"""WD-Ablate: J-space guided PARAMETER-level concept deletion.

Motivation (from H3's negative result + UDS evidence): suppressing the J-lens
readout with a training loss (WD-Train) lowers the observational metric but
leaves causal deletion depth (UDS) flat -- the association survives in the
weights. Methods that DO delete (GradDiff UDS .97, RMU .99) all change weights
substantially, but over-forget or collapse utility.

WD-Ablate instead uses the Jacobian lens only to *localize*, and deletes in
parameter space. For workspace-band layer l, a concept token c is written into
the workspace along direction
    d_{l,c} = J_l^T W_U[c]
(the residual direction whose component determines c's lens logit). We collect
these directions for the forget concepts, remove the part shared with retain
concepts (so general capability is spared), orthonormalize to a rank-k basis Q,
and physically project it out of every matrix that WRITES into the residual at
that layer (attention o_proj, MLP down_proj):
    W <- (I - alpha * Q Q^T) W
This severs the write-path rather than teaching the model to avoid it.

Usage: python wd_ablate.py --model_dir M --lens L --out O [--rank 8] [--alpha 1.0]
"""

import argparse
import json
import sys

import torch
import transformers

sys.path.insert(0, "/workspace/jspace-unlearning/src")
from concepts import FORGET10_AUTHORS, name_parts

RES = "/workspace/jspace-unlearning/results"


def concept_token_ids(tok, names, disc_mask=None):
    """First sub-word tokens of each name/part (leading-space and bare)."""
    ids = set()
    for full in names:
        for v in [full] + name_parts(full):
            for s in (f" {v}", v):
                t = tok(s, add_special_tokens=False).input_ids
                if t:
                    ids.add(t[0])
    return sorted(ids)


def retain_author_names():
    """Retain-side author names (block-frequency extraction, as in build_probes)."""
    import re
    from collections import Counter

    from datasets import load_dataset

    rp = load_dataset("locuslab/TOFU", "retain_perturbed")["train"]
    pat = re.compile(r"\b([A-Z][\w'\-]+(?:\s+[A-Z][\w'\-]+){1,3})\b")
    names = []
    for b in range(20):
        cnt = Counter()
        for i in range(20):
            r = rp[b * 20 + i]
            for m in pat.findall(r["question"] + " " + r["answer"]):
                cnt[m.rstrip("'s") if m.endswith("'s") else m] += 1
        names.append(cnt.most_common(1)[0][0])
    return names


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model_dir", required=True)
    ap.add_argument("--lens", required=True)
    ap.add_argument("--out", required=True)
    ap.add_argument("--band", default="10,11,12,13")
    ap.add_argument("--rank", type=int, default=8, help="concept subspace rank per layer")
    ap.add_argument("--alpha", type=float, default=1.0, help="ablation strength 0..1")
    ap.add_argument("--protect_retain", action="store_true", default=True)
    args = ap.parse_args()

    band = [int(x) for x in args.band.split(",")]
    tok = transformers.AutoTokenizer.from_pretrained(args.model_dir)
    hf = transformers.AutoModelForCausalLM.from_pretrained(
        args.model_dir, dtype=torch.float32
    ).cuda()
    hf.eval()

    ck = torch.load(args.lens, map_location="cpu", weights_only=True)
    J = {l: ck["J"][l].float().cuda() for l in band}

    W_U = hf.lm_head.weight.data  # [V, d]
    forget_ids = torch.tensor(concept_token_ids(tok, FORGET10_AUTHORS), device="cuda")
    retain_ids = torch.tensor(concept_token_ids(tok, retain_author_names()), device="cuda")
    print(f"concepts: {len(forget_ids)} forget tokens, {len(retain_ids)} retain tokens")

    report = {"band": band, "rank": args.rank, "alpha": args.alpha, "layers": {}}
    for l in band:
        # write-directions for forget and retain concepts at this layer
        Df = (J[l].T @ W_U[forget_ids].T).T   # [n_f, d]
        Dr = (J[l].T @ W_U[retain_ids].T).T   # [n_r, d]
        Df = Df / Df.norm(dim=1, keepdim=True).clamp_min(1e-9)
        Dr = Dr / Dr.norm(dim=1, keepdim=True).clamp_min(1e-9)

        if args.protect_retain:
            # remove the retain-shared component: keep only what is specific to forget
            Qr, _ = torch.linalg.qr(Dr.T)          # [d, r_r] orthonormal basis of retain dirs
            Df = Df - (Df @ Qr) @ Qr.T             # orthogonal complement of retain subspace

        # rank-k principal subspace of the forget-specific write directions
        U, S, _ = torch.linalg.svd(Df.T @ Df)
        Q = U[:, : args.rank]                      # [d, k] orthonormal
        energy = float(S[: args.rank].sum() / S.sum())

        P = torch.eye(Q.shape[0], device="cuda") - args.alpha * (Q @ Q.T)

        layer = hf.model.layers[l]
        for mod_name, mod in (("o_proj", layer.self_attn.o_proj),
                              ("down_proj", layer.mlp.down_proj)):
            W = mod.weight.data                    # [d_out=d_model, d_in]
            mod.weight.data = P @ W                # kill the concept write-directions
        report["layers"][l] = {"subspace_energy": round(energy, 4)}
        print(f"  L{l}: ablated rank-{args.rank} subspace (energy {energy:.3f})")

    hf = hf.to(torch.bfloat16)
    hf.save_pretrained(args.out)
    tok.save_pretrained(args.out)
    json.dump(report, open(f"{args.out}/wd_ablate.json", "w"), indent=1)
    print("saved", args.out)


if __name__ == "__main__":
    main()