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

import torch
from configs.paths import dim_paths
from src.utils import read_json, write_json

DIM = "monitoring"
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")

def flat(v):
    if isinstance(v, dict):
        v = v.get("direction", v.get("vec", v.get("vector", v)))
    if isinstance(v, torch.Tensor):
        v = v.detach().float()
    else:
        v = torch.tensor(v).float()
    return v.view(-1)

def cos(a, b):
    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

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)
sel = read_json(SEL_PATH)
selected = set(int(x) for x in sel["selected_layers"])

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)
    mu = acts.mean(0)

    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)

    row = {
        "layer": L,
        "selected": L in selected,
        "n_pos": int(pos.shape[0]),
        "n_neg": int(neg.shape[0]),
        "raw_norm": float(raw_md.norm()),
        "mu_norm": float(mu.norm()),

        "cos_raw_mu": cos(raw_md, mu),
        "cos_noOrtho_mu": cos(d_no_ortho, mu) if d_no_ortho is not None else None,
        "cos_noPCA_mu": cos(d_no_pca, mu) if d_no_pca is not None else None,
        "cos_full_mu": cos(d_full, mu) if d_full is not None else None,

        "abs_cos_raw_mu": abs(cos(raw_md, mu)) if cos(raw_md, mu) is not None else None,
        "abs_cos_noOrtho_mu": abs(cos(d_no_ortho, mu)) if d_no_ortho is not None and cos(d_no_ortho, mu) is not None else None,
        "abs_cos_noPCA_mu": abs(cos(d_no_pca, mu)) if d_no_pca is not None and cos(d_no_pca, mu) is not None else None,
        "abs_cos_full_mu": abs(cos(d_full, mu)) if d_full is not None and cos(d_full, mu) is not None else None,
    }

    if row["abs_cos_noOrtho_mu"] is not None and row["abs_cos_full_mu"] is not None:
        row["ortho_overlap_reduction_mu"] = row["abs_cos_noOrtho_mu"] - row["abs_cos_full_mu"]
    else:
        row["ortho_overlap_reduction_mu"] = None

    rows.append(row)

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_mu": avg([r["abs_cos_raw_mu"] for r in rs]),
        "mean_abs_cos_noOrtho_mu": avg([r["abs_cos_noOrtho_mu"] for r in rs]),
        "mean_abs_cos_noPCA_mu": avg([r["abs_cos_noPCA_mu"] for r in rs]),
        "mean_abs_cos_full_mu": avg([r["abs_cos_full_mu"] for r in rs]),
        "mean_ortho_overlap_reduction_mu": avg([r["ortho_overlap_reduction_mu"] for r in rs]),
    }

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

summary = {
    "note": "Geometry diagnostic from cached contrastive activations. cos_mu tests overlap with general reasoning mean.",
    "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_mu_summary.json")
write_json(summary, out)

print("Saved:", out)
print()
print("| group | n | raw cos μ | noOrtho cos μ | noPCA cos μ | full cos μ | ortho reduction |")
print("|---|---:|---:|---:|---:|---:|---:|")
for g in summary["groups"]:
    print(
        f"| {g['group']} | {g['n_layers']} "
        f"| {g['mean_abs_cos_raw_mu']:.4f} "
        f"| {g['mean_abs_cos_noOrtho_mu']:.4f} "
        f"| {g['mean_abs_cos_noPCA_mu']:.4f} "
        f"| {g['mean_abs_cos_full_mu']:.4f} "
        f"| {g['mean_ortho_overlap_reduction_mu']:.4f} |"
    )