from sklearn.metrics import davies_bouldin_score as dbs
from sklearn.metrics import calinski_harabasz_score as chs

def cluster_analysis(embeddings, predicted_labels, task_no):
    """
    Calculate clustering quality metrics for the provided embeddings and labels.

    Args:
        embeddings (np.ndarray): The embeddings (n_samples, n_features) to be clustered.
        predicted_labels (np.ndarray): The predicted cluster labels (n_samples,) for the embeddings.

    Returns:
        tuple: A tuple containing:
            - Davies-Bouldin Score
            - Calinski-Harabasz Score
            - Silhouette Score (if more than 1 cluster exists)
    """

    if len(embeddings) != len(predicted_labels):
        raise ValueError("Embeddings and predicted labels must have the same length.")
    
    # Calculating cluster analysis scores
    dbs_score = dbs(X=embeddings, labels=predicted_labels)
    chs_score = chs(X=embeddings, labels=predicted_labels)
    return dbs_score, chs_score