/*! firebase-admin v12.1.1 */
"use strict";
/*!
* Copyright 2020 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.MachineLearningApiClient = exports.isGcsTfliteModelOptions = void 0;
const api_request_1 = require("../utils/api-request");
const error_1 = require("../utils/error");
const utils = require("../utils/index");
const validator = require("../utils/validator");
const machine_learning_utils_1 = require("./machine-learning-utils");
const ML_V1BETA2_API = 'https://firebaseml.googleapis.com/v1beta2';
const FIREBASE_VERSION_HEADER = {
'X-Firebase-Client': `fire-admin-node/${utils.getSdkVersion()}`,
};
// Operation polling defaults
const POLL_DEFAULT_MAX_TIME_MILLISECONDS = 120000; // Maximum overall 2 minutes
const POLL_BASE_WAIT_TIME_MILLISECONDS = 3000; // Start with 3 second delay
const POLL_MAX_WAIT_TIME_MILLISECONDS = 30000; // Maximum 30 second delay
function isGcsTfliteModelOptions(options) {
const gcsUri = options?.tfliteModel?.gcsTfliteUri;
return typeof gcsUri !== 'undefined';
}
exports.isGcsTfliteModelOptions = isGcsTfliteModelOptions;
/**
* Class that facilitates sending requests to the Firebase ML backend API.
*
* @internal
*/
class MachineLearningApiClient {
constructor(app) {
this.app = app;
if (!validator.isNonNullObject(app) || !('options' in app)) {
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'First argument passed to admin.machineLearning() must be a valid '
+ 'Firebase app instance.');
}
this.httpClient = new api_request_1.AuthorizedHttpClient(app);
}
createModel(model) {
if (!validator.isNonNullObject(model) ||
!validator.isNonEmptyString(model.displayName)) {
const err = new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Invalid model content.');
return Promise.reject(err);
}
return this.getProjectUrl()
.then((url) => {
const request = {
method: 'POST',
url: `${url}/models`,
data: model,
};
return this.sendRequest(request);
});
}
updateModel(modelId, model, updateMask) {
if (!validator.isNonEmptyString(modelId) ||
!validator.isNonNullObject(model) ||
!validator.isNonEmptyArray(updateMask)) {
const err = new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Invalid model or mask content.');
return Promise.reject(err);
}
return this.getProjectUrl()
.then((url) => {
const request = {
method: 'PATCH',
url: `${url}/models/${modelId}?updateMask=${updateMask.join()}`,
data: model,
};
return this.sendRequest(request);
});
}
getModel(modelId) {
return Promise.resolve()
.then(() => {
return this.getModelName(modelId);
})
.then((modelName) => {
return this.getResourceWithShortName(modelName);
});
}
getOperation(operationName) {
return Promise.resolve()
.then(() => {
return this.getResourceWithFullName(operationName);
});
}
listModels(options = {}) {
if (!validator.isNonNullObject(options)) {
const err = new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Invalid ListModelsOptions');
return Promise.reject(err);
}
if (typeof options.filter !== 'undefined' && !validator.isNonEmptyString(options.filter)) {
const err = new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Invalid list filter.');
return Promise.reject(err);
}
if (typeof options.pageSize !== 'undefined') {
if (!validator.isNumber(options.pageSize)) {
const err = new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Invalid page size.');
return Promise.reject(err);
}
if (options.pageSize < 1 || options.pageSize > 100) {
const err = new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Page size must be between 1 and 100.');
return Promise.reject(err);
}
}
if (typeof options.pageToken !== 'undefined' && !validator.isNonEmptyString(options.pageToken)) {
const err = new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Next page token must be a non-empty string.');
return Promise.reject(err);
}
return this.getProjectUrl()
.then((url) => {
const request = {
method: 'GET',
url: `${url}/models`,
data: options,
};
return this.sendRequest(request);
});
}
deleteModel(modelId) {
return this.getProjectUrl()
.then((url) => {
const modelName = this.getModelName(modelId);
const request = {
method: 'DELETE',
url: `${url}/${modelName}`,
};
return this.sendRequest(request);
});
}
/**
* Handles a Long Running Operation coming back from the server.
*
* @param op - The operation to handle
* @param options - The options for polling
*/
handleOperation(op, options) {
if (op.done) {
if (op.response) {
return Promise.resolve(op.response);
}
else if (op.error) {
const err = machine_learning_utils_1.FirebaseMachineLearningError.fromOperationError(op.error.code, op.error.message);
return Promise.reject(err);
}
// Done operations must have either a response or an error.
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-server-response', 'Invalid operation response.');
}
// Operation is not done
if (options?.wait) {
return this.pollOperationWithExponentialBackoff(op.name, options);
}
const metadata = op.metadata || {};
const metadataType = metadata['@type'] || '';
if (!metadataType.includes('ModelOperationMetadata')) {
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-server-response', `Unknown Metadata type: ${JSON.stringify(metadata)}`);
}
return this.getModel(extractModelId(metadata.name));
}
// baseWaitMillis and maxWaitMillis should only ever be modified by unit tests to run faster.
pollOperationWithExponentialBackoff(opName, options) {
const maxTimeMilliseconds = options?.maxTimeMillis ?? POLL_DEFAULT_MAX_TIME_MILLISECONDS;
const baseWaitMillis = options?.baseWaitMillis ?? POLL_BASE_WAIT_TIME_MILLISECONDS;
const maxWaitMillis = options?.maxWaitMillis ?? POLL_MAX_WAIT_TIME_MILLISECONDS;
const poller = new api_request_1.ExponentialBackoffPoller(baseWaitMillis, maxWaitMillis, maxTimeMilliseconds);
return poller.poll(() => {
return this.getOperation(opName)
.then((responseData) => {
if (!responseData.done) {
return null;
}
if (responseData.error) {
const err = machine_learning_utils_1.FirebaseMachineLearningError.fromOperationError(responseData.error.code, responseData.error.message);
throw err;
}
return responseData.response;
});
});
}
/**
* Gets the specified resource from the ML API. Resource names must be the short names without project
* ID prefix (e.g. `models/123456789`).
*
* @param {string} name Short name of the resource to get. e.g. 'models/12345'
* @returns {Promise<T>} A promise that fulfills with the resource.
*/
getResourceWithShortName(name) {
return this.getProjectUrl()
.then((url) => {
const request = {
method: 'GET',
url: `${url}/${name}`,
};
return this.sendRequest(request);
});
}
/**
* Gets the specified resource from the ML API. Resource names must be the full names including project
* number prefix.
* @param fullName - Full resource name of the resource to get. e.g. projects/123465/operations/987654
* @returns {Promise<T>} A promise that fulfulls with the resource.
*/
getResourceWithFullName(fullName) {
const request = {
method: 'GET',
url: `${ML_V1BETA2_API}/${fullName}`
};
return this.sendRequest(request);
}
sendRequest(request) {
request.headers = FIREBASE_VERSION_HEADER;
return this.httpClient.send(request)
.then((resp) => {
return resp.data;
})
.catch((err) => {
throw this.toFirebaseError(err);
});
}
toFirebaseError(err) {
if (err instanceof error_1.PrefixedFirebaseError) {
return err;
}
const response = err.response;
if (!response.isJson()) {
return new machine_learning_utils_1.FirebaseMachineLearningError('unknown-error', `Unexpected response with status: ${response.status} and body: ${response.text}`);
}
const error = response.data.error || {};
let code = 'unknown-error';
if (error.status && error.status in ERROR_CODE_MAPPING) {
code = ERROR_CODE_MAPPING[error.status];
}
const message = error.message || `Unknown server error: ${response.text}`;
return new machine_learning_utils_1.FirebaseMachineLearningError(code, message);
}
getProjectUrl() {
return this.getProjectIdPrefix()
.then((projectIdPrefix) => {
return `${ML_V1BETA2_API}/${projectIdPrefix}`;
});
}
getProjectIdPrefix() {
if (this.projectIdPrefix) {
return Promise.resolve(this.projectIdPrefix);
}
return utils.findProjectId(this.app)
.then((projectId) => {
if (!validator.isNonEmptyString(projectId)) {
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Failed to determine project ID. Initialize the SDK with service account credentials, or '
+ 'set project ID as an app option. Alternatively, set the GOOGLE_CLOUD_PROJECT '
+ 'environment variable.');
}
this.projectIdPrefix = `projects/${projectId}`;
return this.projectIdPrefix;
});
}
getModelName(modelId) {
if (!validator.isNonEmptyString(modelId)) {
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Model ID must be a non-empty string.');
}
if (modelId.indexOf('/') !== -1) {
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', 'Model ID must not contain any "/" characters.');
}
return `models/${modelId}`;
}
}
exports.MachineLearningApiClient = MachineLearningApiClient;
const ERROR_CODE_MAPPING = {
INVALID_ARGUMENT: 'invalid-argument',
NOT_FOUND: 'not-found',
RESOURCE_EXHAUSTED: 'resource-exhausted',
UNAUTHENTICATED: 'authentication-error',
UNKNOWN: 'unknown-error',
};
function extractModelId(resourceName) {
return resourceName.split('/').pop();
}