diff --git a/CHANGELOG.md b/CHANGELOG.md index 120ba49..32370aa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,8 +9,9 @@ ## Changelog -### **HEAD -> master** 2021/09/10 mandic00@live.com +### **HEAD -> master** 2021/09/11 mandic00@live.com +- enable webgl uniforms ### **1.5.2** 2021/09/10 mandic00@live.com diff --git a/build.json b/build.json index 91d2770..f78478d 100644 --- a/build.json +++ b/build.json @@ -79,6 +79,7 @@ "name": "faceapi/browser/esm/nobundle", "platform": "browser", "format": "esm", + "sourcemap": true, "input": "src/index.ts", "output": "dist/face-api.esm-nobundle.js", "external": ["fs","buffer","util","os","@tensorflow","tfjs.esm.js"] diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index e2aa70b..5cfae1c 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -4520,3 +4520,4 @@ export { validateConfig, version3 as version }; +//# sourceMappingURL=face-api.esm-nobundle.js.map diff --git a/dist/face-api.esm-nobundle.js.map b/dist/face-api.esm-nobundle.js.map new file mode 100644 index 0000000..b866651 --- /dev/null +++ b/dist/face-api.esm-nobundle.js.map @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["tfjs.esm.js", "../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/isNodejs.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/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/imageToSquare.ts", "../src/dom/NetInput.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/bufferToVideo.ts", "../src/dom/fetchVideo.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.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/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.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/DetectFaceLandmarksTasks.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", "../src/index.ts"], + "sourcesContent": ["/*\n Face-API\n homepage: \n author: '\n*/\n\n// src/tfjs/tf-browser.ts\nimport { version as tfjsVersion } from \"@tensorflow/tfjs/package.json\";\nimport { version as tfjsCoreVersion } from \"@tensorflow/tfjs-core/package.json\";\nimport { version as tfjsDataVersion } from \"@tensorflow/tfjs-data/package.json\";\nimport { version as tfjsLayersVersion } from \"@tensorflow/tfjs-layers/package.json\";\nimport { version as tfjsConverterVersion } from \"@tensorflow/tfjs-converter/package.json\";\nimport { version as tfjsBackendCPUVersion } from \"@tensorflow/tfjs-backend-cpu/package.json\";\nimport { version as tfjsBackendWebGLVersion } from \"@tensorflow/tfjs-backend-webgl/package.json\";\nimport { version as tfjsBackendWASMVersion } from \"@tensorflow/tfjs-backend-wasm/package.json\";\nexport * from \"@tensorflow/tfjs-core/dist/index.js\";\nexport * from \"@tensorflow/tfjs-layers/dist/index.js\";\nexport * from \"@tensorflow/tfjs-converter/dist/index.js\";\nimport * as data from \"@tensorflow/tfjs-data/dist/index.js\";\nexport * from \"@tensorflow/tfjs-backend-cpu/dist/index.js\";\nexport * from \"@tensorflow/tfjs-backend-webgl/dist/index.js\";\nexport * from \"@tensorflow/tfjs-backend-wasm/dist/index.js\";\nvar version = {\n tfjs: tfjsVersion,\n \"tfjs-core\": tfjsCoreVersion,\n \"tfjs-data\": tfjsDataVersion,\n \"tfjs-layers\": tfjsLayersVersion,\n \"tfjs-converter\": tfjsConverterVersion,\n \"tfjs-backend-cpu\": tfjsBackendCPUVersion,\n \"tfjs-backend-webgl\": tfjsBackendWebGLVersion,\n \"tfjs-backend-wasm\": tfjsBackendWASMVersion\n};\nexport {\n data,\n version\n};\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 = 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 = 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 ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number\n\n private _y: number\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${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 = 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({ x, y, width, height });\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 { x, y, right, bottom } = 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 = 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 override 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 = 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 = 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 = 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) => 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 = 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 override 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 override 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 = 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(sourceObj: TSource, detection: FaceDetection): 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 createVideoElement: () => document.createElement('video'),\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 // eslint-disable-next-line dot-notation\n const Video = global['Video'] || global.HTMLVideoElement;\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 createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video 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 createVideoElement,\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", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof require === 'function'\n && typeof module !== 'undefined'\n && typeof process !== 'undefined' && !!process.version;\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 { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } 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 { 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)) return resolve(null);\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 { 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 = 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 * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } 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 = false\n\n private _inputDimensions: number[][] = []\n\n private _inputSize = 0\n\n constructor(inputs: Array, treatAsBatchInput = false) {\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 = 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 let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize], false, false);\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) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n\n return batchTensor;\n });\n }\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) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n 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) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\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(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\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(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): 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\n .map((det) => (det instanceof FaceDetection\n ? det.forSize(imgWidth, imgHeight).box\n : det))\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 { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${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 * 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", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions = 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 { 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 = 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 { 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, 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 = 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 = false,\n isScaleDown = 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 { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu = 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: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${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(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${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(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${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 { 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(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 { 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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 override dispose(throwOnRedispose = 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", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n public neutral = 0\n public happy = 0\n public sad = 0\n public angry = 0\n public fearful = 0\n public disgusted = 0\n public surprised = 0\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 * 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 = t.dataSync();\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 { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): 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 { 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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${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 * 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 = 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(255) 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 { 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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[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 override dispose(throwOnRedispose = 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 { 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(landmarkTensor.dataSync());\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 { 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 * 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(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 { 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 { 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 { 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 { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } 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, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${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 * 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(255) 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(() => tf.unstack(this.forwardInput(netInput)));\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 { 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 { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } 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: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${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 { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\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\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\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) conv11 = out;\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\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) return 0.0;\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) * 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(maxOutputSize, numBoxes);\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], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = 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], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 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(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\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(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\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(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\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", "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", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name = '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 * 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 if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\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(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\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 const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\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 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(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\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 { 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 { 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\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 { 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\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 = '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 { 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(255) 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 * 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 = 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 override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override 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 = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): 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", "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 const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\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 override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override 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 { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } 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> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override 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(\n faces.map((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> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override 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>> extends PredictAllFaceExpressionsTask {\n override 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>> extends PredictSingleFaceExpressionsTask {\n override 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 { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } 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> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\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> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\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 return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask>> extends PredictAllAgeAndGenderTask {\n override 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>> extends PredictSingleAgeAndGenderTask {\n override 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 { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } 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>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\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 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>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\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, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\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 * 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 { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } 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> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(\n faces.map((face) => this.landmarkNet.detectLandmarks(face)),\n ) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\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> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\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 const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\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", "/* 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 // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\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 return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = 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 override 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 = 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\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): 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 = 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", "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\n// set webgl defaults\nif (browser) {\n tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n tf.ENV.set('WEBGL_CPU_FORWARD', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n}\n"], + "mappings": ";;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAAA;AAAA;AAAA;AAAA;AAAA;AAeA;AACA;AACA;AAEA;AACA;AACA;AAdA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA,IAAI,UAAU;AAAA,EACZ,MAAM;AAAA,EACN,aAAa;AAAA,EACb,aAAa;AAAA,EACb,eAAe;AAAA,EACf,kBAAkB;AAAA,EAClB,oBAAoB;AAAA,EACpB,sBAAsB;AAAA,EACtB,qBAAqB;AAAA;;;AC9BvB;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;;;ACEO,qBACL,KACA,QACA,WAAW,OACX;AACA,MAAI;AAEJ,SAAO,MAAM,GAAG,QAAQ,CAAC,EAAE,GAAG,KAAK,YAAY;AAC7C,UAAM,OAAO,OAAO;AACpB,QAAI,OAAO,KAAK,GAAG,KAAK;AACxB,QAAI,OAAO,GAAG;AAAA;AAGhB,MAAI,UAAU;AACZ,UAAM,OAAO,OAAO,OAAO,SAAS;AACpC,UAAM,KAAK,OAAO;AAClB,QAAI,CAAC,QAAQ,CAAC,IAAI;AAChB;AAAA;AAGF,QAAI,OAAO,KAAK,GAAG,KAAK;AACxB,QAAI,OAAO,GAAG,GAAG,GAAG;AAAA;AAGtB,MAAI;AAAA;;;AC1BN;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;;;ACOO,uBAAwC;AAAA,EAK7C,YAAY,OAAe,QAAgB;AACzC,QAAI,CAAC,cAAc,UAAU,CAAC,cAAc,SAAS;AACnD,YAAM,IAAI,MAAM,wFAAwF,KAAK,UAAU,EAAE,OAAO;AAAA;AAGlI,SAAK,SAAS;AACd,SAAK,UAAU;AAAA;AAAA,MAGN,QAAgB;AAAE,WAAO,KAAK;AAAA;AAAA,MAE9B,SAAiB;AAAE,WAAO,KAAK;AAAA;AAAA,EAEnC,UAAsB;AAC3B,WAAO,IAAI,WAAW,IAAI,KAAK,OAAO,IAAI,KAAK;AAAA;AAAA;;;ADrB5C,kBAAkB,SAAa,KAAa;AACjD,SAAO,mBAAqB,2BAAU,QAAO,MAAM,WAAW;AAAA;AAGzD,oBAAoB,SAAoC;AAC7D,SAAO,SAAS,SAAQ;AAAA;AAGnB,oBAAoB,SAAoC;AAC7D,SAAO,SAAS,SAAQ;AAAA;AAGnB,oBAAoB,SAAoC;AAC7D,SAAO,SAAS,SAAQ;AAAA;AAGnB,oBAAoB,SAAoC;AAC7D,SAAO,SAAS,SAAQ;AAAA;AAGnB,iBAAiB,KAAa;AACnC,SAAO,MAAM,MAAM;AAAA;AAGd,gBAAgB,KAAa;AAClC,SAAO,MAAM,MAAM;AAAA;AAGd,eAAe,KAAa,OAAO,GAAG;AAC3C,QAAM,IAAI,MAAM;AAChB,SAAO,KAAK,MAAM,MAAM,KAAK;AAAA;AAGxB,sBAAsB,KAAmB;AAC9C,SAAO,OAAO,IAAI,SAAS,IAAI;AAAA;AAG1B,mCAAmC,EAAE,OAAO,UAAuB,WAAmB;AAC3F,QAAM,SAAQ,YAAY,KAAK,IAAI,QAAQ;AAC3C,SAAO,IAAI,WAAW,KAAK,MAAM,QAAQ,SAAQ,KAAK,MAAM,SAAS;AAAA;AAGhE,wBAAwB,KAAqB;AAClD,SAAO,IAAI,OAAO,CAAC,KAAK,OAAO,IAAI,IAAI,KAAK,IAAI,MAAM,GAAG,IACtD,IAAI,IAAI,MAAM,IAAI,QAAQ,IAAI;AAAA;AAG5B,eAAe,KAAa,OAAe,MAAwB;AACxE,SAAO,MAAM,KAAK,KAAK,GAAG,IAAI,CAAC,GAAG,MAAM,QAAS,IAAI;AAAA;AAGhD,uBAAuB,KAAU;AACtC,SAAO,CAAC,CAAC,OAAQ,QAAQ,YAAc,QAAQ,aAAc,CAAC,OAAO,MAAM,QAAQ,QAAQ;AAAA;AAGtF,4BAA4B,KAAU;AAC3C,SAAO,cAAc,QAAQ,OAAO,KAAK,OAAO;AAAA;;;AExD3C,kBAA8B;AAAA,EAKnC,YAAY,GAAW,GAAW;AAChC,SAAK,KAAK;AACV,SAAK,KAAK;AAAA;AAAA,MAGR,IAAY;AAAE,WAAO,KAAK;AAAA;AAAA,MAE1B,IAAY;AAAE,WAAO,KAAK;AAAA;AAAA,EAEvB,IAAI,IAAmB;AAC5B,WAAO,IAAI,MAAM,KAAK,IAAI,GAAG,GAAG,KAAK,IAAI,GAAG;AAAA;AAAA,EAGvC,IAAI,IAAmB;AAC5B,WAAO,IAAI,MAAM,KAAK,IAAI,GAAG,GAAG,KAAK,IAAI,GAAG;AAAA;AAAA,EAGvC,IAAI,IAAmB;AAC5B,WAAO,IAAI,MAAM,KAAK,IAAI,GAAG,GAAG,KAAK,IAAI,GAAG;AAAA;AAAA,EAGvC,IAAI,IAAmB;AAC5B,WAAO,IAAI,MAAM,KAAK,IAAI,GAAG,GAAG,KAAK,IAAI,GAAG;AAAA;AAAA,EAGvC,MAAa;AAClB,WAAO,IAAI,MAAM,KAAK,IAAI,KAAK,IAAI,KAAK,IAAI,KAAK;AAAA;AAAA,EAG5C,YAAoB;AACzB,WAAO,KAAK,KAAM,KAAK,KAAK,IAAM,KAAK,KAAK;AAAA;AAAA,EAGvC,QAAe;AACpB,WAAO,IAAI,MAAM,KAAK,MAAM,KAAK,IAAI,KAAK,MAAM,KAAK;AAAA;AAAA;;;ACtClD,gBAAwD;AAAA,SAC/C,OAAO,MAAoB;AACvC,WAAO,CAAC,CAAC,QAAQ,CAAC,KAAK,GAAG,KAAK,GAAG,KAAK,OAAO,KAAK,QAAQ,MAAM;AAAA;AAAA,SAGrD,iBAAiB,KAAU,QAAgB,0BAA0B,OAAO;AACxF,QAAI,CAAC,IAAI,OAAO,MAAM;AACpB,YAAM,IAAI,MAAM,GAAG,yBAAyB,KAAK,UAAU;AAAA;AAG7D,QAAI,CAAC,2BAA4B,KAAI,QAAQ,KAAK,IAAI,SAAS,IAAI;AACjE,YAAM,IAAI,MAAM,GAAG,mBAAmB,IAAI,sBAAsB,IAAI;AAAA;AAAA;AAAA,EAYxE,YAAY,MAA4B,0BAA0B,MAAM;AACtE,UAAM,MAAO,QAAQ;AAErB,UAAM,SAAS,CAAC,IAAI,MAAM,IAAI,KAAK,IAAI,OAAO,IAAI,QAAQ,MAAM;AAChE,UAAM,SAAS,CAAC,IAAI,GAAG,IAAI,GAAG,IAAI,OAAO,IAAI,QAAQ,MAAM;AAE3D,QAAI,CAAC,UAAU,CAAC,QAAQ;AACtB,YAAM,IAAI,MAAM,2EAA2E,KAAK,UAAU;AAAA;AAG5G,UAAM,CAAC,GAAG,GAAG,OAAO,UAAU,SAC1B,CAAC,IAAI,GAAG,IAAI,GAAG,IAAI,OAAO,IAAI,UAC9B,CAAC,IAAI,MAAM,IAAI,KAAK,IAAI,QAAQ,IAAI,MAAM,IAAI,SAAS,IAAI;AAE/D,QAAI,iBAAiB;AAAA,MACnB;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA,OACZ,mBAAmB;AAEtB,SAAK,KAAK;AACV,SAAK,KAAK;AACV,SAAK,SAAS;AACd,SAAK,UAAU;AAAA;AAAA,MAGN,IAAY;AAAE,WAAO,KAAK;AAAA;AAAA,MAE1B,IAAY;AAAE,WAAO,KAAK;AAAA;AAAA,MAE1B,QAAgB;AAAE,WAAO,KAAK;AAAA;AAAA,MAE9B,SAAiB;AAAE,WAAO,KAAK;AAAA;AAAA,MAE/B,OAAe;AAAE,WAAO,KAAK;AAAA;AAAA,MAE7B,MAAc;AAAE,WAAO,KAAK;AAAA;AAAA,MAE5B,QAAgB;AAAE,WAAO,KAAK,IAAI,KAAK;AAAA;AAAA,MAEvC,SAAiB;AAAE,WAAO,KAAK,IAAI,KAAK;AAAA;AAAA,MAExC,OAAe;AAAE,WAAO,KAAK,QAAQ,KAAK;AAAA;AAAA,MAE1C,UAAiB;AAAE,WAAO,IAAI,MAAM,KAAK,MAAM,KAAK;AAAA;AAAA,MAEpD,WAAkB;AAAE,WAAO,IAAI,MAAM,KAAK,OAAO,KAAK;AAAA;AAAA,MAEtD,aAAoB;AAAE,WAAO,IAAI,MAAM,KAAK,MAAM,KAAK;AAAA;AAAA,MAEvD,cAAqB;AAAE,WAAO,IAAI,MAAM,KAAK,OAAO,KAAK;AAAA;AAAA,EAE7D,QAAsB;AAC3B,UAAM,CAAC,GAAG,GAAG,OAAO,UAAU,CAAC,KAAK,GAAG,KAAK,GAAG,KAAK,OAAO,KAAK,QAC7D,IAAI,CAAC,QAAQ,KAAK,MAAM;AAC3B,WAAO,IAAI,IAAI;AAAA,MACb;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA;AAAA;AAAA,EAIV,QAAsB;AAC3B,UAAM,CAAC,GAAG,GAAG,OAAO,UAAU,CAAC,KAAK,GAAG,KAAK,GAAG,KAAK,OAAO,KAAK,QAC7D,IAAI,CAAC,QAAQ,KAAK,MAAM;AAC3B,WAAO,IAAI,IAAI;AAAA,MACb;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA;AAAA;AAAA,EAIV,WAAyB;AAC9B,QAAI;AAAA,MACF;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA,QACX;AACJ,UAAM,OAAO,KAAK,IAAI,QAAQ;AAC9B,QAAI,QAAQ,QAAQ;AAClB,WAAM,OAAO;AACb,eAAS;AAAA;AAEX,QAAI,SAAS,OAAO;AAClB,WAAM,OAAO;AACb,gBAAU;AAAA;AAGZ,WAAO,IAAI,IAAI,EAAE,GAAG,GAAG,OAAO;AAAA;AAAA,EAGzB,QAAQ,GAAuC;AACpD,UAAM,SAAS,aAAa,KAAM,EAAkB,QAAQ;AAC5D,UAAM,SAAS,aAAa,KAAM,EAAkB,SAAS;AAC7D,WAAO,IAAI,IAAI;AAAA,MACb,GAAG,KAAK,IAAI;AAAA,MACZ,GAAG,KAAK,IAAI;AAAA,MACZ,OAAO,KAAK,QAAQ;AAAA,MACpB,QAAQ,KAAK,SAAS;AAAA;AAAA;AAAA,EAInB,IAAI,MAAc,MAA4B;AACnD,UAAM,CAAC,GAAG,GAAG,OAAO,UAAU;AAAA,MAC5B,KAAK,IAAK,OAAO;AAAA,MACjB,KAAK,IAAK,OAAO;AAAA,MACjB,KAAK,QAAQ;AAAA,MACb,KAAK,SAAS;AAAA;AAEhB,WAAO,IAAI,IAAI;AAAA,MACb;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA;AAAA;AAAA,EAIV,mBAAmB,UAAkB,WAAiC;AAC3E,UAAM,EAAE,GAAG,GAAG,OAAO,WAAW;AAChC,UAAM,WAAW,KAAK,IAAI,GAAG;AAC7B,UAAM,WAAW,KAAK,IAAI,GAAG;AAE7B,UAAM,WAAW,QAAQ;AACzB,UAAM,YAAY,SAAS;AAC3B,UAAM,eAAe,KAAK,IAAI,UAAU,WAAW;AACnD,UAAM,gBAAgB,KAAK,IAAI,WAAW,YAAY;AAEtD,WAAQ,IAAI,IAAI;AAAA,MACd,GAAG;AAAA,MAAU,GAAG;AAAA,MAAU,OAAO;AAAA,MAAc,QAAQ;AAAA,OACrD;AAAA;AAAA,EAGC,MAAM,IAAY,IAA0B;AACjD,UAAM,EAAE,OAAO,WAAW;AAC1B,UAAM,IAAI,KAAK,IAAI;AACnB,UAAM,IAAI,KAAK,IAAI;AAEnB,WAAO,IAAI,IAAI;AAAA,MACb;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA;AAAA;AAAA,EAIV,aAAa,aAAqB,YAAoB;AAC3D,UAAM,IAAI,KAAK,QAAQ;AACvB,UAAM,IAAI,KAAK,SAAS;AAExB,UAAM,KAAK;AACX,UAAM,KAAK;AACX,QAAI,MAAM;AACV,QAAI,MAAM;AAEV,QAAI,IAAI,KAAK;AACb,QAAI,IAAI,KAAK;AACb,QAAI,KAAK,KAAK;AACd,QAAI,KAAK,KAAK;AAEd,QAAI,KAAK,YAAY;AACnB,YAAM,CAAC,KAAK,aAAa;AACzB,WAAK;AAAA;AAEP,QAAI,KAAK,aAAa;AACpB,YAAM,CAAC,KAAK,cAAc;AAC1B,WAAK;AAAA;AAEP,QAAI,IAAI,GAAG;AACT,YAAM,IAAI;AACV,UAAI;AAAA;AAEN,QAAI,IAAI,GAAG;AACT,YAAM,IAAI;AACV,UAAI;AAAA;AAGN,WAAO;AAAA,MACL;AAAA,MAAI;AAAA,MAAK;AAAA,MAAI;AAAA,MAAK;AAAA,MAAG;AAAA,MAAI;AAAA,MAAG;AAAA,MAAI;AAAA,MAAG;AAAA;AAAA;AAAA,EAIhC,UAAU,QAAa;AAC5B,WAAO,IAAI,IAAI;AAAA,MACb,MAAM,KAAK,OAAQ,OAAO,OAAO,KAAK;AAAA,MACtC,KAAK,KAAK,MAAO,OAAO,MAAM,KAAK;AAAA,MACnC,OAAO,KAAK,QAAS,OAAO,QAAQ,KAAK;AAAA,MACzC,QAAQ,KAAK,SAAU,OAAO,SAAS,KAAK;AAAA,OAC3C,WAAW;AAAA;AAAA;;;ACjMX,gCAA0B,IAAyC;AAAA,EACxE,YAAY,MAAc,KAAa,OAAe,QAAgB,0BAA0B,OAAO;AACrG,UAAM;AAAA,MACJ;AAAA,MAAM;AAAA,MAAK;AAAA,MAAO;AAAA,OACjB;AAAA;AAAA;;;ACTA,4BAAsB;AAAA,EAW3B,YACE,OACA,YACA,WACA,aACA,WACA;AACA,SAAK,aAAa,IAAI,WAAW,UAAU,OAAO,UAAU;AAC5D,SAAK,SAAS;AACd,SAAK,cAAc;AACnB,SAAK,aAAa;AAClB,SAAK,OAAO,IAAI,IAAI,aAAa,QAAQ,KAAK;AAAA;AAAA,MAGrC,QAAgB;AAAE,WAAO,KAAK;AAAA;AAAA,MAE9B,aAAqB;AAAE,WAAO,KAAK;AAAA;AAAA,MAEnC,YAAoB;AAAE,WAAO,KAAK;AAAA;AAAA,MAElC,MAAW;AAAE,WAAO,KAAK;AAAA;AAAA,MAEzB,YAAwB;AAAE,WAAO,KAAK;AAAA;AAAA,MAEtC,aAAqB;AAAE,WAAO,KAAK,UAAU;AAAA;AAAA,MAE7C,cAAsB;AAAE,WAAO,KAAK,UAAU;AAAA;AAAA,MAE9C,cAAmB;AAAE,WAAO,IAAI,IAAI,KAAK,MAAM,QAAQ,KAAK,UAAU;AAAA;AAAA,EAE1E,QAAQ,OAAe,QAAiC;AAC7D,WAAO,IAAI,gBACT,KAAK,OACL,KAAK,YACL,KAAK,WACL,KAAK,aACL,EAAE,OAAO;AAAA;AAAA;;;ACzCR,kCAA4B,gBAAyC;AAAA,EAC1E,YACE,OACA,aACA,WACA;AACA,UAAM,OAAO,OAAO,IAAI,aAAa;AAAA;AAAA,EAGvB,QAAQ,OAAe,QAA+B;AACpE,UAAM,EAAE,OAAO,aAAa,cAAc,MAAM,QAAQ,OAAO;AAC/D,WAAO,IAAI,cAAc,OAAO,aAAa;AAAA;AAAA;;;ACnB1C,aAAa,MAAW,MAAW,QAAQ,MAAM;AACtD,QAAM,QAAQ,KAAK,IAAI,GAAK,KAAK,IAAI,KAAK,OAAO,KAAK,SAAS,KAAK,IAAI,KAAK,MAAM,KAAK;AACxF,QAAM,SAAS,KAAK,IAAI,GAAK,KAAK,IAAI,KAAK,QAAQ,KAAK,UAAU,KAAK,IAAI,KAAK,KAAK,KAAK;AAC1F,QAAM,eAAe,QAAQ;AAE7B,SAAO,QACH,eAAgB,MAAK,OAAO,KAAK,OAAO,gBACxC,eAAe,KAAK,IAAI,KAAK,MAAM,KAAK;AAAA;;;ACPvC,iBAAiB,KAA4B;AAClD,QAAM,KAAK,IAAI,IAAI,CAAC,OAAO,GAAG;AAC9B,QAAM,KAAK,IAAI,IAAI,CAAC,OAAO,GAAG;AAC9B,QAAM,OAAO,GAAG,OAAO,CAAC,KAAK,MAAO,IAAI,MAAM,IAAI,KAAM;AACxD,QAAM,OAAO,GAAG,OAAO,CAAC,KAAK,MAAO,IAAI,MAAM,IAAI,KAAM;AACxD,QAAM,OAAO,GAAG,OAAO,CAAC,KAAK,MAAO,MAAM,IAAI,IAAI,KAAM;AACxD,QAAM,OAAO,GAAG,OAAO,CAAC,KAAK,MAAO,MAAM,IAAI,IAAI,KAAM;AAExD,SAAO,IAAI,YAAY,MAAM,MAAM,MAAM;AAAA;;;ACPpC,2BACL,OACA,QACA,cACA,QAAQ,MACE;AACV,MAAI,uBAAuB,OACxB,IAAI,CAAC,OAAO,aAAc,GAAE,OAAO,aACnC,KAAK,CAAC,IAAI,OAAO,GAAG,QAAQ,GAAG,OAC/B,IAAI,CAAC,MAAM,EAAE;AAEhB,QAAM,OAAiB;AAEvB,SAAO,qBAAqB,SAAS,GAAG;AACtC,UAAM,OAAO,qBAAqB;AAClC,SAAK,KAAK;AAEV,UAAM,UAAU;AAEhB,UAAM,UAAoB;AAC1B,aAAS,IAAI,GAAG,IAAI,QAAQ,QAAQ,KAAK;AACvC,YAAM,MAAM,QAAQ;AAEpB,YAAM,UAAU,MAAM;AACtB,YAAM,SAAS,MAAM;AAErB,cAAQ,KAAK,IAAI,SAAS,QAAQ;AAAA;AAGpC,2BAAuB,qBAAqB,OAC1C,CAAC,GAAG,MAAM,QAAQ,MAAM;AAAA;AAI5B,SAAO;AAAA;;;ACnCF,mBAAmB,GAAgB,SAAgC;AACxE,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,CAAC,GAAG,GAAG,KAAK;AAClB,UAAM,QAAQ,AAAG,sBAAK,CAAC,GAAG,EAAE,MAAM,MAAM,GAAG,IAAI,IAAI,GAAG;AACtD,UAAM,QAAQ,AAAG,sBAAK,CAAC,GAAG,EAAE,MAAM,MAAM,GAAG,IAAI,IAAI,GAAG;AACtD,UAAM,QAAQ,AAAG,sBAAK,CAAC,GAAG,EAAE,MAAM,MAAM,GAAG,IAAI,IAAI,GAAG;AACtD,UAAM,UAAU,AAAG,wBAAO,CAAC,OAAO,OAAO,QAAQ;AAEjD,WAAO,AAAG,qBAAI,GAAG;AAAA;AAAA;;;ACAd,qBACL,WACA,gBAAgB,OACH;AACb,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,CAAC,QAAQ,SAAS,UAAU,MAAM,MAAM;AAC9C,QAAI,WAAW,OAAO;AACpB,aAAO;AAAA;AAGT,UAAM,UAAU,KAAK,IAAI,SAAS;AAClC,UAAM,gBAAgB,KAAK,MAAM,UAAW,iBAAgB,MAAM;AAClE,UAAM,cAAc,SAAS,QAAQ,IAAI;AAEzC,UAAM,sBAAsB,CAAC,uBAA0C;AACrE,YAAM,qBAAqB,UAAU,MAAM;AAC3C,yBAAmB,eAAe;AAClC,aAAO,AAAG,sBAAK,oBAAoB,GAAG;AAAA;AAGxC,UAAM,sBAAsB,oBAAoB;AAChD,UAAM,yBAAyB,UAAW,oBAAoB,MAAM;AAEpE,UAAM,uBAAuB,iBAAiB,yBAC1C,oBAAoB,0BACpB;AAEJ,UAAM,iBAAiB;AAAA,MACrB;AAAA,MACA;AAAA,MACA;AAAA,MAEC,OAAO,CAAC,MAAM,CAAC,CAAC,GAChB,IAAI,CAAC,MAAiB,AAAG,sBAAK,GAAG;AACpC,WAAO,AAAG,wBAAO,gBAAgB;AAAA;AAAA;;;AC5C9B,sBAAsB,YAAmB;AAC9C,QAAM,QAAQ,WAAW;AACzB,WAAS,IAAI,MAAM,SAAS,GAAG,IAAI,GAAG,KAAK;AACzC,UAAM,IAAI,KAAK,MAAM,KAAK,WAAY,KAAI;AAC1C,UAAM,IAAI,MAAM;AAChB,UAAM,KAAK,MAAM;AACjB,UAAM,KAAK;AAAA;AAEb,SAAO;AAAA;;;ACDF,iBAAiB,GAAW;AACjC,SAAO,IAAK,KAAI,KAAK,IAAI,CAAC;AAAA;AAGrB,wBAAwB,GAAW;AACxC,SAAO,KAAK,IAAI,IAAK,KAAI;AAAA;;;ACHpB,yBAAmB,IAA2B;AAAA,EACnD,YAAY,GAAW,GAAW,OAAe,QAAgB,0BAA0B,OAAO;AAChG,UAAM;AAAA,MACJ;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA,OACZ;AAAA;AAAA;;;ACHP,IAAM,OAAO;AACb,IAAM,OAAO;AACb,IAAM,WAAW;AAOV,0BAA8C;AAAA,EAOnD,YACE,+BACA,SACA,QAAe,IAAI,MAAM,GAAG,IAC5B;AACA,UAAM,EAAE,OAAO,WAAW;AAC1B,SAAK,WAAW,IAAI,WAAW,OAAO;AACtC,SAAK,SAAS;AACd,SAAK,aAAa,8BAA8B,IAC9C,CAAC,OAAO,GAAG,IAAI,IAAI,MAAM,OAAO,SAAS,IAAI;AAAA;AAAA,MAItC,QAAe;AAAE,WAAO,IAAI,MAAM,KAAK,OAAO,GAAG,KAAK,OAAO;AAAA;AAAA,MAE7D,aAAqB;AAAE,WAAO,KAAK,SAAS;AAAA;AAAA,MAE5C,cAAsB;AAAE,WAAO,KAAK,SAAS;AAAA;AAAA,MAE7C,YAAqB;AAAE,WAAO,KAAK;AAAA;AAAA,MAEnC,oBAA6B;AACtC,WAAO,KAAK,WAAW,IACrB,CAAC,OAAO,GAAG,IAAI,KAAK,QAAQ,IAAI,IAAI,MAAM,KAAK,YAAY,KAAK;AAAA;AAAA,EAI7D,QAAiC,OAAe,QAAmB;AACxE,WAAO,IAAK,KAAK,YACf,KAAK,mBACL,EAAE,OAAO;AAAA;AAAA,EAIN,QAAiC,GAAW,GAAc;AAC/D,WAAO,IAAK,KAAK,YACf,KAAK,mBACL,KAAK,UACL,IAAI,MAAM,GAAG;AAAA;AAAA,EAIV,aAAsC,IAAc;AACzD,WAAO,KAAK,QAAQ,GAAG,GAAG,GAAG;AAAA;AAAA,EAcxB,MACL,WACA,UAAkE,IAC7D;AACL,QAAI,WAAW;AACb,YAAM,MAAM,qBAAqB,gBAC7B,UAAU,IAAI,UACd,IAAI,IAAI;AAEZ,aAAO,KAAK,QAAQ,IAAI,GAAG,IAAI,GAAG,MAAM,MAAM;AAAA;AAGhD,UAAM,EAAE,kBAAkB,kBAAkB,EAAE,kBAAkB,OAAO,eAAe,QAAQ;AAE9F,QAAI,kBAAkB;AACpB,aAAO,KAAK;AAAA;AAGd,WAAO,KAAK,aAAa;AAAA;AAAA,EAGnB,YAAiB;AACvB,UAAM,UAAU,KAAK;AAErB,UAAM,CAAC,eAAe,gBAAgB,eAAe;AACrD,UAAM,cAAc,CAAC,OAAc,YAAY,IAAI,IAAI;AACvD,UAAM,iBAAkB,aAAY,iBAAiB,YAAY,mBAAmB;AAEpF,UAAM,OAAO,KAAK,MAAM,iBAAiB;AAEzC,UAAM,WAAW,eAAe;AAEhC,UAAM,IAAI,KAAK,MAAM,KAAK,IAAI,GAAG,SAAS,IAAK,OAAO;AACtD,UAAM,IAAI,KAAK,MAAM,KAAK,IAAI,GAAG,SAAS,IAAK,OAAO;AAEtD,WAAO,IAAI,KAAK,GAAG,GAAG,KAAK,IAAI,MAAM,KAAK,aAAa,IAAI,KAAK,IAAI,MAAM,KAAK,cAAc;AAAA;AAAA,EAGvF,aAAa,SAAsB;AACzC,UAAM,MAAM,QAAQ,KAAK;AACzB,WAAO,IAAI,IAAI,IAAI,QAAQ,SAAS,IAAI,SAAS;AAAA;AAAA,EAGzC,2BAAoC;AAC5C,UAAM,IAAI,MAAM;AAAA;AAAA;;;AC3Hb,mCAA6B,cAAc;AAAA,EAC7B,2BAAoC;AACrD,UAAM,MAAM,KAAK;AACjB,WAAO;AAAA,MACL,IAAI;AAAA,MACJ,IAAI;AAAA,MACJ,eAAe,CAAC,IAAI,IAAI,IAAI;AAAA;AAAA;AAAA;;;ACN3B,oCAA8B,cAAc;AAAA,EAC1C,gBAAyB;AAC9B,WAAO,KAAK,UAAU,MAAM,GAAG;AAAA;AAAA,EAG1B,iBAA0B;AAC/B,WAAO,KAAK,UAAU,MAAM,IAAI;AAAA;AAAA,EAG3B,kBAA2B;AAChC,WAAO,KAAK,UAAU,MAAM,IAAI;AAAA;AAAA,EAG3B,UAAmB;AACxB,WAAO,KAAK,UAAU,MAAM,IAAI;AAAA;AAAA,EAG3B,aAAsB;AAC3B,WAAO,KAAK,UAAU,MAAM,IAAI;AAAA;AAAA,EAG3B,cAAuB;AAC5B,WAAO,KAAK,UAAU,MAAM,IAAI;AAAA;AAAA,EAG3B,WAAoB;AACzB,WAAO,KAAK,UAAU,MAAM,IAAI;AAAA;AAAA,EAGf,2BAAoC;AACrD,WAAO;AAAA,MACL,KAAK;AAAA,MACL,KAAK;AAAA,MACL,KAAK;AAAA,MACL,IAAI;AAAA;AAAA;;;AC/BH,sBAAsC;AAAA,EAK3C,YAAY,OAAe,UAAkB;AAC3C,SAAK,SAAS;AACd,SAAK,YAAY;AAAA;AAAA,MAGR,QAAgB;AAAE,WAAO,KAAK;AAAA;AAAA,MAE9B,WAAmB;AAAE,WAAO,KAAK;AAAA;AAAA,EAErC,SAAS,eAAe,MAAc;AAC3C,WAAO,GAAG,KAAK,QAAQ,eAAe,KAAK,MAAM,KAAK,eAAe;AAAA;AAAA;;;ACjBlE,+BAAyB,IAAgB;AAAA,SAChC,wBAAwB,KAAU,QAAgB;AAC9D,QAAI,iBAAiB,KAAK;AAE1B,QAAI,CAAC,cAAc,IAAI,QAAQ;AAC7B,YAAM,IAAI,MAAM,GAAG,qCAAqC,IAAI;AAAA;AAAA;AAAA,EAMhE,YAAY,KAAiC,OAAe;AAC1D,UAAM;AACN,SAAK,SAAS;AAAA;AAAA,MAGL,QAAgB;AAAE,WAAO,KAAK;AAAA;AAAA;;;ACrBpC,mCAA6B;AAAA,EAKlC,YAAY,OAAe,aAA6B;AACtD,QAAI,CAAE,QAAO,UAAU,WAAW;AAChC,YAAM,IAAI,MAAM;AAAA;AAGlB,QAAI,CAAC,MAAM,QAAQ,gBAAgB,YAAY,KAAK,CAAC,SAAS,CAAE,iBAAgB,gBAAgB;AAC9F,YAAM,IAAI,MAAM;AAAA;AAGlB,SAAK,SAAS;AACd,SAAK,eAAe;AAAA;AAAA,MAGX,QAAgB;AAAE,WAAO,KAAK;AAAA;AAAA,MAE9B,cAA8B;AAAE,WAAO,KAAK;AAAA;AAAA,EAEhD,SAAc;AACnB,WAAO;AAAA,MACL,OAAO,KAAK;AAAA,MACZ,aAAa,KAAK,YAAY,IAAI,CAAC,MAAM,MAAM,KAAK;AAAA;AAAA;AAAA,SAI1C,SAAS,MAAmC;AACxD,UAAM,cAAc,KAAK,YAAY,IAAI,CAAC,MAAW,IAAI,aAAa;AACtE,WAAO,IAAI,uBAAuB,KAAK,OAAO;AAAA;AAAA;;;AC1B3C,iCAA2B,WAAW;AAAA,SAC7B,0BAA0B,KAAU,QAAgB;AAChE,eAAW,wBAAwB,KAAK;AAExC,QACE,CAAC,mBAAmB,IAAI,UACrB,CAAC,mBAAmB,IAAI,aAC3B;AACA,YAAM,IAAI,MAAM,GAAG,uCAAuC,IAAI,eAAe,IAAI;AAAA;AAAA;AAAA,EAQrF,YAAY,KAAiC,OAAe,OAAe,YAAoB;AAC7F,UAAM,KAAK;AACX,SAAK,SAAS;AACd,SAAK,cAAc;AAAA;AAAA,MAGV,QAAgB;AAAE,WAAO,KAAK;AAAA;AAAA,MAE9B,aAAqB;AAAE,WAAO,KAAK;AAAA;AAAA;;;ACvBzC,6BAA6B,KAAwC;AAC1E,SAAO,IAAI,qBAAqB;AAAA;AAG3B,iCAA0C,WAAoB,WAAsD;AACzH,QAAM,YAAY,EAAE;AACpB,SAAO,KAAK,cAAc;AAAA;;;ACVrB,4BAAyC;AAC9C,QAAM,QAAQ,OAAO;AACrB,MAAI,CAAC;AAAO,UAAM,IAAI,MAAM;AAE5B,QAAM,WAAW,MAAM;AACrB,UAAM,IAAI,MAAM;AAAA;AAGlB,SAAO;AAAA,IACL,QAAQ;AAAA,IACR;AAAA,IACA,OAAO;AAAA,IACP;AAAA,IACA,OAAO;AAAA,IACP,qBAAqB,MAAM,SAAS,cAAc;AAAA,IAClD,oBAAoB,MAAM,SAAS,cAAc;AAAA,IACjD,oBAAoB,MAAM,SAAS,cAAc;AAAA,IACjD;AAAA,IACA;AAAA;AAAA;;;AClBG,0BAA0B,IAAsB;AACrD,MAAI,iBAAiB;AAErB,MAAI,CAAC,IAAI;AACP,QAAI;AAEF,WAAK,UAAQ;AAAA,aACN,KAAP;AACA,uBAAiB,IAAI;AAAA;AAAA;AAIzB,QAAM,WAAW,KACb,CAAC,aAAqB,IAAI,QAAgB,CAAC,SAAS,WAAW;AAC/D,OAAG,SAAS,UAAU,CAAC,KAAU,WAAoB,MAAM,OAAO,OAAO,QAAQ;AAAA,OAEjF,MAAM;AACN,UAAM,IAAI,MAAM,qEAAqE;AAAA;AAGzF,SAAO;AAAA,IACL;AAAA;AAAA;;;ACnBG,2BAAwC;AAE7C,QAAM,SAAS,OAAO,aAAa,OAAO;AAC1C,QAAM,QAAQ,OAAO,SAAS,OAAO;AAErC,QAAM,QAAQ,OAAO,YAAY,OAAO;AAExC,QAAM,sBAAsB,MAAM;AAChC,QAAI;AAAQ,aAAO,IAAI;AACvB,UAAM,IAAI,MAAM;AAAA;AAGlB,QAAM,qBAAqB,MAAM;AAC/B,QAAI;AAAO,aAAO,IAAI;AACtB,UAAM,IAAI,MAAM;AAAA;AAGlB,QAAM,qBAAqB,MAAM;AAC/B,QAAI;AAAO,aAAO,IAAI;AACtB,UAAM,IAAI,MAAM;AAAA;AAGlB,QAAM,QAAQ,OAAO;AAGrB,QAAM,aAAa;AAEnB,SAAO;AAAA,IACL,QAAQ,UAAU,MAAM;AAAA;AAAA,IACxB,0BAA0B,OAAO,4BAA4B,MAAM;AAAA;AAAA,IACnE,OAAO,SAAS,MAAM;AAAA;AAAA,IACtB,WAAW,OAAO,aAAa,MAAM;AAAA;AAAA,IACrC,OAAO,OAAO,oBAAoB,MAAM;AAAA;AAAA,IACxC;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,OACG;AAAA;AAAA;;;ACzCA,qBAA8B;AACnC,SAAO,OAAO,WAAW,YACpB,OAAO,aAAa,eACpB,OAAO,qBAAqB,eAC5B,OAAO,sBAAsB,eAC7B,OAAO,qBAAqB,eAC5B,OAAO,cAAc,eACrB,OAAO,6BAA6B;AAAA;;;ACPpC,oBAA6B;AAClC,SAAO,OAAO,WAAW,YACpB,OAAO,cAAY,cACnB,OAAO,WAAW,eAClB,OAAO,YAAY,eAAe,CAAC,CAAC,QAAQ;AAAA;;;ACGnD,IAAI;AAEJ,kBAA+B;AAC7B,MAAI,CAAC,aAAa;AAChB,UAAM,IAAI,MAAM;AAAA;AAElB,SAAO;AAAA;AAGT,gBAAgB,MAAkB;AAChC,gBAAc;AAAA;AAGhB,sBAAsB;AAGpB,MAAI;AAAa,WAAO,OAAO;AAC/B,MAAI;AAAY,WAAO,OAAO;AAC9B,SAAO;AAAA;AAGT,qBAAqB,MAA2B;AAC9C,MAAI,CAAC,aAAa;AAChB;AAAA;AAGF,MAAI,CAAC,aAAa;AAChB,UAAM,IAAI,MAAM;AAAA;AAGlB,QAAM,EAAE,SAAS,YAAY,QAAQ,QAAQ,YAAY,UAAU;AACnE,cAAY,SAAS;AACrB,cAAY,QAAQ;AACpB,cAAY,sBAAsB,KAAI,uBAAwB,OAAM,IAAI;AACxE,cAAY,qBAAqB,KAAI,sBAAuB,OAAM,IAAI;AAEtE,cAAY,YAAY,KAAI,aAAa,YAAY;AACrD,cAAY,QAAQ,KAAI,SAAS,YAAY;AAC7C,cAAY,QAAQ,KAAI,SAAS,YAAY;AAC7C,cAAY,WAAW,KAAI,YAAY,YAAY;AAAA;AAG9C,IAAM,MAAM;AAAA,EACjB;AAAA,EACA;AAAA,EACA;AAAA,EACA;AAAA,EACA;AAAA,EACA;AAAA,EACA;AAAA,EACA;AAAA,EACA;AAAA;AAGF;;;AC3DO,sBAAsB,KAAmB;AAC9C,MAAI,CAAC,IAAI,cAAc,OAAO,QAAQ,UAAU;AAC9C,WAAO,SAAS,eAAe;AAAA;AAEjC,SAAO;AAAA;;;ACHF,6BAA6B,WAA4F;AAC9H,QAAM,EAAE,QAAQ,wDAA6B,IAAI;AAEjD,MAAI,qBAAqB,2BAA0B;AACjD,WAAO;AAAA;AAGT,QAAM,SAAS,aAAa;AAE5B,MAAI,CAAE,mBAAkB,SAAS;AAC/B,UAAM,IAAI,MAAM;AAAA;AAGlB,QAAM,MAAM,OAAO,WAAW;AAC9B,MAAI,CAAC,KAAK;AACR,UAAM,IAAI,MAAM;AAAA;AAGlB,SAAO;AAAA;;;ACfF,IAAK;AAAL,UAAK,iBAAL;AAEL,gCAAW;AAEX,iCAAY;AAEZ,mCAAc;AAEd,oCAAe;AAAA,GARL;AAoBL,iCAA4D;AAAA,EAajE,YAAY,UAAiC,IAAI;AAC/C,UAAM;AAAA,MACJ;AAAA,MAAgB;AAAA,MAAiB;AAAA,MAAW;AAAA,MAAU;AAAA,MAAW;AAAA,QAC/D;AACJ,SAAK,iBAAiB,kBAAkB,eAAe;AACvD,SAAK,kBAAkB,mBAAmB;AAC1C,SAAK,YAAY,aAAa;AAC9B,SAAK,WAAW,YAAY;AAC5B,SAAK,YAAY,aAAa;AAC9B,SAAK,UAAU,WAAW;AAAA;AAAA;AAIvB,0BAAoB;AAAA,EAOzB,YACE,MACA,QACA,UAAiC,IACjC;AAEA,SAAK,OAAO,OAAO,SAAS,WACxB,CAAC,QACA,gBAAgB,gBAAgB,KAAK,OAAO;AACjD,SAAK,SAAS;AACd,SAAK,UAAU,IAAI,qBAAqB;AAAA;AAAA,EAG1C,aAAa,KAAuC;AAClD,UAAM,EAAE,YAAY,KAAK;AACzB,WAAO,KAAK,KAAK,IAAI,CAAC,MAAM,IAAI,YAAY,GAAG,OAAO,OAAO,CAAC,IAAI,OAAQ,KAAK,KAAK,KAAK,IAAK,KAAM,IAAI;AAAA;AAAA,EAG1G,gBAAwB;AACtB,UAAM,EAAE,UAAU,YAAY,KAAK;AACnC,WAAO,KAAK,KAAK,SAAS,WAAY,IAAI;AAAA;AAAA,EAG5C,aAAa,KAA+B,YAAkC;AAC5E,UAAM,EAAE,mBAAmB,KAAK;AAChC,UAAM,cAAc,mBAAmB,eAAe,gBAAgB,mBAAmB,eAAe;AACxG,UAAM,aAAa,mBAAmB,eAAe,eAAe,mBAAmB,eAAe;AAEtG,UAAM,iBAAiB,KAAK,aAAa;AACzC,UAAM,kBAAkB,KAAK;AAC7B,UAAM,IAAK,cAAc,KAAK,OAAO,IAAI,iBAAiB,KAAK,OAAO;AACtE,UAAM,IAAI,aAAa,KAAK,OAAO,IAAI,kBAAkB,KAAK,OAAO;AAGrE,QAAI,YAAY;AACd,YAAM,EAAE,OAAO,WAAW;AAC1B,YAAM,OAAO,KAAK,IAAI,KAAK,IAAI,GAAG,QAAQ,iBAAiB;AAC3D,YAAM,OAAO,KAAK,IAAI,KAAK,IAAI,GAAG,SAAS,kBAAkB;AAC7D,aAAO,EAAE,GAAG,MAAM,GAAG;AAAA;AAEvB,WAAO,EAAE,GAAG;AAAA;AAAA,EAGd,KAAK,WAAkE;AACrE,UAAM,SAAS,aAAa;AAC5B,UAAM,MAAM,oBAAoB;AAEhC,UAAM;AAAA,MACJ;AAAA,MAAiB;AAAA,MAAW;AAAA,MAAU;AAAA,MAAW;AAAA,QAC/C,KAAK;AAET,QAAI,OAAO,GAAG,cAAc;AAC5B,UAAM,eAAe,KAAK,aAAa;AACvC,UAAM,aAAa,KAAK;AAExB,QAAI,YAAY;AAChB,UAAM,YAAY,KAAK,aAAa,KAAK;AACzC,QAAI,SAAS,UAAU,GAAG,UAAU,GAAG,cAAc;AAErD,QAAI,YAAY;AAChB,SAAK,KAAK,QAAQ,CAAC,UAAU,MAAM;AACjC,YAAM,IAAI,UAAU,UAAU;AAC9B,YAAM,IAAI,UAAU,UAAU,IAAM,KAAI,KAAK;AAC7C,UAAI,SAAS,UAAU,GAAG;AAAA;AAAA;AAAA;;;AC9GzB,2BAAqB;AAAA,EAS1B,YAAY,UAA2B,IAAI;AACzC,UAAM;AAAA,MACJ;AAAA,MAAU;AAAA,MAAW;AAAA,MAAO;AAAA,QAC1B;AACJ,SAAK,WAAW,YAAY;AAC5B,SAAK,YAAY,aAAa;AAC9B,SAAK,QAAQ;AAEb,UAAM,0BAA0B;AAAA,MAC9B,gBAAgB,eAAe;AAAA,MAC/B,iBAAiB,KAAK;AAAA;AAExB,SAAK,mBAAmB,IAAI,qBAAqB,KAAK,4BAA4B;AAAA;AAAA;AAI/E,oBAAc;AAAA,EAKnB,YACE,KACA,UAA2B,IAC3B;AACA,SAAK,MAAM,IAAI,IAAI;AACnB,SAAK,UAAU,IAAI,eAAe;AAAA;AAAA,EAGpC,KAAK,WAAkE;AACrE,UAAM,MAAM,oBAAoB;AAEhC,UAAM,EAAE,UAAU,cAAc,KAAK;AAErC,UAAM;AAAA,MACJ;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA,QACX,KAAK;AACT,QAAI,cAAc;AAClB,QAAI,YAAY;AAChB,QAAI,WAAW,GAAG,GAAG,OAAO;AAE5B,UAAM,EAAE,UAAU,KAAK;AACvB,QAAI,OAAO;AACT,UAAI,cAAc,CAAC,QAAQ,EAAE,GAAG,IAAK,YAAY,GAAI,KAAK,KAAK,QAAQ,kBAAkB,KAAK;AAAA;AAAA;AAAA;;;ACxD7F,wBACL,WACA,YACA;AACA,QAAM,kBAAkB,MAAM,QAAQ,cAAc,aAAa,CAAC;AAElE,kBAAgB,QAAQ,CAAC,QAAQ;AAE/B,UAAM,QAAQ,eAAe,gBACzB,IAAI,QACH,oBAAoB,OAAO,IAAI,UAAU,QAAQ;AAGtD,UAAM,MAAM,eAAe,gBACvB,IAAI,MACH,oBAAoB,OAAO,IAAI,UAAU,MAAM,IAAI,IAAI;AAE5D,UAAM,QAAQ,QAAQ,GAAG,MAAM,WAAW;AAC1C,QAAI,QAAQ,KAAK,EAAE,SAAS,KAAK;AAAA;AAAA;;;ACxB9B,uBAAuB,OAAsD;AAClF,QAAM,EAAE,OAAO,UAAU,IAAI;AAE7B,SAAQ,iBAAiB,SAAS,MAAM,YAClC,iBAAiB,SAAS,MAAM,cAAc;AAAA;;;ACH/C,0BAA0B,OAAgE;AAE/F,SAAO,IAAI,QAAQ,CAAC,SAAS,WAAW;AACtC,QAAI,iBAAiB,IAAI,SAAS,UAAU,cAAc;AAAQ,aAAO,QAAQ;AAEjF,qBAAiB,GAAU;AACzB,UAAI,CAAC,EAAE;AAAe;AAEtB,QAAE,cAAc,oBAAoB,QAAQ;AAC5C,QAAE,cAAc,oBAAoB,SAAS;AAC7C,aAAO;AAAA;AAGT,oBAAgB,GAAU;AACxB,UAAI,CAAC,EAAE;AAAe;AACtB,QAAE,cAAc,oBAAoB,QAAQ;AAC5C,QAAE,cAAc,oBAAoB,SAAS;AAC7C,cAAQ;AAAA;AAGV,UAAM,iBAAiB,QAAQ;AAC/B,UAAM,iBAAiB,SAAS;AAAA;AAAA;;;ACtB7B,uBAAuB,KAAsC;AAClE,SAAO,IAAI,QAAQ,CAAC,SAAS,WAAW;AACtC,QAAI,CAAE,gBAAe;AAAO,aAAO,IAAI,MAAM;AAC7C,UAAM,SAAS,IAAI;AACnB,WAAO,SAAS,MAAM;AACpB,UAAI,OAAO,OAAO,WAAW;AAAU,eAAO,IAAI,MAAM;AACxD,YAAM,MAAM,IAAI,SAAS;AACzB,UAAI,SAAS,MAAM,QAAQ;AAC3B,UAAI,UAAU;AACd,UAAI,MAAM,OAAO;AAAA;AAEnB,WAAO,UAAU;AACjB,WAAO,cAAc;AAAA;AAAA;;;ACXlB,4BAA4B,OAA0F;AAC3H,QAAM,EAAE,OAAO,UAAU,IAAI;AAE7B,MAAI,iBAAiB,OAAO;AAC1B,WAAO,IAAI,WAAW,MAAM,cAAc,MAAM;AAAA;AAElD,MAAI,iBAAiB,OAAO;AAC1B,WAAO,IAAI,WAAW,MAAM,YAAY,MAAM;AAAA;AAEhD,SAAO,IAAI,WAAW,MAAM,OAAO,MAAM;AAAA;;;ACNpC,sBAAsB,EAAE,OAAO,UAA0C;AAC9E,QAAM,EAAE,wBAAwB,IAAI;AACpC,QAAM,SAAS;AACf,SAAO,QAAQ;AACf,SAAO,SAAS;AAChB,SAAO;AAAA;AAGF,+BAA+B,OAAwD,MAAuC;AACnI,QAAM,EAAE,0BAAc,IAAI;AAE1B,MAAI,CAAE,kBAAiB,eAAc,CAAC,cAAc,QAAQ;AAC1D,UAAM,IAAI,MAAM;AAAA;AAGlB,QAAM,EAAE,OAAO,WAAW,QAAQ,mBAAmB;AACrD,QAAM,SAAS,aAAa,EAAE,OAAO;AAErC,MAAI,iBAAiB,YAAW;AAC9B,wBAAoB,QAAQ,aAAa,OAAO,GAAG;AAAA,SAC9C;AACL,wBAAoB,QAAQ,UAAU,OAAO,GAAG,GAAG,OAAO;AAAA;AAE5D,SAAO;AAAA;;;ACxBT,mCACE,WACA,QAC4B;AAC5B,QAAM,eAAe,UAAU,IAAI,SAAS;AAE5C,QAAM,CAAC,QAAQ,OAAO,eAAe,UAAU,MAAM,MAAM,WAAW,aAAa,IAAI;AACvF,QAAM,cAAc,AAAG,sBAAK,MAAM,UAAU,KAAK,QAAQ,OAAO,aAAa;AAC7E,QAAM,AAAG,yBAAQ,SAAS,aAAa;AAEvC,cAAY;AAEZ,SAAO;AAAA;;;ACfF,wBAAwB,OAAY;AACzC,QAAM,EAAE,OAAO,QAAQ,UAAU,IAAI;AAErC,SAAO,iBAAiB,SACnB,iBAAiB,UACjB,iBAAiB;AAAA;;;ACFjB,uBAAuB,OAA6C,WAAmB,cAAc,OAAO;AACjH,QAAM,EAAE,OAAO,WAAW,IAAI;AAE9B,MAAI,CAAE,kBAAiB,SAAS,iBAAiB,SAAS;AACxD,UAAM,IAAI,MAAM;AAAA;AAGlB,MAAI,aAAa;AAAG,WAAO,aAAa,EAAE,OAAO,GAAG,QAAQ;AAC5D,QAAM,OAAO,mBAAmB;AAChC,QAAM,SAAQ,YAAY,KAAK,IAAI,KAAK,QAAQ,KAAK;AACrD,QAAM,QAAQ,SAAQ,KAAK;AAC3B,QAAM,SAAS,SAAQ,KAAK;AAE5B,QAAM,eAAe,aAAa,EAAE,OAAO,WAAW,QAAQ;AAC9D,QAAM,cAAc,iBAAiB,SAAS,QAAQ,sBAAsB;AAE5E,QAAM,SAAS,KAAK,IAAI,QAAQ,UAAU;AAC1C,QAAM,KAAK,eAAe,QAAQ,SAAS,SAAS;AACpD,QAAM,KAAK,eAAe,SAAS,QAAQ,SAAS;AACpD,MAAI,YAAY,QAAQ,KAAK,YAAY,SAAS;AAAG,wBAAoB,cAAc,UAAU,aAAa,IAAI,IAAI,OAAO;AAE7H,SAAO;AAAA;;;AChBF,qBAAe;AAAA,EAapB,YAAY,QAAkC,oBAAoB,OAAO;AAZjE,yBAAkD;AAElD,qBAAiC;AAIjC,8BAAqB;AAErB,4BAA+B;AAE/B,sBAAa;AAGnB,QAAI,CAAC,MAAM,QAAQ,SAAS;AAC1B,YAAM,IAAI,MAAM,4HAA4H;AAAA;AAG9I,SAAK,qBAAqB;AAC1B,SAAK,aAAa,OAAO;AAEzB,WAAO,QAAQ,CAAC,OAAO,QAAQ;AAC7B,UAAI,WAAW,QAAQ;AACrB,aAAK,cAAc,OAAO;AAC1B,aAAK,iBAAiB,OAAO,MAAM;AACnC;AAAA;AAGF,UAAI,WAAW,QAAQ;AACrB,cAAM,YAAa,MAAc,MAAM;AACvC,YAAI,cAAc,GAAG;AACnB,gBAAM,IAAI,MAAM,yCAAyC;AAAA;AAG3D,aAAK,cAAc,OAAO;AAC1B,aAAK,iBAAiB,OAAQ,MAAc,MAAM,MAAM;AACxD;AAAA;AAGF,YAAM,SAAU,iBAAyB,IAAI,SAAS,SAAS,QAAQ,sBAAsB;AAC7F,WAAK,UAAU,OAAO;AACtB,WAAK,iBAAiB,OAAO,CAAC,OAAO,QAAQ,OAAO,OAAO;AAAA;AAAA;AAAA,MAIpD,eAAiD;AAC1D,WAAO,KAAK;AAAA;AAAA,MAGH,WAAgC;AACzC,WAAO,KAAK;AAAA;AAAA,MAGH,eAAwB;AACjC,WAAO,KAAK,YAAY,KAAK,KAAK;AAAA;AAAA,MAGzB,YAAoB;AAC7B,WAAO,KAAK;AAAA;AAAA,MAGH,kBAA8B;AACvC,WAAO,KAAK;AAAA;AAAA,MAGH,YAAgC;AACzC,WAAO,KAAK;AAAA;AAAA,MAGH,0BAAwC;AACjD,WAAO,MAAM,KAAK,WAAW,GAAG,GAAG,IACjC,CAAC,GAAG,aAAa,KAAK,2BAA2B;AAAA;AAAA,EAI9C,SAAS,UAAiE;AAC/E,WAAO,KAAK,SAAS,aAAa,KAAK,aAAa;AAAA;AAAA,EAG/C,mBAAmB,UAA4B;AACpD,WAAO,KAAK,iBAAiB;AAAA;AAAA,EAGxB,eAAe,UAA0B;AAC9C,WAAO,KAAK,iBAAiB,UAAU;AAAA;AAAA,EAGlC,cAAc,UAA0B;AAC7C,WAAO,KAAK,iBAAiB,UAAU;AAAA;AAAA,EAGlC,2BAA2B,UAA8B;AAC9D,QAAI,OAAO,KAAK,cAAc,UAAU;AACtC,YAAM,IAAI,MAAM;AAAA;AAGlB,UAAM,QAAQ,KAAK,cAAc;AACjC,UAAM,SAAS,KAAK,eAAe;AACnC,WAAO,0BAA0B,EAAE,OAAO,UAAU,KAAK;AAAA;AAAA,EAYpD,cAAc,WAAmB,iBAAiB,MAAmB;AAC1E,SAAK,aAAa;AAElB,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,eAAe,MAAM,KAAK,WAAW,GAAG,GAAG,IAAI,CAAC,aAAa;AACjE,cAAM,QAAQ,KAAK,SAAS;AAE5B,YAAI,iBAAoB,yBAAQ;AAC9B,cAAI,YAAY,WAAW,SAAS,QAAQ,AAAG,4BAAW;AAC1D,sBAAY,YAAY,WAAW;AAEnC,cAAI,UAAU,MAAM,OAAO,aAAa,UAAU,MAAM,OAAO,WAAW;AACxE,wBAAY,AAAG,uBAAM,eAAe,WAAW,CAAC,WAAW,YAAY,OAAO;AAAA;AAGhF,iBAAO,UAAU,KAAK,WAAW,WAAW;AAAA;AAG9C,YAAI,iBAAiB,IAAI,SAAS,QAAQ;AACxC,iBAAO,AAAG,yBAAQ,WAAW,cAAc,OAAO,WAAW;AAAA;AAG/D,cAAM,IAAI,MAAM,+BAA+B,qGAAqG;AAAA;AAGtJ,YAAM,cAAc,AAAG,uBAAM,aAAa,IAAI,CAAC,MAAM,AAAG,sBAAK,GAAG,aAAa,KAAK,KAAK,WAAW,WAAW,WAAW;AAExH,aAAO;AAAA;AAAA;AAAA;;;ACrIb,0BAAiC,QAAsC;AACrE,MAAI,kBAAkB;AAAU,WAAO;AACvC,QAAM,gBAAgB,MAAM,QAAQ,UAAU,SAAS,CAAC;AACxD,MAAI,CAAC,cAAc;AAAQ,UAAM,IAAI,MAAM;AAC3C,QAAM,aAAa,CAAC,QAAiB,MAAM,QAAQ,UAAU,mBAAmB,SAAS;AACzF,QAAM,aAAa,cAAc,IAAI;AACrC,aAAW,QAAQ,CAAC,OAAO,MAAM;AAC/B,QAAI,CAAC,eAAe,UAAU,CAAC,WAAW,UAAU,CAAC,WAAW,QAAQ;AACtE,UAAI,OAAO,cAAc,OAAO;AAAU,cAAM,IAAI,MAAM,eAAe,WAAW,sEAAsE,cAAc;AACxK,YAAM,IAAI,MAAM,eAAe,WAAW;AAAA;AAE5C,QAAI,WAAW,QAAQ;AAErB,YAAM,YAAY,MAAM,MAAM;AAC9B,UAAI,cAAc;AAAG,cAAM,IAAI,MAAM,eAAe,WAAW,iCAAiC;AAAA;AAAA;AAIpG,QAAM,QAAQ,IAAI,WAAW,IAAI,CAAC,UAAU,eAAe,UAAU,iBAAiB;AACtF,SAAO,IAAI,SAAS,YAAY,MAAM,QAAQ;AAAA;;;ACjBhD,4BAAmC,OAAkB,YAAuE;AAC1H,QAAM,EAAE,WAAW,IAAI;AACvB,MAAI,SAAS;AACb,MAAI,CAAE,kBAAiB,SAAS;AAC9B,UAAM,WAAW,MAAM,WAAW;AAClC,QAAI,SAAS,YAAY;AAAG,YAAM,IAAI,MAAM;AAC5C,UAAM,iBAAiB,SAAS,SAAS;AACzC,aAAS,0BAA0B,SAAS,iBAAiB,MAAM,oBAAoB;AAAA;AAEzF,QAAM,MAAM,oBAAoB;AAChC,QAAM,QAAQ,WACX,IAAI,CAAC,QAAS,eAAe,gBAAgB,IAAI,QAAQ,OAAO,OAAO,OAAO,QAAQ,IAAI,UAAU,KACpG,IAAI,CAAC,QAAQ,IAAI,mBAAmB,OAAO,OAAO,OAAO;AAC5D,SAAO,MAAM,IAAI,CAAC,EAAE,GAAG,GAAG,OAAO,aAAa;AAC5C,UAAM,UAAU,aAAa,EAAE,OAAO;AACtC,QAAI,QAAQ,KAAK,SAAS;AAAG,0BAAoB,SAAS,aAAa,IAAI,aAAa,GAAG,GAAG,OAAO,SAAS,GAAG;AACjH,WAAO;AAAA;AAAA;;;AChBX,kCAAyC,aAAwC,YAAiE;AAChJ,MAAI,CAAC,WAAW,gBAAgB,CAAC,WAAW,cAAc;AACxD,UAAM,IAAI,MAAM;AAAA;AAGlB,MAAI,WAAW,gBAAgB,YAAY,MAAM,KAAK,GAAG;AACvD,UAAM,IAAI,MAAM;AAAA;AAGlB,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,CAAC,WAAW,UAAU,eAAe,YAAY,MAAM,MAAM,WAAW,eAAe,IAAI;AAEjG,UAAM,QAAQ,WACX,IAAI,CAAC,QAAS,eAAe,gBAC1B,IAAI,QAAQ,UAAU,WAAW,MACjC,KACH,IAAI,CAAC,QAAQ,IAAI,mBAAmB,UAAU;AAEjD,UAAM,cAAc,MAAM,IAAI,CAAC;AAAA,MAC7B;AAAA,MAAG;AAAA,MAAG;AAAA,MAAO;AAAA,UACT,AAAG,yBAAQ,YAAY,KAAK,WAAW,UAAU,cAAc,CAAC,GAAG,GAAG,IAAI,CAAC,QAAQ,OAAO;AAEhG,WAAO;AAAA;AAAA;;;ACpCX,4BACE,KAEA,MACmB;AACnB,QAAM,EAAE,UAAU,IAAI;AACtB,QAAM,MAAM,MAAM,MAAM,KAAK;AAC7B,MAAI,CAAE,KAAI,SAAS,MAAM;AACvB,UAAM,IAAI,MAAM,qBAAqB,IAAI,WAAW,IAAI,yBAAyB,IAAI;AAAA;AAEvF,SAAO;AAAA;;;ACTT,0BAAiC,KAAwC;AACvE,QAAM,MAAM,MAAM,aAAa;AAC/B,QAAM,OAAO,MAAO,IAAK;AAEzB,MAAI,CAAC,KAAK,KAAK,WAAW,WAAW;AACnC,UAAM,IAAI,MAAM,wEAAwE,KAAK,kBAAkB,IAAI;AAAA;AAErH,SAAO,cAAc;AAAA;;;ACRvB,yBAAmC,KAAyB;AAC1D,SAAQ,OAAM,aAAa,MAAM;AAAA;;;ACDnC,+BAAsC,KAAoC;AACxE,SAAO,IAAI,aAAa,MAAO,OAAM,aAAa,MAAM;AAAA;;;ACDnD,uBAAuB,KAAsC;AAClE,SAAO,IAAI,QAAQ,CAAC,SAAS,WAAW;AACtC,QAAI,CAAE,gBAAe;AAAO,aAAO,IAAI,MAAM;AAE7C,UAAM,QAAQ,IAAI,SAAS;AAC3B,UAAM,YAAY,MAAM,QAAQ;AAChC,UAAM,UAAU;AAChB,UAAM,cAAc;AACpB,UAAM,QAAQ;AACd,UAAM,MAAM,IAAI,gBAAgB;AAChC,UAAM;AAAA;AAAA;;;ACTV,0BAAiC,KAAwC;AACvE,QAAM,MAAM,MAAM,aAAa;AAC/B,QAAM,OAAO,MAAO,IAAK;AAEzB,MAAI,CAAC,KAAK,KAAK,WAAW,WAAW;AACnC,UAAM,IAAI,MAAM,wEAAwE,KAAK,kBAAkB,IAAI;AAAA;AAErH,SAAO,cAAc;AAAA;;;ACVhB,sBAAsB,KAAyB,kBAA0B;AAC9E,QAAM,0BAA0B,GAAG;AAEnC,MAAI,CAAC,KAAK;AACR,WAAO;AAAA,MACL,cAAc;AAAA,MACd,aAAa;AAAA;AAAA;AAIjB,MAAI,QAAQ,KAAK;AACf,WAAO;AAAA,MACL,cAAc;AAAA,MACd,aAAa,IAAI;AAAA;AAAA;AAIrB,QAAM,WAAW,IAAI,WAAW,aAAa,YAAY,IAAI,WAAW,cAAc,aAAa;AACnG,QAAM,IAAI,QAAQ,UAAU;AAE5B,QAAM,QAAQ,IAAI,MAAM,KAAK,OAAO,CAAC,MAAM;AAE3C,QAAM,eAAe,IAAI,SAAS,WAC9B,MAAM,MAAM,SAAS,KACrB;AAEJ,MAAI,eAAe,WAAY,KAAI,SAAS,WAAW,MAAM,MAAM,GAAG,MAAM,SAAS,KAAK,OAAO,KAAK;AACtG,iBAAe,IAAI,WAAW,OAAO,IAAI,iBAAiB;AAE1D,SAAO;AAAA,IACL;AAAA,IACA,aAAa,iBAAiB,MAAM,IAAI,iBAAiB,GAAG,gBAAgB;AAAA;AAAA;;;AC1BhF,6BACE,KACA,kBAC4B;AAC5B,QAAM,EAAE,aAAa,iBAAiB,aAAa,KAAK;AACxD,QAAM,WAAW,MAAM,UAAuC;AAE9D,SAAO,AAAG,oBAAG,YAAY,UAAU;AAAA;;;ACT9B,yBAAyB,OAAoB,WAAwB,qBAAqB,OAAO;AACtG,QAAM,EAAE,OAAO,WAAW,qBACtB,mBAAmB,aACnB;AACJ,QAAM,QAAQ;AACd,QAAM,SAAS;AACf,SAAO,EAAE,OAAO;AAAA;;;ACFX,0BAAyC;AAAA,EAC9C,YAAY,MAAc;AAIhB,mBAAkC;AAElC,0BAAiC;AALzC,SAAK,QAAQ;AAAA;AAAA,MASJ,SAAiC;AAAE,WAAO,KAAK;AAAA;AAAA,MAE/C,gBAAgC;AAAE,WAAO,KAAK;AAAA;AAAA,MAE9C,WAAoB;AAAE,WAAO,CAAC,CAAC,KAAK;AAAA;AAAA,EAExC,iBAAiB,WAA8B;AACpD,UAAM,EAAE,KAAK,YAAY,KAAK,qBAAqB;AACnD,WAAO,IAAI;AAAA;AAAA,EAGN,sBAAsB,WAAmB,SAAmB;AACjE,UAAM,EAAE,KAAK,YAAY,KAAK,qBAAqB;AACnD,QAAI,SAAS;AACb,QAAI,WAAW;AAAA;AAAA,EAGV,eAAe;AACpB,WAAO,KAAK,eAAe,IAAI,CAAC,EAAE,gBAAiB;AAAA,MACjD,MAAM;AAAA,MACN,QAAQ,KAAK,iBAAiB;AAAA;AAAA;AAAA,EAI3B,qBAAqB;AAC1B,WAAO,KAAK,eAAe,OAAO,CAAC,UAAU,MAAM,kBAAqB;AAAA;AAAA,EAGnE,kBAAkB;AACvB,WAAO,KAAK,eAAe,OAAO,CAAC,UAAU,CAAE,OAAM,kBAAqB;AAAA;AAAA,EAGrE,WAAW;AAChB,SAAK,kBAAkB,QAAQ,CAAC,EAAE,MAAM,sBAAa;AACnD,WAAK,sBAAsB,MAAM,QAAO;AAAA;AAAA;AAAA,EAIrC,SAAS;AACd,SAAK,qBAAqB,QAAQ,CAAC,EAAE,MAAM,QAAQ,eAAe;AAChE,YAAM,UAAS,AAAG,wBAAO,SAAS;AAClC,eAAS;AACT,WAAK,sBAAsB,MAAM;AAAA;AAAA;AAAA,EAI9B,QAAQ,mBAAmB,MAAM;AACtC,SAAK,eAAe,QAAQ,CAAC,UAAU;AACrC,UAAI,oBAAoB,MAAM,OAAO,YAAY;AAC/C,cAAM,IAAI,MAAM,mDAAmD,MAAM;AAAA;AAE3E,YAAM,OAAO;AAAA;AAEf,SAAK,UAAU;AAAA;AAAA,EAGV,kBAAgC;AACrC,WAAO,IAAI,aACT,KAAK,eACF,IAAI,CAAC,EAAE,sBAAa,MAAM,KAAK,QAAO,aACtC,OAAO,CAAC,MAAM,QAAQ,KAAK,OAAO;AAAA;AAAA,QAI5B,KAAK,cAAgE;AAChF,QAAI,wBAAwB,cAAc;AACxC,WAAK,eAAe;AACpB;AAAA;AAEF,UAAM,KAAK,YAAY;AAAA;AAAA,QAGZ,YAAY,KAAyB;AAChD,QAAI,OAAO,OAAO,QAAQ,UAAU;AAClC,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAE1B,UAAM,YAAY,MAAM,cAAc,KAAK,KAAK;AAChD,SAAK,kBAAkB;AAAA;AAAA,QAGZ,aAAa,UAA8B;AACtD,QAAI,YAAY,OAAO,aAAa,UAAU;AAC5C,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAE1B,UAAM,EAAE,aAAa,IAAI;AACzB,UAAM,EAAE,aAAa,iBAAiB,aAAa,UAAU,KAAK;AAClE,UAAM,uBAAuB,CAAC,cAAwB,QAAQ,IAAI,UAAU,IAAI,CAAC,OAAO,SAAS,IAAI,KAAK,CAAC,QAAQ,IAAI;AACvH,UAAM,cAAc,AAAG,oBAAG,qBAAqB;AAC/C,UAAM,WAAW,KAAK,MAAO,OAAM,SAAS,cAAc;AAC1D,UAAM,YAAY,MAAM,YAAY,UAAU;AAC9C,SAAK,kBAAkB;AAAA;AAAA,EAGlB,kBAAkB,WAA8B;AACrD,UAAM,EAAE,eAAe,WAAW,KAAK,2BAA2B;AAClE,SAAK,iBAAiB;AACtB,SAAK,UAAU;AAAA;AAAA,EAGV,eAAe,SAAuB;AAC3C,UAAM,EAAE,eAAe,WAAW,KAAK,cAAc;AACrD,SAAK,iBAAiB;AACtB,SAAK,UAAU;AAAA;AAAA,EAGT,qBAAqB,WAAmB;AAC9C,QAAI,CAAC,KAAK,QAAQ;AAChB,YAAM,IAAI,MAAM;AAAA;AAGlB,UAAM,SAAS,UAAU,MAAM,KAAK,OAAO,CAAC,KAAoD,aAAY;AAE1G,UAAI,CAAC,IAAI,QAAQ,eAAe,WAAU;AACxC,cAAM,IAAI,MAAM,wDAAwD,sBAAqB;AAAA;AAE/F,aAAO,EAAE,KAAK,IAAI,SAAS,mBAAS,SAAS,IAAI,QAAQ;AAAA,OACxD,EAAE,SAAS,KAAK;AAEnB,UAAM,EAAE,KAAK,YAAY;AACzB,QAAI,CAAC,OAAO,CAAC,WAAW,CAAE,KAAI,oBAAuB,0BAAS;AAC5D,YAAM,IAAI,MAAM,8DAA8D;AAAA;AAGhF,WAAO,EAAE,KAAK;AAAA;AAAA;;;ACzIX,gCACL,GACA,QACA,QACa;AACb,SAAO,AAAG,sBAAK,MAAM;AACnB,QAAI,MAAM,AAAG,iCAAgB,GAAG,OAAO,kBAAkB,OAAO,kBAAkB,QAAQ;AAC1F,UAAM,AAAG,qBAAI,KAAK,OAAO;AACzB,WAAO;AAAA;AAAA;;;ACNJ,qBACL,GACA,kBACA,eAAe,OACF;AACb,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,OAAO,AAAG,sBACd,eACI,AAAG,qBACH,AAAG,wBAAO,GAAI,iBAAiB,MAAqB,SAAS,CAAC,GAAG,IAAI,SACrE,iBAAiB,MAAM,QAEvB,uBAAuB,GAAG,iBAAiB,OAA8B,CAAC,GAAG;AAEnF,UAAM,OAAO,uBAAuB,MAAM,iBAAiB,OAAO,CAAC,GAAG;AAEtE,UAAM,MAAM,AAAG,sBAAK,AAAG,qBAAI,MAAM;AACjC,UAAM,OAAO,uBAAuB,KAAK,iBAAiB,OAAO,CAAC,GAAG;AAErE,WAAO,AAAG,sBAAK,AAAG,qBAAI,MAAM,AAAG,qBAAI,MAAM;AAAA;AAAA;AAItC,qBACL,GACA,kBACA,eAAe,OACf,cAAc,MACD;AACb,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,OAAO,AAAG,sBACd,eACI,AAAG,qBACH,AAAG,wBAAO,GAAI,iBAAiB,MAAqB,SAAS,cAAc,CAAC,GAAG,KAAK,CAAC,GAAG,IAAI,SAC5F,iBAAiB,MAAM,QAEvB,uBAAuB,GAAG,iBAAiB,OAA8B,cAAc,CAAC,GAAG,KAAK,CAAC,GAAG;AAE1G,UAAM,OAAO,uBAAuB,MAAM,iBAAiB,OAAO,CAAC,GAAG;AAEtE,UAAM,MAAM,AAAG,sBAAK,AAAG,qBAAI,MAAM;AACjC,UAAM,OAAO,uBAAuB,KAAK,iBAAiB,OAAO,CAAC,GAAG;AAErE,UAAM,MAAM,AAAG,sBAAK,AAAG,qBAAI,MAAM,AAAG,qBAAI,MAAM;AAC9C,UAAM,OAAO,uBAAuB,KAAK,iBAAiB,OAAO,CAAC,GAAG;AAErE,WAAO,AAAG,sBAAK,AAAG,qBAAI,MAAM,AAAG,qBAAI,MAAM,AAAG,qBAAI,MAAM;AAAA;AAAA;;;AChDnD,mBACL,GACA,QACA,UAA4B,QAC5B,WAAW,OACE;AACb,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,MAAM,AAAG,qBACb,AAAG,wBAAO,GAAG,OAAO,SAAS,CAAC,GAAG,IAAI,UACrC,OAAO;AAGT,WAAO,WAAW,AAAG,sBAAK,OAAO;AAAA;AAAA;;;ACd9B,oCAAoC,WAAgB,eAA+B;AACxF,SAAO,KAAK,WAAW,QAAQ,CAAC,SAAS;AACvC,QAAI,CAAC,cAAc,KAAK,CAAC,OAAO,GAAG,iBAAiB,OAAO;AACzD,gBAAU,MAAM;AAAA;AAAA;AAAA;;;ACDf,kCACL,gBACA,eACA;AACA,SAAO,CACL,YACA,aACA,YACA,iBACe;AACf,UAAM,UAAU,AAAG,0BACjB,eAAe,aAAa,cAAc,aAAa,aACvD,CAAC,YAAY,YAAY,YAAY;AAEvC,UAAM,OAAO,AAAG,0BAAS,eAAe;AAExC,kBAAc,KACZ,EAAE,WAAW,GAAG,0BAChB,EAAE,WAAW,GAAG;AAGlB,WAAO,EAAE,SAAS;AAAA;AAAA;;;ACrBf,gCACL,gBACA,eACA;AACA,SAAO,CACL,YACA,aACA,iBACa;AACb,UAAM,aAAa,AAAG,0BAAS,eAAe,aAAa,cAAc,CAAC,YAAY;AACtF,UAAM,UAAU,AAAG,0BAAS,eAAe;AAE3C,kBAAc,KACZ,EAAE,WAAW,GAAG,0BAChB,EAAE,WAAW,GAAG;AAGlB,WAAO;AAAA,MACL,SAAS;AAAA,MACT,MAAM;AAAA;AAAA;AAAA;;;ACHL,gCAA0B;AAAA,EAE/B,YAES,kBAEA,kBAEA,MAEP;AANO;AAEA;AAEA;AAAA;AAAA;;;ACxBJ,2CACL,gBACA,eACA;AACA,SAAO,CAAC,YAAoB,aAAqB,iBAA8C;AAC7F,UAAM,mBAAmB,AAAG,0BAAS,eAAe,IAAI,IAAI,aAAa,CAAC,GAAG,GAAG,YAAY;AAC5F,UAAM,mBAAmB,AAAG,0BAAS,eAAe,aAAa,cAAc,CAAC,GAAG,GAAG,YAAY;AAClG,UAAM,OAAO,AAAG,0BAAS,eAAe;AAExC,kBAAc,KACZ,EAAE,WAAW,GAAG,mCAChB,EAAE,WAAW,GAAG,mCAChB,EAAE,WAAW,GAAG;AAGlB,WAAO,IAAI,oBACT,kBACA,kBACA;AAAA;AAAA;AAKC,wCAEL,oBACA;AACA,SAAO,CAAC,WAAwC;AAC9C,UAAM,mBAAmB,mBAAgC,GAAG,2BAA2B;AACvF,UAAM,mBAAmB,mBAAgC,GAAG,2BAA2B;AACvF,UAAM,OAAO,mBAAgC,GAAG,eAAe;AAE/D,WAAO,IAAI,oBACT,kBACA,kBACA;AAAA;AAAA;;;ACpCC,mCAAmC,WAAgB,eAA+B;AACvF,SAAO,CAAC,cAAsB,WAAmB,eAAwB;AACvE,UAAM,UAAS,UAAU;AAEzB,QAAI,CAAC,SAAS,SAAQ,YAAY;AAChC,YAAM,IAAI,MAAM,sBAAsB,+BAA+B,4BAA4B;AAAA;AAGnG,kBAAc,KACZ,EAAE,cAAc,WAAW,cAAc;AAG3C,WAAO;AAAA;AAAA;;;ACfJ,+BAA+B,SAAuB;AAC3D,MAAI,mBAAmB;AAEvB,0BAAwB,YAAkC;AACxD,UAAM,MAAM,iBAAiB,MAAM,GAAG;AACtC,uBAAmB,iBAAiB,MAAM;AAC1C,WAAO;AAAA;AAGT,iCAA6C;AAC3C,WAAO;AAAA;AAGT,SAAO;AAAA,IACL;AAAA,IACA;AAAA;AAAA;;;ACZG,2BAA2B,gBAAwC,eAA+B;AACvG,QAAM,oBAAoB,yBAAyB,gBAAgB;AACnE,QAAM,6BAA6B,kCAAkC,gBAAgB;AAErF,oCAAkC,YAAoB,aAAqB,cAAsB,eAAe,OAA0B;AACxI,UAAM,QAAQ,eACV,kBAAkB,YAAY,aAAa,GAAG,GAAG,wBACjD,2BAA2B,YAAY,aAAa,GAAG;AAC3D,UAAM,QAAQ,2BAA2B,aAAa,aAAa,GAAG;AACtE,UAAM,SAAQ,2BAA2B,aAAa,aAAa,GAAG;AAEtE,WAAO,EAAE,OAAO,OAAO;AAAA;AAGzB,oCAAkC,YAAoB,aAAqB,cAAsB,eAAe,OAA0B;AACxI,UAAM,EAAE,OAAO,OAAO,kBAAU,yBAAyB,YAAY,aAAa,cAAc;AAChG,UAAM,QAAQ,2BAA2B,aAAa,aAAa,GAAG;AAEtE,WAAO;AAAA,MACL;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA;AAAA;AAIzB,SAAO;AAAA,IACL;AAAA,IACA;AAAA;AAAA;;;ACxBG,uBAAuB,SAA8F;AAC1H,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAE1B,QAAM;AAAA,IACJ;AAAA,MACE,kBAAkB,gBAAgB;AAEtC,QAAM,SAAS,yBAAyB,GAAG,IAAI,UAAU;AACzD,QAAM,SAAS,yBAAyB,IAAI,IAAI;AAChD,QAAM,SAAS,yBAAyB,IAAI,KAAK;AACjD,QAAM,SAAS,yBAAyB,KAAK,KAAK;AAElD,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAG1E,SAAO;AAAA,IACL;AAAA,IACA,QAAQ;AAAA,MACN;AAAA,MAAQ;AAAA,MAAQ;AAAA,MAAQ;AAAA;AAAA;AAAA;;;ACvBvB,+BAA+B,oBAAuE;AAC3G,SAAO,CAAC,WAA+B;AACrC,UAAM,UAAU,mBAAgC,GAAG,kBAAkB;AACrE,UAAM,OAAO,mBAAgC,GAAG,eAAe;AAE/D,WAAO,EAAE,SAAS;AAAA;AAAA;;;ACNf,2BAA2B,WAAgB,eAA+B;AAC/E,QAAM,qBAAqB,0BAA0B,WAAW;AAEhE,QAAM,oBAAoB,sBAAsB;AAChD,QAAM,6BAA6B,+BAA+B;AAElE,oCAAkC,QAAgB,eAAe,OAA0B;AACzF,UAAM,QAAQ,eACV,kBAAkB,GAAG,kBACrB,2BAA2B,GAAG;AAClC,UAAM,QAAQ,2BAA2B,GAAG;AAC5C,UAAM,SAAQ,2BAA2B,GAAG;AAE5C,WAAO,EAAE,OAAO,OAAO;AAAA;AAGzB,oCAAkC,QAAgB,eAAe,OAA0B;AACzF,UAAM,QAAQ,eACV,kBAAkB,GAAG,kBACrB,2BAA2B,GAAG;AAClC,UAAM,QAAQ,2BAA2B,GAAG;AAC5C,UAAM,SAAQ,2BAA2B,GAAG;AAC5C,UAAM,QAAQ,2BAA2B,GAAG;AAE5C,WAAO;AAAA,MACL;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA;AAAA;AAIzB,SAAO;AAAA,IACL;AAAA,IACA;AAAA;AAAA;;;AC7BG,oCACL,WACuE;AACvE,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,MACE,kBAAkB,WAAW;AAEjC,QAAM,SAAS;AAAA,IACb,QAAQ,yBAAyB,UAAU;AAAA,IAC3C,QAAQ,yBAAyB;AAAA,IACjC,QAAQ,yBAAyB;AAAA,IACjC,QAAQ,yBAAyB;AAAA;AAGnC,6BAA2B,WAAW;AAEtC,SAAO,EAAE,QAAQ;AAAA;;;ACdZ,yCAAmC,cAAuG;AAAA,EAC/I,cAAc;AACZ,UAAM;AAAA;AAAA,EAGD,aAAa,OAA8B;AAChD,UAAM,EAAE,WAAW;AAEnB,QAAI,CAAC,QAAQ;AACX,YAAM,IAAI,MAAM;AAAA;AAGlB,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,cAAc,AAAG,sBAAK,MAAM,cAAc,KAAK,OAAO;AAC5D,YAAM,UAAU,CAAC,SAAS,SAAS;AACnC,YAAM,aAAa,UAAU,aAAa,SAAS,IAAI;AAEvD,UAAI,MAAM,YAAY,YAAY,OAAO,QAAQ;AACjD,YAAM,YAAY,KAAK,OAAO;AAC9B,YAAM,YAAY,KAAK,OAAO;AAC9B,YAAM,YAAY,KAAK,OAAO;AAC9B,YAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AAEtC,aAAO;AAAA;AAAA;AAAA,QAIE,QAAQ,OAAwC;AAC3D,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,EAGlC,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,2BAA2B,WAA8B;AACjE,WAAO,2BAA2B;AAAA;AAAA,EAG1B,cAAc,SAAuB;AAC7C,WAAO,cAAc;AAAA;AAAA;;;AC9ClB,6BACL,GACA,QACa;AACb,SAAO,AAAG,sBAAK,MAAM,AAAG,qBACtB,AAAG,wBAAO,GAAG,OAAO,UACpB,OAAO;AAAA;;;ACPJ,wBAAuB,SAAuB,YAAoB,aAA2E;AAClJ,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAE1B,QAAM,kBAAkB,uBAAuB,gBAAgB;AAE/D,QAAM,KAAK,gBAAgB,YAAY,aAAa;AAEpD,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAG1E,SAAO;AAAA,IACL;AAAA,IACA,QAAQ,EAAE;AAAA;AAAA;;;AChBP,qCACL,WACsD;AACtD,QAAM,gBAAgC;AAEtC,QAAM,qBAAqB,0BAA0B,WAAW;AAEhE,2BAAyB,QAA0B;AACjD,UAAM,UAAU,mBAAmB,GAAG,kBAAkB;AACxD,UAAM,OAAO,mBAAmB,GAAG,eAAe;AAClD,WAAO,EAAE,SAAS;AAAA;AAGpB,QAAM,SAAS;AAAA,IACb,IAAI,gBAAgB;AAAA;AAGtB,6BAA2B,WAAW;AAEtC,SAAO,EAAE,QAAQ;AAAA;;;ACtBZ,4BAA4B,WAA8B;AAC/D,QAAM,sBAAyC;AAC/C,QAAM,gBAAmC;AAEzC,SAAO,KAAK,WAAW,QAAQ,CAAC,QAAQ;AACtC,UAAM,MAAM,IAAI,WAAW,QAAQ,gBAAgB;AACnD,QAAI,OAAO,UAAU;AAAA;AAGvB,SAAO,EAAE,qBAAqB;AAAA;;;ACAzB,kCAGG,cAAyB;AAAA,EAGjC,YAAY,OAAe,sBAA+D;AACxF,UAAM;AACN,SAAK,wBAAwB;AAAA;AAAA,MAGpB,uBAAgE;AACzE,WAAO,KAAK;AAAA;AAAA,EASP,OAAO,OAA4C;AACxD,UAAM,EAAE,WAAW;AAEnB,QAAI,CAAC,QAAQ;AACX,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAG1B,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,qBAAqB,iBAAiB,WACxC,KAAK,qBAAqB,aAAa,SACvC;AACJ,aAAO,oBAAoB,mBAAmB,KAAK,mBAAmB,MAAM,IAAI,KAAK,OAAO;AAAA;AAAA;AAAA,EAIhF,QAAQ,mBAAmB,MAAM;AAC/C,SAAK,qBAAqB,QAAQ;AAClC,UAAM,QAAQ;AAAA;AAAA,EAGT,qBAAqB,SAAuB;AACjD,UAAM,EAAE,QAAQ,kBAAkB,KAAK,wBAAwB;AAC/D,SAAK,UAAU;AACf,SAAK,iBAAiB;AAAA;AAAA,EAGjB,wBAAwB,SAAuB;AACpD,WAAO,eAAc,SAAS,KAAK,2BAA2B,KAAK;AAAA;AAAA,EAG3D,2BAA2B,WAA8B;AACjE,UAAM,EAAE,qBAAqB,kBAAkB,mBAAmB;AAElE,SAAK,qBAAqB,kBAAkB;AAE5C,WAAO,4BAA2B;AAAA;AAAA,EAG1B,cAAc,SAAuB;AAC7C,UAAM,MAAM,KAAK;AACjB,UAAM,OAAO,KAAK;AAClB,UAAM,uBAAwB,OAAO,MAAO;AAE5C,UAAM,0BAA0B,QAAQ,MAAM,GAAG,QAAQ,SAAS;AAClE,UAAM,oBAAoB,QAAQ,MAAM,QAAQ,SAAS;AAEzD,SAAK,qBAAqB,eAAe;AACzC,WAAO,KAAK,wBAAwB;AAAA;AAAA;;;AC/EjC,IAAM,yBAAyB,CAAC,WAAW,SAAS,OAAO,SAAS,WAAW,aAAa;AAE5F,4BAAsB;AAAA,EAS3B,YAAY,eAAwC;AAR7C,mBAAU;AACV,iBAAQ;AACR,eAAM;AACN,iBAAQ;AACR,mBAAU;AACV,qBAAY;AACZ,qBAAY;AAGjB,QAAI,cAAc,WAAW,GAAG;AAC9B,YAAM,IAAI,MAAM,8EAA8E,cAAc;AAAA;AAG9G,2BAAuB,QAAQ,CAAC,YAAY,QAAQ;AAClD,WAAK,cAAc,cAAc;AAAA;AAAA;AAAA,EAIrC,gBAAgB;AACd,WAAO,uBACJ,IAAI,CAAC,eAAgB,GAAE,YAAY,aAAa,KAAK,gBACrD,KAAK,CAAC,IAAI,OAAO,GAAG,cAAc,GAAG;AAAA;AAAA;;;AChBrC,sCAAgC,cAA0C;AAAA,EAC/E,YAAY,uBAA6C,IAAI,wBAAwB;AACnF,UAAM,qBAAqB;AAAA;AAAA,EAGtB,aAAa,OAA4C;AAC9D,WAAO,AAAG,sBAAK,MAAM,AAAG,yBAAQ,KAAK,OAAO;AAAA;AAAA,QAGjC,QAAQ,OAAwC;AAC3D,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,QAG/B,mBAAmB,OAAkB;AAChD,UAAM,WAAW,MAAM,WAAW;AAClC,UAAM,MAAM,MAAM,KAAK,aAAa;AACpC,UAAM,sBAAsB,MAAM,QAAQ,IAAI,AAAG,yBAAQ,KAAK,IAAI,OAAO,MAAM;AAC7E,YAAM,QAAO,EAAE;AACf,QAAE;AACF,aAAO;AAAA;AAET,QAAI;AAEJ,UAAM,qBAAqB,oBACxB,IAAI,CAAC,iBAAiB,IAAI,gBAAgB;AAE7C,WAAO,SAAS,eACZ,qBACA,mBAAmB;AAAA;AAAA,EAGf,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,0BAAkC;AAC1C,WAAO;AAAA;AAAA,EAGC,2BAAmC;AAC3C,WAAO;AAAA;AAAA;;;AC5CJ,+BAA+B,KAA0C;AAC9E,SAAO,IAAI,uBAAuB;AAAA;AAG7B,mCAA4C,WAAoB,aAA4D;AACjI,QAAM,YAAY,EAAE;AACpB,SAAO,KAAK,cAAc;AAAA;;;ACDrB,6BACL,WACA,iBACA,gBAAgB,KAChB,iBACA;AACA,QAAM,uBAAuB,MAAM,QAAQ,mBAAmB,kBAAkB,CAAC;AAEjF,uBAAqB,QAAQ,CAAC,MAAM;AAElC,UAAM,OAAO,aAAa,kBACtB,IACC,sBAAsB,KAAK,EAAE,cAAc;AAChD,QAAI,CAAC,MAAM;AACT,YAAM,IAAI,MAAM;AAAA;AAGlB,UAAM,SAAS,KAAK;AACpB,UAAM,mBAAmB,OAAO,OAAO,CAAC,cAAc,UAAU,cAAc;AAE9E,UAAM,SAAS,oBAAoB,KAC/B,EAAE,UAAU,IAAI,aACf,mBAAmB,IAAI,MAAM,GAAG;AAErC,UAAM,gBAAgB,IAAI,cACxB,iBAAiB,IAAI,CAAC,cAAc,GAAG,UAAU,eAAe,MAAM,UAAU,kBAChF;AAEF,kBAAc,KAAK;AAAA;AAAA;;;ACvBhB,6BAA6B,KAA0E;AAC5G,SAAO,oBAAoB,QAEtB,IAAI,wBAAwB,iBAE5B,IAAI,iCAAiC,iBAErC,IAAI,0BAA0B;AAAA;AAGrC,4BAA4B,MAAM;AAEhC,QAAM,UAAU,CAAC,IAAI,IAAI,IAAI,OAAQ,KAAK,MAAM,KAAK,IAAI,KAAK,MAAM,KAAK;AAGzE,QAAM,UAAU,CAAC,UAAW,QAAQ,MAAO,KAAK;AAEhD,QAAM,QAAQ,EAAE,MAA0B,QAAW,OAA2B,QAAW,KAAyB;AAEpH,MAAI,CAAC,QAAQ,CAAC,KAAK,cAAc,KAAK,WAAW,WAAW;AAAI,WAAO;AACvE,QAAM,KAAK,KAAK;AAOhB,QAAM,OAAO,CAAC,QAAQ,GAAG,IAAI,IAAI,GAAG,IAAI,IAAI,GAAG,IAAI,IAAI,GAAG,IAAI;AAK9D,QAAM,QAAQ,QAAQ,GAAG,KAAK,IAAI,GAAG,GAAG,KAAK,GAAG,IAAI,MAAM,GAAG,IAAI,IAAI,KAAK,IAAI,KAAK,IAAI,GAAG,IAAI,KAAK,GAAG,IAAI,MAAM,GAAG,IAAI;AAMvH,QAAM,SAAS,GAAG,OAAO,CAAC,MAAM,QAAS,OAAO,IAAI,KAAK,OAAO,IAAI,IAAK;AACzE,QAAM,MAAM,GAAG,OAAO,CAAC,MAAM,QAAS,OAAO,IAAI,KAAK,OAAO,IAAI,IAAK;AACtE,QAAM,MAAM,KAAK,KAAM,MAAK,SAAS,UAAW,OAAM,UAAU,MAAO;AAEvE,SAAO;AAAA;AAGF,iCAEoD,WAAoB,oBAAgF;AAC7J,QAAM,EAAE,KAAK,UAAU,UAAU;AACjC,QAAM,YAAY,mBAAmB,QAAwB,MAAM,GAAG,MAAM;AAE5E,QAAM,OAAO,UAAU;AACvB,QAAM,EAAE,cAAc,UAAU;AAChC,QAAM,cAAc,IAAI,cAAc,UAAU,UAAU,OAAO,KAAK,QAAQ,UAAU,YAAY;AACpG,QAAM,QAAQ,mBAAmB;AAEjC,QAAM,YAAY;AAAA,IAChB;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA;AAGF,SAAO,KAAK,cAAc;AAAA;;;AC3DrB,qCAA+B;AAAA,EAapC,YAAY,UAAqC,IAAI;AACnD,UAAM;AAAA,MACJ,YAAY;AAAA,MAAM,aAAa;AAAA,MAAM;AAAA,MAAW;AAAA,MAAW;AAAA,MAAW;AAAA,QACpE;AACJ,SAAK,YAAY;AACjB,SAAK,aAAa;AAClB,SAAK,YAAY,aAAa;AAC9B,SAAK,YAAY,aAAa;AAC9B,SAAK,YAAY,aAAa;AAC9B,SAAK,aAAa,cAAc;AAAA;AAAA;AAI7B,8BAAwB;AAAA,EAK7B,YACE,eACA,UAAqC,IACrC;AACA,SAAK,gBAAgB;AACrB,SAAK,UAAU,IAAI,yBAAyB;AAAA;AAAA,EAG9C,KAAK,WAAkE;AACrE,UAAM,MAAM,oBAAoB;AAEhC,UAAM;AAAA,MACJ;AAAA,MAAW;AAAA,MAAY;AAAA,MAAW;AAAA,MAAW;AAAA,MAAW;AAAA,QACtD,KAAK;AAET,QAAI,aAAa,KAAK,yBAAyB,iBAAiB;AAC9D,UAAI,cAAc;AAClB,UAAI,YAAY;AAChB,kBAAY,KAAK,KAAK,cAAc;AACpC,kBAAY,KAAK,KAAK,cAAc;AACpC,kBAAY,KAAK,KAAK,cAAc;AACpC,kBAAY,KAAK,KAAK,cAAc;AACpC,kBAAY,KAAK,KAAK,cAAc,cAAc;AAClD,kBAAY,KAAK,KAAK,cAAc,eAAe;AACnD,kBAAY,KAAK,KAAK,cAAc,YAAY;AAAA;AAGlD,QAAI,YAAY;AACd,UAAI,cAAc;AAClB,UAAI,YAAY;AAEhB,YAAM,YAAY,CAAC,OAAe;AAChC,YAAI;AACJ,YAAI,IAAI,GAAG,GAAG,GAAG,GAAG,WAAW,GAAG,IAAI,KAAK;AAC3C,YAAI;AAAA;AAEN,WAAK,cAAc,UAAU,QAAQ;AAAA;AAAA;AAAA;AAOpC,2BACL,WACA,eACA;AACA,QAAM,qBAAqB,MAAM,QAAQ,iBAAiB,gBAAgB,CAAC;AAC3E,qBAAmB,QAAQ,CAAC,MAAM;AAEhC,UAAM,YAAY,aAAa,gBAC3B,IACC,oBAAoB,KAAK,EAAE,YAAY;AAC5C,QAAI,CAAC,WAAW;AACd,YAAM,IAAI,MAAM;AAAA;AAGlB,QAAI,kBAAkB,WAAW,KAAK;AAAA;AAAA;;;;;;ACrG1C,4BAA2B,gBAAwC,eAA+B;AAChG,QAAM,oBAAoB,yBAAyB,gBAAgB;AACnE,QAAM,6BAA6B,kCAAkC,gBAAgB;AAErF,uCAAqC,YAAoB,aAAqB,cAA4C;AACxH,UAAM,kBAAkB,2BAA2B,YAAY,aAAa,GAAG;AAC/E,UAAM,kBAAkB,2BAA2B,aAAa,aAAa,GAAG;AAChF,UAAM,iBAAiB,kBAAkB,YAAY,aAAa,GAAG,GAAG;AAExE,WAAO,EAAE,iBAAiB,iBAAiB;AAAA;AAG7C,kCAAgC,UAAkB,cAAuC;AACvF,UAAM,kBAAkB,2BAA2B,UAAU,UAAU,GAAG;AAC1E,UAAM,kBAAkB,2BAA2B,UAAU,UAAU,GAAG;AAC1E,UAAM,kBAAkB,2BAA2B,UAAU,UAAU,GAAG;AAE1E,WAAO,EAAE,iBAAiB,iBAAiB;AAAA;AAG7C,SAAO;AAAA,IACL;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA;AAAA;AAIG,wBAAuB,SAAuB,eAAsF;AACzI,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAE1B,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,MACE,mBAAkB,gBAAgB;AAEtC,QAAM,qBAAqB,kBAAkB,GAAG,IAAI,GAAG;AACvD,QAAM,+BAA+B,4BAA4B,IAAI,IAAI;AACzE,QAAM,+BAA+B,4BAA4B,IAAI,KAAK;AAE1E,QAAM,aAAa;AAAA,IACjB,SAAS;AAAA,IACT,mBAAmB;AAAA,IACnB,mBAAmB;AAAA;AAGrB,QAAM,cAAc;AACpB,QAAM,eAAe,GAAG,GAAG,QAAQ,CAAC,QAAQ;AAC1C,gBAAY,cAAc,SAAS,uBAAuB,KAAK,0BAA0B;AAAA;AAG3F,QAAM,4BAA4B,4BAA4B,KAAK,KAAK;AACxE,QAAM,2BAA2B,2BAA2B,KAAK,KAAK;AAEtE,QAAM,YAAY;AAAA,IAChB,iBAAiB;AAAA,IACjB,gBAAgB;AAAA;AAGlB,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAG1E,SAAO;AAAA,IACL;AAAA,IACA,QAAQ,EAAE,YAAY,aAAa;AAAA;AAAA;;;ACtEvC,4BAA2B,WAAgB,eAA+B;AACxE,QAAM,qBAAqB,0BAA0B,WAAW;AAEhE,QAAM,oBAAoB,sBAAsB;AAChD,QAAM,6BAA6B,+BAA+B;AAElE,uCAAqC,cAA4C;AAC/E,UAAM,kBAAkB,2BAA2B,GAAG;AACtD,UAAM,kBAAkB,2BAA2B,GAAG;AACtD,UAAM,iBAAiB,kBAAkB,GAAG;AAE5C,WAAO,EAAE,iBAAiB,iBAAiB;AAAA;AAG7C,kCAAgC,cAAuC;AACrE,UAAM,kBAAkB,2BAA2B,GAAG;AACtD,UAAM,kBAAkB,2BAA2B,GAAG;AACtD,UAAM,kBAAkB,2BAA2B,GAAG;AAEtD,WAAO,EAAE,iBAAiB,iBAAiB;AAAA;AAG7C,SAAO;AAAA,IACL;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA;AAAA;AAIG,qCACL,WACA,eAC+D;AAC/D,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,MACE,mBAAkB,WAAW;AAEjC,QAAM,qBAAqB,kBAAkB;AAC7C,QAAM,+BAA+B,4BAA4B;AACjE,QAAM,+BAA+B,4BAA4B;AAEjE,QAAM,aAAa;AAAA,IACjB,SAAS;AAAA,IACT,mBAAmB;AAAA,IACnB,mBAAmB;AAAA;AAGrB,QAAM,cAAc;AACpB,QAAM,eAAe,GAAG,GAAG,QAAQ,CAAC,QAAQ;AAC1C,gBAAY,cAAc,SAAS,uBAAuB,0BAA0B;AAAA;AAGtF,QAAM,4BAA4B,4BAA4B;AAC9D,QAAM,2BAA2B,2BAA2B;AAE5D,QAAM,YAAY;AAAA,IAChB,iBAAiB;AAAA,IACjB,gBAAgB;AAAA;AAGlB,6BAA2B,WAAW;AAEtC,SAAO,EAAE,QAAQ,EAAE,YAAY,aAAa,aAAa;AAAA;;;AChE3D,cAAc,GAAgB,QAAoB,QAAuC;AACvF,SAAO,AAAG,qBAAI,AAAG,wBAAO,GAAG,OAAO,SAAS,QAAQ,SAAS,OAAO;AAAA;AAGrE,wBAAwB,GAAgB,QAA8B,kBAAkB,MAAmB;AACzG,MAAI,MAAM,kBAAkB,AAAG,sBAAK,KAAK;AACzC,QAAM,uBAAuB,KAAK,OAAO,iBAAiB,CAAC,GAAG;AAC9D,QAAM,uBAAuB,AAAG,sBAAK,MAAM,OAAO,iBAAiB,CAAC,GAAG;AACvE,QAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,QAAM,AAAG,qBAAI,KAAK,KAAK,GAAG,OAAO,gBAAgB,CAAC,GAAG;AACrD,SAAO;AAAA;AAGT,mBAAmB,GAAgB,QAAsC;AACvE,MAAI,MAAM,uBAAuB,AAAG,sBAAK,IAAI,OAAO,iBAAiB,CAAC,GAAG;AACzE,QAAM,uBAAuB,AAAG,sBAAK,MAAM,OAAO,iBAAiB,CAAC,GAAG;AACvE,QAAM,uBAAuB,AAAG,sBAAK,MAAM,OAAO,iBAAiB,CAAC,GAAG;AACvE,QAAM,AAAG,qBAAI,KAAK;AAClB,SAAO;AAAA;AAGF,iCAA2B,cAAkC;AAAA,EAGlE,YAAY,eAAuB;AACjC,UAAM;AACN,SAAK,iBAAiB;AAAA;AAAA,EAGjB,aAAa,OAA8B;AAChD,UAAM,EAAE,WAAW;AACnB,QAAI,CAAC,QAAQ;AACX,YAAM,IAAI,MAAM;AAAA;AAElB,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,cAAc,AAAG,sBAAK,MAAM,cAAc,KAAK,OAAO;AAC5D,YAAM,UAAU,CAAC,SAAS,SAAS;AACnC,YAAM,aAAa,UAAU,aAAa,SAAS,IAAI;AACvD,UAAI,MAAM,AAAG,sBAAK,KAAK,YAAY,OAAO,WAAW,SAAS,CAAC,GAAG;AAClE,YAAM,eAAe,KAAK,OAAO,WAAW,mBAAmB;AAC/D,YAAM,eAAe,KAAK,OAAO,WAAW;AAC5C,YAAM,KAAK,gBAAgB,GAAG,GAAG,QAAQ,CAAC,QAAQ;AAChD,cAAM,UAAU,KAAK,OAAO,YAAY,cAAc;AAAA;AAExD,YAAM,eAAe,KAAK,OAAO,UAAU;AAC3C,YAAM,AAAG,sBAAK,uBAAuB,KAAK,OAAO,UAAU,gBAAgB,CAAC,GAAG;AAC/E,aAAO;AAAA;AAAA;AAAA,QAIE,QAAQ,OAAwC;AAC3D,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,EAGlC,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,2BAA2B,WAA8B;AACjE,WAAO,4BAA2B,WAAW,KAAK;AAAA;AAAA,EAG1C,cAAc,SAAuB;AAC7C,WAAO,eAAc,SAAS,KAAK;AAAA;AAAA;;;ACvEhC,wBAAuB,SAA6E;AACzG,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAE1B,QAAM,kBAAkB,uBAAuB,gBAAgB;AAE/D,QAAM,MAAM,gBAAgB,KAAK,GAAG;AACpC,QAAM,SAAS,gBAAgB,KAAK,GAAG;AAEvC,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAG1E,SAAO;AAAA,IACL;AAAA,IACA,QAAQ,EAAE,IAAI,EAAE,KAAK;AAAA;AAAA;;;ACjBlB,qCACL,WACsD;AACtD,QAAM,gBAAgC;AAEtC,QAAM,qBAAqB,0BAA0B,WAAW;AAEhE,2BAAyB,QAA0B;AACjD,UAAM,UAAU,mBAAmB,GAAG,kBAAkB;AACxD,UAAM,OAAO,mBAAmB,GAAG,eAAe;AAClD,WAAO,EAAE,SAAS;AAAA;AAGpB,QAAM,SAAS;AAAA,IACb,IAAI;AAAA,MACF,KAAK,gBAAgB;AAAA,MACrB,QAAQ,gBAAgB;AAAA;AAAA;AAI5B,6BAA2B,WAAW;AAEtC,SAAO,EAAE,QAAQ;AAAA;;;ACtBZ,IAAK;AAAL,UAAK,SAAL;AAEL,sBAAS;AAET,oBAAO;AAAA,GAJG;;;ACML,iCAA2B,cAAyB;AAAA,EAGzD,YAAY,uBAAqC,IAAI,aAAa,IAAI;AACpE,UAAM;AACN,SAAK,wBAAwB;AAAA;AAAA,MAGpB,uBAAqC;AAC9C,WAAO,KAAK;AAAA;AAAA,EAGP,OAAO,OAA0C;AACtD,UAAM,EAAE,WAAW;AAEnB,QAAI,CAAC,QAAQ;AACX,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAG1B,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,qBAAqB,iBAAiB,WACxC,KAAK,qBAAqB,aAAa,SACvC;AAEJ,YAAM,SAAS,AAAG,yBAAQ,oBAAoB,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI,SAAS,KAAK,mBAAmB,MAAM,IAAI;AACzG,YAAM,MAAM,oBAAoB,QAAQ,OAAO,GAAG,KAAK;AACvD,YAAM,SAAS,oBAAoB,QAAQ,OAAO,GAAG;AACrD,aAAO,EAAE,KAAK;AAAA;AAAA;AAAA,EAIX,aAAa,OAA0C;AAC5D,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,EAAE,KAAK,WAAW,KAAK,OAAO;AACpC,aAAO,EAAE,KAAK,QAAQ,AAAG,yBAAQ;AAAA;AAAA;AAAA,QAIxB,QAAQ,OAAsC;AACzD,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,QAG/B,oBAAoB,OAA8E;AAC7G,UAAM,WAAW,MAAM,WAAW;AAClC,UAAM,MAAM,MAAM,KAAK,aAAa;AAEpC,UAAM,OAAO,AAAG,yBAAQ,IAAI;AAC5B,UAAM,UAAU,AAAG,yBAAQ,IAAI;AAC/B,UAAM,sBAAsB,KAAK,IAAI,CAAC,WAAW,MAAO;AAAA,MACtD;AAAA,MACA,cAAc,QAAQ;AAAA;AAGxB,UAAM,qBAAqB,MAAM,QAAQ,IACvC,oBAAoB,IAAI,OAAO,EAAE,WAAW,mBAAmB;AAC7D,YAAM,MAAO,UAAU,WAAY;AACnC,YAAM,WAAY,aAAa,WAAY;AAC3C,YAAM,SAAS,WAAW;AAC1B,YAAM,SAAS,SAAS,OAAO,OAAO,OAAO;AAC7C,YAAM,oBAAoB,SAAS,WAAY,IAAI;AAEnD,gBAAU;AACV,mBAAa;AACb,aAAO,EAAE,KAAK,QAAQ;AAAA;AAG1B,QAAI,IAAI;AACR,QAAI,OAAO;AAEX,WAAO,SAAS,eAAe,qBAAiD,mBAAmB;AAAA;AAAA,EAG3F,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGO,QAAQ,mBAAmB,MAAM;AAC/C,SAAK,qBAAqB,QAAQ;AAClC,UAAM,QAAQ;AAAA;AAAA,EAGT,qBAAqB,SAAuB;AACjD,UAAM,EAAE,QAAQ,kBAAkB,KAAK,wBAAwB;AAC/D,SAAK,UAAU;AACf,SAAK,iBAAiB;AAAA;AAAA,EAGjB,wBAAwB,SAAuB;AACpD,WAAO,eAAc;AAAA;AAAA,EAGb,2BAA2B,WAA8B;AACjE,UAAM,EAAE,qBAAqB,kBAAkB,mBAAmB;AAElE,SAAK,qBAAqB,kBAAkB;AAE5C,WAAO,4BAA2B;AAAA;AAAA,EAG1B,cAAc,SAAuB;AAC7C,UAAM,uBAAwB,MAAM,IAAI,IAAM,OAAM,IAAI;AAExD,UAAM,0BAA0B,QAAQ,MAAM,GAAG,QAAQ,SAAS;AAClE,UAAM,oBAAoB,QAAQ,MAAM,QAAQ,SAAS;AAEzD,SAAK,qBAAqB,eAAe;AACzC,WAAO,KAAK,wBAAwB;AAAA;AAAA;;;AC5GjC,0CAGG,cAAgC;AAAA,EACjC,YAAY,QAAqB,WAAmB,oBAAgD;AACzG,UAAM,kBAAkB,mBAAmB,IAAI,CAAC,EAAE,OAAO,aAAa;AACpE,YAAM,SAAQ,YAAY,KAAK,IAAI,QAAQ;AAC3C,aAAO;AAAA,QACL,OAAO,QAAQ;AAAA,QACf,QAAQ,SAAS;AAAA;AAAA;AAIrB,UAAM,YAAY,gBAAgB;AAElC,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,0BAA0B,CAAC,OAAe,UAAkB,AAAG,uBAAM,CAAC,AAAG,sBAAK,CAAC,KAAK,OAAO,YAAY,AAAG,sBAAK,CAAC,KAAK,OAAO,aAAa,GAAG,KAAK,GAAG,KAAK;AAG/J,YAAM,aAAa,CAAC,UAAkB,SAAoD;AACxF,cAAM,EAAE,OAAO,WAAW,gBAAgB;AAC1C,eAAO,KAAK,OAAO,UAAU,KAAK,IAAI,QAAQ,UAAU,IAAI;AAAA;AAG9D,YAAM,cAAc,CAAC,aAAqB,WAAW,UAAU,CAAC,GAAG,MAAM,IAAI;AAC7E,YAAM,cAAc,CAAC,aAAqB,WAAW,UAAU,CAAC,GAAG,MAAM,IAAI;AAE7E,YAAM,kBAAkB,OACrB,IAAI,AAAG,sBAAK,CAAC,WAAW,MAAM,WAAW,YACzC,IAAI,AAAG,uBAAM,MAAM,KAAK,MAAM,YAAY,CAAC,GAAG,aAAa,wBAC1D,YAAY,WACZ,YAAY,cAEb,IAAI,AAAG,uBAAM,MAAM,KAAK,MAAM,YAAY,CAAC,GAAG,aAAa,wBAC1D,gBAAgB,UAAU,OAC1B,gBAAgB,UAAU;AAG9B,aAAO;AAAA;AAAA;AAAA,EAIJ,aAAa,OAA8B;AAChD,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,MAAM,KAAK,OAAO;AACxB,aAAO,KAAK,YACV,KACA,MAAM,WACN,MAAM,gBAAgB,IAAI,CAAC,CAAC,QAAQ,WAAY,GAAE,QAAQ;AAAA;AAAA;AAAA,QAKnD,QAAQ,OAAwC;AAC3D,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,QAG/B,gBAAgB,OAAgE;AAC3F,UAAM,WAAW,MAAM,WAAW;AAClC,UAAM,kBAAkB,AAAG,sBACzB,MAAM,AAAG,yBAAQ,KAAK,aAAa;AAGrC,UAAM,oBAAoB,MAAM,QAAQ,IAAI,gBAAgB,IAC1D,OAAO,gBAAgB,aAAa;AAClC,YAAM,iBAAiB,MAAM,KAAK,eAAe;AACjD,YAAM,UAAU,eAAe,OAAO,CAAC,GAAG,MAAM,OAAO;AACvD,YAAM,UAAU,eAAe,OAAO,CAAC,GAAG,MAAM,CAAC,OAAO;AAExD,aAAO,IAAI,gBACT,MAAM,IAAI,KAAK,GAAG,IAAI,CAAC,GAAG,MAAM,IAAI,MAAM,QAAQ,IAAc,QAAQ,MACxE;AAAA,QACE,QAAQ,SAAS,eAAe;AAAA,QAChC,OAAO,SAAS,cAAc;AAAA;AAAA;AAMtC,oBAAgB,QAAQ,CAAC,MAAM,EAAE;AAEjC,WAAO,SAAS,eAAe,oBAAyC,kBAAkB;AAAA;AAAA,EAGlF,2BAAmC;AAC3C,WAAO;AAAA;AAAA;;;AC1FJ,sCAAgC,sBAAkD;AAAA,EACvF,YAAY,uBAA6C,IAAI,wBAAwB;AACnF,UAAM,qBAAqB;AAAA;AAAA,EAGnB,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,0BAAkC;AAC1C,WAAO;AAAA;AAAA;;;ACRJ,wCACL,WAC2E;AAC3E,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,MACE,kBAAkB,WAAW;AAEjC,QAAM,SAAS;AAAA,IACb,QAAQ,yBAAyB,UAAU;AAAA,IAC3C,QAAQ,yBAAyB;AAAA,IACjC,QAAQ,yBAAyB;AAAA;AAGnC,6BAA2B,WAAW;AAEtC,SAAO,EAAE,QAAQ;AAAA;;;ACnBZ,2BAA2B,SAAkG;AAClI,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAE1B,QAAM;AAAA,IACJ;AAAA,MACE,kBAAkB,gBAAgB;AAEtC,QAAM,SAAS,yBAAyB,GAAG,IAAI,UAAU;AACzD,QAAM,SAAS,yBAAyB,IAAI,IAAI;AAChD,QAAM,SAAS,yBAAyB,IAAI,KAAK;AAEjD,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAG1E,SAAO;AAAA,IACL;AAAA,IACA,QAAQ,EAAE,QAAQ,QAAQ;AAAA;AAAA;;;AChBvB,6CAAuC,cAA+G;AAAA,EAC3J,cAAc;AACZ,UAAM;AAAA;AAAA,EAGD,aAAa,OAA8B;AAChD,UAAM,EAAE,WAAW;AAEnB,QAAI,CAAC,QAAQ;AACX,YAAM,IAAI,MAAM;AAAA;AAGlB,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,cAAc,AAAG,sBAAK,MAAM,cAAc,KAAK,OAAO;AAC5D,YAAM,UAAU,CAAC,SAAS,SAAS;AACnC,YAAM,aAAa,UAAU,aAAa,SAAS,IAAI;AAEvD,UAAI,MAAM,YAAY,YAAY,OAAO,QAAQ;AACjD,YAAM,YAAY,KAAK,OAAO;AAC9B,YAAM,YAAY,KAAK,OAAO;AAC9B,YAAM,AAAG,yBAAQ,KAAK,CAAC,IAAI,KAAK,CAAC,GAAG,IAAI;AAExC,aAAO;AAAA;AAAA;AAAA,QAIE,QAAQ,OAAwC;AAC3D,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,EAGlC,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,2BAA2B,WAA8B;AACjE,WAAO,+BAA+B;AAAA;AAAA,EAG9B,cAAc,SAAuB;AAC7C,WAAO,kBAAkB;AAAA;AAAA;;;AC7CtB,0CAAoC,sBAAsD;AAAA,EAC/F,YAAY,uBAAiD,IAAI,4BAA4B;AAC3F,UAAM,yBAAyB;AAAA;AAAA,EAGvB,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,0BAAkC;AAC1C,WAAO;AAAA;AAAA;;;ACVJ,oCAA8B,kBAAkB;AAAA;;;ACAhD,eAAe,GAAgB,QAAuC;AAC3E,SAAO,AAAG,qBAAI,AAAG,qBAAI,GAAG,OAAO,UAAU,OAAO;AAAA;;;ACAlD,oBACE,GACA,QACA,SACA,UACA,UAA4B,QACf;AACb,QAAM,EAAE,SAAS,SAAS,OAAO;AAEjC,MAAI,MAAM,AAAG,wBAAO,GAAG,SAAS,SAAS;AACzC,QAAM,AAAG,qBAAI,KAAK;AAClB,QAAM,MAAM,KAAK,OAAO;AACxB,SAAO,WAAW,AAAG,sBAAK,OAAO;AAAA;AAG5B,eAAc,GAAgB,QAAyB;AAC5D,SAAO,WAAU,GAAG,QAAQ,CAAC,GAAG,IAAI;AAAA;AAG/B,oBAAoB,GAAgB,QAAyB;AAClE,SAAO,WAAU,GAAG,QAAQ,CAAC,GAAG,IAAI;AAAA;AAG/B,kBAAkB,GAAgB,QAAyB;AAChE,SAAO,WAAU,GAAG,QAAQ,CAAC,GAAG,IAAI,MAAM;AAAA;;;ACvB5C,4BAA2B,gBAAwC,eAA+B;AAChG,+BAA6B,iBAAyB,YAAoB,YAAiC;AACzG,UAAM,UAAU,eAAe;AAC/B,UAAM,QAAQ,QAAQ,SAAU,cAAa,aAAa;AAE1D,QAAI,QAAQ,QAAQ;AAClB,YAAM,IAAI,MAAM,+BAA+B,0BAA0B,QAAQ,uBAAuB,2BAA2B;AAAA;AAGrI,WAAO,AAAG,sBACR,MAAM,AAAG,2BACP,AAAG,0BAAS,SAAS,CAAC,YAAY,OAAO,YAAY,cACrD,CAAC,GAAG,GAAG,GAAG;AAAA;AAKhB,6BACE,iBACA,YACA,YACA,cACY;AACZ,UAAM,UAAU,oBAAoB,iBAAiB,YAAY;AACjE,UAAM,OAAO,AAAG,0BAAS,eAAe;AAExC,kBAAc,KACZ,EAAE,WAAW,GAAG,0BAChB,EAAE,WAAW,GAAG;AAGlB,WAAO,EAAE,SAAS;AAAA;AAGpB,mCAAiC,YAAoB,cAAwC;AAC3F,UAAM,UAAU,AAAG,0BAAS,eAAe;AAC3C,UAAM,SAAS,AAAG,0BAAS,eAAe;AAE1C,kBAAc,KACZ,EAAE,WAAW,GAAG,0BAChB,EAAE,WAAW,GAAG;AAGlB,WAAO;AAAA,MACL;AAAA,MACA;AAAA;AAAA;AAIJ,kCACE,iBACA,YACA,YACA,cACiB;AACjB,UAAM,QAAO,kBAAkB,iBAAiB,YAAY,YAAY,GAAG;AAC3E,UAAM,SAAQ,wBAAwB,YAAY,GAAG;AAErD,WAAO,EAAE,aAAM;AAAA;AAGjB,sCACE,iBACA,YACA,YACA,cACA,SAAS,OACY;AACrB,UAAM,QAAQ,uBAAwB,UAAS,MAAM,KAAK,iBAAiB,YAAY,YAAY,GAAG;AACtG,UAAM,SAAQ,uBAAuB,iBAAiB,YAAY,YAAY,GAAG;AAEjF,WAAO,EAAE,OAAO;AAAA;AAGlB,SAAO;AAAA,IACL;AAAA,IACA;AAAA;AAAA;AAIG,wBAAuB,SAA6E;AACzG,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAE1B,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,mBAAkB,gBAAgB;AAEtC,QAAM,cAAc,uBAAuB,MAAM,IAAI,GAAG;AACxD,QAAM,WAAW,2BAA2B,MAAM,IAAI,GAAG;AACzD,QAAM,WAAW,2BAA2B,MAAM,IAAI,GAAG;AACzD,QAAM,WAAW,2BAA2B,MAAM,IAAI,GAAG;AAEzD,QAAM,cAAc,2BAA2B,OAAO,IAAI,GAAG,eAAe;AAC5E,QAAM,WAAW,2BAA2B,OAAO,IAAI,GAAG;AAC1D,QAAM,WAAW,2BAA2B,OAAO,IAAI,GAAG;AAC1D,QAAM,WAAW,2BAA2B,OAAO,IAAI,GAAG;AAE1D,QAAM,eAAe,2BAA2B,QAAQ,KAAK,GAAG,gBAAgB;AAChF,QAAM,YAAY,2BAA2B,QAAQ,KAAK,GAAG;AAC7D,QAAM,YAAY,2BAA2B,QAAQ,KAAK,GAAG;AAE7D,QAAM,eAAe,2BAA2B,QAAQ,KAAK,GAAG,gBAAgB;AAChF,QAAM,YAAY,2BAA2B,QAAQ,KAAK,GAAG;AAC7D,QAAM,YAAY,2BAA2B,QAAQ,KAAK,GAAG;AAC7D,QAAM,mBAAmB,2BAA2B,QAAQ,KAAK,GAAG;AAEpE,QAAM,KAAK,AAAG,sBACZ,MAAM,AAAG,2BAAU,AAAG,0BAAS,eAAe,MAAM,MAAM,CAAC,KAAK,OAAO,CAAC,GAAG;AAE7E,gBAAc,KAAK,EAAE,WAAW;AAEhC,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAG1E,QAAM,SAAS;AAAA,IACb;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA;AAGF,SAAO,EAAE,QAAQ;AAAA;;;AC5InB,4BAA2B,WAAgB,eAA+B;AACxE,QAAM,qBAAqB,0BAA0B,WAAW;AAEhE,mCAAiC,QAAkC;AACjE,UAAM,UAAU,mBAAmB,GAAG,wBAAwB;AAC9D,UAAM,SAAS,mBAAmB,GAAG,uBAAuB;AAE5D,WAAO,EAAE,SAAS;AAAA;AAGpB,kCAAgC,QAAiC;AAC/D,UAAM,UAAU,mBAAmB,GAAG,uBAAuB;AAC7D,UAAM,OAAO,mBAAmB,GAAG,oBAAoB;AACvD,UAAM,SAAQ,wBAAwB;AAEtC,WAAO,EAAE,MAAM,EAAE,SAAS,QAAQ;AAAA;AAGpC,sCAAoC,QAAqC;AACvE,WAAO;AAAA,MACL,OAAO,uBAAuB,GAAG;AAAA,MACjC,OAAO,uBAAuB,GAAG;AAAA;AAAA;AAIrC,SAAO;AAAA,IACL;AAAA,IACA;AAAA;AAAA;AAIG,qCACL,WACsD;AACtD,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,mBAAkB,WAAW;AAEjC,QAAM,cAAc,uBAAuB;AAC3C,QAAM,WAAW,2BAA2B;AAC5C,QAAM,WAAW,2BAA2B;AAC5C,QAAM,WAAW,2BAA2B;AAE5C,QAAM,cAAc,2BAA2B;AAC/C,QAAM,WAAW,2BAA2B;AAC5C,QAAM,WAAW,2BAA2B;AAC5C,QAAM,WAAW,2BAA2B;AAE5C,QAAM,eAAe,2BAA2B;AAChD,QAAM,YAAY,2BAA2B;AAC7C,QAAM,YAAY,2BAA2B;AAE7C,QAAM,eAAe,2BAA2B;AAChD,QAAM,YAAY,2BAA2B;AAC7C,QAAM,YAAY,2BAA2B;AAC7C,QAAM,mBAAmB,2BAA2B;AAEpD,QAAM,EAAE,OAAO;AACf,gBAAc,KAAK,EAAE,cAAc,MAAM,WAAW;AAEpD,MAAI,CAAC,WAAW,KAAK;AACnB,UAAM,IAAI,MAAM,yDAAyD;AAAA;AAG3E,QAAM,SAAS;AAAA,IACb;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA,IACA;AAAA;AAGF,6BAA2B,WAAW;AAEtC,SAAO,EAAE,QAAQ;AAAA;;;ACzFZ,kBAAkB,GAAgB,QAA0C;AACjF,MAAI,MAAM,MAAK,GAAG,OAAO;AACzB,QAAM,WAAW,KAAK,OAAO;AAC7B,QAAM,AAAG,qBAAI,KAAK;AAClB,QAAM,AAAG,sBAAK;AACd,SAAO;AAAA;AAGF,sBAAsB,GAAgB,QAA0C;AACrF,MAAI,MAAM,SAAS,GAAG,OAAO;AAC7B,QAAM,WAAW,KAAK,OAAO;AAE7B,MAAI,SAAS,AAAG,yBAAQ,GAAG,GAAG,GAAG;AACjC,QAAM,SAAQ,AAAG,uBAAkB,OAAO;AAC1C,QAAM,QAAQ,OAAO,MAAM,OAAO,IAAI,MAAM;AAC5C,QAAM,gBAAgB,OAAO,MAAM,OAAO,IAAI,MAAM,MAAM,OAAO,MAAM,OAAO,IAAI,MAAM;AAExF,MAAI,eAAe;AACjB,UAAM,YAAY,CAAC,GAAG,IAAI;AAC1B,cAAU,KAAK;AACf,UAAM,SAAS,AAAG,uBAAkB;AACpC,UAAM,AAAG,wBAAO,CAAC,KAAK,SAAS;AAE/B,UAAM,YAAY,CAAC,GAAG,IAAI;AAC1B,cAAU,KAAK;AACf,UAAM,SAAS,AAAG,uBAAkB;AACpC,UAAM,AAAG,wBAAO,CAAC,KAAK,SAAS;AAAA;AAGjC,WAAS,QAAQ,AAAG,wBAAO,CAAC,QAAQ,SAAQ,KAAK;AACjD,QAAM,AAAG,qBAAI,QAAQ;AAErB,QAAM,AAAG,sBAAK;AACd,SAAO;AAAA;;;AC3BF,uCAAiC,cAAyB;AAAA,EAC/D,cAAc;AACZ,UAAM;AAAA;AAAA,EAGD,aAAa,OAA8B;AAChD,UAAM,EAAE,WAAW;AAEnB,QAAI,CAAC,QAAQ;AACX,YAAM,IAAI,MAAM;AAAA;AAGlB,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,cAAc,AAAG,sBAAK,MAAM,cAAc,KAAK,OAAO;AAE5D,YAAM,UAAU,CAAC,SAAS,SAAS;AACnC,YAAM,aAAa,UAAU,aAAa,SAAS,IAAI;AAEvD,UAAI,MAAM,SAAS,YAAY,OAAO;AACtC,YAAM,AAAG,yBAAQ,KAAK,GAAG,GAAG;AAE5B,YAAM,SAAS,KAAK,OAAO;AAC3B,YAAM,SAAS,KAAK,OAAO;AAC3B,YAAM,SAAS,KAAK,OAAO;AAE3B,YAAM,aAAa,KAAK,OAAO;AAC/B,YAAM,SAAS,KAAK,OAAO;AAC3B,YAAM,SAAS,KAAK,OAAO;AAC3B,YAAM,SAAS,KAAK,OAAO;AAE3B,YAAM,aAAa,KAAK,OAAO;AAC/B,YAAM,SAAS,KAAK,OAAO;AAC3B,YAAM,SAAS,KAAK,OAAO;AAE3B,YAAM,aAAa,KAAK,OAAO;AAC/B,YAAM,SAAS,KAAK,OAAO;AAC3B,YAAM,SAAS,KAAK,OAAO;AAC3B,YAAM,aAAa,KAAK,OAAO;AAE/B,YAAM,YAAY,IAAI,KAAK,CAAC,GAAG;AAC/B,YAAM,iBAAiB,AAAG,wBAAO,WAAW,OAAO;AAEnD,aAAO;AAAA;AAAA;AAAA,QAIE,QAAQ,OAAwC;AAC3D,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,QAG/B,sBAAsB,OAAwD;AA7D7F;AA8DI,QAAI,qCAAO,UAAP,mBAAc,KAAK,CAAC,QAAQ,OAAO;AAAI,aAAO,IAAI,aAAa;AACnE,UAAM,WAAW,MAAM,WAAW;AAClC,UAAM,wBAAwB,AAAG,sBAAK,MAAM,AAAG,yBAAQ,KAAK,aAAa;AACzE,UAAM,0BAA0B,MAAM,QAAQ,IAAI,sBAAsB,IAAI,CAAC,MAAM,EAAE;AACrF,0BAAsB,QAAQ,CAAC,MAAM,EAAE;AACvC,WAAO,SAAS,eAAe,0BAA0B,wBAAwB;AAAA;AAAA,EAGzE,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,2BAA2B,WAA8B;AACjE,WAAO,4BAA2B;AAAA;AAAA,EAG1B,cAAc,SAAuB;AAC7C,WAAO,eAAc;AAAA;AAAA;;;AC3ElB,kCAAkC,SAAuB;AAC9D,QAAM,MAAM,IAAI;AAChB,MAAI,eAAe;AACnB,SAAO;AAAA;;;ACHF,kCAGL,WACA,YAC6B;AAC7B,QAAM,YAAY,EAAE;AACpB,SAAO,KAAK,cAAc;AAAA;;;ACPrB,mBAAmB,KAA8B;AACtD,SAAO,OAAO,IAAI,QAAQ;AAAA;AAGrB,uBAGL,WACA,KACkB;AAClB,QAAM,YAAY,EAAE;AACpB,SAAO,KAAK,cAAc;AAAA;;;ACPrB,sBAAsB,KAAiC;AAC5D,SAAQ,KAAI,WAAW,OAAO,QAAQ,IAAI,WAAW,OAAO,WACvD,mBAAmB,IAAI;AAAA;AAGvB,0BAGL,WACA,QACA,mBACqB;AACrB,QAAM,YAAY,EAAE,QAAQ;AAC5B,SAAO,KAAK,cAAc;AAAA;;;AChB5B,4BAA2B,gBAAwC,eAA+B;AAChG,sCAAoC,aAAqB,cAAuD;AAC9G,UAAM,UAAU,AAAG,0BAAS,eAAe,IAAI,IAAI,cAAc,CAAC,GAAG,GAAG,aAAa;AACrF,UAAM,mBAAmB,AAAG,0BAAS,eAAe;AACpD,UAAM,oBAAoB,AAAG,0BAAS,eAAe;AACrD,UAAM,kBAAkB,AAAG,0BAAS,eAAe;AACnD,UAAM,sBAAsB,AAAG,0BAAS,eAAe;AAEvD,kBAAc,KACZ,EAAE,WAAW,GAAG,0BAChB,EAAE,WAAW,GAAG,mCAChB,EAAE,WAAW,GAAG,oCAChB,EAAE,WAAW,GAAG,kCAChB,EAAE,WAAW,GAAG;AAGlB,WAAO;AAAA,MACL;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA;AAAA;AAIJ,6BACE,YACA,aACA,YACA,cACA,iBACY;AACZ,UAAM,UAAU,AAAG,0BACjB,eAAe,aAAa,cAAc,aAAa,aACvD,CAAC,YAAY,YAAY,YAAY;AAEvC,UAAM,OAAO,AAAG,0BAAS,eAAe;AAExC,kBAAc,KACZ,EAAE,WAAW,GAAG,0BAChB,EAAE,WAAW,GAAG,gBAAgB,kBAAkB,sBAAsB;AAG1E,WAAO,EAAE,SAAS;AAAA;AAGpB,sCACE,YACA,aACA,YACA,cACqB;AACrB,UAAM;AAAA,MACJ;AAAA,MACA;AAAA,QACE,kBAAkB,YAAY,aAAa,YAAY,cAAc;AAEzE,WAAO;AAAA,MACL;AAAA,MACA,mBAAmB;AAAA;AAAA;AAIvB,iCACE,YACA,aACA,cAC4B;AAC5B,UAAM,iBAAiB,2BAA2B,YAAY,GAAG;AACjE,UAAM,iBAAiB,2BAA2B,YAAY,aAAa,GAAG,GAAG;AAEjF,WAAO,EAAE,gBAAgB;AAAA;AAG3B,sCAAwD;AACtD,UAAM,SAAS,2BAA2B,GAAG,IAAI,GAAG;AACpD,UAAM,SAAS,sBAAsB,IAAI,IAAI;AAC7C,UAAM,SAAS,sBAAsB,IAAI,KAAK;AAC9C,UAAM,SAAS,sBAAsB,KAAK,KAAK;AAC/C,UAAM,SAAS,sBAAsB,KAAK,KAAK;AAC/C,UAAM,SAAS,sBAAsB,KAAK,KAAK;AAC/C,UAAM,SAAS,sBAAsB,KAAK,KAAK;AAC/C,UAAM,SAAS,sBAAsB,KAAK,KAAK;AAC/C,UAAM,SAAS,sBAAsB,KAAK,KAAK;AAC/C,UAAM,SAAS,sBAAsB,KAAK,KAAK;AAC/C,UAAM,UAAU,sBAAsB,KAAK,KAAK;AAChD,UAAM,UAAU,sBAAsB,KAAK,KAAK;AAChD,UAAM,UAAU,sBAAsB,KAAK,MAAM;AACjD,UAAM,UAAU,sBAAsB,MAAM,MAAM;AAClD,WAAO;AAAA,MACL;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA;AAAA;AAIJ,0CAA+D;AAC7D,UAAM,SAAS,2BAA2B,MAAM,KAAK,GAAG;AACxD,UAAM,SAAS,2BAA2B,KAAK,KAAK,GAAG;AACvD,UAAM,SAAS,2BAA2B,KAAK,KAAK,GAAG;AACvD,UAAM,SAAS,2BAA2B,KAAK,KAAK,GAAG;AACvD,UAAM,SAAS,2BAA2B,KAAK,KAAK,GAAG;AACvD,UAAM,SAAS,2BAA2B,KAAK,KAAK,GAAG;AACvD,UAAM,SAAS,2BAA2B,KAAK,IAAI,GAAG;AACtD,UAAM,SAAS,2BAA2B,IAAI,KAAK,GAAG;AACtD,UAAM,2BAA2B,kBAAkB,KAAK,IAAI,GAAG;AAC/D,UAAM,oBAAoB,kBAAkB,KAAK,GAAG,GAAG;AACvD,UAAM,2BAA2B,kBAAkB,MAAM,IAAI,GAAG;AAChE,UAAM,oBAAoB,kBAAkB,MAAM,IAAI,GAAG;AACzD,UAAM,2BAA2B,kBAAkB,KAAK,IAAI,GAAG;AAC/D,UAAM,oBAAoB,kBAAkB,KAAK,IAAI,GAAG;AACxD,UAAM,2BAA2B,kBAAkB,KAAK,IAAI,GAAG;AAC/D,UAAM,oBAAoB,kBAAkB,KAAK,IAAI,GAAG;AACxD,UAAM,2BAA2B,kBAAkB,KAAK,IAAI,GAAG;AAC/D,UAAM,oBAAoB,kBAAkB,KAAK,IAAI,GAAG;AACxD,UAAM,2BAA2B,kBAAkB,KAAK,IAAI,GAAG;AAC/D,UAAM,oBAAoB,kBAAkB,KAAK,IAAI,GAAG;AAExD,UAAM,kBAAkB;AAAA,MACtB,wBAAwB;AAAA,MACxB,iBAAiB;AAAA;AAEnB,UAAM,kBAAkB;AAAA,MACtB,wBAAwB;AAAA,MACxB,iBAAiB;AAAA;AAEnB,UAAM,kBAAkB;AAAA,MACtB,wBAAwB;AAAA,MACxB,iBAAiB;AAAA;AAEnB,UAAM,kBAAkB;AAAA,MACtB,wBAAwB;AAAA,MACxB,iBAAiB;AAAA;AAEnB,UAAM,kBAAkB;AAAA,MACtB,wBAAwB;AAAA,MACxB,iBAAiB;AAAA;AAEnB,UAAM,kBAAkB;AAAA,MACtB,wBAAwB;AAAA,MACxB,iBAAiB;AAAA;AAEnB,WAAO;AAAA,MACL;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA;AAAA;AAIJ,SAAO;AAAA,IACL;AAAA,IACA;AAAA;AAAA;AAIG,wBAAuB,SAA6E;AACzG,QAAM,gBAAgC;AACtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAC1B,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,mBAAkB,gBAAgB;AACtC,QAAM,cAAc;AACpB,QAAM,mBAAmB;AACzB,QAAM,YAAY,AAAG,0BACnB,eAAe,OAAO,IACtB,CAAC,GAAG,MAAM;AAEZ,QAAM,eAAe;AAAA,IACnB;AAAA;AAEF,gBAAc,KAAK,EAAE,WAAW;AAChC,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAG1E,SAAO;AAAA,IACL,QAAQ;AAAA,MACN;AAAA,MACA;AAAA,MACA;AAAA;AAAA,IAEF;AAAA;AAAA;;;AC9MJ,4BAA2B,WAAgB,eAA+B;AACxE,QAAM,qBAAqB,0BAA0B,WAAW;AAEhE,sCAAoC,QAAgB,KAAa,cAA2C;AAC1G,UAAM,UAAU,mBAAmB,GAAG,iBAAiB,yBAAyB,GAAG,GAAG;AACtF,UAAM,oBAAoB,mBAAmB,GAAG,iBAAiB,uCAAuC,GAAG,GAAG;AAC9G,WAAO,EAAE,SAAS;AAAA;AAGpB,iCAA+B,KAAyC;AACtE,UAAM,eAAe,oBAAoB;AACzC,UAAM,sBAAsB,sBAAsB;AAClD,UAAM,4BAA4B,GAAG;AACrC,UAAM,4BAA4B,GAAG;AAErC,UAAM,UAAU,mBAAmB,GAAG,yCAAyC,GAAG,GAAG;AACrF,UAAM,mBAAmB,mBAAmB,GAAG,uCAAuC,GAAG,GAAG;AAC5F,UAAM,oBAAoB,mBAAmB,GAAG,sCAAsC,GAAG,GAAG;AAC5F,UAAM,kBAAkB,mBAAmB,GAAG,6CAA6C,GAAG,GAAG;AACjG,UAAM,sBAAsB,mBAAmB,GAAG,iDAAiD,GAAG,GAAG;AAEzG,WAAO;AAAA,MACL,gBAAgB;AAAA,QACd;AAAA,QACA;AAAA,QACA;AAAA,QACA;AAAA,QACA;AAAA;AAAA,MAEF,gBAAgB,2BAA2B,eAAe,KAAK;AAAA;AAAA;AAInE,sCAAwD;AACtD,WAAO;AAAA,MACL,QAAQ,2BAA2B,eAAe,GAAG;AAAA,MACrD,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,QAAQ,sBAAsB;AAAA,MAC9B,SAAS,sBAAsB;AAAA,MAC/B,SAAS,sBAAsB;AAAA,MAC/B,SAAS,sBAAsB;AAAA,MAC/B,SAAS,sBAAsB;AAAA;AAAA;AAInC,6BAA2B,QAAgB,cAAkC;AAC3E,UAAM,UAAU,mBAAmB,GAAG,kBAAkB,GAAG,GAAG;AAC9D,UAAM,OAAO,mBAAmB,GAAG,iBAAiB,GAAG,GAAG;AAC1D,WAAO,EAAE,SAAS;AAAA;AAGpB,qCAAmC,KAAkC;AACnE,UAAM,yBAAyB,kBAC7B,2BAA2B,4BAC3B,kCAAkC;AAEpC,UAAM,kBAAkB,kBACtB,2BAA2B,sBAC3B,kCAAkC;AAEpC,WAAO,EAAE,wBAAwB;AAAA;AAGnC,0CAA+D;AAC7D,WAAO;AAAA,MACL,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,QAAQ,2BAA2B,cAAc,GAAG;AAAA,MACpD,iBAAiB,0BAA0B;AAAA,MAC3C,iBAAiB,0BAA0B;AAAA,MAC3C,iBAAiB,0BAA0B;AAAA,MAC3C,iBAAiB,0BAA0B;AAAA,MAC3C,iBAAiB,0BAA0B;AAAA,MAC3C,iBAAiB,0BAA0B;AAAA;AAAA;AAI/C,SAAO;AAAA,IACL;AAAA,IACA;AAAA;AAAA;AAIG,qCACL,WACsD;AACtD,QAAM,gBAAgC;AACtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,mBAAkB,WAAW;AACjC,QAAM,YAAY,UAAU;AAC5B,gBAAc,KAAK,EAAE,cAAc,oBAAoB,WAAW;AAClE,MAAI,CAAC,WAAW,YAAY;AAC1B,UAAM,IAAI,MAAM,yEAAyE;AAAA;AAG3F,QAAM,SAAS;AAAA,IACb,aAAa;AAAA,IACb,kBAAkB;AAAA,IAClB,cAAc;AAAA,MACZ;AAAA;AAAA;AAIJ,6BAA2B,WAAW;AACtC,SAAO,EAAE,QAAQ;AAAA;;;ACxHZ,4BAA4B,GAAgB,QAA6B,SAA2B;AACzG,SAAO,AAAG,sBAAK,MAAM;AACnB,QAAI,MAAM,AAAG,wBAAO,GAAG,OAAO,SAAS,SAAS;AAChD,UAAM,AAAG,qBAAI,KAAK,OAAO;AACzB,WAAO,AAAG,6BAAY,KAAK,GAAG;AAAA;AAAA;;;ACHlC,IAAM,UAAU;AAEhB,4BAA4B,GAAgB,QAAyC,SAA2B;AAC9G,SAAO,AAAG,sBAAK,MAAM;AACnB,QAAI,MAAM,AAAG,iCAAgB,GAAG,OAAO,SAAS,SAAS;AACzD,UAAM,AAAG,2BACP,KACA,OAAO,iBACP,OAAO,qBACP,OAAO,mBACP,OAAO,kBACP;AAEF,WAAO,AAAG,6BAAY,KAAK,GAAG;AAAA;AAAA;AAIlC,+BAA+B,UAAoC;AACjE,SAAO,CAAC,GAAG,GAAG,GAAG,IAAI,KAAK,CAAC,QAAQ,QAAQ,YAAY,CAAC,GAAG,KAAK,CAAC,GAAG;AAAA;AAG/D,qBAAqB,GAAgB,QAA4B;AACtE,SAAO,AAAG,sBAAK,MAAM;AACnB,QAAI;AACJ,QAAI,MAAM,mBAAmB,GAAG,OAAO,QAAQ,CAAC,GAAG;AAEnD,UAAM,iBAAiB;AAAA,MACrB,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA,MACP,OAAO;AAAA;AAGT,mBAAe,QAAQ,CAAC,OAAO,MAAM;AACnC,YAAM,WAAW,IAAI;AACrB,YAAM,uBAAuB,sBAAsB;AACnD,YAAM,mBAAmB,KAAK,MAAM,gBAAgB;AACpD,YAAM,mBAAmB,KAAK,MAAM,gBAAgB,CAAC,GAAG;AACxD,UAAI,aAAa;AAAI,iBAAS;AAAA;AAGhC,QAAI,WAAW,MAAM;AACnB,YAAM,IAAI,MAAM;AAAA;AAGlB,WAAO;AAAA,MACL;AAAA,MACA;AAAA;AAAA;AAAA;;;AC3DN,aAAa,OAAoB,GAAW,GAAW;AACrD,QAAM,YAAY,MAAM;AACxB,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAQ,KAAK,IAAI,UAAU,GAAG,IAAI,UAAU,GAAG;AACrD,QAAM,QAAS,SAAQ,SAAU,SAAQ;AACzC,QAAM,QAAS,SAAQ,SAAU,SAAQ;AACzC,MAAI,SAAS,KAAK,SAAS;AAAG,WAAO;AACrC,QAAM,mBAAmB,KAAK,IAAI,OAAO;AACzC,QAAM,mBAAmB,KAAK,IAAI,OAAO;AACzC,QAAM,mBAAmB,KAAK,IAAI,OAAO;AACzC,QAAM,mBAAmB,KAAK,IAAI,OAAO;AACzC,QAAM,mBAAmB,KAAK,IAAI,mBAAmB,kBAAkB,KAAO,KAAK,IAAI,mBAAmB,kBAAkB;AAC5H,SAAO,mBAAoB,SAAQ,QAAQ;AAAA;AAGtC,4BACL,OACA,QACA,eACA,cACA,gBACU;AACV,QAAM,WAAW,MAAM,MAAM;AAC7B,QAAM,aAAa,KAAK,IAAI,eAAe;AAE3C,QAAM,aAAa,OAChB,IAAI,CAAC,OAAO,aAAc,GAAE,OAAO,aACnC,OAAO,CAAC,MAAM,EAAE,QAAQ,gBACxB,KAAK,CAAC,IAAI,OAAO,GAAG,QAAQ,GAAG;AAElC,QAAM,eAAe,CAAC,MAAe,KAAK,eAAe,IAAI;AAC7D,QAAM,WAAqB;AAE3B,aAAW,QAAQ,CAAC,MAAM;AACxB,QAAI,SAAS,UAAU;AAAY;AACnC,UAAM,gBAAgB,EAAE;AACxB,aAAS,IAAI,SAAS,SAAS,GAAG,KAAK,GAAG,EAAE,GAAG;AAC7C,YAAM,OAAM,IAAI,OAAO,EAAE,UAAU,SAAS;AAC5C,UAAI,SAAQ;AAAK;AACjB,QAAE,SAAS,aAAa;AACxB,UAAI,EAAE,SAAS;AAAgB;AAAA;AAEjC,QAAI,kBAAkB,EAAE,OAAO;AAC7B,eAAS,KAAK,EAAE;AAAA;AAAA;AAGpB,SAAO;AAAA;;;AClDT,2CAA2C,GAAgB;AACzD,QAAM,MAAM,AAAG,yBAAQ,AAAG,2BAAU,GAAG,CAAC,GAAG;AAE3C,QAAM,QAAQ;AAAA,IACZ,AAAG,qBAAI,IAAI,IAAI,IAAI;AAAA,IACnB,AAAG,qBAAI,IAAI,IAAI,IAAI;AAAA;AAErB,QAAM,UAAU;AAAA,IACd,AAAG,qBAAI,IAAI,IAAI,AAAG,qBAAI,MAAM,IAAI;AAAA,IAChC,AAAG,qBAAI,IAAI,IAAI,AAAG,qBAAI,MAAM,IAAI;AAAA;AAElC,SAAO,EAAE,OAAO;AAAA;AAGlB,0BAA0B,IAAiB,IAAiB;AAC1D,QAAM,EAAE,OAAO,YAAY,kCAAkC;AAE7D,QAAM,MAAM,AAAG,yBAAQ,AAAG,2BAAU,IAAI,CAAC,GAAG;AAC5C,QAAM,WAAW,AAAG,qBAAI,AAAG,qBAAI,AAAG,qBAAI,AAAG,qBAAI,IAAI,IAAI,KAAK,MAAM,KAAK;AACrE,QAAM,WAAW,AAAG,qBAAI,AAAG,qBAAI,AAAG,qBAAI,IAAI,IAAI,KAAK,MAAM,KAAK,QAAQ;AACtE,QAAM,WAAW,AAAG,qBAAI,AAAG,qBAAI,AAAG,qBAAI,AAAG,qBAAI,IAAI,IAAI,KAAK,MAAM,KAAK;AACrE,QAAM,WAAW,AAAG,qBAAI,AAAG,qBAAI,AAAG,qBAAI,IAAI,IAAI,KAAK,MAAM,KAAK,QAAQ;AAEtE,SAAO,AAAG,2BACR,AAAG,uBAAM;AAAA,IACP,AAAG,qBAAI,UAAU;AAAA,IACjB,AAAG,qBAAI,UAAU;AAAA,IACjB,AAAG,qBAAI,UAAU;AAAA,IACjB,AAAG,qBAAI,UAAU;AAAA,MAEnB,CAAC,GAAG;AAAA;AAID,qBAAqB,gBAA6B,kBAA+B,QAA2B;AACjH,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,YAAY,eAAe,MAAM;AAEvC,QAAI,QAAQ,iBACV,AAAG,yBAAQ,AAAG,sBAAK,OAAO,WAAW,CAAC,WAAW,GAAG,KAAK,CAAC,IAAI,KAC9D,AAAG,yBAAQ,gBAAgB,CAAC,IAAI;AAElC,YAAQ,AAAG,yBAAQ,OAAO,CAAC,WAAY,MAAM,MAAM,KAAK,WAAY;AAEpE,UAAM,mBAAmB,AAAG,yBAAQ,AAAG,uBAAM,kBAAkB,CAAC,GAAG,GAAG,IAAI,CAAC,IAAI,IAAI;AACnF,QAAI,SAAS,AAAG,uBAAM,kBAAkB,CAAC,GAAG,GAAG,IAAI,CAAC,IAAI,IAAI;AAE5D,aAAS,AAAG,yBAAQ,QAAQ,CAAC,WAAW,OAAO,MAAM;AAErD,UAAM,eAAe,AAAG,yBAAQ;AAChC,UAAM,gBAAgB,AAAG,yBAAQ;AAEjC,WAAO,EAAE,OAAO,cAAc,QAAQ;AAAA;AAAA;;;ACnDnC,4BACL,GACA,QACA;AACA,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,YAAY,EAAE,MAAM;AAC1B,UAAM,wBAAwB,AAAG,yBAC/B,UAAU,GAAG,OAAO,yBACpB,CAAC,WAAW,IAAI,GAAG;AAErB,UAAM,kBAAkB,AAAG,yBACzB,UAAU,GAAG,OAAO,kBACpB,CAAC,WAAW,IAAI;AAElB,WAAO,EAAE,uBAAuB;AAAA;AAAA;;;ACb7B,yBACL,GACA,QACA,QACA;AACA,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,QAAQ,mBAAmB,GAAG,OAAO,QAAQ,CAAC,GAAG;AACvD,UAAM,QAAQ,mBAAmB,OAAO,OAAO,QAAQ,CAAC,GAAG;AAC3D,UAAM,SAAQ,mBAAmB,OAAO,OAAO,QAAQ,CAAC,GAAG;AAC3D,UAAM,QAAQ,mBAAmB,QAAO,OAAO,QAAQ,CAAC,GAAG;AAC3D,UAAM,QAAQ,mBAAmB,OAAO,OAAO,QAAQ,CAAC,GAAG;AAC3D,UAAM,QAAQ,mBAAmB,OAAO,OAAO,QAAQ,CAAC,GAAG;AAC3D,UAAM,QAAQ,mBAAmB,OAAO,OAAO,QAAQ,CAAC,GAAG;AAC3D,UAAM,QAAQ,mBAAmB,OAAO,OAAO,QAAQ,CAAC,GAAG;AAE3D,UAAM,iBAAiB,mBAAmB,QAAQ,OAAO;AACzD,UAAM,iBAAiB,mBAAmB,GAAG,OAAO;AACpD,UAAM,iBAAiB,mBAAmB,OAAO,OAAO;AACxD,UAAM,iBAAiB,mBAAmB,OAAO,OAAO;AACxD,UAAM,iBAAiB,mBAAmB,OAAO,OAAO;AACxD,UAAM,iBAAiB,mBAAmB,OAAO,OAAO;AAExD,UAAM,iBAAiB,AAAG,wBAAO;AAAA,MAC/B,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,OACd;AAEH,UAAM,mBAAmB,AAAG,wBAAO;AAAA,MACjC,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,MACf,eAAe;AAAA,OACd;AAEH,WAAO;AAAA,MACL;AAAA,MACA;AAAA;AAAA;AAAA;;;AC3CC,kCAA4B;AAAA,EAOjC,YAAY,EAAE,eAAe,eAAuC,IAAI;AAN9D,iBAAQ;AAOhB,SAAK,iBAAiB,iBAAiB;AACvC,SAAK,cAAc,cAAc;AAEjC,QAAI,OAAO,KAAK,mBAAmB,YAAY,KAAK,kBAAkB,KAAK,KAAK,kBAAkB,GAAG;AACnG,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAG1B,QAAI,OAAO,KAAK,gBAAgB,UAAU;AACxC,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAAA;AAAA,MAIxB,gBAAwB;AAAE,WAAO,KAAK;AAAA;AAAA,MAEtC,aAAqB;AAAE,WAAO,KAAK;AAAA;AAAA;;;ACZlC,mCAA6B,cAAyB;AAAA,EAC3D,cAAc;AACZ,UAAM;AAAA;AAAA,EAGD,aAAa,OAAiB;AACnC,UAAM,EAAE,WAAW;AACnB,QAAI,CAAC;AAAQ,YAAM,IAAI,MAAM;AAC7B,WAAO,AAAG,sBAAK,MAAM;AACnB,YAAM,cAAc,AAAG,sBAAK,MAAM,cAAc,KAAK,QAAQ;AAC7D,YAAM,IAAI,AAAG,qBAAI,AAAG,qBAAI,aAAa,QAAQ;AAC7C,YAAM,WAAW,YAAY,GAAG,OAAO;AACvC,YAAM,EAAE,gBAAgB,qBAAqB,gBAAgB,SAAS,KAAK,SAAS,QAAQ,OAAO;AACnG,aAAO,YAAY,gBAAgB,kBAAkB,OAAO;AAAA;AAAA;AAAA,QAInD,QAAQ,OAAkB;AACrC,WAAO,KAAK,aAAa,MAAM,WAAW;AAAA;AAAA,QAG/B,YAAY,OAAkB,UAAkC,IAA8B;AACzG,UAAM,EAAE,YAAY,kBAAkB,IAAI,sBAAsB;AAChE,UAAM,WAAW,MAAM,WAAW;AAClC,UAAM,EAAE,OAAO,QAAQ,QAAQ,YAAY,KAAK,aAAa;AAC7D,UAAM,QAAQ,OAAO;AACrB,UAAM,SAAS,QAAQ;AACvB,aAAS,IAAI,GAAG,IAAI,OAAO,QAAQ,KAAK;AACtC,aAAO,GAAG;AACV,cAAQ,GAAG;AAAA;AAEb,UAAM,aAAa,MAAM,KAAK,OAAO;AACrC,UAAM,eAAe;AACrB,UAAM,UAAU,mBAAkB,OAAO,YAAwB,YAAY,cAAc;AAC3F,UAAM,eAAe,SAAS,2BAA2B;AACzD,UAAM,YAAY,SAAS;AAC3B,UAAM,OAAO,YAAY,aAAa;AACtC,UAAM,OAAO,YAAY,aAAa;AACtC,UAAM,YAAY,MAAM;AACxB,UAAM,UAAU,QACb,IAAI,CAAC,QAAQ;AACZ,YAAM,CAAC,KAAK,UAAU;AAAA,QACpB,KAAK,IAAI,GAAG,UAAU,KAAK;AAAA,QAC3B,KAAK,IAAI,GAAK,UAAU,KAAK;AAAA,QAC7B,IAAI,CAAC,QAAQ,MAAM;AACrB,YAAM,CAAC,MAAM,SAAS;AAAA,QACpB,KAAK,IAAI,GAAG,UAAU,KAAK;AAAA,QAC3B,KAAK,IAAI,GAAK,UAAU,KAAK;AAAA,QAC7B,IAAI,CAAC,QAAQ,MAAM;AACrB,aAAO,IAAI,cACT,WAAW,MACX,IAAI,KAAK,MAAM,KAAK,QAAQ,MAAM,SAAS,MAC3C,EAAE,QAAQ,SAAS,eAAe,IAAI,OAAO,SAAS,cAAc;AAAA;AAG1E,UAAM;AACN,WAAO;AACP,WAAO;AAAA;AAAA,EAGC,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,2BAA2B,WAA8B;AACjE,WAAO,4BAA2B;AAAA;AAAA,EAG1B,cAAc,SAAuB;AAC7C,WAAO,eAAc;AAAA;AAAA;;;AC/ElB,8BAA8B,SAAuB;AAC1D,QAAM,MAAM,IAAI;AAChB,MAAI,eAAe;AACnB,SAAO;AAAA;AAGF,gCAAgC,SAAuB;AAC5D,SAAO,qBAAqB;AAAA;AAIvB,qCAA+B,eAAe;AAAA;;;ACd9C,IAAM,gBAAgB;AAEtB,IAAM,cAAc;AAAA,EACzB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,SAAS;AAAA,EACnB,IAAI,MAAM,SAAS;AAAA,EACnB,IAAI,MAAM,QAAQ;AAAA,EAClB,IAAI,MAAM,SAAS;AAAA;AAGd,IAAM,wBAAwB;AAAA,EACnC,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA;AAGf,IAAM,qBAA+C,CAAC,SAAS,SAAS;AAExE,IAAM,qBAAqB;AAC3B,IAAM,oCAAoC;;;ACVjD,IAAM,WAAW,CAAC,QAAa,OAAO,QAAQ;AAEvC,wBAAwB,QAAa;AAC1C,MAAI,CAAC,QAAQ;AACX,UAAM,IAAI,MAAM,mBAAmB;AAAA;AAGrC,MAAI,OAAO,OAAO,uBAAuB,WAAW;AAClD,UAAM,IAAI,MAAM,wDAAwD,OAAO;AAAA;AAGjF,MAAI,CAAC,SAAS,OAAO,iBAAiB,OAAO,eAAe,KAAK,OAAO,eAAe,GAAK;AAC1F,UAAM,IAAI,MAAM,gEAAgE,OAAO;AAAA;AAGzF,MACE,CAAC,MAAM,QAAQ,OAAO,YACnB,CAAC,OAAO,QAAQ,UAChB,CAAC,OAAO,QAAQ,MAAM,CAAC,MAAW,OAAO,MAAM,WAClD;AACA,UAAM,IAAI,MAAM,kEAAkE,KAAK,UAAU,OAAO;AAAA;AAG1G,MACE,CAAC,MAAM,QAAQ,OAAO,YACnB,CAAC,OAAO,QAAQ,UAChB,CAAC,OAAO,QAAQ,IAAI,CAAC,MAAW,KAAK,IAAI,MAAM,CAAC,MAAW,SAAS,EAAE,MAAM,SAAS,EAAE,KAC1F;AACA,UAAM,IAAI,MAAM,wEAAwE,KAAK,UAAU,OAAO;AAAA;AAGhH,MAAI,OAAO,WACT,EAAC,MAAM,QAAQ,OAAO,YACnB,OAAO,QAAQ,WAAW,KAC1B,CAAC,OAAO,QAAQ,MAAM,YACxB;AACD,UAAM,IAAI,MAAM,8EAA8E,KAAK,UAAU,OAAO;AAAA;AAAA;;;AC/CjH,eAAe,GAA6B;AACjD,SAAO,AAAG,sBAAK,MAAM;AACnB,UAAM,MAAM,AAAG,qBAAI,GAAG,AAAG,wBAAO;AAChC,WAAO,AAAG,qBAAI,AAAG,sBAAK,AAAG,qBAAI,GAAG,OAAO;AAAA;AAAA;;;ACApC,2BAA2B,GAAgB,QAAwC;AACxF,SAAO,AAAG,sBAAK,MAAM;AACnB,QAAI,MAAM,AAAG,qBAAI,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI,CAAC,GAAG;AACjD,UAAM,AAAG,wBAAO,KAAK,OAAO,KAAK,SAAS,CAAC,GAAG,IAAI;AAClD,UAAM,AAAG,qBAAI,KAAK,OAAO,GAAG;AAC5B,UAAM,AAAG,qBAAI,KAAK,OAAO,GAAG;AAC5B,UAAM,AAAG,qBAAI,KAAK,OAAO,KAAK;AAC9B,WAAO,MAAM;AAAA;AAAA;;;ACPV,iCAAgC,GAAgB,QAA0C;AAC/F,SAAO,AAAG,sBAAK,MAAM;AACnB,QAAI,MAAM,AAAG,qBAAI,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI,CAAC,GAAG;AACjD,UAAM,AAAG,iCAAgB,KAAK,OAAO,kBAAkB,OAAO,kBAAkB,CAAC,GAAG,IAAI;AACxF,UAAM,AAAG,qBAAI,KAAK,OAAO;AACzB,WAAO,MAAM;AAAA;AAAA;;;ACDjB,4BAA2B,gBAAwC,eAA+B;AAChG,QAAM,oBAAoB,yBAAyB,gBAAgB;AAEnE,kCAAgC,MAAc,cAAiC;AAC7E,UAAM,OAAM,AAAG,0BAAS,eAAe;AACvC,UAAM,UAAU,AAAG,0BAAS,eAAe;AAE3C,kBAAc,KACZ,EAAE,WAAW,GAAG,sBAChB,EAAE,WAAW,GAAG;AAElB,WAAO,EAAE,WAAK;AAAA;AAGhB,0CAAwC,YAAoB,aAAqB,cAAyC;AACxH,UAAM,QAAO,kBAAkB,YAAY,aAAa,GAAG,GAAG;AAC9D,UAAM,KAAK,uBAAuB,aAAa,GAAG;AAClD,WAAO,EAAE,aAAM;AAAA;AAEjB,QAAM,6BAA6B,kCAAkC,gBAAgB;AAErF,SAAO;AAAA,IACL;AAAA,IACA;AAAA,IACA;AAAA;AAAA;AAIG,wBACL,SACA,QACA,iBACA,aACgE;AAChE,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,MACE,sBAAsB;AAE1B,QAAM,gBAAgC;AACtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,IACA;AAAA,MACE,mBAAkB,gBAAgB;AACtC,MAAI;AAEJ,MAAI,OAAO,oBAAoB;AAC7B,UAAM,CAAC,IAAI,IAAI,IAAI,IAAI,IAAI,IAAI,IAAI,IAAI,MAAM;AAC7C,UAAM,QAAQ,OAAO,qBACjB,kBAAkB,IAAI,IAAI,GAAG,WAC7B,2BAA2B,IAAI,IAAI;AACvC,UAAM,QAAQ,2BAA2B,IAAI,IAAI;AACjD,UAAM,SAAQ,2BAA2B,IAAI,IAAI;AACjD,UAAM,QAAQ,2BAA2B,IAAI,IAAI;AACjD,UAAM,QAAQ,2BAA2B,IAAI,IAAI;AACjD,UAAM,QAAQ,2BAA2B,IAAI,IAAI;AACjD,UAAM,QAAQ,KAAK,2BAA2B,IAAI,IAAI,WAAW;AACjE,UAAM,QAAQ,KAAK,2BAA2B,IAAI,IAAI,WAAW;AACjE,UAAM,QAAQ,kBAAkB,MAAM,MAAM,IAAI,IAAI,iBAAiB,GAAG;AACxE,aAAS;AAAA,MACP;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA;AAAA,SAErD;AACL,UAAM,CAAC,IAAI,IAAI,IAAI,IAAI,IAAI,IAAI,IAAI,IAAI,MAAM;AAC7C,UAAM,QAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,QAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,SAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,QAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,QAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,QAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,QAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,QAAQ,+BAA+B,IAAI,IAAI;AACrD,UAAM,QAAQ,kBAAkB,IAAI,IAAI,iBAAiB,GAAG;AAC5D,aAAS;AAAA,MACP;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA,MAAO;AAAA;AAAA;AAG5D,MAAI,sBAAsB,WAAW,GAAG;AACtC,UAAM,IAAI,MAAM,kCAAkC,sBAAsB;AAAA;AAE1E,SAAO,EAAE,QAAQ;AAAA;;;AChFnB,4BAA2B,WAAgB,eAA+B;AACxE,QAAM,qBAAqB,0BAA0B,WAAW;AAEhE,kCAAgC,QAA2B;AACzD,UAAM,OAAM,mBAAmB,GAAG,cAAc;AAChD,UAAM,UAAU,mBAAmB,GAAG,kBAAkB;AACxD,WAAO,EAAE,WAAK;AAAA;AAGhB,6BAA2B,QAA4B;AACrD,UAAM,UAAU,mBAAmB,GAAG,kBAAkB;AACxD,UAAM,OAAO,mBAAmB,GAAG,eAAe;AAClD,WAAO,EAAE,SAAS;AAAA;AAGpB,0CAAwC,QAAmC;AACzE,UAAM,QAAO,kBAAkB,GAAG;AAClC,UAAM,KAAK,uBAAuB,GAAG;AACrC,WAAO,EAAE,aAAM;AAAA;AAGjB,QAAM,6BAA6B,+BAA+B;AAClE,SAAO;AAAA,IACL;AAAA,IACA;AAAA,IACA;AAAA;AAAA;AAIG,qCACL,WACA,QACgE;AAChE,QAAM,gBAAgC;AAEtC,QAAM;AAAA,IACJ;AAAA,IACA;AAAA,IACA;AAAA,MACE,mBAAkB,WAAW;AAEjC,MAAI;AAEJ,MAAI,OAAO,oBAAoB;AAE7B,UAAM,aAAc,OAAO,eAAe,OAAO,YAAY,UAAU;AACvE,aAAS;AAAA,MACP,OAAO,OAAO,qBAAqB,kBAAkB,WAAW,2BAA2B;AAAA,MAC3F,OAAO,2BAA2B;AAAA,MAClC,OAAO,2BAA2B;AAAA,MAClC,OAAO,2BAA2B;AAAA,MAClC,OAAO,2BAA2B;AAAA,MAClC,OAAO,2BAA2B;AAAA,MAClC,OAAO,aAAa,IAAI,2BAA2B,WAAW;AAAA,MAC9D,OAAO,aAAa,IAAI,2BAA2B,WAAW;AAAA,MAC9D,OAAO,kBAAkB;AAAA;AAAA,SAEtB;AACL,aAAS;AAAA,MACP,OAAO,+BAA+B;AAAA,MACtC,OAAO,+BAA+B;AAAA,MACtC,OAAO,+BAA+B;AAAA,MACtC,OAAO,+BAA+B;AAAA,MACtC,OAAO,+BAA+B;AAAA,MACtC,OAAO,+BAA+B;AAAA,MACtC,OAAO,+BAA+B;AAAA,MACtC,OAAO,+BAA+B;AAAA,MACtC,OAAO,kBAAkB;AAAA;AAAA;AAI7B,6BAA2B,WAAW;AACtC,SAAO,EAAE,QAAQ;AAAA;;;AC7EZ,8BAAwB;AAAA,EAO7B,YAAY,EAAE,WAAW,mBAAuC,IAAI;AAN1D,iBAAQ;AAOhB,SAAK,aAAa,aAAa;AAC/B,SAAK,kBAAkB,kBAAkB;AAEzC,QAAI,OAAO,KAAK,eAAe,YAAY,KAAK,aAAa,OAAO,GAAG;AACrE,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAG1B,QAAI,OAAO,KAAK,oBAAoB,YAAY,KAAK,mBAAmB,KAAK,KAAK,mBAAmB,GAAG;AACtG,YAAM,IAAI,MAAM,GAAG,KAAK;AAAA;AAAA;AAAA,MAIxB,YAAoB;AAAE,WAAO,KAAK;AAAA;AAAA,MAElC,iBAAyB;AAAE,WAAO,KAAK;AAAA;AAAA;;;ACJtC,oCAA6B,cAAmC;AAAA,EAKrE,YAAY,QAA0B;AACpC,UAAM;AACN,mBAAe;AACf,SAAK,UAAU;AAAA;AAAA,MAGN,SAA2B;AACpC,WAAO,KAAK;AAAA;AAAA,MAGH,kBAA2B;AACpC,WAAO,KAAK,OAAO,mBAAmB,KAAK,OAAO,QAAQ,SAAS;AAAA;AAAA,MAG1D,kBAA0B;AACnC,WAAO,IAAK,MAAK,kBAAkB,KAAK,OAAO,QAAQ,SAAS;AAAA;AAAA,EAG3D,cAAc,GAAgB,QAAiD;AACpF,QAAI,MAAM,kBAAkB,GAAG,OAAO;AACtC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,kBAAkB,KAAK,OAAO;AACpC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,kBAAkB,KAAK,OAAO;AACpC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,kBAAkB,KAAK,OAAO;AACpC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,kBAAkB,KAAK,OAAO;AACpC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,kBAAkB,KAAK,OAAO;AACpC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,kBAAkB,KAAK,OAAO;AACpC,UAAM,kBAAkB,KAAK,OAAO;AACpC,WAAO,UAAU,KAAK,OAAO,OAAO,SAAS;AAAA;AAAA,EAGxC,aAAa,GAAgB,QAAsC;AACxE,QAAI,MAAM,KAAK,OAAO,qBAClB,MAAM,UAAU,GAAG,OAAO,OAAqB,SAAS,UACxD,wBAAuB,GAAG,OAAO;AACrC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,wBAAuB,KAAK,OAAO;AACzC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,wBAAuB,KAAK,OAAO;AACzC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,wBAAuB,KAAK,OAAO;AACzC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,wBAAuB,KAAK,OAAO;AACzC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,wBAAuB,KAAK,OAAO;AACzC,UAAM,AAAG,yBAAQ,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,IAAI;AACtC,UAAM,OAAO,QAAQ,wBAAuB,KAAK,OAAO,SAAS;AACjE,UAAM,OAAO,QAAQ,wBAAuB,KAAK,OAAO,SAAS;AACjE,WAAO,UAAU,KAAK,OAAO,OAAO,SAAS;AAAA;AAAA,EAGxC,aAAa,OAAiB,WAAgC;AACnE,UAAM,EAAE,WAAW;AAEnB,QAAI,CAAC,QAAQ;AACX,YAAM,IAAI,MAAM;AAAA;AAGlB,WAAO,AAAG,sBAAK,MAAM;AACnB,UAAI,cAAc,AAAG,sBAAK,MAAM,cAAc,WAAW,QAAQ;AACjE,oBAAc,KAAK,OAAO,UACtB,UAAU,aAAa,KAAK,OAAO,WACnC;AACJ,oBAAc,YAAY,IAAI;AAC9B,aAAO,KAAK,OAAO,qBACf,KAAK,aAAa,aAAa,UAC/B,KAAK,cAAc,aAAa;AAAA;AAAA;AAAA,QAI3B,QAAQ,OAAkB,WAAyC;AAC9E,WAAO,KAAK,aAAa,MAAM,WAAW,QAAQ;AAAA;AAAA,QAGvC,OAAO,OAAkB,gBAAoC,IAAgC;AACxG,UAAM,EAAE,WAAW,mBAAmB,IAAI,kBAAkB;AAC5D,UAAM,WAAW,MAAM,WAAW;AAClC,UAAM,MAAM,MAAM,KAAK,aAAa,UAAU;AAC9C,UAAM,OAAO,AAAG,sBAAK,MAAM,AAAG,yBAAQ,KAAK,GAAG;AAC9C,UAAM,kBAAkB;AAAA,MACtB,OAAO,SAAS,cAAc;AAAA,MAC9B,QAAQ,SAAS,eAAe;AAAA;AAGlC,UAAM,UAAU,MAAM,KAAK,aAAa,MAAM,SAAS,2BAA2B,IAAI;AACtF,QAAI;AACJ,SAAK;AAEL,UAAM,QAAQ,QAAQ,IAAI,CAAC,QAAQ,IAAI;AACvC,UAAM,SAAS,QAAQ,IAAI,CAAC,QAAQ,IAAI;AACxC,UAAM,cAAc,QAAQ,IAAI,CAAC,QAAQ,IAAI;AAC7C,UAAM,aAAa,QAAQ,IAAI,CAAC,QAAQ,KAAK,OAAO,QAAQ,IAAI;AAEhE,UAAM,UAAU,kBACd,MAAM,IAAI,CAAC,QAAQ,IAAI,QAAQ,aAC/B,QACA,KAAK,OAAO,cACZ;AAGF,UAAM,aAAa,QAAQ,IAAI,CAAC,QAAQ,IAAI,gBAC1C,OAAO,MACP,YAAY,MACZ,WAAW,MACX,MAAM,MACN;AAEF,WAAO;AAAA;AAAA,EAGC,sBAA8B;AACtC,WAAO;AAAA;AAAA,EAGC,2BAA2B,WAA8B;AACjE,WAAO,4BAA2B,WAAW,KAAK;AAAA;AAAA,EAG1C,cAAc,SAAuB;AAC7C,UAAM,cAAc,KAAK,OAAO,eAAe,gBAAe;AAE9D,UAAM,aAAa,cAAc,YAAY,SAAS;AACtD,QAAI,eAAe,KAAK,eAAe,KAAK,eAAe,GAAG;AAC5D,YAAM,IAAI,MAAM,oEAAoE;AAAA;AAEtF,WAAO,eAAc,SAAS,KAAK,QAAQ,KAAK,iBAAiB;AAAA;AAAA,QAGnD,aACd,cACA,qBACA,gBACA;AACA,UAAM,EAAE,OAAO,WAAW;AAC1B,UAAM,YAAY,KAAK,IAAI,OAAO;AAClC,UAAM,oBAAoB,YAAY;AACtC,UAAM,oBAAoB,YAAY;AAEtC,UAAM,WAAW,aAAa,MAAM;AACpC,UAAM,WAAW,KAAK,OAAO,QAAQ;AAErC,UAAM,CAAC,aAAa,cAAc,qBAAqB,AAAG,sBAAK,MAAM;AACnE,YAAM,WAAW,aAAa,QAAQ,CAAC,UAAU,UAAU,UAAU,KAAK;AAE1E,YAAM,QAAQ,SAAS,MAAM,CAAC,GAAG,GAAG,GAAG,IAAI,CAAC,UAAU,UAAU,UAAU;AAC1E,YAAM,SAAS,SAAS,MAAM,CAAC,GAAG,GAAG,GAAG,IAAI,CAAC,UAAU,UAAU,UAAU;AAC3E,YAAM,cAAc,KAAK,kBACrB,AAAG,yBAAQ,SAAS,MAAM,CAAC,GAAG,GAAG,GAAG,IAAI,CAAC,UAAU,UAAU,UAAU,KAAK,OAAO,QAAQ,UAAU,KACrG,AAAG,wBAAO;AACd,aAAO,CAAC,OAAO,QAAQ;AAAA;AAGzB,UAAM,UAAU;AAChB,UAAM,aAAa,MAAM,aAAa;AACtC,UAAM,YAAY,MAAM,YAAY;AACpC,aAAS,MAAM,GAAG,MAAM,UAAU,OAAO;AACvC,eAAS,MAAM,GAAG,MAAM,UAAU,OAAO;AACvC,iBAAS,SAAS,GAAG,SAAS,UAAU,UAAU;AAChD,gBAAM,QAAQ,QAAQ,WAAW,KAAK,KAAK,QAAQ;AACnD,cAAI,CAAC,kBAAkB,QAAQ,gBAAgB;AAC7C,kBAAM,MAAQ,OAAM,QAAQ,UAAU,KAAK,KAAK,QAAQ,OAAO,WAAY;AAC3E,kBAAM,MAAQ,OAAM,QAAQ,UAAU,KAAK,KAAK,QAAQ,OAAO,WAAY;AAC3E,kBAAM,aAAe,KAAK,IAAI,UAAU,KAAK,KAAK,QAAQ,MAAM,KAAK,OAAO,QAAQ,QAAQ,IAAK,WAAY;AAC7G,kBAAM,cAAgB,KAAK,IAAI,UAAU,KAAK,KAAK,QAAQ,MAAM,KAAK,OAAO,QAAQ,QAAQ,IAAK,WAAY;AAC9G,kBAAM,IAAK,MAAO,aAAa;AAC/B,kBAAM,IAAK,MAAO,cAAc;AAChC,kBAAM,MAAM,EAAE,KAAK,KAAK;AACxB,kBAAM,EAAE,YAAY,UAAU,KAAK,kBAC/B,MAAM,KAAK,sBAAsB,mBAAkC,OACnE,EAAE,YAAY,GAAG,OAAO;AAC5B,oBAAQ,KAAK;AAAA,cACX,KAAK,IAAI,YAAY,GAAG,GAAG,IAAI,YAAY,IAAI;AAAA,cAC/C;AAAA,cACA,YAAY,QAAQ;AAAA,cACpB;AAAA,iBACG;AAAA;AAAA;AAAA;AAAA;AAAA;AAOb,gBAAY;AACZ,iBAAa;AACb,sBAAkB;AAClB,WAAO;AAAA;AAAA,QAGK,sBAAsB,eAA4B,KAAmD;AACjH,UAAM,EAAE,KAAK,KAAK,WAAW;AAC7B,UAAM,cAAc,MAAM,cAAc;AACxC,WAAO,MAAM,KAAK,OAAO,QAAQ,QAAQ,KAAK,GAC3C,IAAI,CAAC,GAAG,MAAM,YAAY,KAAK,KAAK,QAAQ,IAC5C,IAAI,CAAC,YAAY,UAAW;AAAA,MAC3B;AAAA,MACA;AAAA,QAED,OAAO,CAAC,KAAK,SAAU,IAAI,aAAa,KAAK,aAAa,MAAM;AAAA;AAAA;AA/MhE;AACS,AADT,eACS,uBAAuB,CAAC,GAAG,IAAI,IAAI,IAAI,KAAK,KAAK,KAAK,MAAM;;;ACPrE,+BAAyB,eAAe;AAAA,EAC7C,YAAY,qBAAqB,MAAM;AACrC,UAAM,SAAS;AAAA,MACb;AAAA,MACA,cAAc;AAAA,MACd,SAAS,CAAC;AAAA,SACN,qBACA;AAAA,QACA,SAAS;AAAA,QACT,SAAS;AAAA,UAET;AAAA,QACA,SAAS;AAAA,QACT,iBAAiB;AAAA;AAAA;AAIvB,UAAM;AAAA;AAAA,MAGG,qBAA8B;AACvC,WAAO,KAAK,OAAO;AAAA;AAAA,MAGV,UAAmB;AAC5B,WAAO,KAAK,OAAO;AAAA;AAAA,QAGR,YAAY,OAAkB,eAA6D;AACtG,UAAM,mBAAmB,MAAM,KAAK,OAAO,OAAO;AAClD,WAAO,iBAAiB,IAAI,CAAC,QAAQ,IAAI,cAAc,IAAI,OAAO,IAAI,aAAa,EAAE,OAAO,IAAI,YAAY,QAAQ,IAAI;AAAA;AAAA,EAGvG,sBAA8B;AAC/C,WAAO,KAAK,qBAAqB,oCAAoC;AAAA;AAAA,EAGpD,2BAA2B,WAA8F;AAC1I,WAAO,MAAM,2BAA2B;AAAA;AAAA;;;AChDrC,0BAA0B,SAAuB,qBAAqB,MAAM;AACjF,QAAM,MAAM,IAAI,WAAW;AAC3B,MAAI,eAAe;AACnB,SAAO;AAAA;;;ACNF,4CAAsC,kBAAkB;AAAA,EAAxD,cAJP;AAIO;AACc,iBAAQ;AAAA;AAAA;;;ACLtB,2BAAwB;AAAA,QAEhB,KAAK,aAA2D;AAC3E,WAAO,YAAY,MAAM,KAAK;AAAA;AAAA,QAGnB,MAAkB;AAC7B,UAAM,IAAI,MAAM;AAAA;AAAA;;;ACApB,gDACE,eACA,OAEA,gBACA,gBAEA,sBAAwF,CAAC,EAAE,kBAAkB,aAC7G;AACA,QAAM,YAAY,cAAc,IAAI,CAAC,iBAAkB,oBAAoB,gBACvE,oBAAoB,gBACpB,aAAa;AACjB,QAAM,QAAgD,kBACpD,kBAAoB,0BAChB,MAAM,mBAAmB,OAAO,aAChC,MAAM,aAAa,OAAO;AAEhC,QAAM,UAAU,MAAM,eAAe;AACrC,QAAM,QAAQ,CAAC,MAAM,aAAgB,2BAAU,EAAE;AACjD,SAAO;AAAA;AAGT,iDACE,cACA,OAEA,eACA,gBAEA,qBACA;AACA,SAAO,iCACL,CAAC,eACD,OACA,OAAO,UAAU,cAAc,MAAM,KACrC,gBACA;AAAA;;;ACzCG,IAAM,iBAAgB;AAEtB,IAAM,eAAc;AAAA,EACzB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA,EACpB,IAAI,MAAM,UAAU;AAAA;AAGf,IAAM,WAAqC,CAAC,SAAS,SAAS;;;ACF9D,qCAA+B,eAAe;AAAA,EACnD,cAAc;AACZ,UAAM,SAAS;AAAA,MACb,oBAAoB;AAAA,MACpB,cAAc;AAAA,MACd,SAAS,CAAC;AAAA,MACV,SAAS;AAAA,MACT,SAAS;AAAA,MACT,oBAAoB;AAAA,MACpB,aAAa,CAAC,GAAG,IAAI,IAAI,IAAI,KAAK,KAAK;AAAA;AAGzC,UAAM;AAAA;AAAA,MAGG,UAAmB;AAC5B,WAAO,KAAK,OAAO;AAAA;AAAA,QAGR,YAAY,OAAkB,eAA6D;AACtG,UAAM,mBAAmB,MAAM,KAAK,OAAO,OAAO;AAClD,WAAO,iBAAiB,IAAI,CAAC,QAAQ,IAAI,cAAc,IAAI,OAAO,IAAI,aAAa,EAAE,OAAO,IAAI,YAAY,QAAQ,IAAI;AAAA;AAAA,EAGvG,sBAA8B;AAC/C,WAAO;AAAA;AAAA,EAGU,2BAA2B,WAA8F;AAC1I,WAAO,MAAM,2BAA2B;AAAA;AAAA;;;ACvBrC,IAAM,OAAO;AAAA,EAClB,gBAAgB,IAAI;AAAA,EACpB,kBAAkB,IAAI;AAAA,EACtB,YAAY,IAAI;AAAA,EAChB,mBAAmB,IAAI;AAAA,EACvB,uBAAuB,IAAI;AAAA,EAC3B,oBAAoB,IAAI;AAAA,EACxB,mBAAmB,IAAI;AAAA,EACvB,cAAc,IAAI;AAAA;AAUb,IAAM,iBAAiB,CAAC,OAAkB,YAA6D,KAAK,eAAe,YAAY,OAAO;AAS9I,IAAM,mBAAmB,CAAC,OAAkB,YAA+D,KAAK,iBAAiB,YAAY,OAAO;AASpJ,IAAM,aAAa,CAAC,OAAkB,YAA0D,KAAK,WAAW,YAAY,OAAO;AASnI,IAAM,sBAAsB,CAAC,UAAmE,KAAK,kBAAkB,gBAAgB;AAWvI,IAAM,0BAA0B,CAAC,UAAmE,KAAK,sBAAsB,gBAAgB;AAY/I,IAAM,wBAAwB,CAAC,UAA6D,KAAK,mBAAmB,sBAAsB;AAS1I,IAAM,2BAA2B,CAAC,UAAmE,KAAK,kBAAkB,mBAAmB;AAS/I,IAAM,sBAAsB,CAAC,UAAiF,KAAK,aAAa,oBAAoB;AAEpJ,IAAM,0BAA0B,CAAC,QAAgB,KAAK,eAAe,KAAK;AAC1E,IAAM,4BAA4B,CAAC,QAAgB,KAAK,iBAAiB,KAAK;AAC9E,IAAM,sBAAsB,CAAC,QAAgB,KAAK,WAAW,KAAK;AAClE,IAAM,wBAAwB,CAAC,QAAgB,KAAK,kBAAkB,KAAK;AAC3E,IAAM,4BAA4B,CAAC,QAAgB,KAAK,sBAAsB,KAAK;AACnF,IAAM,2BAA2B,CAAC,QAAgB,KAAK,mBAAmB,KAAK;AAC/E,IAAM,0BAA0B,CAAC,QAAgB,KAAK,kBAAkB,KAAK;AAC7E,IAAM,qBAAqB,CAAC,QAAgB,KAAK,aAAa,KAAK;AAGnE,IAAM,yBAAyB;AAC/B,IAAM,cAAc;AACpB,IAAM,kBAAkB;;;ACtGxB,mDAAqE,eAAwB;AAAA,EAClG,YAEY,YAEA,OAEA,gBACV;AACA;AANU;AAEA;AAEA;AAAA;AAAA;AAMP,kDAAmF,+BAA0E;AAAA,QAC5I,MAA+C;AACnE,UAAM,gBAAgB,MAAM,KAAK;AAEjC,UAAM,wBAAwB,MAAM,iCAClC,eACA,KAAK,OACL,OAAO,UAAU,QAAQ,IACvB,MAAM,IAAI,CAAC,SAAS,KAAK,kBAAkB,mBAAmB,SAEhE,KAAK;AAGP,WAAO,cAAc,IACnB,CAAC,cAAc,MAAM,0BAAmC,cAAc,sBAAsB;AAAA;AAAA,EAIhG,mBAAmB;AACjB,WAAO,IAAI,2BAA2B,MAAM,KAAK;AAAA;AAAA;AAI9C,qDAAsF,+BAA8F;AAAA,QACnK,MAAyD;AAC7E,UAAM,eAAe,MAAM,KAAK;AAChC,QAAI,CAAC,cAAc;AACjB,aAAO;AAAA;AAGT,UAAM,kBAAkB,MAAM,kCAC5B,cACA,KAAK,OACL,CAAC,SAAS,KAAK,kBAAkB,mBAAmB,OACpD,KAAK;AAGP,WAAO,0BAA0B,cAAc;AAAA;AAAA,EAGjD,mBAAmB;AACjB,WAAO,IAAI,8BAA8B,MAAM,KAAK;AAAA;AAAA;AAIjD,mEAAuH,8BAAuC;AAAA,EAC1J,mBAAmB;AAC1B,WAAO,IAAI,4CAA4C,MAAM,KAAK;AAAA;AAAA,EAGpE,sBAAsB;AACpB,WAAO,IAAI,8BAA8B,MAAM,KAAK;AAAA;AAAA;AAIjD,sEAA0H,iCAA0C;AAAA,EAChK,mBAAmB;AAC1B,WAAO,IAAI,+CAA+C,MAAM,KAAK;AAAA;AAAA,EAGvE,qBAAqB;AACnB,WAAO,IAAI,gCAAgC,MAAM,KAAK;AAAA;AAAA;;;ACzEnD,gDAAkE,eAAwB;AAAA,EAC/F,YAEY,YAEA,OAEA,gBACV;AACA;AANU;AAEA;AAEA;AAAA;AAAA;AAMP,+CAAgF,4BAAuE;AAAA,QACtI,MAA+C;AACnE,UAAM,gBAAgB,MAAM,KAAK;AACjC,UAAM,qBAAqB,MAAM,iCAC/B,eACA,KAAK,OACL,OAAO,UAAU,QAAQ,IAAI,MAAM,IAAI,CAAC,SAAS,KAAK,aAAa,oBAAoB,SACvF,KAAK;AAEP,WAAO,cAAc,IAAI,CAAC,cAAc,MAAM;AAC5C,YAAM,EAAE,KAAK,QAAQ,sBAAsB,mBAAmB;AAC9D,aAAO,cAAc,iBAAiB,cAAc,QAAQ,oBAAoB;AAAA;AAAA;AAAA,EAIpF,sBAAsB;AACpB,WAAO,IAAI,8BAA8B,MAAM,KAAK;AAAA;AAAA;AAIjD,kDAAmF,4BAA2F;AAAA,QAC7J,MAAyD;AAC7E,UAAM,eAAe,MAAM,KAAK;AAChC,QAAI,CAAC;AAAc,aAAO;AAC1B,UAAM,EAAE,KAAK,QAAQ,sBAAsB,MAAM,kCAC/C,cACA,KAAK,OACL,CAAC,SAAS,KAAK,aAAa,oBAAoB,OAChD,KAAK;AAEP,WAAO,cAAc,iBAAiB,cAAc,QAAQ,oBAAoB;AAAA;AAAA,EAGlF,sBAAsB;AACpB,WAAO,IAAI,iCAAiC,MAAM,KAAK;AAAA;AAAA;AAIpD,gEAAoH,2BAAoC;AAAA,EACpJ,sBAAsB;AAC7B,WAAO,IAAI,+CAA+C,MAAM,KAAK;AAAA;AAAA,EAGvE,sBAAsB;AACpB,WAAO,IAAI,8BAA8B,MAAM,KAAK;AAAA;AAAA;AAIjD,mEAAuH,8BAAuC;AAAA,EAC1J,sBAAsB;AAC7B,WAAO,IAAI,kDAAkD,MAAM,KAAK;AAAA;AAAA,EAG1E,qBAAqB;AACnB,WAAO,IAAI,gCAAgC,MAAM,KAAK;AAAA;AAAA;;;ACvEnD,mDAAqE,eAAwB;AAAA,EAClG,YAEY,YAEA,OACV;AACA;AAJU;AAEA;AAAA;AAAA;AAMP,kDAAsG,+BAAyE;AAAA,QAC9J,MAA8C;AAClE,UAAM,gBAAgB,MAAM,KAAK;AACjC,UAAM,cAAc,MAAM,iCACxB,eACA,KAAK,OACL,CAAC,UAAU,QAAQ,IAAI,MAAM,IAAI,CAAC,SAAS,KAAK,mBAAmB,sBAAsB,SACzF,MACA,CAAC,iBAAiB,aAAa,UAAU,MAAM,MAAM,EAAE,kBAAkB;AAE3E,WAAO,YAAY,IAAI,CAAC,YAAY,MAAM,yBAAkC,cAAc,IAAI;AAAA;AAAA,EAGhG,sBAAsB;AACpB,WAAO,IAAI,+CAA+C,MAAM,KAAK;AAAA;AAAA,EAGvE,mBAAmB;AACjB,WAAO,IAAI,4CAA4C,MAAM,KAAK;AAAA;AAAA;AAI/D,oDAAwG,+BAA6F;AAAA,QACpL,MAAwD;AAC5E,UAAM,eAAe,MAAM,KAAK;AAChC,QAAI,CAAC;AAAc,aAAO;AAC1B,UAAM,aAAa,MAAM,kCACvB,cACA,KAAK,OACL,CAAC,SAAS,KAAK,mBAAmB,sBAAsB,OACxD,MAEA,CAAC,kBAAiB,cAAa,UAAU,MAAM,MAAM,EAAE,kBAAkB;AAE3E,WAAO,yBAAyB,cAAc;AAAA;AAAA,EAGhD,sBAAsB;AACpB,WAAO,IAAI,kDAAkD,MAAM,KAAK;AAAA;AAAA,EAG1E,mBAAmB;AACjB,WAAO,IAAI,+CAA+C,MAAM,KAAK;AAAA;AAAA;;;ACjDlE,gDAAkE,eAAwB;AAAA,EAC/F,YAEY,YAEA,OAEA,oBACV;AACA;AANU;AAEA;AAEA;AAAA;AAAA,MAKE,cAAyD;AACrE,WAAO,KAAK,qBACR,KAAK,wBACL,KAAK;AAAA;AAAA;AAIN,+CAAgF,4BAAqE;AAAA,QACpI,MAA6C;AACjE,UAAM,gBAAgB,MAAM,KAAK;AACjC,UAAM,aAAa,cAAc,IAAI,CAAC,QAAQ,IAAI;AAClD,UAAM,QAAgD,KAAK,iBAAoB,0BAC3E,MAAM,mBAAmB,KAAK,OAAO,cACrC,MAAM,aAAa,KAAK,OAAO;AACnC,UAAM,sBAAsB,MAAM,QAAQ,IACxC,MAAM,IAAI,CAAC,SAAS,KAAK,YAAY,gBAAgB;AAEvD,UAAM,QAAQ,CAAC,MAAM,aAAgB,2BAAU,EAAE;AACjD,WAAO,cAAc,IAAI,CAAC,cAAc,MAAM,wBAAiC,cAAc,oBAAoB;AAAA;AAAA,EAGnH,sBAAsB;AACpB,WAAO,IAAI,+CAA+C,MAAM,KAAK;AAAA;AAAA,EAGvE,mBAAmB;AACjB,WAAO,IAAI,4CAA4C,MAAM,KAAK;AAAA;AAAA,EAGpE,sBAAsB;AACpB,WAAO,IAAI,8BAA8B,MAAM,KAAK;AAAA;AAAA;AAIjD,kDAAmF,4BAAyF;AAAA,QAC3J,MAAuD;AAC3E,UAAM,eAAe,MAAM,KAAK;AAChC,QAAI,CAAC,cAAc;AACjB,aAAO;AAAA;AAET,UAAM,EAAE,cAAc;AACtB,UAAM,QAAgD,KAAK,iBAAoB,0BAC3E,MAAM,mBAAmB,KAAK,OAAO,CAAC,cACtC,MAAM,aAAa,KAAK,OAAO,CAAC;AACpC,UAAM,YAAY,MAAM,KAAK,YAAY,gBAAgB,MAAM;AAC/D,UAAM,QAAQ,CAAC,MAAM,aAAgB,2BAAU,EAAE;AACjD,WAAO,wBAAiC,cAAc;AAAA;AAAA,EAGxD,sBAAsB;AACpB,WAAO,IAAI,kDAAkD,MAAM,KAAK;AAAA;AAAA,EAG1E,mBAAmB;AACjB,WAAO,IAAI,+CAA+C,MAAM,KAAK;AAAA;AAAA,EAGvE,qBAAqB;AACnB,WAAO,IAAI,gCAAgC,MAAM,KAAK;AAAA;AAAA;;;ACvEnD,wCAA2C,eAAwB;AAAA,EAExE,YAAsB,OAA4B,UAAgC,IAAI,yBAAyB;AAC7G;AADoB;AAA4B;AAAA;AAAA;AAK7C,uCAAiC,oBAAqC;AAAA,QACrD,MAAgC;AACpD,UAAM,EAAE,OAAO,YAAY;AAC3B,QAAI;AACJ,QAAI,mBAAmB;AAAyB,eAAS,KAAK,iBAAiB,YAAY,OAAO;AAAA,aACzF,mBAAmB;AAAuB,eAAS,KAAK,eAAe,YAAY,OAAO;AAAA,aAC1F,mBAAmB;AAAmB,eAAS,KAAK,WAAW,YAAY,OAAO;AAAA;AACtF,YAAM,IAAI,MAAM;AACrB,WAAO;AAAA;AAAA,EAGD,iCAAmE;AACzE,WAAO,IAAI,QAAiC,CAAC,SAAS,WAAW;AAC/D,WAAK,MACF,KAAK,CAAC,eAAe,QAAQ,WAAW,IAAI,CAAC,cAAc,wBAAwB,IAAI,cACvF,MAAM,CAAC,QAAQ,OAAO;AAAA;AAAA;AAAA,EAI7B,kBAAkB,qBAAqB,OAAO;AAC5C,WAAO,IAAI,2BACT,KAAK,kCACL,KAAK,OACL;AAAA;AAAA,EAIJ,sBAAsB;AACpB,WAAO,IAAI,8BACT,KAAK,kCACL,KAAK;AAAA;AAAA,EAIT,mBAAmB;AACjB,WAAO,IAAI,2BACT,KAAK,kCACL,KAAK;AAAA;AAAA;AAKJ,yCAAmC,oBAA+C;AAAA,QACjE,MAA0C;AAC9D,UAAM,iBAAiB,MAAM,IAAI,mBAAmB,KAAK,OAAO,KAAK;AACrE,QAAI,gCAAgC,eAAe;AACnD,mBAAe,QAAQ,CAAC,kBAAkB;AACxC,UAAI,cAAc,QAAQ,8BAA8B;AAAO,wCAAgC;AAAA;AAEjG,WAAO;AAAA;AAAA,EAGD,gCAA4E;AAElF,WAAO,IAAI,QAA2C,OAAO,YAAY;AACvE,YAAM,YAAY,MAAM,KAAK;AAC7B,cAAQ,YAAY,wBAA4B,IAAI,aAAa;AAAA;AAAA;AAAA,EAIrE,kBAAkB,qBAAqB,OAAO;AAC5C,WAAO,IAAI,8BACT,KAAK,iCACL,KAAK,OACL;AAAA;AAAA,EAIJ,sBAAsB;AACpB,WAAO,IAAI,iCACT,KAAK,iCACL,KAAK;AAAA;AAAA,EAIT,mBAAmB;AACjB,WAAO,IAAI,8BACT,KAAK,iCACL,KAAK;AAAA;AAAA;;;AC9FJ,0BAA0B,OAAkB,UAAgC,IAAI,yBAA+C;AACpI,SAAO,IAAI,qBAAqB,OAAO;AAAA;AAGlC,wBAAwB,OAAkB,UAAgC,IAAI,yBAA6C;AAChI,SAAO,IAAI,mBAAmB,OAAO;AAAA;;;ACJvC,sCAA6C,OAAkB,eAAiG;AAC9J,SAAO,eAAe,OAAO,IAAI,sBAAsB,gBAAgB,EAAE,kBAAkB,KACxF,oBACA;AAAA;AAGL,kCAAyC,OAAkB,gBAAoC,IAA6E;AAC1K,SAAO,eAAe,OAAO,IAAI,kBAAkB,gBAChD,oBACA;AAAA;AAGE,IAAM,WAAW;;;AClBjB,2BAA2B,MAA+B,MAA+B;AAC9F,MAAI,KAAK,WAAW,KAAK;AAAQ,UAAM,IAAI,MAAM;AAEjD,QAAM,QAAQ,MAAM,KAAK;AACzB,QAAM,QAAQ,MAAM,KAAK;AAEzB,SAAO,KAAK,KACV,MACG,IAAI,CAAC,KAAK,MAAM,MAAM,MAAM,IAC5B,OAAO,CAAC,KAAK,SAAS,MAAO,QAAQ,GAAI;AAAA;;;ACJzC,wBAAkB;AAAA,EAKvB,YACE,QACA,oBAAoB,KACpB;AACA,SAAK,qBAAqB;AAE1B,UAAM,aAAa,MAAM,QAAQ,UAAU,SAAS,CAAC;AAErD,QAAI,CAAC,WAAW,QAAQ;AACtB,YAAM,IAAI,MAAM;AAAA;AAGlB,QAAI,QAAQ;AACZ,UAAM,oBAAoB,MAAM,UAAU;AAE1C,SAAK,sBAAsB,WAAW,IAAI,CAAC,SAAS;AAClD,UAAI,gBAAgB,wBAAwB;AAC1C,eAAO;AAAA;AAGT,UAAI,gBAAgB,cAAc;AAChC,eAAO,IAAI,uBAAuB,qBAAqB,CAAC;AAAA;AAG1D,UAAI,KAAK,cAAc,KAAK,sBAAsB,cAAc;AAC9D,eAAO,IAAI,uBAAuB,qBAAqB,CAAC,KAAK;AAAA;AAG/D,YAAM,IAAI,MAAM;AAAA;AAAA;AAAA,MAIT,qBAA+C;AAAE,WAAO,KAAK;AAAA;AAAA,MAE7D,oBAA4B;AAAE,WAAO,KAAK;AAAA;AAAA,EAE9C,oBAAoB,iBAA+B,aAAqC;AAC7F,WAAO,YACJ,IAAI,CAAC,MAAM,kBAAkB,GAAG,kBAChC,OAAO,CAAC,IAAI,OAAO,KAAK,IAAI,KACxB,aAAY,UAAU;AAAA;AAAA,EAGxB,gBAAgB,iBAA0C;AAC/D,WAAO,KAAK,mBACT,IAAI,CAAC,EAAE,aAAa,YAAY,IAAI,UACnC,OACA,KAAK,oBAAoB,iBAAiB,eAE3C,OAAO,CAAC,MAAM,SAAU,KAAK,WAAW,KAAK,WAAW,OAAO;AAAA;AAAA,EAG7D,cAAc,iBAA0C;AAC7D,UAAM,YAAY,KAAK,gBAAgB;AACvC,WAAO,UAAU,WAAW,KAAK,oBAC7B,YACA,IAAI,UAAU,WAAW,UAAU;AAAA;AAAA,EAGlC,SAAc;AACnB,WAAO;AAAA,MACL,mBAAmB,KAAK;AAAA,MACxB,oBAAoB,KAAK,mBAAmB,IAAI,CAAC,OAAO,GAAG;AAAA;AAAA;AAAA,SAIjD,SAAS,MAAwB;AAC7C,UAAM,qBAAqB,KAAK,mBAC7B,IAAI,CAAC,OAAY,uBAAuB,SAAS;AACpD,WAAO,IAAI,YAAY,oBAAoB,KAAK;AAAA;AAAA;;;AC1E7C,gCAAgC,SAAuB;AAC5D,QAAM,MAAM,IAAI;AAChB,MAAI,eAAe;AACnB,SAAO;AAAA;;;ACFF,uBAA0B,SAAY,YAA4B;AACvE,QAAM,EAAE,OAAO,WAAW,IAAI,WAAW,WAAW,OAAO,WAAW;AAEtE,MAAI,SAAS,KAAK,UAAU,GAAG;AAC7B,UAAM,IAAI,MAAM,uCAAuC,KAAK,UAAU,EAAE,OAAO;AAAA;AAGjF,MAAI,MAAM,QAAQ,UAAU;AAE1B,WAAQ,QAAuB,IAAI,CAAC,QAAQ,cAAc,KAAK,EAAE,OAAO;AAAA;AAG1E,MAAI,oBAAoB,UAAU;AAChC,UAAM,mBAAmB,QAAQ,UAAU,QAAQ,OAAO;AAC1D,UAAM,mBAAmB,QAAQ,mBAAmB,QAAQ,iBAAiB,IAAI,OAAO,iBAAiB,IAAI;AAC7G,WAAO,wBAAwB,wBAAwB,SAAS,mBAAmB;AAAA;AAGrF,MAAI,oBAAoB,UAAU;AAChC,WAAO,wBAAwB,SAAS,QAAQ,UAAU,QAAQ,OAAO;AAAA;AAG3E,MAAI,mBAAmB,iBAAiB,mBAAmB,eAAe;AACxE,WAAQ,QAAgB,QAAQ,OAAO;AAAA;AAGzC,SAAO;AAAA;;;ACRT,IAAM,OAAQ,OAAO,YAAY;AACjC,IAAM,WAAW,OAAO,cAAc,eAAiB,OAAO,UAAU,cAAc;AAC/E,IAAM,WAAU,EAAE,SAAa,UAAmB,MAAM;AAG/D,IAAI,UAAS;AACX,EAAG,qBAAI,IAAI,gCAAgC;AAC3C,EAAG,qBAAI,IAAI,qBAAqB;AAChC,EAAG,qBAAI,IAAI,4BAA4B;AACvC,EAAG,qBAAI,IAAI,6BAA6B;AAAA;", + "names": [] +}