/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 * 
 *   http://www.apache.org/licenses/LICENSE-2.0
 * 
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License 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 org.apache.sysds.test.functions.binary.matrix_full_other;

import java.util.HashMap;

import org.junit.AfterClass;
import org.junit.BeforeClass;
import org.junit.Test;
import org.apache.sysds.common.Types.ExecMode;
import org.apache.sysds.lops.LopProperties.ExecType;
import org.apache.sysds.lops.MMTSJ.MMTSJType;
import org.apache.sysds.runtime.matrix.data.MatrixValue.CellIndex;
import org.apache.sysds.test.AutomatedTestBase;
import org.apache.sysds.test.TestConfiguration;
import org.apache.sysds.test.TestUtils;

public class FullMatrixMultiplicationTransposeSelfTest extends AutomatedTestBase 
{
	private final static String TEST_NAME1 = "TransposeSelfMatrixMultiplication1";
	private final static String TEST_NAME2 = "TransposeSelfMatrixMultiplication2";
	private final static String TEST_DIR = "functions/binary/matrix_full_other/";
	private final static String TEST_CLASS_DIR = TEST_DIR + FullMatrixMultiplicationTransposeSelfTest.class.getSimpleName() + "/";
	private final static double eps = 1e-10;
	
	//for CP
	private final static int rows1 = 1100;
	private final static int cols1 = 300;
	//for Spark
	private final static int rows2 = 2500;
	private final static int cols2 = 750; 
	
	private final static double sparsity1 = 0.7;
	private final static double sparsity2 = 0.1;
	
	
	@Override
	public void setUp() 
	{
		addTestConfiguration(
				TEST_NAME1, 
				new TestConfiguration(TEST_CLASS_DIR, TEST_NAME1, 
				new String[] { "B" })   ); 
		addTestConfiguration(
				TEST_NAME2, 
				new TestConfiguration(TEST_CLASS_DIR, TEST_NAME2, 
				new String[] { "B" })   ); 
		if (TEST_CACHE_ENABLED) {
			setOutAndExpectedDeletionDisabled(true);
		}
	}

	@BeforeClass
	public static void init() {
		TestUtils.clearDirectory(TEST_DATA_DIR + TEST_CLASS_DIR);
	}

	@AfterClass
	public static void cleanUp() {
		if (TEST_CACHE_ENABLED) {
			TestUtils.clearDirectory(TEST_DATA_DIR + TEST_CLASS_DIR);
		}
	}

	@Test
	public void testMMLeftDenseCP() {
		runTransposeSelfMatrixMultiplicationTest(MMTSJType.LEFT, ExecType.CP, false);
	}
	
	@Test
	public void testMMRightDenseCP() {
		runTransposeSelfMatrixMultiplicationTest(MMTSJType.RIGHT, ExecType.CP, false);
	}

	@Test
	public void testMMLeftSparseCP() {
		runTransposeSelfMatrixMultiplicationTest(MMTSJType.LEFT, ExecType.CP, true);
	}
	
	@Test
	public void testMMRightSparseCP() {
		runTransposeSelfMatrixMultiplicationTest(MMTSJType.RIGHT, ExecType.CP, true);
	}
		
	@Test
	public void testVVLeftDenseCP() {
		runTransposeSelfVectorMultiplicationTest(MMTSJType.LEFT, ExecType.CP, false);
	}
	
	@Test
	public void testVVRightDenseCP() {
		runTransposeSelfVectorMultiplicationTest(MMTSJType.RIGHT, ExecType.CP, false);
	}

	@Test
	public void testVVLeftSparseCP() {
		runTransposeSelfVectorMultiplicationTest(MMTSJType.LEFT, ExecType.CP, true);
	}
	
	@Test
	public void testVVRightSparseCP() {
		runTransposeSelfVectorMultiplicationTest(MMTSJType.RIGHT, ExecType.CP, true);
	}
	
	
	private void runTransposeSelfMatrixMultiplicationTest( MMTSJType type, ExecType instType, boolean sparse )
	{
		//setup exec type, rows, cols
		int rows = -1, cols = -1;
		String TEST_NAME = null;
		if( type == MMTSJType.LEFT ) {
			if( instType == ExecType.CP ) {
				rows = rows1;
				cols = cols1;
			}
			else { //if type MR
				rows = rows2;
				cols = cols2;
			}
			TEST_NAME = TEST_NAME1;
		}
		else {
			if( instType == ExecType.CP ) {
				rows = cols1;
				cols = rows1;
			}
			else { //if type MR
				rows = cols2;
				cols = rows2;
			}
			TEST_NAME = TEST_NAME2;
		}

		double sparsity = sparse ? sparsity2 : sparsity1;

		String TEST_CACHE_DIR = "";
		if (TEST_CACHE_ENABLED) {
			TEST_CACHE_DIR = rows + "_" + cols + "_" + sparsity + "/";
		}

		//rtplatform for MR
		ExecMode platformOld = rtplatform;
		rtplatform = ExecMode.HYBRID;
	
		try
		{
			TestConfiguration config = getTestConfiguration(TEST_NAME);
			loadTestConfiguration(config, TEST_CACHE_DIR);
			
			/* This is for running the junit test the new way, i.e., construct the arguments directly */
			String HOME = SCRIPT_DIR + TEST_DIR;
			fullDMLScriptName = HOME + TEST_NAME + ".dml";
			programArgs = new String[]{"-stats","-args", input("A"),
				Integer.toString(rows), Integer.toString(cols), output("B") };
			
			fullRScriptName = HOME + TEST_NAME + ".R";
			rCmd = "Rscript" + " " + fullRScriptName + " " + inputDir() + " " + expectedDir();
	
			//generate actual dataset
			double[][] A = getRandomMatrix(rows, cols, 0, 1, sparsity, 7); 
			writeInputMatrix("A", A, true);
	
			setOutputBuffering(false);
			runTest(true, false, null, -1); 
			runRScript(true); 
			
			//compare matrices 
			HashMap<CellIndex, Double> dmlfile = readDMLMatrixFromHDFS("B");
			HashMap<CellIndex, Double> rfile  = readRMatrixFromFS("B");
			TestUtils.compareMatrices(dmlfile, rfile, eps, "Stat-DML", "Stat-R");
		}
		finally {
			rtplatform = platformOld;
		}
	}
	
	private void runTransposeSelfVectorMultiplicationTest( MMTSJType type, ExecType instType, boolean sparse )
	{
		//setup exec type, rows, cols
		int rows = -1, cols = -1;
		String TEST_NAME = null;
		if( type == MMTSJType.LEFT ) {
			if( instType == ExecType.CP ) {
				rows = rows1;
				cols = 1;
			}
			else { //if type MR
				rows = rows2;
				cols = 1;
			}
			TEST_NAME = TEST_NAME1;
		}
		else {
			if( instType == ExecType.CP ) {
				rows = 1;
				cols = rows1;
			}
			else { //if type MR
				rows = 1;
				cols = rows2;
			}
			TEST_NAME = TEST_NAME2;
		}

		double sparsity = sparse ? sparsity2 : sparsity1;

		String TEST_CACHE_DIR = "";
		if (TEST_CACHE_ENABLED) {
			TEST_CACHE_DIR = rows + "_" + cols + "_" + sparsity + "/";
		}

		//rtplatform for MR
		ExecMode platformOld = rtplatform;
		rtplatform = ExecMode.HYBRID;
	
		try
		{
			TestConfiguration config = getTestConfiguration(TEST_NAME);
			loadTestConfiguration(config, TEST_CACHE_DIR);
			
			/* This is for running the junit test the new way, i.e., construct the arguments directly */
			String HOME = SCRIPT_DIR + TEST_DIR;
			fullDMLScriptName = HOME + TEST_NAME + ".dml";
			programArgs = new String[]{"-args", input("A"),
				Integer.toString(rows), Integer.toString(cols), output("B") };
			
			fullRScriptName = HOME + TEST_NAME + ".R";
			rCmd = "Rscript" + " " + fullRScriptName + " " + inputDir() + " " + expectedDir();
	
			//generate actual dataset
			double[][] A = getRandomMatrix(rows, cols, 0, 1, sparsity, 7); 
			writeInputMatrix("A", A, true);
	
			setOutputBuffering(false);
			runTest(true, false, null, -1); 
			runRScript(true); 
			
			//compare matrices 
			HashMap<CellIndex, Double> dmlfile = readDMLMatrixFromHDFS("B");
			HashMap<CellIndex, Double> rfile  = readRMatrixFromFS("B");
			TestUtils.compareMatrices(dmlfile, rfile, eps, "Stat-DML", "Stat-R");
		}
		finally {
			rtplatform = platformOld;
		}
	}
}