"""
Phase 0B: single-target bottleneck trainer for kinematics-vs-mechanics.

Same sender architecture as `_killer_experiment.py` (4-agent × 2-head
Gumbel-Softmax, K=5, HIDDEN_DIM=128) but the receiver is a 3-way classifier
that reads a single message and predicts the binned target class.

Takes `--target <name>` and `--dataset <collision|ramp>` and pulls the
corresponding `_bin` column from the label .npz. Feature file and per-dataset
config are selected by `--backbone <vjepa2|dinov2>`.

Run:
  /usr/bin/python3 _kinematics_train.py --dataset collision --backbone vjepa2 \
      --target mass --seed 0
"""
import argparse, json, math, os, sys, time, warnings
warnings.filterwarnings("ignore")

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

sys.path.insert(0, os.path.dirname(__file__))
from _killer_experiment import (
    TemporalEncoder, DiscreteSender, DiscreteMultiSender
)

DEVICE = torch.device("mps" if torch.backends.mps.is_available() else "cpu")

HIDDEN_DIM = 128
VOCAB_SIZE = 5
N_HEADS = 2
N_AGENTS = 4
MSG_DIM = N_AGENTS * N_HEADS * VOCAB_SIZE   # 40
N_POS = N_AGENTS * N_HEADS                   # 8
BATCH_SIZE = 32
SENDER_LR = 1e-3
RECEIVER_LR = 3e-3
EARLY_STOP_PATIENCE = 50          # matches exp4_faithfulness early-stop style


# ── Classifier receiver ──

class ClassifierReceiver(nn.Module):
    """Reads one message and predicts 3 class logits."""

    def __init__(self, msg_dim, hidden_dim, n_classes=3):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(msg_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim // 2), nn.ReLU(),
            nn.Linear(hidden_dim // 2, n_classes),
        )

    def forward(self, msg):
        return self.net(msg)


# ── Dataset / label loading ──

FEATURE_FILES = {
    ("collision", "vjepa2"): "results/vjepa2_collision_pooled.pt",   # [600, 24, 1024]
    ("collision", "dinov2"): "results/collision_dinov2_features.pt", # [600, 24, 384]
    ("ramp", "vjepa2"): "results/vjepa2_ramp_temporal.pt",           # [300, 16, 1024]
    ("ramp", "dinov2"): "results/phase54b_dino_features.pt",         # [300, 8, 384]
}

LABEL_FILES = {
    "collision": "results/kinematics_vs_mechanics/labels_collision.npz",
    "ramp": "results/kinematics_vs_mechanics/labels_ramp.npz",
}


def load_features(dataset, backbone):
    path = FEATURE_FILES[(dataset, backbone)]
    d = torch.load(path, weights_only=False, map_location="cpu")
    feat = d["features"].float()
    return feat


def load_labels(dataset, target):
    """Return bin labels (int [N])."""
    z = np.load(LABEL_FILES[dataset])
    key = f"{target}_bin"
    if key not in z:
        raise ValueError(f"Unknown target '{target}' for dataset '{dataset}'. "
                          f"Available: {[k.replace('_bin', '') for k in z.files if k.endswith('_bin')]}")
    return z[key].astype(np.int64)


# ── Training loop ──

def train_one(dataset, backbone, target, seed,
              n_epochs=150, verbose=False):
    """
    Returns dict: task_acc, posdis, elapsed_s, seed, dataset, backbone, target.
    """
    t0 = time.time()
    feat = load_features(dataset, backbone)          # [N, T, D]
    labels = load_labels(dataset, target)            # [N]

    N, nf, dim = feat.shape
    fpa = max(1, nf // N_AGENTS)
    agent_views = [feat[:, (i * fpa):(i + 1) * fpa, :] for i in range(N_AGENTS)]

    torch.manual_seed(seed)
    np.random.seed(seed)

    # 80/20 train / holdout split (stratified by target class)
    rng = np.random.RandomState(seed * 1000 + 42)
    train_ids = []
    holdout_ids = []
    for c in np.unique(labels):
        ids_c = np.where(labels == c)[0]
        rng.shuffle(ids_c)
        split = max(1, len(ids_c) // 5)
        holdout_ids.extend(ids_c[:split])
        train_ids.extend(ids_c[split:])
    train_ids = np.array(train_ids)
    holdout_ids = np.array(holdout_ids)

    n_classes = int(labels.max()) + 1
    chance = 1.0 / n_classes

    senders = [DiscreteSender(TemporalEncoder(HIDDEN_DIM, dim, fpa),
                                HIDDEN_DIM, VOCAB_SIZE, N_HEADS)
               for _ in range(N_AGENTS)]
    sender = DiscreteMultiSender(senders).to(DEVICE)
    receivers = [ClassifierReceiver(MSG_DIM, HIDDEN_DIM, n_classes).to(DEVICE)
                 for _ in range(3)]
    so = torch.optim.Adam(sender.parameters(), lr=SENDER_LR)
    ros = [torch.optim.Adam(r.parameters(), lr=RECEIVER_LR) for r in receivers]

    labels_dev = torch.tensor(labels, dtype=torch.long).to(DEVICE)
    me = math.log(VOCAB_SIZE)
    n_batches = max(1, len(train_ids) // BATCH_SIZE)

    best_acc, best_state, best_ep = 0.0, None, 0

    for ep in range(n_epochs):
        if ep - best_ep > EARLY_STOP_PATIENCE and best_acc > chance + 0.05:
            break
        if ep > 0 and ep % 40 == 0:
            for i in range(len(receivers)):
                receivers[i] = ClassifierReceiver(MSG_DIM, HIDDEN_DIM, n_classes).to(DEVICE)
                ros[i] = torch.optim.Adam(receivers[i].parameters(), lr=RECEIVER_LR)

        sender.train(); [r.train() for r in receivers]
        tau = 3.0 + (1.0 - 3.0) * ep / max(1, n_epochs - 1)
        hard = ep >= 30

        rng_ep = np.random.RandomState(seed * 10000 + ep)
        perm = rng_ep.permutation(train_ids)

        for b in range(n_batches):
            batch_ids = perm[b * BATCH_SIZE:(b + 1) * BATCH_SIZE]
            if len(batch_ids) < 4:
                continue
            views = [v[batch_ids].to(DEVICE) for v in agent_views]
            target_batch = labels_dev[batch_ids]

            msg, logits_list = sender(views, tau=tau, hard=hard)
            loss = torch.tensor(0.0, device=DEVICE)
            for r in receivers:
                pred = r(msg)
                loss = loss + F.cross_entropy(pred, target_batch)
            loss = loss / len(receivers)

            # Entropy regularisation on each position
            for lg in logits_list:
                lp = F.log_softmax(lg, -1)
                p = lp.exp().clamp(min=1e-8)
                ent = -(p * lp).sum(-1).mean()
                if ent / me < 0.1:
                    loss = loss - 0.03 * ent

            if torch.isnan(loss):
                so.zero_grad(); [o.zero_grad() for o in ros]
                continue
            so.zero_grad(); [o.zero_grad() for o in ros]
            loss.backward()
            torch.nn.utils.clip_grad_norm_(sender.parameters(), 1.0)
            so.step(); [o.step() for o in ros]

        if ep % 50 == 0 and DEVICE.type == "mps":
            torch.mps.empty_cache()

        # Evaluation every 10 epochs
        if (ep + 1) % 10 == 0 or ep == 0:
            sender.eval(); [r.eval() for r in receivers]
            with torch.no_grad():
                v_ho = [v[holdout_ids].to(DEVICE) for v in agent_views]
                msg_ho, _ = sender(v_ho)
                target_ho = labels_dev[holdout_ids]
                best_per_recv = 0.0
                for r in receivers:
                    preds = r(msg_ho).argmax(-1)
                    acc = (preds == target_ho).float().mean().item()
                    best_per_recv = max(best_per_recv, acc)
                if verbose and ep % 50 == 0:
                    print(f"      ep={ep}  holdout_acc={best_per_recv:.1%}",
                          flush=True)
                if best_per_recv > best_acc:
                    best_acc = best_per_recv
                    best_ep = ep
                    best_state = {k: v.cpu().clone()
                                   for k, v in sender.state_dict().items()}

    if best_state:
        sender.load_state_dict(best_state)
    sender.eval()

    # Extract tokens at best state for posdis computation
    with torch.no_grad():
        toks_list = []
        for i in range(0, N, BATCH_SIZE):
            vs = [v[i:i + BATCH_SIZE].to(DEVICE) for v in agent_views]
            _, logits = sender(vs)
            toks_list.append(np.stack([l.argmax(-1).cpu().numpy() for l in logits], 1))
    tokens = np.concatenate(toks_list, 0)

    # Simple PosDis (target only, MI per position)
    try:
        from _killer_experiment import positional_disentanglement
        attrs = np.stack([labels, labels], axis=1)
        posdis, _, _ = positional_disentanglement(tokens, attrs, VOCAB_SIZE)
    except Exception as e:
        posdis = 0.0

    return {
        "dataset": dataset,
        "backbone": backbone,
        "target": target,
        "seed": int(seed),
        "n_classes": int(n_classes),
        "chance": float(chance),
        "task_acc": float(best_acc),
        "posdis": float(posdis),
        "elapsed_s": float(time.time() - t0),
        "best_ep": int(best_ep),
        "n_train": int(len(train_ids)),
        "n_holdout": int(len(holdout_ids)),
    }


if __name__ == "__main__":
    ap = argparse.ArgumentParser()
    ap.add_argument("--dataset", required=True, choices=["collision", "ramp"])
    ap.add_argument("--backbone", required=True, choices=["vjepa2", "dinov2"])
    ap.add_argument("--target", required=True)
    ap.add_argument("--seed", type=int, default=0)
    ap.add_argument("--epochs", type=int, default=150)
    ap.add_argument("--verbose", action="store_true")
    args = ap.parse_args()
    r = train_one(args.dataset, args.backbone, args.target, args.seed,
                   n_epochs=args.epochs, verbose=args.verbose)
    print(json.dumps(r, indent=2), flush=True)