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package org.eclipse.deeplearning4j.dl4jcore.nn.conf.preprocessor;

import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.eclipse.deeplearning4j.dl4jcore.nn.conf.preprocessor.custom.MyCustomPreprocessor;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertTrue;
import org.junit.jupiter.api.DisplayName;

@DisplayName("Custom Preprocessor Test")
class CustomPreprocessorTest extends BaseDL4JTest {

    @Test
    @DisplayName("Test Custom Preprocessor")
    void testCustomPreprocessor() {
        // Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works...
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).activation(Activation.SOFTMAX).nOut(10).build()).inputPreProcessor(0, new MyCustomPreprocessor()).build();
        String json = conf.toJson();
        String yaml = conf.toYaml();
        // System.out.println(json);
        MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
        assertEquals(conf, confFromJson);
        MultiLayerConfiguration confFromYaml = MultiLayerConfiguration.fromYaml(yaml);
        assertEquals(conf, confFromYaml);
        assertTrue(confFromJson.getInputPreProcess(0) instanceof MyCustomPreprocessor);
    }
}
