import pathlib as pl
import time
import gc
from threading import Event
from typing import Any, Dict

import torch
from bayesian_torch.models.dnn_to_bnn import dnn_to_bnn, get_kl_loss  # type: ignore[import-untyped]
from torch import nn, optim
from torch.utils.data import DataLoader

import model.dataset as ds
import model.training as tn
from model.cnn import CNN3D
from util.progress import ProgressTracker
from util.ui_logger import PipelineLogger


def _release_torch_memory(device: str) -> None:
    gc.collect()
    if device.startswith("cuda"):
        torch.cuda.empty_cache()
    elif device.startswith("mps") and hasattr(torch, "mps"):
        torch.mps.empty_cache()


def _check_control_events(
    stop_event: Event | None,
    pause_event: Event | None,
) -> None:
    if stop_event is not None and stop_event.is_set():
        raise InterruptedError("Pipeline execution stopped by user.")
    while pause_event is not None and pause_event.is_set():
        time.sleep(0.5)
        if stop_event is not None and stop_event.is_set():
            raise InterruptedError("Pipeline execution stopped by user while paused.")


def train_bayesian_task(
    track: ProgressTracker,
    log: PipelineLogger,
    config: Dict[str, Any],
    state: Dict[str, Any],
) -> None:
    log.info("Starting bayesian model training...")
    events = state.get("events")
    stop_event = events.get("stop") if isinstance(events, dict) else None
    pause_event = events.get("pause") if isinstance(events, dict) else None

    # Get the loaded datesets from the state
    train_loader: DataLoader[ds.ADNIDataset] = state["train_loader"]
    val_loader: DataLoader[ds.ADNIDataset] = state["val_loader"]
    test_loader: DataLoader[ds.ADNIDataset] = state["test_loader"]

    bayesian_models_path = pl.Path(config["work_dir"]) / "bayesian_models"

    # Set up intermediate model directory
    intermediate_model_dir = bayesian_models_path / "intermediate_models"
    if not intermediate_model_dir.exists():
        intermediate_model_dir.mkdir(parents=True, exist_ok=True)
    log.info(f"Intermediate models will be saved to {intermediate_model_dir}")

    train_progress = track.get_sub_tracker("Training Progress")
    track.update(total=config["training"]["ensemble_size"], advance=0)

    bnn_prior_parameters = {
        "prior_mu": 0.0,
        "prior_sigma": 1.0,
        "posterior_mu_init": 0.0,
        "posterior_rho_init": -3.0,
        "type": "Reparameterization",
        "moped_enable": False,
        "moped_delta": 0.5,
    }

    for model_num in range(config["training"]["ensemble_size"]):
        _check_control_events(stop_event=stop_event, pause_event=pause_event)
        log.info(
            f"Training model {model_num + 1}/{config['training']['ensemble_size']}..."
        )
        model = (
            CNN3D(
                image_channels=config["data"]["image_channels"],
                clin_data_channels=config["data"]["clin_data_channels"],
                num_classes=config["data"]["num_classes"],
                droprate=config["training"]["droprate"],
            )
            .float()
            .to(config["training"]["device"])
        )
        dnn_to_bnn(model, bnn_prior_parameters)

        optimizer = optim.Adam(
            model.parameters(), lr=config["training"]["learning_rate"]
        )
        criterion = nn.BCELoss()

        model, history = tn.train_model_bayesian(
            log=log,
            progress=train_progress,
            model=model,
            train_loader=train_loader,
            val_loader=val_loader,
            optimizer=optimizer,
            criterion=criterion,
            num_epochs=config["training"]["num_epochs"],
            output_path=bayesian_models_path,
            get_kl_loss=get_kl_loss,
            stop_event=stop_event,
            pause_event=pause_event,
        )
        state.setdefault("bayesian_histories", []).append(history)

        test_loss, test_acc = tn.test_model_bayesian(
            model=model,
            test_loader=test_loader,
            criterion=criterion,
            get_kl_loss=get_kl_loss,
            progress=track.get_sub_tracker("Testing Progress"),
            log=log,
            stop_event=stop_event,
            pause_event=pause_event,
        )

        log.info(
            f"Run {model_num + 1}/{config['training']['ensemble_size']} - "
            f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_acc:.4f}"
        )

        model_save_path = bayesian_models_path / f"model_{model_num + 1}.pt"
        torch.save(model.state_dict(), model_save_path)
        log.info(f"Model saved to {model_save_path}")
        del history
        del optimizer
        del criterion
        del model
        _release_torch_memory(config["training"]["device"])
        track.update(total=config["training"]["ensemble_size"], advance=1)

    log.info("All Bayesian models trained and saved successfully.")