from pathlib import Path
from typing import Any, Dict, Optional, Union

import mlflow  # type: ignore

# Default configuration
MLFLOW_TRACKING_URI = "sqlite:///mlflow/mlflow.db"
EXPERIMENT_NAME = "multilingual-absa"


def setup_mlflow():
    """Initializes MLflow tracking URI and experiment."""
    # Ensure the directory exists
    Path("mlflow").mkdir(parents=True, exist_ok=True)

    mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
    mlflow.set_experiment(EXPERIMENT_NAME)


def log_training_run(
    params: Dict[str, Any],
    metrics: Dict[str, float],
    model_path: Optional[Union[str, Path]] = None,
    run_name: Optional[str] = None,
) -> str:
    """
    Logs parameters, metrics, and optionally a model artifact to MLflow.

    Args:
        params: Dictionary of hyperparameters or configuration.
        metrics: Dictionary of evaluation metrics.
        model_path: Path to the saved model directory or file.
        run_name: Optional name for the run.

    Returns:
        The ID of the created MLflow run.
    """
    setup_mlflow()

    with mlflow.start_run(run_name=run_name) as run:  # type: ignore[attr-defined]
        mlflow.log_params(params)  # type: ignore[attr-defined]
        mlflow.log_metrics(metrics)  # type: ignore[attr-defined]

        if model_path:
            model_path_obj = Path(model_path)
            if model_path_obj.exists():
                mlflow.log_artifact(str(model_path_obj), artifact_path="model")  # type: ignore[attr-defined]
            else:
                print(f"Warning: Model path {model_path} does not exist. Artifact not logged.")

        return run.info.run_id  # type: ignore[no-any-return]


def get_best_run(metric: str = "eval_macro_f1", ascending: bool = False) -> Optional[Any]:  # type: ignore
    """
    Retrieves the best run from the experiment based on a specific metric.

    Args:
        metric: The metric to sort by.
        ascending: True if a lower metric is better (e.g., loss), False for higher is better (e.g., F1).

    Returns:
        The MLflow Run object for the best run, or None if no runs exist.
    """
    setup_mlflow()

    experiment = mlflow.get_experiment_by_name(EXPERIMENT_NAME)  # type: ignore[attr-defined]
    if not experiment:
        return None

    runs = mlflow.search_runs(  # type: ignore[attr-defined]
        experiment_ids=[experiment.experiment_id],
        order_by=[f"metrics.{metric} {'ASC' if ascending else 'DESC'}"],
        max_results=1,
        output_format="list",
    )

    if not runs:
        return None

    return runs[0]