{"code":"import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs';\r\nimport * as socketProxy from 'socket.io-client';\r\nimport { DEFAULT_CLIENT_HYPERPARAMS, deserializeVar, Events, serializeVars } from './common';\r\nimport { FederatedClientTfModel, isFederatedClientModel } from './models';\r\nvar socketio = socketProxy.default || socketProxy;\r\nvar CONNECTION_TIMEOUT = 10 * 1000;\r\nvar UPLOAD_TIMEOUT = 5 * 1000;\r\nvar COOKIE_NAME = 'federated-learner-uuid';\r\nvar YEAR_IN_MS = 365 * 24 * 60 * 60 * 1000;\r\nvar Client = (function () {\r\n    function Client(server, model, config) {\r\n        var _this = this;\r\n        this.server = server;\r\n        if (isFederatedClientModel(model)) {\r\n            this.model = model;\r\n        }\r\n        else {\r\n            var compileConfig = (config || {}).modelCompileConfig || {};\r\n            this.model = new FederatedClientTfModel(model, compileConfig);\r\n        }\r\n        this.uploadCallbacks = [];\r\n        this.versionCallbacks = [function (v1, v2) {\r\n                _this.log(\"Updated model: \" + v1 + \" -> \" + v2);\r\n            }];\r\n        this.versionUpdateCounts = {};\r\n        this.verbose = (config || {}).verbose;\r\n        this.sendMetrics = (config || {}).sendMetrics;\r\n        if ((config || {}).clientId) {\r\n            this.clientId = config.clientId;\r\n        }\r\n        else if (getCookie(COOKIE_NAME)) {\r\n            this.clientId = getCookie(COOKIE_NAME);\r\n        }\r\n        else {\r\n            this.clientId = uuid();\r\n            setCookie(COOKIE_NAME, this.clientId);\r\n        }\r\n        this.hyperparams = (config || {}).hyperparams || {};\r\n    }\r\n    Client.prototype.modelVersion = function () {\r\n        return this.msg == null ? 'unsynced' : this.msg.model.version;\r\n    };\r\n    Client.prototype.onNewVersion = function (callback) {\r\n        this.versionCallbacks.push(callback);\r\n    };\r\n    Client.prototype.onUpload = function (callback) {\r\n        this.uploadCallbacks.push(callback);\r\n    };\r\n    Client.prototype.setup = function () {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var newVersion;\r\n            var _this = this;\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4, this.time('Initial model setup', function () { return tslib_1.__awaiter(_this, void 0, void 0, function () {\r\n                            return tslib_1.__generator(this, function (_a) {\r\n                                switch (_a.label) {\r\n                                    case 0: return [4, this.model.setup()];\r\n                                    case 1:\r\n                                        _a.sent();\r\n                                        return [2];\r\n                                }\r\n                            });\r\n                        }); })];\r\n                    case 1:\r\n                        _a.sent();\r\n                        this.x = tf.tensor([], [0].concat(this.model.inputShape));\r\n                        this.y = tf.tensor([], [0].concat(this.model.outputShape));\r\n                        return [4, this.time('Download weights from server', function () { return tslib_1.__awaiter(_this, void 0, void 0, function () {\r\n                                var _a;\r\n                                return tslib_1.__generator(this, function (_b) {\r\n                                    switch (_b.label) {\r\n                                        case 0:\r\n                                            _a = this;\r\n                                            return [4, this.connectTo(this.server)];\r\n                                        case 1:\r\n                                            _a.msg = _b.sent();\r\n                                            return [2];\r\n                                    }\r\n                                });\r\n                            }); })];\r\n                    case 2:\r\n                        _a.sent();\r\n                        this.setVars(this.msg.model.vars);\r\n                        newVersion = this.modelVersion();\r\n                        this.versionUpdateCounts[newVersion] = 0;\r\n                        this.versionCallbacks.forEach(function (cb) { return cb(null, newVersion); });\r\n                        this.socket.on(Events.Download, function (msg) {\r\n                            var oldVersion = _this.modelVersion();\r\n                            var newVersion = msg.model.version;\r\n                            _this.msg = msg;\r\n                            _this.setVars(msg.model.vars);\r\n                            _this.versionUpdateCounts[newVersion] = 0;\r\n                            _this.versionCallbacks.forEach(function (cb) { return cb(oldVersion, newVersion); });\r\n                        });\r\n                        return [2];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    Client.prototype.dispose = function () {\r\n        this.socket.disconnect();\r\n        this.log('Disconnected');\r\n    };\r\n    Client.prototype.federatedUpdate = function (x, y) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var xNew, yNew, examplesPerUpdate, _loop_1, this_1;\r\n            var _this = this;\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        xNew = addRows(this.x, x, this.model.inputShape);\r\n                        yNew = addRows(this.y, y, this.model.outputShape);\r\n                        tf.dispose([this.x, this.y]);\r\n                        this.x = xNew;\r\n                        this.y = yNew;\r\n                        examplesPerUpdate = this.hyperparam('examplesPerUpdate');\r\n                        _loop_1 = function () {\r\n                            var modelVersion, xTrain, yTrain, fitConfig, metrics, stdDev, newVars, newTensors, uploadMsg, xRest, yRest;\r\n                            return tslib_1.__generator(this, function (_a) {\r\n                                switch (_a.label) {\r\n                                    case 0:\r\n                                        modelVersion = this_1.modelVersion();\r\n                                        xTrain = sliceWithEmptyTensors(this_1.x, 0, examplesPerUpdate);\r\n                                        yTrain = sliceWithEmptyTensors(this_1.y, 0, examplesPerUpdate);\r\n                                        fitConfig = {\r\n                                            epochs: this_1.hyperparam('epochs'),\r\n                                            batchSize: this_1.hyperparam('batchSize'),\r\n                                            learningRate: this_1.hyperparam('learningRate')\r\n                                        };\r\n                                        metrics = null;\r\n                                        if (this_1.sendMetrics) {\r\n                                            metrics = this_1.model.evaluate(xTrain, yTrain);\r\n                                        }\r\n                                        return [4, this_1.time('Fit model', function () { return tslib_1.__awaiter(_this, void 0, void 0, function () {\r\n                                                var err_1;\r\n                                                return tslib_1.__generator(this, function (_a) {\r\n                                                    switch (_a.label) {\r\n                                                        case 0:\r\n                                                            _a.trys.push([0, 2, , 3]);\r\n                                                            return [4, this.model.fit(xTrain, yTrain, fitConfig)];\r\n                                                        case 1:\r\n                                                            _a.sent();\r\n                                                            return [3, 3];\r\n                                                        case 2:\r\n                                                            err_1 = _a.sent();\r\n                                                            console.error(err_1);\r\n                                                            throw err_1;\r\n                                                        case 3: return [2];\r\n                                                    }\r\n                                                });\r\n                                            }); })];\r\n                                    case 1:\r\n                                        _a.sent();\r\n                                        stdDev = this_1.hyperparam('weightNoiseStddev');\r\n                                        newVars = void 0;\r\n                                        if (!stdDev) return [3, 3];\r\n                                        newTensors = tf.tidy(function () {\r\n                                            return _this.model.getVars().map(function (v) {\r\n                                                return v.add(tf.randomNormal(v.shape, 0, stdDev));\r\n                                            });\r\n                                        });\r\n                                        return [4, serializeVars(newTensors)];\r\n                                    case 2:\r\n                                        newVars = _a.sent();\r\n                                        tf.dispose(newTensors);\r\n                                        return [3, 5];\r\n                                    case 3: return [4, serializeVars(this_1.model.getVars())];\r\n                                    case 4:\r\n                                        newVars = _a.sent();\r\n                                        _a.label = 5;\r\n                                    case 5:\r\n                                        this_1.setVars(this_1.msg.model.vars);\r\n                                        uploadMsg = {\r\n                                            model: { version: modelVersion, vars: newVars },\r\n                                            clientId: this_1.clientId,\r\n                                        };\r\n                                        if (this_1.sendMetrics) {\r\n                                            uploadMsg.metrics = metrics;\r\n                                        }\r\n                                        return [4, this_1.time('Upload weights to server', function () { return tslib_1.__awaiter(_this, void 0, void 0, function () {\r\n                                                return tslib_1.__generator(this, function (_a) {\r\n                                                    switch (_a.label) {\r\n                                                        case 0: return [4, this.uploadVars(uploadMsg)];\r\n                                                        case 1:\r\n                                                            _a.sent();\r\n                                                            return [2];\r\n                                                    }\r\n                                                });\r\n                                            }); })];\r\n                                    case 6:\r\n                                        _a.sent();\r\n                                        this_1.uploadCallbacks.forEach(function (cb) { return cb(uploadMsg); });\r\n                                        this_1.versionUpdateCounts[modelVersion] += 1;\r\n                                        tf.dispose([xTrain, yTrain]);\r\n                                        xRest = sliceWithEmptyTensors(this_1.x, examplesPerUpdate);\r\n                                        yRest = sliceWithEmptyTensors(this_1.y, examplesPerUpdate);\r\n                                        tf.dispose([this_1.x, this_1.y]);\r\n                                        this_1.x = xRest;\r\n                                        this_1.y = yRest;\r\n                                        return [2];\r\n                                }\r\n                            });\r\n                        };\r\n                        this_1 = this;\r\n                        _a.label = 1;\r\n                    case 1:\r\n                        if (!(this.x.shape[0] >= examplesPerUpdate)) return [3, 3];\r\n                        return [5, _loop_1()];\r\n                    case 2:\r\n                        _a.sent();\r\n                        return [3, 1];\r\n                    case 3: return [2];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    Client.prototype.evaluate = function (x, y) {\r\n        return this.model.evaluate(x, y);\r\n    };\r\n    Client.prototype.predict = function (x) {\r\n        return this.model.predict(x);\r\n    };\r\n    Object.defineProperty(Client.prototype, \"inputShape\", {\r\n        get: function () {\r\n            return this.model.inputShape;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Client.prototype, \"outputShape\", {\r\n        get: function () {\r\n            return this.model.outputShape;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Client.prototype.numUpdates = function () {\r\n        var _this = this;\r\n        var numTotal = 0;\r\n        Object.keys(this.versionUpdateCounts).forEach(function (k) {\r\n            numTotal += _this.versionUpdateCounts[k];\r\n        });\r\n        return numTotal;\r\n    };\r\n    Client.prototype.numVersions = function () {\r\n        return Object.keys(this.versionUpdateCounts).length;\r\n    };\r\n    Client.prototype.numExamples = function () {\r\n        return this.x.shape[0];\r\n    };\r\n    Client.prototype.hyperparam = function (key) {\r\n        return (this.hyperparams[key] || this.msg.hyperparams[key] ||\r\n            DEFAULT_CLIENT_HYPERPARAMS[key]);\r\n    };\r\n    Client.prototype.numExamplesPerUpdate = function () {\r\n        return this.hyperparam('examplesPerUpdate');\r\n    };\r\n    Client.prototype.numExamplesRemaining = function () {\r\n        return this.numExamplesPerUpdate() - this.numExamples();\r\n    };\r\n    Client.prototype.uploadVars = function (msg) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var prom;\r\n            var _this = this;\r\n            return tslib_1.__generator(this, function (_a) {\r\n                prom = new Promise(function (resolve, reject) {\r\n                    var rejectTimer = setTimeout(function () { return reject(\"uploadVars timed out\"); }, UPLOAD_TIMEOUT);\r\n                    _this.socket.emit(Events.Upload, msg, function () {\r\n                        clearTimeout(rejectTimer);\r\n                        resolve();\r\n                    });\r\n                });\r\n                return [2, prom];\r\n            });\r\n        });\r\n    };\r\n    Client.prototype.setVars = function (newVars) {\r\n        var _this = this;\r\n        tf.tidy(function () {\r\n            _this.model.setVars(newVars.map(function (v) { return deserializeVar(v); }));\r\n        });\r\n    };\r\n    Client.prototype.connectTo = function (server) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            return tslib_1.__generator(this, function (_a) {\r\n                if (typeof server === 'string') {\r\n                    this.socket = socketio(server);\r\n                }\r\n                else {\r\n                    this.socket = server();\r\n                }\r\n                return [2, fromEvent(this.socket, Events.Download, CONNECTION_TIMEOUT)];\r\n            });\r\n        });\r\n    };\r\n    Client.prototype.log = function () {\r\n        var args = [];\r\n        for (var _i = 0; _i < arguments.length; _i++) {\r\n            args[_i] = arguments[_i];\r\n        }\r\n        if (this.verbose) {\r\n            console.log.apply(console, ['Federated Client:'].concat(args));\r\n        }\r\n    };\r\n    Client.prototype.time = function (msg, action) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var t1, t2;\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        t1 = new Date().getTime();\r\n                        return [4, action()];\r\n                    case 1:\r\n                        _a.sent();\r\n                        t2 = new Date().getTime();\r\n                        this.log(msg + \" took \" + (t2 - t1) + \"ms\");\r\n                        return [2];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    return Client;\r\n}());\r\nexport { Client };\r\nfunction fromEvent(emitter, eventName, timeout) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        return tslib_1.__generator(this, function (_a) {\r\n            return [2, new Promise(function (resolve, reject) {\r\n                    var rejectTimer = setTimeout(function () { return reject(eventName + \" event timed out\"); }, timeout);\r\n                    var listener = function (evtArgs) {\r\n                        emitter.removeListener(eventName, listener);\r\n                        clearTimeout(rejectTimer);\r\n                        resolve(evtArgs);\r\n                    };\r\n                    emitter.on(eventName, listener);\r\n                })];\r\n        });\r\n    });\r\n}\r\nfunction concatWithEmptyTensors(a, b) {\r\n    if (a.shape[0] === 0) {\r\n        return b.clone();\r\n    }\r\n    else if (b.shape[0] === 0) {\r\n        return a.clone();\r\n    }\r\n    else {\r\n        return a.concat(b);\r\n    }\r\n}\r\nfunction sliceWithEmptyTensors(a, begin, size) {\r\n    if (begin >= a.shape[0]) {\r\n        return tf.tensor([], [0].concat(a.shape.slice(1)));\r\n    }\r\n    else {\r\n        return a.slice(begin, size);\r\n    }\r\n}\r\nfunction addRows(existing, newEls, unitShape) {\r\n    if (tf.util.arraysEqual(newEls.shape, unitShape)) {\r\n        return tf.tidy(function () { return concatWithEmptyTensors(existing, tf.expandDims(newEls)); });\r\n    }\r\n    else {\r\n        tf.util.assertShapesMatch(newEls.shape.slice(1), unitShape);\r\n        return tf.tidy(function () { return concatWithEmptyTensors(existing, newEls); });\r\n    }\r\n}\r\nfunction getCookie(name) {\r\n    if (typeof document === 'undefined') {\r\n        return null;\r\n    }\r\n    var v = document.cookie.match('(^|;) ?' + name + '=([^;]*)(;|$)');\r\n    return v ? v[2] : null;\r\n}\r\nfunction setCookie(name, value) {\r\n    if (typeof document === 'undefined') {\r\n        return;\r\n    }\r\n    var d = new Date();\r\n    d.setTime(d.getTime() + YEAR_IN_MS);\r\n    document.cookie = name + '=' + value + ';path=/;expires=' + d.toUTCString();\r\n}\r\nfunction uuid() {\r\n    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function (c) {\r\n        var r = Math.random() * 16 | 0, v = c === 'x' ? r : (r & 0x3 | 0x8);\r\n        return v.toString(16);\r\n    });\r\n}\r\n//# 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<reference types=\"socket.io-client\" />\r\nimport * as tf from '@tensorflow/tfjs';\r\nimport { AsyncTfModel, ClientHyperparams, FederatedCompileConfig, SerializedVariable, UploadCallback, VersionCallback } from './common';\r\nimport { FederatedClientModel } from './models';\r\ndeclare type SocketCallback = () => SocketIOClient.Socket;\r\nexport declare type FederatedClientConfig = {\r\n    modelCompileConfig?: FederatedCompileConfig;\r\n    hyperparams?: ClientHyperparams;\r\n    verbose?: boolean;\r\n    clientId?: string;\r\n    sendMetrics?: boolean;\r\n};\r\nexport declare class Client {\r\n    private msg;\r\n    private model;\r\n    private socket;\r\n    private versionCallbacks;\r\n    private uploadCallbacks;\r\n    private x;\r\n    private y;\r\n    private versionUpdateCounts;\r\n    private server;\r\n    private verbose;\r\n    private sendMetrics;\r\n    hyperparams: ClientHyperparams;\r\n    clientId: string;\r\n    constructor(server: string | SocketCallback, model: FederatedClientModel | AsyncTfModel, config?: FederatedClientConfig);\r\n    modelVersion(): string;\r\n    onNewVersion(callback: VersionCallback): void;\r\n    onUpload(callback: UploadCallback): void;\r\n    setup(): Promise<void>;\r\n    dispose(): void;\r\n    federatedUpdate(x: tf.Tensor, y: tf.Tensor): Promise<void>;\r\n    evaluate(x: tf.Tensor, y: tf.Tensor): number[];\r\n    predict(x: tf.Tensor): tf.Tensor;\r\n    readonly inputShape: number[];\r\n    readonly outputShape: number[];\r\n    numUpdates(): number;\r\n    numVersions(): number;\r\n    numExamples(): number;\r\n    private hyperparam;\r\n    numExamplesPerUpdate(): number;\r\n    numExamplesRemaining(): number;\r\n    private uploadVars;\r\n    protected setVars(newVars: SerializedVariable[]): void;\r\n    private connectTo;\r\n    private log;\r\n    private time;\r\n}\r\nexport {};\r\n"}}