"""Topology families and dynamic-topology timelines.

Five families span the structures routing research cares about: Barabási–Albert scale-free
graphs (router-level Internet), Watts–Strogatz small worlds, Erdős–Rényi random graphs, Waxman
geometric graphs (ISP-like, with distance-proportional latencies and node coordinates) and k-ary
fat-trees (data-centre fabrics whose hosts are the only traffic endpoints; host links carry twice
the fabric capacity, i.e. a 2:1 oversubscribed edge, so bursts contend inside the fabric). Random families are
generated at a common mean degree so that size is the only structural factor that changes with
the nominal network size; any rare disconnected sample is stitched into one component instead of
being resampled, so the node count is always exactly the nominal one.

A pre-sampled timeline of link failures and node degradations makes the graph time-varying.
Failures are drawn only from non-bridge links of the currently live graph, so the network never
partitions and traffic can always be routed around the damage.
"""
from __future__ import annotations

from dataclasses import dataclass
from typing import List, Tuple

import networkx as nx
import numpy as np

from .config import SimConfig
from .design import Cell, Dynamics

ROLE_ROUTER, ROLE_CORE, ROLE_AGGREGATION, ROLE_EDGE, ROLE_HOST = 0, 1, 2, 3, 4


@dataclass(frozen=True)
class Topology:
    n_nodes: int
    edges: np.ndarray      # (E, 2) int16, each row (u, v) with u < v, lexicographically sorted
    capacity: np.ndarray   # (E,) int16 packets/step per direction
    latency: np.ndarray    # (E,) int16 steps
    endpoints: np.ndarray  # (K,) int16 nodes eligible as flow sources and sinks
    node_role: np.ndarray  # (N,) int8, ROLE_* constants
    node_xy: np.ndarray    # (N, 2) float32 coordinates, or shape (0, 2) for non-geometric families


@dataclass(frozen=True)
class TopologyEvent:
    kind: str            # "link_failure" | "node_degradation"
    start: int           # first step the event is active
    end: int             # first step it is no longer active (exclusive)
    edge: int = -1       # index into Topology.edges for a link failure
    node: int = -1       # degraded node
    factor: float = 0.0  # capacity multiplier applied to the degraded node's links


def _stitch(graph: nx.Graph, xy: np.ndarray = None, rng: np.random.Generator = None) -> None:
    """Connect a disconnected graph by joining every component to the largest one (rare)."""
    components = sorted(nx.connected_components(graph), key=len, reverse=True)
    main = np.array(sorted(components[0]))
    for comp in components[1:]:
        comp = np.array(sorted(comp))
        if xy is not None:  # geometric: closest pair of nodes
            d = np.linalg.norm(xy[comp][:, None, :] - xy[main][None, :, :], axis=2)
            a, b = np.unravel_index(int(d.argmin()), d.shape)
            graph.add_edge(int(comp[a]), int(main[b]))
        else:
            graph.add_edge(int(rng.choice(comp)), int(rng.choice(main)))
        main = np.concatenate([main, comp])


def _random_family(cfg: SimConfig, family: str, n: int, rng: np.random.Generator):
    seed = int(rng.integers(2**31 - 1))
    xy = np.zeros((0, 2), np.float32)
    if family == "barabasi_albert":
        graph = nx.barabasi_albert_graph(n, cfg.mean_degree // 2, seed=seed)
    elif family == "watts_strogatz":
        graph = nx.connected_watts_strogatz_graph(n, cfg.mean_degree, 0.1, tries=1000, seed=seed)
    elif family == "erdos_renyi":
        graph = nx.gnp_random_graph(n, cfg.mean_degree / (n - 1), seed=seed)
        _stitch(graph, rng=rng)
    elif family == "waxman":
        xy = rng.random((n, 2)).astype(np.float32)
        iu, iv = np.triu_indices(n, 1)
        d = np.linalg.norm(xy[iu] - xy[iv], axis=1)
        kernel = np.exp(-d / (0.15 * np.sqrt(2.0)))   # Waxman link preference, alpha = 0.15 of the diagonal
        n_edges = round(0.5 * cfg.mean_degree * n)     # fixed edge count: exact mean degree at every size
        chosen = rng.choice(len(iu), n_edges, replace=False, p=kernel / kernel.sum())
        graph = nx.Graph()
        graph.add_nodes_from(range(n))
        graph.add_edges_from(zip(iu[chosen].tolist(), iv[chosen].tolist()))
        _stitch(graph, xy=xy)
    else:
        raise ValueError(family)
    edges = np.sort(np.array(graph.edges(), dtype=np.int16), axis=1)
    edges = edges[np.lexsort((edges[:, 1], edges[:, 0]))]
    lo, hi = cfg.capacity_range
    capacity = rng.integers(lo, hi + 1, len(edges)).astype(np.int16)
    if family == "waxman":  # latency proportional to Euclidean distance, spanning latency_range
        lo_l, hi_l = cfg.latency_range
        dist = np.linalg.norm(xy[edges[:, 0]] - xy[edges[:, 1]], axis=1) / np.sqrt(2.0)
        latency = np.round(lo_l + (hi_l - lo_l) * dist).astype(np.int16)
    else:
        lo_l, hi_l = cfg.latency_range
        latency = rng.integers(lo_l, hi_l + 1, len(edges)).astype(np.int16)
    return Topology(n, edges, capacity, latency, np.arange(n, dtype=np.int16),
                    np.zeros(n, np.int8), xy)


def fat_tree_k(nominal_size: int) -> int:
    """Fat-tree arity for a nominal size: k = 4, 6, 8, 10 give 36, 99, 208, 375 nodes."""
    return {32: 4, 64: 6, 128: 8, 256: 10}.get(nominal_size, 2 * max(2, round((nominal_size / 4) ** (1 / 3))))


def _fat_tree(cfg: SimConfig, nominal_size: int) -> Topology:
    k = fat_tree_k(nominal_size)
    half = k // 2
    n_core = half * half

    def core(i, j):
        return i * half + j

    def agg(pod, i):
        return n_core + pod * k + i

    def edge(pod, i):
        return n_core + pod * k + half + i

    def host(pod, i, h):
        return n_core + k * k + (pod * half + i) * half + h

    links = []
    for pod in range(k):
        for i in range(half):
            for j in range(half):
                links.append((core(i, j), agg(pod, i)))
                links.append((agg(pod, i), edge(pod, j)))
            for h in range(half):
                links.append((edge(pod, i), host(pod, i, h)))
    n = n_core + k * k + k * half * half
    edges = np.sort(np.array(links, dtype=np.int16), axis=1)
    edges = edges[np.lexsort((edges[:, 1], edges[:, 0]))]
    role = np.full(n, ROLE_HOST, np.int8)
    role[:n_core] = ROLE_CORE
    for pod in range(k):
        role[agg(pod, 0):agg(pod, 0) + half] = ROLE_AGGREGATION
        role[edge(pod, 0):edge(pod, 0) + half] = ROLE_EDGE
    hosts = np.flatnonzero(role == ROLE_HOST).astype(np.int16)
    host_link = (role[edges[:, 0]] == ROLE_HOST) | (role[edges[:, 1]] == ROLE_HOST)
    capacity = np.where(host_link, 2 * cfg.fat_tree_capacity, cfg.fat_tree_capacity).astype(np.int16)
    return Topology(n, edges, capacity, np.ones(len(edges), np.int16), hosts, role, np.zeros((0, 2), np.float32))


def generate_topology(cfg: SimConfig, cell: Cell, rng: np.random.Generator) -> Topology:
    if cell.topology == "fat_tree":
        return _fat_tree(cfg, cell.size)
    return _random_family(cfg, cell.topology, cell.size, rng)


def effective_capacity(topo: Topology, events: List[TopologyEvent], step: int) -> np.ndarray:
    """Per-link capacity in force at `step`: base × degradation factors of both endpoints, 0 if failed."""
    failed = np.zeros(len(topo.edges), bool)
    factor = np.ones(topo.n_nodes)
    for ev in events:
        if ev.start <= step < ev.end:
            if ev.kind == "link_failure":
                failed[ev.edge] = True
            else:
                factor[ev.node] *= ev.factor
    u, v = topo.edges.T
    cap = np.maximum(1, np.floor(topo.capacity * factor[u] * factor[v])).astype(np.int16)
    cap[failed] = 0
    return cap


def generate_timeline(cfg: SimConfig, topo: Topology, dyn: Dynamics, rng: np.random.Generator
                      ) -> Tuple[List[TopologyEvent], List[Tuple[int, np.ndarray]]]:
    """Pre-sample every topology event of an episode.

    Returns ``(events, changes)`` where ``changes`` lists the ``(step, effective_capacity)`` pairs
    at which link capacities change, starting with ``(0, base capacities)``.
    """
    fail_at = rng.random(cfg.steps) < dyn.link_failure_rate
    degrade_at = rng.random(cfg.steps) < dyn.node_degradation_rate
    lo_d, hi_d = dyn.duration_range
    lo_f, hi_f = dyn.factor_range
    events: List[TopologyEvent] = []
    all_edges = [tuple(e) for e in topo.edges.tolist()]

    for t in range(cfg.steps):
        if fail_at[t]:
            down = {ev.edge for ev in events if ev.kind == "link_failure" and ev.start <= t < ev.end}
            live = nx.Graph()
            live.add_nodes_from(range(topo.n_nodes))
            live.add_edges_from(e for i, e in enumerate(all_edges) if i not in down)
            bridges = {tuple(sorted(b)) for b in nx.bridges(live)}
            candidates = [i for i, e in enumerate(all_edges) if i not in down and e not in bridges]
            if candidates:
                end = min(t + int(rng.integers(lo_d, hi_d + 1)), cfg.steps)
                events.append(TopologyEvent("link_failure", t, end, edge=int(rng.choice(candidates))))
        if degrade_at[t]:
            end = min(t + int(rng.integers(lo_d, hi_d + 1)), cfg.steps)
            events.append(TopologyEvent("node_degradation", t, end,
                                        node=int(rng.integers(topo.n_nodes)),
                                        factor=float(rng.uniform(lo_f, hi_f))))

    steps = {0} | {ev.start for ev in events} | {ev.end for ev in events if ev.end < cfg.steps}
    changes = [(t, effective_capacity(topo, events, t)) for t in sorted(steps)]
    return events, changes