAA4B,EAA4B,IAAK,IAAK,6BAClE,EAA2B,EAA2B,IAAK,IAAK,4BAEhE,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,aAAY,cAAa,cCjEvC,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAqC,EAA4C,CAC/E,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAiB,EAAkB,GAAG,oBAE5C,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAuC,CACrE,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAEtD,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YACL,EACA,EAC+D,CAC/D,GAAM,GAAgC,GAEhC,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAW,GAE3B,EAAqB,EAAkB,sBACvC,EAA+B,EAA4B,gCAC3D,EAA+B,EAA4B,gCAE3D,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,0BAA0B,OAGtF,GAAM,GAA4B,EAA4B,6BACxD,EAA2B,EAA2B,4BAEtD,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,SAA2B,EAAW,GAE/B,CAAE,OAAQ,CAAE,aAAY,cAAa,aAAa,iBFrE3D,YAAc,EAAgB,EAAoB,EAAuC,CACvF,MAAO,AAAG,OAAI,AAAG,SAAO,EAAG,EAAO,QAAS,EAAQ,QAAS,EAAO,MAGrE,YAAwB,EAAgB,EAA8B,EAA2B,GAAmB,CAClH,GAAI,GAAM,EAAkB,AAAG,OAAK,GAAK,EACzC,SAAM,EAAuB,EAAK,EAAO,gBAAiB,CAAC,EAAG,IAC9D,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,AAAG,MAAI,EAAK,GAAK,EAAG,EAAO,eAAgB,CAAC,EAAG,KAC9C,EAGT,YAAmB,EAAgB,EAAsC,CACvE,GAAI,GAAM,EAAuB,AAAG,OAAK,GAAI,EAAO,gBAAiB,CAAC,EAAG,IACzE,SAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,MAAI,EAAK,GACX,EAGF,oBAA2B,EAAkC,CAGlE,YAAY,EAAuB,CACjC,MAAM,gBACN,KAAK,eAAiB,EAGjB,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KACnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,8CAElB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAC7D,EAAM,AAAG,OAAK,GAAK,EAAY,EAAO,WAAW,QAAS,CAAC,EAAG,KAClE,SAAM,GAAe,EAAK,EAAO,WAAW,kBAAmB,IAC/D,EAAM,GAAe,EAAK,EAAO,WAAW,mBAC5C,GAAM,KAAK,eAAgB,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAChD,EAAM,GAAU,EAAK,EAAO,YAAY,cAAc,QAExD,EAAM,GAAe,EAAK,EAAO,UAAU,iBAC3C,EAAM,AAAG,OAAK,EAAuB,EAAK,EAAO,UAAU,eAAgB,CAAC,EAAG,KACxE,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,sBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,gBAG1C,cAAc,EAAuB,CAC7C,MAAO,IAAc,EAAS,KAAK,kBGvEhC,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAkB,GAAuB,EAAgB,GAEzD,EAAM,EAAgB,IAAK,EAAG,UAC9B,EAAS,EAAgB,IAAK,EAAG,aAEvC,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,GAAI,CAAE,MAAK,YCflB,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,CACF,IAAK,EAAgB,UACrB,OAAQ,EAAgB,eAI5B,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,GAAK,IAAL,UAAK,EAAL,CAEL,SAAS,SAET,OAAO,SAJG,aNQL,oBAA2B,EAAyB,CAGzD,YAAY,EAAqC,GAAI,IAAa,GAAI,CACpE,MAAM,gBACN,KAAK,sBAAwB,KAGpB,uBAAqC,CAC9C,MAAO,MAAK,sBAGP,OAAO,EAA0C,CACtD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EAEE,EAAS,AAAG,WAAQ,EAAoB,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAAS,KAAK,EAAmB,MAAM,GAAI,IACnG,EAAM,GAAoB,EAAQ,EAAO,GAAG,KAAK,OACjD,EAAS,GAAoB,EAAQ,EAAO,GAAG,QACrD,MAAO,CAAE,MAAK,YAIX,aAAa,EAA0C,CAC5D,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAE,MAAK,UAAW,KAAK,OAAO,GACpC,MAAO,CAAE,MAAK,OAAQ,AAAG,WAAQ,WAIxB,SAAQ,EAAsC,CACzD,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,qBAAoB,EAA8E,CAC7G,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAE9B,EAAO,AAAG,WAAQ,EAAI,KACtB,EAAU,AAAG,WAAQ,EAAI,QACzB,EAAsB,EAAK,IAAI,CAAC,EAAW,IAAO,EACtD,YACA,aAAc,EAAQ,MAGlB,EAAqB,KAAM,SAAQ,IACvC,EAAoB,IAAI,MAAO,CAAE,YAAW,kBAAmB,CAC7D,GAAM,GAAO,MAAM,GAAU,QAAQ,GAC/B,EAAY,MAAM,GAAa,QAAQ,GACvC,EAAS,EAAW,GACpB,EAAS,EAAS,GAAO,KAAO,GAAO,OACvC,EAAoB,EAAS,EAAY,EAAI,EAEnD,SAAU,UACV,EAAa,UACN,CAAE,MAAK,SAAQ,wBAG1B,SAAI,IAAI,UACR,EAAI,OAAO,UAEJ,EAAS,aAAe,EAAiD,EAAmB,GAG3F,qBAA8B,CACtC,MAAO,mBAGF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,GAGb,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAwB,IAAM,EAAI,EAAM,KAAM,EAAI,GAElD,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KOvHxC,MAAoB,OASb,oBAGG,GAAgC,CACjC,YAAY,EAAqB,EAAmB,EAAgD,CACzG,GAAM,GAAkB,EAAmB,IAAI,CAAC,CAAE,QAAO,YAAa,CACpE,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,CACL,MAAO,EAAQ,EACf,OAAQ,EAAS,KAIf,EAAY,EAAgB,OAElC,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAA0B,CAAC,EAAe,IAAkB,AAAG,QAAM,CAAC,AAAG,OAAK,CAAC,IAAK,EAAO,WAAY,AAAG,OAAK,CAAC,IAAK,EAAO,YAAa,GAAG,KAAK,EAAG,KAAK,OAGzJ,EAAa,CAAC,EAAkB,IAAoD,CACxF,GAAM,CAAE,QAAO,UAAW,EAAgB,GAC1C,MAAO,GAAK,EAAO,GAAU,KAAK,IAAI,EAAQ,GAAU,EAAI,GAGxD,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GACvE,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GAa7E,MAXwB,GACrB,IAAI,AAAG,OAAK,CAAC,EAAW,KAAM,EAAW,YACzC,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAY,GACZ,EAAY,OAEb,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAgB,GAAU,MAC1B,EAAgB,GAAU,aAO3B,aAAa,EAA8B,CAChD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,KAAK,OAAO,GACxB,MAAO,MAAK,YACV,EACA,EAAM,UACN,EAAM,gBAAgB,IAAI,CAAC,CAAC,EAAQ,KAAY,EAAE,SAAQ,mBAKnD,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,iBAAgB,EAAgE,CAC3F,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAkB,AAAG,OACzB,IAAM,AAAG,UAAQ,KAAK,aAAa,KAG/B,EAAoB,KAAM,SAAQ,IAAI,EAAgB,IAC1D,MAAO,EAAgB,IAAa,CAClC,GAAM,GAAiB,MAAM,KAAK,KAAM,GAAe,QACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,GAAO,IACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,CAAC,GAAO,IAExD,MAAO,IAAI,IACT,MAAM,IAAI,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,GAAI,GAAM,EAAQ,GAAc,EAAQ,KACxE,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,QAMtC,SAAgB,QAAQ,AAAC,GAAM,EAAE,WAE1B,EAAS,aAAe,EAAyC,EAAkB,GAGlF,0BAAmC,CAC3C,MAAO,OC1FJ,oBAAgC,GAAkD,CACvF,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGnB,qBAA8B,CACtC,MAAO,yBAGC,yBAAkC,CAC1C,MAAO,OCdX,OAAoB,OCMb,YACL,EAC2E,CAC3E,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCnBZ,YAA2B,EAAkG,CAClI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAEjD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,SAAQ,SAAQ,WFhBvB,oBAAuC,EAA+G,CAC3J,aAAc,CACZ,MAAM,4BAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,0DAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,GAAI,IAAK,CAAC,EAAG,GAAI,SAEjC,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,oCAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA+B,GAG9B,cAAc,EAAuB,CAC7C,MAAO,IAAkB,KG7CtB,oBAAoC,GAAsD,CAC/F,YAAY,EAAiD,GAAI,IAA4B,CAC3F,MAAM,wBAAyB,GAGvB,qBAA8B,CACtC,MAAO,8BAGC,yBAAkC,CAC1C,MAAO,OCVJ,oBAA8B,GAAkB,GCJvD,MAAoB,OCApB,OAAoB,OCApB,OAAoB,OAIb,YAAe,EAAgB,EAAuC,CAC3E,MAAO,AAAG,QAAI,AAAG,OAAI,EAAG,EAAO,SAAU,EAAO,QDAlD,YACE,EACA,EACA,EACA,EACA,EAA4B,OACf,CACb,GAAM,CAAE,UAAS,QAAS,EAAO,KAE7B,EAAM,AAAG,UAAO,EAAG,EAAS,EAAS,GACzC,SAAM,AAAG,OAAI,EAAK,GAClB,EAAM,GAAM,EAAK,EAAO,OACjB,EAAW,AAAG,QAAK,GAAO,EAG5B,YAAc,EAAgB,EAAyB,CAC5D,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAoB,EAAgB,EAAyB,CAClE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAkB,EAAgB,EAAyB,CAChE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,GAAM,SE7B5C,MAAoB,OAUpB,YAA2B,EAAwC,EAA+B,CAChG,WAA6B,EAAyB,EAAoB,EAAiC,CACzG,GAAM,GAAU,EAAe,GACzB,EAAQ,EAAQ,OAAU,GAAa,EAAa,GAE1D,GAAI,GAAQ,GACV,KAAM,IAAI,OAAM,+BAA+B,sBAA0B,EAAQ,uBAAuB,kBAA2B,KAGrI,MAAO,AAAG,QACR,IAAM,AAAG,YACP,AAAG,WAAS,EAAS,CAAC,EAAY,EAAO,EAAY,IACrD,CAAC,EAAG,EAAG,EAAG,KAKhB,WACE,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,EAAoB,EAAiB,EAAY,GAC3D,EAAO,AAAG,WAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,QAGpB,WAAiC,EAAoB,EAAwC,CAC3F,GAAM,GAAU,AAAG,WAAS,EAAe,IACrC,EAAS,AAAG,WAAS,EAAe,IAE1C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,aAGX,CACL,UACA,UAIJ,WACE,EACA,EACA,EACA,EACiB,CACjB,GAAM,GAAO,EAAkB,EAAiB,EAAY,EAAY,GAAG,UACrE,EAAQ,EAAwB,EAAY,GAAG,WAErD,MAAO,CAAE,OAAM,SAGjB,WACE,EACA,EACA,EACA,EACA,EAAkB,GACG,CACrB,GAAM,GAAQ,EAAwB,GAAS,GAAM,GAAK,EAAiB,EAAY,EAAY,GAAG,WAChG,EAAQ,EAAuB,EAAiB,EAAY,EAAY,GAAG,WAEjF,MAAO,CAAE,QAAO,SAGlB,MAAO,CACL,yBACA,8BAIG,YAAuB,EAA6E,CACzG,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAgB,GAEhC,EAAc,EAAuB,KAAM,GAAI,EAAG,eAClD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YAEnD,EAAc,EAA2B,MAAO,GAAI,EAAG,cAAe,IACtE,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YAEpD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aAEvD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAmB,EAA2B,OAAQ,IAAK,EAAG,oBAE9D,EAAK,AAAG,OACZ,IAAM,AAAG,YAAU,AAAG,WAAS,EAAe,IAAM,KAAM,CAAC,IAAK,MAAO,CAAC,EAAG,KAI7E,GAFA,EAAc,KAAK,CAAE,UAAW,OAE5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAsB1E,MAAO,CAAE,OAnBM,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGe,iBChJnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAiC,EAAkC,CACjE,GAAM,GAAU,EAAmB,GAAG,kBAAwB,GACxD,EAAS,EAAmB,GAAG,iBAAuB,GAE5D,MAAO,CAAE,UAAS,UAGpB,WAAgC,EAAiC,CAC/D,GAAM,GAAU,EAAmB,GAAG,iBAAuB,GACvD,EAAO,EAAmB,GAAG,cAAoB,GACjD,EAAQ,EAAwB,GAEtC,MAAO,CAAE,KAAM,CAAE,UAAS,QAAQ,SAGpC,WAAoC,EAAqC,CACvE,MAAO,CACL,MAAO,EAAuB,GAAG,WACjC,MAAO,EAAuB,GAAG,YAIrC,MAAO,CACL,yBACA,8BAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAW,GAE3B,EAAc,EAAuB,eACrC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAc,EAA2B,eACzC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aAEvC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aACvC,EAAmB,EAA2B,oBAE9C,CAAE,MAAO,EAGf,GAFA,EAAc,KAAK,CAAE,aAAc,KAAM,UAAW,OAEhD,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yDAAyD,KAG3E,GAAM,GAAS,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGF,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBC9FnB,MAAoB,OAKb,YAAkB,EAAgB,EAA0C,CACjF,GAAI,GAAM,GAAK,EAAG,EAAO,OACzB,SAAM,GAAW,EAAK,EAAO,OAC7B,EAAM,AAAG,MAAI,EAAK,GAClB,EAAM,AAAG,OAAK,GACP,EAGF,YAAsB,EAAgB,EAA0C,CACrF,GAAI,GAAM,GAAS,EAAG,EAAO,OAC7B,EAAM,GAAW,EAAK,EAAO,OAE7B,GAAI,GAAS,AAAG,UAAQ,EAAG,EAAG,EAAG,SAC3B,EAAQ,AAAG,QAAkB,EAAO,OACpC,EAAQ,EAAO,MAAM,KAAO,EAAI,MAAM,GAG5C,GAFsB,EAAO,MAAM,KAAO,EAAI,MAAM,IAAM,EAAO,MAAM,KAAO,EAAI,MAAM,GAErE,CACjB,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAE/B,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAGjC,SAAS,EAAQ,AAAG,SAAO,CAAC,EAAQ,GAAQ,GAAK,EACjD,EAAM,AAAG,MAAI,EAAQ,GAErB,EAAM,AAAG,OAAK,GACP,EL3BF,oBAAiC,EAAyB,CAC/D,aAAc,CACZ,MAAM,sBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAGtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAE7D,EAAM,GAAS,EAAY,EAAO,aACtC,EAAM,AAAG,UAAQ,EAAK,EAAG,EAAG,SAE5B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,aAC/B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAa,EAAK,EAAO,kBAE/B,GAAM,GAAY,EAAI,KAAK,CAAC,EAAG,IAG/B,MAFuB,AAAG,UAAO,EAAW,EAAO,WAM1C,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,uBAAsB,EAAwD,CA7D7F,MA8DI,GAAI,oBAAO,QAAP,cAAc,KAAK,AAAC,GAAQ,GAAO,GAAI,MAAO,IAAI,cAAa,KACnE,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAwB,AAAG,OAC/B,IAAM,AAAG,UAAQ,KAAK,aAAa,KAE/B,EAA0B,KAAM,SAAQ,IAAI,EAAsB,IAAI,AAAC,GAAM,EAAE,SACrF,SAAsB,QAAQ,AAAC,GAAM,EAAE,WAChC,EAAS,aAAe,EAA0B,EAAwB,GAGzE,qBAA8B,CACtC,MAAO,yBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KM7ElB,YAAkC,EAAuB,CAC9D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECHF,YAGL,EACA,EAC6B,CAE7B,MAAO,IAAK,KADM,CAAE,eCNf,YAAmB,EAA8B,CACtD,MAAO,OAAO,GAAI,KAAQ,SAGrB,YAGL,EACA,EACkB,CAElB,MAAO,IAAK,KADM,CAAE,QCNf,YAAsB,EAAiC,CAC5D,MAAQ,GAAI,SAAW,GAAO,MAAQ,EAAI,SAAW,GAAO,SACvD,GAAmB,EAAI,mBAGvB,YAGL,EACA,EACA,EACqB,CAErB,MAAO,IAAK,KADM,CAAE,SAAQ,sBCpB9B,OAAoB,OCApB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,WAAoC,EAAqB,EAAuD,CAC9G,GAAM,GAAU,AAAG,YAAS,EAAe,EAAI,EAAI,GAAc,CAAC,EAAG,EAAG,EAAa,IAC/E,EAAmB,AAAG,YAAS,EAAe,IAC9C,EAAoB,AAAG,YAAS,EAAe,IAC/C,EAAkB,AAAG,YAAS,EAAe,IAC7C,EAAsB,AAAG,YAAS,EAAe,IAEvD,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,uBAChB,CAAE,UAAW,GAAG,qBAChB,CAAE,UAAW,GAAG,0BAGX,CACL,UACA,mBACA,oBACA,kBACA,uBAIJ,WACE,EACA,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,KAAgB,EAAkB,oBAAsB,WAGnE,CAAE,UAAS,QAGpB,WACE,EACA,EACA,EACA,EACqB,CACrB,GAAM,CACJ,UACA,QACE,EAAkB,EAAY,EAAa,EAAY,EAAc,IAEzE,MAAO,CACL,UACA,kBAAmB,GAIvB,WACE,EACA,EACA,EAC4B,CAC5B,GAAM,GAAiB,EAA2B,EAAY,GAAG,oBAC3D,EAAiB,EAA2B,EAAY,EAAa,EAAG,GAAG,oBAEjF,MAAO,CAAE,iBAAgB,kBAG3B,YAAwD,CACtD,GAAM,GAAS,EAA2B,EAAG,GAAI,EAAG,sBAC9C,EAAS,EAAsB,GAAI,GAAI,sBACvC,EAAS,EAAsB,GAAI,IAAK,sBACxC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,KAAM,uBAC3C,EAAU,EAAsB,KAAM,KAAM,uBAClD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,UACA,UACA,UACA,WAIJ,YAA+D,CAC7D,GAAM,GAAS,EAA2B,KAAM,IAAK,EAAG,2BAClD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,GAAI,EAAG,2BAChD,EAAS,EAA2B,GAAI,IAAK,EAAG,2BAChD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,EAAG,EAAG,oDACjD,EAA2B,EAAkB,KAAM,GAAI,EAAG,2DAC1D,EAAoB,EAAkB,KAAM,GAAI,EAAG,oDACnD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDA0BxD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,gBAjCsB,CACtB,uBAAwB,EACxB,gBAAiB,GAgCjB,gBA9BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA6BjB,gBA3BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA0BjB,gBAxBsB,CACtB,uBAAwB,GACxB,gBAAiB,IAuBjB,gBArBsB,CACtB,uBAAwB,EACxB,gBAAiB,IAoBjB,gBAlBsB,CACtB,uBAAwB,GACxB,gBAAiB,KAoBrB,MAAO,CACL,2BACA,gCAIG,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAChC,CACJ,iBACA,uBACE,EAAsB,GACpB,CACJ,2BACA,gCACE,GAAkB,EAAgB,GAChC,EAAc,IACd,EAAmB,IAKnB,EAAe,CACnB,UALgB,AAAG,YACnB,EAAe,KAAO,GACtB,CAAC,EAAG,KAAM,KAMZ,GADA,EAAc,KAAK,CAAE,UAAW,2BAC5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,OAAQ,CACN,cACA,mBACA,gBAEF,iBC9MJ,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAoC,EAAgB,EAAa,EAA2C,CAC1G,GAAM,GAAU,EAAmB,GAAG,YAAiB,sBAAyB,EAAG,GAAG,aAChF,EAAoB,EAAmB,GAAG,YAAiB,oCAAuC,EAAG,GAAG,uBAC9G,MAAO,CAAE,UAAS,qBAGpB,WAA+B,EAAyC,CACtE,GAAM,GAAe,oBAAoB,IACnC,EAAsB,sBAAsB,cAC5C,EAA4B,GAAG,mBAC/B,EAA4B,GAAG,mBAE/B,EAAU,EAAmB,GAAG,sBAAyC,EAAG,GAAG,aAC/E,EAAmB,EAAmB,GAAG,oBAAuC,EAAG,GAAG,sBACtF,EAAoB,EAAmB,GAAG,mBAAsC,EAAG,GAAG,uBACtF,EAAkB,EAAmB,GAAG,0BAA6C,EAAG,GAAG,qBAC3F,EAAsB,EAAmB,GAAG,8BAAiD,EAAG,GAAG,yBAEzG,MAAO,CACL,eAAgB,CACd,UACA,mBACA,oBACA,kBACA,uBAEF,eAAgB,EAA2B,cAAe,EAAK,IAInE,YAAwD,CACtD,MAAO,CACL,OAAQ,EAA2B,cAAe,EAAG,sBACrD,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,KAInC,WAA2B,EAAgB,EAAkC,CAC3E,GAAM,GAAU,EAAmB,GAAG,YAAkB,EAAG,GAAG,aACxD,EAAO,EAAmB,GAAG,WAAiB,EAAG,GAAG,UAC1D,MAAO,CAAE,UAAS,QAGpB,WAAmC,EAAkC,CACnE,GAAM,GAAyB,EAC7B,2BAA2B,yBAC3B,kCAAkC,4BAE9B,EAAkB,EACtB,2BAA2B,mBAC3B,kCAAkC,qBAEpC,MAAO,CAAE,yBAAwB,mBAGnC,YAA+D,CAC7D,MAAO,CACL,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,IAI/C,MAAO,CACL,2BACA,gCAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAChC,CACJ,2BACA,gCACE,GAAkB,EAAW,GAC3B,EAAY,EAAU,oBAE5B,GADA,EAAc,KAAK,CAAE,aAAc,mBAAoB,UAAW,2BAC9D,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yEAAyE,KAG3F,GAAM,GAAS,CACb,YAAa,IACb,iBAAkB,IAClB,aAAc,CACZ,cAIJ,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBChInB,OAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,UAAO,EAAG,EAAO,QAAS,EAAS,QAChD,SAAM,AAAG,OAAI,EAAK,EAAO,mBAClB,AAAG,eAAY,EAAK,EAAG,KDPlC,GAAM,IAAU,qBAEhB,YACE,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,QAAS,EAAS,QACzD,SAAM,AAAG,aACP,EACA,EAAO,gBACP,EAAO,oBACP,EAAO,kBACP,EAAO,iBACP,IAEK,AAAG,eAAY,EAAK,EAAG,KAIlC,YAA+B,EAAoC,CACjE,MAAO,CAAC,EAAG,EAAG,EAAG,IAAI,KAAK,AAAC,GAAQ,IAAQ,GAAY,CAAC,EAAG,GAAK,CAAC,EAAG,GAG/D,YAAqB,EAAgB,EAA4B,CACtE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GACA,EAAM,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IA4BnD,GAVA,AAhBuB,CACrB,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,QACP,EAAO,QACP,EAAO,QACP,EAAO,SAGM,QAAQ,CAAC,EAAO,IAAM,CACnC,GAAM,GAAW,EAAI,EACf,EAAuB,GAAsB,GACnD,EAAM,GAAmB,EAAK,EAAM,eAAgB,GACpD,EAAM,EAAmB,EAAK,EAAM,eAAgB,CAAC,EAAG,IACpD,IAAa,IACf,GAAS,KAIT,IAAW,KACb,KAAM,IAAI,OAAM,iDAGlB,MAAO,CACL,MACA,OAAQ,KEjEd,YAAa,EAAoB,EAAW,EAAW,CACrD,GAAM,GAAY,EAAM,YAClB,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAS,GAAQ,GAAU,GAAQ,GACnC,EAAS,GAAQ,GAAU,GAAQ,GACzC,GAAI,GAAS,GAAK,GAAS,EACzB,MAAO,GAET,GAAM,GAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAmB,EAAkB,GACjE,KAAK,IAAI,EAAmB,EAAkB,GACpD,MAAO,GAAoB,GAAQ,EAAQ,GAGtC,YACL,EACA,EACA,EACA,EACA,EACU,CACV,GAAM,GAAW,EAAM,MAAM,GACvB,EAAa,KAAK,IACtB,EACA,GAGI,EAAa,EAChB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,OAAO,AAAC,GAAM,EAAE,MAAQ,GACxB,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAE5B,EAAe,AAAC,GAAe,GAAK,EAAe,EAAI,EACvD,EAAqB,GAE3B,SAAW,QAAQ,AAAC,GAAM,CACxB,GAAI,EAAS,QAAU,EAAY,OACnC,GAAM,GAAgB,EAAE,MACxB,OAAS,GAAI,EAAS,OAAS,EAAG,GAAK,EAAG,EAAE,EAAG,CAC7C,GAAM,GAAM,GAAI,EAAO,EAAE,SAAU,EAAS,IAC5C,GAAI,IAAQ,GACZ,GAAE,OAAS,EAAa,GACpB,EAAE,OAAS,GAAgB,MAEjC,AAAI,IAAkB,EAAE,OACtB,EAAS,KAAK,EAAE,YAGb,EC5DT,MAAoB,OAIpB,YAA2C,EAAgB,CACzD,GAAM,GAAM,AAAG,UAAQ,AAAG,YAAU,EAAG,CAAC,EAAG,KAErC,EAAQ,CACZ,AAAG,MAAI,EAAI,GAAI,EAAI,IACnB,AAAG,MAAI,EAAI,GAAI,EAAI,KAEf,EAAU,CACd,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,KAC1C,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,MAE5C,MAAO,CACL,QACA,WAIJ,YAA0B,EAAiB,EAAiB,CAC1D,GAAM,CACJ,QACA,WACE,GAAkC,GAEhC,EAAM,AAAG,UAAQ,AAAG,YAAU,EAAI,CAAC,EAAG,KACtC,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAC3E,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAEjF,MAAO,AAAG,aACR,AAAG,QAAM,CACP,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,KAEnB,CAAC,EAAG,IAID,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAY,EAAe,MAAM,GAEnC,EAAQ,GACV,AAAG,UAAQ,AAAG,OAAK,EAAO,UAAW,CAAC,EAAW,EAAG,IAAK,CAAC,GAAI,IAC9D,AAAG,UAAQ,EAAgB,CAAC,GAAI,KAElC,EAAQ,AAAG,UACT,EACA,CAAC,EAAY,EAAM,MAAM,GAAK,EAAY,IAG5C,GAAM,GAAmB,AAAG,UAAQ,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,MAC/E,EAAS,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,IAE5D,EAAS,AAAG,UACV,EACA,CAAC,EAAW,EAAO,MAAM,KAG3B,GAAM,GAAe,AAAG,UAAQ,GAC1B,EAAgB,AAAG,UAAQ,GAEjC,MAAO,CACL,MAAO,EACP,OAAQ,KC1Ed,OAAoB,OCApB,OAAoB,OAKb,YACL,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAY,EAAE,MAAM,GACpB,EAAwB,AAAG,WAC/B,GAAU,EAAG,EAAO,wBACpB,CAAC,EAAW,GAAI,EAAG,IAEf,EAAkB,AAAG,WACzB,GAAU,EAAG,EAAO,iBACpB,CAAC,EAAW,GAAI,IAElB,MAAO,CAAE,wBAAuB,qBDb7B,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAQ,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IACjD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IAErD,EAAiB,GAAmB,EAAQ,EAAO,iBACnD,EAAiB,GAAmB,EAAG,EAAO,iBAC9C,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAElD,EAAiB,AAAG,UAAO,CAC/B,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,uBACd,GAEG,EAAmB,AAAG,UAAO,CACjC,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,iBACd,GAEH,MAAO,CACL,iBACA,sBE3CC,WAA4B,CAOjC,YAAY,CAAE,gBAAe,cAAuC,GAAI,CAN9D,WAAgB,wBAUxB,GAHA,KAAK,eAAiB,GAAiB,GACvC,KAAK,YAAc,GAAc,IAE7B,MAAO,MAAK,gBAAmB,UAAY,KAAK,gBAAkB,GAAK,KAAK,gBAAkB,EAChG,KAAM,IAAI,OAAM,GAAG,KAAK,iEAG1B,GAAI,MAAO,MAAK,aAAgB,SAC9B,KAAM,IAAI,OAAM,GAAG,KAAK,iDAIxB,gBAAwB,CAAE,MAAO,MAAK,kBAEtC,aAAqB,CAAE,MAAO,MAAK,cTZlC,oBAA6B,EAAyB,CAC3D,aAAc,CACZ,MAAM,kBAGD,aAAa,EAAiB,CACnC,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAQ,WACvD,EAAI,AAAG,OAAI,AAAG,OAAI,EAAa,AAAG,UAAO,sBAAwB,AAAG,UAAO,IAC3E,EAAW,GAAY,EAAG,EAAO,aAEjC,CAAE,iBAAgB,oBAAqB,GAAgB,EAAS,IAAK,EAAS,OAAQ,EAAO,kBAEnG,MAAO,IAAY,EAAgB,EAAkB,EAAO,qBAInD,SAAQ,EAAkB,CACrC,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,aACX,EACA,EAAkC,GACR,CAC1B,GAAM,CAAE,aAAY,iBAAkB,GAAI,GAAsB,GAE1D,EAAW,KAAM,GAAW,GAE5B,CACJ,MAAO,EACP,OAAQ,GACN,KAAK,aAAa,GAEhB,EAAQ,EAAO,GACf,EAAS,EAAQ,GACvB,OAAS,GAAI,EAAG,EAAI,EAAO,OAAQ,IACjC,EAAO,GAAG,UACV,EAAQ,GAAG,UAGb,GAAM,GAAa,MAAM,KAAK,KAAM,GAAO,QAErC,EAAU,GACd,EACA,EACA,EAJmB,GAMnB,GAGI,EAAe,EAAS,2BAA2B,GACnD,EAAY,EAAS,UACrB,EAAO,EAAY,EAAa,MAChC,EAAO,EAAY,EAAa,OAEhC,EAAY,EAAM,YAClB,EAAU,EACb,IAAI,AAAC,GAAQ,CACZ,GAAM,CAAC,EAAK,GAAU,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACf,CAAC,GAAM,IAAS,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACrB,MAAO,IAAI,GACT,EAAW,GACX,GAAI,IACF,GACA,EACA,GAAQ,GACR,EAAS,GAEX,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,OAKtC,SAAM,UACN,EAAO,UACA,EAGC,qBAA8B,CACtC,MAAO,wBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KUhHlB,YAA8B,EAAuB,CAC1D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,EAGF,YAAgC,EAAuB,CAC5D,MAAO,IAAqB,GAIvB,oBAA+B,GAAe,GCd9C,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,QAAU,SACpB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,OAAQ,SAClB,GAAI,GAAM,QAAS,UAGR,GAAwB,CACnC,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAA+C,CAAC,QAAS,QAAS,QAElE,GAAqB,oBACrB,GAAoC,mCCvBjD,MAAoB,OCapB,GAAM,IAAW,AAAC,GAAa,MAAO,IAAQ,SAEvC,YAAwB,EAAa,CAC1C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mBAAmB,KAGrC,GAAI,MAAO,GAAO,oBAAuB,UACvC,KAAM,IAAI,OAAM,wDAAwD,EAAO,sBAGjF,GAAI,CAAC,GAAS,EAAO,eAAiB,EAAO,aAAe,GAAK,EAAO,aAAe,EACrF,KAAM,IAAI,OAAM,gEAAgE,EAAO,gBAGzF,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,MAAM,AAAC,GAAW,MAAO,IAAM,UAElD,KAAM,IAAI,OAAM,kEAAkE,KAAK,UAAU,EAAO,YAG1G,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,IAAI,AAAC,GAAW,GAAK,IAAI,MAAM,AAAC,GAAW,GAAS,EAAE,IAAM,GAAS,EAAE,IAE1F,KAAM,IAAI,OAAM,wEAAwE,KAAK,UAAU,EAAO,YAGhH,GAAI,EAAO,SACT,EAAC,MAAM,QAAQ,EAAO,UACnB,EAAO,QAAQ,SAAW,GAC1B,CAAC,EAAO,QAAQ,MAAM,KAEzB,KAAM,IAAI,OAAM,8EAA8E,KAAK,UAAU,EAAO,YCjDxH,OAAoB,OCApB,OAAoB,OAEb,YAAe,EAA6B,CACjD,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OAAI,EAAG,AAAG,UAAO,qBAChC,MAAO,AAAG,QAAI,AAAG,QAAK,AAAG,OAAI,EAAG,IAAO,KDApC,YAA2B,EAAgB,EAAwC,CACxF,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,UAAO,EAAK,EAAO,KAAK,QAAS,CAAC,EAAG,GAAI,SAClD,EAAM,AAAG,OAAI,EAAK,EAAO,GAAG,KAC5B,EAAM,AAAG,OAAI,EAAK,EAAO,GAAG,SAC5B,EAAM,AAAG,OAAI,EAAK,EAAO,KAAK,MACvB,GAAM,KEZjB,OAAoB,OAKb,YAAgC,EAAgB,EAA0C,CAC/F,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,mBAAgB,EAAK,EAAO,iBAAkB,EAAO,iBAAkB,CAAC,EAAG,GAAI,SACxF,EAAM,AAAG,OAAI,EAAK,EAAO,MAClB,GAAM,KCVjB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAEnE,WAAgC,EAAc,EAAiC,CAC7E,GAAM,GAAM,AAAG,YAAS,EAAe,IACjC,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,SAChB,CAAE,UAAW,GAAG,cAEX,CAAE,MAAK,WAGhB,WAAwC,EAAoB,EAAqB,EAAyC,CACxH,GAAM,GAAO,EAAkB,EAAY,EAAa,EAAG,GAAG,UACxD,EAAK,EAAuB,EAAa,GAAG,QAClD,MAAO,CAAE,OAAM,MAEjB,GAAM,GAA6B,GAAkC,EAAgB,GAErF,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACA,EACA,EACgE,CAChE,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAChC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAgB,GAClC,EAEJ,GAAI,EAAO,mBAAoB,CAC7B,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAAO,mBACjB,EAAkB,EAAI,EAAI,EAAG,SAC7B,EAA2B,EAAI,EAAI,SACjC,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAkB,GAAM,GAAM,EAAI,EAAI,EAAiB,EAAG,SACxE,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,cAErD,CACL,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAAkB,EAAI,EAAI,EAAiB,EAAG,SAC5D,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,UAG5D,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAE1E,MAAO,CAAE,SAAQ,iBChFnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAgC,EAA2B,CACzD,GAAM,GAAM,EAAmB,GAAG,QAAc,GAC1C,EAAU,EAAmB,GAAG,YAAkB,GACxD,MAAO,CAAE,MAAK,WAGhB,WAA2B,EAA4B,CACrD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,WAAwC,EAAmC,CACzE,GAAM,GAAO,EAAkB,GAAG,UAC5B,EAAK,EAAuB,GAAG,QACrC,MAAO,CAAE,OAAM,MAGjB,GAAM,GAA6B,GAA+B,GAClE,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACgE,CAChE,GAAM,GAAgC,GAEhC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAW,GAE7B,EAEJ,GAAI,EAAO,mBAAoB,CAE7B,GAAM,GAAc,EAAO,aAAe,EAAO,YAAY,QAAU,EACvE,EAAS,CACP,MAAO,EAAO,mBAAqB,EAAkB,SAAW,EAA2B,SAC3F,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAkB,cAG3B,GAAS,CACP,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAAkB,UAI7B,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBC7EZ,YAAwB,CAO7B,YAAY,CAAE,YAAW,kBAAuC,GAAI,CAN1D,WAAgB,oBAUxB,GAHA,KAAK,WAAa,GAAa,IAC/B,KAAK,gBAAkB,GAAkB,GAErC,MAAO,MAAK,YAAe,UAAY,KAAK,WAAa,IAAO,EAClE,KAAM,IAAI,OAAM,GAAG,KAAK,6DAG1B,GAAI,MAAO,MAAK,iBAAoB,UAAY,KAAK,iBAAmB,GAAK,KAAK,iBAAmB,EACnG,KAAM,IAAI,OAAM,GAAG,KAAK,qEAIxB,YAAoB,CAAE,MAAO,MAAK,cAElC,iBAAyB,CAAE,MAAO,MAAK,kBPJtC,oBAA6B,EAAmC,CAKrE,YAAY,EAA0B,CACpC,MAAM,cACN,GAAe,GACf,KAAK,QAAU,KAGN,SAA2B,CACpC,MAAO,MAAK,WAGH,kBAA2B,CACpC,MAAO,MAAK,OAAO,iBAAmB,KAAK,OAAO,QAAQ,OAAS,KAG1D,kBAA0B,CACnC,MAAO,GAAK,MAAK,gBAAkB,KAAK,OAAO,QAAQ,OAAS,GAG3D,cAAc,EAAgB,EAAiD,CACpF,GAAI,GAAM,GAAkB,EAAG,EAAO,OACtC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,GAAkB,EAAK,EAAO,OAC7B,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAgB,EAAsC,CACxE,GAAI,GAAM,KAAK,OAAO,mBAClB,GAAM,GAAU,EAAG,EAAO,MAAqB,QAAS,KACxD,GAAuB,EAAG,EAAO,OACrC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EACjE,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EAC1D,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAiB,EAAgC,CACnE,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,4CAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAc,AAAG,OAAK,EAAM,cAAc,EAAW,IAAQ,WACjE,SAAc,KAAK,OAAO,QACtB,EAAU,EAAa,KAAK,OAAO,SACnC,EACJ,EAAc,EAAY,IAAI,AAAG,SAAO,MACjC,KAAK,OAAO,mBACf,KAAK,aAAa,EAAa,GAC/B,KAAK,cAAc,EAAa,UAI3B,SAAQ,EAAkB,EAAyC,CAC9E,MAAO,MAAK,aAAa,KAAM,GAAW,GAAQ,QAGvC,QAAO,EAAkB,EAAoC,GAAgC,CACxG,GAAM,CAAE,YAAW,kBAAmB,GAAI,IAAkB,GACtD,EAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,EAAU,GACxC,EAAO,AAAG,OAAK,IAAM,AAAG,UAAQ,GAAK,GAAG,cACxC,EAAkB,CACtB,MAAO,EAAS,cAAc,GAC9B,OAAQ,EAAS,eAAe,IAG5B,EAAU,KAAM,MAAK,aAAa,EAAM,EAAS,2BAA2B,GAAI,GACtF,EAAI,UACJ,EAAK,UAEL,GAAM,GAAQ,EAAQ,IAAI,AAAC,GAAQ,EAAI,KACjC,EAAS,EAAQ,IAAI,AAAC,GAAQ,EAAI,OAClC,EAAc,EAAQ,IAAI,AAAC,GAAQ,EAAI,YACvC,EAAa,EAAQ,IAAI,AAAC,GAAQ,KAAK,OAAO,QAAQ,EAAI,QAgBhE,MAPmB,AAPH,IACd,EAAM,IAAI,AAAC,GAAQ,EAAI,QAAQ,IAC/B,EACA,KAAK,OAAO,aACZ,IAGyB,IAAI,AAAC,GAAQ,GAAI,IAC1C,EAAO,GACP,EAAY,GACZ,EAAW,GACX,EAAM,GACN,IAKM,qBAA8B,CACtC,MAAO,GAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,QAG1C,cAAc,EAAuB,CAC7C,GAAM,GAAc,KAAK,OAAO,aAAe,GAAe,qBAExD,EAAa,EAAc,EAAY,OAAS,OACtD,GAAI,IAAe,GAAK,IAAe,GAAK,IAAe,EACzD,KAAM,IAAI,OAAM,oEAAoE,2BAEtF,MAAO,IAAc,EAAS,KAAK,OAAQ,KAAK,gBAAiB,QAGnD,cACd,EACA,EACA,EACA,CACA,GAAM,CAAE,QAAO,UAAW,EACpB,EAAY,KAAK,IAAI,EAAO,GAC5B,EAAoB,EAAY,EAChC,EAAoB,EAAY,EAEhC,EAAW,EAAa,MAAM,GAC9B,EAAW,KAAK,OAAO,QAAQ,OAE/B,CAAC,EAAa,EAAc,GAAqB,AAAG,OAAK,IAAM,CACnE,GAAM,GAAW,EAAa,QAAQ,CAAC,EAAU,EAAU,EAAU,KAAK,kBAEpE,EAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACpE,EAAS,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACrE,EAAc,KAAK,gBACrB,AAAG,UAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,KAAK,OAAO,QAAQ,SAAU,GACrG,AAAG,SAAO,GACd,MAAO,CAAC,EAAO,EAAQ,KAGnB,EAAU,GACV,EAAa,KAAM,GAAa,QAChC,EAAY,KAAM,GAAY,QACpC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAS,EAAG,EAAS,EAAU,IAAU,CAChD,GAAM,GAAQ,GAAQ,EAAW,GAAK,GAAK,GAAQ,IACnD,GAAI,CAAC,GAAkB,EAAQ,EAAgB,CAC7C,GAAM,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAe,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACvG,EAAgB,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACxG,GAAK,EAAO,GAAa,EACzB,GAAK,GAAO,EAAc,EAC1B,GAAM,CAAE,MAAK,MAAK,UAClB,CAAE,cAAY,UAAU,KAAK,gBAC/B,KAAM,MAAK,sBAAsB,EAAkC,IACnE,CAAE,WAAY,EAAG,MAAO,GAC5B,EAAQ,KAAK,CACX,IAAK,GAAI,IAAY,GAAG,GAAG,GAAI,GAAY,GAAI,GAC/C,QACA,WAAY,EAAQ,GACpB,YACG,MAOb,SAAY,UACZ,EAAa,UACb,EAAkB,UACX,OAGK,uBAAsB,EAA4B,EAAmD,CACjH,GAAM,CAAE,MAAK,MAAK,UAAW,EACvB,EAAc,KAAM,GAAc,QACxC,MAAO,OAAM,KAAK,OAAO,QAAQ,QAAQ,KAAK,GAC3C,IAAI,CAAC,EAAG,IAAM,EAAY,GAAK,GAAK,GAAQ,IAC5C,IAAI,CAAC,EAAY,IAAW,EAC3B,aACA,WAED,OAAO,CAAC,EAAK,IAAU,EAAI,WAAa,EAAK,WAAa,EAAM,KA/MhE,MACS,AADT,GACS,qBAAuB,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,IAAK,KAAM,MQPrE,oBAAyB,GAAe,CAC7C,YAAY,EAA8B,GAAM,CAC9C,GAAM,GAAS,CACb,qBACA,aAAc,GACd,QAAS,CAAC,WACN,EACA,CACA,QAAS,GACT,QAAS,IAET,CACA,QAAS,GACT,gBAAiB,KAIvB,MAAM,MAGG,qBAA8B,CACvC,MAAO,MAAK,OAAO,sBAGV,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,MAAK,mBAAqB,GAAoC,GAG7D,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KChDrC,YAA0B,EAAuB,EAA8B,GAAM,CAC1F,GAAM,GAAM,GAAI,IAAW,GAC3B,SAAI,eAAe,GACZ,ECNF,oBAAsC,GAAkB,CAAxD,aAJP,CAIO,oBACK,WAAgB,4BCLrB,WAAwB,MAChB,MAEX,EACY,CACZ,MAAO,GAAY,KAAM,MAAK,YAGnB,MAAkB,CAC7B,KAAM,IAAI,OAAM,6CCRpB,OAAoB,OCDpB,OAAoB,OAOpB,kBACE,EACA,EAEA,EACA,EAEA,EAAwF,CAAC,CAAE,iBAAkB,EAC7G,CACA,GAAM,GAAY,EAAc,IAAI,AAAC,GAAkB,GAAoB,GACvE,EAAoB,GACpB,EAAa,WACX,EAAgD,GACpD,aAAoB,WAChB,KAAM,IAAmB,EAAO,GAChC,KAAM,IAAa,EAAO,IAG1B,EAAU,KAAM,GAAe,GAErC,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAGT,kBACE,EACA,EAEA,EACA,EAEA,EACA,CACA,MAAO,IACL,CAAC,GACD,EACA,KAAO,IAAU,EAAc,EAAM,IACrC,EACA,GC5CG,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAAqC,CAAC,QAAS,QAAS,QCF9D,oBAA+B,GAAe,CACnD,aAAc,CACZ,GAAM,GAAS,CACb,mBAAoB,GACpB,aAAc,GACd,QAAS,CAAC,QACV,QAAS,GACT,QAAS,GACT,mBAAoB,GACpB,YAAa,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,MAGzC,MAAM,MAGG,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,2BAGC,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KCvBrC,GAAM,GAAO,CAClB,eAAgB,GAAI,IACpB,iBAAkB,GAAI,IACtB,WAAY,GAAI,IAChB,kBAAmB,GAAI,IACvB,sBAAuB,GAAI,IAC3B,mBAAoB,GAAI,IACxB,kBAAmB,GAAI,IACvB,aAAc,GAAI,KAUP,GAAiB,CAAC,EAAkB,IAA6D,EAAK,eAAe,YAAY,EAAO,GASxI,GAAmB,CAAC,EAAkB,IAA+D,EAAK,iBAAiB,YAAY,EAAO,GAS9I,GAAa,CAAC,EAAkB,IAA0D,EAAK,WAAW,YAAY,EAAO,GAS7H,GAAsB,AAAC,GAAmE,EAAK,kBAAkB,gBAAgB,GAWjI,GAA0B,AAAC,GAAmE,EAAK,sBAAsB,gBAAgB,GAYzI,GAAwB,AAAC,GAA6D,EAAK,mBAAmB,sBAAsB,GASpI,GAA2B,AAAC,GAAmE,EAAK,kBAAkB,mBAAmB,GASzI,GAAsB,AAAC,GAAiF,EAAK,aAAa,oBAAoB,GAE9I,GAA0B,AAAC,GAAgB,EAAK,eAAe,KAAK,GACpE,GAA4B,AAAC,GAAgB,EAAK,iBAAiB,KAAK,GACxE,GAAsB,AAAC,GAAgB,EAAK,WAAW,KAAK,GAC5D,GAAwB,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACrE,GAA4B,AAAC,GAAgB,EAAK,sBAAsB,KAAK,GAC7E,GAA2B,AAAC,GAAgB,EAAK,mBAAmB,KAAK,GACzE,GAA0B,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACvE,GAAqB,AAAC,GAAgB,EAAK,aAAa,KAAK,GAG7D,GAAyB,GACzB,GAAc,GACd,GAAkB,GCjGxB,oBAAqE,EAAwB,CAClG,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAA0E,MACrE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAwB,KAAM,IAClC,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,KAEtD,KAAK,gBAGP,MAAO,GAAc,IACnB,CAAC,EAAc,IAAM,GAAmC,EAAc,EAAsB,KAIhG,kBAAmB,CACjB,MAAO,IAAI,IAA2B,KAAM,KAAK,SAI9C,gBAEG,GAA8F,MACzF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,GAAkB,KAAM,IAC5B,EACA,KAAK,MACL,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,GACpD,KAAK,gBAGP,MAAO,IAA0B,EAAc,GAGjD,kBAAmB,CACjB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA0C,CAClD,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCjFnD,oBAAkE,EAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAAuE,MAClE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAqB,KAAM,IAC/B,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,aAAa,oBAAoB,KAElD,KAAK,gBAGP,MAAO,GAAc,IAAI,CAAC,EAAc,IAAM,CAC5C,GAAM,CAAE,MAAK,SAAQ,qBAAsB,EAAmB,GAC9D,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,KAIpF,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA2F,MACtF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,MAAK,SAAQ,qBAAsB,KAAM,IAC/C,EACA,KAAK,MACL,AAAC,GAAS,EAAK,aAAa,oBAAoB,GAChD,KAAK,gBAGP,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,GAGlF,qBAAsB,CACpB,MAAO,IAAI,IAAiC,KAAM,KAAK,SAIpD,gBAEG,GAAoC,CAC5C,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCtFnD,oBAAqE,EAAwB,CAClG,YAEY,EAEA,EACV,CACA,QAJU,kBAEA,eAMP,gBAEG,GAAyE,MACpE,MAA8C,CACzD,GAAM,GAAgB,KAAM,MAAK,WAUjC,MAAO,AARa,MAAM,IACxB,EACA,KAAK,MACL,AAAC,GAAU,QAAQ,IAAI,EAAM,IAAI,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,KACzF,KACA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,OAGxD,IAAI,CAAC,EAAY,IAAM,GAAkC,EAAc,GAAI,IAGhG,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,SAI/D,gBAEG,GAA6F,MACxF,MAAwD,CACnE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAEF,GAAM,GAAa,KAAM,IACvB,EACA,KAAK,MACL,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,GACxD,KAEA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,MAG3E,MAAO,IAAyB,EAAc,GAGhD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,SP1DlE,oBAAkE,EAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,6BAKE,cAAyD,CACrE,MAAO,MAAK,mBACR,EAAK,sBACL,EAAK,oBAIN,gBAEG,GAAqE,MAChE,MAA6C,CACxD,GAAM,GAAgB,KAAM,MAAK,WAC3B,EAAa,EAAc,IAAI,AAAC,GAAQ,EAAI,WAE5C,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,GACrC,KAAM,IAAa,KAAK,MAAO,GAE7B,EAAsB,KAAM,SAAQ,IAAI,EAAM,IAClD,AAAC,GAAS,KAAK,YAAY,gBAAgB,KAG7C,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAAc,IAAI,CAAC,EAAc,IAAM,GAAiC,EAAc,EAAoB,KAGnH,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAyF,MACpF,MAAuD,CAClE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,aAAc,EAChB,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,CAAC,IACtC,KAAM,IAAa,KAAK,MAAO,CAAC,IAE9B,EAAY,KAAM,MAAK,YAAY,gBAAgB,EAAM,IAE/D,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,GAAiC,EAAc,GAGxD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SQzFnD,oBAA2C,EAAwB,CACxE,YAEY,EAEA,EAAgC,GAAI,GAC9C,CACA,QAJU,aAEA,iBAMP,gBAAiC,GAAqC,MAC9D,MAAgC,CAC3C,GAAM,CAAE,QAAO,WAAY,KAEvB,EACJ,GAAI,YAAmB,IAAyB,EAAS,EAAK,iBAAiB,YAAY,EAAO,WACzF,YAAmB,GAAuB,EAAS,EAAK,eAAe,YAAY,EAAO,WAC1F,YAAmB,IAAmB,EAAS,EAAK,WAAW,YAAY,EAAO,OACtF,MAAM,IAAI,OAAM,wHAErB,MAAO,GAGD,gCAAmE,CAEzE,MAAO,IAAI,SAAiC,KAAO,IAAY,CAC7D,GAAM,GAAa,KAAM,MAAK,MAC9B,EAAQ,EAAW,IAAI,AAAC,GAAc,GAAwB,GAAI,OAItE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,SAKJ,gBAAmC,GAA+C,MAC1E,MAA0C,CACrD,GAAM,GAAiB,KAAM,IAAI,IAAmB,KAAK,MAAO,KAAK,SACjE,EAAgC,EAAe,GACnD,SAAe,QAAQ,AAAC,GAAkB,CACxC,AAAI,EAAc,MAAQ,EAA8B,OAAO,GAAgC,KAE1F,EAGD,+BAA4E,CAElF,MAAO,IAAI,SAA2C,KAAO,IAAY,CACvE,GAAM,GAAY,KAAM,MAAK,MAC7B,EAAQ,EAAY,GAA4B,GAAI,GAAa,UAIrE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,SCpGJ,YAA0B,EAAkB,EAAgC,GAAI,GAA+C,CACpI,MAAO,IAAI,IAAqB,EAAO,GAGlC,YAAwB,EAAkB,EAAgC,GAAI,GAA6C,CAChI,MAAO,IAAI,IAAmB,EAAO,GCFvC,kBACE,EACA,EACyE,CACzE,MAAO,IAAe,EAAO,GAAI,GAAsB,EAAgB,CAAE,iBAAkB,KACxF,oBACA,sBAGL,kBACE,EACA,EAAoC,GACqC,CACzE,MAAO,IAAe,EAAO,GAAI,IAAkB,IAChD,oBACA,sBAGE,GAAM,IAAW,GC1BjB,YAA2B,EAA+B,EAA+B,CAC9F,GAAI,EAAK,SAAW,EAAK,OAAQ,KAAM,IAAI,OAAM,kDAEjD,GAAM,GAAQ,MAAM,KAAK,GACnB,EAAQ,MAAM,KAAK,GAEzB,MAAO,MAAK,KACV,EACG,IAAI,CAAC,EAAK,IAAM,EAAM,EAAM,IAC5B,OAAO,CAAC,EAAK,IAAS,EAAO,GAAQ,EAAI,ICJzC,YAAkB,CAKvB,YACE,EACA,EAA4B,GAC5B,CACA,KAAK,mBAAqB,EAE1B,GAAM,GAAa,MAAM,QAAQ,GAAU,EAAS,CAAC,GAErD,GAAI,CAAC,EAAW,OACd,KAAM,IAAI,OAAM,2DAGlB,GAAI,GAAQ,EACN,EAAoB,IAAM,UAAU,MAE1C,KAAK,oBAAsB,EAAW,IAAI,AAAC,GAAS,CAClD,GAAI,YAAgB,IAClB,MAAO,GAGT,GAAI,YAAgB,cAClB,MAAO,IAAI,IAAuB,IAAqB,CAAC,IAG1D,GAAI,EAAK,YAAc,EAAK,qBAAsB,cAChD,MAAO,IAAI,IAAuB,IAAqB,CAAC,EAAK,aAG/D,KAAM,IAAI,OAAM,4MAIT,qBAA+C,CAAE,MAAO,MAAK,uBAE7D,oBAA4B,CAAE,MAAO,MAAK,mBAE9C,oBAAoB,EAA+B,EAAqC,CAC7F,MAAO,GACJ,IAAI,AAAC,GAAM,GAAkB,EAAG,IAChC,OAAO,CAAC,EAAI,IAAO,EAAK,EAAI,GACxB,GAAY,QAAU,GAGxB,gBAAgB,EAA0C,CAC/D,MAAO,MAAK,mBACT,IAAI,CAAC,CAAE,cAAa,WAAY,GAAI,IACnC,EACA,KAAK,oBAAoB,EAAiB,KAE3C,OAAO,CAAC,EAAM,IAAU,EAAK,SAAW,EAAK,SAAW,EAAO,GAG7D,cAAc,EAA0C,CAC7D,GAAM,GAAY,KAAK,gBAAgB,GACvC,MAAO,GAAU,SAAW,KAAK,kBAC7B,EACA,GAAI,IAAU,UAAW,EAAU,UAGlC,QAAc,CACnB,MAAO,CACL,kBAAmB,KAAK,kBACxB,mBAAoB,KAAK,mBAAmB,IAAI,AAAC,GAAO,EAAG,iBAIjD,UAAS,EAAwB,CAC7C,GAAM,GAAqB,EAAK,mBAC7B,IAAI,AAAC,GAAY,GAAuB,SAAS,IACpD,MAAO,IAAI,IAAY,EAAoB,EAAK,qBC1E7C,YAAgC,EAAuB,CAC5D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECFF,YAA0B,EAAY,EAA4B,CACvE,GAAM,CAAE,QAAO,UAAW,GAAI,GAAW,EAAW,MAAO,EAAW,QAEtE,GAAI,GAAS,GAAK,GAAU,EAC1B,KAAM,IAAI,OAAM,uCAAuC,KAAK,UAAU,CAAE,QAAO,cAGjF,GAAI,MAAM,QAAQ,GAEhB,MAAQ,GAAuB,IAAI,AAAC,GAAQ,GAAc,EAAK,CAAE,QAAO,YAG1E,GAAI,GAAoB,GAAU,CAChC,GAAM,GAAmB,EAAQ,UAAU,QAAQ,EAAO,GACpD,EAAmB,EAAQ,mBAAmB,QAAQ,EAAiB,IAAI,MAAO,EAAiB,IAAI,QAC7G,MAAO,IAAwB,GAAwB,EAAS,GAAmB,GAGrF,MAAI,IAAoB,GACf,GAAwB,EAAS,EAAQ,UAAU,QAAQ,EAAO,IAGvE,YAAmB,IAAiB,YAAmB,GACjD,EAAgB,QAAQ,EAAO,GAGlC,EjJRT,GAAM,IAAQ,MAAO,UAAY,YAC3B,GAAW,MAAO,YAAc,aAAiB,MAAO,WAAU,WAAc,YACzE,GAAU,CAAE,QAAa,GAAmB,QAAM", "names": [] } diff --git a/dist/face-api.node.js b/dist/face-api.node.js index fc6268a..654857b 100644 --- a/dist/face-api.node.js +++ b/dist/face-api.node.js @@ -5,5 +5,5 @@ author: ' */ -var fn=Object.create,Ye=Object.defineProperty,hn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty,bn=Object.getOwnPropertyNames,gn=Object.getOwnPropertyDescriptor;var Er=o=>Ye(o,"__esModule",{value:!0});var fo=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),Ge=(o,t)=>{for(var e in t)Ye(o,e,{get:t[e],enumerable:!0})},vn=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of bn(t))!xn.call(o,r)&&r!=="default"&&Ye(o,r,{get:()=>t[r],enumerable:!(e=gn(t,r))||e.enumerable});return o},b=o=>vn(Er(Ye(o!=null?fn(hn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);var g=fo(Mr=>{var yn=Object.create,Cr=Object.defineProperty,Fn=Object.getPrototypeOf,Tn=Object.prototype.hasOwnProperty,_n=Object.getOwnPropertyNames,wn=Object.getOwnPropertyDescriptor,ho=o=>Cr(o,"__esModule",{value:!0}),xo=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of _n(t))!Tn.call(o,r)&&r!=="default"&&Cr(o,r,{get:()=>t[r],enumerable:!(e=wn(t,r))||e.enumerable});return o},Pn=o=>xo(ho(Cr(o!=null?yn(Fn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);ho(Mr);xo(Mr,Pn(require("@tensorflow/tfjs-node")))});var To=fo((Ln,Fo)=>{Er(Ln);Ge(Ln,{isNodejs:()=>kn});function kn(){return typeof global=="object"&&!0&&typeof Fo!="undefined"&&typeof process!="undefined"&&!!process.version}});Er(exports);Ge(exports,{AgeGenderNet:()=>pr,BoundingBox:()=>re,Box:()=>D,ComposableTask:()=>tt,ComputeAllFaceDescriptorsTask:()=>At,ComputeFaceDescriptorsTaskBase:()=>vr,ComputeSingleFaceDescriptorTask:()=>Wt,DetectAllFaceLandmarksTask:()=>Fr,DetectAllFacesTask:()=>He,DetectFaceLandmarksTaskBase:()=>yr,DetectFacesTaskBase:()=>_r,DetectSingleFaceLandmarksTask:()=>Tr,DetectSingleFaceTask:()=>wr,Dimensions:()=>A,FACE_EXPRESSION_LABELS:()=>qr,FaceDetection:()=>M,FaceDetectionNet:()=>qo,FaceExpressionNet:()=>cr,FaceExpressions:()=>It,FaceLandmark68Net:()=>fe,FaceLandmark68TinyNet:()=>dr,FaceLandmarkNet:()=>Ro,FaceLandmarks:()=>V,FaceLandmarks5:()=>vo,FaceLandmarks68:()=>ne,FaceMatch:()=>Ee,FaceMatcher:()=>Dr,FaceRecognitionNet:()=>xe,Gender:()=>vt,LabeledBox:()=>Me,LabeledFaceDescriptors:()=>xt,NetInput:()=>bt,NeuralNetwork:()=>S,ObjectDetection:()=>Dt,Point:()=>x,PredictedBox:()=>yo,Rect:()=>oe,SsdMobilenetv1:()=>Xt,SsdMobilenetv1Options:()=>Z,TinyFaceDetector:()=>Fe,TinyFaceDetectorOptions:()=>gr,TinyYolov2:()=>ve,TinyYolov2Options:()=>lt,allFaces:()=>Ea,allFacesSsdMobilenetv1:()=>un,allFacesTinyYolov2:()=>Da,awaitMediaLoaded:()=>Gr,bufferToImage:()=>zr,computeFaceDescriptor:()=>ua,createCanvas:()=>Yt,createCanvasFromMedia:()=>Ie,createFaceDetectionNet:()=>aa,createFaceRecognitionNet:()=>Xn,createSsdMobilenetv1:()=>Jo,createTinyFaceDetector:()=>Ma,createTinyYolov2:()=>ca,detectAllFaces:()=>Pr,detectFaceLandmarks:()=>pn,detectFaceLandmarksTiny:()=>da,detectLandmarks:()=>wa,detectSingleFace:()=>Pa,draw:()=>Co,env:()=>P,euclideanDistance:()=>uo,extendWithAge:()=>hr,extendWithFaceDescriptor:()=>fr,extendWithFaceDetection:()=>Ot,extendWithFaceExpressions:()=>mr,extendWithFaceLandmarks:()=>le,extendWithGender:()=>xr,extractFaceTensors:()=>se,extractFaces:()=>ae,fetchImage:()=>Bn,fetchJson:()=>Xr,fetchNetWeights:()=>Rn,fetchOrThrow:()=>Gt,getContext2dOrThrow:()=>$,getMediaDimensions:()=>Ht,imageTensorToCanvas:()=>Vr,imageToSquare:()=>Ur,inverseSigmoid:()=>Mn,iou:()=>kr,isMediaElement:()=>qe,isMediaLoaded:()=>Ne,isWithAge:()=>Jn,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>Zr,isWithFaceLandmarks:()=>Vt,isWithGender:()=>qn,loadAgeGenderModel:()=>Fa,loadFaceDetectionModel:()=>Ta,loadFaceExpressionModel:()=>ya,loadFaceLandmarkModel:()=>ba,loadFaceLandmarkTinyModel:()=>ga,loadFaceRecognitionModel:()=>va,loadSsdMobilenetv1Model:()=>dn,loadTinyFaceDetectorModel:()=>ha,loadTinyYolov2Model:()=>xa,loadWeightMap:()=>Jr,locateFaces:()=>_a,matchDimensions:()=>$n,minBbox:()=>Sr,nets:()=>_,nonMaxSuppression:()=>Ar,normalize:()=>ot,padToSquare:()=>Wr,predictAgeAndGender:()=>fa,recognizeFaceExpressions:()=>la,resizeResults:()=>ln,resolveInput:()=>jt,shuffleArray:()=>En,sigmoid:()=>De,ssdMobilenetv1:()=>mn,tf:()=>Ca,tinyFaceDetector:()=>ma,tinyYolov2:()=>pa,toNetInput:()=>E,utils:()=>bo,validateConfig:()=>ao,version:()=>La});var Ca=b(g());var Co={};Ge(Co,{AnchorPosition:()=>dt,DrawBox:()=>Xe,DrawBoxOptions:()=>Yr,DrawFaceLandmarks:()=>Qr,DrawFaceLandmarksOptions:()=>Kr,DrawTextField:()=>Mt,DrawTextFieldOptions:()=>Ce,drawContour:()=>ft,drawDetections:()=>Wn,drawFaceExpressions:()=>On,drawFaceLandmarks:()=>Hn});function ft(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var bo={};Ge(bo,{computeReshapedDimensions:()=>Lr,getCenterPoint:()=>$t,isDimensions:()=>Ve,isEven:()=>ze,isFloat:()=>Ir,isTensor:()=>Bt,isTensor1D:()=>Dn,isTensor2D:()=>Nr,isTensor3D:()=>ht,isTensor4D:()=>z,isValidNumber:()=>rt,isValidProbablitiy:()=>ee,range:()=>ct,round:()=>Rt});var go=b(g());var A=class{constructor(t,e){if(!rt(t)||!rt(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new A(1/this.width,1/this.height)}};function Bt(o,t){return o instanceof go.Tensor&&o.shape.length===t}function Dn(o){return Bt(o,1)}function Nr(o){return Bt(o,2)}function ht(o){return Bt(o,3)}function z(o){return Bt(o,4)}function Ir(o){return o%1!=0}function ze(o){return o%2==0}function Rt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function Ve(o){return o&&o.width&&o.height}function Lr({width:o,height:t},e){let r=e/Math.max(t,o);return new A(Math.round(o*r),Math.round(t*r))}function $t(o){return o.reduce((t,e)=>t.add(e),new x(0,0)).div(new x(o.length,o.length))}function ct(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function rt(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function ee(o){return rt(o)&&o>=0&&o<=1}var x=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new x(this.x+t.x,this.y+t.y)}sub(t){return new x(this.x-t.x,this.y-t.y)}mul(t){return new x(this.x*t.x,this.y*t.y)}div(t){return new x(this.x/t.x,this.y/t.y)}abs(){return new x(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new x(Math.floor(this.x),Math.floor(this.y))}};var D=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(rt)}static assertIsValidBox(t,e,r=!1){if(!D.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(rt),a=[r.x,r.y,r.width,r.height].every(rt);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];D.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new x(this.left,this.top)}get topRight(){return new x(this.right,this.top)}get bottomLeft(){return new x(this.left,this.bottom)}get bottomRight(){return new x(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new D({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new D({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new D({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var re=class extends D{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var Dt=class{constructor(t,e,r,n,a){this._imageDims=new A(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new D(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new Dt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends Dt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function kr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function Sr(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var mt=b(g());function ot(o,t){return mt.tidy(()=>{let[e,r,n]=t,a=mt.fill([...o.shape.slice(0,3),1],e,"float32"),s=mt.fill([...o.shape.slice(0,3),1],r,"float32"),i=mt.fill([...o.shape.slice(0,3),1],n,"float32"),c=mt.concat([a,s,i],3);return mt.sub(o,c)})}var Et=b(g());function Wr(o,t=!1){return Et.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Et.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Et.cast(u,"float32"));return Et.concat(d,s)})}function En(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function De(o){return 1/(1+Math.exp(-o))}function Mn(o){return Math.log(o/(1-o))}var oe=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var Cn=.5,Nn=.43,In=.45,V=class{constructor(t,e,r=new x(0,0)){let{width:n,height:a}=e;this._imgDims=new A(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new x(n,a)).add(r))}get shift(){return new x(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new x(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new x(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof M?t.box.floor():new D(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/In),c=$t(t),m=Math.floor(Math.max(0,c.x-Cn*i)),p=Math.floor(Math.max(0,c.y-Nn*i));return new oe(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=Sr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var vo=class extends V{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],$t([t[3],t[4]])]}};var ne=class extends V{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map($t)}};var Ee=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Rt(this.distance)})`:""}`}};var Me=class extends D{static assertIsValidLabeledBox(t,e){if(D.assertIsValidBox(t,e),!rt(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var xt=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new xt(t.label,e)}};var yo=class extends Me{static assertIsValidPredictedBox(t,e){if(Me.assertIsValidLabeledBox(t,e),!ee(t.score)||!ee(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function pt(o){return o.detection instanceof M}function Ot(o,t){return{...o,...{detection:t}}}function Br(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function Ue(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Rr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=Ue();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function $r(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Or=b(To()),k;function Sn(){if(!k)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return k}function jr(o){k=o}function Hr(){return $r()?jr(Br()):(0,Or.isNodejs)()?jr(Rr()):null}function An(o){if(k||Hr(),!k)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=k.Canvas,Image:e=k.Image}=o;k.Canvas=t,k.Image=e,k.createCanvasElement=o.createCanvasElement||(()=>new t),k.createImageElement=o.createImageElement||(()=>new e),k.ImageData=o.ImageData||k.ImageData,k.Video=o.Video||k.Video,k.fetch=o.fetch||k.fetch,k.readFile=o.readFile||k.readFile}var P={getEnv:Sn,setEnv:jr,initialize:Hr,createBrowserEnv:Br,createFileSystem:Ue,createNodejsEnv:Rr,monkeyPatch:An,isBrowser:$r,isNodejs:Or.isNodejs};Hr();function jt(o){return!P.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function $(o){let{Canvas:t,CanvasRenderingContext2D:e}=P.getEnv();if(o instanceof e)return o;let r=jt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var dt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(dt||(dt={}));var Ce=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||dt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},Mt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof Mt?t.text:t,this.anchor=e,this.options=new Ce(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let v=c+d.x,w=c+d.y+(l+1)*s;r.fillText(u,v,w)})}};var Yr=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:dt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Ce({...s,...a})}},Xe=class{constructor(t,e={}){this.box=new D(t),this.options=new Yr(e)}draw(t){let e=$(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new Mt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function Wn(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof M?r.score:pt(r)?r.detection.score:void 0,a=r instanceof M?r.box:pt(r)?r.detection.box:new D(r),s=n?`${Rt(n)}`:void 0;new Xe(a,{label:s}).draw(o)})}var ue=b(g());function Ne(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function Gr(o){return new Promise((t,e)=>{if(o instanceof P.getEnv().Canvas||Ne(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function zr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=P.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function Ht(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t?new A(o.naturalWidth,o.naturalHeight):o instanceof e?new A(o.videoWidth,o.videoHeight):new A(o.width,o.height)}function Yt({width:o,height:t}){let{createCanvasElement:e}=P.getEnv(),r=e();return r.width=o,r.height=t,r}function Ie(o,t){let{ImageData:e}=P.getEnv();if(!(o instanceof e)&&!Ne(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||Ht(o),a=Yt({width:r,height:n});return o instanceof e?$(a).putImageData(o,0,0):$(a).drawImage(o,0,0,r,n),a}var Je=b(g());async function Vr(o,t){let e=t||P.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(z(o)?1:0),s=Je.tidy(()=>o.as3D(r,n,a).toInt());return await Je.browser.toPixels(s,e),s.dispose(),e}function qe(o){let{Image:t,Canvas:e,Video:r}=P.getEnv();return o instanceof t||o instanceof e||o instanceof r}var J=b(g());function Ur(o,t,e=!1){let{Image:r,Canvas:n}=P.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Yt({width:1,height:1});let a=Ht(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=Yt({width:t,height:t}),p=o instanceof n?o:Ie(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&$(m).drawImage(p,u,l,i,c),m}var bt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(ht(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(z(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof P.getEnv().Canvas?r:Ie(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ct(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Lr({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,J.tidy(()=>{let r=ct(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof J.Tensor){let i=z(s)?s:s.expandDims();return i=Wr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=J.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof P.getEnv().Canvas)return J.browser.fromPixels(Ur(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return J.stack(r.map(a=>J.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function E(o){if(o instanceof bt)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(jt);return r.forEach((n,a)=>{if(!qe(n)&&!ht(n)&&!z(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(z(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>qe(n)&&Gr(n))),new bt(r,Array.isArray(o))}async function ae(o,t){let{Canvas:e}=P.getEnv(),r=o;if(!(o instanceof e)){let s=await E(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await Vr(i)}let n=$(r);return t.map(s=>s instanceof M?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=Yt({width:c,height:m});return c>0&&m>0&&$(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var Ze=b(g());async function se(o,t){if(!ht(o)&&!z(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(z(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Ze.tidy(()=>{let[e,r,n]=o.shape.slice(z(o)?1:0);return t.map(i=>i instanceof M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>Ze.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Gt(o,t){let{fetch:e}=P.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Bn(o){let t=await Gt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return zr(e)}async function Xr(o){return(await Gt(o)).json()}async function Rn(o){return new Float32Array(await(await Gt(o)).arrayBuffer())}var _o=b(g());function Ke(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function Jr(o,t){let{manifestUri:e,modelBaseUri:r}=Ke(o,t),n=await Xr(e);return _o.io.loadWeights(n,r)}function $n(o,t,e=!1){let{width:r,height:n}=e?Ht(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Nt=b(g());var gt=b(g());var S=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof gt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof gt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=gt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await Jr(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=P.getEnv(),{manifestUri:r,modelBaseUri:n}=Ke(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=gt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof gt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var C=b(g());var ie=b(g());function O(o,t,e){return ie.tidy(()=>{let r=ie.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=ie.add(r,t.bias),r})}function Qe(o,t,e=!1){return C.tidy(()=>{let r=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):O(o,t.conv0,[2,2])),n=O(r,t.conv1,[1,1]),a=C.relu(C.add(r,n)),s=O(a,t.conv2,[1,1]);return C.relu(C.add(r,C.add(n,s)))})}function Le(o,t,e=!1,r=!0){return C.tidy(()=>{let n=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):O(o,t.conv0,r?[2,2]:[1,1])),a=O(n,t.conv1,[1,1]),s=C.relu(C.add(n,a)),i=O(s,t.conv2,[1,1]),c=C.relu(C.add(n,C.add(a,i))),m=O(c,t.conv3,[1,1]);return C.relu(C.add(n,C.add(a,C.add(i,m))))})}var Ct=b(g());function zt(o,t,e="same",r=!1){return Ct.tidy(()=>{let n=Ct.add(Ct.conv2d(o,t.filters,[1,1],e),t.bias);return r?Ct.relu(n):n})}function W(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var tr=b(g());function ce(o,t){return(e,r,n,a)=>{let s=tr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=tr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var er=b(g());function rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function B(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function nr(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function wo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock4Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function ar(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function Po(o){let t=[],{extractDenseBlock4Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return W(o,t),{params:r,paramMappings:t}}var Se=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Nt.tidy(()=>{let r=Nt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Nt.scalar(255)),s=Le(a,e.dense0,!0);return s=Le(s,e.dense1),s=Le(s,e.dense2),s=Le(s,e.dense3),s=Nt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return Po(t)}extractParams(t){return wo(t)}};var Mo=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Do(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Eo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Mo.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Do(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Eo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var qr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);qr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return qr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var cr=class extends We{constructor(t=new Se){super("FaceExpressionNet",t)}forwardInput(t){return ue.tidy(()=>ue.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(ue.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new It(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Zr(o){return o.expressions instanceof It}function mr(o,t){return{...o,...{expressions:t}}}function On(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof It?a:Zr(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=pt(a)?a.detection.box.bottomLeft:r||new x(0,0);new Mt(c.map(d=>`${d.expression} (${Rt(d.probability)})`),m).draw(o)})}function Vt(o){return pt(o)&&o.landmarks instanceof V&&o.unshiftedLandmarks instanceof V&&o.alignedRect instanceof M}function jn(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function le(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new M(o.detection.score,n.rescale(a.reverse()),a),i=jn(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var Kr=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},Qr=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new Kr(e)}draw(t){let e=$(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof ne&&(e.strokeStyle=s,e.lineWidth=a,ft(e,this.faceLandmarks.getJawOutline()),ft(e,this.faceLandmarks.getLeftEyeBrow()),ft(e,this.faceLandmarks.getRightEyeBrow()),ft(e,this.faceLandmarks.getNose()),ft(e,this.faceLandmarks.getLeftEye(),!0),ft(e,this.faceLandmarks.getRightEye(),!0),ft(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function Hn(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof V?r:Vt(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new Qr(n).draw(o)})}var No="1.0.1";var ut=b(g());var I=b(g());function Yn(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Io(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=B(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=Yn(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};ct(t,0,1).forEach(y=>{l[`main_block_${y}`]=c(128,`middle_flow/main_block_${y}`)});let v=i(128,256,"exit_flow/reduction_block"),w=s(256,512,"exit_flow/separable_conv"),h={reduction_block:v,separable_conv:w};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:h}}}function Gn(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function Lo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=Gn(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};ct(t,0,1).forEach(w=>{d[`main_block_${w}`]=s(`middle_flow/main_block_${w}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return W(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function ko(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function to(o,t,e=!0){let r=e?I.relu(o):o;return r=O(r,t.separable_conv0,[1,1]),r=O(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,ko(o,t.expansion_conv,[2,2])),r}function zn(o,t){let e=O(I.relu(o),t.separable_conv0,[1,1]);return e=O(I.relu(e),t.separable_conv1,[1,1]),e=O(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var eo=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(ko(a,e.entry_flow.conv_in,[2,2]));return s=to(s,e.entry_flow.reduction_block_0,!1),s=to(s,e.entry_flow.reduction_block_1),ct(this._numMainBlocks,0,1).forEach(i=>{s=zn(s,e.middle_flow[`main_block_${i}`])}),s=to(s,e.exit_flow.reduction_block),s=I.relu(O(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return Lo(t,this._numMainBlocks)}extractParams(t){return Io(t,this._numMainBlocks)}};function So(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),n=rr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ao(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return W(o,t),{params:n,paramMappings:t}}var vt;(function(o){o.FEMALE="female",o.MALE="male"})(vt||(vt={}));var pr=class extends S{constructor(t=new eo(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return ut.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t,n=ut.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Ae(n,e.fc.age).as1D(),s=Ae(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return ut.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:ut.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?vt.MALE:vt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return So(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ao(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var H=b(g());var Be=class extends We{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return H.tidy(()=>{let s=(d,u)=>H.stack([H.fill([68],d,"float32"),H.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return u(l,v)?Math.abs(l-v)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(H.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return H.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await E(t))}async detectLandmarks(t){let e=await E(t),r=H.tidy(()=>H.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>ze(d)),m=i.filter((p,d)=>!ze(d));return new ne(Array(68).fill(0).map((p,d)=>new x(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var fe=class extends Be{constructor(t=new Se){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Lt=b(g());function Wo(o){let t=[],{extractDenseBlock3Params:e}=sr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return W(o,t),{params:r,paramMappings:t}}function Bo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock3Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var ro=class extends S{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(112,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(Lt.scalar(255)),s=Qe(a,e.dense0,!0);return s=Qe(s,e.dense1),s=Qe(s,e.dense2),s=Lt.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Wo(t)}extractParams(t){return Bo(t)}};var dr=class extends Be{constructor(t=new ro){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Ro=class extends fe{};var U=b(g());var he=b(g());var ur=b(g());function $o(o,t){return ur.add(ur.mul(o,t.weights),t.biases)}function oo(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=he.conv2d(o,a,e,n);return i=he.add(i,s),i=$o(i,t.scale),r?he.relu(i):i}function Oo(o,t){return oo(o,t,[1,1],!0)}function no(o,t){return oo(o,t,[1,1],!1)}function lr(o,t){return oo(o,t,[2,2],!0,"valid")}var Y=b(g());function Vn(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Ir(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return Y.tidy(()=>Y.transpose(Y.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=Y.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=Y.tensor1d(o(i)),p=Y.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function jo(o){let{extractWeights:t,getRemainingWeights:e}=B(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Vn(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),v=a(147456,128,3,"conv128_down",!0),w=a(147456,128,3,"conv128_1"),h=a(147456,128,3,"conv128_2"),y=a(589824,256,3,"conv256_down",!0),T=a(589824,256,3,"conv256_1"),F=a(589824,256,3,"conv256_2"),L=a(589824,256,3,"conv256_down_out"),G=Y.tidy(()=>Y.transpose(Y.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:v,conv128_1:w,conv128_2:h,conv256_down:y,conv256_1:T,conv256_2:F,conv256_down_out:L,fc:G},paramMappings:r}}function Un(o,t){let e=j(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function Ho(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=Un(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),v=r("conv128_2"),w=r("conv256_down"),h=r("conv256_1"),y=r("conv256_2"),T=r("conv256_down_out"),{fc:F}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!Nr(F))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${F}`);let L={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:v,conv256_down:w,conv256_1:h,conv256_2:y,conv256_down_out:T,fc:F};return W(o,t),{params:L,paramMappings:t}}var R=b(g());function nt(o,t){let e=Oo(o,t.conv1);return e=no(e,t.conv2),e=R.add(e,o),e=R.relu(e),e}function Re(o,t){let e=lr(o,t.conv1);e=no(e,t.conv2);let r=R.avgPool(o,2,2,"valid"),n=R.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=R.zeros(i);e=R.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=R.zeros(m);e=R.concat([e,p],2)}return r=a?R.concat([r,n],3):r,e=R.add(r,e),e=R.relu(e),e}var xe=class extends S{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return U.tidy(()=>{let r=U.cast(t.toBatchTensor(150,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(U.scalar(256)),s=lr(a,e.conv32_down);s=U.maxPool(s,3,2,"valid"),s=nt(s,e.conv32_1),s=nt(s,e.conv32_2),s=nt(s,e.conv32_3),s=Re(s,e.conv64_down),s=nt(s,e.conv64_1),s=nt(s,e.conv64_2),s=nt(s,e.conv64_3),s=Re(s,e.conv128_down),s=nt(s,e.conv128_1),s=nt(s,e.conv128_2),s=Re(s,e.conv256_down),s=nt(s,e.conv256_1),s=nt(s,e.conv256_2),s=Re(s,e.conv256_down_out);let i=s.mean([1,2]);return U.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=U.tidy(()=>U.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return Ho(t)}extractParams(t){return jo(t)}};function Xn(o){let t=new xe;return t.extractWeights(o),t}function fr(o,t){return{...o,...{descriptor:t}}}function Jn(o){return typeof o.age=="number"}function hr(o,t){return{...o,...{age:t}}}function qn(o){return(o.gender===vt.MALE||o.gender===vt.FEMALE)&&ee(o.genderProbability)}function xr(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var st=b(g());var at=b(g());function Zn(o,t){function e(c,m){let p=at.tensor4d(o(3*3*c),[3,3,c,1]),d=at.tensor1d(o(c)),u=at.tensor1d(o(c)),l=at.tensor1d(o(c)),v=at.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),v=a(256,512,"mobilenetv1/conv_6"),w=a(512,512,"mobilenetv1/conv_7"),h=a(512,512,"mobilenetv1/conv_8"),y=a(512,512,"mobilenetv1/conv_9"),T=a(512,512,"mobilenetv1/conv_10"),F=a(512,512,"mobilenetv1/conv_11"),L=a(512,1024,"mobilenetv1/conv_12"),G=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,conv_8:h,conv_9:y,conv_10:T,conv_11:F,conv_12:L,conv_13:G}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),v=n(256,64,1,"prediction_layer/conv_6"),w=n(64,128,3,"prediction_layer/conv_7"),h=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),T=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),F=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),L=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),G=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),et=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),it=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),X=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),_t=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),wt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Pt=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,box_predictor_0:{box_encoding_predictor:h,class_predictor:y},box_predictor_1:{box_encoding_predictor:T,class_predictor:F},box_predictor_2:{box_encoding_predictor:L,class_predictor:G},box_predictor_3:{box_encoding_predictor:et,class_predictor:it},box_predictor_4:{box_encoding_predictor:X,class_predictor:_t},box_predictor_5:{box_encoding_predictor:wt,class_predictor:Pt}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function Yo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Zn(e,t),s=n(),i=a(),m={extra_dim:at.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Kn(o,t){let e=j(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,v=e(`${d}/depthwise_weights`,4,`${u}/filters`),w=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),h=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),y=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),T=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:v,batch_norm_scale:w,batch_norm_offset:h,batch_norm_mean:y,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function Go(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Kn(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!ht(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return W(o,t),{params:a,paramMappings:t}}var yt=b(g());var kt=b(g());function q(o,t,e){return kt.tidy(()=>{let r=kt.conv2d(o,t.filters,e,"same");return r=kt.add(r,t.batch_norm_offset),kt.clipByValue(r,0,6)})}var Qn=.0010000000474974513;function ta(o,t,e){return yt.tidy(()=>{let r=yt.depthwiseConv2d(o,t.filters,e,"same");return r=yt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Qn),yt.clipByValue(r,0,6)})}function ea(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function zo(o,t){return yt.tidy(()=>{let e,r=q(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=ea(i);r=ta(r,a.depthwise_conv,c),r=q(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function ra(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let v=Math.max(n,c),w=Math.max(a,m),h=Math.min(s,p),y=Math.min(i,d),T=Math.max(h-v,0)*Math.max(y-w,0);return T/(u+l-T)}function Vo(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=ra(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function oa(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function na(o,t){let{sizes:e,centers:r}=oa(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function Uo(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=na(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Oe=b(g());var $e=b(g());function Ut(o,t){return $e.tidy(()=>{let e=o.shape[0],r=$e.reshape(zt(o,t.box_encoding_predictor),[e,-1,1,4]),n=$e.reshape(zt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Xo(o,t,e){return Oe.tidy(()=>{let r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),l=Ut(n,e.box_predictor_2),v=Ut(s,e.box_predictor_3),w=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),y=Oe.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,v.boxPredictionEncoding,w.boxPredictionEncoding,h.boxPredictionEncoding],1),T=Oe.concat([d.classPrediction,u.classPrediction,l.classPrediction,v.classPrediction,w.classPrediction,h.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var Z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=zo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Xo(a.out,a.conv11,e.prediction_layer);return Uo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*w);return new M(p[F],new oe(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Go(t)}extractParams(t){return Yo(t)}};function Jo(o){let t=new Xt;return t.extractWeights(o),t}function aa(o){return Jo(o)}var qo=class extends Xt{};var Zo=.4,Ko=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Qo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],tn=[117.001,114.697,97.404],en="tiny_yolov2_model",rn="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function ao(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!br(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var so=b(g());function sa(o,t){let e=ce(o,t);function r(s,i){let c=so.tensor1d(o(s)),m=so.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=sa(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,w,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,l,"conv1"),et=m(l,v,"conv2"),it=m(v,w,"conv3"),X=m(w,h,"conv4"),_t=m(h,y,"conv5"),wt=T?m(y,T,"conv6"):void 0,Pt=F?m(T,F,"conv7"):void 0,te=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}else{let[d,u,l,v,w,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,l,"conv1"),et=c(l,v,"conv2"),it=c(v,w,"conv3"),X=c(w,h,"conv4"),_t=c(h,y,"conv5"),wt=c(y,T,"conv6"),Pt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Pt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function ia(o,t){let e=j(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=ia(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var io=class extends S{constructor(t){super("TinyYolov2");ao(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(zt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||io.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],w=await u.array(),h=await d.array();for(let y=0;yr){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,_t=G-it/2,wt=et-X/2,Pt={row:y,col:T,anchor:F},{classScore:te,label:lo}=this.withClassScores?await this.extractPredictedClass(l,Pt):{classScore:1,label:0};v.push({box:new re(_t,wt,_t+it,wt+X),score:L,classScore:L*te,label:lo,...Pt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=io;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ca(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var co=b(g());async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof co.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof co.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var _={ssdMobilenetv1:new Xt,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),ma=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),pa=(o,t)=>_.tinyYolov2.locateFaces(o,t),pn=o=>_.faceLandmark68Net.detectLandmarks(o),da=o=>_.faceLandmark68TinyNet.detectLandmarks(o),ua=o=>_.faceRecognitionNet.computeFaceDescriptor(o),la=o=>_.faceExpressionNet.predictExpressions(o),fa=o=>_.ageGenderNet.predictAgeAndGender(o),dn=o=>_.ssdMobilenetv1.load(o),ha=o=>_.tinyFaceDetector.load(o),xa=o=>_.tinyYolov2.load(o),ba=o=>_.faceLandmark68Net.load(o),ga=o=>_.faceLandmark68TinyNet.load(o),va=o=>_.faceRecognitionNet.load(o),ya=o=>_.faceExpressionNet.load(o),Fa=o=>_.ageGenderNet.load(o),Ta=dn,_a=mn,wa=pn;var mo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends mo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},Pe=class extends mo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Qt=class extends Pe{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends po{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends po{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new Pe(this,this.input)}},qt=class extends Te{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>_.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var _r=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends _r{async run(){let{input:t,options:e}=this,r;if(e instanceof gr)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof Z)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof lt)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Ot({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},wr=class extends _r{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Ot({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function Pa(o,t=new Z){return new wr(o,t)}function Pr(o,t=new Z){return new He(o,t)}async function un(o,t){return Pr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Da(o,t={}){return Pr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Ea=un;function uo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Dr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>uo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ma(o){let t=new Fe;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(Ot(o,n),a)}return pt(o)?Ot(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Na=typeof process!="undefined",Ia=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",La={faceapi:No,node:Na,browser:Ia}; +var Vn=Object.create,sr=Object.defineProperty,Un=Object.getPrototypeOf,Xn=Object.prototype.hasOwnProperty,Jn=Object.getOwnPropertyNames,qn=Object.getOwnPropertyDescriptor;var jr=o=>sr(o,"__esModule",{value:!0});var Do=(o,t)=>()=>(t||(t={exports:{}},o(t.exports,t)),t.exports),ir=(o,t)=>{for(var e in t)sr(o,e,{get:t[e],enumerable:!0})},Zn=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Jn(t))!Xn.call(o,r)&&r!=="default"&&sr(o,r,{get:()=>t[r],enumerable:!(e=qn(t,r))||e.enumerable});return o},b=o=>Zn(jr(sr(o!=null?Vn(Un(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);var g=Do(Hr=>{var Kn=Object.create,Yr=Object.defineProperty,Qn=Object.getPrototypeOf,ta=Object.prototype.hasOwnProperty,ea=Object.getOwnPropertyNames,ra=Object.getOwnPropertyDescriptor,Eo=o=>Yr(o,"__esModule",{value:!0}),Mo=(o,t,e)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of ea(t))!ta.call(o,r)&&r!=="default"&&Yr(o,r,{get:()=>t[r],enumerable:!(e=ra(t,r))||e.enumerable});return o},oa=o=>Mo(Eo(Yr(o!=null?Kn(Qn(o)):{},"default",o&&o.__esModule&&"default"in o?{get:()=>o.default,enumerable:!0}:{value:o,enumerable:!0})),o);Eo(Hr);Mo(Hr,oa(require("@tensorflow/tfjs-node")))});var ko=Do((ca,Lo)=>{jr(ca);ir(ca,{isNodejs:()=>ma});function ma(){return typeof global=="object"&&!0&&typeof Lo!="undefined"&&typeof process!="undefined"&&!!process.version}});jr(exports);ir(exports,{AgeGenderNet:()=>je,BoundingBox:()=>Ut,Box:()=>D,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>Pt,ComputeFaceDescriptorsTaskBase:()=>Ze,ComputeSingleFaceDescriptorTask:()=>Dt,DetectAllFaceLandmarksTask:()=>tr,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Qe,DetectFacesTaskBase:()=>rr,DetectSingleFaceLandmarksTask:()=>er,DetectSingleFaceTask:()=>or,Dimensions:()=>A,FACE_EXPRESSION_LABELS:()=>Sr,FaceDetection:()=>M,FaceDetectionNet:()=>fo,FaceExpressionNet:()=>$e,FaceExpressions:()=>Ft,FaceLandmark68Net:()=>te,FaceLandmark68TinyNet:()=>Ye,FaceLandmarkNet:()=>mo,FaceLandmarks:()=>j,FaceLandmarks5:()=>Xr,FaceLandmarks68:()=>Jt,FaceMatch:()=>le,FaceMatcher:()=>ar,FaceRecognitionNet:()=>ee,Gender:()=>ht,LabeledBox:()=>fe,LabeledFaceDescriptors:()=>ut,NetInput:()=>ft,NeuralNetwork:()=>S,ObjectDetection:()=>vt,Point:()=>h,PredictedBox:()=>Jr,Rect:()=>Xt,SsdMobilenetv1:()=>Ht,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ae,TinyFaceDetectorOptions:()=>qe,TinyYolov2:()=>oe,TinyYolov2Options:()=>ct,allFaces:()=>Hn,allFacesSsdMobilenetv1:()=>_o,allFacesTinyYolov2:()=>jn,awaitMediaLoaded:()=>xr,bufferToImage:()=>br,computeFaceDescriptor:()=>En,createCanvas:()=>At,createCanvasFromMedia:()=>xe,createFaceDetectionNet:()=>dn,createFaceRecognitionNet:()=>en,createSsdMobilenetv1:()=>lo,createTinyFaceDetector:()=>Yn,createTinyYolov2:()=>yn,detectAllFaces:()=>nr,detectFaceLandmarks:()=>vo,detectFaceLandmarksTiny:()=>Dn,detectLandmarks:()=>$n,detectSingleFace:()=>On,draw:()=>ao,env:()=>P,euclideanDistance:()=>Or,extendWithAge:()=>Ve,extendWithFaceDescriptor:()=>ze,extendWithFaceDetection:()=>It,extendWithFaceExpressions:()=>Oe,extendWithFaceLandmarks:()=>Qt,extendWithGender:()=>Ue,extractFaceTensors:()=>Zt,extractFaces:()=>qt,fetchImage:()=>So,fetchJson:()=>Tr,fetchNetWeights:()=>Ao,fetchOrThrow:()=>Wt,getContext2dOrThrow:()=>W,getMediaDimensions:()=>St,imageTensorToCanvas:()=>vr,imageToSquare:()=>yr,inverseSigmoid:()=>Io,iou:()=>pr,isMediaElement:()=>ke,isMediaLoaded:()=>he,isWithAge:()=>rn,isWithFaceDetection:()=>nt,isWithFaceExpressions:()=>Ar,isWithFaceLandmarks:()=>$t,isWithGender:()=>on,loadAgeGenderModel:()=>Wn,loadFaceDetectionModel:()=>Bn,loadFaceExpressionModel:()=>An,loadFaceLandmarkModel:()=>Ln,loadFaceLandmarkTinyModel:()=>kn,loadFaceRecognitionModel:()=>Sn,loadSsdMobilenetv1Model:()=>yo,loadTinyFaceDetectorModel:()=>Nn,loadTinyYolov2Model:()=>In,loadWeightMap:()=>wr,locateFaces:()=>Rn,matchDimensions:()=>Bo,minBbox:()=>dr,nets:()=>_,nonMaxSuppression:()=>ur,normalize:()=>Z,padToSquare:()=>lr,predictAgeAndGender:()=>Cn,recognizeFaceExpressions:()=>Mn,resizeResults:()=>wo,resolveInput:()=>Lt,shuffleArray:()=>No,sigmoid:()=>ue,ssdMobilenetv1:()=>go,tf:()=>Gn,tinyFaceDetector:()=>wn,tinyYolov2:()=>Pn,toNetInput:()=>E,utils:()=>Gr,validateConfig:()=>$r,version:()=>zn});var Gn=b(g());var ao={};ir(ao,{AnchorPosition:()=>lt,DrawBox:()=>hr,DrawBoxOptions:()=>ro,DrawFaceLandmarks:()=>no,DrawFaceLandmarksOptions:()=>oo,DrawTextField:()=>kt,DrawTextFieldOptions:()=>Le,drawContour:()=>bt,drawDetections:()=>ua,drawFaceExpressions:()=>la,drawFaceLandmarks:()=>ha});function bt(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:n},a)=>{let s=t[a];o.moveTo(s.x,s.y),o.lineTo(r,n)}),e){let r=t[t.length-1],n=t[0];if(!r||!n)return;o.moveTo(r.x,r.y),o.lineTo(n.x,n.y)}o.stroke()}var Gr={};ir(Gr,{computeReshapedDimensions:()=>Ur,getCenterPoint:()=>Vt,isDimensions:()=>mr,isEven:()=>cr,isFloat:()=>Vr,isTensor:()=>Gt,isTensor1D:()=>na,isTensor2D:()=>zr,isTensor3D:()=>gt,isTensor4D:()=>V,isValidNumber:()=>ot,isValidProbablitiy:()=>de,range:()=>pt,round:()=>zt});var Co=b(g());var A=class{constructor(t,e){if(!ot(t)||!ot(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new A(1/this.width,1/this.height)}};function Gt(o,t){return o instanceof Co.Tensor&&o.shape.length===t}function na(o){return Gt(o,1)}function zr(o){return Gt(o,2)}function gt(o){return Gt(o,3)}function V(o){return Gt(o,4)}function Vr(o){return o%1!=0}function cr(o){return o%2==0}function zt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function mr(o){return o&&o.width&&o.height}function Ur({width:o,height:t},e){let r=e/Math.max(t,o);return new A(Math.round(o*r),Math.round(t*r))}function Vt(o){return o.reduce((t,e)=>t.add(e),new h(0,0)).div(new h(o.length,o.length))}function pt(o,t,e){return Array(o).fill(0).map((r,n)=>t+n*e)}function ot(o){return!!o&&o!==Infinity&&o!==-Infinity&&!Number.isNaN(o)||o===0}function de(o){return ot(o)&&o>=0&&o<=1}var h=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new h(this.x+t.x,this.y+t.y)}sub(t){return new h(this.x-t.x,this.y-t.y)}mul(t){return new h(this.x*t.x,this.y*t.y)}div(t){return new h(this.x/t.x,this.y/t.y)}abs(){return new h(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new h(Math.floor(this.x),Math.floor(this.y))}};var D=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(ot)}static assertIsValidBox(t,e,r=!1){if(!D.isRect(t))throw new Error(`${e} - invalid box: ${JSON.stringify(t)}, expected object with properties x, y, width, height`);if(!r&&(t.width<0||t.height<0))throw new Error(`${e} - width (${t.width}) and height (${t.height}) must be positive numbers`)}constructor(t,e=!0){let r=t||{},n=[r.left,r.top,r.right,r.bottom].every(ot),a=[r.x,r.y,r.width,r.height].every(ot);if(!a&&!n)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(r)}`);let[s,i,c,m]=a?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];D.assertIsValidBox({x:s,y:i,width:c,height:m},"Box.constructor",e),this._x=s,this._y=i,this._width=c,this._height=m}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new h(this.left,this.top)}get topRight(){return new h(this.right,this.top)}get bottomLeft(){return new h(this.left,this.bottom)}get bottomRight(){return new h(this.right,this.bottom)}round(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new D({x:t,y:e,width:r,height:n})}floor(){let[t,e,r,n]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new D({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-d+e+r,d=e),u>t&&(c=-u+t+n,u=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:u,x:m,ex:d,w:r,h:n}}calibrate(t){return new D({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var Ut=class extends D{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var vt=class{constructor(t,e,r,n,a){this._imageDims=new A(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new D(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new vt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends vt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function pr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function dr(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var dt=b(g());function Z(o,t){return dt.tidy(()=>{let[e,r,n]=t,a=dt.fill([...o.shape.slice(0,3),1],e,"float32"),s=dt.fill([...o.shape.slice(0,3),1],r,"float32"),i=dt.fill([...o.shape.slice(0,3),1],n,"float32"),c=dt.concat([a,s,i],3);return dt.sub(o,c)})}var Nt=b(g());function lr(o,t=!1){return Nt.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=u=>{let l=o.shape.slice();return l[s]=u,Nt.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],d=[t&&m?i(m):null,o,c].filter(u=>!!u).map(u=>Nt.cast(u,"float32"));return Nt.concat(d,s)})}function No(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function ue(o){return 1/(1+Math.exp(-o))}function Io(o){return Math.log(o/(1-o))}var Xt=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var aa=.5,sa=.43,ia=.45,j=class{constructor(t,e,r=new h(0,0)){let{width:n,height:a}=e;this._imgDims=new A(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new h(n,a)).add(r))}get shift(){return new h(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new h(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new h(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof M?t.box.floor():new D(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=d=>n.sub(d).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/ia),c=Vt(t),m=Math.floor(Math.max(0,c.x-aa*i)),p=Math.floor(Math.max(0,c.y-sa*i));return new Xt(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=dr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Xr=class extends j{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Vt([t[3],t[4]])]}};var Jt=class extends j{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Vt)}};var le=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${zt(this.distance)})`:""}`}};var fe=class extends D{static assertIsValidLabeledBox(t,e){if(D.assertIsValidBox(t,e),!ot(t.label))throw new Error(`${e} - expected property label (${t.label}) to be a number`)}constructor(t,e){super(t);this._label=e}get label(){return this._label}};var ut=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new ut(t.label,e)}};var Jr=class extends fe{static assertIsValidPredictedBox(t,e){if(fe.assertIsValidLabeledBox(t,e),!de(t.score)||!de(t.classScore))throw new Error(`${e} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,e,r,n){super(t,e);this._score=r,this._classScore=n}get score(){return this._score}get classScore(){return this._classScore}};function nt(o){return o.detection instanceof M}function It(o,t){return{...o,...{detection:t}}}function qr(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function fr(o){let t="";if(!o)try{o=require("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((n,a)=>{o.readFile(r,(s,i)=>s?a(s):n(i))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Zr(){let o=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,e=()=>{if(o)return new o;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},n=global.fetch,a=fr();return{Canvas:o||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:e,createImageElement:r,fetch:n,...a}}function Kr(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var Qr=b(ko()),k;function pa(){if(!k)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return k}function to(o){k=o}function eo(){return Kr()?to(qr()):(0,Qr.isNodejs)()?to(Zr()):null}function da(o){if(k||eo(),!k)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=k.Canvas,Image:e=k.Image}=o;k.Canvas=t,k.Image=e,k.createCanvasElement=o.createCanvasElement||(()=>new t),k.createImageElement=o.createImageElement||(()=>new e),k.ImageData=o.ImageData||k.ImageData,k.Video=o.Video||k.Video,k.fetch=o.fetch||k.fetch,k.readFile=o.readFile||k.readFile}var P={getEnv:pa,setEnv:to,initialize:eo,createBrowserEnv:qr,createFileSystem:fr,createNodejsEnv:Zr,monkeyPatch:da,isBrowser:Kr,isNodejs:Qr.isNodejs};eo();function Lt(o){return!P.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function W(o){let{Canvas:t,CanvasRenderingContext2D:e}=P.getEnv();if(o instanceof e)return o;let r=Lt(o);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let n=r.getContext("2d");if(!n)throw new Error("resolveContext2d - canvas 2d context is null");return n}var lt;(function(o){o.TOP_LEFT="TOP_LEFT",o.TOP_RIGHT="TOP_RIGHT",o.BOTTOM_LEFT="BOTTOM_LEFT",o.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lt||(lt={}));var Le=class{constructor(t={}){let{anchorPosition:e,backgroundColor:r,fontColor:n,fontSize:a,fontStyle:s,padding:i}=t;this.anchorPosition=e||lt.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=s||"Georgia",this.padding=i||4}},kt=class{constructor(t,e,r={}){this.text=typeof t=="string"?[t]:t instanceof kt?t.text:t,this.anchor=e,this.options=new Le(r)}measureWidth(t){let{padding:e}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,n)=>r{let v=c+d.x,w=c+d.y+(l+1)*s;r.fillText(u,v,w)})}};var ro=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:n,drawLabelOptions:a}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=n;let s={anchorPosition:lt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Le({...s,...a})}},hr=class{constructor(t,e={}){this.box=new D(t),this.options=new ro(e)}draw(t){let e=W(t),{boxColor:r,lineWidth:n}=this.options,{x:a,y:s,width:i,height:c}=this.box;e.strokeStyle=r,e.lineWidth=n,e.strokeRect(a,s,i,c);let{label:m}=this.options;m&&new kt([m],{x:a-n/2,y:s},this.options.drawLabelOptions).draw(t)}};function ua(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof M?r.score:nt(r)?r.detection.score:void 0,a=r instanceof M?r.box:nt(r)?r.detection.box:new D(r),s=n?`${zt(n)}`:void 0;new hr(a,{label:s}).draw(o)})}var Te=b(g());function he(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function xr(o){return new Promise((t,e)=>{if(o instanceof P.getEnv().Canvas||he(o))return t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),e(a))}function n(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",n),a.currentTarget.removeEventListener("error",r),t(a))}o.addEventListener("load",n),o.addEventListener("error",r)})}function br(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let n=P.getEnv().createImageElement();n.onload=()=>t(n),n.onerror=e,n.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function St(o){let{Image:t,Video:e}=P.getEnv();return o instanceof t?new A(o.naturalWidth,o.naturalHeight):o instanceof e?new A(o.videoWidth,o.videoHeight):new A(o.width,o.height)}function At({width:o,height:t}){let{createCanvasElement:e}=P.getEnv(),r=e();return r.width=o,r.height=t,r}function xe(o,t){let{ImageData:e}=P.getEnv();if(!(o instanceof e)&&!he(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:n}=t||St(o),a=At({width:r,height:n});return o instanceof e?W(a).putImageData(o,0,0):W(a).drawImage(o,0,0,r,n),a}var gr=b(g());async function vr(o,t){let e=t||P.getEnv().createCanvasElement(),[r,n,a]=o.shape.slice(V(o)?1:0),s=gr.tidy(()=>o.as3D(r,n,a).toInt());return await gr.browser.toPixels(s,e),s.dispose(),e}function ke(o){let{Image:t,Canvas:e,Video:r}=P.getEnv();return o instanceof t||o instanceof e||o instanceof r}var K=b(g());function yr(o,t,e=!1){let{Image:r,Canvas:n}=P.getEnv();if(!(o instanceof r||o instanceof n))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return At({width:1,height:1});let a=St(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=At({width:t,height:t}),p=o instanceof n?o:xe(o),d=Math.abs(i-c)/2,u=e&&i0&&p.height>0&&W(m).drawImage(p,u,l,i,c),m}var ft=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(gt(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(V(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof P.getEnv().Canvas?r:xe(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return pt(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Ur({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,K.tidy(()=>{let r=pt(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof K.Tensor){let i=V(s)?s:s.expandDims();return i=lr(i,e),(i.shape[1]!==t||i.shape[2]!==t)&&(i=K.image.resizeBilinear(i,[t,t])),i.as3D(t,t,3)}if(s instanceof P.getEnv().Canvas)return K.browser.fromPixels(yr(s,t,e));throw new Error(`toBatchTensor - at batchIdx ${a}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${s}`)});return K.stack(r.map(a=>K.cast(a,"float32"))).as4D(this.batchSize,t,t,3)})}};async function E(o){if(o instanceof ft)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=n=>Array.isArray(o)?` at input index ${n}:`:"",r=t.map(Lt);return r.forEach((n,a)=>{if(!ke(n)&&!gt(n)&&!V(n))throw typeof t[a]=="string"?new Error(`toNetInput -${e(a)} string passed, but could not resolve HTMLElement for element id ${t[a]}`):new Error(`toNetInput -${e(a)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(V(n)){let s=n.shape[0];if(s!==1)throw new Error(`toNetInput -${e(a)} tf.Tensor4D with batchSize ${s} passed, but not supported in input array`)}}),await Promise.all(r.map(n=>ke(n)&&xr(n))),new ft(r,Array.isArray(o))}async function qt(o,t){let{Canvas:e}=P.getEnv(),r=o;if(!(o instanceof e)){let s=await E(o);if(s.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let i=s.getInput(0);r=i instanceof e?i:await vr(i)}let n=W(r);return t.map(s=>s instanceof M?s.forSize(r.width,r.height).box.floor():s).map(s=>s.clipAtImageBorders(r.width,r.height)).map(({x:s,y:i,width:c,height:m})=>{let p=At({width:c,height:m});return c>0&&m>0&&W(p).putImageData(n.getImageData(s,i,c,m),0,0),p})}var Fr=b(g());async function Zt(o,t){if(!gt(o)&&!V(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(V(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Fr.tidy(()=>{let[e,r,n]=o.shape.slice(V(o)?1:0);return t.map(i=>i instanceof M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).map(({x:i,y:c,width:m,height:p})=>Fr.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function Wt(o,t){let{fetch:e}=P.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function So(o){let t=await Wt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return br(e)}async function Tr(o){return(await Wt(o)).json()}async function Ao(o){return new Float32Array(await(await Wt(o)).arrayBuffer())}var Wo=b(g());function _r(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function wr(o,t){let{manifestUri:e,modelBaseUri:r}=_r(o,t),n=await Tr(e);return Wo.io.loadWeights(n,r)}function Bo(o,t,e=!1){let{width:r,height:n}=e?St(t):t;return o.width=r,o.height=n,{width:r,height:n}}var Rt=b(g());var yt=b(g());var S=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:e,objProp:r}=this.traversePropertyPath(t);return e[r]}reassignParamFromPath(t,e){let{obj:r,objProp:n}=this.traversePropertyPath(t);r[n].dispose(),r[n]=e}getParamList(){return this._paramMappings.map(({paramPath:t})=>({path:t,tensor:this.getParamFromPath(t)}))}getTrainableParams(){return this.getParamList().filter(t=>t.tensor instanceof yt.Variable)}getFrozenParams(){return this.getParamList().filter(t=>!(t.tensor instanceof yt.Variable))}variable(){this.getFrozenParams().forEach(({path:t,tensor:e})=>{this.reassignParamFromPath(t,e.variable())})}freeze(){this.getTrainableParams().forEach(({path:t,tensor:e})=>{let r=yt.tensor(e.dataSync());e.dispose(),this.reassignParamFromPath(t,r)})}dispose(t=!0){this.getParamList().forEach(e=>{if(t&&e.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${e.path}`);e.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:t})=>Array.from(t.dataSync())).reduce((t,e)=>t.concat(e)))}async load(t){if(t instanceof Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let e=await wr(t,this.getDefaultModelName());this.loadFromWeightMap(e)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:e}=P.getEnv(),{manifestUri:r,modelBaseUri:n}=_r(t,this.getDefaultModelName()),a=m=>Promise.all(m.map(p=>e(p).then(d=>d.buffer))),s=yt.io.weightsLoaderFactory(a),i=JSON.parse((await e(r)).toString()),c=await s(i,n);this.loadFromWeightMap(c)}loadFromWeightMap(t){let{paramMappings:e,params:r}=this.extractParamsFromWeightMap(t);this._paramMappings=e,this._params=r}extractWeights(t){let{paramMappings:e,params:r}=this.extractParams(t);this._paramMappings=e,this._params=r}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let e=t.split("/").reduce((a,s)=>{if(!a.nextObj.hasOwnProperty(s))throw new Error(`traversePropertyPath - object does not have property ${s}, for path ${t}`);return{obj:a.nextObj,objProp:s,nextObj:a.nextObj[s]}},{nextObj:this.params}),{obj:r,objProp:n}=e;if(!r||!n||!(r[n]instanceof yt.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:r,objProp:n}}};var C=b(g());var be=b(g());function O(o,t,e){return be.tidy(()=>{let r=be.separableConv2d(o,t.depthwise_filter,t.pointwise_filter,e,"same");return r=be.add(r,t.bias),r})}function Pr(o,t,e=!1){return C.tidy(()=>{let r=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,[2,2],"same"),t.conv0.bias):O(o,t.conv0,[2,2])),n=O(r,t.conv1,[1,1]),a=C.relu(C.add(r,n)),s=O(a,t.conv2,[1,1]);return C.relu(C.add(r,C.add(n,s)))})}function Se(o,t,e=!1,r=!0){return C.tidy(()=>{let n=C.relu(e?C.add(C.conv2d(o,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):O(o,t.conv0,r?[2,2]:[1,1])),a=O(n,t.conv1,[1,1]),s=C.relu(C.add(n,a)),i=O(s,t.conv2,[1,1]),c=C.relu(C.add(n,C.add(a,i))),m=O(c,t.conv3,[1,1]);return C.relu(C.add(n,C.add(a,C.add(i,m))))})}var Bt=b(g());function Kt(o,t,e="same",r=!1){return Bt.tidy(()=>{let n=Bt.add(Bt.conv2d(o,t.filters,[1,1],e),t.bias);return r?Bt.relu(n):n})}function B(o,t){Object.keys(o).forEach(e=>{t.some(r=>r.originalPath===e)||o[e].dispose()})}var Dr=b(g());function ge(o,t){return(e,r,n,a)=>{let s=Dr.tensor4d(o(e*r*n*n),[n,n,e,r]),i=Dr.tensor1d(o(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:s,bias:i}}}var Er=b(g());function Mr(o,t){return(e,r,n)=>{let a=Er.tensor2d(o(e*r),[e,r]),s=Er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var Ae=b(g());var Cr=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function ve(o,t){return(e,r,n)=>{let a=Ae.tensor4d(o(3*3*e),[3,3,e,1]),s=Ae.tensor4d(o(e*r),[1,1,e,r]),i=Ae.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new Cr(a,s,i)}}function ye(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new Cr(e,r,n)}}function H(o,t){return(e,r,n)=>{let a=o[e];if(!Gt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function R(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function Nr(o,t){let e=ge(o,t),r=ve(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function Ro(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractDenseBlock4Params:n}=Nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function Ir(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function Lr(o,t){let e=H(o,t),r=Ir(e),n=ye(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function s(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`),u=n(`${i}/conv3`);return{conv0:m,conv1:p,conv2:d,conv3:u}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:s}}function $o(o){let t=[],{extractDenseBlock4Params:e}=Lr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2"),dense3:e("dense3")};return B(o,t),{params:r,paramMappings:t}}var We=class extends S{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceFeatureExtractor - load model before inference");return Rt.tidy(()=>{let r=Rt.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(Rt.scalar(255)),s=Se(a,e.dense0,!0);return s=Se(s,e.dense1),s=Se(s,e.dense2),s=Se(s,e.dense3),s=Rt.avgPool(s,[7,7],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return $o(t)}extractParams(t){return Ro(t)}};var Ho=b(g());var Fe=b(g());function Be(o,t){return Fe.tidy(()=>Fe.add(Fe.matMul(o,t.weights),t.bias))}function Oo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=R(o),i=Mr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function jo(o){let t=[],e=H(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return B(o,t),{params:n,paramMappings:t}}function kr(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var Re=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Ho.tidy(()=>{let r=t instanceof ft?this.faceFeatureExtractor.forwardInput(t):t;return Be(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Oo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=kr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),jo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Sr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ft=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Sr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return Sr.map(t=>({expression:t,probability:this[t]})).sort((t,e)=>e.probability-t.probability)}};var $e=class extends Re{constructor(t=new We){super("FaceExpressionNet",t)}forwardInput(t){return Te.tidy(()=>Te.softmax(this.runNet(t)))}async forward(t){return this.forwardInput(await E(t))}async predictExpressions(t){let e=await E(t),r=await this.forwardInput(e),n=await Promise.all(Te.unstack(r).map(async s=>{let i=await s.data();return s.dispose(),i}));r.dispose();let a=n.map(s=>new Ft(s));return e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ar(o){return o.expressions instanceof Ft}function Oe(o,t){return{...o,...{expressions:t}}}function la(o,t,e=.1,r){(Array.isArray(t)?t:[t]).forEach(a=>{let s=a instanceof Ft?a:Ar(a)?a.expressions:void 0;if(!s)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let c=s.asSortedArray().filter(d=>d.probability>e),m=nt(a)?a.detection.box.bottomLeft:r||new h(0,0);new kt(c.map(d=>`${d.expression} (${zt(d.probability)})`),m).draw(o)})}function $t(o){return nt(o)&&o.landmarks instanceof j&&o.unshiftedLandmarks instanceof j&&o.alignedRect instanceof M}function fa(o){let t=(i,c,m,p)=>Math.atan2(p-c,m-i)%Math.PI,e=i=>i*180/Math.PI,r={roll:void 0,pitch:void 0,yaw:void 0};if(!o||!o._positions||o._positions.length!==68)return r;let n=o._positions;r.roll=-t(n[36]._x,n[36]._y,n[45]._x,n[45]._y),r.pitch=t(0,Math.abs(n[0]._x-n[30]._x)/n[30]._x,Math.PI,Math.abs(n[16]._x-n[30]._x)/n[30]._x);let a=n.reduce((i,c)=>ii>c._y?i:c._y,-Infinity);return r.yaw=Math.PI*(o._imgDims._height/(s-a)/1.4-1),r}function Qt(o,t){let{box:e}=o.detection,r=t.shiftBy(e.x,e.y),n=r.align(),{imageDims:a}=o.detection,s=new M(o.detection.score,n.rescale(a.reverse()),a),i=fa(t);return{...o,...{landmarks:r,unshiftedLandmarks:t,alignedRect:s,angle:i}}}var oo=class{constructor(t={}){let{drawLines:e=!0,drawPoints:r=!0,lineWidth:n,lineColor:a,pointSize:s,pointColor:i}=t;this.drawLines=e,this.drawPoints=r,this.lineWidth=n||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},no=class{constructor(t,e={}){this.faceLandmarks=t,this.options=new oo(e)}draw(t){let e=W(t),{drawLines:r,drawPoints:n,lineWidth:a,lineColor:s,pointSize:i,pointColor:c}=this.options;if(r&&this.faceLandmarks instanceof Jt&&(e.strokeStyle=s,e.lineWidth=a,bt(e,this.faceLandmarks.getJawOutline()),bt(e,this.faceLandmarks.getLeftEyeBrow()),bt(e,this.faceLandmarks.getRightEyeBrow()),bt(e,this.faceLandmarks.getNose()),bt(e,this.faceLandmarks.getLeftEye(),!0),bt(e,this.faceLandmarks.getRightEye(),!0),bt(e,this.faceLandmarks.getMouth(),!0)),n){e.strokeStyle=c,e.fillStyle=c;let m=p=>{e.beginPath(),e.arc(p.x,p.y,i,0,2*Math.PI),e.fill()};this.faceLandmarks.positions.forEach(m)}}};function ha(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let n=r instanceof j?r:$t(r)?r.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new no(n).draw(o)})}var Yo="1.0.2";var xt=b(g());var I=b(g());function xa(o,t){let e=ge(o,t),r=ve(o,t);function n(s,i,c){let m=r(s,i,`${c}/separable_conv0`),p=r(i,i,`${c}/separable_conv1`),d=e(s,i,1,`${c}/expansion_conv`);return{separable_conv0:m,separable_conv1:p,expansion_conv:d}}function a(s,i){let c=r(s,s,`${i}/separable_conv0`),m=r(s,s,`${i}/separable_conv1`),p=r(s,s,`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:e,extractSeparableConvParams:r,extractReductionBlockParams:n,extractMainBlockParams:a}}function Go(o,t){let e=[],{extractWeights:r,getRemainingWeights:n}=R(o),{extractConvParams:a,extractSeparableConvParams:s,extractReductionBlockParams:i,extractMainBlockParams:c}=xa(r,e),m=a(3,32,3,"entry_flow/conv_in"),p=i(32,64,"entry_flow/reduction_block_0"),d=i(64,128,"entry_flow/reduction_block_1"),u={conv_in:m,reduction_block_0:p,reduction_block_1:d},l={};pt(t,0,1).forEach(y=>{l[`main_block_${y}`]=c(128,`middle_flow/main_block_${y}`)});let v=i(128,256,"exit_flow/reduction_block"),w=s(256,512,"exit_flow/separable_conv"),x={reduction_block:v,separable_conv:w};if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:e,params:{entry_flow:u,middle_flow:l,exit_flow:x}}}function ba(o,t){let e=H(o,t),r=Ir(e),n=ye(e);function a(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=r(`${i}/expansion_conv`);return{separable_conv0:c,separable_conv1:m,expansion_conv:p}}function s(i){let c=n(`${i}/separable_conv0`),m=n(`${i}/separable_conv1`),p=n(`${i}/separable_conv2`);return{separable_conv0:c,separable_conv1:m,separable_conv2:p}}return{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}}function zo(o,t){let e=[],{extractConvParams:r,extractSeparableConvParams:n,extractReductionBlockParams:a,extractMainBlockParams:s}=ba(o,e),i=r("entry_flow/conv_in"),c=a("entry_flow/reduction_block_0"),m=a("entry_flow/reduction_block_1"),p={conv_in:i,reduction_block_0:c,reduction_block_1:m},d={};pt(t,0,1).forEach(w=>{d[`main_block_${w}`]=s(`middle_flow/main_block_${w}`)});let u=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),v={reduction_block:u,separable_conv:l};return B(o,e),{params:{entry_flow:p,middle_flow:d,exit_flow:v},paramMappings:e}}function Vo(o,t,e){return I.add(I.conv2d(o,t.filters,e,"same"),t.bias)}function so(o,t,e=!0){let r=e?I.relu(o):o;return r=O(r,t.separable_conv0,[1,1]),r=O(I.relu(r),t.separable_conv1,[1,1]),r=I.maxPool(r,[3,3],[2,2],"same"),r=I.add(r,Vo(o,t.expansion_conv,[2,2])),r}function ga(o,t){let e=O(I.relu(o),t.separable_conv0,[1,1]);return e=O(I.relu(e),t.separable_conv1,[1,1]),e=O(I.relu(e),t.separable_conv2,[1,1]),e=I.add(e,o),e}var io=class extends S{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return I.tidy(()=>{let r=I.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(I.scalar(256)),s=I.relu(Vo(a,e.entry_flow.conv_in,[2,2]));return s=so(s,e.entry_flow.reduction_block_0,!1),s=so(s,e.entry_flow.reduction_block_1),pt(this._numMainBlocks,0,1).forEach(i=>{s=ga(s,e.middle_flow[`main_block_${i}`])}),s=so(s,e.exit_flow.reduction_block),s=I.relu(O(s,e.exit_flow.separable_conv,[1,1])),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return zo(t,this._numMainBlocks)}extractParams(t){return Go(t,this._numMainBlocks)}};function Uo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),n=Mr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Xo(o){let t=[],e=H(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return B(o,t),{params:n,paramMappings:t}}var ht;(function(o){o.FEMALE="female",o.MALE="male"})(ht||(ht={}));var je=class extends S{constructor(t=new io(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return xt.tidy(()=>{let r=t instanceof ft?this.faceFeatureExtractor.forwardInput(t):t,n=xt.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Be(n,e.fc.age).as1D(),s=Be(n,e.fc.gender);return{age:a,gender:s}})}forwardInput(t){return xt.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:xt.softmax(r)}})}async forward(t){return this.forwardInput(await E(t))}async predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=xt.unstack(r.age),a=xt.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,l=u?ht.MALE:ht.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:l,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Uo(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=kr(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Xo(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(n)}};var Y=b(g());var He=class extends Re{postProcess(t,e,r){let n=r.map(({width:s,height:i})=>{let c=e/Math.max(i,s);return{width:s*c,height:i*c}}),a=n.length;return Y.tidy(()=>{let s=(d,u)=>Y.stack([Y.fill([68],d,"float32"),Y.fill([68],u,"float32")],1).as2D(1,136).as1D(),i=(d,u)=>{let{width:l,height:v}=n[d];return u(l,v)?Math.abs(l-v)/2:0},c=d=>i(d,(u,l)=>ui(d,(u,l)=>ls(c(u),m(u))))).div(Y.stack(Array.from(Array(a),(d,u)=>s(n[u].width,n[u].height))))})}forwardInput(t){return Y.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,n])=>({height:r,width:n})))})}async forward(t){return this.forwardInput(await E(t))}async detectLandmarks(t){let e=await E(t),r=Y.tidy(()=>Y.unstack(this.forwardInput(e))),n=await Promise.all(r.map(async(a,s)=>{let i=Array.from(await a.data()),c=i.filter((p,d)=>cr(d)),m=i.filter((p,d)=>!cr(d));return new Jt(Array(68).fill(0).map((p,d)=>new h(c[d],m[d])),{height:e.getInputHeight(s),width:e.getInputWidth(s)})}));return r.forEach(a=>a.dispose()),e.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}};var te=class extends He{constructor(t=new We){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Ot=b(g());function Jo(o){let t=[],{extractDenseBlock3Params:e}=Lr(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return B(o,t),{params:r,paramMappings:t}}function qo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractDenseBlock3Params:n}=Nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i}}}var co=class extends S{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Ot.tidy(()=>{let r=Ot.cast(t.toBatchTensor(112,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(Ot.scalar(255)),s=Pr(a,e.dense0,!0);return s=Pr(s,e.dense1),s=Pr(s,e.dense2),s=Ot.avgPool(s,[14,14],[2,2],"valid"),s})}async forward(t){return this.forwardInput(await E(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return Jo(t)}extractParams(t){return qo(t)}};var Ye=class extends He{constructor(t=new co){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var mo=class extends te{};var U=b(g());var _e=b(g());var Wr=b(g());function Zo(o,t){return Wr.add(Wr.mul(o,t.weights),t.biases)}function po(o,t,e,r,n="same"){let{filters:a,bias:s}=t.conv,i=_e.conv2d(o,a,e,n);return i=_e.add(i,s),i=Zo(i,t.scale),r?_e.relu(i):i}function Ko(o,t){return po(o,t,[1,1],!0)}function uo(o,t){return po(o,t,[1,1],!1)}function Br(o,t){return po(o,t,[2,2],!0,"valid")}var G=b(g());function va(o,t){function e(i,c,m){let p=o(i),d=p.length/(c*m*m);if(Vr(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${c}, filterSize: ${m}`);return G.tidy(()=>G.transpose(G.tensor4d(p,[c,d,m,m]),[2,3,1,0]))}function r(i,c,m,p){let d=e(i,c,m),u=G.tensor1d(o(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:u}}function n(i,c){let m=G.tensor1d(o(i)),p=G.tensor1d(o(i));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:m,biases:p}}function a(i,c,m,p){let d=r(i,c,m,`${p}/conv`),u=n(c,`${p}/scale`);return{conv:d,scale:u}}function s(i,c,m,p,d=!1){let u=a((d?.5:1)*i,c,m,`${p}/conv1`),l=a(i,c,m,`${p}/conv2`);return{conv1:u,conv2:l}}return{extractConvLayerParams:a,extractResidualLayerParams:s}}function Qo(o){let{extractWeights:t,getRemainingWeights:e}=R(o),r=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=va(t,r),s=n(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),c=a(9216,32,3,"conv32_2"),m=a(9216,32,3,"conv32_3"),p=a(36864,64,3,"conv64_down",!0),d=a(36864,64,3,"conv64_1"),u=a(36864,64,3,"conv64_2"),l=a(36864,64,3,"conv64_3"),v=a(147456,128,3,"conv128_down",!0),w=a(147456,128,3,"conv128_1"),x=a(147456,128,3,"conv128_2"),y=a(589824,256,3,"conv256_down",!0),T=a(589824,256,3,"conv256_1"),F=a(589824,256,3,"conv256_2"),L=a(589824,256,3,"conv256_down_out"),z=G.tidy(()=>G.transpose(G.tensor2d(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),e().length!==0)throw new Error(`weights remaing after extract: ${e().length}`);return{params:{conv32_down:s,conv32_1:i,conv32_2:c,conv32_3:m,conv64_down:p,conv64_1:d,conv64_2:u,conv64_3:l,conv128_down:v,conv128_1:w,conv128_2:x,conv256_down:y,conv256_1:T,conv256_2:F,conv256_down_out:L,fc:z},paramMappings:r}}function ya(o,t){let e=H(o,t);function r(s){let i=e(`${s}/scale/weights`,1),c=e(`${s}/scale/biases`,1);return{weights:i,biases:c}}function n(s){let i=e(`${s}/conv/filters`,4),c=e(`${s}/conv/bias`,1),m=r(s);return{conv:{filters:i,bias:c},scale:m}}function a(s){return{conv1:n(`${s}/conv1`),conv2:n(`${s}/conv2`)}}return{extractConvLayerParams:n,extractResidualLayerParams:a}}function tn(o){let t=[],{extractConvLayerParams:e,extractResidualLayerParams:r}=ya(o,t),n=e("conv32_down"),a=r("conv32_1"),s=r("conv32_2"),i=r("conv32_3"),c=r("conv64_down"),m=r("conv64_1"),p=r("conv64_2"),d=r("conv64_3"),u=r("conv128_down"),l=r("conv128_1"),v=r("conv128_2"),w=r("conv256_down"),x=r("conv256_1"),y=r("conv256_2"),T=r("conv256_down_out"),{fc:F}=o;if(t.push({originalPath:"fc",paramPath:"fc"}),!zr(F))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${F}`);let L={conv32_down:n,conv32_1:a,conv32_2:s,conv32_3:i,conv64_down:c,conv64_1:m,conv64_2:p,conv64_3:d,conv128_down:u,conv128_1:l,conv128_2:v,conv256_down:w,conv256_1:x,conv256_2:y,conv256_down_out:T,fc:F};return B(o,t),{params:L,paramMappings:t}}var $=b(g());function at(o,t){let e=Ko(o,t.conv1);return e=uo(e,t.conv2),e=$.add(e,o),e=$.relu(e),e}function Ge(o,t){let e=Br(o,t.conv1);e=uo(e,t.conv2);let r=$.avgPool(o,2,2,"valid"),n=$.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=$.zeros(i);e=$.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=$.zeros(m);e=$.concat([e,p],2)}return r=a?$.concat([r,n],3):r,e=$.add(r,e),e=$.relu(e),e}var ee=class extends S{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return U.tidy(()=>{let r=U.cast(t.toBatchTensor(150,!0),"float32"),a=Z(r,[122.782,117.001,104.298]).div(U.scalar(256)),s=Br(a,e.conv32_down);s=U.maxPool(s,3,2,"valid"),s=at(s,e.conv32_1),s=at(s,e.conv32_2),s=at(s,e.conv32_3),s=Ge(s,e.conv64_down),s=at(s,e.conv64_1),s=at(s,e.conv64_2),s=at(s,e.conv64_3),s=Ge(s,e.conv128_down),s=at(s,e.conv128_1),s=at(s,e.conv128_2),s=Ge(s,e.conv256_down),s=at(s,e.conv256_1),s=at(s,e.conv256_2),s=Ge(s,e.conv256_down_out);let i=s.mean([1,2]);return U.matMul(i,e.fc)})}async forward(t){return this.forwardInput(await E(t))}async computeFaceDescriptor(t){var a;if((a=t==null?void 0:t.shape)==null?void 0:a.some(s=>s<=0))return new Float32Array(128);let e=await E(t),r=U.tidy(()=>U.unstack(this.forwardInput(e))),n=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?n:n[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return tn(t)}extractParams(t){return Qo(t)}};function en(o){let t=new ee;return t.extractWeights(o),t}function ze(o,t){return{...o,...{descriptor:t}}}function rn(o){return typeof o.age=="number"}function Ve(o,t){return{...o,...{age:t}}}function on(o){return(o.gender===ht.MALE||o.gender===ht.FEMALE)&&de(o.genderProbability)}function Ue(o,t,e){return{...o,...{gender:t,genderProbability:e}}}var it=b(g());var st=b(g());function Fa(o,t){function e(c,m){let p=st.tensor4d(o(3*3*c),[3,3,c,1]),d=st.tensor1d(o(c)),u=st.tensor1d(o(c)),l=st.tensor1d(o(c)),v=st.tensor1d(o(c));return t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=st.tensor4d(o(c*m*p*p),[p,p,c,m]),v=st.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let c=n(3,32,3,"mobilenetv1/conv_0"),m=a(32,64,"mobilenetv1/conv_1"),p=a(64,128,"mobilenetv1/conv_2"),d=a(128,128,"mobilenetv1/conv_3"),u=a(128,256,"mobilenetv1/conv_4"),l=a(256,256,"mobilenetv1/conv_5"),v=a(256,512,"mobilenetv1/conv_6"),w=a(512,512,"mobilenetv1/conv_7"),x=a(512,512,"mobilenetv1/conv_8"),y=a(512,512,"mobilenetv1/conv_9"),T=a(512,512,"mobilenetv1/conv_10"),F=a(512,512,"mobilenetv1/conv_11"),L=a(512,1024,"mobilenetv1/conv_12"),z=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,conv_8:x,conv_9:y,conv_10:T,conv_11:F,conv_12:L,conv_13:z}}function i(){let c=n(1024,256,1,"prediction_layer/conv_0"),m=n(256,512,3,"prediction_layer/conv_1"),p=n(512,128,1,"prediction_layer/conv_2"),d=n(128,256,3,"prediction_layer/conv_3"),u=n(256,128,1,"prediction_layer/conv_4"),l=n(128,256,3,"prediction_layer/conv_5"),v=n(256,64,1,"prediction_layer/conv_6"),w=n(64,128,3,"prediction_layer/conv_7"),x=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),T=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),F=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),L=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),z=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),mt=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),q=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Et=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Mt=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Ct=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:c,conv_1:m,conv_2:p,conv_3:d,conv_4:u,conv_5:l,conv_6:v,conv_7:w,box_predictor_0:{box_encoding_predictor:x,class_predictor:y},box_predictor_1:{box_encoding_predictor:T,class_predictor:F},box_predictor_2:{box_encoding_predictor:L,class_predictor:z},box_predictor_3:{box_encoding_predictor:rt,class_predictor:mt},box_predictor_4:{box_encoding_predictor:q,class_predictor:Et},box_predictor_5:{box_encoding_predictor:Mt,class_predictor:Ct}}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:i}}function nn(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=Fa(e,t),s=n(),i=a(),m={extra_dim:st.tensor3d(e(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:s,prediction_layer:i,output_layer:m},paramMappings:t}}function Ta(o,t){let e=H(o,t);function r(m,p,d){let u=e(`${m}/Conv2d_${p}_pointwise/weights`,4,`${d}/filters`),l=e(`${m}/Conv2d_${p}_pointwise/convolution_bn_offset`,1,`${d}/batch_norm_offset`);return{filters:u,batch_norm_offset:l}}function n(m){let p=`mobilenetv1/conv_${m}`,d=`MobilenetV1/Conv2d_${m}_depthwise`,u=`${p}/depthwise_conv`,l=`${p}/pointwise_conv`,v=e(`${d}/depthwise_weights`,4,`${u}/filters`),w=e(`${d}/BatchNorm/gamma`,1,`${u}/batch_norm_scale`),x=e(`${d}/BatchNorm/beta`,1,`${u}/batch_norm_offset`),y=e(`${d}/BatchNorm/moving_mean`,1,`${u}/batch_norm_mean`),T=e(`${d}/BatchNorm/moving_variance`,1,`${u}/batch_norm_variance`);return{depthwise_conv:{filters:v,batch_norm_scale:w,batch_norm_offset:x,batch_norm_mean:y,batch_norm_variance:T},pointwise_conv:r("MobilenetV1",m,l)}}function a(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}}function s(m,p){let d=e(`${m}/weights`,4,`${p}/filters`),u=e(`${m}/biases`,1,`${p}/bias`);return{filters:d,bias:u}}function i(m){let p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function an(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Ta(o,t),n=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!gt(n))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${n}`);let a={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:n}};return B(o,t),{params:a,paramMappings:t}}var Tt=b(g());var jt=b(g());function Q(o,t,e){return jt.tidy(()=>{let r=jt.conv2d(o,t.filters,e,"same");return r=jt.add(r,t.batch_norm_offset),jt.clipByValue(r,0,6)})}var _a=.0010000000474974513;function wa(o,t,e){return Tt.tidy(()=>{let r=Tt.depthwiseConv2d(o,t.filters,e,"same");return r=Tt.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,_a),Tt.clipByValue(r,0,6)})}function Pa(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function sn(o,t){return Tt.tidy(()=>{let e,r=Q(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,s)=>{let i=s+1,c=Pa(i);r=wa(r,a.depthwise_conv,c),r=Q(r,a.pointwise_conv,[1,1]),i===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function Da(o,t,e){let r=o.arraySync(),n=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[e][0],r[e][2]),m=Math.min(r[e][1],r[e][3]),p=Math.max(r[e][0],r[e][2]),d=Math.max(r[e][1],r[e][3]),u=(s-n)*(i-a),l=(p-c)*(d-m);if(u<=0||l<=0)return 0;let v=Math.max(n,c),w=Math.max(a,m),x=Math.min(s,p),y=Math.min(i,d),T=Math.max(x-v,0)*Math.max(y-w,0);return T/(u+l-T)}function cn(o,t,e,r,n){let a=o.shape[0],s=Math.min(e,a),i=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>n).sort((p,d)=>d.score-p.score),c=p=>p<=r?1:0,m=[];return i.forEach(p=>{if(m.length>=s)return;let d=p.score;for(let u=m.length-1;u>=0;--u){let l=Da(o,p.boxIndex,m[u]);if(l!==0&&(p.score*=c(l),p.score<=n))break}d===p.score&&m.push(p.boxIndex)}),m}var f=b(g());function Ea(o){let t=f.unstack(f.transpose(o,[1,0])),e=[f.sub(t[2],t[0]),f.sub(t[3],t[1])],r=[f.add(t[0],f.div(e[0],f.scalar(2))),f.add(t[1],f.div(e[1],f.scalar(2)))];return{sizes:e,centers:r}}function Ma(o,t){let{sizes:e,centers:r}=Ea(o),n=f.unstack(f.transpose(t,[1,0])),a=f.div(f.mul(f.exp(f.div(n[2],f.scalar(5))),e[0]),f.scalar(2)),s=f.add(f.mul(f.div(n[0],f.scalar(10)),e[0]),r[0]),i=f.div(f.mul(f.exp(f.div(n[3],f.scalar(5))),e[1]),f.scalar(2)),c=f.add(f.mul(f.div(n[1],f.scalar(10)),e[1]),r[1]);return f.transpose(f.stack([f.sub(s,a),f.sub(c,i),f.add(s,a),f.add(c,i)]),[1,0])}function mn(o,t,e){return f.tidy(()=>{let r=o.shape[0],n=Ma(f.reshape(f.tile(e.extra_dim,[r,1,1]),[-1,4]),f.reshape(o,[-1,4]));n=f.reshape(n,[r,n.shape[0]/r,4]);let a=f.sigmoid(f.slice(t,[0,0,1],[-1,-1,-1])),s=f.slice(a,[0,0,0],[-1,-1,1]);s=f.reshape(s,[r,s.shape[1]]);let i=f.unstack(n),c=f.unstack(s);return{boxes:i,scores:c}})}var Je=b(g());var Xe=b(g());function re(o,t){return Xe.tidy(()=>{let e=o.shape[0],r=Xe.reshape(Kt(o,t.box_encoding_predictor),[e,-1,1,4]),n=Xe.reshape(Kt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function pn(o,t,e){return Je.tidy(()=>{let r=Q(o,e.conv_0,[1,1]),n=Q(r,e.conv_1,[2,2]),a=Q(n,e.conv_2,[1,1]),s=Q(a,e.conv_3,[2,2]),i=Q(s,e.conv_4,[1,1]),c=Q(i,e.conv_5,[2,2]),m=Q(c,e.conv_6,[1,1]),p=Q(m,e.conv_7,[2,2]),d=re(t,e.box_predictor_0),u=re(o,e.box_predictor_1),l=re(n,e.box_predictor_2),v=re(s,e.box_predictor_3),w=re(c,e.box_predictor_4),x=re(p,e.box_predictor_5),y=Je.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,l.boxPredictionEncoding,v.boxPredictionEncoding,w.boxPredictionEncoding,x.boxPredictionEncoding],1),T=Je.concat([d.classPrediction,u.classPrediction,l.classPrediction,v.classPrediction,w.classPrediction,x.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ht=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return it.tidy(()=>{let r=it.cast(t.toBatchTensor(512,!1),"float32"),n=it.sub(it.mul(r,it.scalar(.007843137718737125)),it.scalar(1)),a=sn(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=pn(a.out,a.conv11,e.prediction_layer);return mn(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new X(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F{let[L,z]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(q=>q*x),[rt,mt]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(q=>q*w);return new M(p[F],new Xt(rt,L,mt-rt,z-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return an(t)}extractParams(t){return nn(t)}};function lo(o){let t=new Ht;return t.extractWeights(o),t}function dn(o){return lo(o)}var fo=class extends Ht{};var un=.4,ln=[new h(.738768,.874946),new h(2.42204,2.65704),new h(4.30971,7.04493),new h(10.246,4.59428),new h(12.6868,11.8741)],fn=[new h(1.603231,2.094468),new h(6.041143,7.080126),new h(2.882459,3.518061),new h(4.266906,5.178857),new h(9.041765,10.66308)],hn=[117.001,114.697,97.404],xn="tiny_yolov2_model",bn="tiny_yolov2_separable_conv_model";var N=b(g());var Rr=o=>typeof o=="number";function $r(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Rr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Rr(t.x)&&Rr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Rr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var et=b(g());var tt=b(g());function we(o){return tt.tidy(()=>{let t=tt.mul(o,tt.scalar(.10000000149011612));return tt.add(tt.relu(tt.sub(o,t)),t)})}function _t(o,t){return et.tidy(()=>{let e=et.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=et.conv2d(e,t.conv.filters,[1,1],"valid"),e=et.sub(e,t.bn.sub),e=et.mul(e,t.bn.truediv),e=et.add(e,t.conv.bias),we(e)})}var Yt=b(g());function wt(o,t){return Yt.tidy(()=>{let e=Yt.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Yt.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=Yt.add(e,t.bias),we(e)})}var ho=b(g());function Ca(o,t){let e=ge(o,t);function r(s,i){let c=ho.tensor1d(o(s)),m=ho.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=ve(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function gn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=Ca(n,s),p;if(t.withSeparableConvs){let[d,u,l,v,w,x,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),z=m(u,l,"conv1"),rt=m(l,v,"conv2"),mt=m(v,w,"conv3"),q=m(w,x,"conv4"),Et=m(x,y,"conv5"),Mt=T?m(y,T,"conv6"):void 0,Ct=F?m(T,F,"conv7"):void 0,pe=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}else{let[d,u,l,v,w,x,y,T,F]=r,L=c(d,u,"conv0"),z=c(u,l,"conv1"),rt=c(l,v,"conv2"),mt=c(v,w,"conv3"),q=c(w,x,"conv4"),Et=c(x,y,"conv5"),Mt=c(y,T,"conv6"),Ct=c(T,F,"conv7"),pe=i(F,5*e,1,"conv8");p={conv0:L,conv1:z,conv2:rt,conv3:mt,conv4:q,conv5:Et,conv6:Mt,conv7:Ct,conv8:pe}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Na(o,t){let e=H(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=ye(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function vn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Na(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var ct=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var xo=class extends S{constructor(t){super("TinyYolov2");$r(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=_t(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=_t(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=_t(r,e.conv6),r=_t(r,e.conv7),Kt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?we(Kt(t,e.conv0,"valid",!1)):wt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=wt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?wt(r,e.conv6):r,r=e.conv7?wt(r,e.conv7):r,Kt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?Z(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ct(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(x=>x.box),d=m.map(x=>x.score),u=m.map(x=>x.classScore),l=m.map(x=>this.config.classes[x.label]);return ur(p.map(x=>x.rescale(r)),d,this.config.iouThreshold,!0).map(x=>new vt(d[x],u[x],l[x],p[x],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return vn(t,this.config)}extractParams(t){let e=this.config.filterSizes||xo.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return gn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],w=await u.array(),x=await d.array();for(let y=0;yr){let z=(T+ue(x[y][T][F][0]))/m*i,rt=(y+ue(x[y][T][F][1]))/m*c,mt=Math.exp(x[y][T][F][2])*this.config.anchors[F].x/m*i,q=Math.exp(x[y][T][F][3])*this.config.anchors[F].y/m*c,Et=z-mt/2,Mt=rt-q/2,Ct={row:y,col:T,anchor:F},{classScore:pe,label:Po}=this.withClassScores?await this.extractPredictedClass(l,Ct):{classScore:1,label:0};v.push({box:new Ut(Et,Mt,Et+mt,Mt+q),score:L,classScore:L*pe,label:Po,...Ct})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},Pe=xo;Pe.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var oe=class extends Pe{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:un,classes:["face"],...t?{anchors:fn,meanRgb:hn}:{anchors:ln,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?bn:xn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function yn(o,t=!0){let e=new oe(t);return e.extractWeights(o),e}var qe=class extends ct{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Ke=b(g());var bo=b(g());async function ne(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>$t(c)?n(c):c.detection),s=r||(t instanceof bo.Tensor?await Zt(t,a):await qt(t,a)),i=await e(s);return s.forEach(c=>c instanceof bo.Tensor&&c.dispose()),i}async function De(o,t,e,r,n){return ne([o],t,async a=>e(a[0]),r,n)}var Fn=.4,Tn=[new h(1.603231,2.094468),new h(6.041143,7.080126),new h(2.882459,3.518061),new h(4.266906,5.178857),new h(9.041765,10.66308)],_n=[117.001,114.697,97.404];var ae=class extends Pe{constructor(){let t={withSeparableConvs:!0,iouThreshold:Fn,classes:["face"],anchors:Tn,meanRgb:_n,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var _={ssdMobilenetv1:new Ht,tinyFaceDetector:new ae,tinyYolov2:new oe,faceLandmark68Net:new te,faceLandmark68TinyNet:new Ye,faceRecognitionNet:new ee,faceExpressionNet:new $e,ageGenderNet:new je},go=(o,t)=>_.ssdMobilenetv1.locateFaces(o,t),wn=(o,t)=>_.tinyFaceDetector.locateFaces(o,t),Pn=(o,t)=>_.tinyYolov2.locateFaces(o,t),vo=o=>_.faceLandmark68Net.detectLandmarks(o),Dn=o=>_.faceLandmark68TinyNet.detectLandmarks(o),En=o=>_.faceRecognitionNet.computeFaceDescriptor(o),Mn=o=>_.faceExpressionNet.predictExpressions(o),Cn=o=>_.ageGenderNet.predictAgeAndGender(o),yo=o=>_.ssdMobilenetv1.load(o),Nn=o=>_.tinyFaceDetector.load(o),In=o=>_.tinyYolov2.load(o),Ln=o=>_.faceLandmark68Net.load(o),kn=o=>_.faceLandmark68TinyNet.load(o),Sn=o=>_.faceRecognitionNet.load(o),An=o=>_.faceExpressionNet.load(o),Wn=o=>_.ageGenderNet.load(o),Bn=yo,Rn=go,$n=vo;var Fo=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ce=class extends Fo{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>Oe(r,e[n]))}withAgeAndGender(){return new Ee(this,this.input)}},Ne=class extends Fo{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Oe(t,e)}withAgeAndGender(){return new Me(this,this.input)}},ce=class extends Ce{withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},me=class extends Ne{withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var To=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Ee=class extends To{async run(){let t=await this.parentTask,e=await ne(t,this.input,async r=>Promise.all(r.map(n=>_.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Ve(Ue(r,s,i),a)})}withFaceExpressions(){return new Ce(this,this.input)}},Me=class extends To{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await De(t,this.input,a=>_.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Ve(Ue(t,r,n),e)}withFaceExpressions(){return new Ne(this,this.input)}},se=class extends Ee{withFaceExpressions(){return new ce(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},ie=class extends Me{withFaceExpressions(){return new me(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var Ze=class extends J{constructor(t,e){super();this.parentTask=t;this.input=e}},Pt=class extends Ze{async run(){let t=await this.parentTask;return(await ne(t,this.input,r=>Promise.all(r.map(n=>_.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>ze(t[n],r))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}},Dt=class extends Ze{async run(){let t=await this.parentTask;if(!t)return;let e=await De(t,this.input,r=>_.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ze(t,e)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}};var Qe=class extends J{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?_.faceLandmark68TinyNet:_.faceLandmark68Net}},tr=class extends Qe{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Ke.Tensor?await Zt(this.input,e):await qt(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),t.map((a,s)=>Qt(a,n[s]))}withFaceExpressions(){return new ce(this,this.input)}withAgeAndGender(){return new se(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},er=class extends Qe{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Ke.Tensor?await Zt(this.input,[e]):await qt(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ke.Tensor&&a.dispose()),Qt(t,n)}withFaceExpressions(){return new me(this,this.input)}withAgeAndGender(){return new ie(this,this.input)}withFaceDescriptor(){return new Dt(this,this.input)}};var rr=class extends J{constructor(t,e=new X){super();this.input=t;this.options=e}},Ie=class extends rr{async run(){let{input:t,options:e}=this,r;if(e instanceof qe)r=_.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=_.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof ct)r=_.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>It({},r)))})}withFaceLandmarks(t=!1){return new tr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ce(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ee(this.runAndExtendWithFaceDetections(),this.input)}},or=class extends rr{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?It({},e):void 0)})}withFaceLandmarks(t=!1){return new er(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Me(this.runAndExtendWithFaceDetection(),this.input)}};function On(o,t=new X){return new or(o,t)}function nr(o,t=new X){return new Ie(o,t)}async function _o(o,t){return nr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function jn(o,t={}){return nr(o,new ct(t)).withFaceLandmarks().withFaceDescriptors()}var Hn=_o;function Or(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var ar=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof ut)return s;if(s instanceof Float32Array)return new ut(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new ut(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>Or(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new le(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>ut.fromJSON(r));return new ar(e,t.distanceThreshold)}};function Yn(o){let t=new ae;return t.extractWeights(o),t}function wo(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>wo(n,{width:e,height:r}));if($t(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Qt(It(o,n),a)}return nt(o)?It(o,o.detection.forSize(e,r)):o instanceof j||o instanceof M?o.forSize(e,r):o}var Ia=typeof process!="undefined",La=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",zn={faceapi:Yo,node:Ia,browser:La};0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); //# sourceMappingURL=face-api.node.js.map diff --git a/dist/face-api.node.js.map b/dist/face-api.node.js.map index 755c5a0..ab8e4ae 100644 --- a/dist/face-api.node.js.map +++ b/dist/face-api.node.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["../src/tfjs/tf-node.ts", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"], "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\nexport * from '@tensorflow/tfjs-node';\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof require === 'function'\n && typeof module !== 'undefined'\n // issues with gatsby.js: module.exports is undefined\n // && !!module.exports\n && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed: boolean = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number\n\n private _height: number\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number\n\n private _y: number\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number\n\n private _y: number\n\n private _width: number\n\n private _height: number\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({\n x, y, width, height,\n });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({\n x, y, width, height,\n });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const {\n x, y, right, bottom,\n } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({\n x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({\n x, y, width, height,\n });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return {\n dy, edy, dx, edx, y, ey, x, ex, w, h,\n };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n super({\n left, top, right, bottom,\n }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number\n\n private _classScore: number\n\n private _className: string\n\n private _box: Rect\n\n private _imageDims: Dimensions\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU: boolean = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n imgTensor: tf.Tensor4D,\n isCenterImage: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) {\n return imgTensor;\n }\n\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n ? createPaddingTensor(remainingPaddingAmount)\n : null;\n\n const tensorsToStack = [\n paddingTensorPrepend,\n imgTensor,\n paddingTensorAppend,\n ]\n .filter((t) => !!t)\n // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n super({\n x, y, width, height,\n }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point\n\n protected _positions: Point[]\n\n protected _imgDims: Dimensions\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string\n\n private _distance: number\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance: boolean = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string\n\n private _descriptors: Float32Array[]\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number\n\n private _classScore: number\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n TSource\n>(\n sourceObj: TSource,\n detection: FaceDetection,\n): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n fetch,\n readFile,\n };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n\n if (!fs) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = err.toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => {\n fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n })\n : () => {\n throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n };\n\n return {\n readFile,\n };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition\n\n public backgroundColor: string\n\n public fontColor: string\n\n public fontSize: number\n\n public fontStyle: string\n\n public padding: number\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[]\n\n public anchor : IPoint\n\n public options: DrawTextFieldOptions\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string\n\n public lineWidth: number\n\n public drawLabelOptions: DrawTextFieldOptions\n\n public label?: string\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box\n\n public options: DrawBoxOptions\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = await t.data();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n return resolve(null);\n }\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = []\n\n private _canvases: HTMLCanvasElement[] = []\n\n private _batchSize: number\n\n private _treatAsBatchInput: boolean = false\n\n private _inputDimensions: number[][] = []\n\n private _inputSize: number\n\n constructor(\n inputs: Array,\n treatAsBatchInput: boolean = false,\n ) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor'.\n let imgTensor = isTensor4D(input) ? input : input.expandDims();\n // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor'.\n imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n return batchTensor;\n });\n }\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) {\n return inputs;\n }\n\n const inputArgArray = Array.isArray(inputs)\n ? inputs\n : [inputs];\n\n if (!inputArgArray.length) {\n throw new Error('toNetInput - empty array passed as input');\n }\n\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n const inputArray = inputArgArray.map(resolveInput);\n\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') {\n throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n }\n\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) {\n throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n }\n });\n\n // wait for all media elements being loaded\n await Promise.all(\n inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n );\n\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n input: TNetInput,\n detections: Array,\n): Promise {\n const { Canvas } = env.getEnv();\n\n let canvas = input as HTMLCanvasElement;\n\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n\n if (netInput.batchSize > 1) {\n throw new Error('extractFaces - batchSize > 1 not supported');\n }\n\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas\n ? tensorOrCanvas\n : await imageTensorToCanvas(tensorOrCanvas);\n }\n\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections.map(\n (det) => (det instanceof FaceDetection\n ? det.forSize(canvas.width, canvas.height).box.floor()\n : det),\n )\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n return boxes.map(({\n x, y, width, height,\n }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n imageTensor: tf.Tensor3D | tf.Tensor4D,\n detections: Array,\n): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n const boxes = detections.map(\n (det) => (det instanceof FaceDetection\n ? det.forSize(imgWidth, imgHeight).box\n : det),\n )\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n const faceTensors = boxes.map(({\n x, y, width, height,\n }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined\n\n protected _paramMappings: ParamMapping[] = []\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer: boolean = false,\n isScaleDown: boolean = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import {\n extractConvParamsFactory,\n extractSeparableConvParamsFactory,\n ExtractWeightsFunction,\n ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n FaceFeatureExtractorParams,\n IFaceFeatureExtractor,\n TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n public neutral: number\n\n public happy: number\n\n public sad: number\n\n public angry: number\n\n public fearful: number\n\n public disgusted: number\n\n public surprised: number\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & {\n expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n TSource\n>(\n sourceObj: TSource,\n expressions: FaceExpressions,\n): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n canvasArg: string | HTMLCanvasElement,\n faceExpressions: DrawFaceExpressionsInput | Array,\n minConfidence = 0.1,\n textFieldAnchor?: IPoint,\n) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n landmarks: TFaceLandmarks,\n unshiftedLandmarks: TFaceLandmarks,\n alignedRect: FaceDetection,\n angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks, FaceLandmarks> {\n return isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n // convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (theta) => (theta * 180) / Math.PI;\n\n const angle = { roll: undefined, pitch: undefined, yaw: undefined };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n // value of 0 means center\n\n // roll is face lean from left to right\n // comparing x,y of outside corners of leftEye and rightEye\n angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n // pitch is face turn from left right\n // comparing x distance of top of nose to left and right edge of face\n // precision is lacking since coordinates are not precise enough\n angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n // yaw is face move from up to down\n // comparing size of the box around the face with top and bottom of detected landmarks\n // silly hack, but this gives us face compression on y-axis\n // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n return angle;\n}\n\nexport function extendWithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n const angle = calculateFaceAngle(unshiftedLandmarks);\n\n const extension = {\n landmarks,\n unshiftedLandmarks,\n alignedRect,\n angle,\n };\n\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean\n\n public drawPoints: boolean\n\n public lineWidth: number\n\n public pointSize: number\n\n public lineColor: string\n\n public pointColor: string\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks\n\n public options: DrawFaceLandmarksOptions\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (await ageTensor.data())[0];\n const probMale = (await genderTensor.data())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public dispose(throwOnRedispose: boolean = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors,\n extractWeightEntryFactory,\n loadSeparableConvParamsFactory,\n ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(await landmarkTensor.data());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown: boolean = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n\n if (!params) {\n throw new Error('SsdMobilenetv1 - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n const features = mobileNetV1(x, params.mobilenetv1);\n\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(\n input: TNetInput,\n options: ISsdMobilenetv1Options = {},\n ): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n const netInput = await toNetInput(input);\n\n const {\n boxes: _boxes,\n scores: _scores,\n } = this.forwardInput(netInput);\n\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n\n const scoresData = Array.from(await scores.data());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(\n boxes,\n scoresData as number[],\n maxResults,\n iouThreshold,\n minConfidence,\n );\n\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(\n left,\n top,\n right - left,\n bottom - top,\n ),\n {\n height: netInput.getInputHeight(0),\n width: netInput.getInputWidth(0),\n },\n );\n });\n\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n x: tf.Tensor4D,\n params: MobileNetV1.DepthwiseConvParams,\n strides: [number, number],\n) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) {\n conv11 = out;\n }\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n x: tf.Tensor4D,\n params: PointwiseConvParams,\n strides: [number, number],\n) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) {\n return 0.0;\n }\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(\n maxOutputSize,\n numBoxes,\n );\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n ];\n return {\n sizes,\n centers,\n };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const {\n sizes,\n centers,\n } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(\n boxPredictions: tf.Tensor4D,\n classPredictions: tf.Tensor4D,\n params: OutputLayerParams,\n) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(\n boxes,\n [batchSize, (boxes.shape[0] / batchSize), 4],\n );\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(\n scores,\n [batchSize, scores.shape[1] as number],\n );\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return {\n boxes: boxesByBatch,\n scores: scoresByBatch,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name: string = 'SsdMobilenetv1Options'\n\n private _minConfidence: number\n\n private _maxResults: number\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name: string = 'TinyYolov2Options'\n\n private _inputSize: number\n\n private _scoreThreshold: number\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs: boolean = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask {\n public async then(\n // eslint-disable-next-line no-unused-vars\n onfulfilled: (value: T) => T | PromiseLike,\n ): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask<\n TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n\n const faceLandmarksByFace = await Promise.all(faces.map(\n (face) => this.landmarkNet.detectLandmarks(face),\n )) as FaceLandmarks68[];\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n\n const results = await computeResults(faces);\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderTask,\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map(\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n )),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictAllFaceExpressionsTask {\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleFaceExpressionsTask {\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllFaceExpressionsTask,\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map(\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n )),\n this.extractedFaces,\n );\n\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictAllAgeAndGenderTask {\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleAgeAndGenderTask {\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n PredictAllAgeAndGenderWithFaceAlignmentTask,\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n PredictAllFaceExpressionsWithFaceAlignmentTask,\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n ) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public async run(): Promise {\n const { input, options } = this;\n\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise[]>(async (resolve) => {\n const detections = await this.run();\n resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n input: TNetInput,\n minConfidence?: number,\n): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n input: TNetInput,\n forwardParams: ITinyYolov2Options = {},\n): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff ** 2), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[]\n\n private _distanceThreshold: number\n\n constructor(\n inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>,\n distanceThreshold: number = 0.6,\n ) {\n this._distanceThreshold = distanceThreshold;\n\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n if (!inputArray.length) {\n throw new Error('FaceRecognizer.constructor - expected atleast one input');\n }\n\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) {\n return desc;\n }\n\n if (desc instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n }\n\n if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n }\n\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0)\n / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(\n label,\n this.computeMeanDistance(queryDescriptor, descriptors),\n ))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return bestMatch.distance < this.distanceThreshold\n ? bestMatch\n : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this.distanceThreshold,\n labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors\n .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n"], - "mappings": ";;;;;;;4qCAAA,GAAA,IAGA,GAAA,GAAc,GAAA,qCCHd,oDAAO,aAA6B,CAClC,MAAO,OAAO,SAAW,UACpB,IACA,MAAO,KAAW,aAGlB,MAAO,UAAY,aAAe,CAAC,CAAC,QAAQ,WCNnD,yyFAAA,OAAoB,OCApB,qRCEO,YACL,EACA,EACA,EAAoB,GACpB,CASA,GARA,EAAI,YAEJ,EAAO,MAAM,GAAG,QAAQ,CAAC,CAAE,IAAG,KAAK,IAAY,CAC7C,GAAM,GAAO,EAAO,GACpB,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,KAGZ,EAAU,CACZ,GAAM,GAAO,EAAO,EAAO,OAAS,GAC9B,EAAK,EAAO,GAClB,GAAI,CAAC,GAAQ,CAAC,EACZ,OAGF,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,EAAG,EAAG,GAGtB,EAAI,SC1BN,2RAAA,OAAoB,OCOb,WAAwC,CAK7C,YAAY,EAAe,EAAgB,CACzC,GAAI,CAAC,GAAc,IAAU,CAAC,GAAc,GAC1C,KAAM,IAAI,OAAM,wFAAwF,KAAK,UAAU,CAAE,QAAO,cAGlI,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,QAEnC,SAAsB,CAC3B,MAAO,IAAI,GAAW,EAAI,KAAK,MAAO,EAAI,KAAK,UDrB5C,YAAkB,EAAa,EAAa,CACjD,MAAO,aAAqB,YAAU,EAAO,MAAM,SAAW,EAGzD,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,WAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAiB,EAAa,CACnC,MAAO,GAAM,GAAM,EAGd,YAAgB,EAAa,CAClC,MAAO,GAAM,GAAM,EAGd,YAAe,EAAa,EAAe,EAAG,CACnD,GAAM,GAAI,IAAM,EAChB,MAAO,MAAK,MAAM,EAAM,GAAK,EAGxB,YAAsB,EAAmB,CAC9C,MAAO,IAAO,EAAI,OAAS,EAAI,OAG1B,YAAmC,CAAE,QAAO,UAAuB,EAAmB,CAC3F,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,IAAI,GAAW,KAAK,MAAM,EAAQ,GAAQ,KAAK,MAAM,EAAS,IAGhE,YAAwB,EAAqB,CAClD,MAAO,GAAI,OAAO,CAAC,EAAK,IAAO,EAAI,IAAI,GAAK,GAAI,GAAM,EAAG,IACtD,IAAI,GAAI,GAAM,EAAI,OAAQ,EAAI,SAG5B,YAAe,EAAa,EAAe,EAAwB,CACxE,MAAO,OAAM,GAAK,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,EAAS,EAAI,GAGhD,YAAuB,EAAU,CACtC,MAAO,CAAC,CAAC,GAAQ,IAAQ,UAAc,IAAQ,WAAc,CAAC,OAAO,MAAM,IAAQ,IAAQ,EAGtF,YAA4B,EAAU,CAC3C,MAAO,IAAc,IAAQ,GAAO,GAAK,GAAO,EExD3C,WAA8B,CAKnC,YAAY,EAAW,EAAW,CAChC,KAAK,GAAK,EACV,KAAK,GAAK,KAGR,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,GAEvB,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,KAAa,CAClB,MAAO,IAAI,GAAM,KAAK,IAAI,KAAK,GAAI,KAAK,IAAI,KAAK,IAG5C,WAAoB,CACzB,MAAO,MAAK,KAAM,KAAK,GAAK,EAAM,KAAK,GAAK,GAGvC,OAAe,CACpB,MAAO,IAAI,GAAM,KAAK,MAAM,KAAK,GAAI,KAAK,MAAM,KAAK,MCtClD,WAAwD,OAC/C,QAAO,EAAoB,CACvC,MAAO,CAAC,CAAC,GAAQ,CAAC,EAAK,EAAG,EAAK,EAAG,EAAK,MAAO,EAAK,QAAQ,MAAM,UAGrD,kBAAiB,EAAU,EAAgB,EAAmC,GAAO,CACjG,GAAI,CAAC,EAAI,OAAO,GACd,KAAM,IAAI,OAAM,GAAG,oBAAyB,KAAK,UAAU,2DAG7D,GAAI,CAAC,GAA4B,GAAI,MAAQ,GAAK,EAAI,OAAS,GAC7D,KAAM,IAAI,OAAM,GAAG,cAAmB,EAAI,sBAAsB,EAAI,oCAYxE,YAAY,EAA4B,EAAmC,GAAM,CAC/E,GAAM,GAAO,GAAQ,GAEf,EAAS,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAO,EAAI,QAAQ,MAAM,IAC1D,EAAS,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAAQ,MAAM,IAE3D,GAAI,CAAC,GAAU,CAAC,EACd,KAAM,IAAI,OAAM,2EAA2E,KAAK,UAAU,MAG5G,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,EAC1B,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAC9B,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAQ,EAAI,KAAM,EAAI,OAAS,EAAI,KAE/D,EAAI,iBAAiB,CACnB,IAAG,IAAG,QAAO,UACZ,kBAAmB,GAEtB,KAAK,GAAK,EACV,KAAK,GAAK,EACV,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,MAE1B,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,WAE/B,OAAe,CAAE,MAAO,MAAK,KAE7B,MAAc,CAAE,MAAO,MAAK,KAE5B,QAAgB,CAAE,MAAO,MAAK,EAAI,KAAK,SAEvC,SAAiB,CAAE,MAAO,MAAK,EAAI,KAAK,UAExC,OAAe,CAAE,MAAO,MAAK,MAAQ,KAAK,UAE1C,UAAiB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,QAEpD,WAAkB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAEtD,aAAoB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,WAEvD,cAAqB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAE7D,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,UAAyB,CAC9B,GAAI,CACF,IAAG,IAAG,QAAO,UACX,KACE,EAAO,KAAK,IAAI,EAAQ,GAC9B,MAAI,GAAQ,GACV,IAAM,EAAO,EACb,GAAS,GAEP,EAAS,GACX,IAAM,EAAO,EACb,GAAU,GAGL,GAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,QAAQ,EAAuC,CACpD,GAAM,GAAS,GAAa,GAAM,EAAkB,MAAQ,EACtD,EAAS,GAAa,GAAM,EAAkB,OAAS,EAC7D,MAAO,IAAI,GAAI,CACb,EAAG,KAAK,EAAI,EACZ,EAAG,KAAK,EAAI,EACZ,MAAO,KAAK,MAAQ,EACpB,OAAQ,KAAK,OAAS,IAInB,IAAI,EAAc,EAA4B,CACnD,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAC5B,KAAK,EAAK,EAAO,EACjB,KAAK,EAAK,EAAO,EACjB,KAAK,MAAQ,EACb,KAAK,OAAS,GAEhB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,mBAAmB,EAAkB,EAAiC,CAC3E,GAAM,CACJ,IAAG,IAAG,QAAO,UACX,KACE,EAAW,KAAK,IAAI,EAAG,GACvB,EAAW,KAAK,IAAI,EAAG,GAEvB,EAAW,EAAQ,EACnB,EAAY,EAAS,EACrB,EAAe,KAAK,IAAI,EAAU,EAAW,GAC7C,EAAgB,KAAK,IAAI,EAAW,EAAY,GAEtD,MAAQ,IAAI,GAAI,CACd,EAAG,EAAU,EAAG,EAAU,MAAO,EAAc,OAAQ,IACrD,QAGC,MAAM,EAAY,EAA0B,CACjD,GAAM,CAAE,QAAO,UAAW,KACpB,EAAI,KAAK,EAAI,EACb,EAAI,KAAK,EAAI,EAEnB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,aAAa,EAAqB,EAAoB,CAC3D,GAAM,GAAI,KAAK,MAAQ,EACjB,EAAI,KAAK,OAAS,EAElB,EAAK,EACL,EAAK,EACP,EAAM,EACN,EAAM,EAEN,EAAI,KAAK,KACT,EAAI,KAAK,IACT,EAAK,KAAK,MACV,EAAK,KAAK,OAEd,MAAI,GAAK,GACP,GAAM,CAAC,EAAK,EAAa,EACzB,EAAK,GAEH,EAAK,GACP,GAAM,CAAC,EAAK,EAAc,EAC1B,EAAK,GAEH,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAEF,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAGC,CACL,KAAI,MAAK,KAAI,MAAK,IAAG,KAAI,IAAG,KAAI,IAAG,KAIhC,UAAU,EAAa,CAC5B,MAAO,IAAI,GAAI,CACb,KAAM,KAAK,KAAQ,EAAO,KAAO,KAAK,MACtC,IAAK,KAAK,IAAO,EAAO,IAAM,KAAK,OACnC,MAAO,KAAK,MAAS,EAAO,MAAQ,KAAK,MACzC,OAAQ,KAAK,OAAU,EAAO,OAAS,KAAK,SAC3C,WAAW,UCrMX,oBAA0B,EAAyC,CACxE,YAAY,EAAc,EAAa,EAAe,EAAgB,EAAmC,GAAO,CAC9G,MAAM,CACJ,OAAM,MAAK,QAAO,UACjB,KCTA,YAAsB,CAW3B,YACE,EACA,EACA,EACA,EACA,EACA,CACA,KAAK,WAAa,GAAI,GAAW,EAAU,MAAO,EAAU,QAC5D,KAAK,OAAS,EACd,KAAK,YAAc,EACnB,KAAK,WAAa,EAClB,KAAK,KAAO,GAAI,GAAI,GAAa,QAAQ,KAAK,eAGrC,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,eAEnC,YAAoB,CAAE,MAAO,MAAK,cAElC,MAAW,CAAE,MAAO,MAAK,QAEzB,YAAwB,CAAE,MAAO,MAAK,cAEtC,aAAqB,CAAE,MAAO,MAAK,UAAU,SAE7C,cAAsB,CAAE,MAAO,MAAK,UAAU,UAE9C,cAAmB,CAAE,MAAO,IAAI,GAAI,KAAK,MAAM,QAAQ,KAAK,UAAU,WAE1E,QAAQ,EAAe,EAAiC,CAC7D,MAAO,IAAI,IACT,KAAK,MACL,KAAK,WACL,KAAK,UACL,KAAK,YACL,CAAE,QAAO,aCzCR,mBAA4B,GAAyC,CAC1E,YACE,EACA,EACA,EACA,CACA,MAAM,EAAO,EAAO,GAAI,EAAa,GAGhC,QAAQ,EAAe,EAA+B,CAC3D,GAAM,CAAE,QAAO,cAAa,aAAc,MAAM,QAAQ,EAAO,GAC/D,MAAO,IAAI,GAAc,EAAO,EAAa,KCnB1C,YAAa,EAAW,EAAW,EAAiB,GAAM,CAC/D,GAAM,GAAQ,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,MAAO,EAAK,OAAS,KAAK,IAAI,EAAK,KAAM,EAAK,OAClF,EAAS,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,OAAQ,EAAK,QAAU,KAAK,IAAI,EAAK,IAAK,EAAK,MACpF,EAAe,EAAQ,EAE7B,MAAO,GACH,EAAgB,GAAK,KAAO,EAAK,KAAO,GACxC,EAAe,KAAK,IAAI,EAAK,KAAM,EAAK,MCPvC,YAAiB,EAA4B,CAClD,GAAM,GAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAExD,MAAO,IAAI,IAAY,EAAM,EAAM,EAAM,GCPpC,YACL,EACA,EACA,EACA,EAAiB,GACP,CACV,GAAI,GAAuB,EACxB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAC/B,IAAI,AAAC,GAAM,EAAE,UAEV,EAAiB,GAEvB,KAAO,EAAqB,OAAS,GAAG,CACtC,GAAM,GAAO,EAAqB,MAClC,EAAK,KAAK,GAEV,GAAM,GAAU,EAEV,EAAoB,GAC1B,OAAS,GAAI,EAAG,EAAI,EAAQ,OAAQ,IAAK,CACvC,GAAM,GAAM,EAAQ,GAEd,EAAU,EAAM,GAChB,EAAS,EAAM,GAErB,EAAQ,KAAK,GAAI,EAAS,EAAQ,IAGpC,EAAuB,EAAqB,OAC1C,CAAC,EAAG,IAAM,EAAQ,IAAM,GAI5B,MAAO,GCrCT,OAAoB,OAEb,YAAmB,EAAgB,EAAgC,CACxE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAG,EAAG,GAAK,EACZ,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAU,AAAG,UAAO,CAAC,EAAO,EAAO,GAAQ,GAEjD,MAAO,AAAG,QAAI,EAAG,KCVrB,OAAoB,OAUb,YACL,EACA,EAAyB,GACZ,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAQ,GAAS,EAAU,MAAM,MAAM,GAC9C,GAAI,IAAW,EACb,MAAO,GAGT,GAAM,GAAU,KAAK,IAAI,EAAS,GAC5B,EAAgB,KAAK,MAAM,EAAW,GAAgB,GAAM,IAC5D,EAAc,EAAS,EAAQ,EAAI,EAEnC,EAAsB,AAAC,GAA0C,CACrE,GAAM,GAAqB,EAAU,MAAM,QAC3C,SAAmB,GAAe,EAC3B,AAAG,QAAK,EAAoB,EAAG,YAGlC,EAAsB,EAAoB,GAC1C,EAAyB,EAAW,EAAoB,MAAM,GAM9D,EAAiB,CAJM,GAAiB,EAC1C,EAAoB,GACpB,KAIF,EACA,GAEC,OAAO,AAAC,GAAM,CAAC,CAAC,GAEhB,IAAI,AAAC,GAAiB,AAAG,QAAK,EAAG,YACpC,MAAO,AAAG,WAAO,EAAgB,KC7C9B,YAAsB,EAAmB,CAC9C,GAAM,GAAQ,EAAW,QACzB,OAAS,GAAI,EAAM,OAAS,EAAG,EAAI,EAAG,IAAK,CACzC,GAAM,GAAI,KAAK,MAAM,KAAK,SAAY,GAAI,IACpC,EAAI,EAAM,GAChB,EAAM,GAAK,EAAM,GACjB,EAAM,GAAK,EAEb,MAAO,GCDF,YAAiB,EAAW,CACjC,MAAO,GAAK,GAAI,KAAK,IAAI,CAAC,IAGrB,YAAwB,EAAW,CACxC,MAAO,MAAK,IAAI,EAAK,GAAI,ICHpB,oBAAmB,EAA2B,CACnD,YAAY,EAAW,EAAW,EAAe,EAAgB,EAAmC,GAAO,CACzG,MAAM,CACJ,IAAG,IAAG,QAAO,UACZ,KCHP,GAAM,IAAO,GACP,GAAO,IACP,GAAW,IAOV,OAA8C,CAOnD,YACE,EACA,EACA,EAAe,GAAI,GAAM,EAAG,GAC5B,CACA,GAAM,CAAE,QAAO,UAAW,EAC1B,KAAK,SAAW,GAAI,GAAW,EAAO,GACtC,KAAK,OAAS,EACd,KAAK,WAAa,EAA8B,IAC9C,AAAC,GAAO,EAAG,IAAI,GAAI,GAAM,EAAO,IAAS,IAAI,OAItC,QAAe,CAAE,MAAO,IAAI,GAAM,KAAK,OAAO,EAAG,KAAK,OAAO,MAE7D,aAAqB,CAAE,MAAO,MAAK,SAAS,SAE5C,cAAsB,CAAE,MAAO,MAAK,SAAS,UAE7C,YAAqB,CAAE,MAAO,MAAK,cAEnC,oBAA6B,CACtC,MAAO,MAAK,WAAW,IACrB,AAAC,GAAO,EAAG,IAAI,KAAK,QAAQ,IAAI,GAAI,GAAM,KAAK,WAAY,KAAK,eAI7D,QAAiC,EAAe,EAAmB,CACxE,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,CAAE,QAAO,WAIN,QAAiC,EAAW,EAAc,CAC/D,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,KAAK,SACL,GAAI,GAAM,EAAG,IAIV,aAAsC,EAAc,CACzD,MAAO,MAAK,QAAQ,EAAG,EAAG,EAAG,GAcxB,MACL,EACA,EAAkE,GAC7D,CACL,GAAI,EAAW,CACb,GAAM,GAAM,YAAqB,GAC7B,EAAU,IAAI,QACd,GAAI,GAAI,GAEZ,MAAO,MAAK,QAAQ,EAAI,EAAG,EAAI,GAAG,MAAM,KAAM,GAGhD,GAAM,CAAE,mBAAkB,iBAAkB,CAAE,iBAAkB,GAAO,cAAe,MAAQ,GAE9F,MAAI,GACK,KAAK,YAGP,KAAK,aAAa,GAGnB,WAAiB,CACvB,GAAM,GAAU,KAAK,2BAEf,CAAC,EAAe,EAAgB,GAAe,EAC/C,EAAc,AAAC,GAAc,EAAY,IAAI,GAAI,YACjD,EAAkB,GAAY,GAAiB,EAAY,IAAmB,EAE9E,EAAO,KAAK,MAAM,EAAiB,IAEnC,EAAW,GAAe,GAE1B,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAChD,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAEtD,MAAO,IAAI,IAAK,EAAG,EAAG,KAAK,IAAI,EAAM,KAAK,WAAa,GAAI,KAAK,IAAI,EAAM,KAAK,YAAc,IAGvF,aAAa,EAAsB,CACzC,GAAM,GAAM,GAAQ,KAAK,WACzB,MAAO,GAAI,IAAI,EAAI,MAAQ,EAAS,EAAI,OAAS,GAGzC,0BAAoC,CAC5C,KAAM,IAAI,OAAM,4DC3Hb,oBAA6B,EAAc,CACtC,0BAAoC,CAC5C,GAAM,GAAM,KAAK,UACjB,MAAO,CACL,EAAI,GACJ,EAAI,GACJ,GAAe,CAAC,EAAI,GAAI,EAAI,QCN3B,oBAA8B,EAAc,CAC1C,eAAyB,CAC9B,MAAO,MAAK,UAAU,MAAM,EAAG,IAG1B,gBAA0B,CAC/B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,iBAA2B,CAChC,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,SAAmB,CACxB,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,YAAsB,CAC3B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,aAAuB,CAC5B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,UAAoB,CACzB,MAAO,MAAK,UAAU,MAAM,GAAI,IAGxB,0BAAoC,CAC5C,MAAO,CACL,KAAK,aACL,KAAK,cACL,KAAK,YACL,IAAI,MC/BH,YAAsC,CAK3C,YAAY,EAAe,EAAkB,CAC3C,KAAK,OAAS,EACd,KAAK,UAAY,KAGR,QAAgB,CAAE,MAAO,MAAK,UAE9B,WAAmB,CAAE,MAAO,MAAK,UAErC,SAAS,EAAwB,GAAc,CACpD,MAAO,GAAG,KAAK,QAAQ,EAAe,KAAK,GAAM,KAAK,aAAe,OCjBlE,oBAAyB,EAAgB,OAChC,yBAAwB,EAAU,EAAgB,CAG9D,GAFA,EAAI,iBAAiB,EAAK,GAEtB,CAAC,GAAc,EAAI,OACrB,KAAM,IAAI,OAAM,GAAG,gCAAqC,EAAI,yBAMhE,YAAY,EAAiC,EAAe,CAC1D,MAAM,GACN,KAAK,OAAS,KAGL,QAAgB,CAAE,MAAO,MAAK,SCrBpC,YAA6B,CAKlC,YAAY,EAAe,EAA6B,CACtD,GAAM,MAAO,IAAU,SACrB,KAAM,IAAI,OAAM,sEAGlB,GAAI,CAAC,MAAM,QAAQ,IAAgB,EAAY,KAAK,AAAC,GAAS,CAAE,aAAgB,gBAC9E,KAAM,IAAI,OAAM,4FAGlB,KAAK,OAAS,EACd,KAAK,aAAe,KAGX,QAAgB,CAAE,MAAO,MAAK,UAE9B,cAA8B,CAAE,MAAO,MAAK,aAEhD,QAAc,CACnB,MAAO,CACL,MAAO,KAAK,MACZ,YAAa,KAAK,YAAY,IAAI,AAAC,GAAM,MAAM,KAAK,WAI1C,UAAS,EAAmC,CACxD,GAAM,GAAc,EAAK,YAAY,IAAI,AAAC,GAAW,GAAI,cAAa,IACtE,MAAO,IAAI,IAAuB,EAAK,MAAO,KC1B3C,oBAA2B,GAAW,OAC7B,2BAA0B,EAAU,EAAgB,CAGhE,GAFA,GAAW,wBAAwB,EAAK,GAGtC,CAAC,GAAmB,EAAI,QACrB,CAAC,GAAmB,EAAI,YAE3B,KAAM,IAAI,OAAM,GAAG,kCAAuC,EAAI,eAAe,EAAI,6CAQrF,YAAY,EAAiC,EAAe,EAAe,EAAoB,CAC7F,MAAM,EAAK,GACX,KAAK,OAAS,EACd,KAAK,YAAc,KAGV,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,cCvBzC,YAA6B,EAAwC,CAC1E,MAAO,GAAI,oBAAqB,GAG3B,YAGL,EACA,EAC4B,CAE5B,MAAO,IAAK,KADM,CAAE,cCdf,aAAyC,CAC9C,GAAM,GAAQ,OAAO,MACrB,GAAI,CAAC,EAAO,KAAM,IAAI,OAAM,gEAM5B,MAAO,CACL,OAAQ,kBACR,yBACA,MAAO,iBACP,UACA,MAAO,iBACP,oBAAqB,IAAM,SAAS,cAAc,UAClD,mBAAoB,IAAM,SAAS,cAAc,OACjD,QACA,SAbe,IAAM,CACrB,KAAM,IAAI,OAAM,iECLb,YAA0B,EAAsB,CACrD,GAAI,GAAiB,GAErB,GAAI,CAAC,EACH,GAAI,CAEF,EAAa,oBACN,EAAP,CACA,EAAiB,EAAI,WAYzB,MAAO,CACL,SATe,EACb,AAAC,GAAqB,GAAI,SAAgB,CAAC,EAAS,IAAW,CAC/D,EAAG,SAAS,EAAU,CAAC,EAAU,IAAoB,EAAM,EAAO,GAAO,EAAQ,MAEjF,IAAM,CACN,KAAM,IAAI,OAAM,qEAAqE,OCfpF,aAAwC,CAE7C,GAAM,GAAS,OAAO,QAAa,OAAO,kBACpC,EAAQ,OAAO,OAAS,OAAO,iBAE/B,EAAsB,IAAM,CAChC,GAAI,EAAQ,MAAO,IAAI,GACvB,KAAM,IAAI,OAAM,+EAGZ,EAAqB,IAAM,CAC/B,GAAI,EAAO,MAAO,IAAI,GACtB,KAAM,IAAI,OAAM,6EAGZ,EAAQ,OAAO,MAGf,EAAa,KAEnB,MAAO,CACL,OAAQ,GAAU,KAAM,GACxB,yBAA0B,OAAO,0BAA4B,KAAM,GACnE,MAAO,GAAS,KAAM,GACtB,UAAW,OAAO,WAAa,KAAM,GACrC,MAAO,OAAO,kBAAoB,KAAM,GACxC,sBACA,qBACA,WACG,GCjCA,aAA8B,CACnC,MAAO,OAAO,SAAW,UACpB,MAAO,WAAa,aACpB,MAAO,mBAAqB,aAC5B,MAAO,oBAAsB,aAC7B,MAAO,mBAAqB,aAC5B,MAAO,YAAc,aACrB,MAAO,2BAA6B,YCH3C,OAAyB,QAGrB,EAEJ,aAA+B,CAC7B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,yEAElB,MAAO,GAGT,YAAgB,EAAkB,CAChC,EAAc,EAGhB,aAAsB,CAGpB,MAAI,MAAoB,GAAO,MAC3B,kBAAmB,GAAO,MACvB,KAGT,YAAqB,EAA2B,CAK9C,GAJK,GACH,KAGE,CAAC,EACH,KAAM,IAAI,OAAM,8EAGlB,GAAM,CAAE,SAAS,EAAY,OAAQ,QAAQ,EAAY,OAAU,EACnE,EAAY,OAAS,EACrB,EAAY,MAAQ,EACpB,EAAY,oBAAsB,EAAI,qBAAwB,KAAM,GAAI,IACxE,EAAY,mBAAqB,EAAI,oBAAuB,KAAM,GAAI,IAEtE,EAAY,UAAY,EAAI,WAAa,EAAY,UACrD,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,SAAW,EAAI,UAAY,EAAY,SAG9C,GAAM,GAAM,CACjB,UACA,UACA,cACA,oBACA,oBACA,mBACA,eACA,aACA,sBAGF,KC3DO,YAAsB,EAAmB,CAC9C,MAAI,CAAC,EAAI,YAAc,MAAO,IAAQ,SAC7B,SAAS,eAAe,GAE1B,ECHF,WAA6B,EAA4F,CAC9H,GAAM,CAAE,SAAQ,4BAA6B,EAAI,SAEjD,GAAI,YAAqB,GACvB,MAAO,GAGT,GAAM,GAAS,GAAa,GAE5B,GAAI,CAAE,aAAkB,IACtB,KAAM,IAAI,OAAM,kEAGlB,GAAM,GAAM,EAAO,WAAW,MAC9B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,GCfF,GAAK,IAAL,UAAK,EAAL,CAEL,WAAW,WAEX,YAAY,YAEZ,cAAc,cAEd,eAAe,iBARL,aAoBL,YAA4D,CAajE,YAAY,EAAiC,GAAI,CAC/C,GAAM,CACJ,iBAAgB,kBAAiB,YAAW,WAAU,YAAW,WAC/D,EACJ,KAAK,eAAiB,GAAkB,GAAe,SACvD,KAAK,gBAAkB,GAAmB,qBAC1C,KAAK,UAAY,GAAa,yBAC9B,KAAK,SAAW,GAAY,GAC5B,KAAK,UAAY,GAAa,UAC9B,KAAK,QAAU,GAAW,IAIvB,QAAoB,CAOzB,YACE,EACA,EACA,EAAiC,GACjC,CAEA,KAAK,KAAO,MAAO,IAAS,SACxB,CAAC,GACA,YAAgB,IAAgB,EAAK,KAAO,EACjD,KAAK,OAAS,EACd,KAAK,QAAU,GAAI,IAAqB,GAG1C,aAAa,EAAuC,CAClD,GAAM,CAAE,WAAY,KAAK,QACzB,MAAO,MAAK,KAAK,IAAI,AAAC,GAAM,EAAI,YAAY,GAAG,OAAO,OAAO,CAAC,EAAI,IAAQ,EAAK,EAAK,EAAK,EAAK,GAAM,EAAI,EAG1G,eAAwB,CACtB,GAAM,CAAE,WAAU,WAAY,KAAK,QACnC,MAAO,MAAK,KAAK,OAAS,EAAY,EAAI,EAG5C,aAAa,EAA+B,EAAkC,CAC5E,GAAM,CAAE,kBAAmB,KAAK,QAC1B,EAAc,IAAmB,GAAe,cAAgB,IAAmB,GAAe,UAClG,EAAa,IAAmB,GAAe,aAAe,IAAmB,GAAe,aAEhG,EAAiB,KAAK,aAAa,GACnC,EAAkB,KAAK,gBACvB,EAAK,EAAc,KAAK,OAAO,EAAI,EAAiB,KAAK,OAAO,EAChE,EAAI,EAAa,KAAK,OAAO,EAAI,EAAkB,KAAK,OAAO,EAGrE,GAAI,EAAY,CACd,GAAM,CAAE,QAAO,UAAW,EACpB,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAQ,GAAiB,GACrD,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAS,GAAkB,GAC7D,MAAO,CAAE,EAAG,EAAM,EAAG,GAEvB,MAAO,CAAE,IAAG,KAGd,KAAK,EAAkE,CACrE,GAAM,GAAS,GAAa,GACtB,EAAM,EAAoB,GAE1B,CACJ,kBAAiB,YAAW,WAAU,YAAW,WAC/C,KAAK,QAET,EAAI,KAAO,GAAG,OAAc,IAC5B,GAAM,GAAe,KAAK,aAAa,GACjC,EAAa,KAAK,gBAExB,EAAI,UAAY,EAChB,GAAM,GAAY,KAAK,aAAa,EAAK,GACzC,EAAI,SAAS,EAAU,EAAG,EAAU,EAAG,EAAc,GAErD,EAAI,UAAY,EAChB,KAAK,KAAK,QAAQ,CAAC,EAAU,IAAM,CACjC,GAAM,GAAI,EAAU,EAAU,EACxB,EAAI,EAAU,EAAU,EAAM,GAAI,GAAK,EAC7C,EAAI,SAAS,EAAU,EAAG,OC5GzB,YAAqB,CAS1B,YAAY,EAA2B,GAAI,CACzC,GAAM,CACJ,WAAU,YAAW,QAAO,oBAC1B,EACJ,KAAK,SAAW,GAAY,qBAC5B,KAAK,UAAY,GAAa,EAC9B,KAAK,MAAQ,EAEb,GAAM,GAA0B,CAC9B,eAAgB,GAAe,YAC/B,gBAAiB,KAAK,UAExB,KAAK,iBAAmB,GAAI,IAAqB,IAAK,KAA4B,MAI/E,QAAc,CAKnB,YACE,EACA,EAA2B,GAC3B,CACA,KAAK,IAAM,GAAI,GAAI,GACnB,KAAK,QAAU,GAAI,IAAe,GAGpC,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CAAE,WAAU,aAAc,KAAK,QAE/B,CACJ,IAAG,IAAG,QAAO,UACX,KAAK,IACT,EAAI,YAAc,EAClB,EAAI,UAAY,EAChB,EAAI,WAAW,EAAG,EAAG,EAAO,GAE5B,GAAM,CAAE,SAAU,KAAK,QACvB,AAAI,GACF,GAAI,IAAc,CAAC,GAAQ,CAAE,EAAG,EAAK,EAAY,EAAI,KAAK,KAAK,QAAQ,kBAAkB,KAAK,KC1D7F,YACL,EACA,EACA,CAGA,AAFwB,OAAM,QAAQ,GAAc,EAAa,CAAC,IAElD,QAAQ,AAAC,GAAQ,CAE/B,GAAM,GAAQ,YAAe,GACzB,EAAI,MACH,GAAoB,GAAO,EAAI,UAAU,MAAQ,OAGhD,EAAM,YAAe,GACvB,EAAI,IACH,GAAoB,GAAO,EAAI,UAAU,IAAM,GAAI,GAAI,GAEtD,EAAQ,EAAQ,GAAG,GAAM,KAAW,OAC1C,GAAI,IAAQ,EAAK,CAAE,UAAS,KAAK,KC1BrC,OAAoB,OCEb,YAAuB,EAAsD,CAClF,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAQ,aAAiB,IAAS,EAAM,UAClC,YAAiB,IAAS,EAAM,YAAc,ECH/C,YAA0B,EAAgE,CAE/F,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,GAAI,YAAiB,GAAI,SAAS,QAAU,GAAc,GACxD,MAAO,GAAQ,MAGjB,WAAiB,EAAU,CACzB,AAAI,CAAC,EAAE,eAEP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAO,IAGT,WAAgB,EAAU,CACxB,AAAI,CAAC,EAAE,eACP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAQ,IAGV,EAAM,iBAAiB,OAAQ,GAC/B,EAAM,iBAAiB,QAAS,KCxB7B,YAAuB,EAAsC,CAClE,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,AAAM,YAAe,OAAO,EAAO,GAAI,OAAM,qDAC7C,GAAM,GAAS,GAAI,YACnB,EAAO,OAAS,IAAM,CACpB,AAAI,MAAO,GAAO,QAAW,UAAU,EAAO,GAAI,OAAM,qEACxD,GAAM,GAAM,EAAI,SAAS,qBACzB,EAAI,OAAS,IAAM,EAAQ,GAC3B,EAAI,QAAU,EACd,EAAI,IAAM,EAAO,QAEnB,EAAO,QAAU,EACjB,EAAO,cAAc,KCXlB,YAA4B,EAA0F,CAC3H,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAI,aAAiB,GACZ,GAAI,GAAW,EAAM,aAAc,EAAM,eAE9C,YAAiB,GACZ,GAAI,GAAW,EAAM,WAAY,EAAM,aAEzC,GAAI,GAAW,EAAM,MAAO,EAAM,QCNpC,YAAsB,CAAE,QAAO,UAA0C,CAC9E,GAAM,CAAE,uBAAwB,EAAI,SAC9B,EAAS,IACf,SAAO,MAAQ,EACf,EAAO,OAAS,EACT,EAGF,YAA+B,EAAwD,EAAuC,CACnI,GAAM,CAAE,aAAc,EAAI,SAE1B,GAAI,CAAE,aAAiB,KAAc,CAAC,GAAc,GAClD,KAAM,IAAI,OAAM,8DAGlB,GAAM,CAAE,QAAO,UAAW,GAAQ,GAAmB,GAC/C,EAAS,GAAa,CAAE,QAAO,WAErC,MAAI,aAAiB,GACnB,EAAoB,GAAQ,aAAa,EAAO,EAAG,GAEnD,EAAoB,GAAQ,UAAU,EAAO,EAAG,EAAG,EAAO,GAErD,EC7BT,OAAoB,OAKpB,kBACE,EACA,EAC4B,CAC5B,GAAM,GAAe,GAAU,EAAI,SAAS,sBAEtC,CAAC,EAAQ,EAAO,GAAe,EAAU,MAAM,MAAM,EAAW,GAAa,EAAI,GACjF,EAAc,AAAG,QAAK,IAAM,EAAU,KAAK,EAAQ,EAAO,GAAa,SAC7E,YAAM,AAAG,YAAQ,SAAS,EAAa,GAEvC,EAAY,UAEL,ECfF,YAAwB,EAAY,CACzC,GAAM,CAAE,QAAO,SAAQ,SAAU,EAAI,SAErC,MAAO,aAAiB,IACnB,YAAiB,IACjB,YAAiB,GCPxB,MAAoB,OCKb,YAAuB,EAA6C,EAAmB,EAAuB,GAAO,CAC1H,GAAM,CAAE,QAAO,UAAW,EAAI,SAE9B,GAAI,CAAE,aAAiB,IAAS,YAAiB,IAC/C,KAAM,IAAI,OAAM,4EAGlB,GAAI,GAAa,EAAG,MAAO,IAAa,CAAE,MAAO,EAAG,OAAQ,IAC5D,GAAM,GAAO,GAAmB,GAC1B,EAAQ,EAAY,KAAK,IAAI,EAAK,OAAQ,EAAK,OAC/C,EAAQ,EAAQ,EAAK,MACrB,EAAS,EAAQ,EAAK,OAEtB,EAAe,GAAa,CAAE,MAAO,EAAW,OAAQ,IACxD,EAAc,YAAiB,GAAS,EAAQ,GAAsB,GAEtE,EAAS,KAAK,IAAI,EAAQ,GAAU,EACpC,EAAK,GAAe,EAAQ,EAAS,EAAS,EAC9C,EAAK,GAAe,EAAS,EAAQ,EAAS,EACpD,MAAI,GAAY,MAAQ,GAAK,EAAY,OAAS,GAAG,EAAoB,GAAc,UAAU,EAAa,EAAI,EAAI,EAAO,GAEtH,EDdF,YAAe,CAapB,YACE,EACA,EAA6B,GAC7B,CAfM,mBAAkD,GAElD,eAAiC,GAIjC,wBAA8B,GAE9B,sBAA+B,GAQrC,GAAI,CAAC,MAAM,QAAQ,GACjB,KAAM,IAAI,OAAM,4HAA4H,KAG9I,KAAK,mBAAqB,EAC1B,KAAK,WAAa,EAAO,OAEzB,EAAO,QAAQ,CAAC,EAAO,IAAQ,CAC7B,GAAI,GAAW,GAAQ,CACrB,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAO,EAAM,MACnC,OAGF,GAAI,EAAW,GAAQ,CACrB,GAAM,GAAa,EAAc,MAAM,GACvC,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,yCAAyC,8CAG3D,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAQ,EAAc,MAAM,MAAM,GACxD,OAGF,GAAM,GAAU,YAAyB,GAAI,SAAS,OAAS,EAAQ,GAAsB,GAC7F,KAAK,UAAU,GAAO,EACtB,KAAK,iBAAiB,GAAO,CAAC,EAAO,OAAQ,EAAO,MAAO,QAIpD,eAAiD,CAC1D,MAAO,MAAK,iBAGH,WAAgC,CACzC,MAAO,MAAK,aAGH,eAAwB,CACjC,MAAO,MAAK,UAAY,GAAK,KAAK,sBAGzB,YAAoB,CAC7B,MAAO,MAAK,cAGH,kBAA8B,CACvC,MAAO,MAAK,oBAGH,YAAgC,CACzC,MAAO,MAAK,cAGH,0BAAwC,CACjD,MAAO,IAAM,KAAK,UAAW,EAAG,GAAG,IACjC,CAAC,EAAG,IAAa,KAAK,2BAA2B,IAI9C,SAAS,EAAiE,CAC/E,MAAO,MAAK,SAAS,IAAa,KAAK,aAAa,GAG/C,mBAAmB,EAA4B,CACpD,MAAO,MAAK,iBAAiB,GAGxB,eAAe,EAA0B,CAC9C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,cAAc,EAA0B,CAC7C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,2BAA2B,EAA8B,CAC9D,GAAI,MAAO,MAAK,WAAc,SAC5B,KAAM,IAAI,OAAM,yFAGlB,GAAM,GAAQ,KAAK,cAAc,GAC3B,EAAS,KAAK,eAAe,GACnC,MAAO,IAA0B,CAAE,QAAO,UAAU,KAAK,WAYpD,cAAc,EAAmB,EAA0B,GAAmB,CACnF,YAAK,WAAa,EAEX,AAAG,OAAK,IAAM,CACnB,GAAM,GAAe,GAAM,KAAK,UAAW,EAAG,GAAG,IAAI,AAAC,GAAa,CACjE,GAAM,GAAQ,KAAK,SAAS,GAE5B,GAAI,YAAoB,UAAQ,CAE9B,GAAI,GAAY,EAAW,GAAS,EAAQ,EAAM,aAElD,SAAY,GAAY,EAAW,GAE/B,GAAU,MAAM,KAAO,GAAa,EAAU,MAAM,KAAO,IAC7D,GAAY,AAAG,QAAM,eAAe,EAAW,CAAC,EAAW,KAGtD,EAAU,KAAK,EAAW,EAAW,GAG9C,GAAI,YAAiB,GAAI,SAAS,OAChC,MAAO,AAAG,WAAQ,WAAW,GAAc,EAAO,EAAW,IAG/D,KAAM,IAAI,OAAM,+BAA+B,8FAAqG,OAOtJ,MAHoB,AAAG,SAAM,EAAa,IAAI,AAAC,GAAM,AAAG,OAAK,EAAG,aAAa,KAAK,KAAK,UAAW,EAAW,EAAW,OE3I9H,iBAAiC,EAAsC,CACrE,GAAI,YAAkB,IACpB,MAAO,GAGT,GAAM,GAAgB,MAAM,QAAQ,GAChC,EACA,CAAC,GAEL,GAAI,CAAC,EAAc,OACjB,KAAM,IAAI,OAAM,4CAGlB,GAAM,GAAa,AAAC,GAAiB,MAAM,QAAQ,GAAU,mBAAmB,KAAS,GAEnF,EAAa,EAAc,IAAI,IAErC,SAAW,QAAQ,CAAC,EAAO,IAAM,CAC/B,GAAI,CAAC,GAAe,IAAU,CAAC,GAAW,IAAU,CAAC,EAAW,GAC9D,KAAI,OAAO,GAAc,IAAO,SACxB,GAAI,OAAM,eAAe,EAAW,sEAAsE,EAAc,MAG1H,GAAI,OAAM,eAAe,EAAW,iIAG5C,GAAI,EAAW,GAAQ,CAErB,GAAM,GAAY,EAAM,MAAM,GAC9B,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,eAAe,EAAW,iCAAiC,iDAMjF,KAAM,SAAQ,IACZ,EAAW,IAAI,AAAC,GAAU,GAAe,IAAU,GAAiB,KAG/D,GAAI,IAAS,EAAY,MAAM,QAAQ,ICtChD,kBACE,EACA,EAC8B,CAC9B,GAAM,CAAE,UAAW,EAAI,SAEnB,EAAS,EAEb,GAAI,CAAE,aAAiB,IAAS,CAC9B,GAAM,GAAW,KAAM,GAAW,GAElC,GAAI,EAAS,UAAY,EACvB,KAAM,IAAI,OAAM,8CAGlB,GAAM,GAAiB,EAAS,SAAS,GACzC,EAAS,YAA0B,GAC/B,EACA,KAAM,IAAoB,GAGhC,GAAM,GAAM,EAAoB,GAQhC,MAAO,AAPO,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAO,MAAO,EAAO,QAAQ,IAAI,QAC7C,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAO,MAAO,EAAO,SAE/C,IAAI,CAAC,CAChB,IAAG,IAAG,QAAO,YACT,CACJ,GAAM,GAAU,GAAa,CAAE,QAAO,WACtC,MAAI,GAAQ,GAAK,EAAS,GAAG,EAAoB,GAAS,aAAa,EAAI,aAAa,EAAG,EAAG,EAAO,GAAS,EAAG,GAC1G,IClDX,OAAoB,OAgBpB,kBACE,EACA,EACwB,CACxB,GAAI,CAAC,GAAW,IAAgB,CAAC,EAAW,GAC1C,KAAM,IAAI,OAAM,6DAGlB,GAAI,EAAW,IAAgB,EAAY,MAAM,GAAK,EACpD,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAW,EAAU,GAAe,EAAY,MAAM,MAAM,EAAW,GAAe,EAAI,GAajG,MAJoB,AAPN,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAU,GAAW,IACjC,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAU,IAEvB,IAAI,CAAC,CAC7B,IAAG,IAAG,QAAO,YACT,AAAG,WAAQ,EAAY,KAAK,EAAW,EAAU,GAAc,CAAC,EAAG,EAAG,GAAI,CAAC,EAAQ,EAAO,OCtCpG,kBACE,EAEA,EACmB,CACnB,GAAM,CAAE,SAAU,EAAI,SAChB,EAAM,KAAM,GAAM,EAAK,GAC7B,GAAI,CAAE,GAAI,OAAS,KACjB,KAAM,IAAI,OAAM,qBAAqB,EAAI,WAAW,EAAI,yBAAyB,EAAI,OAEvF,MAAO,GCTT,kBAAiC,EAAwC,CACvE,GAAM,GAAM,KAAM,IAAa,GACzB,EAAO,KAAO,GAAK,OAEzB,GAAI,CAAC,EAAK,KAAK,WAAW,UACxB,KAAM,IAAI,OAAM,wEAAwE,EAAK,kBAAkB,EAAI,OAErH,MAAO,IAAc,GCRvB,kBAAmC,EAAyB,CAC1D,MAAQ,MAAM,IAAa,IAAM,OCDnC,kBAAsC,EAAoC,CACxE,MAAO,IAAI,cAAa,KAAO,MAAM,IAAa,IAAM,eCH1D,OAAoB,OCAb,YAAsB,EAAyB,EAA0B,CAC9E,GAAM,GAA0B,GAAG,0BAEnC,GAAI,CAAC,EACH,MAAO,CACL,aAAc,GACd,YAAa,GAIjB,GAAI,IAAQ,IACV,MAAO,CACL,aAAc,IACd,YAAa,IAAI,KAIrB,GAAM,GAAW,EAAI,WAAW,WAAa,UAAY,EAAI,WAAW,YAAc,WAAa,GACnG,EAAM,EAAI,QAAQ,EAAU,IAE5B,GAAM,GAAQ,EAAI,MAAM,KAAK,OAAO,AAAC,GAAM,GAErC,EAAe,EAAI,SAAS,SAC9B,EAAM,EAAM,OAAS,GACrB,EAEA,EAAe,EAAY,GAAI,SAAS,SAAW,EAAM,MAAM,EAAG,EAAM,OAAS,GAAK,GAAO,KAAK,KACtG,SAAe,EAAI,WAAW,KAAO,IAAI,IAAiB,EAEnD,CACL,eACA,YAAa,IAAiB,IAAM,IAAI,IAAiB,GAAG,KAAgB,KD1BhF,kBACE,EACA,EAC4B,CAC5B,GAAM,CAAE,cAAa,gBAAiB,GAAa,EAAK,GAClD,EAAW,KAAM,IAAuC,GAE9D,MAAO,AAAG,OAAG,YAAY,EAAU,GET9B,YAAyB,EAAoB,EAAwB,EAA8B,GAAO,CAC/G,GAAM,CAAE,QAAO,UAAW,EACtB,GAAmB,GACnB,EACJ,SAAM,MAAQ,EACd,EAAM,OAAS,EACR,CAAE,QAAO,UCTlB,OAAoB,OCApB,OAAoB,OAOb,WAAyC,CAC9C,YAAY,EAAc,CAIhB,aAAkC,OAElC,oBAAiC,GALzC,KAAK,MAAQ,KASJ,SAAiC,CAAE,MAAO,MAAK,WAE/C,gBAAgC,CAAE,MAAO,MAAK,kBAE9C,WAAoB,CAAE,MAAO,CAAC,CAAC,KAAK,OAExC,iBAAiB,EAA8B,CACpD,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,MAAO,GAAI,GAGN,sBAAsB,EAAmB,EAAmB,CACjE,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,EAAI,GAAS,UACb,EAAI,GAAW,EAGV,cAAe,CACpB,MAAO,MAAK,eAAe,IAAI,CAAC,CAAE,eAAiB,EACjD,KAAM,EACN,OAAQ,KAAK,iBAAiB,MAI3B,oBAAqB,CAC1B,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,EAAM,iBAAqB,cAGnE,iBAAkB,CACvB,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,CAAE,GAAM,iBAAqB,eAGrE,UAAW,CAChB,KAAK,kBAAkB,QAAQ,CAAC,CAAE,OAAM,YAAa,CACnD,KAAK,sBAAsB,EAAM,EAAO,cAIrC,QAAS,CACd,KAAK,qBAAqB,QAAQ,CAAC,CAAE,OAAM,OAAQ,KAAe,CAChE,GAAM,GAAS,AAAG,UAAO,EAAS,YAClC,EAAS,UACT,KAAK,sBAAsB,EAAM,KAI9B,QAAQ,EAA4B,GAAM,CAC/C,KAAK,eAAe,QAAQ,AAAC,GAAU,CACrC,GAAI,GAAoB,EAAM,OAAO,WACnC,KAAM,IAAI,OAAM,mDAAmD,EAAM,QAE3E,EAAM,OAAO,YAEf,KAAK,QAAU,OAGV,iBAAgC,CACrC,MAAO,IAAI,cACT,KAAK,eACF,IAAI,CAAC,CAAE,YAAa,MAAM,KAAK,EAAO,aACtC,OAAO,CAAC,EAAM,IAAQ,EAAK,OAAO,UAI5B,MAAK,EAAgE,CAChF,GAAI,YAAwB,cAAc,CACxC,KAAK,eAAe,GACpB,OAEF,KAAM,MAAK,YAAY,QAGZ,aAAY,EAAyB,CAChD,GAAI,GAAO,MAAO,IAAQ,SACxB,KAAM,IAAI,OAAM,GAAG,KAAK,0CAE1B,GAAM,GAAY,KAAM,IAAc,EAAK,KAAK,uBAChD,KAAK,kBAAkB,QAGZ,cAAa,EAA8B,CACtD,GAAI,GAAY,MAAO,IAAa,SAClC,KAAM,IAAI,OAAM,GAAG,KAAK,iDAE1B,GAAM,CAAE,YAAa,EAAI,SACnB,CAAE,cAAa,gBAAiB,GAAa,EAAU,KAAK,uBAC5D,EAAuB,AAAC,GAAwB,QAAQ,IAAI,EAAU,IAAI,AAAC,GAAO,EAAS,GAAI,KAAK,AAAC,GAAQ,EAAI,UACjH,EAAc,AAAG,MAAG,qBAAqB,GACzC,EAAW,KAAK,MAAO,MAAM,GAAS,IAAc,YACpD,EAAY,KAAM,GAAY,EAAU,GAC9C,KAAK,kBAAkB,GAGlB,kBAAkB,EAA8B,CACrD,GAAM,CAAE,gBAAe,UAAW,KAAK,2BAA2B,GAClE,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGV,eAAe,EAAuB,CAC3C,GAAM,CAAE,gBAAe,UAAW,KAAK,cAAc,GACrD,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGT,qBAAqB,EAAmB,CAC9C,GAAI,CAAC,KAAK,OACR,KAAM,IAAI,OAAM,qDAGlB,GAAM,GAAS,EAAU,MAAM,KAAK,OAAO,CAAC,EAAoD,IAAY,CAE1G,GAAI,CAAC,EAAI,QAAQ,eAAe,GAC9B,KAAM,IAAI,OAAM,wDAAwD,eAAqB,KAE/F,MAAO,CAAE,IAAK,EAAI,QAAS,UAAS,QAAS,EAAI,QAAQ,KACxD,CAAE,QAAS,KAAK,SAEb,CAAE,MAAK,WAAY,EACzB,GAAI,CAAC,GAAO,CAAC,GAAW,CAAE,GAAI,YAAuB,YACnD,KAAM,IAAI,OAAM,8DAA8D,KAGhF,MAAO,CAAE,MAAK,aC7IlB,MAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,iBAAkB,EAAO,iBAAkB,EAAQ,QAC1F,SAAM,AAAG,OAAI,EAAK,EAAO,MAClB,IDNJ,YACL,EACA,EACA,EAAwB,GACX,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,CAAC,EAAG,GAAI,QACrE,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,CAAC,EAAG,KAE7E,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,OAItC,YACL,EACA,EACA,EAAwB,GACxB,EAAuB,GACV,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,GAAI,QAC5F,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,KAEpG,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAE/D,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,KACxC,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,QEpD1D,OAAoB,OAIb,YACL,EACA,EACA,EAA4B,OAC5B,EAAoB,GACP,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OACb,AAAG,UAAO,EAAG,EAAO,QAAS,CAAC,EAAG,GAAI,GACrC,EAAO,MAGT,MAAO,GAAW,AAAG,QAAK,GAAO,ICd9B,WAAoC,EAAgB,EAA+B,CACxF,OAAO,KAAK,GAAW,QAAQ,AAAC,GAAS,CACvC,AAAK,EAAc,KAAK,AAAC,GAAO,EAAG,eAAiB,IAClD,EAAU,GAAM,YCLtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,EACA,IACe,CACf,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,SCzBtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,IACa,CACb,GAAM,GAAa,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAY,IAChF,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CACL,QAAS,EACT,KAAM,ICvBZ,OAAoB,OCoBb,YAA0B,CAE/B,YAES,EAEA,EAEA,EAEP,CANO,wBAEA,wBAEA,cDxBJ,YACL,EACA,EACA,CACA,MAAO,CAAC,EAAoB,EAAqB,IAA8C,CAC7F,GAAM,GAAmB,AAAG,YAAS,EAAe,EAAI,EAAI,GAAa,CAAC,EAAG,EAAG,EAAY,IACtF,EAAmB,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAG,EAAG,EAAY,IAC5F,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,WAGX,GAAI,IACT,EACA,EACA,IAKC,YAEL,EACA,CACA,MAAO,AAAC,IAAwC,CAC9C,GAAM,GAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,IAAI,IACT,EACA,EACA,IEpCC,WAAmC,EAAgB,EAA+B,CACvF,MAAO,CAAC,EAAsB,EAAmB,IAAwB,CACvE,GAAM,GAAS,EAAU,GAEzB,GAAI,CAAC,GAAS,EAAQ,GACpB,KAAM,IAAI,OAAM,sBAAsB,oBAA+B,oBAA4B,KAGnG,SAAc,KACZ,CAAE,eAAc,UAAW,GAAc,IAGpC,GCfJ,WAA+B,EAAuB,CAC3D,GAAI,GAAmB,EAEvB,WAAwB,EAAkC,CACxD,GAAM,GAAM,EAAiB,MAAM,EAAG,GACtC,SAAmB,EAAiB,MAAM,GACnC,EAGT,YAA6C,CAC3C,MAAO,GAGT,MAAO,CACL,iBACA,uBCPG,YAA2B,EAAwC,EAA+B,CACvG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,GAAQ,EACV,EAAkB,EAAY,EAAa,EAAG,GAAG,WACjD,EAA2B,EAAY,EAAa,GAAG,WACrD,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAChE,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,CAAE,QAAO,QAAO,SAAU,EAAyB,EAAY,EAAa,EAAc,GAC1F,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YAAuB,EAA8F,CAC1H,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAC3C,EAAS,EAAyB,IAAK,IAAK,UAElD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CACN,SAAQ,SAAQ,SAAQ,WCvBvB,YAA+B,EAAuE,CAC3G,MAAO,AAAC,IAA+B,CACrC,GAAM,GAAU,EAAgC,GAAG,YAAkB,GAC/D,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,CAAE,UAAS,SCNf,YAA2B,EAAgB,EAA+B,CAC/E,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YACL,EACuE,CACvE,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBhBdZ,oBAAmC,EAAuG,CAC/I,aAAc,CACZ,MAAM,wBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,sDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAE/B,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,+BAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KiBlDzB,OAAoB,OCApB,OAAoB,OAIb,YACL,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,AAAG,OACtB,AAAG,UAAO,EAAG,EAAO,SACpB,EAAO,OCPJ,YAAuB,EAAuB,EAAoB,EAA2E,CAClJ,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAIpB,EAAK,AAFa,GAAuB,EAAgB,GAEpC,EAAY,EAAa,MAEpD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,OCdP,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,EAAgB,OAGtB,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,YAA4B,EAA8B,CAC/D,GAAM,GAAyC,GACzC,EAAmC,GAEzC,cAAO,KAAK,GAAW,QAAQ,AAAC,GAAQ,CACtC,GAAM,GAAM,EAAI,WAAW,MAAQ,EAAgB,EACnD,EAAI,GAAO,EAAU,KAGhB,CAAE,sBAAqB,iBJIzB,oBAGG,EAAyB,CAGjC,YAAY,EAAe,EAA+D,CACxF,MAAM,GACN,KAAK,sBAAwB,KAGpB,uBAAgE,CACzE,MAAO,MAAK,sBASP,OAAO,EAA4C,CACxD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EACJ,MAAO,IAAoB,EAAmB,KAAK,EAAmB,MAAM,GAAI,IAAK,EAAO,MAIzF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,EAAS,KAAK,0BAA2B,KAAK,4BAG3D,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAM,KAAK,0BACX,EAAO,KAAK,2BACZ,EAAwB,EAAO,EAAO,EAEtC,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KKnFjC,GAAM,IAAyB,CAAC,UAAW,QAAS,MAAO,QAAS,UAAW,YAAa,aAE5F,QAAsB,CAe3B,YAAY,EAAwC,CAClD,GAAI,EAAc,SAAW,EAC3B,KAAM,IAAI,OAAM,8EAA8E,EAAc,UAG9G,GAAuB,QAAQ,CAAC,EAAY,IAAQ,CAClD,KAAK,GAAc,EAAc,KAIrC,eAAgB,CACd,MAAO,IACJ,IAAI,AAAC,GAAgB,EAAE,aAAY,YAAa,KAAK,MACrD,KAAK,CAAC,EAAI,IAAO,EAAG,YAAc,EAAG,e1CtBrC,oBAAgC,GAA0C,CAC/E,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGtB,aAAa,EAA4C,CAC9D,MAAO,AAAG,SAAK,IAAM,AAAG,WAAQ,KAAK,OAAO,UAGjC,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,oBAAmB,EAAkB,CAChD,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAC9B,EAAsB,KAAM,SAAQ,IAAI,AAAG,WAAQ,GAAK,IAAI,KAAO,IAAM,CAC7E,GAAM,GAAO,KAAM,GAAE,OACrB,SAAE,UACK,KAET,EAAI,UAEJ,GAAM,GAAqB,EACxB,IAAI,AAAC,GAAiB,GAAI,IAAgB,IAE7C,MAAO,GAAS,aACZ,EACA,EAAmB,GAGf,qBAA8B,CACtC,MAAO,wBAGC,yBAAkC,CAC1C,MAAO,KAGC,0BAAmC,CAC3C,MAAO,K2C1CJ,YAA+B,EAA0C,CAC9E,MAAO,GAAI,sBAAuB,IAG7B,YAGL,EACA,EAC8B,CAE9B,MAAO,IAAK,KADM,CAAE,gBCPf,YACL,EACA,EACA,EAAgB,GAChB,EACA,CAGA,AAF6B,OAAM,QAAQ,GAAmB,EAAkB,CAAC,IAE5D,QAAQ,AAAC,GAAM,CAElC,GAAM,GAAO,YAAa,IACtB,EACC,GAAsB,GAAK,EAAE,YAAc,OAChD,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mHAIlB,GAAM,GAAmB,AADV,EAAK,gBACY,OAAO,AAAC,GAAc,EAAU,YAAc,GAExE,EAAS,GAAoB,GAC/B,EAAE,UAAU,IAAI,WACf,GAAmB,GAAI,GAAM,EAAG,GAMrC,AAJsB,GAAI,IACxB,EAAiB,IAAI,AAAC,GAAc,GAAG,EAAU,eAAe,GAAM,EAAU,iBAChF,GAEY,KAAK,KCvBhB,YAA6B,EAA0E,CAC5G,MAAO,IAAoB,IAEtB,EAAI,oBAAwB,IAE5B,EAAI,6BAAiC,IAErC,EAAI,sBAA0B,GAGrC,YAA4B,EAAM,CAEhC,GAAM,GAAU,CAAC,EAAI,EAAI,EAAI,IAAQ,KAAK,MAAM,EAAK,EAAI,EAAK,GAAM,KAAK,GAGnE,EAAU,AAAC,GAAW,EAAQ,IAAO,KAAK,GAE1C,EAAQ,CAAE,KAA0B,OAAW,MAA2B,OAAW,IAAyB,QAEpH,GAAI,CAAC,GAAQ,CAAC,EAAK,YAAc,EAAK,WAAW,SAAW,GAAI,MAAO,GACvE,GAAM,GAAK,EAAK,WAOhB,EAAM,KAAO,CAAC,EAAQ,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,IAK9D,EAAM,MAAQ,EAAQ,EAAG,KAAK,IAAI,EAAG,GAAG,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,GAAI,KAAK,GAAI,KAAK,IAAI,EAAG,IAAI,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,IAMvH,GAAM,GAAS,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,UACnE,EAAM,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,WACtE,SAAM,IAAM,KAAK,GAAM,GAAK,SAAS,QAAW,GAAM,GAAU,IAAO,GAEhE,EAGF,YAEoD,EAAoB,EAAgF,CAC7J,GAAM,CAAE,IAAK,GAAU,EAAU,UAC3B,EAAY,EAAmB,QAAwB,EAAM,EAAG,EAAM,GAEtE,EAAO,EAAU,QACjB,CAAE,aAAc,EAAU,UAC1B,EAAc,GAAI,GAAc,EAAU,UAAU,MAAO,EAAK,QAAQ,EAAU,WAAY,GAC9F,EAAQ,GAAmB,GASjC,MAAO,IAAK,KAPM,CAChB,YACA,qBACA,cACA,UCxDG,YAA+B,CAapC,YAAY,EAAqC,GAAI,CACnD,GAAM,CACJ,YAAY,GAAM,aAAa,GAAM,YAAW,YAAW,YAAW,cACpE,EACJ,KAAK,UAAY,EACjB,KAAK,WAAa,EAClB,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,uBAC9B,KAAK,WAAa,GAAc,yBAI7B,QAAwB,CAK7B,YACE,EACA,EAAqC,GACrC,CACA,KAAK,cAAgB,EACrB,KAAK,QAAU,GAAI,IAAyB,GAG9C,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CACJ,YAAW,aAAY,YAAW,YAAW,YAAW,cACtD,KAAK,QAcT,GAZI,GAAa,KAAK,wBAAyB,KAC7C,GAAI,YAAc,EAClB,EAAI,UAAY,EAChB,GAAY,EAAK,KAAK,cAAc,iBACpC,GAAY,EAAK,KAAK,cAAc,kBACpC,GAAY,EAAK,KAAK,cAAc,mBACpC,GAAY,EAAK,KAAK,cAAc,WACpC,GAAY,EAAK,KAAK,cAAc,aAAc,IAClD,GAAY,EAAK,KAAK,cAAc,cAAe,IACnD,GAAY,EAAK,KAAK,cAAc,WAAY,KAG9C,EAAY,CACd,EAAI,YAAc,EAClB,EAAI,UAAY,EAEhB,GAAM,GAAY,AAAC,GAAe,CAChC,EAAI,YACJ,EAAI,IAAI,EAAG,EAAG,EAAG,EAAG,EAAW,EAAG,EAAI,KAAK,IAC3C,EAAI,QAEN,KAAK,cAAc,UAAU,QAAQ,MAOpC,YACL,EACA,EACA,CAEA,AAD2B,OAAM,QAAQ,GAAiB,EAAgB,CAAC,IACxD,QAAQ,AAAC,GAAM,CAEhC,GAAM,GAAY,YAAa,GAC3B,EACC,GAAoB,GAAK,EAAE,UAAY,OAC5C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gIAGlB,GAAI,IAAkB,GAAW,KAAK,oBC1G1C,OAAoB,OCApB,MAAoB,OCKpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAqC,EAAoB,EAAqB,EAA4C,CACxH,GAAM,GAAkB,EAA2B,EAAY,EAAa,GAAG,qBACzE,EAAkB,EAA2B,EAAa,EAAa,GAAG,qBAC1E,EAAiB,EAAkB,EAAY,EAAa,EAAG,GAAG,oBAExE,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAkB,EAAuC,CACvF,GAAM,GAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBAE1E,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YAAuB,EAAuB,EAAsF,CACzI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAgB,GAEhC,EAAqB,EAAkB,EAAG,GAAI,EAAG,sBACjD,EAA+B,EAA4B,GAAI,GAAI,gCACnE,EAA+B,EAA4B,GAAI,IAAK,gCAEpE,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,IAAK,0BAA0B,OAG3F,GAAM,GAA4B,EAA4B,IAAK,IAAK,6BAClE,EAA2B,EAA2B,IAAK,IAAK,4BAEhE,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,aAAY,cAAa,cCjEvC,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAqC,EAA4C,CAC/E,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAiB,EAAkB,GAAG,oBAE5C,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAuC,CACrE,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAEtD,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YACL,EACA,EAC+D,CAC/D,GAAM,GAAgC,GAEhC,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAW,GAE3B,EAAqB,EAAkB,sBACvC,EAA+B,EAA4B,gCAC3D,EAA+B,EAA4B,gCAE3D,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,0BAA0B,OAGtF,GAAM,GAA4B,EAA4B,6BACxD,EAA2B,EAA2B,4BAEtD,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,SAA2B,EAAW,GAE/B,CAAE,OAAQ,CAAE,aAAY,cAAa,aAAa,iBFrE3D,YAAc,EAAgB,EAAoB,EAAuC,CACvF,MAAO,AAAG,OAAI,AAAG,SAAO,EAAG,EAAO,QAAS,EAAQ,QAAS,EAAO,MAGrE,YAAwB,EAAgB,EAA8B,EAA2B,GAAmB,CAClH,GAAI,GAAM,EAAkB,AAAG,OAAK,GAAK,EACzC,SAAM,EAAuB,EAAK,EAAO,gBAAiB,CAAC,EAAG,IAC9D,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,AAAG,MAAI,EAAK,GAAK,EAAG,EAAO,eAAgB,CAAC,EAAG,KAC9C,EAGT,YAAmB,EAAgB,EAAsC,CACvE,GAAI,GAAM,EAAuB,AAAG,OAAK,GAAI,EAAO,gBAAiB,CAAC,EAAG,IACzE,SAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,MAAI,EAAK,GACX,EAGF,oBAA2B,EAAkC,CAGlE,YAAY,EAAuB,CACjC,MAAM,gBACN,KAAK,eAAiB,EAGjB,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KACnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,8CAElB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAC7D,EAAM,AAAG,OAAK,GAAK,EAAY,EAAO,WAAW,QAAS,CAAC,EAAG,KAClE,SAAM,GAAe,EAAK,EAAO,WAAW,kBAAmB,IAC/D,EAAM,GAAe,EAAK,EAAO,WAAW,mBAC5C,GAAM,KAAK,eAAgB,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAChD,EAAM,GAAU,EAAK,EAAO,YAAY,cAAc,QAExD,EAAM,GAAe,EAAK,EAAO,UAAU,iBAC3C,EAAM,AAAG,OAAK,EAAuB,EAAK,EAAO,UAAU,eAAgB,CAAC,EAAG,KACxE,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,sBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,gBAG1C,cAAc,EAAuB,CAC7C,MAAO,IAAc,EAAS,KAAK,kBGvEhC,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAkB,GAAuB,EAAgB,GAEzD,EAAM,EAAgB,IAAK,EAAG,UAC9B,EAAS,EAAgB,IAAK,EAAG,aAEvC,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,GAAI,CAAE,MAAK,YCflB,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,CACF,IAAK,EAAgB,UACrB,OAAQ,EAAgB,eAI5B,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,GAAK,IAAL,UAAK,EAAL,CAEL,SAAS,SAET,OAAO,SAJG,aNQL,oBAA2B,EAAyB,CAGzD,YAAY,EAAqC,GAAI,IAAa,GAAI,CACpE,MAAM,gBACN,KAAK,sBAAwB,KAGpB,uBAAqC,CAC9C,MAAO,MAAK,sBAGP,OAAO,EAA0C,CACtD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EAEE,EAAS,AAAG,WAAQ,EAAoB,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAAS,KAAK,EAAmB,MAAM,GAAI,IACnG,EAAM,GAAoB,EAAQ,EAAO,GAAG,KAAK,OACjD,EAAS,GAAoB,EAAQ,EAAO,GAAG,QACrD,MAAO,CAAE,MAAK,YAIX,aAAa,EAA0C,CAC5D,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAE,MAAK,UAAW,KAAK,OAAO,GACpC,MAAO,CAAE,MAAK,OAAQ,AAAG,WAAQ,WAIxB,SAAQ,EAAsC,CACzD,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,qBAAoB,EAA8E,CAC7G,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAE9B,EAAO,AAAG,WAAQ,EAAI,KACtB,EAAU,AAAG,WAAQ,EAAI,QACzB,EAAsB,EAAK,IAAI,CAAC,EAAW,IAAO,EACtD,YACA,aAAc,EAAQ,MAGlB,EAAqB,KAAM,SAAQ,IACvC,EAAoB,IAAI,MAAO,CAAE,YAAW,kBAAmB,CAC7D,GAAM,GAAO,MAAM,GAAU,QAAQ,GAC/B,EAAY,MAAM,GAAa,QAAQ,GACvC,EAAS,EAAW,GACpB,EAAS,EAAS,GAAO,KAAO,GAAO,OACvC,EAAoB,EAAS,EAAY,EAAI,EAEnD,SAAU,UACV,EAAa,UACN,CAAE,MAAK,SAAQ,wBAG1B,SAAI,IAAI,UACR,EAAI,OAAO,UAEJ,EAAS,aAAe,EAAiD,EAAmB,GAG3F,qBAA8B,CACtC,MAAO,mBAGF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,GAGb,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAwB,IAAM,EAAI,EAAM,KAAM,EAAI,GAElD,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KOvHxC,MAAoB,OASb,oBAGG,GAAgC,CACjC,YAAY,EAAqB,EAAmB,EAAgD,CACzG,GAAM,GAAkB,EAAmB,IAAI,CAAC,CAAE,QAAO,YAAa,CACpE,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,CACL,MAAO,EAAQ,EACf,OAAQ,EAAS,KAIf,EAAY,EAAgB,OAElC,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAA0B,CAAC,EAAe,IAAkB,AAAG,QAAM,CAAC,AAAG,OAAK,CAAC,IAAK,EAAO,WAAY,AAAG,OAAK,CAAC,IAAK,EAAO,YAAa,GAAG,KAAK,EAAG,KAAK,OAGzJ,EAAa,CAAC,EAAkB,IAAoD,CACxF,GAAM,CAAE,QAAO,UAAW,EAAgB,GAC1C,MAAO,GAAK,EAAO,GAAU,KAAK,IAAI,EAAQ,GAAU,EAAI,GAGxD,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GACvE,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GAa7E,MAXwB,GACrB,IAAI,AAAG,OAAK,CAAC,EAAW,KAAM,EAAW,YACzC,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAY,GACZ,EAAY,OAEb,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAgB,GAAU,MAC1B,EAAgB,GAAU,aAO3B,aAAa,EAA8B,CAChD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,KAAK,OAAO,GACxB,MAAO,MAAK,YACV,EACA,EAAM,UACN,EAAM,gBAAgB,IAAI,CAAC,CAAC,EAAQ,KAAY,EAAE,SAAQ,mBAKnD,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,iBAAgB,EAAgE,CAC3F,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAkB,AAAG,OACzB,IAAM,AAAG,UAAQ,KAAK,aAAa,KAG/B,EAAoB,KAAM,SAAQ,IAAI,EAAgB,IAC1D,MAAO,EAAgB,IAAa,CAClC,GAAM,GAAiB,MAAM,KAAK,KAAM,GAAe,QACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,GAAO,IACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,CAAC,GAAO,IAExD,MAAO,IAAI,IACT,MAAM,IAAI,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,GAAI,GAAM,EAAQ,GAAc,EAAQ,KACxE,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,QAMtC,SAAgB,QAAQ,AAAC,GAAM,EAAE,WAE1B,EAAS,aAAe,EAAyC,EAAkB,GAGlF,0BAAmC,CAC3C,MAAO,OC1FJ,oBAAgC,GAAkD,CACvF,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGnB,qBAA8B,CACtC,MAAO,yBAGC,yBAAkC,CAC1C,MAAO,OCdX,OAAoB,OCMb,YACL,EAC2E,CAC3E,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCnBZ,YAA2B,EAAkG,CAClI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAEjD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,SAAQ,SAAQ,WFhBvB,oBAAuC,EAA+G,CAC3J,aAAc,CACZ,MAAM,4BAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,0DAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,GAAI,IAAK,CAAC,EAAG,GAAI,SAEjC,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,oCAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA+B,GAG9B,cAAc,EAAuB,CAC7C,MAAO,IAAkB,KG7CtB,oBAAoC,GAAsD,CAC/F,YAAY,EAAiD,GAAI,IAA4B,CAC3F,MAAM,wBAAyB,GAGvB,qBAA8B,CACtC,MAAO,8BAGC,yBAAkC,CAC1C,MAAO,OCVJ,oBAA8B,GAAkB,GCJvD,MAAoB,OCApB,OAAoB,OCApB,OAAoB,OAIb,YAAe,EAAgB,EAAuC,CAC3E,MAAO,AAAG,QAAI,AAAG,OAAI,EAAG,EAAO,SAAU,EAAO,QDAlD,YACE,EACA,EACA,EACA,EACA,EAA4B,OACf,CACb,GAAM,CAAE,UAAS,QAAS,EAAO,KAE7B,EAAM,AAAG,UAAO,EAAG,EAAS,EAAS,GACzC,SAAM,AAAG,OAAI,EAAK,GAClB,EAAM,GAAM,EAAK,EAAO,OACjB,EAAW,AAAG,QAAK,GAAO,EAG5B,YAAc,EAAgB,EAAyB,CAC5D,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAoB,EAAgB,EAAyB,CAClE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAkB,EAAgB,EAAyB,CAChE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,GAAM,SE7B5C,MAAoB,OAUpB,YAA2B,EAAwC,EAA+B,CAChG,WAA6B,EAAyB,EAAoB,EAAiC,CACzG,GAAM,GAAU,EAAe,GACzB,EAAQ,EAAQ,OAAU,GAAa,EAAa,GAE1D,GAAI,GAAQ,GACV,KAAM,IAAI,OAAM,+BAA+B,sBAA0B,EAAQ,uBAAuB,kBAA2B,KAGrI,MAAO,AAAG,QACR,IAAM,AAAG,YACP,AAAG,WAAS,EAAS,CAAC,EAAY,EAAO,EAAY,IACrD,CAAC,EAAG,EAAG,EAAG,KAKhB,WACE,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,EAAoB,EAAiB,EAAY,GAC3D,EAAO,AAAG,WAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,QAGpB,WAAiC,EAAoB,EAAwC,CAC3F,GAAM,GAAU,AAAG,WAAS,EAAe,IACrC,EAAS,AAAG,WAAS,EAAe,IAE1C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,aAGX,CACL,UACA,UAIJ,WACE,EACA,EACA,EACA,EACiB,CACjB,GAAM,GAAO,EAAkB,EAAiB,EAAY,EAAY,GAAG,UACrE,EAAQ,EAAwB,EAAY,GAAG,WAErD,MAAO,CAAE,OAAM,SAGjB,WACE,EACA,EACA,EACA,EACA,EAAkB,GACG,CACrB,GAAM,GAAQ,EAAwB,GAAS,GAAM,GAAK,EAAiB,EAAY,EAAY,GAAG,WAChG,EAAQ,EAAuB,EAAiB,EAAY,EAAY,GAAG,WAEjF,MAAO,CAAE,QAAO,SAGlB,MAAO,CACL,yBACA,8BAIG,YAAuB,EAA6E,CACzG,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAgB,GAEhC,EAAc,EAAuB,KAAM,GAAI,EAAG,eAClD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YAEnD,EAAc,EAA2B,MAAO,GAAI,EAAG,cAAe,IACtE,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YAEpD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aAEvD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAmB,EAA2B,OAAQ,IAAK,EAAG,oBAE9D,EAAK,AAAG,OACZ,IAAM,AAAG,YAAU,AAAG,WAAS,EAAe,IAAM,KAAM,CAAC,IAAK,MAAO,CAAC,EAAG,KAI7E,GAFA,EAAc,KAAK,CAAE,UAAW,OAE5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAsB1E,MAAO,CAAE,OAnBM,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGe,iBChJnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAiC,EAAkC,CACjE,GAAM,GAAU,EAAmB,GAAG,kBAAwB,GACxD,EAAS,EAAmB,GAAG,iBAAuB,GAE5D,MAAO,CAAE,UAAS,UAGpB,WAAgC,EAAiC,CAC/D,GAAM,GAAU,EAAmB,GAAG,iBAAuB,GACvD,EAAO,EAAmB,GAAG,cAAoB,GACjD,EAAQ,EAAwB,GAEtC,MAAO,CAAE,KAAM,CAAE,UAAS,QAAQ,SAGpC,WAAoC,EAAqC,CACvE,MAAO,CACL,MAAO,EAAuB,GAAG,WACjC,MAAO,EAAuB,GAAG,YAIrC,MAAO,CACL,yBACA,8BAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAW,GAE3B,EAAc,EAAuB,eACrC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAc,EAA2B,eACzC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aAEvC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aACvC,EAAmB,EAA2B,oBAE9C,CAAE,MAAO,EAGf,GAFA,EAAc,KAAK,CAAE,aAAc,KAAM,UAAW,OAEhD,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yDAAyD,KAG3E,GAAM,GAAS,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGF,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBC9FnB,MAAoB,OAKb,YAAkB,EAAgB,EAA0C,CACjF,GAAI,GAAM,GAAK,EAAG,EAAO,OACzB,SAAM,GAAW,EAAK,EAAO,OAC7B,EAAM,AAAG,MAAI,EAAK,GAClB,EAAM,AAAG,OAAK,GACP,EAGF,YAAsB,EAAgB,EAA0C,CACrF,GAAI,GAAM,GAAS,EAAG,EAAO,OAC7B,EAAM,GAAW,EAAK,EAAO,OAE7B,GAAI,GAAS,AAAG,UAAQ,EAAG,EAAG,EAAG,SAC3B,EAAQ,AAAG,QAAkB,EAAO,OACpC,EAAQ,EAAO,MAAM,KAAO,EAAI,MAAM,GAG5C,GAFsB,EAAO,MAAM,KAAO,EAAI,MAAM,IAAM,EAAO,MAAM,KAAO,EAAI,MAAM,GAErE,CACjB,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAE/B,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAGjC,SAAS,EAAQ,AAAG,SAAO,CAAC,EAAQ,GAAQ,GAAK,EACjD,EAAM,AAAG,MAAI,EAAQ,GAErB,EAAM,AAAG,OAAK,GACP,EL3BF,oBAAiC,EAAyB,CAC/D,aAAc,CACZ,MAAM,sBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAGtD,EAAa,GAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAE7D,EAAM,GAAS,EAAY,EAAO,aACtC,EAAM,AAAG,UAAQ,EAAK,EAAG,EAAG,SAE5B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,aAC/B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAa,EAAK,EAAO,kBAE/B,GAAM,GAAY,EAAI,KAAK,CAAC,EAAG,IAG/B,MAFuB,AAAG,UAAO,EAAW,EAAO,WAM1C,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,uBAAsB,EAAwD,CA7D7F,MA8DI,GAAI,oBAAO,QAAP,cAAc,KAAK,AAAC,GAAQ,GAAO,GAAI,MAAO,IAAI,cAAa,KACnE,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAwB,AAAG,OAC/B,IAAM,AAAG,UAAQ,KAAK,aAAa,KAE/B,EAA0B,KAAM,SAAQ,IAAI,EAAsB,IAAI,AAAC,GAAM,EAAE,SACrF,SAAsB,QAAQ,AAAC,GAAM,EAAE,WAChC,EAAS,aAAe,EAA0B,EAAwB,GAGzE,qBAA8B,CACtC,MAAO,yBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KM7ElB,YAAkC,EAAuB,CAC9D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECHF,YAGL,EACA,EAC6B,CAE7B,MAAO,IAAK,KADM,CAAE,eCNf,YAAmB,EAA8B,CACtD,MAAO,OAAO,GAAI,KAAQ,SAGrB,YAGL,EACA,EACkB,CAElB,MAAO,IAAK,KADM,CAAE,QCNf,YAAsB,EAAiC,CAC5D,MAAQ,GAAI,SAAW,GAAO,MAAQ,EAAI,SAAW,GAAO,SACvD,GAAmB,EAAI,mBAGvB,YAGL,EACA,EACA,EACqB,CAErB,MAAO,IAAK,KADM,CAAE,SAAQ,sBCpB9B,OAAoB,OCApB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,WAAoC,EAAqB,EAAuD,CAC9G,GAAM,GAAU,AAAG,YAAS,EAAe,EAAI,EAAI,GAAc,CAAC,EAAG,EAAG,EAAa,IAC/E,EAAmB,AAAG,YAAS,EAAe,IAC9C,EAAoB,AAAG,YAAS,EAAe,IAC/C,EAAkB,AAAG,YAAS,EAAe,IAC7C,EAAsB,AAAG,YAAS,EAAe,IAEvD,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,uBAChB,CAAE,UAAW,GAAG,qBAChB,CAAE,UAAW,GAAG,0BAGX,CACL,UACA,mBACA,oBACA,kBACA,uBAIJ,WACE,EACA,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,KAAgB,EAAkB,oBAAsB,WAGnE,CAAE,UAAS,QAGpB,WACE,EACA,EACA,EACA,EACqB,CACrB,GAAM,CACJ,UACA,QACE,EAAkB,EAAY,EAAa,EAAY,EAAc,IAEzE,MAAO,CACL,UACA,kBAAmB,GAIvB,WACE,EACA,EACA,EAC4B,CAC5B,GAAM,GAAiB,EAA2B,EAAY,GAAG,oBAC3D,EAAiB,EAA2B,EAAY,EAAa,EAAG,GAAG,oBAEjF,MAAO,CAAE,iBAAgB,kBAG3B,YAAwD,CACtD,GAAM,GAAS,EAA2B,EAAG,GAAI,EAAG,sBAC9C,EAAS,EAAsB,GAAI,GAAI,sBACvC,EAAS,EAAsB,GAAI,IAAK,sBACxC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,KAAM,uBAC3C,EAAU,EAAsB,KAAM,KAAM,uBAClD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,UACA,UACA,UACA,WAIJ,YAA+D,CAC7D,GAAM,GAAS,EAA2B,KAAM,IAAK,EAAG,2BAClD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,GAAI,EAAG,2BAChD,EAAS,EAA2B,GAAI,IAAK,EAAG,2BAChD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,EAAG,EAAG,oDACjD,EAA2B,EAAkB,KAAM,GAAI,EAAG,2DAC1D,EAAoB,EAAkB,KAAM,GAAI,EAAG,oDACnD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDA0BxD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,gBAjCsB,CACtB,uBAAwB,EACxB,gBAAiB,GAgCjB,gBA9BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA6BjB,gBA3BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA0BjB,gBAxBsB,CACtB,uBAAwB,GACxB,gBAAiB,IAuBjB,gBArBsB,CACtB,uBAAwB,EACxB,gBAAiB,IAoBjB,gBAlBsB,CACtB,uBAAwB,GACxB,gBAAiB,KAoBrB,MAAO,CACL,2BACA,gCAIG,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAChC,CACJ,iBACA,uBACE,EAAsB,GACpB,CACJ,2BACA,gCACE,GAAkB,EAAgB,GAChC,EAAc,IACd,EAAmB,IAKnB,EAAe,CACnB,UALgB,AAAG,YACnB,EAAe,KAAO,GACtB,CAAC,EAAG,KAAM,KAMZ,GADA,EAAc,KAAK,CAAE,UAAW,2BAC5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,OAAQ,CACN,cACA,mBACA,gBAEF,iBC9MJ,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAoC,EAAgB,EAAa,EAA2C,CAC1G,GAAM,GAAU,EAAmB,GAAG,YAAiB,sBAAyB,EAAG,GAAG,aAChF,EAAoB,EAAmB,GAAG,YAAiB,oCAAuC,EAAG,GAAG,uBAC9G,MAAO,CAAE,UAAS,qBAGpB,WAA+B,EAAyC,CACtE,GAAM,GAAe,oBAAoB,IACnC,EAAsB,sBAAsB,cAC5C,EAA4B,GAAG,mBAC/B,EAA4B,GAAG,mBAE/B,EAAU,EAAmB,GAAG,sBAAyC,EAAG,GAAG,aAC/E,EAAmB,EAAmB,GAAG,oBAAuC,EAAG,GAAG,sBACtF,EAAoB,EAAmB,GAAG,mBAAsC,EAAG,GAAG,uBACtF,EAAkB,EAAmB,GAAG,0BAA6C,EAAG,GAAG,qBAC3F,EAAsB,EAAmB,GAAG,8BAAiD,EAAG,GAAG,yBAEzG,MAAO,CACL,eAAgB,CACd,UACA,mBACA,oBACA,kBACA,uBAEF,eAAgB,EAA2B,cAAe,EAAK,IAInE,YAAwD,CACtD,MAAO,CACL,OAAQ,EAA2B,cAAe,EAAG,sBACrD,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,KAInC,WAA2B,EAAgB,EAAkC,CAC3E,GAAM,GAAU,EAAmB,GAAG,YAAkB,EAAG,GAAG,aACxD,EAAO,EAAmB,GAAG,WAAiB,EAAG,GAAG,UAC1D,MAAO,CAAE,UAAS,QAGpB,WAAmC,EAAkC,CACnE,GAAM,GAAyB,EAC7B,2BAA2B,yBAC3B,kCAAkC,4BAE9B,EAAkB,EACtB,2BAA2B,mBAC3B,kCAAkC,qBAEpC,MAAO,CAAE,yBAAwB,mBAGnC,YAA+D,CAC7D,MAAO,CACL,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,IAI/C,MAAO,CACL,2BACA,gCAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAChC,CACJ,2BACA,gCACE,GAAkB,EAAW,GAC3B,EAAY,EAAU,oBAE5B,GADA,EAAc,KAAK,CAAE,aAAc,mBAAoB,UAAW,2BAC9D,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yEAAyE,KAG3F,GAAM,GAAS,CACb,YAAa,IACb,iBAAkB,IAClB,aAAc,CACZ,cAIJ,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBChInB,OAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,UAAO,EAAG,EAAO,QAAS,EAAS,QAChD,SAAM,AAAG,OAAI,EAAK,EAAO,mBAClB,AAAG,eAAY,EAAK,EAAG,KDPlC,GAAM,IAAU,qBAEhB,YACE,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,QAAS,EAAS,QACzD,SAAM,AAAG,aACP,EACA,EAAO,gBACP,EAAO,oBACP,EAAO,kBACP,EAAO,iBACP,IAEK,AAAG,eAAY,EAAK,EAAG,KAIlC,YAA+B,EAAoC,CACjE,MAAO,CAAC,EAAG,EAAG,EAAG,IAAI,KAAK,AAAC,GAAQ,IAAQ,GAAY,CAAC,EAAG,GAAK,CAAC,EAAG,GAG/D,YAAqB,EAAgB,EAA4B,CACtE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GACA,EAAM,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IA4BnD,GAVA,AAhBuB,CACrB,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,QACP,EAAO,QACP,EAAO,QACP,EAAO,SAGM,QAAQ,CAAC,EAAO,IAAM,CACnC,GAAM,GAAW,EAAI,EACf,EAAuB,GAAsB,GACnD,EAAM,GAAmB,EAAK,EAAM,eAAgB,GACpD,EAAM,EAAmB,EAAK,EAAM,eAAgB,CAAC,EAAG,IACpD,IAAa,IACf,GAAS,KAIT,IAAW,KACb,KAAM,IAAI,OAAM,iDAGlB,MAAO,CACL,MACA,OAAQ,KEjEd,YAAa,EAAoB,EAAW,EAAW,CACrD,GAAM,GAAY,EAAM,YAClB,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAS,GAAQ,GAAU,GAAQ,GACnC,EAAS,GAAQ,GAAU,GAAQ,GACzC,GAAI,GAAS,GAAK,GAAS,EACzB,MAAO,GAET,GAAM,GAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAmB,EAAkB,GACjE,KAAK,IAAI,EAAmB,EAAkB,GACpD,MAAO,GAAoB,GAAQ,EAAQ,GAGtC,YACL,EACA,EACA,EACA,EACA,EACU,CACV,GAAM,GAAW,EAAM,MAAM,GACvB,EAAa,KAAK,IACtB,EACA,GAGI,EAAa,EAChB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,OAAO,AAAC,GAAM,EAAE,MAAQ,GACxB,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAE5B,EAAe,AAAC,GAAe,GAAK,EAAe,EAAI,EACvD,EAAqB,GAE3B,SAAW,QAAQ,AAAC,GAAM,CACxB,GAAI,EAAS,QAAU,EAAY,OACnC,GAAM,GAAgB,EAAE,MACxB,OAAS,GAAI,EAAS,OAAS,EAAG,GAAK,EAAG,EAAE,EAAG,CAC7C,GAAM,GAAM,GAAI,EAAO,EAAE,SAAU,EAAS,IAC5C,GAAI,IAAQ,GACZ,GAAE,OAAS,EAAa,GACpB,EAAE,OAAS,GAAgB,MAEjC,AAAI,IAAkB,EAAE,OACtB,EAAS,KAAK,EAAE,YAGb,EC5DT,MAAoB,OAIpB,YAA2C,EAAgB,CACzD,GAAM,GAAM,AAAG,UAAQ,AAAG,YAAU,EAAG,CAAC,EAAG,KAErC,EAAQ,CACZ,AAAG,MAAI,EAAI,GAAI,EAAI,IACnB,AAAG,MAAI,EAAI,GAAI,EAAI,KAEf,EAAU,CACd,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,KAC1C,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,MAE5C,MAAO,CACL,QACA,WAIJ,YAA0B,EAAiB,EAAiB,CAC1D,GAAM,CACJ,QACA,WACE,GAAkC,GAEhC,EAAM,AAAG,UAAQ,AAAG,YAAU,EAAI,CAAC,EAAG,KACtC,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAC3E,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAEjF,MAAO,AAAG,aACR,AAAG,QAAM,CACP,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,KAEnB,CAAC,EAAG,IAID,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAY,EAAe,MAAM,GAEnC,EAAQ,GACV,AAAG,UAAQ,AAAG,OAAK,EAAO,UAAW,CAAC,EAAW,EAAG,IAAK,CAAC,GAAI,IAC9D,AAAG,UAAQ,EAAgB,CAAC,GAAI,KAElC,EAAQ,AAAG,UACT,EACA,CAAC,EAAY,EAAM,MAAM,GAAK,EAAY,IAG5C,GAAM,GAAmB,AAAG,UAAQ,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,MAC/E,EAAS,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,IAE5D,EAAS,AAAG,UACV,EACA,CAAC,EAAW,EAAO,MAAM,KAG3B,GAAM,GAAe,AAAG,UAAQ,GAC1B,EAAgB,AAAG,UAAQ,GAEjC,MAAO,CACL,MAAO,EACP,OAAQ,KC1Ed,OAAoB,OCApB,OAAoB,OAKb,YACL,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAY,EAAE,MAAM,GACpB,EAAwB,AAAG,WAC/B,GAAU,EAAG,EAAO,wBACpB,CAAC,EAAW,GAAI,EAAG,IAEf,EAAkB,AAAG,WACzB,GAAU,EAAG,EAAO,iBACpB,CAAC,EAAW,GAAI,IAElB,MAAO,CAAE,wBAAuB,qBDb7B,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAQ,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IACjD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IAErD,EAAiB,GAAmB,EAAQ,EAAO,iBACnD,EAAiB,GAAmB,EAAG,EAAO,iBAC9C,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAElD,EAAiB,AAAG,UAAO,CAC/B,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,uBACd,GAEG,EAAmB,AAAG,UAAO,CACjC,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,iBACd,GAEH,MAAO,CACL,iBACA,sBE3CC,WAA4B,CAOjC,YAAY,CAAE,gBAAe,cAAuC,GAAI,CAN9D,WAAgB,wBAUxB,GAHA,KAAK,eAAiB,GAAiB,GACvC,KAAK,YAAc,GAAc,IAE7B,MAAO,MAAK,gBAAmB,UAAY,KAAK,gBAAkB,GAAK,KAAK,gBAAkB,EAChG,KAAM,IAAI,OAAM,GAAG,KAAK,iEAG1B,GAAI,MAAO,MAAK,aAAgB,SAC9B,KAAM,IAAI,OAAM,GAAG,KAAK,iDAIxB,gBAAwB,CAAE,MAAO,MAAK,kBAEtC,aAAqB,CAAE,MAAO,MAAK,cTZlC,oBAA6B,EAAyB,CAC3D,aAAc,CACZ,MAAM,kBAGD,aAAa,EAAiB,CACnC,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAQ,WACvD,EAAI,AAAG,OAAI,AAAG,OAAI,EAAa,AAAG,UAAO,sBAAwB,AAAG,UAAO,IAC3E,EAAW,GAAY,EAAG,EAAO,aAEjC,CAAE,iBAAgB,oBAAqB,GAAgB,EAAS,IAAK,EAAS,OAAQ,EAAO,kBAEnG,MAAO,IAAY,EAAgB,EAAkB,EAAO,qBAInD,SAAQ,EAAkB,CACrC,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,aACX,EACA,EAAkC,GACR,CAC1B,GAAM,CAAE,aAAY,iBAAkB,GAAI,GAAsB,GAE1D,EAAW,KAAM,GAAW,GAE5B,CACJ,MAAO,EACP,OAAQ,GACN,KAAK,aAAa,GAEhB,EAAQ,EAAO,GACf,EAAS,EAAQ,GACvB,OAAS,GAAI,EAAG,EAAI,EAAO,OAAQ,IACjC,EAAO,GAAG,UACV,EAAQ,GAAG,UAGb,GAAM,GAAa,MAAM,KAAK,KAAM,GAAO,QAErC,EAAU,GACd,EACA,EACA,EAJmB,GAMnB,GAGI,EAAe,EAAS,2BAA2B,GACnD,EAAY,EAAS,UACrB,EAAO,EAAY,EAAa,MAChC,EAAO,EAAY,EAAa,OAEhC,EAAY,EAAM,YAClB,EAAU,EACb,IAAI,AAAC,GAAQ,CACZ,GAAM,CAAC,EAAK,GAAU,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACf,CAAC,GAAM,IAAS,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACrB,MAAO,IAAI,GACT,EAAW,GACX,GAAI,IACF,GACA,EACA,GAAQ,GACR,EAAS,GAEX,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,OAKtC,SAAM,UACN,EAAO,UACA,EAGC,qBAA8B,CACtC,MAAO,wBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KUhHlB,YAA8B,EAAuB,CAC1D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,EAGF,YAAgC,EAAuB,CAC5D,MAAO,IAAqB,GAIvB,oBAA+B,GAAe,GCd9C,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,QAAU,SACpB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,OAAQ,SAClB,GAAI,GAAM,QAAS,UAGR,GAAwB,CACnC,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAA+C,CAAC,QAAS,QAAS,QAElE,GAAqB,oBACrB,GAAoC,mCCvBjD,MAAoB,OCapB,GAAM,IAAW,AAAC,GAAa,MAAO,IAAQ,SAEvC,YAAwB,EAAa,CAC1C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mBAAmB,KAGrC,GAAI,MAAO,GAAO,oBAAuB,UACvC,KAAM,IAAI,OAAM,wDAAwD,EAAO,sBAGjF,GAAI,CAAC,GAAS,EAAO,eAAiB,EAAO,aAAe,GAAK,EAAO,aAAe,EACrF,KAAM,IAAI,OAAM,gEAAgE,EAAO,gBAGzF,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,MAAM,AAAC,GAAW,MAAO,IAAM,UAElD,KAAM,IAAI,OAAM,kEAAkE,KAAK,UAAU,EAAO,YAG1G,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,IAAI,AAAC,GAAW,GAAK,IAAI,MAAM,AAAC,GAAW,GAAS,EAAE,IAAM,GAAS,EAAE,IAE1F,KAAM,IAAI,OAAM,wEAAwE,KAAK,UAAU,EAAO,YAGhH,GAAI,EAAO,SACT,EAAC,MAAM,QAAQ,EAAO,UACnB,EAAO,QAAQ,SAAW,GAC1B,CAAC,EAAO,QAAQ,MAAM,KAEzB,KAAM,IAAI,OAAM,8EAA8E,KAAK,UAAU,EAAO,YCjDxH,MAAoB,OCApB,MAAoB,OAEb,YAAe,EAA6B,CACjD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,AAAG,MAAI,EAAG,AAAG,SAAO,qBAChC,MAAO,AAAG,OAAI,AAAG,OAAK,AAAG,MAAI,EAAG,IAAO,KDApC,YAA2B,EAAgB,EAAwC,CACxF,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAM,AAAG,MAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,SAAO,EAAK,EAAO,KAAK,QAAS,CAAC,EAAG,GAAI,SAClD,EAAM,AAAG,MAAI,EAAK,EAAO,GAAG,KAC5B,EAAM,AAAG,MAAI,EAAK,EAAO,GAAG,SAC5B,EAAM,AAAG,MAAI,EAAK,EAAO,KAAK,MACvB,GAAM,KEZjB,OAAoB,OAKb,YAAgC,EAAgB,EAA0C,CAC/F,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,mBAAgB,EAAK,EAAO,iBAAkB,EAAO,iBAAkB,CAAC,EAAG,GAAI,SACxF,EAAM,AAAG,OAAI,EAAK,EAAO,MAClB,GAAM,KCVjB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAEnE,WAAgC,EAAc,EAAiC,CAC7E,GAAM,GAAM,AAAG,YAAS,EAAe,IACjC,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,SAChB,CAAE,UAAW,GAAG,cAEX,CAAE,MAAK,WAGhB,WAAwC,EAAoB,EAAqB,EAAyC,CACxH,GAAM,GAAO,EAAkB,EAAY,EAAa,EAAG,GAAG,UACxD,EAAK,EAAuB,EAAa,GAAG,QAClD,MAAO,CAAE,OAAM,MAEjB,GAAM,GAA6B,GAAkC,EAAgB,GAErF,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACA,EACA,EACgE,CAChE,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAChC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAgB,GAClC,EAEJ,GAAI,EAAO,mBAAoB,CAC7B,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAAO,mBACjB,EAAkB,EAAI,EAAI,EAAG,SAC7B,EAA2B,EAAI,EAAI,SACjC,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAkB,GAAM,GAAM,EAAI,EAAI,EAAiB,EAAG,SACxE,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,cAErD,CACL,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAAkB,EAAI,EAAI,EAAiB,EAAG,SAC5D,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,UAG5D,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAE1E,MAAO,CAAE,SAAQ,iBChFnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAgC,EAA2B,CACzD,GAAM,GAAM,EAAmB,GAAG,QAAc,GAC1C,EAAU,EAAmB,GAAG,YAAkB,GACxD,MAAO,CAAE,MAAK,WAGhB,WAA2B,EAA4B,CACrD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,WAAwC,EAAmC,CACzE,GAAM,GAAO,EAAkB,GAAG,UAC5B,EAAK,EAAuB,GAAG,QACrC,MAAO,CAAE,OAAM,MAGjB,GAAM,GAA6B,GAA+B,GAClE,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACgE,CAChE,GAAM,GAAgC,GAEhC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAW,GAE7B,EAEJ,GAAI,EAAO,mBAAoB,CAE7B,GAAM,GAAc,EAAO,aAAe,EAAO,YAAY,QAAU,EACvE,EAAS,CACP,MAAO,EAAO,mBAAqB,EAAkB,SAAW,EAA2B,SAC3F,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAkB,cAG3B,GAAS,CACP,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAAkB,UAI7B,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBC7EZ,YAAwB,CAO7B,YAAY,CAAE,YAAW,kBAAuC,GAAI,CAN1D,WAAgB,oBAUxB,GAHA,KAAK,WAAa,GAAa,IAC/B,KAAK,gBAAkB,GAAkB,GAErC,MAAO,MAAK,YAAe,UAAY,KAAK,WAAa,IAAO,EAClE,KAAM,IAAI,OAAM,GAAG,KAAK,6DAG1B,GAAI,MAAO,MAAK,iBAAoB,UAAY,KAAK,iBAAmB,GAAK,KAAK,iBAAmB,EACnG,KAAM,IAAI,OAAM,GAAG,KAAK,qEAIxB,YAAoB,CAAE,MAAO,MAAK,cAElC,iBAAyB,CAAE,MAAO,MAAK,kBPJtC,oBAA6B,EAAmC,CAKrE,YAAY,EAA0B,CACpC,MAAM,cACN,GAAe,GACf,KAAK,QAAU,KAGN,SAA2B,CACpC,MAAO,MAAK,WAGH,kBAA2B,CACpC,MAAO,MAAK,OAAO,iBAAmB,KAAK,OAAO,QAAQ,OAAS,KAG1D,kBAA0B,CACnC,MAAO,GAAK,MAAK,gBAAkB,KAAK,OAAO,QAAQ,OAAS,GAG3D,cAAc,EAAgB,EAAiD,CACpF,GAAI,GAAM,GAAkB,EAAG,EAAO,OACtC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,GAAkB,EAAK,EAAO,OAC7B,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAgB,EAAsC,CACxE,GAAI,GAAM,KAAK,OAAO,mBAClB,GAAM,GAAU,EAAG,EAAO,MAAqB,QAAS,KACxD,GAAuB,EAAG,EAAO,OACrC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EACjE,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EAC1D,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAiB,EAAgC,CACnE,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,4CAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAc,AAAG,OAAK,EAAM,cAAc,EAAW,IAAQ,WACjE,SAAc,KAAK,OAAO,QACtB,GAAU,EAAa,KAAK,OAAO,SACnC,EACJ,EAAc,EAAY,IAAI,AAAG,SAAO,MACjC,KAAK,OAAO,mBACf,KAAK,aAAa,EAAa,GAC/B,KAAK,cAAc,EAAa,UAI3B,SAAQ,EAAkB,EAAyC,CAC9E,MAAO,MAAK,aAAa,KAAM,GAAW,GAAQ,QAGvC,QAAO,EAAkB,EAAoC,GAAgC,CACxG,GAAM,CAAE,YAAW,kBAAmB,GAAI,IAAkB,GACtD,EAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,EAAU,GACxC,EAAO,AAAG,OAAK,IAAM,AAAG,UAAQ,GAAK,GAAG,cACxC,EAAkB,CACtB,MAAO,EAAS,cAAc,GAC9B,OAAQ,EAAS,eAAe,IAG5B,EAAU,KAAM,MAAK,aAAa,EAAM,EAAS,2BAA2B,GAAI,GACtF,EAAI,UACJ,EAAK,UAEL,GAAM,GAAQ,EAAQ,IAAI,AAAC,GAAQ,EAAI,KACjC,EAAS,EAAQ,IAAI,AAAC,GAAQ,EAAI,OAClC,EAAc,EAAQ,IAAI,AAAC,GAAQ,EAAI,YACvC,EAAa,EAAQ,IAAI,AAAC,GAAQ,KAAK,OAAO,QAAQ,EAAI,QAgBhE,MAPmB,AAPH,IACd,EAAM,IAAI,AAAC,GAAQ,EAAI,QAAQ,IAC/B,EACA,KAAK,OAAO,aACZ,IAGyB,IAAI,AAAC,GAAQ,GAAI,IAC1C,EAAO,GACP,EAAY,GACZ,EAAW,GACX,EAAM,GACN,IAKM,qBAA8B,CACtC,MAAO,GAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,QAG1C,cAAc,EAAuB,CAC7C,GAAM,GAAc,KAAK,OAAO,aAAe,GAAe,qBAExD,EAAa,EAAc,EAAY,OAAS,OACtD,GAAI,IAAe,GAAK,IAAe,GAAK,IAAe,EACzD,KAAM,IAAI,OAAM,oEAAoE,2BAEtF,MAAO,IAAc,EAAS,KAAK,OAAQ,KAAK,gBAAiB,QAGnD,cACd,EACA,EACA,EACA,CACA,GAAM,CAAE,QAAO,UAAW,EACpB,EAAY,KAAK,IAAI,EAAO,GAC5B,EAAoB,EAAY,EAChC,EAAoB,EAAY,EAEhC,EAAW,EAAa,MAAM,GAC9B,EAAW,KAAK,OAAO,QAAQ,OAE/B,CAAC,EAAa,EAAc,GAAqB,AAAG,OAAK,IAAM,CACnE,GAAM,GAAW,EAAa,QAAQ,CAAC,EAAU,EAAU,EAAU,KAAK,kBAEpE,EAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACpE,EAAS,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACrE,EAAc,KAAK,gBACrB,AAAG,UAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,KAAK,OAAO,QAAQ,SAAU,GACrG,AAAG,SAAO,GACd,MAAO,CAAC,EAAO,EAAQ,KAGnB,EAAU,GACV,EAAa,KAAM,GAAa,QAChC,EAAY,KAAM,GAAY,QACpC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAS,EAAG,EAAS,EAAU,IAAU,CAChD,GAAM,GAAQ,GAAQ,EAAW,GAAK,GAAK,GAAQ,IACnD,GAAI,CAAC,GAAkB,EAAQ,EAAgB,CAC7C,GAAM,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAe,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACvG,EAAgB,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACxG,GAAK,EAAO,GAAa,EACzB,GAAK,GAAO,EAAc,EAC1B,GAAM,CAAE,MAAK,MAAK,UAClB,CAAE,cAAY,UAAU,KAAK,gBAC/B,KAAM,MAAK,sBAAsB,EAAkC,IACnE,CAAE,WAAY,EAAG,MAAO,GAC5B,EAAQ,KAAK,CACX,IAAK,GAAI,IAAY,GAAG,GAAG,GAAI,GAAY,GAAI,GAC/C,QACA,WAAY,EAAQ,GACpB,YACG,MAOb,SAAY,UACZ,EAAa,UACb,EAAkB,UACX,OAGK,uBAAsB,EAA4B,EAAmD,CACjH,GAAM,CAAE,MAAK,MAAK,UAAW,EACvB,EAAc,KAAM,GAAc,QACxC,MAAO,OAAM,KAAK,OAAO,QAAQ,QAAQ,KAAK,GAC3C,IAAI,CAAC,EAAG,IAAM,EAAY,GAAK,GAAK,GAAQ,IAC5C,IAAI,CAAC,EAAY,IAAW,EAC3B,aACA,WAED,OAAO,CAAC,EAAK,IAAU,EAAI,WAAa,EAAK,WAAa,EAAM,KA/MhE,MACS,AADT,GACS,qBAAuB,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,IAAK,KAAM,MQPrE,oBAAyB,GAAe,CAC7C,YAAY,EAA8B,GAAM,CAC9C,GAAM,GAAS,CACb,qBACA,aAAc,GACd,QAAS,CAAC,WACN,EACA,CACA,QAAS,GACT,QAAS,IAET,CACA,QAAS,GACT,gBAAiB,KAIvB,MAAM,MAGG,qBAA8B,CACvC,MAAO,MAAK,OAAO,sBAGV,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,MAAK,mBAAqB,GAAoC,GAG7D,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KChDrC,YAA0B,EAAuB,EAA8B,GAAM,CAC1F,GAAM,GAAM,GAAI,IAAW,GAC3B,SAAI,eAAe,GACZ,ECNF,oBAAsC,GAAkB,CAAxD,aAJP,CAIO,oBACK,WAAgB,4BCLrB,YAAwB,MAChB,MAEX,EACY,CACZ,MAAO,GAAY,KAAM,MAAK,YAGnB,MAAkB,CAC7B,KAAM,IAAI,OAAM,6CCRpB,OAAoB,OCDpB,OAAoB,OAOpB,kBACE,EACA,EAEA,EACA,EAEA,EAAwF,CAAC,CAAE,iBAAkB,EAC7G,CACA,GAAM,GAAY,EAAc,IAAI,AAAC,GAAkB,GAAoB,GACvE,EAAoB,GACpB,EAAa,WACX,EAAgD,GACpD,aAAoB,WAChB,KAAM,IAAmB,EAAO,GAChC,KAAM,IAAa,EAAO,IAG1B,EAAU,KAAM,GAAe,GAErC,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAGT,kBACE,EACA,EAEA,EACA,EAEA,EACA,CACA,MAAO,IACL,CAAC,GACD,EACA,KAAO,IAAU,EAAc,EAAM,IACrC,EACA,GC5CG,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAAqC,CAAC,QAAS,QAAS,QCF9D,oBAA+B,GAAe,CACnD,aAAc,CACZ,GAAM,GAAS,CACb,mBAAoB,GACpB,aAAc,GACd,QAAS,CAAC,QACV,QAAS,GACT,QAAS,GACT,mBAAoB,GACpB,YAAa,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,MAGzC,MAAM,MAGG,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,2BAGC,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KCvBrC,GAAM,GAAO,CAClB,eAAgB,GAAI,IACpB,iBAAkB,GAAI,IACtB,WAAY,GAAI,IAChB,kBAAmB,GAAI,IACvB,sBAAuB,GAAI,IAC3B,mBAAoB,GAAI,IACxB,kBAAmB,GAAI,IACvB,aAAc,GAAI,KAUP,GAAiB,CAAC,EAAkB,IAA6D,EAAK,eAAe,YAAY,EAAO,GASxI,GAAmB,CAAC,EAAkB,IAA+D,EAAK,iBAAiB,YAAY,EAAO,GAS9I,GAAa,CAAC,EAAkB,IAA0D,EAAK,WAAW,YAAY,EAAO,GAS7H,GAAsB,AAAC,GAAmE,EAAK,kBAAkB,gBAAgB,GAWjI,GAA0B,AAAC,GAAmE,EAAK,sBAAsB,gBAAgB,GAYzI,GAAwB,AAAC,GAA6D,EAAK,mBAAmB,sBAAsB,GASpI,GAA2B,AAAC,GAAmE,EAAK,kBAAkB,mBAAmB,GASzI,GAAsB,AAAC,GAAiF,EAAK,aAAa,oBAAoB,GAE9I,GAA0B,AAAC,GAAgB,EAAK,eAAe,KAAK,GACpE,GAA4B,AAAC,GAAgB,EAAK,iBAAiB,KAAK,GACxE,GAAsB,AAAC,GAAgB,EAAK,WAAW,KAAK,GAC5D,GAAwB,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACrE,GAA4B,AAAC,GAAgB,EAAK,sBAAsB,KAAK,GAC7E,GAA2B,AAAC,GAAgB,EAAK,mBAAmB,KAAK,GACzE,GAA0B,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACvE,GAAqB,AAAC,GAAgB,EAAK,aAAa,KAAK,GAG7D,GAAyB,GACzB,GAAc,GACd,GAAkB,GCjGxB,oBAAqE,GAAwB,CAClG,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAA0E,MACrE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAwB,KAAM,IAClC,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,KAEtD,KAAK,gBAGP,MAAO,GAAc,IACnB,CAAC,EAAc,IAAM,GAAmC,EAAc,EAAsB,KAIhG,kBAAmB,CACjB,MAAO,IAAI,IAA2B,KAAM,KAAK,SAI9C,gBAEG,GAA8F,MACzF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,GAAkB,KAAM,IAC5B,EACA,KAAK,MACL,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,GACpD,KAAK,gBAGP,MAAO,IAA0B,EAAc,GAGjD,kBAAmB,CACjB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA0C,CAClD,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCjFnD,oBAAkE,GAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAAuE,MAClE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAqB,KAAM,IAC/B,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,aAAa,oBAAoB,KAElD,KAAK,gBAGP,MAAO,GAAc,IAAI,CAAC,EAAc,IAAM,CAC5C,GAAM,CAAE,MAAK,SAAQ,qBAAsB,EAAmB,GAC9D,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,KAIpF,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA2F,MACtF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,MAAK,SAAQ,qBAAsB,KAAM,IAC/C,EACA,KAAK,MACL,AAAC,GAAS,EAAK,aAAa,oBAAoB,GAChD,KAAK,gBAGP,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,GAGlF,qBAAsB,CACpB,MAAO,IAAI,IAAiC,KAAM,KAAK,SAIpD,gBAEG,GAAoC,CAC5C,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCtFnD,oBAAqE,GAAwB,CAClG,YAEY,EAEA,EACV,CACA,QAJU,kBAEA,eAMP,gBAEG,GAAyE,MACpE,MAA8C,CACzD,GAAM,GAAgB,KAAM,MAAK,WAUjC,MAAO,AARa,MAAM,IACxB,EACA,KAAK,MACL,AAAC,GAAU,QAAQ,IAAI,EAAM,IAAI,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,KACzF,KACA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,OAGxD,IAAI,CAAC,EAAY,IAAM,GAAkC,EAAc,GAAI,IAGhG,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,SAI/D,gBAEG,GAA6F,MACxF,MAAwD,CACnE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAEF,GAAM,GAAa,KAAM,IACvB,EACA,KAAK,MACL,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,GACxD,KAEA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,MAG3E,MAAO,IAAyB,EAAc,GAGhD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,SP1DlE,oBAAkE,GAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,6BAKE,cAAyD,CACrE,MAAO,MAAK,mBACR,EAAK,sBACL,EAAK,oBAIN,gBAEG,GAAqE,MAChE,MAA6C,CACxD,GAAM,GAAgB,KAAM,MAAK,WAC3B,EAAa,EAAc,IAAI,AAAC,GAAQ,EAAI,WAE5C,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,GACrC,KAAM,IAAa,KAAK,MAAO,GAE7B,EAAsB,KAAM,SAAQ,IAAI,EAAM,IAClD,AAAC,GAAS,KAAK,YAAY,gBAAgB,KAG7C,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAAc,IAAI,CAAC,EAAc,IAAM,GAAiC,EAAc,EAAoB,KAGnH,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAyF,MACpF,MAAuD,CAClE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,aAAc,EAChB,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,CAAC,IACtC,KAAM,IAAa,KAAK,MAAO,CAAC,IAE9B,EAAY,KAAM,MAAK,YAAY,gBAAgB,EAAM,IAE/D,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,GAAiC,EAAc,GAGxD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SQzFnD,oBAA2C,GAAwB,CACxE,YAEY,EAEA,EAAgC,GAAI,GAC9C,CACA,QAJU,aAEA,iBAMP,gBAAiC,GAAqC,MAC9D,MAAgC,CAC3C,GAAM,CAAE,QAAO,WAAY,KAEvB,EACJ,GAAI,YAAmB,IAAyB,EAAS,EAAK,iBAAiB,YAAY,EAAO,WACzF,YAAmB,GAAuB,EAAS,EAAK,eAAe,YAAY,EAAO,WAC1F,YAAmB,IAAmB,EAAS,EAAK,WAAW,YAAY,EAAO,OACtF,MAAM,IAAI,OAAM,wHAErB,MAAO,GAGD,gCAAmE,CAEzE,MAAO,IAAI,SAAiC,KAAO,IAAY,CAC7D,GAAM,GAAa,KAAM,MAAK,MAC9B,EAAQ,EAAW,IAAI,AAAC,GAAc,GAAwB,GAAI,OAItE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,SAKJ,gBAAmC,GAA+C,MAC1E,MAA0C,CACrD,GAAM,GAAiB,KAAM,IAAI,IAAmB,KAAK,MAAO,KAAK,SACjE,EAAgC,EAAe,GACnD,SAAe,QAAQ,AAAC,GAAkB,CACxC,AAAI,EAAc,MAAQ,EAA8B,OAAO,GAAgC,KAE1F,EAGD,+BAA4E,CAElF,MAAO,IAAI,SAA2C,KAAO,IAAY,CACvE,GAAM,GAAY,KAAM,MAAK,MAC7B,EAAQ,EAAY,GAA4B,GAAI,GAAa,UAIrE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,SCpGJ,YAA0B,EAAkB,EAAgC,GAAI,GAA+C,CACpI,MAAO,IAAI,IAAqB,EAAO,GAGlC,YAAwB,EAAkB,EAAgC,GAAI,GAA6C,CAChI,MAAO,IAAI,IAAmB,EAAO,GCFvC,kBACE,EACA,EACyE,CACzE,MAAO,IAAe,EAAO,GAAI,GAAsB,EAAgB,CAAE,iBAAkB,KACxF,oBACA,sBAGL,kBACE,EACA,EAAoC,GACqC,CACzE,MAAO,IAAe,EAAO,GAAI,IAAkB,IAChD,oBACA,sBAGE,GAAM,IAAW,GC1BjB,YAA2B,EAA+B,EAA+B,CAC9F,GAAI,EAAK,SAAW,EAAK,OAAQ,KAAM,IAAI,OAAM,kDAEjD,GAAM,GAAQ,MAAM,KAAK,GACnB,EAAQ,MAAM,KAAK,GAEzB,MAAO,MAAK,KACV,EACG,IAAI,CAAC,EAAK,IAAM,EAAM,EAAM,IAC5B,OAAO,CAAC,EAAK,IAAS,EAAO,GAAQ,EAAI,ICJzC,YAAkB,CAKvB,YACE,EACA,EAA4B,GAC5B,CACA,KAAK,mBAAqB,EAE1B,GAAM,GAAa,MAAM,QAAQ,GAAU,EAAS,CAAC,GAErD,GAAI,CAAC,EAAW,OACd,KAAM,IAAI,OAAM,2DAGlB,GAAI,GAAQ,EACN,EAAoB,IAAM,UAAU,MAE1C,KAAK,oBAAsB,EAAW,IAAI,AAAC,GAAS,CAClD,GAAI,YAAgB,IAClB,MAAO,GAGT,GAAI,YAAgB,cAClB,MAAO,IAAI,IAAuB,IAAqB,CAAC,IAG1D,GAAI,EAAK,YAAc,EAAK,qBAAsB,cAChD,MAAO,IAAI,IAAuB,IAAqB,CAAC,EAAK,aAG/D,KAAM,IAAI,OAAM,4MAIT,qBAA+C,CAAE,MAAO,MAAK,uBAE7D,oBAA4B,CAAE,MAAO,MAAK,mBAE9C,oBAAoB,EAA+B,EAAqC,CAC7F,MAAO,GACJ,IAAI,AAAC,GAAM,GAAkB,EAAG,IAChC,OAAO,CAAC,EAAI,IAAO,EAAK,EAAI,GACxB,GAAY,QAAU,GAGxB,gBAAgB,EAA0C,CAC/D,MAAO,MAAK,mBACT,IAAI,CAAC,CAAE,cAAa,WAAY,GAAI,IACnC,EACA,KAAK,oBAAoB,EAAiB,KAE3C,OAAO,CAAC,EAAM,IAAU,EAAK,SAAW,EAAK,SAAW,EAAO,GAG7D,cAAc,EAA0C,CAC7D,GAAM,GAAY,KAAK,gBAAgB,GACvC,MAAO,GAAU,SAAW,KAAK,kBAC7B,EACA,GAAI,IAAU,UAAW,EAAU,UAGlC,QAAc,CACnB,MAAO,CACL,kBAAmB,KAAK,kBACxB,mBAAoB,KAAK,mBAAmB,IAAI,AAAC,GAAO,EAAG,iBAIjD,UAAS,EAAwB,CAC7C,GAAM,GAAqB,EAAK,mBAC7B,IAAI,AAAC,GAAY,GAAuB,SAAS,IACpD,MAAO,IAAI,IAAY,EAAoB,EAAK,qBC1E7C,YAAgC,EAAuB,CAC5D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECFF,YAA0B,EAAY,EAA4B,CACvE,GAAM,CAAE,QAAO,UAAW,GAAI,GAAW,EAAW,MAAO,EAAW,QAEtE,GAAI,GAAS,GAAK,GAAU,EAC1B,KAAM,IAAI,OAAM,uCAAuC,KAAK,UAAU,CAAE,QAAO,cAGjF,GAAI,MAAM,QAAQ,GAEhB,MAAQ,GAAuB,IAAI,AAAC,GAAQ,GAAc,EAAK,CAAE,QAAO,YAG1E,GAAI,GAAoB,GAAU,CAChC,GAAM,GAAmB,EAAQ,UAAU,QAAQ,EAAO,GACpD,EAAmB,EAAQ,mBAAmB,QAAQ,EAAiB,IAAI,MAAO,EAAiB,IAAI,QAC7G,MAAO,IAAwB,GAAwB,EAAS,GAAmB,GAGrF,MAAI,IAAoB,GACf,GAAwB,EAAS,EAAQ,UAAU,QAAQ,EAAO,IAGvE,YAAmB,IAAiB,YAAmB,GACjD,EAAgB,QAAQ,EAAO,GAGlC,EjJRT,GAAM,IAAQ,MAAO,UAAY,YAC3B,GAAW,MAAO,YAAc,aAAiB,MAAO,WAAU,WAAc,YACzE,GAAU,CAAE,QAAa,GAAmB,QAAM", + "mappings": ";;;;;;;4qCAAA,GAAA,IAGA,GAAA,GAAc,GAAA,qCCHd,oDAAO,aAA6B,CAClC,MAAO,OAAO,SAAW,UACpB,IACA,MAAO,KAAW,aAGlB,MAAO,UAAY,aAAe,CAAC,CAAC,QAAQ,WCNnD,uyFAAA,OAAoB,OCApB,qRCEO,YACL,EACA,EACA,EAAoB,GACpB,CASA,GARA,EAAI,YAEJ,EAAO,MAAM,GAAG,QAAQ,CAAC,CAAE,IAAG,KAAK,IAAY,CAC7C,GAAM,GAAO,EAAO,GACpB,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,KAGZ,EAAU,CACZ,GAAM,GAAO,EAAO,EAAO,OAAS,GAC9B,EAAK,EAAO,GAClB,GAAI,CAAC,GAAQ,CAAC,EACZ,OAGF,EAAI,OAAO,EAAK,EAAG,EAAK,GACxB,EAAI,OAAO,EAAG,EAAG,EAAG,GAGtB,EAAI,SC1BN,2RAAA,OAAoB,OCOb,WAAwC,CAK7C,YAAY,EAAe,EAAgB,CACzC,GAAI,CAAC,GAAc,IAAU,CAAC,GAAc,GAC1C,KAAM,IAAI,OAAM,wFAAwF,KAAK,UAAU,CAAE,QAAO,cAGlI,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,QAEnC,SAAsB,CAC3B,MAAO,IAAI,GAAW,EAAI,KAAK,MAAO,EAAI,KAAK,UDrB5C,YAAkB,EAAa,EAAa,CACjD,MAAO,aAAqB,YAAU,EAAO,MAAM,SAAW,EAGzD,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,WAAoB,EAAoC,CAC7D,MAAO,IAAS,EAAQ,GAGnB,YAAiB,EAAa,CACnC,MAAO,GAAM,GAAM,EAGd,YAAgB,EAAa,CAClC,MAAO,GAAM,GAAM,EAGd,YAAe,EAAa,EAAe,EAAG,CACnD,GAAM,GAAI,IAAM,EAChB,MAAO,MAAK,MAAM,EAAM,GAAK,EAGxB,YAAsB,EAAmB,CAC9C,MAAO,IAAO,EAAI,OAAS,EAAI,OAG1B,YAAmC,CAAE,QAAO,UAAuB,EAAmB,CAC3F,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,IAAI,GAAW,KAAK,MAAM,EAAQ,GAAQ,KAAK,MAAM,EAAS,IAGhE,YAAwB,EAAqB,CAClD,MAAO,GAAI,OAAO,CAAC,EAAK,IAAO,EAAI,IAAI,GAAK,GAAI,GAAM,EAAG,IACtD,IAAI,GAAI,GAAM,EAAI,OAAQ,EAAI,SAG5B,YAAe,EAAa,EAAe,EAAwB,CACxE,MAAO,OAAM,GAAK,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,EAAS,EAAI,GAGhD,YAAuB,EAAU,CACtC,MAAO,CAAC,CAAC,GAAQ,IAAQ,UAAc,IAAQ,WAAc,CAAC,OAAO,MAAM,IAAQ,IAAQ,EAGtF,YAA4B,EAAU,CAC3C,MAAO,IAAc,IAAQ,GAAO,GAAK,GAAO,EExD3C,WAA8B,CAKnC,YAAY,EAAW,EAAW,CAChC,KAAK,GAAK,EACV,KAAK,GAAK,KAGR,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,GAEvB,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,IAAI,EAAmB,CAC5B,MAAO,IAAI,GAAM,KAAK,EAAI,EAAG,EAAG,KAAK,EAAI,EAAG,GAGvC,KAAa,CAClB,MAAO,IAAI,GAAM,KAAK,IAAI,KAAK,GAAI,KAAK,IAAI,KAAK,IAG5C,WAAoB,CACzB,MAAO,MAAK,KAAM,KAAK,GAAK,EAAM,KAAK,GAAK,GAGvC,OAAe,CACpB,MAAO,IAAI,GAAM,KAAK,MAAM,KAAK,GAAI,KAAK,MAAM,KAAK,MCtClD,WAAwD,OAC/C,QAAO,EAAoB,CACvC,MAAO,CAAC,CAAC,GAAQ,CAAC,EAAK,EAAG,EAAK,EAAG,EAAK,MAAO,EAAK,QAAQ,MAAM,UAGrD,kBAAiB,EAAU,EAAgB,EAAmC,GAAO,CACjG,GAAI,CAAC,EAAI,OAAO,GACd,KAAM,IAAI,OAAM,GAAG,oBAAyB,KAAK,UAAU,2DAG7D,GAAI,CAAC,GAA4B,GAAI,MAAQ,GAAK,EAAI,OAAS,GAC7D,KAAM,IAAI,OAAM,GAAG,cAAmB,EAAI,sBAAsB,EAAI,oCAYxE,YAAY,EAA4B,EAAmC,GAAM,CAC/E,GAAM,GAAO,GAAQ,GAEf,EAAS,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAO,EAAI,QAAQ,MAAM,IAC1D,EAAS,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAAQ,MAAM,IAE3D,GAAI,CAAC,GAAU,CAAC,EACd,KAAM,IAAI,OAAM,2EAA2E,KAAK,UAAU,MAG5G,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,EAC1B,CAAC,EAAI,EAAG,EAAI,EAAG,EAAI,MAAO,EAAI,QAC9B,CAAC,EAAI,KAAM,EAAI,IAAK,EAAI,MAAQ,EAAI,KAAM,EAAI,OAAS,EAAI,KAE/D,EAAI,iBAAiB,CACnB,IAAG,IAAG,QAAO,UACZ,kBAAmB,GAEtB,KAAK,GAAK,EACV,KAAK,GAAK,EACV,KAAK,OAAS,EACd,KAAK,QAAU,KAGN,IAAY,CAAE,MAAO,MAAK,MAE1B,IAAY,CAAE,MAAO,MAAK,MAE1B,QAAgB,CAAE,MAAO,MAAK,UAE9B,SAAiB,CAAE,MAAO,MAAK,WAE/B,OAAe,CAAE,MAAO,MAAK,KAE7B,MAAc,CAAE,MAAO,MAAK,KAE5B,QAAgB,CAAE,MAAO,MAAK,EAAI,KAAK,SAEvC,SAAiB,CAAE,MAAO,MAAK,EAAI,KAAK,UAExC,OAAe,CAAE,MAAO,MAAK,MAAQ,KAAK,UAE1C,UAAiB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,QAEpD,WAAkB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAEtD,aAAoB,CAAE,MAAO,IAAI,GAAM,KAAK,KAAM,KAAK,WAEvD,cAAqB,CAAE,MAAO,IAAI,GAAM,KAAK,MAAO,KAAK,QAE7D,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,OAAsB,CAC3B,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAAC,KAAK,EAAG,KAAK,EAAG,KAAK,MAAO,KAAK,QAC7D,IAAI,AAAC,GAAQ,KAAK,MAAM,IAC3B,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,UAAyB,CAC9B,GAAI,CACF,IAAG,IAAG,QAAO,UACX,KACE,EAAO,KAAK,IAAI,EAAQ,GAC9B,MAAI,GAAQ,GACV,IAAM,EAAO,EACb,GAAS,GAEP,EAAS,GACX,IAAM,EAAO,EACb,GAAU,GAGL,GAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,QAAQ,EAAuC,CACpD,GAAM,GAAS,GAAa,GAAM,EAAkB,MAAQ,EACtD,EAAS,GAAa,GAAM,EAAkB,OAAS,EAC7D,MAAO,IAAI,GAAI,CACb,EAAG,KAAK,EAAI,EACZ,EAAG,KAAK,EAAI,EACZ,MAAO,KAAK,MAAQ,EACpB,OAAQ,KAAK,OAAS,IAInB,IAAI,EAAc,EAA4B,CACnD,GAAM,CAAC,EAAG,EAAG,EAAO,GAAU,CAC5B,KAAK,EAAK,EAAO,EACjB,KAAK,EAAK,EAAO,EACjB,KAAK,MAAQ,EACb,KAAK,OAAS,GAEhB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,mBAAmB,EAAkB,EAAiC,CAC3E,GAAM,CACJ,IAAG,IAAG,QAAO,UACX,KACE,EAAW,KAAK,IAAI,EAAG,GACvB,EAAW,KAAK,IAAI,EAAG,GAEvB,EAAW,EAAQ,EACnB,EAAY,EAAS,EACrB,EAAe,KAAK,IAAI,EAAU,EAAW,GAC7C,EAAgB,KAAK,IAAI,EAAW,EAAY,GAEtD,MAAQ,IAAI,GAAI,CACd,EAAG,EAAU,EAAG,EAAU,MAAO,EAAc,OAAQ,IACrD,QAGC,MAAM,EAAY,EAA0B,CACjD,GAAM,CAAE,QAAO,UAAW,KACpB,EAAI,KAAK,EAAI,EACb,EAAI,KAAK,EAAI,EAEnB,MAAO,IAAI,GAAI,CACb,IAAG,IAAG,QAAO,WAIV,aAAa,EAAqB,EAAoB,CAC3D,GAAM,GAAI,KAAK,MAAQ,EACjB,EAAI,KAAK,OAAS,EAElB,EAAK,EACL,EAAK,EACP,EAAM,EACN,EAAM,EAEN,EAAI,KAAK,KACT,EAAI,KAAK,IACT,EAAK,KAAK,MACV,EAAK,KAAK,OAEd,MAAI,GAAK,GACP,GAAM,CAAC,EAAK,EAAa,EACzB,EAAK,GAEH,EAAK,GACP,GAAM,CAAC,EAAK,EAAc,EAC1B,EAAK,GAEH,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAEF,EAAI,GACN,GAAM,EAAI,EACV,EAAI,GAGC,CACL,KAAI,MAAK,KAAI,MAAK,IAAG,KAAI,IAAG,KAAI,IAAG,KAIhC,UAAU,EAAa,CAC5B,MAAO,IAAI,GAAI,CACb,KAAM,KAAK,KAAQ,EAAO,KAAO,KAAK,MACtC,IAAK,KAAK,IAAO,EAAO,IAAM,KAAK,OACnC,MAAO,KAAK,MAAS,EAAO,MAAQ,KAAK,MACzC,OAAQ,KAAK,OAAU,EAAO,OAAS,KAAK,SAC3C,WAAW,UCrMX,oBAA0B,EAAyC,CACxE,YAAY,EAAc,EAAa,EAAe,EAAgB,EAAmC,GAAO,CAC9G,MAAM,CACJ,OAAM,MAAK,QAAO,UACjB,KCTA,YAAsB,CAW3B,YACE,EACA,EACA,EACA,EACA,EACA,CACA,KAAK,WAAa,GAAI,GAAW,EAAU,MAAO,EAAU,QAC5D,KAAK,OAAS,EACd,KAAK,YAAc,EACnB,KAAK,WAAa,EAClB,KAAK,KAAO,GAAI,GAAI,GAAa,QAAQ,KAAK,eAGrC,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,eAEnC,YAAoB,CAAE,MAAO,MAAK,cAElC,MAAW,CAAE,MAAO,MAAK,QAEzB,YAAwB,CAAE,MAAO,MAAK,cAEtC,aAAqB,CAAE,MAAO,MAAK,UAAU,SAE7C,cAAsB,CAAE,MAAO,MAAK,UAAU,UAE9C,cAAmB,CAAE,MAAO,IAAI,GAAI,KAAK,MAAM,QAAQ,KAAK,UAAU,WAE1E,QAAQ,EAAe,EAAiC,CAC7D,MAAO,IAAI,IACT,KAAK,MACL,KAAK,WACL,KAAK,UACL,KAAK,YACL,CAAE,QAAO,aCzCR,mBAA4B,GAAyC,CAC1E,YACE,EACA,EACA,EACA,CACA,MAAM,EAAO,EAAO,GAAI,EAAa,GAGhC,QAAQ,EAAe,EAA+B,CAC3D,GAAM,CAAE,QAAO,cAAa,aAAc,MAAM,QAAQ,EAAO,GAC/D,MAAO,IAAI,GAAc,EAAO,EAAa,KCnB1C,YAAa,EAAW,EAAW,EAAiB,GAAM,CAC/D,GAAM,GAAQ,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,MAAO,EAAK,OAAS,KAAK,IAAI,EAAK,KAAM,EAAK,OAClF,EAAS,KAAK,IAAI,EAAK,KAAK,IAAI,EAAK,OAAQ,EAAK,QAAU,KAAK,IAAI,EAAK,IAAK,EAAK,MACpF,EAAe,EAAQ,EAE7B,MAAO,GACH,EAAgB,GAAK,KAAO,EAAK,KAAO,GACxC,EAAe,KAAK,IAAI,EAAK,KAAM,EAAK,MCPvC,YAAiB,EAA4B,CAClD,GAAM,GAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAK,EAAI,IAAI,AAAC,GAAO,EAAG,GACxB,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAI,EAAM,EAAI,EAAM,UAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAClD,EAAO,EAAG,OAAO,CAAC,EAAK,IAAO,EAAM,EAAI,EAAI,EAAM,GAExD,MAAO,IAAI,IAAY,EAAM,EAAM,EAAM,GCPpC,YACL,EACA,EACA,EACA,EAAiB,GACP,CACV,GAAI,GAAuB,EACxB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAC/B,IAAI,AAAC,GAAM,EAAE,UAEV,EAAiB,GAEvB,KAAO,EAAqB,OAAS,GAAG,CACtC,GAAM,GAAO,EAAqB,MAClC,EAAK,KAAK,GAEV,GAAM,GAAU,EAEV,EAAoB,GAC1B,OAAS,GAAI,EAAG,EAAI,EAAQ,OAAQ,IAAK,CACvC,GAAM,GAAM,EAAQ,GAEd,EAAU,EAAM,GAChB,EAAS,EAAM,GAErB,EAAQ,KAAK,GAAI,EAAS,EAAQ,IAGpC,EAAuB,EAAqB,OAC1C,CAAC,EAAG,IAAM,EAAQ,IAAM,GAI5B,MAAO,GCrCT,OAAoB,OAEb,WAAmB,EAAgB,EAAgC,CACxE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAG,EAAG,GAAK,EACZ,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAQ,AAAG,QAAK,CAAC,GAAG,EAAE,MAAM,MAAM,EAAG,GAAI,GAAI,EAAG,WAChD,EAAU,AAAG,UAAO,CAAC,EAAO,EAAO,GAAQ,GAEjD,MAAO,AAAG,QAAI,EAAG,KCVrB,OAAoB,OAUb,YACL,EACA,EAAyB,GACZ,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAQ,GAAS,EAAU,MAAM,MAAM,GAC9C,GAAI,IAAW,EACb,MAAO,GAGT,GAAM,GAAU,KAAK,IAAI,EAAS,GAC5B,EAAgB,KAAK,MAAM,EAAW,GAAgB,GAAM,IAC5D,EAAc,EAAS,EAAQ,EAAI,EAEnC,EAAsB,AAAC,GAA0C,CACrE,GAAM,GAAqB,EAAU,MAAM,QAC3C,SAAmB,GAAe,EAC3B,AAAG,QAAK,EAAoB,EAAG,YAGlC,EAAsB,EAAoB,GAC1C,EAAyB,EAAW,EAAoB,MAAM,GAM9D,EAAiB,CAJM,GAAiB,EAC1C,EAAoB,GACpB,KAIF,EACA,GAEC,OAAO,AAAC,GAAM,CAAC,CAAC,GAEhB,IAAI,AAAC,GAAiB,AAAG,QAAK,EAAG,YACpC,MAAO,AAAG,WAAO,EAAgB,KC7C9B,YAAsB,EAAmB,CAC9C,GAAM,GAAQ,EAAW,QACzB,OAAS,GAAI,EAAM,OAAS,EAAG,EAAI,EAAG,IAAK,CACzC,GAAM,GAAI,KAAK,MAAM,KAAK,SAAY,GAAI,IACpC,EAAI,EAAM,GAChB,EAAM,GAAK,EAAM,GACjB,EAAM,GAAK,EAEb,MAAO,GCDF,YAAiB,EAAW,CACjC,MAAO,GAAK,GAAI,KAAK,IAAI,CAAC,IAGrB,YAAwB,EAAW,CACxC,MAAO,MAAK,IAAI,EAAK,GAAI,ICHpB,oBAAmB,EAA2B,CACnD,YAAY,EAAW,EAAW,EAAe,EAAgB,EAAmC,GAAO,CACzG,MAAM,CACJ,IAAG,IAAG,QAAO,UACZ,KCHP,GAAM,IAAO,GACP,GAAO,IACP,GAAW,IAOV,OAA8C,CAOnD,YACE,EACA,EACA,EAAe,GAAI,GAAM,EAAG,GAC5B,CACA,GAAM,CAAE,QAAO,UAAW,EAC1B,KAAK,SAAW,GAAI,GAAW,EAAO,GACtC,KAAK,OAAS,EACd,KAAK,WAAa,EAA8B,IAC9C,AAAC,GAAO,EAAG,IAAI,GAAI,GAAM,EAAO,IAAS,IAAI,OAItC,QAAe,CAAE,MAAO,IAAI,GAAM,KAAK,OAAO,EAAG,KAAK,OAAO,MAE7D,aAAqB,CAAE,MAAO,MAAK,SAAS,SAE5C,cAAsB,CAAE,MAAO,MAAK,SAAS,UAE7C,YAAqB,CAAE,MAAO,MAAK,cAEnC,oBAA6B,CACtC,MAAO,MAAK,WAAW,IACrB,AAAC,GAAO,EAAG,IAAI,KAAK,QAAQ,IAAI,GAAI,GAAM,KAAK,WAAY,KAAK,eAI7D,QAAiC,EAAe,EAAmB,CACxE,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,CAAE,QAAO,WAIN,QAAiC,EAAW,EAAc,CAC/D,MAAO,IAAK,MAAK,YACf,KAAK,kBACL,KAAK,SACL,GAAI,GAAM,EAAG,IAIV,aAAsC,EAAc,CACzD,MAAO,MAAK,QAAQ,EAAG,EAAG,EAAG,GAcxB,MACL,EACA,EAAkE,GAC7D,CACL,GAAI,EAAW,CACb,GAAM,GAAM,YAAqB,GAC7B,EAAU,IAAI,QACd,GAAI,GAAI,GAEZ,MAAO,MAAK,QAAQ,EAAI,EAAG,EAAI,GAAG,MAAM,KAAM,GAGhD,GAAM,CAAE,mBAAkB,iBAAkB,CAAE,iBAAkB,GAAO,cAAe,MAAQ,GAE9F,MAAI,GACK,KAAK,YAGP,KAAK,aAAa,GAGnB,WAAiB,CACvB,GAAM,GAAU,KAAK,2BAEf,CAAC,EAAe,EAAgB,GAAe,EAC/C,EAAc,AAAC,GAAc,EAAY,IAAI,GAAI,YACjD,EAAkB,GAAY,GAAiB,EAAY,IAAmB,EAE9E,EAAO,KAAK,MAAM,EAAiB,IAEnC,EAAW,GAAe,GAE1B,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAChD,EAAI,KAAK,MAAM,KAAK,IAAI,EAAG,EAAS,EAAK,GAAO,IAEtD,MAAO,IAAI,IAAK,EAAG,EAAG,KAAK,IAAI,EAAM,KAAK,WAAa,GAAI,KAAK,IAAI,EAAM,KAAK,YAAc,IAGvF,aAAa,EAAsB,CACzC,GAAM,GAAM,GAAQ,KAAK,WACzB,MAAO,GAAI,IAAI,EAAI,MAAQ,EAAS,EAAI,OAAS,GAGzC,0BAAoC,CAC5C,KAAM,IAAI,OAAM,4DC3Hb,oBAA6B,EAAc,CACtC,0BAAoC,CAC5C,GAAM,GAAM,KAAK,UACjB,MAAO,CACL,EAAI,GACJ,EAAI,GACJ,GAAe,CAAC,EAAI,GAAI,EAAI,QCN3B,oBAA8B,EAAc,CAC1C,eAAyB,CAC9B,MAAO,MAAK,UAAU,MAAM,EAAG,IAG1B,gBAA0B,CAC/B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,iBAA2B,CAChC,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,SAAmB,CACxB,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,YAAsB,CAC3B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,aAAuB,CAC5B,MAAO,MAAK,UAAU,MAAM,GAAI,IAG3B,UAAoB,CACzB,MAAO,MAAK,UAAU,MAAM,GAAI,IAGxB,0BAAoC,CAC5C,MAAO,CACL,KAAK,aACL,KAAK,cACL,KAAK,YACL,IAAI,MC/BH,YAAsC,CAK3C,YAAY,EAAe,EAAkB,CAC3C,KAAK,OAAS,EACd,KAAK,UAAY,KAGR,QAAgB,CAAE,MAAO,MAAK,UAE9B,WAAmB,CAAE,MAAO,MAAK,UAErC,SAAS,EAAwB,GAAc,CACpD,MAAO,GAAG,KAAK,QAAQ,EAAe,KAAK,GAAM,KAAK,aAAe,OCjBlE,oBAAyB,EAAgB,OAChC,yBAAwB,EAAU,EAAgB,CAG9D,GAFA,EAAI,iBAAiB,EAAK,GAEtB,CAAC,GAAc,EAAI,OACrB,KAAM,IAAI,OAAM,GAAG,gCAAqC,EAAI,yBAMhE,YAAY,EAAiC,EAAe,CAC1D,MAAM,GACN,KAAK,OAAS,KAGL,QAAgB,CAAE,MAAO,MAAK,SCrBpC,YAA6B,CAKlC,YAAY,EAAe,EAA6B,CACtD,GAAM,MAAO,IAAU,SACrB,KAAM,IAAI,OAAM,sEAGlB,GAAI,CAAC,MAAM,QAAQ,IAAgB,EAAY,KAAK,AAAC,GAAS,CAAE,aAAgB,gBAC9E,KAAM,IAAI,OAAM,4FAGlB,KAAK,OAAS,EACd,KAAK,aAAe,KAGX,QAAgB,CAAE,MAAO,MAAK,UAE9B,cAA8B,CAAE,MAAO,MAAK,aAEhD,QAAc,CACnB,MAAO,CACL,MAAO,KAAK,MACZ,YAAa,KAAK,YAAY,IAAI,AAAC,GAAM,MAAM,KAAK,WAI1C,UAAS,EAAmC,CACxD,GAAM,GAAc,EAAK,YAAY,IAAI,AAAC,GAAW,GAAI,cAAa,IACtE,MAAO,IAAI,IAAuB,EAAK,MAAO,KC1B3C,oBAA2B,GAAW,OAC7B,2BAA0B,EAAU,EAAgB,CAGhE,GAFA,GAAW,wBAAwB,EAAK,GAGtC,CAAC,GAAmB,EAAI,QACrB,CAAC,GAAmB,EAAI,YAE3B,KAAM,IAAI,OAAM,GAAG,kCAAuC,EAAI,eAAe,EAAI,6CAQrF,YAAY,EAAiC,EAAe,EAAe,EAAoB,CAC7F,MAAM,EAAK,GACX,KAAK,OAAS,EACd,KAAK,YAAc,KAGV,QAAgB,CAAE,MAAO,MAAK,UAE9B,aAAqB,CAAE,MAAO,MAAK,cCvBzC,YAA6B,EAAwC,CAC1E,MAAO,GAAI,oBAAqB,GAG3B,YAGL,EACA,EAC4B,CAE5B,MAAO,IAAK,KADM,CAAE,cCdf,aAAyC,CAC9C,GAAM,GAAQ,OAAO,MACrB,GAAI,CAAC,EAAO,KAAM,IAAI,OAAM,gEAM5B,MAAO,CACL,OAAQ,kBACR,yBACA,MAAO,iBACP,UACA,MAAO,iBACP,oBAAqB,IAAM,SAAS,cAAc,UAClD,mBAAoB,IAAM,SAAS,cAAc,OACjD,QACA,SAbe,IAAM,CACrB,KAAM,IAAI,OAAM,iECLb,YAA0B,EAAsB,CACrD,GAAI,GAAiB,GAErB,GAAI,CAAC,EACH,GAAI,CAEF,EAAa,oBACN,EAAP,CACA,EAAiB,EAAI,WAYzB,MAAO,CACL,SATe,EACb,AAAC,GAAqB,GAAI,SAAgB,CAAC,EAAS,IAAW,CAC/D,EAAG,SAAS,EAAU,CAAC,EAAU,IAAoB,EAAM,EAAO,GAAO,EAAQ,MAEjF,IAAM,CACN,KAAM,IAAI,OAAM,qEAAqE,OCfpF,aAAwC,CAE7C,GAAM,GAAS,OAAO,QAAa,OAAO,kBACpC,EAAQ,OAAO,OAAS,OAAO,iBAE/B,EAAsB,IAAM,CAChC,GAAI,EAAQ,MAAO,IAAI,GACvB,KAAM,IAAI,OAAM,+EAGZ,EAAqB,IAAM,CAC/B,GAAI,EAAO,MAAO,IAAI,GACtB,KAAM,IAAI,OAAM,6EAGZ,EAAQ,OAAO,MAGf,EAAa,KAEnB,MAAO,CACL,OAAQ,GAAU,KAAM,GACxB,yBAA0B,OAAO,0BAA4B,KAAM,GACnE,MAAO,GAAS,KAAM,GACtB,UAAW,OAAO,WAAa,KAAM,GACrC,MAAO,OAAO,kBAAoB,KAAM,GACxC,sBACA,qBACA,WACG,GCjCA,aAA8B,CACnC,MAAO,OAAO,SAAW,UACpB,MAAO,WAAa,aACpB,MAAO,mBAAqB,aAC5B,MAAO,oBAAsB,aAC7B,MAAO,mBAAqB,aAC5B,MAAO,YAAc,aACrB,MAAO,2BAA6B,YCH3C,OAAyB,QAGrB,EAEJ,aAA+B,CAC7B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,yEAElB,MAAO,GAGT,YAAgB,EAAkB,CAChC,EAAc,EAGhB,aAAsB,CAGpB,MAAI,MAAoB,GAAO,MAC3B,kBAAmB,GAAO,MACvB,KAGT,YAAqB,EAA2B,CAK9C,GAJK,GACH,KAGE,CAAC,EACH,KAAM,IAAI,OAAM,8EAGlB,GAAM,CAAE,SAAS,EAAY,OAAQ,QAAQ,EAAY,OAAU,EACnE,EAAY,OAAS,EACrB,EAAY,MAAQ,EACpB,EAAY,oBAAsB,EAAI,qBAAwB,KAAM,GAAI,IACxE,EAAY,mBAAqB,EAAI,oBAAuB,KAAM,GAAI,IAEtE,EAAY,UAAY,EAAI,WAAa,EAAY,UACrD,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,MAAQ,EAAI,OAAS,EAAY,MAC7C,EAAY,SAAW,EAAI,UAAY,EAAY,SAG9C,GAAM,GAAM,CACjB,UACA,UACA,cACA,oBACA,oBACA,mBACA,eACA,aACA,sBAGF,KC3DO,YAAsB,EAAmB,CAC9C,MAAI,CAAC,EAAI,YAAc,MAAO,IAAQ,SAC7B,SAAS,eAAe,GAE1B,ECHF,WAA6B,EAA4F,CAC9H,GAAM,CAAE,SAAQ,4BAA6B,EAAI,SAEjD,GAAI,YAAqB,GACvB,MAAO,GAGT,GAAM,GAAS,GAAa,GAE5B,GAAI,CAAE,aAAkB,IACtB,KAAM,IAAI,OAAM,kEAGlB,GAAM,GAAM,EAAO,WAAW,MAC9B,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,GCfF,GAAK,IAAL,UAAK,EAAL,CAEL,WAAW,WAEX,YAAY,YAEZ,cAAc,cAEd,eAAe,iBARL,aAoBL,YAA4D,CAajE,YAAY,EAAiC,GAAI,CAC/C,GAAM,CACJ,iBAAgB,kBAAiB,YAAW,WAAU,YAAW,WAC/D,EACJ,KAAK,eAAiB,GAAkB,GAAe,SACvD,KAAK,gBAAkB,GAAmB,qBAC1C,KAAK,UAAY,GAAa,yBAC9B,KAAK,SAAW,GAAY,GAC5B,KAAK,UAAY,GAAa,UAC9B,KAAK,QAAU,GAAW,IAIvB,QAAoB,CAOzB,YACE,EACA,EACA,EAAiC,GACjC,CAEA,KAAK,KAAO,MAAO,IAAS,SACxB,CAAC,GACA,YAAgB,IAAgB,EAAK,KAAO,EACjD,KAAK,OAAS,EACd,KAAK,QAAU,GAAI,IAAqB,GAG1C,aAAa,EAAuC,CAClD,GAAM,CAAE,WAAY,KAAK,QACzB,MAAO,MAAK,KAAK,IAAI,AAAC,GAAM,EAAI,YAAY,GAAG,OAAO,OAAO,CAAC,EAAI,IAAQ,EAAK,EAAK,EAAK,EAAK,GAAM,EAAI,EAG1G,eAAwB,CACtB,GAAM,CAAE,WAAU,WAAY,KAAK,QACnC,MAAO,MAAK,KAAK,OAAS,EAAY,EAAI,EAG5C,aAAa,EAA+B,EAAkC,CAC5E,GAAM,CAAE,kBAAmB,KAAK,QAC1B,EAAc,IAAmB,GAAe,cAAgB,IAAmB,GAAe,UAClG,EAAa,IAAmB,GAAe,aAAe,IAAmB,GAAe,aAEhG,EAAiB,KAAK,aAAa,GACnC,EAAkB,KAAK,gBACvB,EAAK,EAAc,KAAK,OAAO,EAAI,EAAiB,KAAK,OAAO,EAChE,EAAI,EAAa,KAAK,OAAO,EAAI,EAAkB,KAAK,OAAO,EAGrE,GAAI,EAAY,CACd,GAAM,CAAE,QAAO,UAAW,EACpB,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAQ,GAAiB,GACrD,EAAO,KAAK,IAAI,KAAK,IAAI,EAAG,EAAS,GAAkB,GAC7D,MAAO,CAAE,EAAG,EAAM,EAAG,GAEvB,MAAO,CAAE,IAAG,KAGd,KAAK,EAAkE,CACrE,GAAM,GAAS,GAAa,GACtB,EAAM,EAAoB,GAE1B,CACJ,kBAAiB,YAAW,WAAU,YAAW,WAC/C,KAAK,QAET,EAAI,KAAO,GAAG,OAAc,IAC5B,GAAM,GAAe,KAAK,aAAa,GACjC,EAAa,KAAK,gBAExB,EAAI,UAAY,EAChB,GAAM,GAAY,KAAK,aAAa,EAAK,GACzC,EAAI,SAAS,EAAU,EAAG,EAAU,EAAG,EAAc,GAErD,EAAI,UAAY,EAChB,KAAK,KAAK,QAAQ,CAAC,EAAU,IAAM,CACjC,GAAM,GAAI,EAAU,EAAU,EACxB,EAAI,EAAU,EAAU,EAAM,GAAI,GAAK,EAC7C,EAAI,SAAS,EAAU,EAAG,OC5GzB,YAAqB,CAS1B,YAAY,EAA2B,GAAI,CACzC,GAAM,CACJ,WAAU,YAAW,QAAO,oBAC1B,EACJ,KAAK,SAAW,GAAY,qBAC5B,KAAK,UAAY,GAAa,EAC9B,KAAK,MAAQ,EAEb,GAAM,GAA0B,CAC9B,eAAgB,GAAe,YAC/B,gBAAiB,KAAK,UAExB,KAAK,iBAAmB,GAAI,IAAqB,IAAK,KAA4B,MAI/E,QAAc,CAKnB,YACE,EACA,EAA2B,GAC3B,CACA,KAAK,IAAM,GAAI,GAAI,GACnB,KAAK,QAAU,GAAI,IAAe,GAGpC,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CAAE,WAAU,aAAc,KAAK,QAE/B,CACJ,IAAG,IAAG,QAAO,UACX,KAAK,IACT,EAAI,YAAc,EAClB,EAAI,UAAY,EAChB,EAAI,WAAW,EAAG,EAAG,EAAO,GAE5B,GAAM,CAAE,SAAU,KAAK,QACvB,AAAI,GACF,GAAI,IAAc,CAAC,GAAQ,CAAE,EAAG,EAAK,EAAY,EAAI,KAAK,KAAK,QAAQ,kBAAkB,KAAK,KC1D7F,YACL,EACA,EACA,CAGA,AAFwB,OAAM,QAAQ,GAAc,EAAa,CAAC,IAElD,QAAQ,AAAC,GAAQ,CAE/B,GAAM,GAAQ,YAAe,GACzB,EAAI,MACH,GAAoB,GAAO,EAAI,UAAU,MAAQ,OAGhD,EAAM,YAAe,GACvB,EAAI,IACH,GAAoB,GAAO,EAAI,UAAU,IAAM,GAAI,GAAI,GAEtD,EAAQ,EAAQ,GAAG,GAAM,KAAW,OAC1C,GAAI,IAAQ,EAAK,CAAE,UAAS,KAAK,KC1BrC,OAAoB,OCEb,YAAuB,EAAsD,CAClF,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAQ,aAAiB,IAAS,EAAM,UAClC,YAAiB,IAAS,EAAM,YAAc,ECH/C,YAA0B,EAAgE,CAE/F,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,GAAI,YAAiB,GAAI,SAAS,QAAU,GAAc,GACxD,MAAO,GAAQ,MAGjB,WAAiB,EAAU,CACzB,AAAI,CAAC,EAAE,eAEP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAO,IAGT,WAAgB,EAAU,CACxB,AAAI,CAAC,EAAE,eACP,GAAE,cAAc,oBAAoB,OAAQ,GAC5C,EAAE,cAAc,oBAAoB,QAAS,GAC7C,EAAQ,IAGV,EAAM,iBAAiB,OAAQ,GAC/B,EAAM,iBAAiB,QAAS,KCxB7B,YAAuB,EAAsC,CAClE,MAAO,IAAI,SAAQ,CAAC,EAAS,IAAW,CACtC,AAAM,YAAe,OAAO,EAAO,GAAI,OAAM,qDAC7C,GAAM,GAAS,GAAI,YACnB,EAAO,OAAS,IAAM,CACpB,AAAI,MAAO,GAAO,QAAW,UAAU,EAAO,GAAI,OAAM,qEACxD,GAAM,GAAM,EAAI,SAAS,qBACzB,EAAI,OAAS,IAAM,EAAQ,GAC3B,EAAI,QAAU,EACd,EAAI,IAAM,EAAO,QAEnB,EAAO,QAAU,EACjB,EAAO,cAAc,KCXlB,YAA4B,EAA0F,CAC3H,GAAM,CAAE,QAAO,SAAU,EAAI,SAE7B,MAAI,aAAiB,GACZ,GAAI,GAAW,EAAM,aAAc,EAAM,eAE9C,YAAiB,GACZ,GAAI,GAAW,EAAM,WAAY,EAAM,aAEzC,GAAI,GAAW,EAAM,MAAO,EAAM,QCNpC,YAAsB,CAAE,QAAO,UAA0C,CAC9E,GAAM,CAAE,uBAAwB,EAAI,SAC9B,EAAS,IACf,SAAO,MAAQ,EACf,EAAO,OAAS,EACT,EAGF,YAA+B,EAAwD,EAAuC,CACnI,GAAM,CAAE,aAAc,EAAI,SAE1B,GAAI,CAAE,aAAiB,KAAc,CAAC,GAAc,GAClD,KAAM,IAAI,OAAM,8DAGlB,GAAM,CAAE,QAAO,UAAW,GAAQ,GAAmB,GAC/C,EAAS,GAAa,CAAE,QAAO,WAErC,MAAI,aAAiB,GACnB,EAAoB,GAAQ,aAAa,EAAO,EAAG,GAEnD,EAAoB,GAAQ,UAAU,EAAO,EAAG,EAAG,EAAO,GAErD,EC7BT,OAAoB,OAKpB,kBACE,EACA,EAC4B,CAC5B,GAAM,GAAe,GAAU,EAAI,SAAS,sBAEtC,CAAC,EAAQ,EAAO,GAAe,EAAU,MAAM,MAAM,EAAW,GAAa,EAAI,GACjF,EAAc,AAAG,QAAK,IAAM,EAAU,KAAK,EAAQ,EAAO,GAAa,SAC7E,YAAM,AAAG,YAAQ,SAAS,EAAa,GAEvC,EAAY,UAEL,ECfF,YAAwB,EAAY,CACzC,GAAM,CAAE,QAAO,SAAQ,SAAU,EAAI,SAErC,MAAO,aAAiB,IACnB,YAAiB,IACjB,YAAiB,GCPxB,MAAoB,OCKb,YAAuB,EAA6C,EAAmB,EAAuB,GAAO,CAC1H,GAAM,CAAE,QAAO,UAAW,EAAI,SAE9B,GAAI,CAAE,aAAiB,IAAS,YAAiB,IAC/C,KAAM,IAAI,OAAM,4EAGlB,GAAI,GAAa,EAAG,MAAO,IAAa,CAAE,MAAO,EAAG,OAAQ,IAC5D,GAAM,GAAO,GAAmB,GAC1B,EAAQ,EAAY,KAAK,IAAI,EAAK,OAAQ,EAAK,OAC/C,EAAQ,EAAQ,EAAK,MACrB,EAAS,EAAQ,EAAK,OAEtB,EAAe,GAAa,CAAE,MAAO,EAAW,OAAQ,IACxD,EAAc,YAAiB,GAAS,EAAQ,GAAsB,GAEtE,EAAS,KAAK,IAAI,EAAQ,GAAU,EACpC,EAAK,GAAe,EAAQ,EAAS,EAAS,EAC9C,EAAK,GAAe,EAAS,EAAQ,EAAS,EACpD,MAAI,GAAY,MAAQ,GAAK,EAAY,OAAS,GAAG,EAAoB,GAAc,UAAU,EAAa,EAAI,EAAI,EAAO,GAEtH,EDdF,YAAe,CAapB,YACE,EACA,EAA6B,GAC7B,CAfM,mBAAkD,GAElD,eAAiC,GAIjC,wBAA8B,GAE9B,sBAA+B,GAQrC,GAAI,CAAC,MAAM,QAAQ,GACjB,KAAM,IAAI,OAAM,4HAA4H,KAG9I,KAAK,mBAAqB,EAC1B,KAAK,WAAa,EAAO,OAEzB,EAAO,QAAQ,CAAC,EAAO,IAAQ,CAC7B,GAAI,GAAW,GAAQ,CACrB,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAO,EAAM,MACnC,OAGF,GAAI,EAAW,GAAQ,CACrB,GAAM,GAAa,EAAc,MAAM,GACvC,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,yCAAyC,8CAG3D,KAAK,cAAc,GAAO,EAC1B,KAAK,iBAAiB,GAAQ,EAAc,MAAM,MAAM,GACxD,OAGF,GAAM,GAAU,YAAyB,GAAI,SAAS,OAAS,EAAQ,GAAsB,GAC7F,KAAK,UAAU,GAAO,EACtB,KAAK,iBAAiB,GAAO,CAAC,EAAO,OAAQ,EAAO,MAAO,QAIpD,eAAiD,CAC1D,MAAO,MAAK,iBAGH,WAAgC,CACzC,MAAO,MAAK,aAGH,eAAwB,CACjC,MAAO,MAAK,UAAY,GAAK,KAAK,sBAGzB,YAAoB,CAC7B,MAAO,MAAK,cAGH,kBAA8B,CACvC,MAAO,MAAK,oBAGH,YAAgC,CACzC,MAAO,MAAK,cAGH,0BAAwC,CACjD,MAAO,IAAM,KAAK,UAAW,EAAG,GAAG,IACjC,CAAC,EAAG,IAAa,KAAK,2BAA2B,IAI9C,SAAS,EAAiE,CAC/E,MAAO,MAAK,SAAS,IAAa,KAAK,aAAa,GAG/C,mBAAmB,EAA4B,CACpD,MAAO,MAAK,iBAAiB,GAGxB,eAAe,EAA0B,CAC9C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,cAAc,EAA0B,CAC7C,MAAO,MAAK,iBAAiB,GAAU,GAGlC,2BAA2B,EAA8B,CAC9D,GAAI,MAAO,MAAK,WAAc,SAC5B,KAAM,IAAI,OAAM,yFAGlB,GAAM,GAAQ,KAAK,cAAc,GAC3B,EAAS,KAAK,eAAe,GACnC,MAAO,IAA0B,CAAE,QAAO,UAAU,KAAK,WAYpD,cAAc,EAAmB,EAA0B,GAAmB,CACnF,YAAK,WAAa,EAEX,AAAG,OAAK,IAAM,CACnB,GAAM,GAAe,GAAM,KAAK,UAAW,EAAG,GAAG,IAAI,AAAC,GAAa,CACjE,GAAM,GAAQ,KAAK,SAAS,GAE5B,GAAI,YAAoB,UAAQ,CAE9B,GAAI,GAAY,EAAW,GAAS,EAAQ,EAAM,aAElD,SAAY,GAAY,EAAW,GAE/B,GAAU,MAAM,KAAO,GAAa,EAAU,MAAM,KAAO,IAC7D,GAAY,AAAG,QAAM,eAAe,EAAW,CAAC,EAAW,KAGtD,EAAU,KAAK,EAAW,EAAW,GAG9C,GAAI,YAAiB,GAAI,SAAS,OAChC,MAAO,AAAG,WAAQ,WAAW,GAAc,EAAO,EAAW,IAG/D,KAAM,IAAI,OAAM,+BAA+B,8FAAqG,OAOtJ,MAHoB,AAAG,SAAM,EAAa,IAAI,AAAC,GAAM,AAAG,OAAK,EAAG,aAAa,KAAK,KAAK,UAAW,EAAW,EAAW,OE3I9H,iBAAiC,EAAsC,CACrE,GAAI,YAAkB,IACpB,MAAO,GAGT,GAAM,GAAgB,MAAM,QAAQ,GAChC,EACA,CAAC,GAEL,GAAI,CAAC,EAAc,OACjB,KAAM,IAAI,OAAM,4CAGlB,GAAM,GAAa,AAAC,GAAiB,MAAM,QAAQ,GAAU,mBAAmB,KAAS,GAEnF,EAAa,EAAc,IAAI,IAErC,SAAW,QAAQ,CAAC,EAAO,IAAM,CAC/B,GAAI,CAAC,GAAe,IAAU,CAAC,GAAW,IAAU,CAAC,EAAW,GAC9D,KAAI,OAAO,GAAc,IAAO,SACxB,GAAI,OAAM,eAAe,EAAW,sEAAsE,EAAc,MAG1H,GAAI,OAAM,eAAe,EAAW,iIAG5C,GAAI,EAAW,GAAQ,CAErB,GAAM,GAAY,EAAM,MAAM,GAC9B,GAAI,IAAc,EAChB,KAAM,IAAI,OAAM,eAAe,EAAW,iCAAiC,iDAMjF,KAAM,SAAQ,IACZ,EAAW,IAAI,AAAC,GAAU,GAAe,IAAU,GAAiB,KAG/D,GAAI,IAAS,EAAY,MAAM,QAAQ,ICtChD,kBACE,EACA,EAC8B,CAC9B,GAAM,CAAE,UAAW,EAAI,SAEnB,EAAS,EAEb,GAAI,CAAE,aAAiB,IAAS,CAC9B,GAAM,GAAW,KAAM,GAAW,GAElC,GAAI,EAAS,UAAY,EACvB,KAAM,IAAI,OAAM,8CAGlB,GAAM,GAAiB,EAAS,SAAS,GACzC,EAAS,YAA0B,GAC/B,EACA,KAAM,IAAoB,GAGhC,GAAM,GAAM,EAAoB,GAQhC,MAAO,AAPO,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAO,MAAO,EAAO,QAAQ,IAAI,QAC7C,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAO,MAAO,EAAO,SAE/C,IAAI,CAAC,CAChB,IAAG,IAAG,QAAO,YACT,CACJ,GAAM,GAAU,GAAa,CAAE,QAAO,WACtC,MAAI,GAAQ,GAAK,EAAS,GAAG,EAAoB,GAAS,aAAa,EAAI,aAAa,EAAG,EAAG,EAAO,GAAS,EAAG,GAC1G,IClDX,OAAoB,OAgBpB,kBACE,EACA,EACwB,CACxB,GAAI,CAAC,GAAW,IAAgB,CAAC,EAAW,GAC1C,KAAM,IAAI,OAAM,6DAGlB,GAAI,EAAW,IAAgB,EAAY,MAAM,GAAK,EACpD,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAC,EAAW,EAAU,GAAe,EAAY,MAAM,MAAM,EAAW,GAAe,EAAI,GAajG,MAJoB,AAPN,GAAW,IACvB,AAAC,GAAS,YAAe,GACrB,EAAI,QAAQ,EAAU,GAAW,IACjC,GAEH,IAAI,AAAC,GAAQ,EAAI,mBAAmB,EAAU,IAEvB,IAAI,CAAC,CAC7B,IAAG,IAAG,QAAO,YACT,AAAG,WAAQ,EAAY,KAAK,EAAW,EAAU,GAAc,CAAC,EAAG,EAAG,GAAI,CAAC,EAAQ,EAAO,OCtCpG,kBACE,EAEA,EACmB,CACnB,GAAM,CAAE,SAAU,EAAI,SAChB,EAAM,KAAM,GAAM,EAAK,GAC7B,GAAI,CAAE,GAAI,OAAS,KACjB,KAAM,IAAI,OAAM,qBAAqB,EAAI,WAAW,EAAI,yBAAyB,EAAI,OAEvF,MAAO,GCTT,kBAAiC,EAAwC,CACvE,GAAM,GAAM,KAAM,IAAa,GACzB,EAAO,KAAO,GAAK,OAEzB,GAAI,CAAC,EAAK,KAAK,WAAW,UACxB,KAAM,IAAI,OAAM,wEAAwE,EAAK,kBAAkB,EAAI,OAErH,MAAO,IAAc,GCRvB,kBAAmC,EAAyB,CAC1D,MAAQ,MAAM,IAAa,IAAM,OCDnC,kBAAsC,EAAoC,CACxE,MAAO,IAAI,cAAa,KAAO,MAAM,IAAa,IAAM,eCH1D,OAAoB,OCAb,YAAsB,EAAyB,EAA0B,CAC9E,GAAM,GAA0B,GAAG,0BAEnC,GAAI,CAAC,EACH,MAAO,CACL,aAAc,GACd,YAAa,GAIjB,GAAI,IAAQ,IACV,MAAO,CACL,aAAc,IACd,YAAa,IAAI,KAIrB,GAAM,GAAW,EAAI,WAAW,WAAa,UAAY,EAAI,WAAW,YAAc,WAAa,GACnG,EAAM,EAAI,QAAQ,EAAU,IAE5B,GAAM,GAAQ,EAAI,MAAM,KAAK,OAAO,AAAC,GAAM,GAErC,EAAe,EAAI,SAAS,SAC9B,EAAM,EAAM,OAAS,GACrB,EAEA,EAAe,EAAY,GAAI,SAAS,SAAW,EAAM,MAAM,EAAG,EAAM,OAAS,GAAK,GAAO,KAAK,KACtG,SAAe,EAAI,WAAW,KAAO,IAAI,IAAiB,EAEnD,CACL,eACA,YAAa,IAAiB,IAAM,IAAI,IAAiB,GAAG,KAAgB,KD1BhF,kBACE,EACA,EAC4B,CAC5B,GAAM,CAAE,cAAa,gBAAiB,GAAa,EAAK,GAClD,EAAW,KAAM,IAAuC,GAE9D,MAAO,AAAG,OAAG,YAAY,EAAU,GET9B,YAAyB,EAAoB,EAAwB,EAA8B,GAAO,CAC/G,GAAM,CAAE,QAAO,UAAW,EACtB,GAAmB,GACnB,EACJ,SAAM,MAAQ,EACd,EAAM,OAAS,EACR,CAAE,QAAO,UCTlB,OAAoB,OCApB,OAAoB,OAOb,WAAyC,CAC9C,YAAY,EAAc,CAIhB,aAAkC,OAElC,oBAAiC,GALzC,KAAK,MAAQ,KASJ,SAAiC,CAAE,MAAO,MAAK,WAE/C,gBAAgC,CAAE,MAAO,MAAK,kBAE9C,WAAoB,CAAE,MAAO,CAAC,CAAC,KAAK,OAExC,iBAAiB,EAA8B,CACpD,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,MAAO,GAAI,GAGN,sBAAsB,EAAmB,EAAmB,CACjE,GAAM,CAAE,MAAK,WAAY,KAAK,qBAAqB,GACnD,EAAI,GAAS,UACb,EAAI,GAAW,EAGV,cAAe,CACpB,MAAO,MAAK,eAAe,IAAI,CAAC,CAAE,eAAiB,EACjD,KAAM,EACN,OAAQ,KAAK,iBAAiB,MAI3B,oBAAqB,CAC1B,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,EAAM,iBAAqB,cAGnE,iBAAkB,CACvB,MAAO,MAAK,eAAe,OAAO,AAAC,GAAU,CAAE,GAAM,iBAAqB,eAGrE,UAAW,CAChB,KAAK,kBAAkB,QAAQ,CAAC,CAAE,OAAM,YAAa,CACnD,KAAK,sBAAsB,EAAM,EAAO,cAIrC,QAAS,CACd,KAAK,qBAAqB,QAAQ,CAAC,CAAE,OAAM,OAAQ,KAAe,CAChE,GAAM,GAAS,AAAG,UAAO,EAAS,YAClC,EAAS,UACT,KAAK,sBAAsB,EAAM,KAI9B,QAAQ,EAA4B,GAAM,CAC/C,KAAK,eAAe,QAAQ,AAAC,GAAU,CACrC,GAAI,GAAoB,EAAM,OAAO,WACnC,KAAM,IAAI,OAAM,mDAAmD,EAAM,QAE3E,EAAM,OAAO,YAEf,KAAK,QAAU,OAGV,iBAAgC,CACrC,MAAO,IAAI,cACT,KAAK,eACF,IAAI,CAAC,CAAE,YAAa,MAAM,KAAK,EAAO,aACtC,OAAO,CAAC,EAAM,IAAQ,EAAK,OAAO,UAI5B,MAAK,EAAgE,CAChF,GAAI,YAAwB,cAAc,CACxC,KAAK,eAAe,GACpB,OAEF,KAAM,MAAK,YAAY,QAGZ,aAAY,EAAyB,CAChD,GAAI,GAAO,MAAO,IAAQ,SACxB,KAAM,IAAI,OAAM,GAAG,KAAK,0CAE1B,GAAM,GAAY,KAAM,IAAc,EAAK,KAAK,uBAChD,KAAK,kBAAkB,QAGZ,cAAa,EAA8B,CACtD,GAAI,GAAY,MAAO,IAAa,SAClC,KAAM,IAAI,OAAM,GAAG,KAAK,iDAE1B,GAAM,CAAE,YAAa,EAAI,SACnB,CAAE,cAAa,gBAAiB,GAAa,EAAU,KAAK,uBAC5D,EAAuB,AAAC,GAAwB,QAAQ,IAAI,EAAU,IAAI,AAAC,GAAO,EAAS,GAAI,KAAK,AAAC,GAAQ,EAAI,UACjH,EAAc,AAAG,MAAG,qBAAqB,GACzC,EAAW,KAAK,MAAO,MAAM,GAAS,IAAc,YACpD,EAAY,KAAM,GAAY,EAAU,GAC9C,KAAK,kBAAkB,GAGlB,kBAAkB,EAA8B,CACrD,GAAM,CAAE,gBAAe,UAAW,KAAK,2BAA2B,GAClE,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGV,eAAe,EAAuB,CAC3C,GAAM,CAAE,gBAAe,UAAW,KAAK,cAAc,GACrD,KAAK,eAAiB,EACtB,KAAK,QAAU,EAGT,qBAAqB,EAAmB,CAC9C,GAAI,CAAC,KAAK,OACR,KAAM,IAAI,OAAM,qDAGlB,GAAM,GAAS,EAAU,MAAM,KAAK,OAAO,CAAC,EAAoD,IAAY,CAE1G,GAAI,CAAC,EAAI,QAAQ,eAAe,GAC9B,KAAM,IAAI,OAAM,wDAAwD,eAAqB,KAE/F,MAAO,CAAE,IAAK,EAAI,QAAS,UAAS,QAAS,EAAI,QAAQ,KACxD,CAAE,QAAS,KAAK,SAEb,CAAE,MAAK,WAAY,EACzB,GAAI,CAAC,GAAO,CAAC,GAAW,CAAE,GAAI,YAAuB,YACnD,KAAM,IAAI,OAAM,8DAA8D,KAGhF,MAAO,CAAE,MAAK,aC7IlB,MAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,iBAAkB,EAAO,iBAAkB,EAAQ,QAC1F,SAAM,AAAG,OAAI,EAAK,EAAO,MAClB,IDNJ,YACL,EACA,EACA,EAAwB,GACX,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,CAAC,EAAG,GAAI,QACrE,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,CAAC,EAAG,KAE7E,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,OAItC,YACL,EACA,EACA,EAAwB,GACxB,EAAuB,GACV,CACb,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAO,AAAG,OACd,EACI,AAAG,MACH,AAAG,SAAO,EAAI,EAAiB,MAAqB,QAAS,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,GAAI,QAC5F,EAAiB,MAAM,MAEvB,EAAuB,EAAG,EAAiB,MAA8B,EAAc,CAAC,EAAG,GAAK,CAAC,EAAG,KAEpG,EAAO,EAAuB,EAAM,EAAiB,MAAO,CAAC,EAAG,IAEhE,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,IAC3B,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAE/D,EAAM,AAAG,OAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,KACxC,EAAO,EAAuB,EAAK,EAAiB,MAAO,CAAC,EAAG,IAErE,MAAO,AAAG,QAAK,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,AAAG,MAAI,EAAM,QEpD1D,OAAoB,OAIb,YACL,EACA,EACA,EAA4B,OAC5B,EAAoB,GACP,CACb,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OACb,AAAG,UAAO,EAAG,EAAO,QAAS,CAAC,EAAG,GAAI,GACrC,EAAO,MAGT,MAAO,GAAW,AAAG,QAAK,GAAO,ICd9B,WAAoC,EAAgB,EAA+B,CACxF,OAAO,KAAK,GAAW,QAAQ,AAAC,GAAS,CACvC,AAAK,EAAc,KAAK,AAAC,GAAO,EAAG,eAAiB,IAClD,EAAU,GAAM,YCLtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,EACA,IACe,CACf,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,SCzBtB,OAAoB,OAIb,YACL,EACA,EACA,CACA,MAAO,CACL,EACA,EACA,IACa,CACb,GAAM,GAAa,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAY,IAChF,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CACL,QAAS,EACT,KAAM,ICvBZ,OAAoB,OCoBb,YAA0B,CAE/B,YAES,EAEA,EAEA,EAEP,CANO,wBAEA,wBAEA,cDxBJ,YACL,EACA,EACA,CACA,MAAO,CAAC,EAAoB,EAAqB,IAA8C,CAC7F,GAAM,GAAmB,AAAG,YAAS,EAAe,EAAI,EAAI,GAAa,CAAC,EAAG,EAAG,EAAY,IACtF,EAAmB,AAAG,YAAS,EAAe,EAAa,GAAc,CAAC,EAAG,EAAG,EAAY,IAC5F,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,WAGX,GAAI,IACT,EACA,EACA,IAKC,YAEL,EACA,CACA,MAAO,AAAC,IAAwC,CAC9C,GAAM,GAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAmB,EAAgC,GAAG,qBAA2B,GACjF,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,IAAI,IACT,EACA,EACA,IEpCC,WAAmC,EAAgB,EAA+B,CACvF,MAAO,CAAC,EAAsB,EAAmB,IAAwB,CACvE,GAAM,GAAS,EAAU,GAEzB,GAAI,CAAC,GAAS,EAAQ,GACpB,KAAM,IAAI,OAAM,sBAAsB,oBAA+B,oBAA4B,KAGnG,SAAc,KACZ,CAAE,eAAc,UAAW,GAAc,IAGpC,GCfJ,WAA+B,EAAuB,CAC3D,GAAI,GAAmB,EAEvB,WAAwB,EAAkC,CACxD,GAAM,GAAM,EAAiB,MAAM,EAAG,GACtC,SAAmB,EAAiB,MAAM,GACnC,EAGT,YAA6C,CAC3C,MAAO,GAGT,MAAO,CACL,iBACA,uBCPG,YAA2B,EAAwC,EAA+B,CACvG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,GAAQ,EACV,EAAkB,EAAY,EAAa,EAAG,GAAG,WACjD,EAA2B,EAAY,EAAa,GAAG,WACrD,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAChE,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAoB,EAAqB,EAAsB,EAAwB,GAA0B,CACjJ,GAAM,CAAE,QAAO,QAAO,SAAU,EAAyB,EAAY,EAAa,EAAc,GAC1F,EAAQ,EAA2B,EAAa,EAAa,GAAG,WAEtE,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YAAuB,EAA8F,CAC1H,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAC3C,EAAS,EAAyB,IAAK,IAAK,UAElD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CACN,SAAQ,SAAQ,SAAQ,WCvBvB,YAA+B,EAAuE,CAC3G,MAAO,AAAC,IAA+B,CACrC,GAAM,GAAU,EAAgC,GAAG,YAAkB,GAC/D,EAAO,EAAgC,GAAG,SAAe,GAE/D,MAAO,CAAE,UAAS,SCNf,YAA2B,EAAgB,EAA+B,CAC/E,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CAAE,QAAO,QAAO,SAGzB,WAAkC,EAAgB,EAAwB,GAA0B,CAClG,GAAM,GAAQ,EACV,EAAkB,GAAG,WACrB,EAA2B,GAAG,WAC5B,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WACtC,EAAQ,EAA2B,GAAG,WAE5C,MAAO,CACL,QAAO,QAAO,QAAO,SAIzB,MAAO,CACL,2BACA,4BC7BG,YACL,EACuE,CACvE,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBhBdZ,oBAAmC,EAAuG,CAC/I,aAAc,CACZ,MAAM,wBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,sDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAE/B,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,+BAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KiBlDzB,OAAoB,OCApB,OAAoB,OAIb,YACL,EACA,EACa,CACb,MAAO,AAAG,SAAK,IAAM,AAAG,OACtB,AAAG,UAAO,EAAG,EAAO,SACpB,EAAO,OCPJ,YAAuB,EAAuB,EAAoB,EAA2E,CAClJ,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAIpB,EAAK,AAFa,GAAuB,EAAgB,GAEpC,EAAY,EAAa,MAEpD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,OCdP,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,EAAgB,OAGtB,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,YAA4B,EAA8B,CAC/D,GAAM,GAAyC,GACzC,EAAmC,GAEzC,cAAO,KAAK,GAAW,QAAQ,AAAC,GAAQ,CACtC,GAAM,GAAM,EAAI,WAAW,MAAQ,EAAgB,EACnD,EAAI,GAAO,EAAU,KAGhB,CAAE,sBAAqB,iBJIzB,oBAGG,EAAyB,CAGjC,YAAY,EAAe,EAA+D,CACxF,MAAM,GACN,KAAK,sBAAwB,KAGpB,uBAAgE,CACzE,MAAO,MAAK,sBASP,OAAO,EAA4C,CACxD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EACJ,MAAO,IAAoB,EAAmB,KAAK,EAAmB,MAAM,GAAI,IAAK,EAAO,MAIzF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,EAAS,KAAK,0BAA2B,KAAK,4BAG3D,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAM,KAAK,0BACX,EAAO,KAAK,2BACZ,EAAwB,EAAO,EAAO,EAEtC,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KKnFjC,GAAM,IAAyB,CAAC,UAAW,QAAS,MAAO,QAAS,UAAW,YAAa,aAE5F,QAAsB,CAe3B,YAAY,EAAwC,CAClD,GAAI,EAAc,SAAW,EAC3B,KAAM,IAAI,OAAM,8EAA8E,EAAc,UAG9G,GAAuB,QAAQ,CAAC,EAAY,IAAQ,CAClD,KAAK,GAAc,EAAc,KAIrC,eAAgB,CACd,MAAO,IACJ,IAAI,AAAC,GAAgB,EAAE,aAAY,YAAa,KAAK,MACrD,KAAK,CAAC,EAAI,IAAO,EAAG,YAAc,EAAG,e1CtBrC,oBAAgC,GAA0C,CAC/E,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGtB,aAAa,EAA4C,CAC9D,MAAO,AAAG,SAAK,IAAM,AAAG,WAAQ,KAAK,OAAO,UAGjC,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,oBAAmB,EAAkB,CAChD,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAC9B,EAAsB,KAAM,SAAQ,IAAI,AAAG,WAAQ,GAAK,IAAI,KAAO,IAAM,CAC7E,GAAM,GAAO,KAAM,GAAE,OACrB,SAAE,UACK,KAET,EAAI,UAEJ,GAAM,GAAqB,EACxB,IAAI,AAAC,GAAiB,GAAI,IAAgB,IAE7C,MAAO,GAAS,aACZ,EACA,EAAmB,GAGf,qBAA8B,CACtC,MAAO,wBAGC,yBAAkC,CAC1C,MAAO,KAGC,0BAAmC,CAC3C,MAAO,K2C1CJ,YAA+B,EAA0C,CAC9E,MAAO,GAAI,sBAAuB,IAG7B,YAGL,EACA,EAC8B,CAE9B,MAAO,IAAK,KADM,CAAE,gBCPf,YACL,EACA,EACA,EAAgB,GAChB,EACA,CAGA,AAF6B,OAAM,QAAQ,GAAmB,EAAkB,CAAC,IAE5D,QAAQ,AAAC,GAAM,CAElC,GAAM,GAAO,YAAa,IACtB,EACC,GAAsB,GAAK,EAAE,YAAc,OAChD,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mHAIlB,GAAM,GAAmB,AADV,EAAK,gBACY,OAAO,AAAC,GAAc,EAAU,YAAc,GAExE,EAAS,GAAoB,GAC/B,EAAE,UAAU,IAAI,WACf,GAAmB,GAAI,GAAM,EAAG,GAMrC,AAJsB,GAAI,IACxB,EAAiB,IAAI,AAAC,GAAc,GAAG,EAAU,eAAe,GAAM,EAAU,iBAChF,GAEY,KAAK,KCvBhB,YAA6B,EAA0E,CAC5G,MAAO,IAAoB,IAEtB,EAAI,oBAAwB,IAE5B,EAAI,6BAAiC,IAErC,EAAI,sBAA0B,GAGrC,YAA4B,EAAM,CAEhC,GAAM,GAAU,CAAC,EAAI,EAAI,EAAI,IAAQ,KAAK,MAAM,EAAK,EAAI,EAAK,GAAM,KAAK,GAGnE,EAAU,AAAC,GAAW,EAAQ,IAAO,KAAK,GAE1C,EAAQ,CAAE,KAA0B,OAAW,MAA2B,OAAW,IAAyB,QAEpH,GAAI,CAAC,GAAQ,CAAC,EAAK,YAAc,EAAK,WAAW,SAAW,GAAI,MAAO,GACvE,GAAM,GAAK,EAAK,WAOhB,EAAM,KAAO,CAAC,EAAQ,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,GAAI,EAAG,IAAI,IAK9D,EAAM,MAAQ,EAAQ,EAAG,KAAK,IAAI,EAAG,GAAG,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,GAAI,KAAK,GAAI,KAAK,IAAI,EAAG,IAAI,GAAK,EAAG,IAAI,IAAM,EAAG,IAAI,IAMvH,GAAM,GAAS,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,UACnE,EAAM,EAAG,OAAO,CAAC,EAAM,IAAS,EAAO,EAAI,GAAK,EAAO,EAAI,GAAK,WACtE,SAAM,IAAM,KAAK,GAAM,GAAK,SAAS,QAAW,GAAM,GAAU,IAAO,GAEhE,EAGF,YAEoD,EAAoB,EAAgF,CAC7J,GAAM,CAAE,IAAK,GAAU,EAAU,UAC3B,EAAY,EAAmB,QAAwB,EAAM,EAAG,EAAM,GAEtE,EAAO,EAAU,QACjB,CAAE,aAAc,EAAU,UAC1B,EAAc,GAAI,GAAc,EAAU,UAAU,MAAO,EAAK,QAAQ,EAAU,WAAY,GAC9F,EAAQ,GAAmB,GASjC,MAAO,IAAK,KAPM,CAChB,YACA,qBACA,cACA,UCxDG,YAA+B,CAapC,YAAY,EAAqC,GAAI,CACnD,GAAM,CACJ,YAAY,GAAM,aAAa,GAAM,YAAW,YAAW,YAAW,cACpE,EACJ,KAAK,UAAY,EACjB,KAAK,WAAa,EAClB,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,EAC9B,KAAK,UAAY,GAAa,uBAC9B,KAAK,WAAa,GAAc,yBAI7B,QAAwB,CAK7B,YACE,EACA,EAAqC,GACrC,CACA,KAAK,cAAgB,EACrB,KAAK,QAAU,GAAI,IAAyB,GAG9C,KAAK,EAAkE,CACrE,GAAM,GAAM,EAAoB,GAE1B,CACJ,YAAW,aAAY,YAAW,YAAW,YAAW,cACtD,KAAK,QAcT,GAZI,GAAa,KAAK,wBAAyB,KAC7C,GAAI,YAAc,EAClB,EAAI,UAAY,EAChB,GAAY,EAAK,KAAK,cAAc,iBACpC,GAAY,EAAK,KAAK,cAAc,kBACpC,GAAY,EAAK,KAAK,cAAc,mBACpC,GAAY,EAAK,KAAK,cAAc,WACpC,GAAY,EAAK,KAAK,cAAc,aAAc,IAClD,GAAY,EAAK,KAAK,cAAc,cAAe,IACnD,GAAY,EAAK,KAAK,cAAc,WAAY,KAG9C,EAAY,CACd,EAAI,YAAc,EAClB,EAAI,UAAY,EAEhB,GAAM,GAAY,AAAC,GAAe,CAChC,EAAI,YACJ,EAAI,IAAI,EAAG,EAAG,EAAG,EAAG,EAAW,EAAG,EAAI,KAAK,IAC3C,EAAI,QAEN,KAAK,cAAc,UAAU,QAAQ,MAOpC,YACL,EACA,EACA,CAEA,AAD2B,OAAM,QAAQ,GAAiB,EAAgB,CAAC,IACxD,QAAQ,AAAC,GAAM,CAEhC,GAAM,GAAY,YAAa,GAC3B,EACC,GAAoB,GAAK,EAAE,UAAY,OAC5C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gIAGlB,GAAI,IAAkB,GAAW,KAAK,oBC1G1C,OAAoB,OCApB,MAAoB,OCKpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAC7D,EAA6B,GAAkC,EAAgB,GAErF,WAAqC,EAAoB,EAAqB,EAA4C,CACxH,GAAM,GAAkB,EAA2B,EAAY,EAAa,GAAG,qBACzE,EAAkB,EAA2B,EAAa,EAAa,GAAG,qBAC1E,EAAiB,EAAkB,EAAY,EAAa,EAAG,GAAG,oBAExE,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAkB,EAAuC,CACvF,GAAM,GAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBACpE,EAAkB,EAA2B,EAAU,EAAU,GAAG,qBAE1E,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YAAuB,EAAuB,EAAsF,CACzI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAgB,GAEhC,EAAqB,EAAkB,EAAG,GAAI,EAAG,sBACjD,EAA+B,EAA4B,GAAI,GAAI,gCACnE,EAA+B,EAA4B,GAAI,IAAK,gCAEpE,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,IAAK,0BAA0B,OAG3F,GAAM,GAA4B,EAA4B,IAAK,IAAK,6BAClE,EAA2B,EAA2B,IAAK,IAAK,4BAEhE,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,aAAY,cAAa,cCjEvC,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAE1D,EAAoB,GAAsB,GAC1C,EAA6B,GAA+B,GAElE,WAAqC,EAA4C,CAC/E,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAiB,EAAkB,GAAG,oBAE5C,MAAO,CAAE,kBAAiB,kBAAiB,kBAG7C,WAAgC,EAAuC,CACrE,GAAM,GAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAChD,EAAkB,EAA2B,GAAG,qBAEtD,MAAO,CAAE,kBAAiB,kBAAiB,mBAG7C,MAAO,CACL,oBACA,6BACA,8BACA,0BAIG,YACL,EACA,EAC+D,CAC/D,GAAM,GAAgC,GAEhC,CACJ,oBACA,6BACA,8BACA,0BACE,GAAkB,EAAW,GAE3B,EAAqB,EAAkB,sBACvC,EAA+B,EAA4B,gCAC3D,EAA+B,EAA4B,gCAE3D,EAAa,CACjB,QAAS,EACT,kBAAmB,EACnB,kBAAmB,GAGf,EAAc,GACpB,GAAM,EAAe,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAC1C,EAAY,cAAc,KAAS,EAAuB,0BAA0B,OAGtF,GAAM,GAA4B,EAA4B,6BACxD,EAA2B,EAA2B,4BAEtD,EAAY,CAChB,gBAAiB,EACjB,eAAgB,GAGlB,SAA2B,EAAW,GAE/B,CAAE,OAAQ,CAAE,aAAY,cAAa,aAAa,iBFrE3D,YAAc,EAAgB,EAAoB,EAAuC,CACvF,MAAO,AAAG,OAAI,AAAG,SAAO,EAAG,EAAO,QAAS,EAAQ,QAAS,EAAO,MAGrE,YAAwB,EAAgB,EAA8B,EAA2B,GAAmB,CAClH,GAAI,GAAM,EAAkB,AAAG,OAAK,GAAK,EACzC,SAAM,EAAuB,EAAK,EAAO,gBAAiB,CAAC,EAAG,IAC9D,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,AAAG,MAAI,EAAK,GAAK,EAAG,EAAO,eAAgB,CAAC,EAAG,KAC9C,EAGT,YAAmB,EAAgB,EAAsC,CACvE,GAAI,GAAM,EAAuB,AAAG,OAAK,GAAI,EAAO,gBAAiB,CAAC,EAAG,IACzE,SAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,EAAuB,AAAG,OAAK,GAAM,EAAO,gBAAiB,CAAC,EAAG,IACvE,EAAM,AAAG,MAAI,EAAK,GACX,EAGF,oBAA2B,EAAkC,CAGlE,YAAY,EAAuB,CACjC,MAAM,gBACN,KAAK,eAAiB,EAGjB,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KACnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,8CAElB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAC7D,EAAM,AAAG,OAAK,GAAK,EAAY,EAAO,WAAW,QAAS,CAAC,EAAG,KAClE,SAAM,GAAe,EAAK,EAAO,WAAW,kBAAmB,IAC/D,EAAM,GAAe,EAAK,EAAO,WAAW,mBAC5C,GAAM,KAAK,eAAgB,EAAG,GAAG,QAAQ,AAAC,GAAQ,CAChD,EAAM,GAAU,EAAK,EAAO,YAAY,cAAc,QAExD,EAAM,GAAe,EAAK,EAAO,UAAU,iBAC3C,EAAM,AAAG,OAAK,EAAuB,EAAK,EAAO,UAAU,eAAgB,CAAC,EAAG,KACxE,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,sBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,gBAG1C,cAAc,EAAuB,CAC7C,MAAO,IAAc,EAAS,KAAK,kBGvEhC,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAkB,GAAuB,EAAgB,GAEzD,EAAM,EAAgB,IAAK,EAAG,UAC9B,EAAS,EAAgB,IAAK,EAAG,aAEvC,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,GAAI,CAAE,MAAK,YCflB,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,EAAqB,EAA0B,EAAW,GAEhE,WAAyB,EAA0B,CACjD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,GAAM,GAAS,CACb,GAAI,CACF,IAAK,EAAgB,UACrB,OAAQ,EAAgB,eAI5B,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCxBZ,GAAK,IAAL,UAAK,EAAL,CAEL,SAAS,SAET,OAAO,SAJG,aNQL,oBAA2B,EAAyB,CAGzD,YAAY,EAAqC,GAAI,IAAa,GAAI,CACpE,MAAM,gBACN,KAAK,sBAAwB,KAGpB,uBAAqC,CAC9C,MAAO,MAAK,sBAGP,OAAO,EAA0C,CACtD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,GAAG,KAAK,uCAG1B,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAqB,YAAiB,IACxC,KAAK,qBAAqB,aAAa,GACvC,EAEE,EAAS,AAAG,WAAQ,EAAoB,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,SAAS,KAAK,EAAmB,MAAM,GAAI,IACnG,EAAM,GAAoB,EAAQ,EAAO,GAAG,KAAK,OACjD,EAAS,GAAoB,EAAQ,EAAO,GAAG,QACrD,MAAO,CAAE,MAAK,YAIX,aAAa,EAA0C,CAC5D,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,CAAE,MAAK,UAAW,KAAK,OAAO,GACpC,MAAO,CAAE,MAAK,OAAQ,AAAG,WAAQ,WAIxB,SAAQ,EAAsC,CACzD,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,qBAAoB,EAA8E,CAC7G,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,GAE9B,EAAO,AAAG,WAAQ,EAAI,KACtB,EAAU,AAAG,WAAQ,EAAI,QACzB,EAAsB,EAAK,IAAI,CAAC,EAAW,IAAO,EACtD,YACA,aAAc,EAAQ,MAGlB,EAAqB,KAAM,SAAQ,IACvC,EAAoB,IAAI,MAAO,CAAE,YAAW,kBAAmB,CAC7D,GAAM,GAAO,MAAM,GAAU,QAAQ,GAC/B,EAAY,MAAM,GAAa,QAAQ,GACvC,EAAS,EAAW,GACpB,EAAS,EAAS,GAAO,KAAO,GAAO,OACvC,EAAoB,EAAS,EAAY,EAAI,EAEnD,SAAU,UACV,EAAa,UACN,CAAE,MAAK,SAAQ,wBAG1B,SAAI,IAAI,UACR,EAAI,OAAO,UAEJ,EAAS,aAAe,EAAiD,EAAmB,GAG3F,qBAA8B,CACtC,MAAO,mBAGF,QAAQ,EAA4B,GAAM,CAC/C,KAAK,qBAAqB,QAAQ,GAClC,MAAM,QAAQ,GAGT,qBAAqB,EAAuB,CACjD,GAAM,CAAE,SAAQ,iBAAkB,KAAK,wBAAwB,GAC/D,KAAK,QAAU,EACf,KAAK,eAAiB,EAGjB,wBAAwB,EAAuB,CACpD,MAAO,IAAc,GAGb,2BAA2B,EAA8B,CACjE,GAAM,CAAE,sBAAqB,iBAAkB,GAAmB,GAElE,YAAK,qBAAqB,kBAAkB,GAErC,GAA2B,GAG1B,cAAc,EAAuB,CAC7C,GAAM,GAAwB,IAAM,EAAI,EAAM,KAAM,EAAI,GAElD,EAA0B,EAAQ,MAAM,EAAG,EAAQ,OAAS,GAC5D,EAAoB,EAAQ,MAAM,EAAQ,OAAS,GAEzD,YAAK,qBAAqB,eAAe,GAClC,KAAK,wBAAwB,KOvHxC,MAAoB,OASb,oBAGG,GAAgC,CACjC,YAAY,EAAqB,EAAmB,EAAgD,CACzG,GAAM,GAAkB,EAAmB,IAAI,CAAC,CAAE,QAAO,YAAa,CACpE,GAAM,GAAQ,EAAY,KAAK,IAAI,EAAQ,GAC3C,MAAO,CACL,MAAO,EAAQ,EACf,OAAQ,EAAS,KAIf,EAAY,EAAgB,OAElC,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAA0B,CAAC,EAAe,IAAkB,AAAG,QAAM,CAAC,AAAG,OAAK,CAAC,IAAK,EAAO,WAAY,AAAG,OAAK,CAAC,IAAK,EAAO,YAAa,GAAG,KAAK,EAAG,KAAK,OAGzJ,EAAa,CAAC,EAAkB,IAAoD,CACxF,GAAM,CAAE,QAAO,UAAW,EAAgB,GAC1C,MAAO,GAAK,EAAO,GAAU,KAAK,IAAI,EAAQ,GAAU,EAAI,GAGxD,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GACvE,EAAc,AAAC,GAAqB,EAAW,EAAU,CAAC,EAAG,IAAM,EAAI,GAa7E,MAXwB,GACrB,IAAI,AAAG,OAAK,CAAC,EAAW,KAAM,EAAW,YACzC,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAY,GACZ,EAAY,OAEb,IAAI,AAAG,QAAM,MAAM,KAAK,MAAM,GAAY,CAAC,EAAG,IAAa,EAC1D,EAAgB,GAAU,MAC1B,EAAgB,GAAU,aAO3B,aAAa,EAA8B,CAChD,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAM,KAAK,OAAO,GACxB,MAAO,MAAK,YACV,EACA,EAAM,UACN,EAAM,gBAAgB,IAAI,CAAC,CAAC,EAAQ,KAAY,EAAE,SAAQ,mBAKnD,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,iBAAgB,EAAgE,CAC3F,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAkB,AAAG,OACzB,IAAM,AAAG,UAAQ,KAAK,aAAa,KAG/B,EAAoB,KAAM,SAAQ,IAAI,EAAgB,IAC1D,MAAO,EAAgB,IAAa,CAClC,GAAM,GAAiB,MAAM,KAAK,KAAM,GAAe,QACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,GAAO,IACjD,EAAU,EAAe,OAAO,CAAC,EAAG,IAAM,CAAC,GAAO,IAExD,MAAO,IAAI,IACT,MAAM,IAAI,KAAK,GAAG,IAAI,CAAC,EAAG,IAAM,GAAI,GAAM,EAAQ,GAAc,EAAQ,KACxE,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,QAMtC,SAAgB,QAAQ,AAAC,GAAM,EAAE,WAE1B,EAAS,aAAe,EAAyC,EAAkB,GAGlF,0BAAmC,CAC3C,MAAO,OC1FJ,oBAAgC,GAAkD,CACvF,YAAY,EAA6C,GAAI,IAAwB,CACnF,MAAM,oBAAqB,GAGnB,qBAA8B,CACtC,MAAO,yBAGC,yBAAkC,CAC1C,MAAO,OCdX,OAAoB,OCMb,YACL,EAC2E,CAC3E,GAAM,GAAgC,GAEhC,CACJ,4BACE,GAAkB,EAAW,GAE3B,EAAS,CACb,OAAQ,EAAyB,SAAU,IAC3C,OAAQ,EAAyB,UACjC,OAAQ,EAAyB,WAGnC,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBCnBZ,YAA2B,EAAkG,CAClI,GAAM,GAAgC,GAEhC,CACJ,iBACA,uBACE,EAAsB,GAEpB,CACJ,4BACE,GAAkB,EAAgB,GAEhC,EAAS,EAAyB,EAAG,GAAI,SAAU,IACnD,EAAS,EAAyB,GAAI,GAAI,UAC1C,EAAS,EAAyB,GAAI,IAAK,UAEjD,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,gBACA,OAAQ,CAAE,SAAQ,SAAQ,WFhBvB,oBAAuC,EAA+G,CAC3J,aAAc,CACZ,MAAM,4BAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,0DAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAO,WAEtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,UAAO,MAE7D,EAAM,GAAY,EAAY,EAAO,OAAQ,IACjD,SAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,GAAY,EAAK,EAAO,QAC9B,EAAM,AAAG,WAAQ,EAAK,CAAC,GAAI,IAAK,CAAC,EAAG,GAAI,SAEjC,SAIE,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,IAGlC,qBAA8B,CACtC,MAAO,oCAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA+B,GAG9B,cAAc,EAAuB,CAC7C,MAAO,IAAkB,KG7CtB,oBAAoC,GAAsD,CAC/F,YAAY,EAAiD,GAAI,IAA4B,CAC3F,MAAM,wBAAyB,GAGvB,qBAA8B,CACtC,MAAO,8BAGC,yBAAkC,CAC1C,MAAO,OCVJ,oBAA8B,GAAkB,GCJvD,MAAoB,OCApB,OAAoB,OCApB,OAAoB,OAIb,YAAe,EAAgB,EAAuC,CAC3E,MAAO,AAAG,QAAI,AAAG,OAAI,EAAG,EAAO,SAAU,EAAO,QDAlD,YACE,EACA,EACA,EACA,EACA,EAA4B,OACf,CACb,GAAM,CAAE,UAAS,QAAS,EAAO,KAE7B,EAAM,AAAG,UAAO,EAAG,EAAS,EAAS,GACzC,SAAM,AAAG,OAAI,EAAK,GAClB,EAAM,GAAM,EAAK,EAAO,OACjB,EAAW,AAAG,QAAK,GAAO,EAG5B,YAAc,EAAgB,EAAyB,CAC5D,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAoB,EAAgB,EAAyB,CAClE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,IAG/B,YAAkB,EAAgB,EAAyB,CAChE,MAAO,IAAU,EAAG,EAAQ,CAAC,EAAG,GAAI,GAAM,SE7B5C,MAAoB,OAUpB,YAA2B,EAAwC,EAA+B,CAChG,WAA6B,EAAyB,EAAoB,EAAiC,CACzG,GAAM,GAAU,EAAe,GACzB,EAAQ,EAAQ,OAAU,GAAa,EAAa,GAE1D,GAAI,GAAQ,GACV,KAAM,IAAI,OAAM,+BAA+B,sBAA0B,EAAQ,uBAAuB,kBAA2B,KAGrI,MAAO,AAAG,QACR,IAAM,AAAG,YACP,AAAG,WAAS,EAAS,CAAC,EAAY,EAAO,EAAY,IACrD,CAAC,EAAG,EAAG,EAAG,KAKhB,WACE,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,EAAoB,EAAiB,EAAY,GAC3D,EAAO,AAAG,WAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,WAGX,CAAE,UAAS,QAGpB,WAAiC,EAAoB,EAAwC,CAC3F,GAAM,GAAU,AAAG,WAAS,EAAe,IACrC,EAAS,AAAG,WAAS,EAAe,IAE1C,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,aAGX,CACL,UACA,UAIJ,WACE,EACA,EACA,EACA,EACiB,CACjB,GAAM,GAAO,EAAkB,EAAiB,EAAY,EAAY,GAAG,UACrE,EAAQ,EAAwB,EAAY,GAAG,WAErD,MAAO,CAAE,OAAM,SAGjB,WACE,EACA,EACA,EACA,EACA,EAAkB,GACG,CACrB,GAAM,GAAQ,EAAwB,GAAS,GAAM,GAAK,EAAiB,EAAY,EAAY,GAAG,WAChG,EAAQ,EAAuB,EAAiB,EAAY,EAAY,GAAG,WAEjF,MAAO,CAAE,QAAO,SAGlB,MAAO,CACL,yBACA,8BAIG,YAAuB,EAA6E,CACzG,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAgB,GAEhC,EAAc,EAAuB,KAAM,GAAI,EAAG,eAClD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YACnD,EAAW,EAA2B,KAAM,GAAI,EAAG,YAEnD,EAAc,EAA2B,MAAO,GAAI,EAAG,cAAe,IACtE,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YACpD,EAAW,EAA2B,MAAO,GAAI,EAAG,YAEpD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aAEvD,EAAe,EAA2B,OAAQ,IAAK,EAAG,eAAgB,IAC1E,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAY,EAA2B,OAAQ,IAAK,EAAG,aACvD,EAAmB,EAA2B,OAAQ,IAAK,EAAG,oBAE9D,EAAK,AAAG,OACZ,IAAM,AAAG,YAAU,AAAG,WAAS,EAAe,IAAM,KAAM,CAAC,IAAK,MAAO,CAAC,EAAG,KAI7E,GAFA,EAAc,KAAK,CAAE,UAAW,OAE5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAsB1E,MAAO,CAAE,OAnBM,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGe,iBChJnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAiC,EAAkC,CACjE,GAAM,GAAU,EAAmB,GAAG,kBAAwB,GACxD,EAAS,EAAmB,GAAG,iBAAuB,GAE5D,MAAO,CAAE,UAAS,UAGpB,WAAgC,EAAiC,CAC/D,GAAM,GAAU,EAAmB,GAAG,iBAAuB,GACvD,EAAO,EAAmB,GAAG,cAAoB,GACjD,EAAQ,EAAwB,GAEtC,MAAO,CAAE,KAAM,CAAE,UAAS,QAAQ,SAGpC,WAAoC,EAAqC,CACvE,MAAO,CACL,MAAO,EAAuB,GAAG,WACjC,MAAO,EAAuB,GAAG,YAIrC,MAAO,CACL,yBACA,8BAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAEhC,CACJ,yBACA,8BACE,GAAkB,EAAW,GAE3B,EAAc,EAAuB,eACrC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAc,EAA2B,eACzC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YACtC,EAAW,EAA2B,YAEtC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aAEvC,EAAe,EAA2B,gBAC1C,EAAY,EAA2B,aACvC,EAAY,EAA2B,aACvC,EAAmB,EAA2B,oBAE9C,CAAE,MAAO,EAGf,GAFA,EAAc,KAAK,CAAE,aAAc,KAAM,UAAW,OAEhD,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yDAAyD,KAG3E,GAAM,GAAS,CACb,cACA,WACA,WACA,WACA,cACA,WACA,WACA,WACA,eACA,YACA,YACA,eACA,YACA,YACA,mBACA,MAGF,SAA2B,EAAW,GAE/B,CAAE,SAAQ,iBC9FnB,MAAoB,OAKb,YAAkB,EAAgB,EAA0C,CACjF,GAAI,GAAM,GAAK,EAAG,EAAO,OACzB,SAAM,GAAW,EAAK,EAAO,OAC7B,EAAM,AAAG,MAAI,EAAK,GAClB,EAAM,AAAG,OAAK,GACP,EAGF,YAAsB,EAAgB,EAA0C,CACrF,GAAI,GAAM,GAAS,EAAG,EAAO,OAC7B,EAAM,GAAW,EAAK,EAAO,OAE7B,GAAI,GAAS,AAAG,UAAQ,EAAG,EAAG,EAAG,SAC3B,EAAQ,AAAG,QAAkB,EAAO,OACpC,EAAQ,EAAO,MAAM,KAAO,EAAI,MAAM,GAG5C,GAFsB,EAAO,MAAM,KAAO,EAAI,MAAM,IAAM,EAAO,MAAM,KAAO,EAAI,MAAM,GAErE,CACjB,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAE/B,GAAM,GAAY,CAAC,GAAG,EAAI,OAC1B,EAAU,GAAK,EACf,GAAM,GAAS,AAAG,QAAkB,GACpC,EAAM,AAAG,SAAO,CAAC,EAAK,GAAS,GAGjC,SAAS,EAAQ,AAAG,SAAO,CAAC,EAAQ,GAAQ,GAAK,EACjD,EAAM,AAAG,MAAI,EAAQ,GAErB,EAAM,AAAG,OAAK,GACP,EL3BF,oBAAiC,EAAyB,CAC/D,aAAc,CACZ,MAAM,sBAGD,aAAa,EAA8B,CAChD,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,oDAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAc,AAAG,OAAK,EAAM,cAAc,IAAK,IAAO,WAGtD,EAAa,EAAU,EADb,CAAC,QAAS,QAAS,UACgB,IAAI,AAAG,SAAO,MAE7D,EAAM,GAAS,EAAY,EAAO,aACtC,EAAM,AAAG,UAAQ,EAAK,EAAG,EAAG,SAE5B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,aAC/B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAC3B,EAAM,GAAS,EAAK,EAAO,UAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAE3B,EAAM,GAAa,EAAK,EAAO,cAC/B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAS,EAAK,EAAO,WAC3B,EAAM,GAAa,EAAK,EAAO,kBAE/B,GAAM,GAAY,EAAI,KAAK,CAAC,EAAG,IAG/B,MAFuB,AAAG,UAAO,EAAW,EAAO,WAM1C,SAAQ,EAAwC,CAC3D,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,uBAAsB,EAAwD,CA7D7F,MA8DI,GAAI,oBAAO,QAAP,cAAc,KAAK,AAAC,GAAQ,GAAO,GAAI,MAAO,IAAI,cAAa,KACnE,GAAM,GAAW,KAAM,GAAW,GAC5B,EAAwB,AAAG,OAC/B,IAAM,AAAG,UAAQ,KAAK,aAAa,KAE/B,EAA0B,KAAM,SAAQ,IAAI,EAAsB,IAAI,AAAC,GAAM,EAAE,SACrF,SAAsB,QAAQ,AAAC,GAAM,EAAE,WAChC,EAAS,aAAe,EAA0B,EAAwB,GAGzE,qBAA8B,CACtC,MAAO,yBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KM7ElB,YAAkC,EAAuB,CAC9D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECHF,YAGL,EACA,EAC6B,CAE7B,MAAO,IAAK,KADM,CAAE,eCNf,YAAmB,EAA8B,CACtD,MAAO,OAAO,GAAI,KAAQ,SAGrB,YAGL,EACA,EACkB,CAElB,MAAO,IAAK,KADM,CAAE,QCNf,YAAsB,EAAiC,CAC5D,MAAQ,GAAI,SAAW,GAAO,MAAQ,EAAI,SAAW,GAAO,SACvD,GAAmB,EAAI,mBAGvB,YAGL,EACA,EACA,EACqB,CAErB,MAAO,IAAK,KADM,CAAE,SAAQ,sBCpB9B,OAAoB,OCApB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,WAAoC,EAAqB,EAAuD,CAC9G,GAAM,GAAU,AAAG,YAAS,EAAe,EAAI,EAAI,GAAc,CAAC,EAAG,EAAG,EAAa,IAC/E,EAAmB,AAAG,YAAS,EAAe,IAC9C,EAAoB,AAAG,YAAS,EAAe,IAC/C,EAAkB,AAAG,YAAS,EAAe,IAC7C,EAAsB,AAAG,YAAS,EAAe,IAEvD,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,sBAChB,CAAE,UAAW,GAAG,uBAChB,CAAE,UAAW,GAAG,qBAChB,CAAE,UAAW,GAAG,0BAGX,CACL,UACA,mBACA,oBACA,kBACA,uBAIJ,WACE,EACA,EACA,EACA,EACA,EACY,CACZ,GAAM,GAAU,AAAG,YACjB,EAAe,EAAa,EAAc,EAAa,GACvD,CAAC,EAAY,EAAY,EAAY,IAEjC,EAAO,AAAG,YAAS,EAAe,IAExC,SAAc,KACZ,CAAE,UAAW,GAAG,aAChB,CAAE,UAAW,GAAG,KAAgB,EAAkB,oBAAsB,WAGnE,CAAE,UAAS,QAGpB,WACE,EACA,EACA,EACA,EACqB,CACrB,GAAM,CACJ,UACA,QACE,EAAkB,EAAY,EAAa,EAAY,EAAc,IAEzE,MAAO,CACL,UACA,kBAAmB,GAIvB,WACE,EACA,EACA,EAC4B,CAC5B,GAAM,GAAiB,EAA2B,EAAY,GAAG,oBAC3D,EAAiB,EAA2B,EAAY,EAAa,EAAG,GAAG,oBAEjF,MAAO,CAAE,iBAAgB,kBAG3B,YAAwD,CACtD,GAAM,GAAS,EAA2B,EAAG,GAAI,EAAG,sBAC9C,EAAS,EAAsB,GAAI,GAAI,sBACvC,EAAS,EAAsB,GAAI,IAAK,sBACxC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAS,EAAsB,IAAK,IAAK,sBACzC,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,IAAK,uBAC1C,EAAU,EAAsB,IAAK,KAAM,uBAC3C,EAAU,EAAsB,KAAM,KAAM,uBAClD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,UACA,UACA,UACA,WAIJ,YAA+D,CAC7D,GAAM,GAAS,EAA2B,KAAM,IAAK,EAAG,2BAClD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,IAAK,EAAG,2BACjD,EAAS,EAA2B,IAAK,GAAI,EAAG,2BAChD,EAAS,EAA2B,GAAI,IAAK,EAAG,2BAChD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,EAAG,EAAG,oDACjD,EAA2B,EAAkB,KAAM,GAAI,EAAG,2DAC1D,EAAoB,EAAkB,KAAM,GAAI,EAAG,oDACnD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,EAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,EAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDAClD,GAA2B,EAAkB,IAAK,GAAI,EAAG,2DACzD,GAAoB,EAAkB,IAAK,GAAI,EAAG,oDA0BxD,MAAO,CACL,SACA,SACA,SACA,SACA,SACA,SACA,SACA,SACA,gBAjCsB,CACtB,uBAAwB,EACxB,gBAAiB,GAgCjB,gBA9BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA6BjB,gBA3BsB,CACtB,uBAAwB,EACxB,gBAAiB,GA0BjB,gBAxBsB,CACtB,uBAAwB,GACxB,gBAAiB,IAuBjB,gBArBsB,CACtB,uBAAwB,EACxB,gBAAiB,IAoBjB,gBAlBsB,CACtB,uBAAwB,GACxB,gBAAiB,KAoBrB,MAAO,CACL,2BACA,gCAIG,YAAuB,EAA6E,CACzG,GAAM,GAAgC,GAChC,CACJ,iBACA,uBACE,EAAsB,GACpB,CACJ,2BACA,gCACE,GAAkB,EAAgB,GAChC,EAAc,IACd,EAAmB,IAKnB,EAAe,CACnB,UALgB,AAAG,YACnB,EAAe,KAAO,GACtB,CAAC,EAAG,KAAM,KAMZ,GADA,EAAc,KAAK,CAAE,UAAW,2BAC5B,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAG1E,MAAO,CACL,OAAQ,CACN,cACA,mBACA,gBAEF,iBC9MJ,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAoC,EAAgB,EAAa,EAA2C,CAC1G,GAAM,GAAU,EAAmB,GAAG,YAAiB,sBAAyB,EAAG,GAAG,aAChF,EAAoB,EAAmB,GAAG,YAAiB,oCAAuC,EAAG,GAAG,uBAC9G,MAAO,CAAE,UAAS,qBAGpB,WAA+B,EAAyC,CACtE,GAAM,GAAe,oBAAoB,IACnC,EAAsB,sBAAsB,cAC5C,EAA4B,GAAG,mBAC/B,EAA4B,GAAG,mBAE/B,EAAU,EAAmB,GAAG,sBAAyC,EAAG,GAAG,aAC/E,EAAmB,EAAmB,GAAG,oBAAuC,EAAG,GAAG,sBACtF,EAAoB,EAAmB,GAAG,mBAAsC,EAAG,GAAG,uBACtF,EAAkB,EAAmB,GAAG,0BAA6C,EAAG,GAAG,qBAC3F,EAAsB,EAAmB,GAAG,8BAAiD,EAAG,GAAG,yBAEzG,MAAO,CACL,eAAgB,CACd,UACA,mBACA,oBACA,kBACA,uBAEF,eAAgB,EAA2B,cAAe,EAAK,IAInE,YAAwD,CACtD,MAAO,CACL,OAAQ,EAA2B,cAAe,EAAG,sBACrD,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,OAAQ,EAAsB,GAC9B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,IAC/B,QAAS,EAAsB,KAInC,WAA2B,EAAgB,EAAkC,CAC3E,GAAM,GAAU,EAAmB,GAAG,YAAkB,EAAG,GAAG,aACxD,EAAO,EAAmB,GAAG,WAAiB,EAAG,GAAG,UAC1D,MAAO,CAAE,UAAS,QAGpB,WAAmC,EAAkC,CACnE,GAAM,GAAyB,EAC7B,2BAA2B,yBAC3B,kCAAkC,4BAE9B,EAAkB,EACtB,2BAA2B,mBAC3B,kCAAkC,qBAEpC,MAAO,CAAE,yBAAwB,mBAGnC,YAA+D,CAC7D,MAAO,CACL,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,OAAQ,EAA2B,aAAc,EAAG,2BACpD,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,GAC3C,gBAAiB,EAA0B,IAI/C,MAAO,CACL,2BACA,gCAIG,YACL,EACsD,CACtD,GAAM,GAAgC,GAChC,CACJ,2BACA,gCACE,GAAkB,EAAW,GAC3B,EAAY,EAAU,oBAE5B,GADA,EAAc,KAAK,CAAE,aAAc,mBAAoB,UAAW,2BAC9D,CAAC,GAAW,GACd,KAAM,IAAI,OAAM,yEAAyE,KAG3F,GAAM,GAAS,CACb,YAAa,IACb,iBAAkB,IAClB,aAAc,CACZ,cAIJ,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBChInB,OAAoB,OCApB,OAAoB,OAIb,WACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,UAAO,EAAG,EAAO,QAAS,EAAS,QAChD,SAAM,AAAG,OAAI,EAAK,EAAO,mBAClB,AAAG,eAAY,EAAK,EAAG,KDPlC,GAAM,IAAU,qBAEhB,YACE,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,mBAAgB,EAAG,EAAO,QAAS,EAAS,QACzD,SAAM,AAAG,aACP,EACA,EAAO,gBACP,EAAO,oBACP,EAAO,kBACP,EAAO,iBACP,IAEK,AAAG,eAAY,EAAK,EAAG,KAIlC,YAA+B,EAAoC,CACjE,MAAO,CAAC,EAAG,EAAG,EAAG,IAAI,KAAK,AAAC,GAAQ,IAAQ,GAAY,CAAC,EAAG,GAAK,CAAC,EAAG,GAG/D,YAAqB,EAAgB,EAA4B,CACtE,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GACA,EAAM,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IA4BnD,GAVA,AAhBuB,CACrB,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,OACP,EAAO,QACP,EAAO,QACP,EAAO,QACP,EAAO,SAGM,QAAQ,CAAC,EAAO,IAAM,CACnC,GAAM,GAAW,EAAI,EACf,EAAuB,GAAsB,GACnD,EAAM,GAAmB,EAAK,EAAM,eAAgB,GACpD,EAAM,EAAmB,EAAK,EAAM,eAAgB,CAAC,EAAG,IACpD,IAAa,IACf,GAAS,KAIT,IAAW,KACb,KAAM,IAAI,OAAM,iDAGlB,MAAO,CACL,MACA,OAAQ,KEjEd,YAAa,EAAoB,EAAW,EAAW,CACrD,GAAM,GAAY,EAAM,YAClB,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAQ,KAAK,IAAI,EAAU,GAAG,GAAI,EAAU,GAAG,IAC/C,EAAS,GAAQ,GAAU,GAAQ,GACnC,EAAS,GAAQ,GAAU,GAAQ,GACzC,GAAI,GAAS,GAAK,GAAS,EACzB,MAAO,GAET,GAAM,GAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAO,GACnC,EAAmB,KAAK,IAAI,EAAmB,EAAkB,GACjE,KAAK,IAAI,EAAmB,EAAkB,GACpD,MAAO,GAAoB,GAAQ,EAAQ,GAGtC,YACL,EACA,EACA,EACA,EACA,EACU,CACV,GAAM,GAAW,EAAM,MAAM,GACvB,EAAa,KAAK,IACtB,EACA,GAGI,EAAa,EAChB,IAAI,CAAC,EAAO,IAAc,EAAE,QAAO,cACnC,OAAO,AAAC,GAAM,EAAE,MAAQ,GACxB,KAAK,CAAC,EAAI,IAAO,EAAG,MAAQ,EAAG,OAE5B,EAAe,AAAC,GAAe,GAAK,EAAe,EAAI,EACvD,EAAqB,GAE3B,SAAW,QAAQ,AAAC,GAAM,CACxB,GAAI,EAAS,QAAU,EAAY,OACnC,GAAM,GAAgB,EAAE,MACxB,OAAS,GAAI,EAAS,OAAS,EAAG,GAAK,EAAG,EAAE,EAAG,CAC7C,GAAM,GAAM,GAAI,EAAO,EAAE,SAAU,EAAS,IAC5C,GAAI,IAAQ,GACZ,GAAE,OAAS,EAAa,GACpB,EAAE,OAAS,GAAgB,MAEjC,AAAI,IAAkB,EAAE,OACtB,EAAS,KAAK,EAAE,YAGb,EC5DT,MAAoB,OAIpB,YAA2C,EAAgB,CACzD,GAAM,GAAM,AAAG,UAAQ,AAAG,YAAU,EAAG,CAAC,EAAG,KAErC,EAAQ,CACZ,AAAG,MAAI,EAAI,GAAI,EAAI,IACnB,AAAG,MAAI,EAAI,GAAI,EAAI,KAEf,EAAU,CACd,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,KAC1C,AAAG,MAAI,EAAI,GAAI,AAAG,MAAI,EAAM,GAAI,AAAG,SAAO,MAE5C,MAAO,CACL,QACA,WAIJ,YAA0B,EAAiB,EAAiB,CAC1D,GAAM,CACJ,QACA,WACE,GAAkC,GAEhC,EAAM,AAAG,UAAQ,AAAG,YAAU,EAAI,CAAC,EAAG,KACtC,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAC3E,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,AAAG,SAAO,IACpF,EAAW,AAAG,MAAI,AAAG,MAAI,AAAG,MAAI,EAAI,GAAI,AAAG,SAAO,KAAM,EAAM,IAAK,EAAQ,IAEjF,MAAO,AAAG,aACR,AAAG,QAAM,CACP,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,GACjB,AAAG,MAAI,EAAU,KAEnB,CAAC,EAAG,IAID,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,QAAK,IAAM,CACnB,GAAM,GAAY,EAAe,MAAM,GAEnC,EAAQ,GACV,AAAG,UAAQ,AAAG,OAAK,EAAO,UAAW,CAAC,EAAW,EAAG,IAAK,CAAC,GAAI,IAC9D,AAAG,UAAQ,EAAgB,CAAC,GAAI,KAElC,EAAQ,AAAG,UACT,EACA,CAAC,EAAY,EAAM,MAAM,GAAK,EAAY,IAG5C,GAAM,GAAmB,AAAG,UAAQ,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,MAC/E,EAAS,AAAG,QAAM,EAAkB,CAAC,EAAG,EAAG,GAAI,CAAC,GAAI,GAAI,IAE5D,EAAS,AAAG,UACV,EACA,CAAC,EAAW,EAAO,MAAM,KAG3B,GAAM,GAAe,AAAG,UAAQ,GAC1B,EAAgB,AAAG,UAAQ,GAEjC,MAAO,CACL,MAAO,EACP,OAAQ,KC1Ed,OAAoB,OCApB,OAAoB,OAKb,YACL,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAY,EAAE,MAAM,GACpB,EAAwB,AAAG,WAC/B,GAAU,EAAG,EAAO,wBACpB,CAAC,EAAW,GAAI,EAAG,IAEf,EAAkB,AAAG,WACzB,GAAU,EAAG,EAAO,iBACpB,CAAC,EAAW,GAAI,IAElB,MAAO,CAAE,wBAAuB,qBDb7B,YACL,EACA,EACA,EACA,CACA,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAQ,EAAmB,EAAG,EAAO,OAAQ,CAAC,EAAG,IACjD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IACrD,EAAQ,EAAmB,EAAO,EAAO,OAAQ,CAAC,EAAG,IAErD,EAAiB,GAAmB,EAAQ,EAAO,iBACnD,EAAiB,GAAmB,EAAG,EAAO,iBAC9C,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAClD,EAAiB,GAAmB,EAAO,EAAO,iBAElD,EAAiB,AAAG,UAAO,CAC/B,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,sBACf,EAAe,uBACd,GAEG,EAAmB,AAAG,UAAO,CACjC,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,gBACf,EAAe,iBACd,GAEH,MAAO,CACL,iBACA,sBE3CC,WAA4B,CAOjC,YAAY,CAAE,gBAAe,cAAuC,GAAI,CAN9D,WAAgB,wBAUxB,GAHA,KAAK,eAAiB,GAAiB,GACvC,KAAK,YAAc,GAAc,IAE7B,MAAO,MAAK,gBAAmB,UAAY,KAAK,gBAAkB,GAAK,KAAK,gBAAkB,EAChG,KAAM,IAAI,OAAM,GAAG,KAAK,iEAG1B,GAAI,MAAO,MAAK,aAAgB,SAC9B,KAAM,IAAI,OAAM,GAAG,KAAK,iDAIxB,gBAAwB,CAAE,MAAO,MAAK,kBAEtC,aAAqB,CAAE,MAAO,MAAK,cTZlC,oBAA6B,EAAyB,CAC3D,aAAc,CACZ,MAAM,kBAGD,aAAa,EAAiB,CACnC,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,gDAGlB,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAc,AAAG,QAAK,EAAM,cAAc,IAAK,IAAQ,WACvD,EAAI,AAAG,OAAI,AAAG,OAAI,EAAa,AAAG,UAAO,sBAAwB,AAAG,UAAO,IAC3E,EAAW,GAAY,EAAG,EAAO,aAEjC,CAAE,iBAAgB,oBAAqB,GAAgB,EAAS,IAAK,EAAS,OAAQ,EAAO,kBAEnG,MAAO,IAAY,EAAgB,EAAkB,EAAO,qBAInD,SAAQ,EAAkB,CACrC,MAAO,MAAK,aAAa,KAAM,GAAW,SAG/B,aACX,EACA,EAAkC,GACR,CAC1B,GAAM,CAAE,aAAY,iBAAkB,GAAI,GAAsB,GAE1D,EAAW,KAAM,GAAW,GAE5B,CACJ,MAAO,EACP,OAAQ,GACN,KAAK,aAAa,GAEhB,EAAQ,EAAO,GACf,EAAS,EAAQ,GACvB,OAAS,GAAI,EAAG,EAAI,EAAO,OAAQ,IACjC,EAAO,GAAG,UACV,EAAQ,GAAG,UAGb,GAAM,GAAa,MAAM,KAAK,KAAM,GAAO,QAErC,EAAU,GACd,EACA,EACA,EAJmB,GAMnB,GAGI,EAAe,EAAS,2BAA2B,GACnD,EAAY,EAAS,UACrB,EAAO,EAAY,EAAa,MAChC,EAAO,EAAY,EAAa,OAEhC,EAAY,EAAM,YAClB,EAAU,EACb,IAAI,AAAC,GAAQ,CACZ,GAAM,CAAC,EAAK,GAAU,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACf,CAAC,GAAM,IAAS,CACpB,KAAK,IAAI,EAAG,EAAU,GAAK,IAC3B,KAAK,IAAI,EAAK,EAAU,GAAK,KAC7B,IAAI,AAAC,GAAQ,EAAM,GACrB,MAAO,IAAI,GACT,EAAW,GACX,GAAI,IACF,GACA,EACA,GAAQ,GACR,EAAS,GAEX,CACE,OAAQ,EAAS,eAAe,GAChC,MAAO,EAAS,cAAc,OAKtC,SAAM,UACN,EAAO,UACA,EAGC,qBAA8B,CACtC,MAAO,wBAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,GAG1B,cAAc,EAAuB,CAC7C,MAAO,IAAc,KUhHlB,YAA8B,EAAuB,CAC1D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,EAGF,YAAgC,EAAuB,CAC5D,MAAO,IAAqB,GAIvB,oBAA+B,GAAe,GCd9C,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,QAAU,SACpB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,QAAS,SACnB,GAAI,GAAM,OAAQ,SAClB,GAAI,GAAM,QAAS,UAGR,GAAwB,CACnC,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAA+C,CAAC,QAAS,QAAS,QAElE,GAAqB,oBACrB,GAAoC,mCCvBjD,MAAoB,OCapB,GAAM,IAAW,AAAC,GAAa,MAAO,IAAQ,SAEvC,YAAwB,EAAa,CAC1C,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,mBAAmB,KAGrC,GAAI,MAAO,GAAO,oBAAuB,UACvC,KAAM,IAAI,OAAM,wDAAwD,EAAO,sBAGjF,GAAI,CAAC,GAAS,EAAO,eAAiB,EAAO,aAAe,GAAK,EAAO,aAAe,EACrF,KAAM,IAAI,OAAM,gEAAgE,EAAO,gBAGzF,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,MAAM,AAAC,GAAW,MAAO,IAAM,UAElD,KAAM,IAAI,OAAM,kEAAkE,KAAK,UAAU,EAAO,YAG1G,GACE,CAAC,MAAM,QAAQ,EAAO,UACnB,CAAC,EAAO,QAAQ,QAChB,CAAC,EAAO,QAAQ,IAAI,AAAC,GAAW,GAAK,IAAI,MAAM,AAAC,GAAW,GAAS,EAAE,IAAM,GAAS,EAAE,IAE1F,KAAM,IAAI,OAAM,wEAAwE,KAAK,UAAU,EAAO,YAGhH,GAAI,EAAO,SACT,EAAC,MAAM,QAAQ,EAAO,UACnB,EAAO,QAAQ,SAAW,GAC1B,CAAC,EAAO,QAAQ,MAAM,KAEzB,KAAM,IAAI,OAAM,8EAA8E,KAAK,UAAU,EAAO,YCjDxH,OAAoB,OCApB,OAAoB,OAEb,YAAe,EAA6B,CACjD,MAAO,AAAG,SAAK,IAAM,CACnB,GAAM,GAAM,AAAG,OAAI,EAAG,AAAG,UAAO,qBAChC,MAAO,AAAG,QAAI,AAAG,QAAK,AAAG,OAAI,EAAG,IAAO,KDApC,YAA2B,EAAgB,EAAwC,CACxF,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,UAAO,EAAK,EAAO,KAAK,QAAS,CAAC,EAAG,GAAI,SAClD,EAAM,AAAG,OAAI,EAAK,EAAO,GAAG,KAC5B,EAAM,AAAG,OAAI,EAAK,EAAO,GAAG,SAC5B,EAAM,AAAG,OAAI,EAAK,EAAO,KAAK,MACvB,GAAM,KEZjB,OAAoB,OAKb,YAAgC,EAAgB,EAA0C,CAC/F,MAAO,AAAG,SAAK,IAAM,CACnB,GAAI,GAAM,AAAG,OAAI,EAAG,CAAC,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,CAAC,EAAG,KACjD,SAAM,AAAG,mBAAgB,EAAK,EAAO,iBAAkB,EAAO,iBAAkB,CAAC,EAAG,GAAI,SACxF,EAAM,AAAG,OAAI,EAAK,EAAO,MAClB,GAAM,KCVjB,OAAoB,OASpB,YAA2B,EAAwC,EAA+B,CAChG,GAAM,GAAoB,GAAyB,EAAgB,GAEnE,WAAgC,EAAc,EAAiC,CAC7E,GAAM,GAAM,AAAG,YAAS,EAAe,IACjC,EAAU,AAAG,YAAS,EAAe,IAE3C,SAAc,KACZ,CAAE,UAAW,GAAG,SAChB,CAAE,UAAW,GAAG,cAEX,CAAE,MAAK,WAGhB,WAAwC,EAAoB,EAAqB,EAAyC,CACxH,GAAM,GAAO,EAAkB,EAAY,EAAa,EAAG,GAAG,UACxD,EAAK,EAAuB,EAAa,GAAG,QAClD,MAAO,CAAE,OAAM,MAEjB,GAAM,GAA6B,GAAkC,EAAgB,GAErF,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACA,EACA,EACgE,CAChE,GAAM,CACJ,iBACA,uBACE,EAAsB,GAEpB,EAAgC,GAChC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAgB,GAClC,EAEJ,GAAI,EAAO,mBAAoB,CAC7B,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAAO,mBACjB,EAAkB,EAAI,EAAI,EAAG,SAC7B,EAA2B,EAAI,EAAI,SACjC,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,EAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAA2B,EAAI,EAAI,SAC3C,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAK,EAA2B,EAAI,EAAI,SAAW,OAC3D,GAAQ,EAAkB,GAAM,GAAM,EAAI,EAAI,EAAiB,EAAG,SACxE,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,cAErD,CACL,GAAM,CAAC,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,EAAI,GAAM,EACvC,EAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,EAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAA+B,EAAI,EAAI,SAC/C,GAAQ,EAAkB,EAAI,EAAI,EAAiB,EAAG,SAC5D,EAAS,CACP,QAAO,QAAO,SAAO,SAAO,QAAO,SAAO,SAAO,SAAO,UAG5D,GAAI,IAAsB,SAAW,EACnC,KAAM,IAAI,OAAM,kCAAkC,IAAsB,UAE1E,MAAO,CAAE,SAAQ,iBChFnB,YAA2B,EAAgB,EAA+B,CACxE,GAAM,GAAqB,EAA0B,EAAW,GAEhE,WAAgC,EAA2B,CACzD,GAAM,GAAM,EAAmB,GAAG,QAAc,GAC1C,EAAU,EAAmB,GAAG,YAAkB,GACxD,MAAO,CAAE,MAAK,WAGhB,WAA2B,EAA4B,CACrD,GAAM,GAAU,EAAmB,GAAG,YAAkB,GAClD,EAAO,EAAmB,GAAG,SAAe,GAClD,MAAO,CAAE,UAAS,QAGpB,WAAwC,EAAmC,CACzE,GAAM,GAAO,EAAkB,GAAG,UAC5B,EAAK,EAAuB,GAAG,QACrC,MAAO,CAAE,OAAM,MAGjB,GAAM,GAA6B,GAA+B,GAClE,MAAO,CACL,oBACA,iCACA,8BAIG,YACL,EACA,EACgE,CAChE,GAAM,GAAgC,GAEhC,CACJ,oBACA,iCACA,8BACE,GAAkB,EAAW,GAE7B,EAEJ,GAAI,EAAO,mBAAoB,CAE7B,GAAM,GAAc,EAAO,aAAe,EAAO,YAAY,QAAU,EACvE,EAAS,CACP,MAAO,EAAO,mBAAqB,EAAkB,SAAW,EAA2B,SAC3F,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAA2B,SAClC,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAa,EAAI,EAA2B,SAAW,OAC9D,MAAO,EAAkB,cAG3B,GAAS,CACP,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAA+B,SACtC,MAAO,EAAkB,UAI7B,SAA2B,EAAW,GAC/B,CAAE,SAAQ,iBC7EZ,YAAwB,CAO7B,YAAY,CAAE,YAAW,kBAAuC,GAAI,CAN1D,WAAgB,oBAUxB,GAHA,KAAK,WAAa,GAAa,IAC/B,KAAK,gBAAkB,GAAkB,GAErC,MAAO,MAAK,YAAe,UAAY,KAAK,WAAa,IAAO,EAClE,KAAM,IAAI,OAAM,GAAG,KAAK,6DAG1B,GAAI,MAAO,MAAK,iBAAoB,UAAY,KAAK,iBAAmB,GAAK,KAAK,iBAAmB,EACnG,KAAM,IAAI,OAAM,GAAG,KAAK,qEAIxB,YAAoB,CAAE,MAAO,MAAK,cAElC,iBAAyB,CAAE,MAAO,MAAK,kBPJtC,oBAA6B,EAAmC,CAKrE,YAAY,EAA0B,CACpC,MAAM,cACN,GAAe,GACf,KAAK,QAAU,KAGN,SAA2B,CACpC,MAAO,MAAK,WAGH,kBAA2B,CACpC,MAAO,MAAK,OAAO,iBAAmB,KAAK,OAAO,QAAQ,OAAS,KAG1D,kBAA0B,CACnC,MAAO,GAAK,MAAK,gBAAkB,KAAK,OAAO,QAAQ,OAAS,GAG3D,cAAc,EAAgB,EAAiD,CACpF,GAAI,GAAM,GAAkB,EAAG,EAAO,OACtC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAkB,EAAK,EAAO,OACpC,EAAM,GAAkB,EAAK,EAAO,OAC7B,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAgB,EAAsC,CACxE,GAAI,GAAM,KAAK,OAAO,mBAClB,GAAM,GAAU,EAAG,EAAO,MAAqB,QAAS,KACxD,GAAuB,EAAG,EAAO,OACrC,SAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,GAAuB,EAAK,EAAO,OACzC,EAAM,AAAG,UAAQ,EAAK,CAAC,EAAG,GAAI,CAAC,EAAG,GAAI,QACtC,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EACjE,EAAM,EAAO,MAAQ,GAAuB,EAAK,EAAO,OAAS,EAC1D,GAAU,EAAK,EAAO,MAAO,QAAS,IAGxC,aAAa,EAAiB,EAAgC,CACnE,GAAM,CAAE,UAAW,KAEnB,GAAI,CAAC,EACH,KAAM,IAAI,OAAM,4CAGlB,MAAO,AAAG,QAAK,IAAM,CACnB,GAAI,GAAc,AAAG,OAAK,EAAM,cAAc,EAAW,IAAQ,WACjE,SAAc,KAAK,OAAO,QACtB,EAAU,EAAa,KAAK,OAAO,SACnC,EACJ,EAAc,EAAY,IAAI,AAAG,SAAO,MACjC,KAAK,OAAO,mBACf,KAAK,aAAa,EAAa,GAC/B,KAAK,cAAc,EAAa,UAI3B,SAAQ,EAAkB,EAAyC,CAC9E,MAAO,MAAK,aAAa,KAAM,GAAW,GAAQ,QAGvC,QAAO,EAAkB,EAAoC,GAAgC,CACxG,GAAM,CAAE,YAAW,kBAAmB,GAAI,IAAkB,GACtD,EAAW,KAAM,GAAW,GAC5B,EAAM,KAAM,MAAK,aAAa,EAAU,GACxC,EAAO,AAAG,OAAK,IAAM,AAAG,UAAQ,GAAK,GAAG,cACxC,EAAkB,CACtB,MAAO,EAAS,cAAc,GAC9B,OAAQ,EAAS,eAAe,IAG5B,EAAU,KAAM,MAAK,aAAa,EAAM,EAAS,2BAA2B,GAAI,GACtF,EAAI,UACJ,EAAK,UAEL,GAAM,GAAQ,EAAQ,IAAI,AAAC,GAAQ,EAAI,KACjC,EAAS,EAAQ,IAAI,AAAC,GAAQ,EAAI,OAClC,EAAc,EAAQ,IAAI,AAAC,GAAQ,EAAI,YACvC,EAAa,EAAQ,IAAI,AAAC,GAAQ,KAAK,OAAO,QAAQ,EAAI,QAgBhE,MAPmB,AAPH,IACd,EAAM,IAAI,AAAC,GAAQ,EAAI,QAAQ,IAC/B,EACA,KAAK,OAAO,aACZ,IAGyB,IAAI,AAAC,GAAQ,GAAI,IAC1C,EAAO,GACP,EAAY,GACZ,EAAW,GACX,EAAM,GACN,IAKM,qBAA8B,CACtC,MAAO,GAGC,2BAA2B,EAA8B,CACjE,MAAO,IAA2B,EAAW,KAAK,QAG1C,cAAc,EAAuB,CAC7C,GAAM,GAAc,KAAK,OAAO,aAAe,GAAe,qBAExD,EAAa,EAAc,EAAY,OAAS,OACtD,GAAI,IAAe,GAAK,IAAe,GAAK,IAAe,EACzD,KAAM,IAAI,OAAM,oEAAoE,2BAEtF,MAAO,IAAc,EAAS,KAAK,OAAQ,KAAK,gBAAiB,QAGnD,cACd,EACA,EACA,EACA,CACA,GAAM,CAAE,QAAO,UAAW,EACpB,EAAY,KAAK,IAAI,EAAO,GAC5B,EAAoB,EAAY,EAChC,EAAoB,EAAY,EAEhC,EAAW,EAAa,MAAM,GAC9B,EAAW,KAAK,OAAO,QAAQ,OAE/B,CAAC,EAAa,EAAc,GAAqB,AAAG,OAAK,IAAM,CACnE,GAAM,GAAW,EAAa,QAAQ,CAAC,EAAU,EAAU,EAAU,KAAK,kBAEpE,EAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACpE,EAAS,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,IACrE,EAAc,KAAK,gBACrB,AAAG,UAAQ,EAAS,MAAM,CAAC,EAAG,EAAG,EAAG,GAAI,CAAC,EAAU,EAAU,EAAU,KAAK,OAAO,QAAQ,SAAU,GACrG,AAAG,SAAO,GACd,MAAO,CAAC,EAAO,EAAQ,KAGnB,EAAU,GACV,EAAa,KAAM,GAAa,QAChC,EAAY,KAAM,GAAY,QACpC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAM,EAAG,EAAM,EAAU,IAChC,OAAS,GAAS,EAAG,EAAS,EAAU,IAAU,CAChD,GAAM,GAAQ,GAAQ,EAAW,GAAK,GAAK,GAAQ,IACnD,GAAI,CAAC,GAAkB,EAAQ,EAAgB,CAC7C,GAAM,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAQ,GAAM,GAAQ,EAAU,GAAK,GAAK,GAAQ,KAAO,EAAY,EACrE,GAAe,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACvG,EAAgB,KAAK,IAAI,EAAU,GAAK,GAAK,GAAQ,IAAM,KAAK,OAAO,QAAQ,GAAQ,EAAK,EAAY,EACxG,GAAK,EAAO,GAAa,EACzB,GAAK,GAAO,EAAc,EAC1B,GAAM,CAAE,MAAK,MAAK,UAClB,CAAE,cAAY,UAAU,KAAK,gBAC/B,KAAM,MAAK,sBAAsB,EAAkC,IACnE,CAAE,WAAY,EAAG,MAAO,GAC5B,EAAQ,KAAK,CACX,IAAK,GAAI,IAAY,GAAG,GAAG,GAAI,GAAY,GAAI,GAC/C,QACA,WAAY,EAAQ,GACpB,YACG,MAOb,SAAY,UACZ,EAAa,UACb,EAAkB,UACX,OAGK,uBAAsB,EAA4B,EAAmD,CACjH,GAAM,CAAE,MAAK,MAAK,UAAW,EACvB,EAAc,KAAM,GAAc,QACxC,MAAO,OAAM,KAAK,OAAO,QAAQ,QAAQ,KAAK,GAC3C,IAAI,CAAC,EAAG,IAAM,EAAY,GAAK,GAAK,GAAQ,IAC5C,IAAI,CAAC,EAAY,IAAW,EAC3B,aACA,WAED,OAAO,CAAC,EAAK,IAAU,EAAI,WAAa,EAAK,WAAa,EAAM,KA/MhE,MACS,AADT,GACS,qBAAuB,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,IAAK,KAAM,MQPrE,oBAAyB,GAAe,CAC7C,YAAY,EAA8B,GAAM,CAC9C,GAAM,GAAS,CACb,qBACA,aAAc,GACd,QAAS,CAAC,WACN,EACA,CACA,QAAS,GACT,QAAS,IAET,CACA,QAAS,GACT,gBAAiB,KAIvB,MAAM,MAGG,qBAA8B,CACvC,MAAO,MAAK,OAAO,sBAGV,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,MAAK,mBAAqB,GAAoC,GAG7D,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KChDrC,YAA0B,EAAuB,EAA8B,GAAM,CAC1F,GAAM,GAAM,GAAI,IAAW,GAC3B,SAAI,eAAe,GACZ,ECNF,oBAAsC,GAAkB,CAAxD,aAJP,CAIO,oBACK,WAAgB,4BCLrB,WAAwB,MAChB,MAEX,EACY,CACZ,MAAO,GAAY,KAAM,MAAK,YAGnB,MAAkB,CAC7B,KAAM,IAAI,OAAM,6CCRpB,OAAoB,OCDpB,OAAoB,OAOpB,kBACE,EACA,EAEA,EACA,EAEA,EAAwF,CAAC,CAAE,iBAAkB,EAC7G,CACA,GAAM,GAAY,EAAc,IAAI,AAAC,GAAkB,GAAoB,GACvE,EAAoB,GACpB,EAAa,WACX,EAAgD,GACpD,aAAoB,WAChB,KAAM,IAAmB,EAAO,GAChC,KAAM,IAAa,EAAO,IAG1B,EAAU,KAAM,GAAe,GAErC,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAGT,kBACE,EACA,EAEA,EACA,EAEA,EACA,CACA,MAAO,IACL,CAAC,GACD,EACA,KAAO,IAAU,EAAc,EAAM,IACrC,EACA,GC5CG,GAAM,IAAgB,GAEhB,GAAc,CACzB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,UACpB,GAAI,GAAM,SAAU,WAGT,GAAqC,CAAC,QAAS,QAAS,QCF9D,oBAA+B,GAAe,CACnD,aAAc,CACZ,GAAM,GAAS,CACb,mBAAoB,GACpB,aAAc,GACd,QAAS,CAAC,QACV,QAAS,GACT,QAAS,GACT,mBAAoB,GACpB,YAAa,CAAC,EAAG,GAAI,GAAI,GAAI,IAAK,IAAK,MAGzC,MAAM,MAGG,UAAmB,CAC5B,MAAO,MAAK,OAAO,aAGR,aAAY,EAAkB,EAA6D,CAEtG,MAAO,AADkB,MAAM,MAAK,OAAO,EAAO,IAC1B,IAAI,AAAC,GAAQ,GAAI,GAAc,EAAI,MAAO,EAAI,YAAa,CAAE,MAAO,EAAI,WAAY,OAAQ,EAAI,eAGhH,qBAA8B,CACtC,MAAO,2BAGC,2BAA2B,EAA8F,CACjI,MAAO,OAAM,2BAA2B,KCvBrC,GAAM,GAAO,CAClB,eAAgB,GAAI,IACpB,iBAAkB,GAAI,IACtB,WAAY,GAAI,IAChB,kBAAmB,GAAI,IACvB,sBAAuB,GAAI,IAC3B,mBAAoB,GAAI,IACxB,kBAAmB,GAAI,IACvB,aAAc,GAAI,KAUP,GAAiB,CAAC,EAAkB,IAA6D,EAAK,eAAe,YAAY,EAAO,GASxI,GAAmB,CAAC,EAAkB,IAA+D,EAAK,iBAAiB,YAAY,EAAO,GAS9I,GAAa,CAAC,EAAkB,IAA0D,EAAK,WAAW,YAAY,EAAO,GAS7H,GAAsB,AAAC,GAAmE,EAAK,kBAAkB,gBAAgB,GAWjI,GAA0B,AAAC,GAAmE,EAAK,sBAAsB,gBAAgB,GAYzI,GAAwB,AAAC,GAA6D,EAAK,mBAAmB,sBAAsB,GASpI,GAA2B,AAAC,GAAmE,EAAK,kBAAkB,mBAAmB,GASzI,GAAsB,AAAC,GAAiF,EAAK,aAAa,oBAAoB,GAE9I,GAA0B,AAAC,GAAgB,EAAK,eAAe,KAAK,GACpE,GAA4B,AAAC,GAAgB,EAAK,iBAAiB,KAAK,GACxE,GAAsB,AAAC,GAAgB,EAAK,WAAW,KAAK,GAC5D,GAAwB,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACrE,GAA4B,AAAC,GAAgB,EAAK,sBAAsB,KAAK,GAC7E,GAA2B,AAAC,GAAgB,EAAK,mBAAmB,KAAK,GACzE,GAA0B,AAAC,GAAgB,EAAK,kBAAkB,KAAK,GACvE,GAAqB,AAAC,GAAgB,EAAK,aAAa,KAAK,GAG7D,GAAyB,GACzB,GAAc,GACd,GAAkB,GCjGxB,oBAAqE,EAAwB,CAClG,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAA0E,MACrE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAwB,KAAM,IAClC,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,KAEtD,KAAK,gBAGP,MAAO,GAAc,IACnB,CAAC,EAAc,IAAM,GAAmC,EAAc,EAAsB,KAIhG,kBAAmB,CACjB,MAAO,IAAI,IAA2B,KAAM,KAAK,SAI9C,gBAEG,GAA8F,MACzF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,GAAkB,KAAM,IAC5B,EACA,KAAK,MACL,AAAC,GAAS,EAAK,kBAAkB,mBAAmB,GACpD,KAAK,gBAGP,MAAO,IAA0B,EAAc,GAGjD,kBAAmB,CACjB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA0C,CAClD,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCjFnD,oBAAkE,EAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,wBAMP,gBAEG,GAAuE,MAClE,MAA+C,CAC1D,GAAM,GAAgB,KAAM,MAAK,WAE3B,EAAqB,KAAM,IAC/B,EACA,KAAK,MACL,KAAO,IAAU,QAAQ,IAAI,EAAM,IACjC,AAAC,GAAS,EAAK,aAAa,oBAAoB,KAElD,KAAK,gBAGP,MAAO,GAAc,IAAI,CAAC,EAAc,IAAM,CAC5C,GAAM,CAAE,MAAK,SAAQ,qBAAsB,EAAmB,GAC9D,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,KAIpF,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAA2F,MACtF,MAAyD,CACpE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,MAAK,SAAQ,qBAAsB,KAAM,IAC/C,EACA,KAAK,MACL,AAAC,GAAS,EAAK,aAAa,oBAAoB,GAChD,KAAK,gBAGP,MAAO,IAAc,GAAiB,EAAc,EAAQ,GAAoB,GAGlF,qBAAsB,CACpB,MAAO,IAAI,IAAiC,KAAM,KAAK,SAIpD,gBAEG,GAAoC,CAC5C,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAuC,CAC/C,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SCtFnD,oBAAqE,EAAwB,CAClG,YAEY,EAEA,EACV,CACA,QAJU,kBAEA,eAMP,gBAEG,GAAyE,MACpE,MAA8C,CACzD,GAAM,GAAgB,KAAM,MAAK,WAUjC,MAAO,AARa,MAAM,IACxB,EACA,KAAK,MACL,AAAC,GAAU,QAAQ,IAAI,EAAM,IAAI,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,KACzF,KACA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,OAGxD,IAAI,CAAC,EAAY,IAAM,GAAkC,EAAc,GAAI,IAGhG,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,SAI/D,gBAEG,GAA6F,MACxF,MAAwD,CACnE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAEF,GAAM,GAAa,KAAM,IACvB,EACA,KAAK,MACL,AAAC,GAAS,EAAK,mBAAmB,sBAAsB,GACxD,KAEA,AAAC,GAAiB,EAAa,UAAU,MAAM,KAAM,CAAE,iBAAkB,MAG3E,MAAO,IAAyB,EAAc,GAGhD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,SP1DlE,oBAAkE,EAAwB,CAC/F,YAEY,EAEA,EAEA,EACV,CACA,QANU,kBAEA,aAEA,6BAKE,cAAyD,CACrE,MAAO,MAAK,mBACR,EAAK,sBACL,EAAK,oBAIN,gBAEG,GAAqE,MAChE,MAA6C,CACxD,GAAM,GAAgB,KAAM,MAAK,WAC3B,EAAa,EAAc,IAAI,AAAC,GAAQ,EAAI,WAE5C,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,GACrC,KAAM,IAAa,KAAK,MAAO,GAE7B,EAAsB,KAAM,SAAQ,IAAI,EAAM,IAClD,AAAC,GAAS,KAAK,YAAY,gBAAgB,KAG7C,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,EAAc,IAAI,CAAC,EAAc,IAAM,GAAiC,EAAc,EAAoB,KAGnH,qBAAsB,CACpB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,kBAAmB,CACjB,MAAO,IAAI,IAA4C,KAAM,KAAK,OAGpE,qBAAsB,CACpB,MAAO,IAAI,IAA8B,KAAM,KAAK,SAIjD,gBAEG,GAAyF,MACpF,MAAuD,CAClE,GAAM,GAAe,KAAM,MAAK,WAChC,GAAI,CAAC,EACH,OAGF,GAAM,CAAE,aAAc,EAChB,EAAgD,KAAK,gBAAoB,WAC3E,KAAM,IAAmB,KAAK,MAAO,CAAC,IACtC,KAAM,IAAa,KAAK,MAAO,CAAC,IAE9B,EAAY,KAAM,MAAK,YAAY,gBAAgB,EAAM,IAE/D,SAAM,QAAQ,AAAC,GAAM,YAAgB,YAAU,EAAE,WAE1C,GAAiC,EAAc,GAGxD,qBAAsB,CACpB,MAAO,IAAI,IAAkD,KAAM,KAAK,OAG1E,kBAAmB,CACjB,MAAO,IAAI,IAA+C,KAAM,KAAK,OAGvE,oBAAqB,CACnB,MAAO,IAAI,IAAgC,KAAM,KAAK,SQzFnD,oBAA2C,EAAwB,CACxE,YAEY,EAEA,EAAgC,GAAI,GAC9C,CACA,QAJU,aAEA,iBAMP,gBAAiC,GAAqC,MAC9D,MAAgC,CAC3C,GAAM,CAAE,QAAO,WAAY,KAEvB,EACJ,GAAI,YAAmB,IAAyB,EAAS,EAAK,iBAAiB,YAAY,EAAO,WACzF,YAAmB,GAAuB,EAAS,EAAK,eAAe,YAAY,EAAO,WAC1F,YAAmB,IAAmB,EAAS,EAAK,WAAW,YAAY,EAAO,OACtF,MAAM,IAAI,OAAM,wHAErB,MAAO,GAGD,gCAAmE,CAEzE,MAAO,IAAI,SAAiC,KAAO,IAAY,CAC7D,GAAM,GAAa,KAAM,MAAK,MAC9B,EAAQ,EAAW,IAAI,AAAC,GAAc,GAAwB,GAAI,OAItE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,iCACL,KAAK,SAKJ,gBAAmC,GAA+C,MAC1E,MAA0C,CACrD,GAAM,GAAiB,KAAM,IAAI,IAAmB,KAAK,MAAO,KAAK,SACjE,EAAgC,EAAe,GACnD,SAAe,QAAQ,AAAC,GAAkB,CACxC,AAAI,EAAc,MAAQ,EAA8B,OAAO,GAAgC,KAE1F,EAGD,+BAA4E,CAElF,MAAO,IAAI,SAA2C,KAAO,IAAY,CACvE,GAAM,GAAY,KAAM,MAAK,MAC7B,EAAQ,EAAY,GAA4B,GAAI,GAAa,UAIrE,kBAAkB,EAA8B,GAAO,CACrD,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,MACL,GAIJ,qBAAsB,CACpB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,OAIT,kBAAmB,CACjB,MAAO,IAAI,IACT,KAAK,gCACL,KAAK,SCpGJ,YAA0B,EAAkB,EAAgC,GAAI,GAA+C,CACpI,MAAO,IAAI,IAAqB,EAAO,GAGlC,YAAwB,EAAkB,EAAgC,GAAI,GAA6C,CAChI,MAAO,IAAI,IAAmB,EAAO,GCFvC,kBACE,EACA,EACyE,CACzE,MAAO,IAAe,EAAO,GAAI,GAAsB,EAAgB,CAAE,iBAAkB,KACxF,oBACA,sBAGL,kBACE,EACA,EAAoC,GACqC,CACzE,MAAO,IAAe,EAAO,GAAI,IAAkB,IAChD,oBACA,sBAGE,GAAM,IAAW,GC1BjB,YAA2B,EAA+B,EAA+B,CAC9F,GAAI,EAAK,SAAW,EAAK,OAAQ,KAAM,IAAI,OAAM,kDAEjD,GAAM,GAAQ,MAAM,KAAK,GACnB,EAAQ,MAAM,KAAK,GAEzB,MAAO,MAAK,KACV,EACG,IAAI,CAAC,EAAK,IAAM,EAAM,EAAM,IAC5B,OAAO,CAAC,EAAK,IAAS,EAAO,GAAQ,EAAI,ICJzC,YAAkB,CAKvB,YACE,EACA,EAA4B,GAC5B,CACA,KAAK,mBAAqB,EAE1B,GAAM,GAAa,MAAM,QAAQ,GAAU,EAAS,CAAC,GAErD,GAAI,CAAC,EAAW,OACd,KAAM,IAAI,OAAM,2DAGlB,GAAI,GAAQ,EACN,EAAoB,IAAM,UAAU,MAE1C,KAAK,oBAAsB,EAAW,IAAI,AAAC,GAAS,CAClD,GAAI,YAAgB,IAClB,MAAO,GAGT,GAAI,YAAgB,cAClB,MAAO,IAAI,IAAuB,IAAqB,CAAC,IAG1D,GAAI,EAAK,YAAc,EAAK,qBAAsB,cAChD,MAAO,IAAI,IAAuB,IAAqB,CAAC,EAAK,aAG/D,KAAM,IAAI,OAAM,4MAIT,qBAA+C,CAAE,MAAO,MAAK,uBAE7D,oBAA4B,CAAE,MAAO,MAAK,mBAE9C,oBAAoB,EAA+B,EAAqC,CAC7F,MAAO,GACJ,IAAI,AAAC,GAAM,GAAkB,EAAG,IAChC,OAAO,CAAC,EAAI,IAAO,EAAK,EAAI,GACxB,GAAY,QAAU,GAGxB,gBAAgB,EAA0C,CAC/D,MAAO,MAAK,mBACT,IAAI,CAAC,CAAE,cAAa,WAAY,GAAI,IACnC,EACA,KAAK,oBAAoB,EAAiB,KAE3C,OAAO,CAAC,EAAM,IAAU,EAAK,SAAW,EAAK,SAAW,EAAO,GAG7D,cAAc,EAA0C,CAC7D,GAAM,GAAY,KAAK,gBAAgB,GACvC,MAAO,GAAU,SAAW,KAAK,kBAC7B,EACA,GAAI,IAAU,UAAW,EAAU,UAGlC,QAAc,CACnB,MAAO,CACL,kBAAmB,KAAK,kBACxB,mBAAoB,KAAK,mBAAmB,IAAI,AAAC,GAAO,EAAG,iBAIjD,UAAS,EAAwB,CAC7C,GAAM,GAAqB,EAAK,mBAC7B,IAAI,AAAC,GAAY,GAAuB,SAAS,IACpD,MAAO,IAAI,IAAY,EAAoB,EAAK,qBC1E7C,YAAgC,EAAuB,CAC5D,GAAM,GAAM,GAAI,IAChB,SAAI,eAAe,GACZ,ECFF,YAA0B,EAAY,EAA4B,CACvE,GAAM,CAAE,QAAO,UAAW,GAAI,GAAW,EAAW,MAAO,EAAW,QAEtE,GAAI,GAAS,GAAK,GAAU,EAC1B,KAAM,IAAI,OAAM,uCAAuC,KAAK,UAAU,CAAE,QAAO,cAGjF,GAAI,MAAM,QAAQ,GAEhB,MAAQ,GAAuB,IAAI,AAAC,GAAQ,GAAc,EAAK,CAAE,QAAO,YAG1E,GAAI,GAAoB,GAAU,CAChC,GAAM,GAAmB,EAAQ,UAAU,QAAQ,EAAO,GACpD,EAAmB,EAAQ,mBAAmB,QAAQ,EAAiB,IAAI,MAAO,EAAiB,IAAI,QAC7G,MAAO,IAAwB,GAAwB,EAAS,GAAmB,GAGrF,MAAI,IAAoB,GACf,GAAwB,EAAS,EAAQ,UAAU,QAAQ,EAAO,IAGvE,YAAmB,IAAiB,YAAmB,GACjD,EAAgB,QAAQ,EAAO,GAGlC,EjJRT,GAAM,IAAQ,MAAO,UAAY,YAC3B,GAAW,MAAO,YAAc,aAAiB,MAAO,WAAU,WAAc,YACzE,GAAU,CAAE,QAAa,GAAmB,QAAM", "names": [] } diff --git a/package-lock.json b/package-lock.json index f55389a..9ccba28 100644 --- a/package-lock.json +++ b/package-lock.json @@ -13,21 +13,21 @@ "@tensorflow/tfjs-backend-wasm": "^3.3.0", "@tensorflow/tfjs-node": "^3.3.0", "@tensorflow/tfjs-node-gpu": "^3.3.0", - "@types/node": "^14.14.33", + "@types/node": "^14.14.34", "@typescript-eslint/eslint-plugin": "^4.17.0", "@typescript-eslint/parser": "^4.17.0", "@vladmandic/pilogger": "^0.2.14", "chokidar": "^3.5.1", "dayjs": "^1.10.4", - "esbuild": "^0.9.0", - "eslint": "^7.21.0", + "esbuild": "^0.9.2", + "eslint": "^7.22.0", "eslint-config-airbnb-base": "^14.2.1", "eslint-plugin-import": "^2.22.1", "eslint-plugin-json": "^2.1.2", "eslint-plugin-node": "^11.1.0", "eslint-plugin-promise": "^4.3.1", "rimraf": "^3.0.2", - "simple-git": "^2.36.1", + "simple-git": "^2.36.2", "tslib": "^2.1.0", "typescript": "^4.2.3" }, @@ -413,9 +413,9 @@ "dev": true }, "node_modules/@types/node": { - "version": "14.14.33", - "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.33.tgz", - "integrity": "sha512-oJqcTrgPUF29oUP8AsUqbXGJNuPutsetaa9kTQAQce5Lx5dTYWV02ScBiT/k1BX/Z7pKeqedmvp39Wu4zR7N7g==", + "version": "14.14.34", + "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.34.tgz", + "integrity": "sha512-dBPaxocOK6UVyvhbnpFIj2W+S+1cBTkHQbFQfeeJhoKFbzYcVUGHvddeWPSucKATb3F0+pgDq0i6ghEaZjsugA==", "dev": true }, "node_modules/@types/node-fetch": { @@ -1237,9 +1237,9 @@ } }, "node_modules/esbuild": { - "version": "0.9.0", - "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.9.0.tgz", - "integrity": "sha512-IqYFO7ZKHf0y4uJpJfGqInmSRn8jMPMbyI1W0Y2PSjSjJcVP538tC8TleJAS4Y8QeqwajqBTwFKayWVzYlMIgg==", + "version": "0.9.2", + "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.9.2.tgz", + "integrity": "sha512-xE3oOILjnmN8PSjkG3lT9NBbd1DbxNqolJ5qNyrLhDWsFef3yTp/KTQz1C/x7BYFKbtrr9foYtKA6KA1zuNAUQ==", "dev": true, "hasInstallScript": true, "bin": { @@ -1265,9 +1265,9 @@ } }, "node_modules/eslint": { - "version": "7.21.0", - "resolved": "https://registry.npmjs.org/eslint/-/eslint-7.21.0.tgz", - "integrity": "sha512-W2aJbXpMNofUp0ztQaF40fveSsJBjlSCSWpy//gzfTvwC+USs/nceBrKmlJOiM8r1bLwP2EuYkCqArn/6QTIgg==", + "version": "7.22.0", + "resolved": "https://registry.npmjs.org/eslint/-/eslint-7.22.0.tgz", + "integrity": "sha512-3VawOtjSJUQiiqac8MQc+w457iGLfuNGLFn8JmF051tTKbh5/x/0vlcEj8OgDCaw7Ysa2Jn8paGshV7x2abKXg==", "dev": true, "dependencies": { "@babel/code-frame": "7.12.11", @@ -1287,7 +1287,7 @@ "file-entry-cache": "^6.0.1", "functional-red-black-tree": "^1.0.1", "glob-parent": "^5.0.0", - "globals": "^12.1.0", + "globals": "^13.6.0", "ignore": "^4.0.6", "import-fresh": "^3.0.0", "imurmurhash": "^0.1.4", @@ -1295,7 +1295,7 @@ "js-yaml": "^3.13.1", "json-stable-stringify-without-jsonify": "^1.0.1", "levn": "^0.4.1", - "lodash": "^4.17.20", + "lodash": "^4.17.21", "minimatch": "^3.0.4", "natural-compare": "^1.4.0", "optionator": "^0.9.1", @@ -1569,6 +1569,33 @@ "node": ">=10" } }, + "node_modules/eslint/node_modules/globals": { + "version": "13.6.0", + "resolved": "https://registry.npmjs.org/globals/-/globals-13.6.0.tgz", + "integrity": "sha512-YFKCX0SiPg7l5oKYCJ2zZGxcXprVXHcSnVuvzrT3oSENQonVLqM5pf9fN5dLGZGyCjhw8TN8Btwe/jKnZ0pjvQ==", + "dev": true, + "dependencies": { + "type-fest": "^0.20.2" + }, + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/eslint/node_modules/type-fest": { + "version": "0.20.2", + "resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.20.2.tgz", + "integrity": "sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ==", + "dev": true, + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/espree": { "version": "7.3.1", "resolved": "https://registry.npmjs.org/espree/-/espree-7.3.1.tgz", @@ -3292,9 +3319,9 @@ "dev": true }, "node_modules/simple-git": { - "version": "2.36.1", - "resolved": "https://registry.npmjs.org/simple-git/-/simple-git-2.36.1.tgz", - "integrity": "sha512-bN18Ea/4IJgqgbZyE9VpVEUkAu9vyP0VWP7acP0CRC1p/N80GGJ0HhIVeFJsm8TdJLBowiJpdLesQuAZ5TFSKw==", + "version": "2.36.2", + "resolved": "https://registry.npmjs.org/simple-git/-/simple-git-2.36.2.tgz", + "integrity": "sha512-orBEf65GfSiQMsYedbJXSiRNnIRvhbeE5rrxZuEimCpWxDZOav0KLy2IEiPi1YJCF+zaC2quiJF8A4TsxI9/tw==", "dev": true, "dependencies": { "@kwsites/file-exists": "^1.1.1", @@ -4230,9 +4257,9 @@ "dev": true }, "@types/node": { - "version": "14.14.33", - "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.33.tgz", - "integrity": "sha512-oJqcTrgPUF29oUP8AsUqbXGJNuPutsetaa9kTQAQce5Lx5dTYWV02ScBiT/k1BX/Z7pKeqedmvp39Wu4zR7N7g==", + "version": "14.14.34", + "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.34.tgz", + "integrity": "sha512-dBPaxocOK6UVyvhbnpFIj2W+S+1cBTkHQbFQfeeJhoKFbzYcVUGHvddeWPSucKATb3F0+pgDq0i6ghEaZjsugA==", "dev": true }, "@types/node-fetch": { @@ -4835,9 +4862,9 @@ } }, "esbuild": { - "version": "0.9.0", - "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.9.0.tgz", - "integrity": "sha512-IqYFO7ZKHf0y4uJpJfGqInmSRn8jMPMbyI1W0Y2PSjSjJcVP538tC8TleJAS4Y8QeqwajqBTwFKayWVzYlMIgg==", + "version": "0.9.2", + "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.9.2.tgz", + "integrity": "sha512-xE3oOILjnmN8PSjkG3lT9NBbd1DbxNqolJ5qNyrLhDWsFef3yTp/KTQz1C/x7BYFKbtrr9foYtKA6KA1zuNAUQ==", "dev": true }, "escalade": { @@ -4853,9 +4880,9 @@ "dev": true }, "eslint": { - "version": "7.21.0", - "resolved": "https://registry.npmjs.org/eslint/-/eslint-7.21.0.tgz", - "integrity": "sha512-W2aJbXpMNofUp0ztQaF40fveSsJBjlSCSWpy//gzfTvwC+USs/nceBrKmlJOiM8r1bLwP2EuYkCqArn/6QTIgg==", + "version": "7.22.0", + "resolved": "https://registry.npmjs.org/eslint/-/eslint-7.22.0.tgz", + "integrity": "sha512-3VawOtjSJUQiiqac8MQc+w457iGLfuNGLFn8JmF051tTKbh5/x/0vlcEj8OgDCaw7Ysa2Jn8paGshV7x2abKXg==", "dev": true, "requires": { "@babel/code-frame": "7.12.11", @@ -4875,7 +4902,7 @@ "file-entry-cache": "^6.0.1", "functional-red-black-tree": "^1.0.1", "glob-parent": "^5.0.0", - "globals": "^12.1.0", + "globals": "^13.6.0", "ignore": "^4.0.6", "import-fresh": "^3.0.0", "imurmurhash": "^0.1.4", @@ -4883,7 +4910,7 @@ "js-yaml": "^3.13.1", "json-stable-stringify-without-jsonify": "^1.0.1", "levn": "^0.4.1", - "lodash": "^4.17.20", + "lodash": "^4.17.21", "minimatch": "^3.0.4", "natural-compare": "^1.4.0", "optionator": "^0.9.1", @@ -4895,6 +4922,23 @@ "table": "^6.0.4", "text-table": "^0.2.0", "v8-compile-cache": "^2.0.3" + }, + "dependencies": { + "globals": { + "version": "13.6.0", + "resolved": "https://registry.npmjs.org/globals/-/globals-13.6.0.tgz", + "integrity": "sha512-YFKCX0SiPg7l5oKYCJ2zZGxcXprVXHcSnVuvzrT3oSENQonVLqM5pf9fN5dLGZGyCjhw8TN8Btwe/jKnZ0pjvQ==", + "dev": true, + "requires": { + "type-fest": "^0.20.2" + } + }, + "type-fest": { + "version": "0.20.2", + "resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.20.2.tgz", + "integrity": "sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ==", + "dev": true + } } }, "eslint-config-airbnb-base": { @@ -6416,9 +6460,9 @@ "dev": true }, "simple-git": { - "version": "2.36.1", - "resolved": "https://registry.npmjs.org/simple-git/-/simple-git-2.36.1.tgz", - "integrity": "sha512-bN18Ea/4IJgqgbZyE9VpVEUkAu9vyP0VWP7acP0CRC1p/N80GGJ0HhIVeFJsm8TdJLBowiJpdLesQuAZ5TFSKw==", + "version": "2.36.2", + "resolved": "https://registry.npmjs.org/simple-git/-/simple-git-2.36.2.tgz", + "integrity": "sha512-orBEf65GfSiQMsYedbJXSiRNnIRvhbeE5rrxZuEimCpWxDZOav0KLy2IEiPi1YJCF+zaC2quiJF8A4TsxI9/tw==", "dev": true, "requires": { "@kwsites/file-exists": "^1.1.1", diff --git a/package.json b/package.json index 6ba799b..7ac2b9b 100644 --- a/package.json +++ b/package.json @@ -44,21 +44,21 @@ "@tensorflow/tfjs-backend-wasm": "^3.3.0", "@tensorflow/tfjs-node": "^3.3.0", "@tensorflow/tfjs-node-gpu": "^3.3.0", - "@types/node": "^14.14.33", + "@types/node": "^14.14.34", "@typescript-eslint/eslint-plugin": "^4.17.0", "@typescript-eslint/parser": "^4.17.0", "@vladmandic/pilogger": "^0.2.14", "chokidar": "^3.5.1", "dayjs": "^1.10.4", - "esbuild": "^0.9.0", - "eslint": "^7.21.0", + "esbuild": "^0.9.2", + "eslint": "^7.22.0", "eslint-config-airbnb-base": "^14.2.1", "eslint-plugin-import": "^2.22.1", "eslint-plugin-json": "^2.1.2", "eslint-plugin-node": "^11.1.0", "eslint-plugin-promise": "^4.3.1", "rimraf": "^3.0.2", - "simple-git": "^2.36.1", + "simple-git": "^2.36.2", "tslib": "^2.1.0", "typescript": "^4.2.3" }