"""
Facial embedding extraction using FaceNet (InceptionResnetV1).

This module wraps the FaceNet model from facenet_pytorch to produce
512‑dimensional embeddings for detected faces.  It relies on MTCNN
for cropping the largest face in the image.  If no face is detected, it
returns ``None``.
"""

from typing import Optional

import numpy as np
from PIL import Image
import torch

try:
    from facenet_pytorch import MTCNN, InceptionResnetV1
except ImportError as exc:
    raise ImportError(
        "facenet_pytorch is required for embedding extraction. Install it with `pip install facenet-pytorch`."
    ) from exc

_mtcnn: Optional[MTCNN] = None
_resnet: Optional[InceptionResnetV1] = None


def _get_models(device: str = "cpu") -> tuple[MTCNN, InceptionResnetV1]:
    """Initialise and cache MTCNN and InceptionResnet models.

    Parameters
    ----------
    device: str, optional
        Device on which to run the models.  Defaults to ``"cpu"``.

    Returns
    -------
    tuple[MTCNN, InceptionResnetV1]
        The face detector and feature extractor.
    """
    global _mtcnn, _resnet
    if _mtcnn is None:
        _mtcnn = MTCNN(image_size=160, margin=0, select_largest=True, device=device)
    if _resnet is None:
        _resnet = InceptionResnetV1(pretrained="vggface2").eval().to(device)
    return _mtcnn, _resnet


def extract_embedding(image: Image.Image, device: str = "cpu") -> Optional[np.ndarray]:
    """Extract a 512‑dimensional face embedding from an image.

    Parameters
    ----------
    image: PIL.Image.Image
        The input image containing a face.
    device: str, optional
        Device on which to run the models.  Defaults to ``"cpu"``.

    Returns
    -------
    np.ndarray or None
        A numpy array of shape (512,) containing the embedding.  If no face
        is detected, returns ``None``.
    """
    mtcnn, resnet = _get_models(device)
    # Detect face and crop to 160x160.  MTCNN returns a tensor of shape (3, 160, 160).
    face, prob = mtcnn(image, return_prob=True)
    if face is None:
        return None
    # Add batch dimension and send to device.
    face = face.to(device).unsqueeze(0)
    # Extract embedding.
    with torch.no_grad():
        emb = resnet(face)
    # Return as 1D numpy array on CPU.
    return emb.squeeze(0).cpu().numpy()


__all__ = ["extract_embedding"]