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/*
 * Copyright (c) "Neo4j"
 * Neo4j Sweden AB [http://neo4j.com]
 *
 * This file is part of Neo4j.
 *
 * Neo4j is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */
package org.neo4j.gds.similarity.nodesim;

import com.carrotsearch.hppc.BitSet;
import org.neo4j.gds.mem.MemoryEstimateDefinition;
import org.neo4j.gds.collections.ha.HugeLongArray;
import org.neo4j.gds.collections.ha.HugeObjectArray;
import org.neo4j.gds.collections.haa.HugeAtomicLongArray;
import org.neo4j.gds.mem.MemoryEstimation;
import org.neo4j.gds.mem.MemoryEstimations;
import org.neo4j.gds.mem.MemoryRange;
import org.neo4j.gds.similarity.SimilarityGraphBuilder;
import org.neo4j.gds.wcc.WccMemoryEstimateDefinition;

import static org.neo4j.gds.mem.Estimate.sizeOfDoubleArray;
import static org.neo4j.gds.mem.Estimate.sizeOfLongArray;

public class NodeSimilarityMemoryEstimateDefinition implements MemoryEstimateDefinition {
    private final NodeSimilarityEstimateParameters parameters;

    public NodeSimilarityMemoryEstimateDefinition(
        NodeSimilarityEstimateParameters parameters
    ) {
        this.parameters = parameters;
    }

    @Override
    public MemoryEstimation memoryEstimation() {

        var topK = Math.abs(parameters.normalizedK());
        var topN = Math.abs(parameters.normalizedN());

        MemoryEstimations.Builder builder = MemoryEstimations.builder(NodeSimilarity.class.getSimpleName())
            .perNode("node filter", nodeCount -> sizeOfLongArray(BitSet.bits2words(nodeCount)))
            .add(
                "vectors",
                MemoryEstimations.setup("", (dimensions, concurrency) -> {
                    int averageDegree = dimensions.nodeCount() == 0
                        ? 0
                        : Math.toIntExact(dimensions.relCountUpperBound() / dimensions.nodeCount());
                    long averageVectorSize = sizeOfLongArray(averageDegree);
                    return MemoryEstimations.builder(HugeObjectArray.class)
                        .perNode("array", nodeCount -> nodeCount * averageVectorSize).build();
                })
            )
            .add("weights",
                MemoryEstimations.setup("", (dimensions, concurrency) -> {
                    int averageDegree = dimensions.nodeCount() == 0
                        ? 0
                        : Math.toIntExact(dimensions.relCountUpperBound() / dimensions.nodeCount());
                    long averageVectorSize = sizeOfDoubleArray(averageDegree);
                    return MemoryEstimations.builder(HugeObjectArray.class)
                        .rangePerNode("array", nodeCount -> MemoryRange.of(0, nodeCount * averageVectorSize))
                        .build();
                }));
        if (parameters.useComponents()) {
            builder.perNode("nodes sorted by component", HugeLongArray::memoryEstimation);
            builder.perNode("upper bound per component", HugeAtomicLongArray::memoryEstimation);

            if (parameters.runWCC()) {
                builder.add("wcc", new WccMemoryEstimateDefinition(false).memoryEstimation());
            } else {
                builder.perNode("component mapping", HugeLongArray::memoryEstimation);
            }
        }
        if (parameters.computeToGraph() && !parameters.hasTopK()) {
            builder.add(
                "similarity graph",
                SimilarityGraphBuilder.memoryEstimation(
                    topK,
                    topN
                )
            );
        }
        if (parameters.hasTopK()) {
            builder.add(
                "topK map",
                MemoryEstimations.setup("", (dimensions, concurrency) ->
                    TopKMap.memoryEstimation(dimensions.nodeCount(), topK))
            );
        }
        if (parameters.hasTopN()) {
            builder.add(
                "topN list",
                MemoryEstimations.setup("", (dimensions, concurrency) ->
                    TopNList.memoryEstimation(dimensions.nodeCount(), topN))
            );
        }
        return builder.build();
    }
}