"""Collapse monitor: track latent variance, effective rank, cross-modal cosine sim.

Hard-stop criterion (per RESEARCH_DEVELOPMENT.md Pitfall 3):
    mean cosine sim > 0.99 for 500 consecutive logged steps -> abort
"""
from __future__ import annotations

import collections
from dataclasses import dataclass, field

import torch


def effective_rank(z: torch.Tensor, eps: float = 1e-9) -> float:
    """Entropy-based effective rank of the covariance matrix."""
    z = z - z.mean(dim=0, keepdim=True)
    cov = (z.t() @ z) / max(z.shape[0] - 1, 1)
    eig = torch.linalg.eigvalsh(cov.float())
    eig = torch.clamp(eig, min=0)
    total = eig.sum() + eps
    p = eig / total
    entropy = -(p * torch.log(p + eps)).sum()
    return float(torch.exp(entropy).item())


def cross_modal_cosine(z_a: torch.Tensor, z_b: torch.Tensor) -> float:
    a = torch.nn.functional.normalize(z_a, dim=-1)
    b = torch.nn.functional.normalize(z_b, dim=-1)
    return float((a * b).sum(dim=-1).mean().item())


@dataclass
class CollapseMonitor:
    window: int = 500
    threshold: float = 0.99
    history: collections.deque = field(default_factory=lambda: collections.deque(maxlen=500))

    def update(self, cosine: float) -> bool:
        self.history.append(cosine)
        if len(self.history) < self.window:
            return False
        return all(c > self.threshold for c in self.history)