# Ultralytics YOLO 🚀, AGPL-3.0 license

from functools import partial
from pathlib import Path

import torch

from ultralytics.utils import IterableSimpleNamespace, yaml_load
from ultralytics.utils.checks import check_yaml

from .bot_sort import BOTSORT
from .byte_tracker import BYTETracker

# A mapping of tracker types to corresponding tracker classes
TRACKER_MAP = {"bytetrack": BYTETracker, "botsort": BOTSORT}


def on_predict_start(predictor: object, persist: bool = False) -> None:
    """
    Initialize trackers for object tracking during prediction.

    Args:
        predictor (object): The predictor object to initialize trackers for.
        persist (bool): Whether to persist the trackers if they already exist.

    Raises:
        AssertionError: If the tracker_type is not 'bytetrack' or 'botsort'.

    Examples:
        Initialize trackers for a predictor object:
        >>> predictor = SomePredictorClass()
        >>> on_predict_start(predictor, persist=True)
    """
    if hasattr(predictor, "trackers") and persist:
        return

    tracker = check_yaml(predictor.args.tracker)
    cfg = IterableSimpleNamespace(**yaml_load(tracker))

    if cfg.tracker_type not in {"bytetrack", "botsort"}:
        raise AssertionError(f"Only 'bytetrack' and 'botsort' are supported for now, but got '{cfg.tracker_type}'")

    trackers = []
    for _ in range(predictor.dataset.bs):
        tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
        trackers.append(tracker)
        if predictor.dataset.mode != "stream":  # only need one tracker for other modes.
            break
    predictor.trackers = trackers
    predictor.vid_path = [None] * predictor.dataset.bs  # for determining when to reset tracker on new video


def on_predict_postprocess_end(predictor: object, persist: bool = False) -> None:
    """
    Postprocess detected boxes and update with object tracking.

    Args:
        predictor (object): The predictor object containing the predictions.
        persist (bool): Whether to persist the trackers if they already exist.

    Examples:
        Postprocess predictions and update with tracking
        >>> predictor = YourPredictorClass()
        >>> on_predict_postprocess_end(predictor, persist=True)
    """
    path, im0s = predictor.batch[:2]

    is_obb = predictor.args.task == "obb"
    is_stream = predictor.dataset.mode == "stream"
    for i in range(len(im0s)):
        tracker = predictor.trackers[i if is_stream else 0]
        vid_path = predictor.save_dir / Path(path[i]).name
        if not persist and predictor.vid_path[i if is_stream else 0] != vid_path:
            tracker.reset()
            predictor.vid_path[i if is_stream else 0] = vid_path

        det = (predictor.results[i].obb if is_obb else predictor.results[i].boxes).cpu().numpy()
        if len(det) == 0:
            continue
        tracks = tracker.update(det, im0s[i])
        if len(tracks) == 0:
            continue
        idx = tracks[:, -1].astype(int)
        predictor.results[i] = predictor.results[i][idx]

        update_args = {"obb" if is_obb else "boxes": torch.as_tensor(tracks[:, :-1])}
        predictor.results[i].update(**update_args)


def register_tracker(model: object, persist: bool) -> None:
    """
    Register tracking callbacks to the model for object tracking during prediction.

    Args:
        model (object): The model object to register tracking callbacks for.
        persist (bool): Whether to persist the trackers if they already exist.

    Examples:
        Register tracking callbacks to a YOLO model
        >>> model = YOLOModel()
        >>> register_tracker(model, persist=True)
    """
    model.add_callback("on_predict_start", partial(on_predict_start, persist=persist))
    model.add_callback("on_predict_postprocess_end", partial(on_predict_postprocess_end, persist=persist))
