import os, sys, json, re
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import torch
from tqdm import tqdm

from configs.paths import dim_paths
from src.utils import load_model_and_tokenizer, get_device, write_json


DIM = "monitoring"
LIMIT = 40
MAX_WINDOW_TOKENS = 4096

p = dim_paths(DIM)

ACT_PATH = p.ACTIVATIONS
FULL_PATH = p.DIRECTIONS
NO_ORTHO_PATH = os.path.join(p.CHECKPOINT_DIR, "directions_monitoring_noOrtho.pt")
NO_PCA_PATH = os.path.join(p.CHECKPOINT_DIR, "directions_monitoring_noPCA.pt")
SEL_PATH = os.path.join(p.CHECKPOINT_DIR, "selected_layers_monitoring_allmonoV2.json")

GPQA_BASELINE_CANDIDATES = [
    os.path.join(p.RESULTS_DIR, "run_gpqa_d_gpqa_d_s64.jsonl"),
    os.path.join(p.RESULTS_DIR, "run_gpqa_d_gpqa_d_s0.jsonl"),
]


def flat(v):
    if isinstance(v, dict):
        for k in ["direction", "vec", "vector", "v"]:
            if k in v:
                v = v[k]
                break
    if not torch.is_tensor(v):
        v = torch.tensor(v)
    return v.detach().float().view(-1)


def cos(a, b):
    if a is None or b is None:
        return None
    a = flat(a)
    b = flat(b)
    if a.numel() != b.numel():
        return None
    an = a.norm()
    bn = b.norm()
    if an < 1e-8 or bn < 1e-8:
        return None
    return float(torch.dot(a, b) / (an * bn))


def get_dir(blob, L):
    d = blob["directions"]
    if L in d:
        return flat(d[L])
    if str(L) in d:
        return flat(d[str(L)])
    return None


def resolve_layers(model):
    candidates = [
        ("model.layers", lambda m: m.model.layers),
        ("base_model.model.layers", lambda m: m.base_model.model.layers),
        ("transformer.h", lambda m: m.transformer.h),
        ("gpt_neox.layers", lambda m: m.gpt_neox.layers),
    ]
    for name, getter in candidates:
        try:
            layers = getter(model)
            if layers is not None and len(layers) > 0:
                return layers, name
        except Exception:
            pass
    raise RuntimeError("Cannot locate transformer layers.")


def load_gpqa_cots(limit):
    rows = []
    used_path = None

    for path in GPQA_BASELINE_CANDIDATES:
        if not os.path.exists(path):
            continue
        used_path = path
        for line in open(path, encoding="utf-8"):
            if not line.strip():
                continue
            try:
                r = json.loads(line)
            except Exception:
                continue

            try:
                alpha = float(r.get("alpha", -999))
            except Exception:
                alpha = -999

            if abs(alpha - 1.0) > 1e-6:
                continue

            cot = r.get("cot", "")
            if "</think>" not in cot:
                continue

            rows.append({
                "problem_idx": r.get("problem_idx"),
                "cot": cot,
            })

            if len(rows) >= limit:
                break

        if rows:
            break

    return rows, used_path


def avg(xs):
    xs = [x for x in xs if x is not None]
    return sum(xs) / len(xs) if xs else None


def group_summary(name, rs):
    return {
        "group": name,
        "n_layers": len(rs),
        "mean_abs_cos_raw_eos": avg([r["abs_cos_raw_eos"] for r in rs]),
        "mean_abs_cos_noOrtho_eos": avg([r["abs_cos_noOrtho_eos"] for r in rs]),
        "mean_abs_cos_noPCA_eos": avg([r["abs_cos_noPCA_eos"] for r in rs]),
        "mean_abs_cos_full_eos": avg([r["abs_cos_full_eos"] for r in rs]),
        "mean_ortho_overlap_reduction_eos": avg([r["ortho_overlap_reduction_eos"] for r in rs]),
    }


def main():
    print("Loading cached activation/directions...")

    acts_blob = torch.load(ACT_PATH, map_location="cpu", weights_only=False)
    full_blob = torch.load(FULL_PATH, map_location="cpu", weights_only=False)
    no_ortho_blob = torch.load(NO_ORTHO_PATH, map_location="cpu", weights_only=False)
    no_pca_blob = torch.load(NO_PCA_PATH, map_location="cpu", weights_only=False)

    selected = set(int(x) for x in json.load(open(SEL_PATH))["selected_layers"])

    cots, cot_path = load_gpqa_cots(LIMIT)
    if not cots:
        raise FileNotFoundError("No GPQA-D alpha=1 cot file found with </think>. Expected run_gpqa_d_gpqa_d_s64.jsonl.")

    print(f"Using cots from: {cot_path}")
    print(f"n_cots={len(cots)}")

    device = get_device()
    model, tokenizer = load_model_and_tokenizer(device=device)
    layers, layer_path = resolve_layers(model)

    layer_ids = sorted(int(L) for L in acts_blob["per_layer"].keys())
    eos_states = {L: [] for L in layer_ids}
    mean_states = {L: [] for L in layer_ids}

    cache = {}

    handles = []

    def make_hook(L):
        def hook(module, inputs, output):
            h = output[0] if isinstance(output, tuple) else output
            # h: [1, seq, hidden]
            if h.shape[1] < 2:
                return output
            eos_states[L].append(h[0, -1, :].detach().float().cpu())
            mean_states[L].append(h[0, :-1, :].mean(dim=0).detach().float().cpu())
            return output
        return hook

    for L in layer_ids:
        if L < len(layers):
            handles.append(layers[L].register_forward_hook(make_hook(L)))

    model.eval()

    for r in tqdm(cots, desc="forward_eos_windows"):
        cot = r["cot"]
        end = cot.find("</think>") + len("</think>")
        text = cot[:end]

        ids = tokenizer(text, add_special_tokens=False)["input_ids"]
        ids = ids[-MAX_WINDOW_TOKENS:]

        input_ids = torch.tensor([ids], device=device)

        with torch.no_grad():
            model(input_ids=input_ids, use_cache=False)

    for h in handles:
        h.remove()

    eos_dirs = {}
    for L in layer_ids:
        if not eos_states[L] or not mean_states[L]:
            continue
        eos_mean = torch.stack(eos_states[L]).mean(0)
        mean_mean = torch.stack(mean_states[L]).mean(0)
        eos_dirs[L] = eos_mean - mean_mean

    rows = []
    for L_raw, data in acts_blob["per_layer"].items():
        L = int(L_raw)
        acts = data["acts"].float()
        labels = data["labels"]

        pos = acts[labels == 1]
        neg = acts[labels == 0]
        if pos.shape[0] < 5 or neg.shape[0] < 5:
            continue

        raw_md = pos.mean(0) - neg.mean(0)
        eos_dir = eos_dirs.get(L)

        d_full = get_dir(full_blob, L)
        d_no_ortho = get_dir(no_ortho_blob, L)
        d_no_pca = get_dir(no_pca_blob, L)

        c_raw = cos(raw_md, eos_dir)
        c_no_ortho = cos(d_no_ortho, eos_dir)
        c_no_pca = cos(d_no_pca, eos_dir)
        c_full = cos(d_full, eos_dir)

        row = {
            "layer": L,
            "selected": L in selected,
            "n_cots": len(cots),
            "cos_raw_eos": c_raw,
            "cos_noOrtho_eos": c_no_ortho,
            "cos_noPCA_eos": c_no_pca,
            "cos_full_eos": c_full,
            "abs_cos_raw_eos": abs(c_raw) if c_raw is not None else None,
            "abs_cos_noOrtho_eos": abs(c_no_ortho) if c_no_ortho is not None else None,
            "abs_cos_noPCA_eos": abs(c_no_pca) if c_no_pca is not None else None,
            "abs_cos_full_eos": abs(c_full) if c_full is not None else None,
        }

        if row["abs_cos_noOrtho_eos"] is not None and row["abs_cos_full_eos"] is not None:
            row["ortho_overlap_reduction_eos"] = row["abs_cos_noOrtho_eos"] - row["abs_cos_full_eos"]
        else:
            row["ortho_overlap_reduction_eos"] = None

        rows.append(row)

    selected_rows = [r for r in rows if r["selected"]]
    rejected_rows = [r for r in rows if not r["selected"]]

    summary = {
        "note": "EOS / </think> termination-direction geometry diagnostic. termination_direction = mean hidden at </think> token minus mean hidden over preceding window.",
        "cot_path": cot_path,
        "n_cots": len(cots),
        "max_window_tokens": MAX_WINDOW_TOKENS,
        "activation_path": ACT_PATH,
        "full_direction_path": FULL_PATH,
        "no_ortho_path": NO_ORTHO_PATH,
        "no_pca_path": NO_PCA_PATH,
        "selected_layer_file": SEL_PATH,
        "groups": [
            group_summary("selected_layers", selected_rows),
            group_summary("rejected_or_unselected_layers", rejected_rows),
            group_summary("all_layers", rows),
        ],
        "rows": rows,
    }

    out = os.path.join(p.RESULTS_DIR, "orthogonalization_geometry_eos_summary.json")
    write_json(summary, out)

    print("Saved:", out)
    print()
    print("| group | n | raw cos EOS | noOrtho cos EOS | noPCA cos EOS | full cos EOS | ortho reduction |")
    print("|---|---:|---:|---:|---:|---:|---:|")
    for g in summary["groups"]:
        def f(x):
            return "NA" if x is None else f"{x:.4f}"
        print(
            f"| {g['group']} | {g['n_layers']} "
            f"| {f(g['mean_abs_cos_raw_eos'])} "
            f"| {f(g['mean_abs_cos_noOrtho_eos'])} "
            f"| {f(g['mean_abs_cos_noPCA_eos'])} "
            f"| {f(g['mean_abs_cos_full_eos'])} "
            f"| {f(g['mean_ortho_overlap_reduction_eos'])} |"
        )


if __name__ == "__main__":
    main()