import type { MetadataBearer as __MetadataBearer } from "@smithy/types";
import type { CreateDataSourceFromRDSInput, CreateDataSourceFromRDSOutput } from "../models/models_0";
/**
 * @public
 */
export type { __MetadataBearer };
/**
 * @public
 *
 * The input for {@link CreateDataSourceFromRDSCommand}.
 */
export interface CreateDataSourceFromRDSCommandInput extends CreateDataSourceFromRDSInput {
}
/**
 * @public
 *
 * The output of {@link CreateDataSourceFromRDSCommand}.
 */
export interface CreateDataSourceFromRDSCommandOutput extends CreateDataSourceFromRDSOutput, __MetadataBearer {
}
declare const CreateDataSourceFromRDSCommand_base: {
    new (input: CreateDataSourceFromRDSCommandInput): import("@smithy/core/client").CommandImpl<CreateDataSourceFromRDSCommandInput, CreateDataSourceFromRDSCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    new (input: CreateDataSourceFromRDSCommandInput): import("@smithy/core/client").CommandImpl<CreateDataSourceFromRDSCommandInput, CreateDataSourceFromRDSCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    getEndpointParameterInstructions(): import("@smithy/types").EndpointParameterInstructions;
};
/**
 * <p>Creates a <code>DataSource</code> object from an <a href="http://aws.amazon.com/rds/"> Amazon Relational Database Service</a> (Amazon RDS). A <code>DataSource</code> references data that can be used to perform <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.</p>
 *
 *         <p>
 *             <code>CreateDataSourceFromRDS</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRDS</code>,
 *          Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>.
 *             After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>.
 *             <code>DataSource</code> in the <code>COMPLETED</code> or <code>PENDING</code> state can
 *           be used only to perform <code>>CreateMLModel</code>>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.
 *         </p>
 *         <p>
 *           If Amazon ML cannot accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response.
 *         </p>
 * @example
 * Use a bare-bones client and the command you need to make an API call.
 * ```javascript
 * import { MachineLearningClient, CreateDataSourceFromRDSCommand } from "@aws-sdk/client-machine-learning"; // ES Modules import
 * // const { MachineLearningClient, CreateDataSourceFromRDSCommand } = require("@aws-sdk/client-machine-learning"); // CommonJS import
 * // import type { MachineLearningClientConfig } from "@aws-sdk/client-machine-learning";
 * const config = {}; // type is MachineLearningClientConfig
 * const client = new MachineLearningClient(config);
 * const input = { // CreateDataSourceFromRDSInput
 *   DataSourceId: "STRING_VALUE", // required
 *   DataSourceName: "STRING_VALUE",
 *   RDSData: { // RDSDataSpec
 *     DatabaseInformation: { // RDSDatabase
 *       InstanceIdentifier: "STRING_VALUE", // required
 *       DatabaseName: "STRING_VALUE", // required
 *     },
 *     SelectSqlQuery: "STRING_VALUE", // required
 *     DatabaseCredentials: { // RDSDatabaseCredentials
 *       Username: "STRING_VALUE", // required
 *       Password: "STRING_VALUE", // required
 *     },
 *     S3StagingLocation: "STRING_VALUE", // required
 *     DataRearrangement: "STRING_VALUE",
 *     DataSchema: "STRING_VALUE",
 *     DataSchemaUri: "STRING_VALUE",
 *     ResourceRole: "STRING_VALUE", // required
 *     ServiceRole: "STRING_VALUE", // required
 *     SubnetId: "STRING_VALUE", // required
 *     SecurityGroupIds: [ // EDPSecurityGroupIds // required
 *       "STRING_VALUE",
 *     ],
 *   },
 *   RoleARN: "STRING_VALUE", // required
 *   ComputeStatistics: true || false,
 * };
 * const command = new CreateDataSourceFromRDSCommand(input);
 * const response = await client.send(command);
 * // { // CreateDataSourceFromRDSOutput
 * //   DataSourceId: "STRING_VALUE",
 * // };
 *
 * ```
 *
 * @param CreateDataSourceFromRDSCommandInput - {@link CreateDataSourceFromRDSCommandInput}
 * @returns {@link CreateDataSourceFromRDSCommandOutput}
 * @see {@link CreateDataSourceFromRDSCommandInput} for command's `input` shape.
 * @see {@link CreateDataSourceFromRDSCommandOutput} for command's `response` shape.
 * @see {@link MachineLearningClientResolvedConfig | config} for MachineLearningClient's `config` shape.
 *
 * @throws {@link IdempotentParameterMismatchException} (client fault)
 *  <p>A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.</p>
 *
 * @throws {@link InternalServerException} (server fault)
 *  <p>An error on the server occurred when trying to process a request.</p>
 *
 * @throws {@link InvalidInputException} (client fault)
 *  <p>An error on the client occurred. Typically, the cause is an invalid input value.</p>
 *
 * @throws {@link MachineLearningServiceException}
 * <p>Base exception class for all service exceptions from MachineLearning service.</p>
 *
 *
 * @public
 */
export declare class CreateDataSourceFromRDSCommand extends CreateDataSourceFromRDSCommand_base {
    /** @internal type navigation helper, not in runtime. */
    protected static __types: {
        api: {
            input: CreateDataSourceFromRDSInput;
            output: CreateDataSourceFromRDSOutput;
        };
        sdk: {
            input: CreateDataSourceFromRDSCommandInput;
            output: CreateDataSourceFromRDSCommandOutput;
        };
    };
}