"""ub_d3_common.py (UBD3, 16 Oct 2026): one rendering of a typed decision (Decision Index wire format: state + {type, instructions,
criteria}) into the UBENC row contract, shared by the d3 data prep (training rows) and the Decision Index engine (inference), so
train and test see byte-identical inputs.

  state        -> one part, role "user": the string as is, or a JSON object as compact JSON (the kit's base.text rendering);
  instructions -> the question text (string as is, object as compact JSON);
  choice       -> options {key: description or ""} in criteria order (the encoder reads "key: description", or "key" alone);
  noul         -> a 2-option choice {"no": "", "yes": ""}; the answer is p(yes).
The question is marked nodrop: training never drops options (d3 trains on the full option set, only re-permuted), and has no [ABS]."""
import json

NOUL_OPTIONS = {"no": "", "yes": ""}


def text(x):
    return x if isinstance(x, str) else json.dumps(x, ensure_ascii=False, separators=(",", ":"))


def options_of(q):
    if q["type"] == "noul":
        return dict(NOUL_OPTIONS)
    return {str(k): ("" if v is None else text(v)) for k, v in q["criteria"].items()}


def to_row(rid, state, q, gold=None, soft=None, weight=1.0):
    """state: str | object; q: {type, instructions, criteria?}. gold: option name (noul: "yes"/"no"); soft: {option name: prob}."""
    opts = options_of(q)
    qq = dict(id="d3:" + q["type"], type="choice", over="user", text=text(q.get("instructions", "")), question_paraphrases=[],
              options=opts, abstain=False, nodrop=True, gold=gold if gold is not None else next(iter(opts)))
    if soft is not None:
        qq["soft"] = soft
    if weight != 1.0:
        qq["weight"] = weight
    return dict(id=rid, parts=[dict(role="user", text=text(state if state is not None else ""))], questions=[qq])