"""The Probe Stability Score (claim C2).

Both components are computed from IID training activations ONLY (no labels from any OOD
distribution, no new human annotation) — that is the whole point: it must predict OOD
transfer *a priori*.

Component 1 — dispersion
    Train K probes on K bootstrap resamples of the SAME IID training set. Each yields a
    concept direction. dispersion = 1 - mean resultant length of the (unit) directions
    (0 = all directions identical/stable, ->1 = scattered/unstable).

Component 2 — augmentation_consistency
    Build `n_aug` label-preserving augmentations of the IID training set, fit a probe on
    each, and measure the mean cosine of each augmented direction to the original.
    (1 = direction unchanged by surface form, ->0 = direction is surface-form artefact.)

Pre-registered hypothesis: the *combination* predicts OOD drop; dispersion alone is
necessary-but-not-sufficient. `combine()` returns the headline scalar; we also report
each component separately and run a sensitivity sweep over the weights.
"""
from __future__ import annotations

import numpy as np

from config import StabilityConfig
from metrics import mean_class_cosine, subspace_principal_angle
from probes import make_probe


def _resultant_dispersion(directions: list[np.ndarray]) -> float:
    """1 - |mean of unit directions|, averaged over class rows. directions: list of [C,H]."""
    if len(directions) < 2:
        return float("nan")
    stacked = np.stack(directions)                      # [K, C, H]
    norms = np.linalg.norm(stacked, axis=2, keepdims=True)
    norms[norms == 0] = 1.0
    units = stacked / norms                             # [K, C, H]
    mean_vec = units.mean(0)                            # [C, H]
    resultant = np.linalg.norm(mean_vec, axis=1)        # [C]  in [0,1]
    return float(1.0 - resultant.mean())


def dispersion(
    X_iid: np.ndarray,
    y_iid: np.ndarray,
    num_labels: int,
    probe_kind: str = "logreg",
    k: int = 20,
    seed: int = 0,
    **probe_kwargs,
) -> float:
    rng = np.random.default_rng(seed)
    n = len(X_iid)
    dirs = []
    for j in range(k):
        idx = rng.integers(0, n, n)                     # bootstrap resample
        p = make_probe(probe_kind, num_labels=num_labels, seed=seed + j, **probe_kwargs)
        p.fit(X_iid[idx], y_iid[idx])
        if p.direction is None:
            return float("nan")                          # e.g. MLP — no linear direction
        dirs.append(p.direction)
    return _resultant_dispersion(dirs)


def augmentation_consistency(
    X_iid: np.ndarray,
    y_iid: np.ndarray,
    X_aug_list: list[np.ndarray],     # activations of n_aug label-preserving augmentations
    num_labels: int,
    probe_kind: str = "logreg",
    seed: int = 0,
    **probe_kwargs,
) -> float:
    base = make_probe(probe_kind, num_labels=num_labels, seed=seed, **probe_kwargs)
    base.fit(X_iid, y_iid)
    if base.direction is None:
        return float("nan")
    cosines = []
    for Xa in X_aug_list:
        p = make_probe(probe_kind, num_labels=num_labels, seed=seed, **probe_kwargs)
        p.fit(Xa, y_iid)                                 # same labels, augmented inputs
        try:
            cosines.append(mean_class_cosine(base.direction, p.direction))
        except ValueError:
            # multiclass row mismatch -> fall back to subspace agreement
            ang = subspace_principal_angle(base.direction, p.direction)
            cosines.append(float(np.cos(ang)))
    return float(np.mean(cosines)) if cosines else float("nan")


def combine(disp: float, aug_cons: float, cfg: StabilityConfig) -> float:
    """Headline scalar. Higher = more stable = predicted to transfer better OOD.

    stability = w_d * (1 - dispersion) + w_c * augmentation_consistency
    """
    stable_from_disp = 1.0 - disp
    z = cfg.w_dispersion + cfg.w_consistency
    return float((cfg.w_dispersion * stable_from_disp + cfg.w_consistency * aug_cons) / z)


def stability_score(
    X_iid: np.ndarray,
    y_iid: np.ndarray,
    X_aug_list: list[np.ndarray],
    num_labels: int,
    cfg: StabilityConfig,
    probe_kind: str = "logreg",
    seed: int = 0,
    **probe_kwargs,
) -> dict:
    """Return {dispersion, augmentation_consistency, score} for one (model, dataset, layer)."""
    disp = dispersion(X_iid, y_iid, num_labels, probe_kind, cfg.k_bootstrap, seed, **probe_kwargs)
    aug = augmentation_consistency(
        X_iid, y_iid, X_aug_list, num_labels, probe_kind, seed, **probe_kwargs
    )
    return {
        "dispersion": disp,
        "augmentation_consistency": aug,
        "score": combine(disp, aug, cfg),
    }