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package org.eclipse.deeplearning4j.frameworkimport.keras.configurations;

import org.deeplearning4j.nn.conf.distribution.*;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.layers.core.KerasDense;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.WeightInitIdentity;
import org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanIn;
import org.junit.jupiter.api.Tag;
import org.junit.jupiter.api.Test;
import java.util.HashMap;
import java.util.Map;
import static org.junit.jupiter.api.Assertions.assertEquals;
import org.junit.jupiter.api.DisplayName;
import org.nd4j.common.tests.tags.NativeTag;
import org.nd4j.common.tests.tags.TagNames;

@DisplayName("Keras Initilization Test")
@Tag(TagNames.FILE_IO)
@Tag(TagNames.KERAS)
@NativeTag
class KerasInitilizationTest extends BaseDL4JTest {

    private double minValue = -0.2;

    private double maxValue = 0.2;

    private double mean = 0.0;

    private double stdDev = 0.2;

    private double value = 42.0;

    private double gain = 0.2;

    private Keras1LayerConfiguration conf1 = new Keras1LayerConfiguration();

    private Keras2LayerConfiguration conf2 = new Keras2LayerConfiguration();

    @Test
    @DisplayName("Test Initializers")
    void testInitializers() throws Exception {
        Integer keras1 = 1;
        Integer keras2 = 2;
        String[] keras1Inits = initializers(conf1);
        String[] keras2Inits = initializers(conf2);
        IWeightInit[] dl4jInits = dl4jInitializers();
        for (int i = 0; i < dl4jInits.length - 1; i++) {
            initilizationDenseLayer(conf1, keras1, keras1Inits[i], dl4jInits[i]);
            initilizationDenseLayer(conf2, keras2, keras2Inits[i], dl4jInits[i]);
            initilizationDenseLayer(conf2, keras2, keras2Inits[dl4jInits.length - 1], dl4jInits[dl4jInits.length - 1]);
        }
    }

    private String[] initializers(KerasLayerConfiguration conf) {
        return new String[] { conf.getINIT_GLOROT_NORMAL(), conf.getINIT_GLOROT_UNIFORM_ALIAS(), conf.getINIT_LECUN_NORMAL(), conf.getINIT_LECUN_UNIFORM(), conf.getINIT_RANDOM_UNIFORM(), conf.getINIT_HE_NORMAL(), conf.getINIT_HE_UNIFORM(), conf.getINIT_ONES(), conf.getINIT_ZERO(), conf.getINIT_IDENTITY(), conf.getINIT_NORMAL(), conf.getINIT_ORTHOGONAL(), conf.getINIT_CONSTANT(), conf.getINIT_VARIANCE_SCALING() };
    }

    private IWeightInit[] dl4jInitializers() {
        return new IWeightInit[] { WeightInit.XAVIER.getWeightInitFunction(), WeightInit.XAVIER_UNIFORM.getWeightInitFunction(), WeightInit.LECUN_NORMAL.getWeightInitFunction(), WeightInit.LECUN_UNIFORM.getWeightInitFunction(), WeightInit.DISTRIBUTION.getWeightInitFunction(new UniformDistribution(minValue, maxValue)), WeightInit.RELU.getWeightInitFunction(), WeightInit.RELU_UNIFORM.getWeightInitFunction(), WeightInit.ONES.getWeightInitFunction(), WeightInit.ZERO.getWeightInitFunction(), new WeightInitIdentity(0.2), WeightInit.DISTRIBUTION.getWeightInitFunction(new NormalDistribution(mean, stdDev)), WeightInit.DISTRIBUTION.getWeightInitFunction(new OrthogonalDistribution(gain)), WeightInit.DISTRIBUTION.getWeightInitFunction(new ConstantDistribution(value)), new WeightInitVarScalingNormalFanIn(0.2) };
    }

    private Distribution[] dl4jDistributions() {
        return new Distribution[] { null, null, null, null, new UniformDistribution(minValue, maxValue), null, null, null, null, null, new NormalDistribution(mean, stdDev), new OrthogonalDistribution(gain), new ConstantDistribution(value), null };
    }

    private void initilizationDenseLayer(KerasLayerConfiguration conf, Integer kerasVersion, String initializer, IWeightInit dl4jInitializer) throws Exception {
        Map<String, Object> layerConfig = new HashMap<>();
        layerConfig.put(conf.getLAYER_FIELD_CLASS_NAME(), conf.getLAYER_CLASS_NAME_DENSE());
        Map<String, Object> config = new HashMap<>();
        config.put(conf.getLAYER_FIELD_ACTIVATION(), "linear");
        config.put(conf.getLAYER_FIELD_NAME(), "init_test");
        double scale = 0.2;
        if (kerasVersion == 1) {
            config.put(conf.getLAYER_FIELD_INIT(), initializer);
            config.put(conf.getLAYER_FIELD_INIT_MEAN(), mean);
            config.put(conf.getLAYER_FIELD_INIT_STDDEV(), stdDev);
            config.put(conf.getLAYER_FIELD_INIT_SCALE(), scale);
            config.put(conf.getLAYER_FIELD_INIT_MINVAL(), minValue);
            config.put(conf.getLAYER_FIELD_INIT_MAXVAL(), maxValue);
            config.put(conf.getLAYER_FIELD_INIT_VALUE(), value);
            config.put(conf.getLAYER_FIELD_INIT_GAIN(), gain);
        } else {
            Map<String, Object> init = new HashMap<>();
            init.put("class_name", initializer);
            Map<String, Object> innerInit = new HashMap<>();
            innerInit.put(conf.getLAYER_FIELD_INIT_MEAN(), mean);
            innerInit.put(conf.getLAYER_FIELD_INIT_STDDEV(), stdDev);
            innerInit.put(conf.getLAYER_FIELD_INIT_SCALE(), scale);
            innerInit.put(conf.getLAYER_FIELD_INIT_MINVAL(), minValue);
            innerInit.put(conf.getLAYER_FIELD_INIT_MAXVAL(), maxValue);
            innerInit.put(conf.getLAYER_FIELD_INIT_VALUE(), value);
            innerInit.put(conf.getLAYER_FIELD_INIT_GAIN(), gain);
            String mode = "fan_in";
            innerInit.put(conf.getLAYER_FIELD_INIT_MODE(), mode);
            String distribution = "normal";
            innerInit.put(conf.getLAYER_FIELD_INIT_DISTRIBUTION(), distribution);
            init.put(conf.getLAYER_FIELD_CONFIG(), innerInit);
            config.put(conf.getLAYER_FIELD_INIT(), init);
        }
        config.put(conf.getLAYER_FIELD_OUTPUT_DIM(), 1337);
        layerConfig.put(conf.getLAYER_FIELD_CONFIG(), config);
        layerConfig.put(conf.getLAYER_FIELD_KERAS_VERSION(), kerasVersion);
        DenseLayer layer = new KerasDense(layerConfig, false).getDenseLayer();
        assertEquals(dl4jInitializer, layer.getWeightInitFn());
    }
}
