"""Static architecture portrait of the spiking LM -- straight from a checkpoint, no
training information.

This is the sibling of `visualize_checkpoint.py` (the live "training scope"), with
every training-history element removed: no metrics CSV, no learning curve, no
influence-over-training curves. It draws ONLY what is intrinsic to the saved model
-- the layers, the neurons, their gene-program identity, and their learned wiring --
and writes a single PNG suitable for posting to the model repo.

What it shows
-------------
  * one stacked plane per LIF layer (embed at the bottom, readout on top); every hidden
    neuron sits at a fixed (x,y) so it is a vertical column through the depth
  * neuron COLOUR = its dominant cell-program identity from modulation.P
  * neuron SIZE   = its recurrent wiring strength at that layer (||W_rec row||)
  * a sample of the strongest inter-layer weights as faint edges
  * side panels (all derived from the final weights, NOT from training history):
        - per-program modulation influence  = ||(thr,beta,in)_gain|| x std(P_column)
        - neuron census per gene program
        - the learned threshold/beta/gain strength for each program

Only torch + numpy + matplotlib are needed (no `ml` package, no GPU, no data).

Usage
-----
    PYTHONPATH=src python src/scripts/visualize_architecture.py \
        --checkpoint snn_stream_program.pth --out spikewhale_architecture.png
"""
from __future__ import annotations

import argparse
import math

import numpy as np
import torch
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.colors as mcol
from matplotlib.lines import Line2D
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401  (registers 3d projection)

# stable colour per program NAME (shared with the other visualisers)
PALETTE = {
    "cell_division": "#E24B4A", "neuron_identity": "#639922", "pluripotency": "#378ADD",
    "endocrine": "#D4537E", "metabolism": "#BA7517", "signaling_identity": "#7F77DD",
}
_FALLBACK = ["#E24B4A", "#639922", "#378ADD", "#D4537E", "#BA7517", "#7F77DD", "#888780", "#1D9E75"]
_DEFAULT_PROGRAMS = ["cell_division", "neuron_identity", "pluripotency",
                     "endocrine", "metabolism", "signaling_identity"]

FG = "#e6edf3"; MUTED = "#8b949e"; BG = "#0d1117"; PANEL = "#161b22"; GRID = "#30363d"


def _base(name):
    return str(name).split("#", 1)[0]


def base_programs(names):
    seen = []
    for nm in names or []:
        b = _base(nm)
        if b not in seen:
            seen.append(b)
    return seen or list(_DEFAULT_PROGRAMS)


def prog_color(base, i):
    return PALETTE.get(base, _FALLBACK[i % len(_FALLBACK)])


def grid_positions(n):
    cols = int(math.ceil(math.sqrt(n)))
    rows = int(math.ceil(n / cols))
    idx = np.arange(n)
    x = (idx % cols) / max(cols - 1, 1)
    y = (idx // cols) / max(rows - 1, 1)
    return x, y


def program_scores(P, program_names):
    """[H,K] identity matrix + names -> ([H,n_base] per-program scores, base list)."""
    bases = base_programs(program_names)
    H, n = P.shape[0], len(bases)
    bi = {b: i for i, b in enumerate(bases)}
    scores = np.zeros((H, n), np.float32)
    counts = np.zeros(n, np.float32)
    for j, nm in enumerate(program_names or []):
        b = _base(nm)
        if b in bi:
            scores[:, bi[b]] += P[:, j]; counts[bi[b]] += 1
    counts[counts == 0] = 1
    scores /= counts
    return scores, bases


def extract(ckpt, stride, max_edges):
    sd = ckpt["model_state_dict"]
    cfg = ckpt["config"]
    L = cfg["num_layers"]; H = cfg["hidden"]
    mod = ckpt.get("modulation", {})
    P = mod.get("P")
    P = P.numpy() if torch.is_tensor(P) else np.zeros((H, len(_DEFAULT_PROGRAMS)), np.float32)
    names = mod.get("program_names") or list(_DEFAULT_PROGRAMS)

    scores, bases = program_scores(P, names)
    dom = scores.argmax(1)
    rgb = np.array([mcol.to_rgb(prog_color(bases[d], d)) for d in dom], np.float32)
    enabled = bool(mod.get("enabled", False))
    if not enabled or np.allclose(P, 0):
        rgb[:] = 0.6                                    # modulation off -> neutral grey
    counts = [int((dom == i).sum()) for i in range(len(bases))]

    sel = np.arange(0, H, max(stride, 1))
    gx, gy = grid_positions(H)
    gx, gy = gx[sel], gy[sel]
    rgb_sel = rgb[sel]

    layers = []
    for l in range(L):
        w = sd[f"W_rec.{l}.weight"].float()
        rownorm = w.norm(dim=1).numpy()[sel]
        rownorm = rownorm / (rownorm.mean() + 1e-6)
        layers.append({"z": l + 1, "size": 8.0 * rownorm + 2.0})

    edges = []
    for l in range(1, L):
        w = sd[f"W_in.{l}.weight"].float().abs()
        k = min(max_edges, w.numel())
        flat = torch.topk(w.flatten(), k).indices.numpy()
        out_n = flat // w.shape[1]; in_n = flat % w.shape[1]
        for o, i in zip(out_n, in_n):
            oi, ii = o // max(stride, 1), i // max(stride, 1)
            if oi < len(gx) and ii < len(gx):
                edges.append(((gx[ii], gy[ii], l), (gx[oi], gy[oi], l + 1)))

    # per-program influence + learned gains (from the FINAL weights only)
    tg = sd.get("modulation.thr_gain"); bg = sd.get("modulation.beta_gain")
    ig = sd.get("modulation.in_gain")
    infl = {b: 0.0 for b in bases}
    gains = {b: {"thr": 0.0, "beta": 0.0, "in": 0.0, "_n": 0} for b in bases}
    if tg is not None and P.shape[1] == len(tg):
        tg, bg, ig = tg.numpy(), bg.numpy(), ig.numpy()
        for j, nm in enumerate(names):
            b = _base(nm)
            if b in infl:
                infl[b] += float(math.sqrt(tg[j]**2 + bg[j]**2 + ig[j]**2) * P[:, j].std())
                gains[b]["thr"] += float(tg[j]); gains[b]["beta"] += float(bg[j])
                gains[b]["in"] += float(ig[j]); gains[b]["_n"] += 1
        for b in gains:
            n = max(gains[b].pop("_n"), 1)
            for k in gains[b]:
                gains[b][k] /= n

    tens = [v for v in sd.values() if torch.is_tensor(v)]
    nparam = sum(v.numel() for v in tens)
    rms = float(torch.sqrt(sum((v.float() ** 2).sum() for v in tens) / nparam))
    fam = cfg.get("family", {})
    trait_map = [("use_engram", "Engram"), ("use_hrm", "HRM"), ("use_moe", "MoE"),
                 ("use_mtp", "MTP"), ("use_spikeattn", "SpikeAttn"),
                 ("use_progsem", "ProgSem"), ("use_kuramoto", "Kuramoto"),
                 ("use_jepa", "JEPA")]
    traits = [lbl for key, lbl in trait_map if fam.get(key)]
    n_seeds = P.shape[1] // max(len(bases), 1)

    return {
        "L": L, "H": H, "gx": gx, "gy": gy, "rgb": rgb_sel, "layers": layers, "edges": edges,
        "step": int(ckpt.get("step", 0)), "nparam": nparam, "rms": rms,
        "traits": traits or ["none"], "P_cols": P.shape[1], "n_seeds": n_seeds,
        "enabled": enabled, "infl": infl, "bases": bases, "counts": counts, "gains": gains,
    }


def draw_3d(ax, d):
    ax.set_facecolor(BG)
    for z, lbl in [(0, "embed"), (d["L"] + 1, "readout")]:
        ax.scatter(d["gx"], d["gy"], np.full_like(d["gx"], z), s=3, c=[(0.5, 0.5, 0.5)], alpha=0.15)
        ax.text(0.0, 1.05, z, lbl, color=MUTED, fontsize=8)
    for (a, b) in d["edges"]:
        ax.plot([a[0], b[0]], [a[1], b[1]], [a[2], b[2]], color="0.6", linewidth=0.3, alpha=0.22)
    for lay in d["layers"]:
        z = np.full_like(d["gx"], lay["z"])
        ax.scatter(d["gx"], d["gy"], z, s=lay["size"], c=d["rgb"], depthshade=True, edgecolors="none")
        ax.text(0.0, 1.05, lay["z"], f"L{lay['z']}", color=MUTED, fontsize=8)
    ax.set_zlim(-0.5, d["L"] + 1.5)
    ax.set_xticks([]); ax.set_yticks([])
    ax.set_zlabel("layer (depth)", color=FG)
    ax.tick_params(colors=MUTED)
    ax.view_init(elev=18, azim=-60)
    for pane in (ax.xaxis, ax.yaxis, ax.zaxis):
        pane.set_pane_color((0.05, 0.06, 0.09, 1.0))


def _style(ax):
    ax.set_facecolor(PANEL)
    for s in ax.spines.values():
        s.set_color(GRID)
    ax.tick_params(colors=MUTED)
    ax.title.set_color(FG)
    ax.xaxis.label.set_color(FG); ax.yaxis.label.set_color(FG)


def draw_influence(ax, d):
    _style(ax)
    items = sorted(d["infl"].items(), key=lambda kv: kv[1])
    bases_i = {b: i for i, b in enumerate(d["bases"])}
    ys = np.arange(len(items))
    ax.barh(ys, [v for _, v in items],
            color=[prog_color(b, bases_i[b]) for b, _ in items], edgecolor="none")
    ax.set_yticks(ys); ax.set_yticklabels([b.replace("_", " ") for b, _ in items], fontsize=9)
    ax.set_xlabel("modulation influence   ||gain|| x std(P)")
    ax.set_title("how strongly each gene program shapes the neurons", fontsize=9)
    ax.grid(axis="x", alpha=0.15)


def draw_census(ax, d):
    _style(ax)
    bases = d["bases"]; counts = d["counts"]
    xs = np.arange(len(bases))
    ax.bar(xs, counts, color=[prog_color(b, i) for i, b in enumerate(bases)], edgecolor="none")
    for x, c in zip(xs, counts):
        ax.text(x, c, f"{c}", ha="center", va="bottom", color=FG, fontsize=8)
    ax.set_xticks(xs)
    ax.set_xticklabels([b.replace("_", "\n") for b in bases], fontsize=8)
    ax.set_ylabel("neurons (dominant program)")
    ax.set_title("neuron census by gene program", fontsize=9)
    ax.grid(axis="y", alpha=0.15)


def main():
    p = argparse.ArgumentParser(description=__doc__,
                                formatter_class=argparse.RawDescriptionHelpFormatter)
    p.add_argument("--checkpoint", default="snn_models/snn_stream_program.pth")
    p.add_argument("--out", default="spikewhale_architecture.png")
    p.add_argument("--max-edges", type=int, default=40, help="sampled edges per layer gap")
    p.add_argument("--stride", type=int, default=1, help="plot every Nth neuron (speed)")
    p.add_argument("--dpi", type=int, default=150)
    p.add_argument("--title", default="SpikeWhale-SNN-216M")
    args = p.parse_args()

    ckpt = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
    d = extract(ckpt, args.stride, args.max_edges)

    fig = plt.figure(figsize=(16, 9), dpi=args.dpi)
    fig.patch.set_facecolor(BG)
    gs = fig.add_gridspec(2, 2, width_ratios=[1.5, 1.0], hspace=0.32, wspace=0.22)
    ax3d = fig.add_subplot(gs[:, 0], projection="3d")
    ax_inf = fig.add_subplot(gs[0, 1])
    ax_cen = fig.add_subplot(gs[1, 1])

    draw_3d(ax3d, d)
    draw_influence(ax_inf, d)
    draw_census(ax_cen, d)

    legend = [Line2D([0], [0], marker="o", ls="", ms=9, mec="none",
                     mfc=prog_color(b, i), label=b.replace("_", " "))
              for i, b in enumerate(d["bases"])]
    leg = fig.legend(handles=legend, loc="lower center", ncol=len(d["bases"]),
                     frameon=True, facecolor=PANEL, edgecolor=GRID, labelcolor=FG,
                     fontsize=9, bbox_to_anchor=(0.5, 0.005),
                     title="neuron colour = dominant gene program")
    leg.get_title().set_color(FG)

    fig.suptitle(
        f"{args.title}  ·  a from-scratch spiking neural network, neurons modulated by a virtual-cell gene-program model",
        color=FG, fontsize=14, y=0.975)
    fig.text(0.03, 0.90,
             f"{d['H']:,} neurons x {d['L']} LIF layers   |   {d['nparam']/1e6:.1f}M params   |   weight RMS {d['rms']:.3f}\n"
             f"traits: {', '.join(d['traits'])}\n"
             f"modulation {'ON' if d['enabled'] else 'off'}  "
             f"({len(d['bases'])} gene programs x {d['n_seeds']} seeds = {d['P_cols']} identity cols)",
             color=MUTED, fontsize=10, va="top", ha="left", linespacing=1.5)
    fig.subplots_adjust(top=0.90, bottom=0.10, left=0.02, right=0.97)
    fig.savefig(args.out, facecolor=fig.get_facecolor())
    print("wrote", args.out)
    print("programs:", d["bases"])
    print("neurons/program:", dict(zip(d["bases"], d["counts"])))


if __name__ == "__main__":
    main()