"""
Functions for hashing graphs to strings.
Isomorphic graphs should be assigned identical hashes.
For now, only Weisfeiler-Lehman hashing is implemented.
"""

from collections import Counter, defaultdict
from hashlib import blake2b

import networkx as nx

__all__ = ["weisfeiler_lehman_graph_hash", "weisfeiler_lehman_subgraph_hashes"]


def _hash_label(label, digest_size):
    return blake2b(label.encode("ascii"), digest_size=digest_size).hexdigest()


def _init_node_labels(G, edge_attr, node_attr):
    if node_attr:
        return {u: str(dd[node_attr]) for u, dd in G.nodes(data=True)}
    elif edge_attr:
        return {u: "" for u in G}
    else:
        return {u: str(deg) for u, deg in G.degree()}


def _neighborhood_aggregate(G, node, node_labels, edge_attr=None):
    """
    Compute new labels for given node by aggregating
    the labels of each node's neighbors.
    """
    label_list = []
    for nbr in G.neighbors(node):
        prefix = "" if edge_attr is None else str(G[node][nbr][edge_attr])
        label_list.append(prefix + node_labels[nbr])
    return node_labels[node] + "".join(sorted(label_list))


@nx.utils.not_implemented_for("multigraph")
@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
def weisfeiler_lehman_graph_hash(
    G, edge_attr=None, node_attr=None, iterations=3, digest_size=16
):
    """Return Weisfeiler Lehman (WL) graph hash.

    The function iteratively aggregates and hashes neighborhoods of each node.
    After each node's neighbors are hashed to obtain updated node labels,
    a hashed histogram of resulting labels is returned as the final hash.

    Hashes are identical for isomorphic graphs and strong guarantees that
    non-isomorphic graphs will get different hashes. See [1]_ for details.

    If no node or edge attributes are provided, the degree of each node
    is used as its initial label.
    Otherwise, node and/or edge labels are used to compute the hash.

    Parameters
    ----------
    G : graph
        The graph to be hashed.
        Can have node and/or edge attributes. Can also have no attributes.
    edge_attr : string, optional (default=None)
        The key in edge attribute dictionary to be used for hashing.
        If None, edge labels are ignored.
    node_attr: string, optional (default=None)
        The key in node attribute dictionary to be used for hashing.
        If None, and no edge_attr given, use the degrees of the nodes as labels.
    iterations: int, optional (default=3)
        Number of neighbor aggregations to perform.
        Should be larger for larger graphs.
    digest_size: int, optional (default=16)
        Size (in bits) of blake2b hash digest to use for hashing node labels.

    Returns
    -------
    h : string
        Hexadecimal string corresponding to hash of the input graph.

    Examples
    --------
    Two graphs with edge attributes that are isomorphic, except for
    differences in the edge labels.

    >>> G1 = nx.Graph()
    >>> G1.add_edges_from(
    ...     [
    ...         (1, 2, {"label": "A"}),
    ...         (2, 3, {"label": "A"}),
    ...         (3, 1, {"label": "A"}),
    ...         (1, 4, {"label": "B"}),
    ...     ]
    ... )
    >>> G2 = nx.Graph()
    >>> G2.add_edges_from(
    ...     [
    ...         (5, 6, {"label": "B"}),
    ...         (6, 7, {"label": "A"}),
    ...         (7, 5, {"label": "A"}),
    ...         (7, 8, {"label": "A"}),
    ...     ]
    ... )

    Omitting the `edge_attr` option, results in identical hashes.

    >>> nx.weisfeiler_lehman_graph_hash(G1)
    '7bc4dde9a09d0b94c5097b219891d81a'
    >>> nx.weisfeiler_lehman_graph_hash(G2)
    '7bc4dde9a09d0b94c5097b219891d81a'

    With edge labels, the graphs are no longer assigned
    the same hash digest.

    >>> nx.weisfeiler_lehman_graph_hash(G1, edge_attr="label")
    'c653d85538bcf041d88c011f4f905f10'
    >>> nx.weisfeiler_lehman_graph_hash(G2, edge_attr="label")
    '3dcd84af1ca855d0eff3c978d88e7ec7'

    Notes
    -----
    To return the WL hashes of each subgraph of a graph, use
    `weisfeiler_lehman_subgraph_hashes`

    Similarity between hashes does not imply similarity between graphs.

    References
    ----------
    .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen,
       Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman
       Graph Kernels. Journal of Machine Learning Research. 2011.
       http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf

    See also
    --------
    weisfeiler_lehman_subgraph_hashes
    """

    def weisfeiler_lehman_step(G, labels, edge_attr=None):
        """
        Apply neighborhood aggregation to each node
        in the graph.
        Computes a dictionary with labels for each node.
        """
        new_labels = {}
        for node in G.nodes():
            label = _neighborhood_aggregate(G, node, labels, edge_attr=edge_attr)
            new_labels[node] = _hash_label(label, digest_size)
        return new_labels

    # set initial node labels
    node_labels = _init_node_labels(G, edge_attr, node_attr)

    subgraph_hash_counts = []
    for _ in range(iterations):
        node_labels = weisfeiler_lehman_step(G, node_labels, edge_attr=edge_attr)
        counter = Counter(node_labels.values())
        # sort the counter, extend total counts
        subgraph_hash_counts.extend(sorted(counter.items(), key=lambda x: x[0]))

    # hash the final counter
    return _hash_label(str(tuple(subgraph_hash_counts)), digest_size)


@nx.utils.not_implemented_for("multigraph")
@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
def weisfeiler_lehman_subgraph_hashes(
    G,
    edge_attr=None,
    node_attr=None,
    iterations=3,
    digest_size=16,
    include_initial_labels=False,
):
    """
    Return a dictionary of subgraph hashes by node.

    Dictionary keys are nodes in `G`, and values are a list of hashes.
    Each hash corresponds to a subgraph rooted at a given node u in `G`.
    Lists of subgraph hashes are sorted in increasing order of depth from
    their root node, with the hash at index i corresponding to a subgraph
    of nodes at most i edges distance from u. Thus, each list will contain
    `iterations` elements - a hash for a subgraph at each depth. If
    `include_initial_labels` is set to `True`, each list will additionally
    have contain a hash of the initial node label (or equivalently a
    subgraph of depth 0) prepended, totalling ``iterations + 1`` elements.

    The function iteratively aggregates and hashes neighborhoods of each node.
    This is achieved for each step by replacing for each node its label from
    the previous iteration with its hashed 1-hop neighborhood aggregate.
    The new node label is then appended to a list of node labels for each
    node.

    To aggregate neighborhoods for a node $u$ at each step, all labels of
    nodes adjacent to $u$ are concatenated. If the `edge_attr` parameter is set,
    labels for each neighboring node are prefixed with the value of this attribute
    along the connecting edge from this neighbor to node $u$. The resulting string
    is then hashed to compress this information into a fixed digest size.

    Thus, at the $i$-th iteration, nodes within $i$ hops influence any given
    hashed node label. We can therefore say that at depth $i$ for node $u$
    we have a hash for a subgraph induced by the $i$-hop neighborhood of $u$.

    The output can be used to create general Weisfeiler-Lehman graph kernels,
    or generate features for graphs or nodes - for example to generate 'words' in
    a graph as seen in the 'graph2vec' algorithm.
    See [1]_ & [2]_ respectively for details.

    Hashes are identical for isomorphic subgraphs and there exist strong
    guarantees that non-isomorphic graphs will get different hashes.
    See [1]_ for details.

    If no node or edge attributes are provided, the degree of each node
    is used as its initial label.
    Otherwise, node and/or edge labels are used to compute the hash.

    Parameters
    ----------
    G : graph
        The graph to be hashed.
        Can have node and/or edge attributes. Can also have no attributes.
    edge_attr : string, optional (default=None)
        The key in edge attribute dictionary to be used for hashing.
        If None, edge labels are ignored.
    node_attr : string, optional (default=None)
        The key in node attribute dictionary to be used for hashing.
        If None, and no edge_attr given, use the degrees of the nodes as labels.
        If None, and edge_attr is given, each node starts with an identical label.
    iterations : int, optional (default=3)
        Number of neighbor aggregations to perform.
        Should be larger for larger graphs.
    digest_size : int, optional (default=16)
        Size (in bits) of blake2b hash digest to use for hashing node labels.
        The default size is 16 bits.
    include_initial_labels : bool, optional (default=False)
        If True, include the hashed initial node label as the first subgraph
        hash for each node.

    Returns
    -------
    node_subgraph_hashes : dict
        A dictionary with each key given by a node in G, and each value given
        by the subgraph hashes in order of depth from the key node.

    Examples
    --------
    Finding similar nodes in different graphs:

    >>> G1 = nx.Graph()
    >>> G1.add_edges_from([(1, 2), (2, 3), (2, 4), (3, 5), (4, 6), (5, 7), (6, 7)])
    >>> G2 = nx.Graph()
    >>> G2.add_edges_from([(1, 3), (2, 3), (1, 6), (1, 5), (4, 6)])
    >>> g1_hashes = nx.weisfeiler_lehman_subgraph_hashes(
    ...     G1, iterations=3, digest_size=8
    ... )
    >>> g2_hashes = nx.weisfeiler_lehman_subgraph_hashes(
    ...     G2, iterations=3, digest_size=8
    ... )

    Even though G1 and G2 are not isomorphic (they have different numbers of edges),
    the hash sequence of depth 3 for node 1 in G1 and node 5 in G2 are similar:

    >>> g1_hashes[1]
    ['a93b64973cfc8897', 'db1b43ae35a1878f', '57872a7d2059c1c0']
    >>> g2_hashes[5]
    ['a93b64973cfc8897', 'db1b43ae35a1878f', '1716d2a4012fa4bc']

    The first 2 WL subgraph hashes match. From this we can conclude that it's very
    likely the neighborhood of 2 hops around these nodes are isomorphic.

    However the 3-hop neighborhoods of ``G1`` and ``G2`` are not isomorphic since the
    3rd hashes in the lists above are not equal.

    These nodes may be candidates to be classified together since their local topology
    is similar.

    Notes
    -----
    To hash the full graph when subgraph hashes are not needed, use
    `weisfeiler_lehman_graph_hash` for efficiency.

    Similarity between hashes does not imply similarity between graphs.

    References
    ----------
    .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen,
       Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman
       Graph Kernels. Journal of Machine Learning Research. 2011.
       http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
    .. [2] Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan,
       Lihui Chen, Yang Liu and Shantanu Jaiswa. graph2vec: Learning
       Distributed Representations of Graphs. arXiv. 2017
       https://arxiv.org/pdf/1707.05005.pdf

    See also
    --------
    weisfeiler_lehman_graph_hash
    """

    def weisfeiler_lehman_step(G, labels, node_subgraph_hashes, edge_attr=None):
        """
        Apply neighborhood aggregation to each node
        in the graph.
        Computes a dictionary with labels for each node.
        Appends the new hashed label to the dictionary of subgraph hashes
        originating from and indexed by each node in G
        """
        new_labels = {}
        for node in G.nodes():
            label = _neighborhood_aggregate(G, node, labels, edge_attr=edge_attr)
            hashed_label = _hash_label(label, digest_size)
            new_labels[node] = hashed_label
            node_subgraph_hashes[node].append(hashed_label)
        return new_labels

    node_labels = _init_node_labels(G, edge_attr, node_attr)
    if include_initial_labels:
        node_subgraph_hashes = {
            k: [_hash_label(v, digest_size)] for k, v in node_labels.items()
        }
    else:
        node_subgraph_hashes = defaultdict(list)

    for _ in range(iterations):
        node_labels = weisfeiler_lehman_step(
            G, node_labels, node_subgraph_hashes, edge_attr
        )

    return dict(node_subgraph_hashes)