using MetagraphOptimization
using Random
using Test

Random.seed!(0)

function test_known_graphs()
    g_ABAB = import_txt(joinpath(@__DIR__, "..", "examples", "AB->AB.txt"))
    props = graph_properties(g_ABAB)

    @test length(g_ABAB.nodes) == 34
    @test props.compute_effort == 185
    @test props.data == 0x68

    @test length(get_operations(g_ABAB).nodeFusions) == 10

    test_node_fusion(g_ABAB)
    test_random_walk(g_ABAB, 100)


    g_ABAB3 = import_txt(joinpath(@__DIR__, "..", "examples", "AB->ABBB.txt"))
    props = graph_properties(g_ABAB3)

    @test length(g_ABAB3.nodes) == 280
    @test props.compute_effort == 2007
    @test props.data == 0x498

    test_node_fusion(g_ABAB3)
    test_random_walk(g_ABAB3, 1000)
end

function test_node_fusion(g::DAG)
    props = graph_properties(g)

    options = get_operations(g)

    nodes_number = length(g.nodes)
    data = props.data
    compute_effort = props.compute_effort

    while !isempty(options.nodeFusions)
        fusion = pop!(options.nodeFusions)

        @test typeof(fusion) <: NodeFusion

        push_operation!(g, fusion)

        props = graph_properties(g)
        @test props.data < data
        @test props.compute_effort == compute_effort

        nodes_number = length(g.nodes)
        data = props.data
        compute_effort = props.compute_effort

        options = get_operations(g)
    end
end

function test_random_walk(g::DAG, n::Int64)
    # the purpose here is to do "random" operations and reverse them again and validate that the graph stays the same and doesn't diverge
    reset_graph!(g)

    properties = graph_properties(g)

    println("Random Walking... ")

    for i = 1:n
        print("\r", i)
        # choose push or pop
        if rand(Bool)
            # push
            opt = get_operations(g)

            # choose one of fuse/split/reduce
            option = rand(1:3)
            if option == 1 && !isempty(opt.nodeFusions)
                push_operation!(g, rand(collect(opt.nodeFusions)))
            elseif option == 2 && !isempty(opt.nodeReductions)
                push_operation!(g, rand(collect(opt.nodeReductions)))
            elseif option == 3 && !isempty(opt.nodeSplits)
                push_operation!(g, rand(collect(opt.nodeSplits)))
            else
                i = i-1
            end
        else
            # pop
            if (can_pop(g))
                pop_operation!(g)
            else
                i = i-1
            end
        end
    end

    println("\rDone.")

    reset_graph!(g)

    @test properties == graph_properties(g)
end