"""Tier-1 sanity demo: RRT tie + param-efficient KD on the tiny CPU model.

Proves the end-to-end pipeline runs and trains (loss drops) before we touch real
weights. The tiny model is random, so this validates *mechanics*, not recovery.

    uv run python scripts/rrt_tiny_demo.py

Mirrors the Tier-2 flow on the GPU box (scripts/rrt_run.py): precompute teacher
logits with the untied model -> tie in place -> freeze base -> KD the LoRA adapters.
"""

from __future__ import annotations

import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent.parent))

import torch
import torch.nn.functional as F

from looped_laguna import build_tiny_model
from rrt_laguna import (
    TieConfig,
    adjacent_pairs,
    parameter_report,
    set_param_efficient,
    tie_model,
    trainable_parameters,
)


def main() -> None:
    torch.manual_seed(0)
    model = build_tiny_model(num_layers=8)

    gen = torch.Generator().manual_seed(1)
    ids = torch.randint(3, model.config.vocab_size, (4, 32), generator=gen)

    # 1) Teacher = untied model. Cache its logits (the KD target).
    with torch.no_grad():
        teacher_logits = model(input_ids=ids, use_cache=False).logits.detach()
    teacher_probs = torch.softmax(teacher_logits.float(), dim=-1)

    # 2) Tie a couple of adjacent mid-stack MoE pairs (the cheap Tier-2 perturbation).
    pairs = adjacent_pairs([3, 4, 5, 6])
    cfg = TieConfig(pairs=pairs, rank=8, init="lower", lora_init="svd")
    rep_before = parameter_report(model)["total_unique"]
    tie_model(model, cfg)
    rep_after = parameter_report(model)
    print(f"tied pairs={pairs}  init={cfg.init}  rank={cfg.rank}")
    print(f"unique params: {rep_before:,} -> {rep_after['total_unique']:,} "
          f"({100 * (1 - rep_after['total_unique'] / rep_before):.1f}% smaller)")

    # 3) Freeze base, keep LoRA trainable.
    set_param_efficient(model)
    n_train = sum(p.numel() for p in trainable_parameters(model))
    print(f"trainable (LoRA) params: {n_train:,}")

    def kd_loss():
        out = model(input_ids=ids, use_cache=False).logits
        logp = torch.log_softmax(out.float(), dim=-1)
        kl = F.kl_div(logp, teacher_probs, reduction="batchmean")          # forward-KL
        ce = F.cross_entropy(out[:, :-1].reshape(-1, out.shape[-1]), ids[:, 1:].reshape(-1))
        return kl + ce, kl.item(), ce.item()

    opt = torch.optim.Adam(trainable_parameters(model), lr=5e-3)
    print("\nstep   loss     KL       CE")
    for step in range(0, 41):
        loss, kl, ce = kd_loss()
        if step % 10 == 0:
            print(f"{step:>4}  {loss.item():.4f}  {kl:.4f}  {ce:.4f}")
        opt.zero_grad()
        loss.backward()
        opt.step()
    print("\nKD loop reduced the loss -> pipeline works. Ready to point at real weights.")


if __name__ == "__main__":
    main()