/*
 * 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.runtime.compress;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.io.ObjectInput;
import java.io.ObjectOutput;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Future;

import org.apache.commons.lang.NotImplementedException;
import org.apache.commons.lang3.tuple.ImmutablePair;
import org.apache.commons.lang3.tuple.Pair;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.sysds.api.DMLScript;
import org.apache.sysds.conf.ConfigurationManager;
import org.apache.sysds.conf.DMLConfig;
import org.apache.sysds.lops.MMTSJ.MMTSJType;
import org.apache.sysds.lops.MapMultChain.ChainType;
import org.apache.sysds.runtime.DMLCompressionException;
import org.apache.sysds.runtime.DMLRuntimeException;
import org.apache.sysds.runtime.compress.colgroup.ColGroup;
import org.apache.sysds.runtime.compress.colgroup.ColGroup.CompressionType;
import org.apache.sysds.runtime.compress.colgroup.ColGroupConverter;
import org.apache.sysds.runtime.compress.colgroup.ColGroupIO;
import org.apache.sysds.runtime.compress.colgroup.ColGroupUncompressed;
import org.apache.sysds.runtime.compress.colgroup.ColGroupValue;
import org.apache.sysds.runtime.compress.lib.LibBinaryCellOp;
import org.apache.sysds.runtime.compress.lib.LibCompAgg;
import org.apache.sysds.runtime.compress.lib.LibLeftMultBy;
import org.apache.sysds.runtime.compress.lib.LibRightMultBy;
import org.apache.sysds.runtime.compress.lib.LibScalar;
import org.apache.sysds.runtime.compress.utils.LinearAlgebraUtils;
import org.apache.sysds.runtime.controlprogram.parfor.stat.Timing;
import org.apache.sysds.runtime.data.SparseBlock;
import org.apache.sysds.runtime.functionobjects.Builtin;
import org.apache.sysds.runtime.functionobjects.Builtin.BuiltinCode;
import org.apache.sysds.runtime.functionobjects.KahanPlus;
import org.apache.sysds.runtime.functionobjects.KahanPlusSq;
import org.apache.sysds.runtime.functionobjects.Mean;
import org.apache.sysds.runtime.functionobjects.Multiply;
import org.apache.sysds.runtime.functionobjects.SwapIndex;
import org.apache.sysds.runtime.matrix.data.LibMatrixBincell;
import org.apache.sysds.runtime.matrix.data.MatrixBlock;
import org.apache.sysds.runtime.matrix.data.MatrixIndexes;
import org.apache.sysds.runtime.matrix.data.MatrixValue;
import org.apache.sysds.runtime.matrix.operators.AggregateBinaryOperator;
import org.apache.sysds.runtime.matrix.operators.AggregateUnaryOperator;
import org.apache.sysds.runtime.matrix.operators.BinaryOperator;
import org.apache.sysds.runtime.matrix.operators.ReorgOperator;
import org.apache.sysds.runtime.matrix.operators.ScalarOperator;
import org.apache.sysds.runtime.util.CommonThreadPool;
import org.apache.sysds.utils.DMLCompressionStatistics;

public class CompressedMatrixBlock extends AbstractCompressedMatrixBlock {
	private static final Log LOG = LogFactory.getLog(CompressedMatrixBlock.class.getName());
	private static final long serialVersionUID = 7319372019143154058L;

	/**
	 * Constructor for building an empty Compressed Matrix block object.
	 * 
	 * OBS! Only to be used for serialization.
	 */
	public CompressedMatrixBlock() {
		super();
	}

	/**
	 * Create a base Compressed matrix block with overlapping column groups.
	 * 
	 * @param overLapping boolean specifier of the if the groups are overlapping.
	 */
	public CompressedMatrixBlock(boolean overLapping) {
		super(overLapping);
	}

	/**
	 * Main constructor for building a block from scratch.
	 * 
	 * Use with caution, since it constructs an empty matrix block with nothing inside.
	 * 
	 * @param rl     number of rows in the block
	 * @param cl     number of columns
	 */
	public CompressedMatrixBlock(int rl, int cl) {
		super(rl, cl, true);
		sparseBlock = null;
		denseBlock = null;
		nonZeros = -1;
	}

	/**
	 * "Copy" constructor to populate this compressed block with the uncompressed metadata contents of a conventional
	 * block. Does not compress the block.
	 * 
	 * @param that matrix block
	 */
	protected CompressedMatrixBlock(MatrixBlock that) {
		super(that.getNumRows(), that.getNumColumns(), true);
		sparseBlock = null;
		denseBlock = null;
		nonZeros = that.getNonZeros();
	}

	public boolean isSingleUncompressedGroup() {
		return(_colGroups != null && _colGroups.size() == 1 &&
			_colGroups.get(0).getCompType() == CompressionType.UNCOMPRESSED);
	}

	public void allocateColGroupList(List<ColGroup> colGroups) {
		_colGroups = colGroups;
	}

	public List<ColGroup> getColGroups() {
		return _colGroups;
	}

	/**
	 * Decompress block.
	 * 
	 * @return a new uncompressed matrix block containing the contents of this block
	 */
	public MatrixBlock decompress() {

		Timing time = new Timing(true);

		// preallocation sparse rows to avoid repeated reallocations
		MatrixBlock ret = new MatrixBlock(rlen, clen, false, -1);
		ret.allocateDenseBlock();
		// (nonZeros == -1) ?
		// .allocateBlock() : new MatrixBlock(rlen, clen, sparse, nonZeros).allocateBlock();

		// if(ret.isInSparseFormat()) {
		// int[] rnnz = new int[rlen];
		// // for(ColGroup grp : _colGroups)
		// // grp.countNonZerosPerRow(rnnz, 0, rlen);
		// ret.allocateSparseRowsBlock();
		// SparseBlock rows = ret.getSparseBlock();
		// for(int i = 0; i < rlen; i++)
		// rows.allocate(i, rnnz[i]);
		// }

		// core decompression (append if sparse)
		for(ColGroup grp : _colGroups)
			grp.decompressToBlockSafe(ret, 0, rlen, 0, grp.getValues(), false);

		// post-processing (for append in decompress)
		if(ret.getNonZeros() == -1 || nonZeros == -1) {
			ret.recomputeNonZeros();
		}
		else {
			ret.setNonZeros(nonZeros);
		}
		if(ret.isInSparseFormat())
			ret.sortSparseRows();

		if(DMLScript.STATISTICS || LOG.isDebugEnabled()) {
			double t = time.stop();
			LOG.debug("decompressed block w/ k=" + 1 + " in " + t + "ms.");
			DMLCompressionStatistics.addDecompressTime(t, 1);
		}
		return ret;
	}

	/**
	 * Decompress block.
	 * 
	 * @param k degree of parallelism
	 * @return a new uncompressed matrix block containing the contents of this block
	 */
	public MatrixBlock decompress(int k) {

		if(k <= 1)
			return decompress();

		Timing time = new Timing(true);

		MatrixBlock ret =  new MatrixBlock(rlen, clen, false, -1).allocateBlock();
		
		// (nonZeros == -1) ? new MatrixBlock(rlen, clen, false, -1)
			// .allocateBlock() : new MatrixBlock(rlen, clen, sparse, nonZeros).allocateBlock();
		// multi-threaded decompression
		nonZeros = 0;
		boolean overlapping = isOverlapping();
		try {
			ExecutorService pool = CommonThreadPool.get(k);
			int rlen = getNumRows();
			final int blkz = CompressionSettings.BITMAP_BLOCK_SZ;
			int blklen = (int) Math.ceil((double) rlen / k);
			blklen += (blklen % blkz != 0) ? blkz - blklen % blkz : 0;
			ArrayList<DecompressTask> tasks = new ArrayList<>();
			for(int i = 0; i < k & i * blklen < getNumRows(); i++)
				tasks.add(
					new DecompressTask(_colGroups, ret, i * blklen, Math.min((i + 1) * blklen, rlen), overlapping));
			List<Future<Long>> rtasks = pool.invokeAll(tasks);
			pool.shutdown();
			for(Future<Long> rt : rtasks)
				nonZeros += rt.get(); // error handling
		}
		catch(InterruptedException | ExecutionException ex) {
			LOG.error("Parallel decompression failed defaulting to non parallel implementation " + ex.getMessage());
			nonZeros = -1;
			ex.printStackTrace();
			return decompress();
		}
		if(overlapping) {
			ret.recomputeNonZeros();
		}
		else {
			ret.setNonZeros(nonZeros);
		}

		if(DMLScript.STATISTICS || LOG.isDebugEnabled()) {
			double t = time.stop();
			LOG.debug("decompressed block w/ k=" + k + " in " + time.stop() + "ms.");
			DMLCompressionStatistics.addDecompressTime(t, k);
		}
		return ret;
	}

	/**
	 * Obtain an upper bound on the memory used to store the compressed block.
	 * 
	 * @return an upper bound on the memory used to store this compressed block considering class overhead.
	 */
	public long estimateCompressedSizeInMemory() {
		long total = baseSizeInMemory();

		for(ColGroup grp : _colGroups)
			total += grp.estimateInMemorySize();

		return total;
	}

	public static long baseSizeInMemory() {
		long total = 16; // Object header

		total += 40; // Matrix Block elements
		total += 8; // Col Group Ref
		total += 2 + 6; // Booleans plus padding

		total += 40; // Col Group Array List
		return total;
	}

	@Override
	public double quickGetValue(int r, int c) {

		// TODO Optimize Quick Get Value, to located the correct column group without having to search for it
		double v = 0.0;
		for(ColGroup group : _colGroups) {
			if(Arrays.binarySearch(group.getColIndices(), c) >= 0) {
				v += group.get(r, c);
				if(!isOverlapping())
					break;
			}
		}

		// find row value
		return v;
	}

	//////////////////////////////////////////
	// Serialization / Deserialization

	@Override
	public long getExactSizeOnDisk() {
		// header information
		long ret = 20;
		for(ColGroup grp : _colGroups) {
			ret += 1; // type info
			ret += grp.getExactSizeOnDisk();
		}
		return ret;
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		// deserialize compressed block
		rlen = in.readInt();
		clen = in.readInt();
		nonZeros = in.readLong();
		overlappingColGroups = in.readBoolean();
		_colGroups = ColGroupIO.readGroups(in);
	}

	@Override
	public void write(DataOutput out) throws IOException {
		// serialize compressed matrix block
		out.writeInt(rlen);
		out.writeInt(clen);
		out.writeLong(nonZeros);
		out.writeBoolean(overlappingColGroups);
		ColGroupIO.writeGroups(out, _colGroups);
	}

	/**
	 * Redirects the default java serialization via externalizable to our default hadoop writable serialization for
	 * efficient broadcast/rdd de-serialization.
	 * 
	 * @param is object input
	 * @throws IOException if IOException occurs
	 */
	@Override
	public void readExternal(ObjectInput is) throws IOException {
		readFields(is);
	}

	/**
	 * Redirects the default java serialization via externalizable to our default hadoop writable serialization for
	 * efficient broadcast/rdd serialization.
	 * 
	 * @param os object output
	 * @throws IOException if IOException occurs
	 */
	@Override
	public void writeExternal(ObjectOutput os) throws IOException {
		write(os);
	}

	@Override
	public MatrixBlock scalarOperations(ScalarOperator sop, MatrixValue result) {
		return LibScalar.scalarOperations(sop, this, result);
	}

	@Override
	public MatrixBlock binaryOperations(BinaryOperator op, MatrixValue thatValue, MatrixValue result) {
		return LibBinaryCellOp.binaryOperations(op, this, thatValue, result);
	}

	@Override
	public MatrixBlock append(MatrixBlock that, MatrixBlock ret) {

		final int m = rlen;
		final int n = clen + that.getNumColumns();
		final long nnz = nonZeros + that.getNonZeros();

		// init result matrix
		CompressedMatrixBlock ret2 = null;
		if(ret == null || !(ret instanceof CompressedMatrixBlock)) {
			ret2 = new CompressedMatrixBlock(m, n);
		}
		else {
			ret2 = (CompressedMatrixBlock) ret;
			ret2.reset(m, n);
		}

		// shallow copy of lhs column groups
		ret2.allocateColGroupList(new ArrayList<ColGroup>());
		ret2._colGroups.addAll(_colGroups);

		// copy of rhs column groups w/ col index shifting
		if(!(that instanceof CompressedMatrixBlock)) {
			that = CompressedMatrixBlockFactory.compress(that).getLeft();
		}

		List<ColGroup> inColGroups = ((CompressedMatrixBlock) that)._colGroups;
		for(ColGroup group : inColGroups) {
			ColGroup tmp = ColGroupConverter.copyColGroup(group);
			tmp.shiftColIndices(clen);
			ret2._colGroups.add(tmp);
		}

		// meta data maintenance
		ret2.setNonZeros(nnz);
		return ret2;
	}

	@Override
	public MatrixBlock chainMatrixMultOperations(MatrixBlock v, MatrixBlock w, MatrixBlock out, ChainType ctype) {
		return chainMatrixMultOperations(v, w, out, ctype, 1);
	}

	@Override
	public MatrixBlock chainMatrixMultOperations(MatrixBlock v, MatrixBlock w, MatrixBlock out, ChainType ctype,
		int k) {

		if(this.getNumColumns() != v.getNumRows())
			throw new DMLRuntimeException(
				"Dimensions mismatch on mmchain operation (" + this.getNumColumns() + " != " + v.getNumRows() + ")");
		if(v.getNumColumns() != 1)
			throw new DMLRuntimeException(
				"Invalid input vector (column vector expected, but ncol=" + v.getNumColumns() + ")");
		if(w != null && w.getNumColumns() != 1)
			throw new DMLRuntimeException(
				"Invalid weight vector (column vector expected, but ncol=" + w.getNumColumns() + ")");

		// multi-threaded MMChain of single uncompressed ColGroup
		if(isSingleUncompressedGroup()) {
			return ((ColGroupUncompressed) _colGroups.get(0)).getData().chainMatrixMultOperations(v, w, out, ctype, k);
		}

		// Timing time = LOG.isDebugEnabled() ? new Timing(true) : null;

		// prepare result
		if(out != null)
			out.reset(clen, 1, false);
		else
			out = new MatrixBlock(clen, 1, false);

		// empty block handling
		if(isEmptyBlock(false))
			return out;

		// compute matrix mult
		MatrixBlock tmp = new MatrixBlock(rlen, 1, false);
		tmp = LibRightMultBy.rightMultByMatrix(_colGroups, v, tmp, k, getMaxNumValues(), false);
		if(ctype == ChainType.XtwXv) {
			BinaryOperator bop = new BinaryOperator(Multiply.getMultiplyFnObject());
			LibMatrixBincell.bincellOpInPlace(tmp, w, bop);
		}
		LibLeftMultBy.leftMultByVectorTranspose(_colGroups, tmp, out, true, k, getMaxNumValues(), isOverlapping());

		return out;
	}

	@Override
	public MatrixBlock aggregateBinaryOperations(MatrixBlock m1, MatrixBlock m2, MatrixBlock ret,
		AggregateBinaryOperator op) {
		return aggregateBinaryOperations(m1, m2, ret, op, false, false);
	}

	public MatrixBlock aggregateBinaryOperations(MatrixBlock m1, MatrixBlock m2, MatrixBlock ret,
		AggregateBinaryOperator op, boolean transposeLeft, boolean transposeRight) {

		if(m1 instanceof CompressedMatrixBlock && m2 instanceof CompressedMatrixBlock) {
			return doubleCompressedAggregateBinaryOperations((CompressedMatrixBlock) m1,
				(CompressedMatrixBlock) m2,
				ret,
				op,
				transposeLeft,
				transposeRight);
		}
		boolean transposeOutput = false;
		if(transposeLeft || transposeRight) {
			ReorgOperator r_op = new ReorgOperator(SwapIndex.getSwapIndexFnObject(), op.getNumThreads());

			if((m1 instanceof CompressedMatrixBlock && transposeLeft) ||
				(m2 instanceof CompressedMatrixBlock && transposeRight)) {
				// change operation from m1 %*% m2 -> t( t(m2) %*% t(m1) )
				transposeOutput = true;
				MatrixBlock tmp = m1;
				m1 = m2;
				m2 = tmp;
				boolean tmpLeft = transposeLeft;
				transposeLeft = !transposeRight;
				transposeRight = !tmpLeft;

			}

			if(!(m1 instanceof CompressedMatrixBlock) && transposeLeft) {
				m1 = new MatrixBlock().copyShallow(m1).reorgOperations(r_op, new MatrixBlock(), 0, 0, 0);
				transposeLeft = false;
			}
			else if(!(m2 instanceof CompressedMatrixBlock) && transposeRight) {
				m2 = new MatrixBlock().copyShallow(m2).reorgOperations(r_op, new MatrixBlock(), 0, 0, 0);
				transposeRight = false;
			}
		}

		// setup meta data (dimensions, sparsity)
		boolean right = (m1 == this);
		MatrixBlock that = right ? m2 : m1;
		if(!right && m2 != this) {
			throw new DMLRuntimeException(
				"Invalid inputs for aggregate Binary Operation which expect either m1 or m2 to be equal to the object calling");
		}

		// create output matrix block
		if(right) {
			boolean allowOverlap = ConfigurationManager.getDMLConfig()
				.getBooleanValue(DMLConfig.COMPRESSED_OVERLAPPING);
			ret = LibRightMultBy
				.rightMultByMatrix(_colGroups, that, ret, op.getNumThreads(), getMaxNumValues(), allowOverlap);
		}
		else {
			ret = LibLeftMultBy.leftMultByMatrix(this, that, ret, op.getNumThreads());
		}

		if(transposeOutput) {
			ReorgOperator r_op = new ReorgOperator(SwapIndex.getSwapIndexFnObject(), op.getNumThreads());
			return ret.reorgOperations(r_op, new MatrixBlock(), 0, 0, 0);
		}
		else
			return ret;

	}

	private MatrixBlock doubleCompressedAggregateBinaryOperations(CompressedMatrixBlock m1, CompressedMatrixBlock m2,
		MatrixBlock ret, AggregateBinaryOperator op, boolean transposeLeft, boolean transposeRight) {

		if(!transposeLeft && !transposeRight) {
			// If both are not transposed, decompress the right hand side. to enable compressed overlapping output.
			LOG.warn("Matrix decompression from multiplying two compressed matrices.");
			return aggregateBinaryOperations(m1, getUncompressed(m2), ret, op, transposeLeft, transposeRight);
		}
		else if(transposeLeft && !transposeRight) {
			// Select witch compressed matrix to decompress.
			if(m1.getNumColumns() > m2.getNumColumns()) {
				ret = LibLeftMultBy.leftMultByMatrixTransposed(m1, m2, ret, op.getNumThreads());
				ReorgOperator r_op = new ReorgOperator(SwapIndex.getSwapIndexFnObject(), op.getNumThreads());
				return ret.reorgOperations(r_op, new MatrixBlock(), 0, 0, 0);
			}
			else
				return LibLeftMultBy.leftMultByMatrixTransposed(m2, m1, ret, op.getNumThreads());

		}
		else if(!transposeLeft && transposeRight) {
			throw new DMLCompressionException("Not Implemented compressed Matrix Mult, to produce larger matrix");
			// worst situation since it blows up the result matrix in number of rows in either compressed matrix.
		}
		else {
			ret = aggregateBinaryOperations(m2, m1, ret, op);
			ReorgOperator r_op = new ReorgOperator(SwapIndex.getSwapIndexFnObject(), op.getNumThreads());
			return ret.reorgOperations(r_op, new MatrixBlock(), 0, 0, 0);
		}
	}

	@Override
	public MatrixBlock aggregateUnaryOperations(AggregateUnaryOperator op, MatrixValue result, int blen,
		MatrixIndexes indexesIn) {
		return aggregateUnaryOperations(op, result, blen, indexesIn, false);
	}

	@Override
	public MatrixBlock aggregateUnaryOperations(AggregateUnaryOperator op, MatrixValue result, int blen,
		MatrixIndexes indexesIn, boolean inCP) {

		// check for supported operations
		if(!(op.aggOp.increOp.fn instanceof KahanPlus || op.aggOp.increOp.fn instanceof KahanPlusSq ||
			op.aggOp.increOp.fn instanceof Mean ||
			(op.aggOp.increOp.fn instanceof Builtin &&
				(((Builtin) op.aggOp.increOp.fn).getBuiltinCode() == BuiltinCode.MIN ||
					((Builtin) op.aggOp.increOp.fn).getBuiltinCode() == BuiltinCode.MAX)))) {
			throw new NotImplementedException("Unary aggregate " + op.aggOp.increOp.fn + " not supported yet.");
		}

		// prepare output dimensions
		CellIndex tempCellIndex = new CellIndex(-1, -1);
		op.indexFn.computeDimension(rlen, clen, tempCellIndex);
		// Correction no long exists
		if(op.aggOp.existsCorrection()) {
			switch(op.aggOp.correction) {
				case LASTROW:
					tempCellIndex.row++;
					break;
				case LASTCOLUMN:
					tempCellIndex.column++;
					break;
				case LASTTWOROWS:
					tempCellIndex.row += 2;
					break;
				case LASTTWOCOLUMNS:
					tempCellIndex.column += 2;
					break;
				default:
					throw new DMLRuntimeException("unrecognized correctionLocation: " + op.aggOp.correction);
			}
		}

		// initialize and allocate the result
		if(result == null)
			result = new MatrixBlock(tempCellIndex.row, tempCellIndex.column, false);
		else
			result.reset(tempCellIndex.row, tempCellIndex.column, false);
		MatrixBlock ret = (MatrixBlock) result;
		ret.allocateDenseBlock();

		return LibCompAgg.aggregateUnary(this, ret, op, blen, indexesIn, inCP);
	}

	@Override
	public MatrixBlock transposeSelfMatrixMultOperations(MatrixBlock out, MMTSJType tstype) {
		return transposeSelfMatrixMultOperations(out, tstype, 1);
	}

	@Override
	public MatrixBlock transposeSelfMatrixMultOperations(MatrixBlock out, MMTSJType tstype, int k) {
		// check for transpose type
		if(tstype != MMTSJType.LEFT) // right not supported yet
			throw new DMLRuntimeException("Invalid MMTSJ type '" + tstype.toString() + "'.");

		// create output matrix block
		if(out == null)
			out = new MatrixBlock(clen, clen, false);
		else
			out.reset(clen, clen, false);
		out.allocateDenseBlock();

		if(!isEmptyBlock(false)) {
			// compute matrix mult
			LibLeftMultBy
				.leftMultByTransposeSelf(_colGroups, out, k, getNumColumns(), getMaxNumValues(), isOverlapping());
			// post-processing
			out.setNonZeros(LinearAlgebraUtils.copyUpperToLowerTriangle(out));
		}
		return out;
	}

	@Override
	public MatrixBlock replaceOperations(MatrixValue result, double pattern, double replacement) {
		printDecompressWarning("replaceOperations " + pattern + "  -> " + replacement);
		LOG.error("Overlapping? : " + isOverlapping() + " If not then wite a proper replace command");
		MatrixBlock tmp = getUncompressed(this);
		return tmp.replaceOperations(result, pattern, replacement);
	}

	@Override
	public MatrixBlock reorgOperations(ReorgOperator op, MatrixValue ret, int startRow, int startColumn, int length) {
		printDecompressWarning(op.getClass().getSimpleName() + " -- " + op.fn.getClass().getSimpleName());
		// TODO make transposed decompress.
		MatrixBlock tmp = decompress(op.getNumThreads());
		return tmp.reorgOperations(op, ret, startRow, startColumn, length);
	}

	public ColGroupUncompressed getUncompressedColGroup() {
		for(ColGroup grp : _colGroups)
			if(grp instanceof ColGroupUncompressed)
				return (ColGroupUncompressed) grp;
		return null;
	}

	public Pair<Integer, int[]> getMaxNumValues() {
		if(v == null) {

			int numVals = 1;
			int[] numValues = new int[_colGroups.size()];
			int nr;
			for(int i = 0; i < _colGroups.size(); i++)
				if(_colGroups.get(i) instanceof ColGroupValue) {
					nr = ((ColGroupValue) _colGroups.get(i)).getNumValues();
					numValues[i] = nr;
					numVals = Math.max(numVals, nr);
				}
				else {
					numValues[i] = -1;
				}
			v = new ImmutablePair<>(numVals, numValues);
			return v;
		}
		else {
			return v;
		}
	}

	private static class DecompressTask implements Callable<Long> {
		private final List<ColGroup> _colGroups;
		private final MatrixBlock _ret;
		private final int _rl;
		private final int _ru;
		private final boolean _overlapping;

		protected DecompressTask(List<ColGroup> colGroups, MatrixBlock ret, int rl, int ru, boolean overlapping) {
			_colGroups = colGroups;
			_ret = ret;
			_rl = rl;
			_ru = ru;
			_overlapping = overlapping;
		}

		@Override
		public Long call() {

			// preallocate sparse rows to avoid repeated alloc
			if(!_overlapping && _ret.isInSparseFormat()) {
				int[] rnnz = new int[_ru - _rl];
				for(ColGroup grp : _colGroups)
					grp.countNonZerosPerRow(rnnz, _rl, _ru);
				SparseBlock rows = _ret.getSparseBlock();
				for(int i = _rl; i < _ru; i++)
					rows.allocate(i, rnnz[i - _rl]);
			}

			// decompress row partition
			for(ColGroup grp : _colGroups)
				grp.decompressToBlockSafe(_ret, _rl, _ru, grp.getValues(), false);

			// post processing (sort due to append)
			if(_ret.isInSparseFormat())
				_ret.sortSparseRows(_rl, _ru);

			return _overlapping ? 0 : _ret.recomputeNonZeros(_rl, _ru - 1);
		}
	}

	@Override
	public String toString() {
		StringBuilder sb = new StringBuilder();
		sb.append("\nCompressed Matrix:");
		sb.append("\nCols:" + getNumColumns() + " Rows:" + getNumRows());
		if(_colGroups != null)
			for(ColGroup cg : _colGroups) {
				sb.append("\n" + cg);
			}
		else
			sb.append("EmptyColGroups");
		return sb.toString();
	}

	public boolean isOverlapping() {
		return _colGroups.size() != 1 && overlappingColGroups;
	}

	public void setOverlapping(boolean overlapping) {
		overlappingColGroups = overlapping;
	}
}