import json
import sys

VARIABLE_TO_CODE_MAP = {
    "agg901": "00H86",
    "agg902": "00H86",
    "agg908": "00H86",
    "agg911": "00H86",
    "all231": "00ASH",
    "at28a": "00WR3",
    "at28b": "00WR3",
    "at36s": "00WR3",
    "balmag01": "00H88",
    "balmag02": "00H88",
    "balmag04": "00AI5",
    "bc102s": "00WR3",
    "bc104s": "00WR3",
    "bc107s": "00WR3",
    "bc20s": "00WR3",
    "bc21s": "00WR3",
    "bc28s": "00WR3",
    "bc36s": "00WR3",
    "bc97a": "00WR3",
    "bc98a": "00WR3",
    "bkc14": "00ASF",
    "bkc321": "00ASI",
    "bkc322": "00ASI",
    "bkc323": "00ASI",
    "bkc324": "00ASI",
    "bkc327": "00ASI",
    "bkc328": "00ASI",
    "bkc84": "00ASF",
    "br20s": "00WR3",
    "ct319": "00AI9",
    "ct320": "00AI9",
    "cta11": "00AI9",
    "cta20": "00AI9",
    "cta21": "00AI9",
    "cv13": "00V26",
    "cv17": "00V26",
    "cv21": "00V26",
    "cv25": "00V26",
    "duemag01": "00AI5",
    "fi21s": "00WR3",
    "fi34s": "00WR3",
    "fi35s": "00WR3",
    "g051s": "00WR3",
    "g102s": "00WR3",
    "g105s": "00WR3",
    "g106s": "00WR3",
    "g201a": "00WR3",
    "g221d": "00WR3",
    "g232s": "00WR3",
    "g242b": "00WR3",
    "g250a": "00WR3",
    "g403s": "00WR3",
    "g405s": "00WR3",
    "g408s": "00WR3",
    "g411s": "00WR3",
    "g416s": "00WR3",
    "g417s": "00WR3",
    "g960s": "00WR3",
    "g990s": "00WR3",
    "index01": "00V53",
    "index02": "00V53",
    "mnpmag03": "00AI5",
    "mt20s": "00WR3",
    "mt34s": "00WR3",
    "p02d": "00WBO",
    "p02h": "00WBO",
    "paymnt08": "00H91",
    "pb28s": "00WR3",
    "pb34s": "00WR3",
    "re102s": "00WR3",
    "re28s": "00WR3",
    "re36s": "00WR3",
    "ret12": "00ASF",
    "rev12": "00ASF",
    "rev13": "00ASF",
    "rev14": "00ASF",
    "rev201": "00ASG",
    "rev202": "00ASG",
    "rev203": "00ASG",
    "rev223": "00ASG",
    "rev224": "00ASG",
    "rev225": "00ASG",
    "rev231": "00ASH",
    "rev232": "00ASH",
    "rev233": "00ASH",
    "rev252": "00ASH",
    "rev253": "00ASH",
    "rev321": "00ASI",
    "rev322": "00ASI",
    "rev54": "00ASF",
    "rev84": "00ASF",
    "rle904": "00ASJ",
    "rvlr75": "00WP4",
    "rvlr77": "00WP4",
    "s004s": "00WR3",
    "s114s": "00WR3",
    "st32s": "00WR3",
    "trv01": "00H87",
    "trv02": "00H87",
    "us21s": "00WR3",
    "us34s": "00WR3",
    "utlmag01": "00AI5",
    "utlmag02": "00AI5",
    "utlmag03": "00AI5",
    "utlmag04": "00AI5",
    "walshr02": "00H90",
}

SCORE_TO_CODE_MAP = {
    "evtg04": "001NN",
    "eads66": "00WDC"
}


def safe_get(d, *keys):
    """Attempts both snake_case and camelCase keys in a dict, handles None safely."""
    if not isinstance(d, dict):
        return {}
    for key in keys:
        if key in d:
            return d[key]
    return {}


def extract_model_variables(creditBureau: dict) -> dict:
    if not creditBureau:
        return {}
    variable_code_map = VARIABLE_TO_CODE_MAP
    score_code_map = SCORE_TO_CODE_MAP
    # Step 0: Extract application_id
    extracted = {}

    # Step 1: Locate add-on products with fallback casing
    credit_bureau = safe_get(creditBureau, "creditBureau", "credit_bureau")

    subject_node = safe_get(
        safe_get(
            safe_get(credit_bureau, "product"),
            "subject"
        ),
        "subject_record", "subjectRecord"
    )

    add_ons = safe_get(subject_node, "add_on_product", "addOnProduct")
    value_map = {}


    # Step 2: Flatten values
    for product in add_ons if isinstance(add_ons, list) else []:
        code = product.get("code")
        score_model = safe_get(product, "score_model", "scoreModel")

        # 2a. Flatten characteristics (already done)
        characteristics = score_model.get("characteristic", [])
        for char in characteristics:
            if isinstance(char, dict) and "id" in char and "value" in char:
                key = char["id"].lower()
                value_map[(code, key)] = char["value"]

        # 2b. Also capture score result if present
        score = safe_get(score_model, "score")
        if score:
            # Capture score result
            results = score.get("results")
            if isinstance(results, str):
                value_map[(code, "score")] = results

            # Capture top 4 score factors as list of {code, rank}
            raw_factors = score.get("factors", {}).get("factor", [])
            if not isinstance(raw_factors, list):
                raw_factors = []

            top_factors = [
                {"code": f.get("code"), "rank": f.get("rank")}
                for f in raw_factors[:4]
                if isinstance(f, dict) and "code" in f and "rank" in f
            ]
            if top_factors:
                value_map[(code, "factors")] = top_factors


    # Step 3.a: Use variable_to_code_map to fetch final vars
    for var, code in variable_code_map.items():
        extracted[var] = value_map.get((code, var))
    
    # Step 3.b: Use score_to_code_map to fetch score vars
    for key, code in SCORE_TO_CODE_MAP.items():
        value = value_map.get((code, "score"))
        if value:
            extracted[key] = value.lstrip("+")

        # Extract factor list, if available
        factor_list = value_map.get((code, "factors"))
        if factor_list:
            extracted[f"{key}_factors"] = factor_list

    return extracted

if __name__ == "__main__":
    for filename in sys.argv[1:]:
        with open(filename) as f:
            data = json.load(f)
        print(f"\n--- Extracting from: {filename} ---")
        result = extract_model_variables(data)
        for k, v in result.items():
            print(f"{k}: {v}")