import logging
import numpy as np

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
)
logger = logging.getLogger(__name__)

G2_PREDICTORS = [
    "hd_score_m2",
    "rejected_app_count",
    "hd_score_m2_connected_max",
    "hd_score_m2_connected_avg",
    "applicant_age_connected_max",
    "applicant_age_connected_avg",
    "account_tel_first_seen_min_conn",
    "account_tel_first_seen_max_conn",
    "account_tel_first_seen_avg_conn",
    "ssn_hash_first_seen_min_conn",
    "ssn_hash_first_seen_avg_conn",
    "account_login_first_seen_min_conn",
    "digital_id_first_seen_max_conn",
    "true_ip_first_seen_min_conn",
    "true_ip_first_seen_max_conn",
    "dist_em_ip_ref_km_min_conn",
    "pct_acc_email_attr_challenged_1_conn",
    "account_login_first_seen_range_conn",
    "account_login_first_seen_stddev_conn",
    "cpu_clock_range_conn",
    "summary_risk_score_max_conn",
]


def _coerce_float(value):
    if value is None:
        return np.nan
    try:
        return float(value)
    except (TypeError, ValueError):
        return np.nan


def pre_processing_g1(results):
    result = results[0]
    dtypes = {
        "hd_score_m1": float,
        "cluster_size_users_v2": float,
        "target_connected_30_sum": float,
        "email_cnt": float,
        "rejected_app_count": float,
        "app_dt_day_cnt": float,
        "hd_score_iso_m2": float
    }
    data = {
        "hd_score_m1": result["hd_score_m1"],
        "cluster_size_users_v2": result["cluster_size_users_v2"],
        "target_connected_30_sum": result["target_connected_30_sum"],
        "email_cnt": result["email_cnt"],
        "rejected_app_count": result["rejected_app_count"],
        "app_dt_day_cnt": result["app_dt_day_cnt"],
        "cluster_size": result["cluster_size"],
        "hd_score_iso_m2": result["hd_score_iso_m2"],
    }

    for col, dtype in dtypes.items():
        if col in data:
            value = str(data[col]).strip()
            data[col] = dtype(value) if value.replace(".", "", 1).isdigit() else None

    return data


def pre_processing_g2(results):
    result = results[0]
    working = dict(result)
    if "rejected_app_count_g2" in working:
        # Always prefer the G2-specific count for G2 preprocessing
        working["rejected_app_count"] = working.get("rejected_app_count_g2")

    data = {}
    for feature in G2_PREDICTORS:
        data[feature] = _coerce_float(working.get(feature))

    data["cluster_size"] = working.get("cluster_size")
    return data


# Backward compatibility alias
pre_processing = pre_processing_g1