"""Tier-1 refit analyses (read cache, refit probes). Run on the server pointing PROBE_CACHE
at a seed cache. Reads predictors from results/<predfile> (default predictors_1aug.jsonl, the
A run, since Tier 2 rewrites predictors.jsonl).

    PROBE_CACHE=/root/rivermind-fs/cache_seed0 python analyze_refit.py layer
    PROBE_CACHE=/root/rivermind-fs/cache_seed0 python analyze_refit.py whitened [predfile]

layer    : mean paraphrase rotation per (relative) layer over a sample — is the rotation a
           property of the chosen layer or pervasive?  (defends "not layer-cherry-picked")
whitened : recompute paraphrase rotation with ID-whitened (Mahalanobis) cosine and re-rank the
           predictors — does the circularity / ranking conclusion survive a better metric? (M3)
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import numpy as np
from scipy import linalg

import cache
import metrics
from config import DATASETS, MODELS, EXTRACT, RESULTS_DIR
from probes import make_probe
from run_pipeline import _select_layer, _rank_predictors
from baselines import PREDICTORS

SAMPLE_DS = ["sst2", "ag_news", "counterfact", "emotion", "subj"]


def _read(name):
    p = RESULTS_DIR / name
    return [json.loads(l) for l in p.read_text().splitlines() if l.strip()] if p.exists() else []


def _dir(X, y, nl, W=None):
    if W is not None:
        X = X @ W
    return make_probe("logreg", num_labels=nl, seed=0, max_iter=500).fit(X, y).direction


def layer_curve(models=None, dsets=None):
    models = models or list(MODELS)
    dsets = dsets or SAMPLE_DS
    print("\n########## LAYER ROBUSTNESS: paraphrase rotation by relative depth ##########")
    buckets = {q: [] for q in [0.1, 0.25, 0.5, 0.75, 0.9, 1.0]}
    for m in models:
        for ds in dsets:
            if not (cache.exists(m, ds, "train") and cache.exists(m, ds, "paraphrase")):
                continue
            nl = DATASETS[ds].num_labels
            meta = cache.load_meta(m, ds, "train")
            Xtr, ytr, _ = cache.load_shard(m, ds, "train")
            Xpa, ypa, _ = cache.load_shard(m, ds, "paraphrase")
            nl_layers = meta["n_layers"]
            for q in buckets:
                L = max(1, min(nl_layers - 1, int(q * (nl_layers - 1))))
                da = _dir(np.asarray(Xtr[:, L, :], np.float32), ytr, nl)
                db = _dir(np.asarray(Xpa[:, L, :], np.float32), ypa, nl)
                buckets[q].append(_rotmag(da, db))
    print(f"{'rel-depth':>10s}{'mean rotation (1-cos)':>24s}{'n':>5s}")
    for q in sorted(buckets):
        v = [x for x in buckets[q] if x == x]
        if v:
            print(f"{q:>10.2f}{np.mean(v):>24.3f}{len(v):>5d}")
    print("(rotation present across depths -> not an artefact of the IID-selected layer)")


def _rotmag(da, db):
    try:
        return 1.0 - metrics.mean_class_cosine(da, db)
    except Exception:
        return 1.0 - float(np.cos(metrics.subspace_principal_angle(da, db)))


def whitened_target(predfile="predictors_1aug.jsonl"):
    evals = _read("eval.jsonl")
    preds = {(p["model"], p["dataset"], p["seed"]): p["predictors"] for p in _read(predfile)}
    print(f"\n########## METRIC ABLATION: ID-whitened paraphrase rotation as target ##########")
    print(f"(predictors from {predfile}; recomputing whitened rotation by refit on cache)")
    rows = []
    for e in evals:
        if e.get("probe") != "logreg":
            continue
        m, ds, seed = e["model"], e["dataset"], e["seed"]
        if (m, ds, seed) not in preds:
            continue
        if not (cache.exists(m, ds, "train") and cache.exists(m, ds, "paraphrase")):
            continue
        nl = DATASETS[ds].num_labels
        layer = e.get("layer") or _select_layer(m, ds, nl, seed)
        Xtr, ytr, _ = cache.load_shard(m, ds, "train", layer=layer)
        Xpa, ypa, _ = cache.load_shard(m, ds, "paraphrase", layer=layer)
        C = np.cov(np.asarray(Xtr, np.float64), rowvar=False) + 1e-3 * np.eye(Xtr.shape[1])
        W = linalg.fractional_matrix_power(C, -0.5).real.astype(np.float32)
        da = _dir(Xtr, ytr, nl, W=W)
        db = _dir(Xpa, ypa, nl, W=W)
        rows.append({"concept": DATASETS[ds].concept, "wrot": _rotmag(da, db), **preds[(m, ds, seed)]})
    if len(rows) < 8:
        print(f"only {len(rows)} configs — skipped")
        return
    _rank_predictors(rows, "wrot", f"WHITENED paraphrase rotation (n={len(rows)})")


if __name__ == "__main__":
    mode = sys.argv[1] if len(sys.argv) > 1 else "layer"
    if mode == "layer":
        layer_curve()
    elif mode == "whitened":
        whitened_target(sys.argv[2] if len(sys.argv) > 2 else "predictors_1aug.jsonl")