"""
Reinforced Disentangled HTML/DOM Representation Learning with Hard-Sample Mining
(Yoon, Buu, & Kim, 2025)
Separates structural DOM tree topology from surface text semantics to expose obfuscated phishing templates.
"""
import numpy as np


class DisentangledDOMEncoder:
    """
    Projects raw HTML/DOM tree metrics into two orthogonal subspaces:
    1. Structural Subspace (z_struct): DOM depth, tag transitions, hidden form/iframe manipulations.
    2. Semantic Subspace (z_sem): Textual token distribution and form field labels.
    Includes Hard-Sample Mining loss weighting to focus gradient updates on deceptive clones.
    """

    def __init__(self, input_dim: int = 24, latent_dim: int = 32, seed: int = 101):
        rng = np.random.default_rng(seed)
        self.W_struct = rng.normal(0, np.sqrt(2.0 / input_dim), size=(input_dim, latent_dim))
        self.b_struct = np.zeros(latent_dim)
        self.W_sem = rng.normal(0, np.sqrt(2.0 / input_dim), size=(input_dim, latent_dim))
        self.b_sem = np.zeros(latent_dim)

    def forward(self, x_dom: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
        """
        Computes disentangled structural and semantic representations and their orthogonality penalty.
        """
        z_struct = np.tanh(x_dom @ self.W_struct + self.b_struct)
        z_sem = np.tanh(x_dom @ self.W_sem + self.b_sem)

        # Orthogonality regularization loss: || Z_struct^T @ Z_sem ||_F^2
        corr_matrix = (z_struct.T @ z_struct) if z_struct.shape[0] == 0 else (z_struct.T @ z_sem) / max(1, x_dom.shape[0])
        ortho_loss = float(np.mean(corr_matrix ** 2))

        z_combined = np.concatenate([z_struct, z_sem], axis=-1)
        return z_combined, z_struct, ortho_loss

    @staticmethod
    def mine_hard_samples(probs: np.ndarray, labels: np.ndarray, margin: float = 0.25) -> np.ndarray:
        """
        Hard-Sample Mining (Yoon et al., 2025): Assigns amplified sample weights (2.5x)
        to borderline or misclassified samples where |y - p| > margin.
        """
        errors = np.abs(labels - probs)
        weights = np.where(errors >= margin, 2.5, 1.0)
        return weights