"""
EFIQA Adapter Weights
Backbone: DINOv3-ViT-L (1024-dim)
Adapter: 1x1 Conv (1024 → 1)
Total: 1025 parameters (float32)

Paper: "EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors"
"""

import numpy as np

W = np.float32([
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])

b = np.float32(+0.02818561)


def forward(features: np.ndarray) -> np.ndarray:
    """
    Args:
        features: (H, W, 1024) DINO patch features
    Returns:
        quality_map: (H, W) quality logits (apply sigmoid for probabilities)
    """
    return features @ W + b