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/*
 * Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
 * with the License. A copy of the License is located at
 *
 * http://aws.amazon.com/apache2.0/
 *
 * or in the "license" file accompanying this file. This file 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.
 */
package ai.djl.translate;

import ai.djl.Model;
import ai.djl.inference.Predictor;
import ai.djl.ndarray.NDManager;
import java.io.IOException;

/**
 * The {@code Translator} interface provides model pre-processing and postprocessing functionality.
 *
 * Users can use this in {@link Predictor} with input and output objects specified. The following
 * is an example of processing an image and creating classification output:
 *
 * 
 * private static final class MyTranslator implements Translator<Image, Classification> {
 *
 *     private int imageWidth;
 *     private int imageHeight;
 *
 *     public MyTranslator(int imageWidth, int imageHeight) {
 *         this.imageWidth = imageWidth;
 *         this.imageHeight = imageHeight;
 *     }
 *
 *     @Override
 *     public NDList processInput(TranslatorContext ctx, Image input) {
 *         NDArray imageND = input.toNDArray(ctx.getNDManager());
 *         return new NDList(NDImageUtils.resize(imageND, imageWidth, imageHeight);
 *     }
 *
 *     @Override
 *     public Classification processOutput(TranslatorContext ctx, NDList list) throws TranslateException {
 *         Model model = ctx.getModel();
 *         NDArray array = list.get(0).at(0);
 *         NDArray sorted = array.argSort(-1, false);
 *         NDArray top = sorted.at(0);
 *
 *         float[] probabilities = array.toFloatArray();
 *         int topIndex = top.toIntArray()[0];
 *
 *         String[] synset;
 *         try {
 *             synset = model.getArtifact("synset.txt", MyTranslator::loadSynset);
 *         } catch (IOException e) {
 *             throw new TranslateException(e);
 *         }
 *         return new Classification(synset[topIndex], probabilities[topIndex]);
 *     }
 *
 *     private static String[] loadSynset(InputStream is) {
 *         ...
 *     }
 * }
 * 
 *
 * @param  the input type
 * @param  the output type
 */
public interface Translator extends PreProcessor, PostProcessor {

    /**
     * Gets the {@link Batchifier}.
     *
     * @return the {@link Batchifier}
     */
    Batchifier getBatchifier();

    /**
     * Prepares the translator with the manager and model to use.
     *
     * @param manager the manager for the translator
     * @param model the model to translate for
     * @throws IOException if there is an error reading inputs for preparing the translator
     */
    default void prepare(NDManager manager, Model model) throws IOException {}
}