"""
Equitable coloring of graphs with bounded degree.
"""

import networkx as nx
from collections import defaultdict

__all__ = ["equitable_color"]


def is_coloring(G, coloring):
    """Determine if the coloring is a valid coloring for the graph G."""
    # Verify that the coloring is valid.
    for (s, d) in G.edges:
        if coloring[s] == coloring[d]:
            return False
    return True


def is_equitable(G, coloring, num_colors=None):
    """Determines if the coloring is valid and equitable for the graph G."""

    if not is_coloring(G, coloring):
        return False

    # Verify whether it is equitable.
    color_set_size = defaultdict(int)
    for color in coloring.values():
        color_set_size[color] += 1

    if num_colors is not None:
        for color in range(num_colors):
            if color not in color_set_size:
                # These colors do not have any vertices attached to them.
                color_set_size[color] = 0

    # If there are more than 2 distinct values, the coloring cannot be equitable
    all_set_sizes = set(color_set_size.values())
    if len(all_set_sizes) == 0 and num_colors is None:  # Was an empty graph
        return True
    elif len(all_set_sizes) == 1:
        return True
    elif len(all_set_sizes) == 2:
        a, b = list(all_set_sizes)
        return abs(a - b) <= 1
    else:  # len(all_set_sizes) > 2:
        return False


def make_C_from_F(F):
    C = defaultdict(lambda: [])
    for node, color in F.items():
        C[color].append(node)

    return C


def make_N_from_L_C(L, C):
    nodes = L.keys()
    colors = C.keys()
    return {
        (node, color): sum(1 for v in L[node] if v in C[color])
        for node in nodes
        for color in colors
    }


def make_H_from_C_N(C, N):
    return {
        (c1, c2): sum(1 for node in C[c1] if N[(node, c2)] == 0)
        for c1 in C.keys()
        for c2 in C.keys()
    }


def change_color(u, X, Y, N, H, F, C, L):
    """Change the color of 'u' from X to Y and update N, H, F, C."""
    assert F[u] == X and X != Y

    # Change the class of 'u' from X to Y
    F[u] = Y

    for k in C.keys():
        # 'u' witnesses an edge from k -> Y instead of from k -> X now.
        if N[u, k] == 0:
            H[(X, k)] -= 1
            H[(Y, k)] += 1

    for v in L[u]:
        # 'v' has lost a neighbor in X and gained one in Y
        N[(v, X)] -= 1
        N[(v, Y)] += 1

        if N[(v, X)] == 0:
            # 'v' witnesses F[v] -> X
            H[(F[v], X)] += 1

        if N[(v, Y)] == 1:
            # 'v' no longer witnesses F[v] -> Y
            H[(F[v], Y)] -= 1

    C[X].remove(u)
    C[Y].append(u)


def move_witnesses(src_color, dst_color, N, H, F, C, T_cal, L):
    """Move witness along a path from src_color to dst_color."""
    X = src_color
    while X != dst_color:
        Y = T_cal[X]
        # Move _any_ witness from X to Y = T_cal[X]
        w = [x for x in C[X] if N[(x, Y)] == 0][0]
        change_color(w, X, Y, N=N, H=H, F=F, C=C, L=L)
        X = Y


def pad_graph(G, num_colors):
    """Add a disconnected complete clique K_p such that the number of nodes in
    the graph becomes a multiple of `num_colors`.

    Assumes that the graph's nodes are labelled using integers.

    Returns the number of nodes with each color.
    """

    n_ = len(G)
    r = num_colors - 1

    # Ensure that the number of nodes in G is a multiple of (r + 1)
    s = n_ // (r + 1)
    if n_ != s * (r + 1):
        p = (r + 1) - n_ % (r + 1)
        s += 1

        # Complete graph K_p between (imaginary) nodes [n_, ... , n_ + p]
        K = nx.relabel_nodes(nx.complete_graph(p), {idx: idx + n_ for idx in range(p)})
        G.add_edges_from(K.edges)

    return s


def procedure_P(V_minus, V_plus, N, H, F, C, L, excluded_colors=None):
    """Procedure P as described in the paper."""

    if excluded_colors is None:
        excluded_colors = set()

    A_cal = set()
    T_cal = {}
    R_cal = []

    # BFS to determine A_cal, i.e. colors reachable from V-
    reachable = [V_minus]
    marked = set(reachable)
    idx = 0

    while idx < len(reachable):
        pop = reachable[idx]
        idx += 1

        A_cal.add(pop)
        R_cal.append(pop)

        # TODO: Checking whether a color has been visited can be made faster by
        # using a look-up table instead of testing for membership in a set by a
        # logarithmic factor.
        next_layer = []
        for k in C.keys():
            if (
                H[(k, pop)] > 0
                and k not in A_cal
                and k not in excluded_colors
                and k not in marked
            ):
                next_layer.append(k)

        for dst in next_layer:
            # Record that `dst` can reach `pop`
            T_cal[dst] = pop

        marked.update(next_layer)
        reachable.extend(next_layer)

    # Variables for the algorithm
    b = len(C) - len(A_cal)

    if V_plus in A_cal:
        # Easy case: V+ is in A_cal
        # Move one node from V+ to V- using T_cal to find the parents.
        move_witnesses(V_plus, V_minus, N=N, H=H, F=F, C=C, T_cal=T_cal, L=L)
    else:
        # If there is a solo edge, we can resolve the situation by
        # moving witnesses from B to A, making G[A] equitable and then
        # recursively balancing G[B - w] with a different V_minus and
        # but the same V_plus.

        A_0 = set()
        A_cal_0 = set()
        num_terminal_sets_found = 0
        made_equitable = False

        for W_1 in R_cal[::-1]:

            for v in C[W_1]:
                X = None

                for U in C.keys():
                    if N[(v, U)] == 0 and U in A_cal and U != W_1:
                        X = U

                # v does not witness an edge in H[A_cal]
                if X is None:
                    continue

                for U in C.keys():
                    # Note: Departing from the paper here.
                    if N[(v, U)] >= 1 and U not in A_cal:
                        X_prime = U
                        w = v

                        # Finding the solo neighbor of w in X_prime
                        y_candidates = [
                            node
                            for node in L[w]
                            if F[node] == X_prime and N[(node, W_1)] == 1
                        ]

                        if len(y_candidates) > 0:
                            y = y_candidates[0]
                            W = W_1

                            # Move w from W to X, now X has one extra node.
                            change_color(w, W, X, N=N, H=H, F=F, C=C, L=L)

                            # Move witness from X to V_minus, making the coloring
                            # equitable.
                            move_witnesses(
                                src_color=X,
                                dst_color=V_minus,
                                N=N,
                                H=H,
                                F=F,
                                C=C,
                                T_cal=T_cal,
                                L=L,
                            )

                            # Move y from X_prime to W, making W the correct size.
                            change_color(y, X_prime, W, N=N, H=H, F=F, C=C, L=L)

                            # Then call the procedure on G[B - y]
                            procedure_P(
                                V_minus=X_prime,
                                V_plus=V_plus,
                                N=N,
                                H=H,
                                C=C,
                                F=F,
                                L=L,
                                excluded_colors=excluded_colors.union(A_cal),
                            )
                            made_equitable = True
                            break

                if made_equitable:
                    break
            else:
                # No node in W_1 was found such that
                # it had a solo-neighbor.
                A_cal_0.add(W_1)
                A_0.update(C[W_1])
                num_terminal_sets_found += 1

            if num_terminal_sets_found == b:
                # Otherwise, construct the maximal independent set and find
                # a pair of z_1, z_2 as in Case II.

                # BFS to determine B_cal': the set of colors reachable from V+
                B_cal_prime = set()
                T_cal_prime = {}

                reachable = [V_plus]
                marked = set(reachable)
                idx = 0
                while idx < len(reachable):
                    pop = reachable[idx]
                    idx += 1

                    B_cal_prime.add(pop)

                    # No need to check for excluded_colors here because
                    # they only exclude colors from A_cal
                    next_layer = [
                        k
                        for k in C.keys()
                        if H[(pop, k)] > 0 and k not in B_cal_prime and k not in marked
                    ]

                    for dst in next_layer:
                        T_cal_prime[pop] = dst

                    marked.update(next_layer)
                    reachable.extend(next_layer)

                # Construct the independent set of G[B']
                I_set = set()
                I_covered = set()
                W_covering = {}

                B_prime = [node for k in B_cal_prime for node in C[k]]

                # Add the nodes in V_plus to I first.
                for z in C[V_plus] + B_prime:
                    if z in I_covered or F[z] not in B_cal_prime:
                        continue

                    I_set.add(z)
                    I_covered.add(z)
                    I_covered.update([nbr for nbr in L[z]])

                    for w in L[z]:
                        if F[w] in A_cal_0 and N[(z, F[w])] == 1:
                            if w not in W_covering:
                                W_covering[w] = z
                            else:
                                # Found z1, z2 which have the same solo
                                # neighbor in some W
                                z_1 = W_covering[w]
                                # z_2 = z

                                Z = F[z_1]
                                W = F[w]

                                # shift nodes along W, V-
                                move_witnesses(
                                    W, V_minus, N=N, H=H, F=F, C=C, T_cal=T_cal, L=L
                                )

                                # shift nodes along V+ to Z
                                move_witnesses(
                                    V_plus,
                                    Z,
                                    N=N,
                                    H=H,
                                    F=F,
                                    C=C,
                                    T_cal=T_cal_prime,
                                    L=L,
                                )

                                # change color of z_1 to W
                                change_color(z_1, Z, W, N=N, H=H, F=F, C=C, L=L)

                                # change color of w to some color in B_cal
                                W_plus = [
                                    k
                                    for k in C.keys()
                                    if N[(w, k)] == 0 and k not in A_cal
                                ][0]
                                change_color(w, W, W_plus, N=N, H=H, F=F, C=C, L=L)

                                # recurse with G[B \cup W*]
                                excluded_colors.update(
                                    [
                                        k
                                        for k in C.keys()
                                        if k != W and k not in B_cal_prime
                                    ]
                                )
                                procedure_P(
                                    V_minus=W,
                                    V_plus=W_plus,
                                    N=N,
                                    H=H,
                                    C=C,
                                    F=F,
                                    L=L,
                                    excluded_colors=excluded_colors,
                                )

                                made_equitable = True
                                break

                    if made_equitable:
                        break
                else:
                    assert False, (
                        "Must find a w which is the solo neighbor "
                        "of two vertices in B_cal_prime."
                    )

            if made_equitable:
                break


def equitable_color(G, num_colors):
    """Provides equitable (r + 1)-coloring for nodes of G in O(r * n^2) time
     if deg(G) <= r. The algorithm is described in [1]_.

     Attempts to color a graph using r colors, where no neighbors of a node
     can have same color as the node itself and the number of nodes with each
     color differ by at most 1.

     Parameters
     ----------
     G : networkX graph
        The nodes of this graph will be colored.

     num_colors : number of colors to use
        This number must be at least one more than the maximum degree of nodes
        in the graph.

     Returns
     -------
     A dictionary with keys representing nodes and values representing
     corresponding coloring.

     Examples
     --------
     >>> G = nx.cycle_graph(4)
     >>> d = nx.coloring.equitable_color(G, num_colors=3)
     >>> nx.algorithms.coloring.equitable_coloring.is_equitable(G, d)
     True

     Raises
     ------
     NetworkXAlgorithmError
         If the maximum degree of the graph ``G`` is greater than
         ``num_colors``.

     References
     ----------
     .. [1] Kierstead, H. A., Kostochka, A. V., Mydlarz, M., & Szemerédi, E.
         (2010). A fast algorithm for equitable coloring. Combinatorica, 30(2),
         217-224.
    """

    # Map nodes to integers for simplicity later.
    nodes_to_int = {}
    int_to_nodes = {}

    for idx, node in enumerate(G.nodes):
        nodes_to_int[node] = idx
        int_to_nodes[idx] = node

    G = nx.relabel_nodes(G, nodes_to_int, copy=True)

    # Basic graph statistics and sanity check.
    if len(G.nodes) > 0:
        r_ = max([G.degree(node) for node in G.nodes])
    else:
        r_ = 0

    if r_ >= num_colors:
        raise nx.NetworkXAlgorithmError(
            f"Graph has maximum degree {r_}, needs "
            f"{r_ + 1} (> {num_colors}) colors for guaranteed coloring."
        )

    # Ensure that the number of nodes in G is a multiple of (r + 1)
    pad_graph(G, num_colors)

    # Starting the algorithm.
    # L = {node: list(G.neighbors(node)) for node in G.nodes}
    L_ = {node: [] for node in G.nodes}

    # Arbitrary equitable allocation of colors to nodes.
    F = {node: idx % num_colors for idx, node in enumerate(G.nodes)}

    C = make_C_from_F(F)

    # The neighborhood is empty initially.
    N = make_N_from_L_C(L_, C)

    # Currently all nodes witness all edges.
    H = make_H_from_C_N(C, N)

    # Start of algorithm.
    edges_seen = set()

    for u in sorted(G.nodes):
        for v in sorted(G.neighbors(u)):

            # Do not double count edges if (v, u) has already been seen.
            if (v, u) in edges_seen:
                continue

            edges_seen.add((u, v))

            L_[u].append(v)
            L_[v].append(u)

            N[(u, F[v])] += 1
            N[(v, F[u])] += 1

            if F[u] != F[v]:
                # Were 'u' and 'v' witnesses for F[u] -> F[v] or F[v] -> F[u]?
                if N[(u, F[v])] == 1:
                    H[F[u], F[v]] -= 1  # u cannot witness an edge between F[u], F[v]

                if N[(v, F[u])] == 1:
                    H[F[v], F[u]] -= 1  # v cannot witness an edge between F[v], F[u]

        if N[(u, F[u])] != 0:
            # Find the first color where 'u' does not have any neighbors.
            Y = [k for k in C.keys() if N[(u, k)] == 0][0]
            X = F[u]
            change_color(u, X, Y, N=N, H=H, F=F, C=C, L=L_)

            # Procedure P
            procedure_P(V_minus=X, V_plus=Y, N=N, H=H, F=F, C=C, L=L_)

    return {int_to_nodes[x]: F[x] for x in int_to_nodes}
