"""Probes.

All probes share a tiny interface so the pipeline is agnostic to type:

    probe = make_probe("logreg", num_labels=2, seed=0).fit(X_train, y_train)
    y_hat = probe.predict(X_eval)
    D = probe.direction          # [C, H] concept-direction matrix, or None (MLP)

`direction` is what claim C1/C2 measure rotation on. For LogReg it is the weight matrix;
for mass-mean it is (class_centroid - global_mean). MLP has no single linear direction, so
it returns None and the eval step skips rotation for it (still reports accuracy).
"""
from __future__ import annotations

import numpy as np
from sklearn.linear_model import LogisticRegression


class BaseProbe:
    direction: np.ndarray | None = None

    def fit(self, X: np.ndarray, y: np.ndarray) -> "BaseProbe":
        raise NotImplementedError

    def predict(self, X: np.ndarray) -> np.ndarray:
        raise NotImplementedError


class LogRegProbe(BaseProbe):
    """L2-regularised multinomial logistic regression (the default diagnostic probe)."""

    def __init__(self, num_labels: int, l2: float = 1.0, seed: int = 0, max_iter: int = 500, **_):
        self.num_labels = num_labels
        self.C = 1.0 / max(l2, 1e-8)
        self.seed = seed
        self.max_iter = max_iter
        self._clf: LogisticRegression | None = None

    def fit(self, X, y):
        self._clf = LogisticRegression(
            C=self.C, max_iter=self.max_iter, random_state=self.seed,
        ).fit(X, y)
        coef = self._clf.coef_                       # [1,H] binary, [C,H] multiclass
        self.direction = coef.copy()
        return self

    def predict(self, X):
        return self._clf.predict(X)


class MassMeanProbe(BaseProbe):
    """Difference-of-means / mass-mean probe. Predicts by nearest class centroid.

    Often *more* OOD-robust than LogReg (a hypothesis under claim C3).
    """

    def __init__(self, num_labels: int, **_):
        self.num_labels = num_labels
        self._centroids: np.ndarray | None = None
        self._classes: np.ndarray | None = None

    def fit(self, X, y):
        y = np.asarray(y)
        self._classes = np.unique(y)
        centroids = np.stack([X[y == c].mean(0) for c in self._classes])  # [C,H]
        self._centroids = centroids
        self.direction = centroids - X.mean(0, keepdims=True)             # [C,H]
        return self

    def predict(self, X):
        # nearest centroid (euclidean)
        d = np.linalg.norm(X[:, None, :] - self._centroids[None, :, :], axis=2)  # [N,C]
        return self._classes[np.argmin(d, axis=1)]


class MLPProbe(BaseProbe):
    """2-layer MLP probe (torch). No single linear direction -> direction stays None."""

    def __init__(self, num_labels: int, hidden: int = 128, epochs: int = 50,
                 lr: float = 1e-3, seed: int = 0, **_):
        self.num_labels = num_labels
        self.hidden = hidden
        self.epochs = epochs
        self.lr = lr
        self.seed = seed
        self._model = None
        self._device = None

    def fit(self, X, y):
        import torch
        import torch.nn as nn

        torch.manual_seed(self.seed)
        self._device = "cuda" if torch.cuda.is_available() else "cpu"
        Xt = torch.tensor(np.asarray(X), dtype=torch.float32, device=self._device)
        yt = torch.tensor(np.asarray(y), dtype=torch.long, device=self._device)
        self._model = nn.Sequential(
            nn.Linear(Xt.shape[1], self.hidden), nn.ReLU(),
            nn.Linear(self.hidden, self.num_labels),
        ).to(self._device)
        opt = torch.optim.Adam(self._model.parameters(), lr=self.lr)
        lossfn = nn.CrossEntropyLoss()
        self._model.train()
        for _ in range(self.epochs):
            opt.zero_grad()
            loss = lossfn(self._model(Xt), yt)
            loss.backward()
            opt.step()
        return self

    def predict(self, X):
        import torch
        self._model.eval()
        with torch.no_grad():
            Xt = torch.tensor(np.asarray(X), dtype=torch.float32, device=self._device)
            return self._model(Xt).argmax(1).cpu().numpy()


class ControlTaskProbe(LogRegProbe):
    """Hewitt & Liang control-task probe: fit on RANDOM labels to measure selectivity.

    selectivity = real_task_acc - control_task_acc. A high-selectivity probe reflects the
    representation, not probe memorisation. We report this to pre-empt the standard
    "your probe is just memorising" reviewer attack.
    """

    def fit(self, X, y):
        rng = np.random.default_rng(self.seed)
        y_rand = rng.permutation(np.asarray(y))     # destroy structure, keep label marginal
        return super().fit(X, y_rand)


_REGISTRY = {
    "logreg": LogRegProbe,
    "mass_mean": MassMeanProbe,
    "mlp": MLPProbe,
    "control": ControlTaskProbe,
}


def make_probe(kind: str, num_labels: int, **kwargs) -> BaseProbe:
    if kind not in _REGISTRY:
        raise KeyError(f"unknown probe '{kind}', choose from {list(_REGISTRY)}")
    return _REGISTRY[kind](num_labels=num_labels, **kwargs)