"""Read the first frame of a list of clips into a unified IMAGE batch."""

import logging
from typing import List

import numpy as np
import torch

logger = logging.getLogger(__name__)


def read_first_frames(clip_paths: List[str]) -> torch.Tensor:
    """Return (N, H, W, 3) float32 tensor in 0..1. Uses max-dim resize to first clip's size.

    On read failure for any clip, substitute a black frame of the reference size.
    """
    import decord
    decord.bridge.set_bridge("native")

    frames: List[np.ndarray] = []
    ref_h, ref_w = None, None
    for path in clip_paths:
        try:
            vr = decord.VideoReader(path)
            f = vr[0].asnumpy()  # HxWx3 uint8 RGB
            if ref_h is None:
                ref_h, ref_w = f.shape[0], f.shape[1]
            if f.shape[0] != ref_h or f.shape[1] != ref_w:
                import cv2
                f = cv2.resize(f, (ref_w, ref_h), interpolation=cv2.INTER_AREA)
            frames.append(f)
        except Exception as e:
            logger.warning("first-frame read failed for %s: %s", path, e)
            if ref_h is None:
                ref_h, ref_w = 64, 64
            frames.append(np.zeros((ref_h, ref_w, 3), dtype=np.uint8))

    if not frames:
        return torch.zeros((0, 64, 64, 3), dtype=torch.float32)

    stacked = np.stack(frames, axis=0).astype(np.float32) / 255.0
    return torch.from_numpy(stacked)