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package org.deeplearning4j.parallelism;

import lombok.val;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.parallelism.inference.InferenceMode;
import org.deeplearning4j.parallelism.inference.LoadBalanceMode;
import org.junit.jupiter.api.Tag;
import org.junit.jupiter.api.Test;
import org.nd4j.common.tests.tags.NativeTag;
import org.nd4j.common.tests.tags.TagNames;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

import static org.junit.jupiter.api.Assertions.*;
@Tag(TagNames.FILE_IO)
@NativeTag
@Tag(TagNames.LARGE_RESOURCES)
@Tag(TagNames.LONG_TEST)
public class InplaceParallelInferenceTest extends BaseDL4JTest {

    @Test
    public void testUpdateModel() {
        int nIn = 5;

        val conf = new NeuralNetConfiguration.Builder()
                .graphBuilder()
                .addInputs("in")
                .layer("out0", new OutputLayer.Builder().nIn(nIn).nOut(4).activation(Activation.SOFTMAX).build(), "in")
                .layer("out1", new OutputLayer.Builder().nIn(nIn).nOut(6).activation(Activation.SOFTMAX).build(), "in")
                .setOutputs("out0", "out1")
                .build();

        val net = new ComputationGraph(conf);
        net.init();

        val pi = new ParallelInference.Builder(net)
                .inferenceMode(InferenceMode.INPLACE)
                .workers(2)
                .build();
        try {

            assertTrue(pi instanceof InplaceParallelInference);

            val models = pi.getCurrentModelsFromWorkers();

            assertTrue(models.length > 0);

            for (val m : models) {
                assertNotNull(m);
                assertEquals(net.params(), m.params());
            }

            val conf2 = new NeuralNetConfiguration.Builder()
                    .graphBuilder()
                    .addInputs("in")
                    .layer("out0", new OutputLayer.Builder().nIn(nIn).nOut(4).activation(Activation.SOFTMAX).build(), "in")
                    .layer("out1", new OutputLayer.Builder().nIn(nIn).nOut(6).activation(Activation.SOFTMAX).build(), "in")
                    .layer("out2", new OutputLayer.Builder().nIn(nIn).nOut(8).activation(Activation.SOFTMAX).build(), "in")
                    .setOutputs("out0", "out1", "out2")
                    .build();

            val net2 = new ComputationGraph(conf2);
            net2.init();

            assertNotEquals(net.params(), net2.params());

            pi.updateModel(net2);

            val models2 = pi.getCurrentModelsFromWorkers();

            assertTrue(models2.length > 0);

            for (val m : models2) {
                assertNotNull(m);
                assertEquals(net2.params(), m.params());
            }
        } finally {
            pi.shutdown();
        }
    }

    @Test
    public void testOutput_RoundRobin_1() throws Exception {
        int nIn = 5;

        val conf = new NeuralNetConfiguration.Builder()
                .graphBuilder()
                .addInputs("in")
                .layer("out0", new OutputLayer.Builder().nIn(nIn).nOut(4).activation(Activation.SOFTMAX).build(), "in")
                .layer("out1", new OutputLayer.Builder().nIn(nIn).nOut(6).activation(Activation.SOFTMAX).build(), "in")
                .setOutputs("out0", "out1")
                .build();

        val net = new ComputationGraph(conf);
        net.init();

        val pi = new ParallelInference.Builder(net)
                .inferenceMode(InferenceMode.INPLACE)
                .loadBalanceMode(LoadBalanceMode.ROUND_ROBIN)
                .workers(2)
                .build();

        try {

            val result0 = pi.output(new INDArray[]{Nd4j.create(new double[]{1.0, 2.0, 3.0, 4.0, 5.0}, new long[]{1, 5})}, null)[0];
            val result1 = pi.output(new INDArray[]{Nd4j.create(new double[]{1.0, 2.0, 3.0, 4.0, 5.0}, new long[]{1, 5})}, null)[0];

            assertNotNull(result0);
            assertEquals(result0, result1);
        } finally {
            pi.shutdown();
        }
    }

    @Test
    public void testOutput_FIFO_1() throws Exception {
        int nIn = 5;

        val conf = new NeuralNetConfiguration.Builder()
                .graphBuilder()
                .addInputs("in")
                .layer("out0", new OutputLayer.Builder().nIn(nIn).nOut(4).activation(Activation.SOFTMAX).build(), "in")
                .layer("out1", new OutputLayer.Builder().nIn(nIn).nOut(6).activation(Activation.SOFTMAX).build(), "in")
                .setOutputs("out0", "out1")
                .build();

        val net = new ComputationGraph(conf);
        net.init();

        val pi = new ParallelInference.Builder(net)
                .inferenceMode(InferenceMode.INPLACE)
                .loadBalanceMode(LoadBalanceMode.FIFO)
                .workers(2)
                .build();

        try {

            val result0 = pi.output(new INDArray[]{Nd4j.create(new double[]{1.0, 2.0, 3.0, 4.0, 5.0}, new long[]{1, 5})}, null)[0];
            val result1 = pi.output(new INDArray[]{Nd4j.create(new double[]{1.0, 2.0, 3.0, 4.0, 5.0}, new long[]{1, 5})}, null)[0];

            assertNotNull(result0);
            assertEquals(result0, result1);
        } finally {
            pi.shutdown();
        }
    }
}