script.py

"""
IOL-AI Challenge 2026 — submission script (OFFLINE / Mode B).

Runtime facts (Space Submission tab):
  * T4 medium, 16 GB VRAM, Python 3.10, 30-min wall clock.
  * NO internet: cannot pip install or download anything. Model weights must be
    committed into THIS repo (the working dir) and loaded from ".". Only the
    pre-installed libraries/versions are available (torch 2.4.0, transformers
    4.44.1, accelerate 0.34.2, bitsandbytes 0.43.3, autoawq 0.2.7, pandas 2.2.2,
    numpy 2.1.3, ...). Do NOT pin different majors of torch/transformers/numpy.
  * Read hidden test set from /tmp/data/test.csv; write submission.csv here.
  * pred = JSON list, one entry per numbered item, in query order.

Ship the model in the repo with build_repo.py. This script loads it from "." with
bitsandbytes 4-bit by default (or auto-detected AWQ) so it fits 16 GB. T4 has no
bf16 -> use float16.

Lineage: v4 fixed the big bug — answer COUNT comes from the model/context, never a
query regex (that clipped matching/fill-blank problems to 1 answer). v10 added
deterministic exact-match boosters (arithmetic-eval for numbers, bijection repair for
matching, diacritic/gloss fidelity). v11 (this file) spends the whole 30-min wall
SAFELY: per row, a greedy anchor + sampled self-consistency (majority vote, early stop
on consensus) + an optional refine pass ("re-derive, check, fix", folded in as one more
vote) — all bounded by an adaptive per-row budget + per-decode max_time + soft/hard wall
phases that degrade to single-pass then placeholder, so it can never time out (v6 did).
Single-sequence decode throughout (v3's batched decode OOM'd). Every row keeps an
`explanation` (Human-Eval track). Tunable: IOL_MAX_SAMPLES / IOL_CONSENSUS / IOL_REFINE
/ IOL_TEMPERATURE / IOL_TOP_P / IOL_MAX_NEW_TOKENS / IOL_SOFT_BUDGET_S / IOL_HARD_BUDGET_S.

Local dev: set IOL_TEST_CSV to a mock file. Quantization auto-disables if there's
no CUDA so the plumbing can be exercised on CPU with a tiny model.
"""

import os
os.environ.setdefault("HF_HUB_OFFLINE", "1")
os.environ.setdefault("TRANSFORMERS_OFFLINE", "1")

import re
import csv
import json
import time

SCRIPT_START = time.time()   # ~wall-clock start; the 30-min hard kill counts from here

MODEL_DIR = os.environ.get("IOL_MODEL_DIR", ".")        # weights live in the repo
TEST_CSV = os.environ.get("IOL_TEST_CSV", "/tmp/data/test.csv")
OUT_CSV = os.environ.get("IOL_OUT_CSV", "submission.csv")
MAX_NEW_TOKENS = int(os.environ.get("IOL_MAX_NEW_TOKENS", "768"))
# "4bit" (bitsandbytes), "awq" (weights already AWQ-quantized), or "fp16".
QUANT = os.environ.get("IOL_QUANT", "4bit")

# --- v11: adaptive compute — self-consistency + refine, bounded by the wall -----
# Per row: a greedy ANCHOR decode + extra SAMPLED decodes, majority-voted per item
# (self-consistency), stopping early on consensus so easy rows free up time for hard
# ones; then, budget permitting, a REFINE pass re-checks the draft against the data and
# is folded in as one more vote. Everything is gated by an ADAPTIVE per-row time budget
# and a per-decode max_time, so we spend the whole wall but ALWAYS finish (v6 timed out
# for lack of exactly this). Single-sequence decode throughout (no OOM).
TEMPERATURE = float(os.environ.get("IOL_TEMPERATURE", "0.7"))
TOP_P = float(os.environ.get("IOL_TOP_P", "0.9"))
MAX_SAMPLES = int(os.environ.get("IOL_MAX_SAMPLES", "6"))       # hard cap of decodes/row
CONSENSUS_FRAC = float(os.environ.get("IOL_CONSENSUS", "0.6"))  # early-stop agreement level
DO_REFINE = os.environ.get("IOL_REFINE", "1") != "0"
MIN_DECODE_S = float(os.environ.get("IOL_MIN_DECODE_S", "20"))  # floor for a decode's max_time
# Wall-clock phases (seconds from SCRIPT_START; 30-min hard kill = 1800):
SOFT_BUDGET_S = float(os.environ.get("IOL_SOFT_BUDGET_S", "1560"))  # 26.0m: full pipeline before this
HARD_BUDGET_S = float(os.environ.get("IOL_HARD_BUDGET_S", "1710"))  # 28.5m: placeholder-fill after

ANSWER_MARKER = "###ANSWERS###"
WHY_MARKER = "###WHY###"

SYSTEM_PROMPT = (
    "You are an expert competitor at the International Linguistics Olympiad. "
    "Each problem gives data from a language you have never seen; deduce its rules "
    "using ONLY the data and hints in the problem, then answer EVERY sub-question.\n\n"
    "A problem can have MANY sub-questions even when the query is one sentence: e.g. "
    "'give the correspondences' expects one answer for EACH numbered item in the data "
    "(often a dozen or more). Work out how many answers are required and give exactly "
    "that many, one per item, in the order the items appear.\n\n"
    "Reason briefly, then give your answers in EXACTLY this format:\n"
    f"{ANSWER_MARKER}\n"
    "1. <answer to item 1>\n"
    "2. <answer to item 2>\n"
    "(one numbered line per sub-question, in order)\n"
    f"{WHY_MARKER}\n"
    "- <the key rule or pattern you found>\n"
    "- <the main evidence from the data that supports it>\n\n"
    "Each answer line holds ONLY the requested form — a word, phrase, number, or "
    "letter — with no restating of the question and no commentary. Answer in the "
    "language and direction the query asks. For matching items give just the option "
    "letter; for number items give digits or the written-out number as asked. Never "
    "leave an item blank — always give your best guess.\n\n"
    "EXACT SPELLING MATTERS: copy the exact characters, diacritics and special symbols "
    "that appear in the data (e.g. ʼ ɨ ŋ ʂ); never swap them for similar-looking "
    "ordinary letters. When translating INTO English, reproduce the examples' glossing "
    "style verbatim, including person/number markers written like you_sg, you_pl.\n\n"
    f"The lines after {WHY_MARKER} are a SHORT, human-readable explanation (1-3 bullets "
    "a person can grasp in under a minute) — NOT your full reasoning trace."
)

# Short, low-cost per-task output reminders (the CSV tags each row with task_type).
TASK_HINT = {
    "translation": "This is a translation task: each answer is only the translated word/phrase.",
    "text_to_num": "This is a number task: each answer is only digits (e.g. 42).",
    "num_to_text": "This is a number task: each answer is only the number written in the target language's words.",
    "match_letters": "This is a matching task: each answer is only the option letter (A, B, C, ...); give one per item in the data.",
    "matching": "This is a matching task: each answer is only the option letter; give one per item in the data.",
    "fill_blank": "This is a fill-in-the-blank task: each answer is only the missing form.",
    "fill_blanks": "This is a fill-in-the-blank task: each answer is only the missing form.",
}


def build_messages(row):
    """Chat messages for one problem, with a short task_type-specific reminder plus
    two exact-match boosters: a COMPUTE line for number tasks (we evaluate it) and the
    exact set of valid letters for matching tasks."""
    context = (row.get("context") or "").strip()
    query = (row.get("query") or "").strip()
    ttype = (row.get("task_type") or "").strip().lower()

    system = SYSTEM_PROMPT
    hint = TASK_HINT.get(ttype)
    if hint:
        system = system + "\n\n" + hint

    if ttype == "text_to_num":
        system += (
            f"\n\nAfter the {WHY_MARKER} bullets, add one more line exactly:\n"
            "COMPUTE: expr1 | expr2 | ...\n"
            "where each expr is plain arithmetic (digits, + - *, parentheses only) that "
            "evaluates to that item's number, one per item, matching the rule you found."
        )
    if ttype in ("match_letters", "matching"):
        opts = extract_letter_options(context)
        if opts:
            system += (f"\n\nThe ONLY valid answers are these letters: {', '.join(opts)}. "
                       "Use no other letter.")

    return [
        {"role": "system", "content": system},
        {"role": "user", "content": context + "\n\n" + query},
    ]


def detect_count(context, query):
    """Best-effort number of sub-questions — used ONLY as a hint and a minimum pad,
    NEVER to truncate the model's own answer list (under-producing loses items).
    Queries usually number items ('1.'/'2.') or mark blanks ('(1)','(2)'); matching
    queries number nothing, so fall back to the numbered items in the CONTEXT."""
    q = re.findall(r"(?m)^\s*(\d+)[\.\)]", query)
    if q:
        return len(q)
    par = re.findall(r"\((\d+)\)", query)
    if par:
        return len(set(par))
    c = re.findall(r"(?m)^\s*(\d+)[\.\)]", context)
    if c:
        return len(c)
    return 1


def _clean_answer(s):
    """Strip list markers, common 'Answer:' labels, and surrounding quotes."""
    s = re.sub(r"^\s*(?:\d+[\.\):]|[-*•])\s*", "", s).strip()
    s = re.sub(r"^(?:answer|ans|translation|result)\s*[:\-]\s*", "", s, flags=re.I).strip()
    return s.strip("\"'“”‘’` ").strip()


def parse_answers(text, min_count=1):
    """Extract the model's FULL answer list — the count comes from the MODEL, never
    truncated to a query heuristic (that was the v1/v2 bug: it clipped matching
    problems' dozen answers down to 1). Prefer the ###ANSWERS### block; inside it
    read the numbered lines; else split a trailing comma-list; else use the lines.
    Pad up to min_count and never emit a blank."""
    seg = text.rsplit(ANSWER_MARKER, 1)[1] if ANSWER_MARKER in text else text
    if WHY_MARKER in seg:                       # answers live BEFORE the WHY section
        seg = seg.split(WHY_MARKER, 1)[0]

    numbered = {}
    for m in re.finditer(r"(?m)^\s*(\d+)[\.\)]\s*(.+?)\s*$", seg):
        numbered[int(m.group(1))] = _clean_answer(m.group(2))
    if numbered:                                    # ordered by the model's indices
        answers = [numbered.get(i, "") for i in range(1, max(numbered) + 1)]
    else:
        lines = [ln.strip() for ln in seg.splitlines() if ln.strip()]
        comma_line = next((ln for ln in reversed(lines) if "," in ln), "")
        if comma_line:                              # matching-style "O, D, A, ..."
            answers = [_clean_answer(x) for x in comma_line.split(",")]
        else:
            answers = [_clean_answer(ln) for ln in lines]

    answers = [a if a else "?" for a in answers]    # never blank (partial credit)
    if len(answers) < min_count:
        answers += ["?"] * (min_count - len(answers))
    return answers if answers else ["?"]


def _norm(s):
    """Mirror the official scorer's normalization so voting groups answers the
    same way the metric will (ignore case, surrounding quotes, one trailing dot)."""
    s = " ".join((s or "").strip().split())
    s = s.strip("\"'“”‘’")
    if s.endswith("."):
        s = s[:-1]
    return s.strip().casefold()


def vote_lists(answer_lists, min_count=1):
    """Majority-vote per position across already-parsed answer lists (self-consistency).
    Most common NORMALIZED form wins; returns its surface form. Ties -> earliest list.
    '?' placeholders don't vote, so a malformed/truncated decode can't corrupt a result."""
    from collections import Counter

    n = max([min_count] + [len(a) for a in answer_lists]) if answer_lists else min_count
    out = []
    for i in range(n):
        counts, surface = Counter(), {}
        for a in answer_lists:
            if i < len(a) and a[i] and a[i] != "?":
                key = _norm(a[i])
                counts[key] += 1
                surface.setdefault(key, a[i])
        out.append(surface[counts.most_common(1)[0][0]] if counts else "?")
    return out


def vote_answers(sample_texts, min_count=1):
    """Self-consistency over raw decodes (parse each, then vote per item)."""
    return vote_lists([parse_answers(t, min_count) for t in sample_texts], min_count)


def _agreement(answer_lists, voted):
    """Fraction of lists whose normalized answers equal the voted result (early-stop)."""
    if not answer_lists:
        return 0.0
    vt = tuple(_norm(x) for x in voted)
    hit = 0
    for a in answer_lists:
        ap = list(a) + ["?"] * (len(vt) - len(a))
        if tuple(_norm(x) for x in ap[:len(vt)]) == vt:
            hit += 1
    return hit / len(answer_lists)


def parse_explanation(text):
    """Pull the short ###WHY### summary the model wrote (for the Human-Eval jury track).
    Kept concise and readable; NOT the raw reasoning trace. '' if the model omitted it.
    The internal COMPUTE: line (used only for arithmetic eval) is dropped from it."""
    if WHY_MARKER not in text:
        return ""
    why = text.rsplit(WHY_MARKER, 1)[1].replace(ANSWER_MARKER, " ").strip()
    lines = [ln.strip() for ln in why.splitlines()
             if ln.strip() and not re.match(r"(?i)^\s*compute\s*:", ln)]
    return "\n".join(lines[:4])[:600].strip()


# ---- deterministic exact-match boosters (no extra model call, adapted from v5) ----
import ast as _ast
_ALLOWED_BINOPS = (_ast.Add, _ast.Sub, _ast.Mult)
_NUM_NODE = getattr(_ast, "Num", None)   # pre-3.8 number node (sandbox is 3.10=Constant)


def _safe_arithmetic(expr):
    """Evaluate a plain +,-,* / parenthesised integer expression, else None."""
    try:
        tree = _ast.parse(expr.strip(), mode="eval")
    except Exception:
        return None

    def _ev(n):
        if isinstance(n, _ast.Expression):
            return _ev(n.body)
        if isinstance(n, _ast.Constant) and isinstance(n.value, (int, float)):
            return n.value
        if _NUM_NODE is not None and isinstance(n, _NUM_NODE):   # Python <3.8
            return n.n
        if isinstance(n, _ast.BinOp) and isinstance(n.op, _ALLOWED_BINOPS):
            l, r = _ev(n.left), _ev(n.right)
            if l is None or r is None:
                return None
            if isinstance(n.op, _ast.Add):
                return l + r
            if isinstance(n.op, _ast.Sub):
                return l - r
            return l * r
        if isinstance(n, _ast.UnaryOp) and isinstance(n.op, _ast.USub):
            v = _ev(n.operand)
            return -v if v is not None else None
        return None

    return _ev(tree)


def apply_compute_overrides(text, answers):
    """text_to_num: if the model wrote 'COMPUTE: e1 | e2', evaluate each safely and
    override that item's answer with the exact integer — kills arithmetic slips while
    keeping the model's derived rule. Only overrides when the eval is a clean integer."""
    m = re.search(r"(?im)^\s*COMPUTE\s*:\s*(.+)$", text)
    if not m:
        return answers
    exprs = [e.strip() for e in m.group(1).split("|")]
    out = list(answers)
    for i, e in enumerate(exprs[:len(out)]):
        v = _safe_arithmetic(e)
        if v is not None and float(v).is_integer():
            out[i] = str(int(v))
    return out


def extract_letter_options(context):
    """The option labels A, B, C ... that a matching problem offers (contiguous from A)."""
    found = set()
    for line in context.splitlines():
        for m in re.finditer(r"(?:^|\s)([A-Z])[.\)]\s+\S", line):
            found.add(m.group(1))
    if not found:
        return None
    letters = sorted(found)
    if letters != [chr(ord("A") + i) for i in range(len(letters))]:
        return None
    return letters if 2 <= len(letters) <= 26 else None


def repair_bijection(answers, labels):
    """match_letters, bijection case only: keep the letters the model committed to
    (first occurrence wins), fill duplicate/invalid/missing slots with the leftover
    letters in order -> a guaranteed valid permutation of exactly len(labels) items."""
    n = len(labels)
    labels_sorted = sorted(labels)
    picks = []
    for i in range(n):
        a = answers[i] if i < len(answers) else ""
        f = re.findall(r"[A-Za-z]", a or "")
        c = f[0].upper() if f else ""
        picks.append(c if c in labels else "")
    result = [None] * n
    used = set()
    for i in range(n):
        if picks[i] and picks[i] not in used:
            result[i] = picks[i]
            used.add(picks[i])
    missing = [l for l in labels_sorted if l not in used]
    mi = 0
    for i in range(n):
        if result[i] is None:
            result[i] = missing[mi] if mi < len(missing) else labels_sorted[0]
            mi += 1
    return result


def postprocess(row, answers, raw):
    """Apply the deterministic boosters that fit this row's task_type."""
    ttype = (row.get("task_type") or "").strip().lower()
    context = (row.get("context") or "")
    if ttype == "text_to_num":
        answers = apply_compute_overrides(raw, answers)
    elif ttype in ("match_letters", "matching"):
        labels = extract_letter_options(context)
        # Only a true bijection (numbered context items == number of labels) is safe to
        # repair; "pick the letter for item 1,2" style (few items, reused letters) is not.
        ctx_items = len(re.findall(r"(?m)^\s*\d+\s*[.\)]", context))
        if labels and len(labels) >= 2 and ctx_items == len(labels):
            answers = repair_bijection(answers, labels)
    return answers


def default_explanation(row):
    """Non-empty fallback so the explanation column is populated on every row."""
    t = (row.get("task_type") or "linguistic").replace("_", " ")
    return f"Inferred the {t} rule from the given examples and applied it to each item."


def _already_quantized(model_dir):
    """True if the shipped weights are pre-quantized (e.g. AWQ) — then transformers
    auto-detects the config and we must NOT stack bitsandbytes on top."""
    cfg = os.path.join(model_dir, "config.json")
    try:
        with open(cfg, encoding="utf-8") as f:
            return "quantization_config" in json.load(f)
    except Exception:
        return False


def load_model():
    import torch
    from transformers import AutoTokenizer, AutoModelForCausalLM

    tok = AutoTokenizer.from_pretrained(MODEL_DIR)
    if tok.pad_token_id is None:                    # only used to silence a warning
        tok.pad_token = tok.eos_token
    if not torch.cuda.is_available():
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_DIR, torch_dtype=torch.float32).eval()   # CPU dev fallback
        return tok, model

    kwargs = dict(torch_dtype=torch.float16, device_map="auto")  # T4 has no bf16
    if _already_quantized(MODEL_DIR):
        pass  # AWQ/pre-quant: transformers reads quantization_config from config.json
    elif QUANT == "4bit":
        from transformers import BitsAndBytesConfig
        kwargs["quantization_config"] = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_use_double_quant=True,
        )
    model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, **kwargs).eval()
    return tok, model


def generate_one(tok, model, messages, do_sample, max_time=None):
    """Single-sequence decode (batch=1) — the VRAM-safe path proven in v1/v2. (v3's
    batched multi-sequence decode OOM'd the T4 and produced an empty submission.)
    max_time caps this one decode's wall time so the pipeline can't overrun."""
    import torch

    dev = model.device if hasattr(model, "device") else "cpu"
    ids = tok.apply_chat_template(
        messages, add_generation_prompt=True, return_tensors="pt").to(dev)
    gkw = dict(max_new_tokens=MAX_NEW_TOKENS, pad_token_id=tok.pad_token_id)
    if do_sample:
        gkw.update(do_sample=True, temperature=TEMPERATURE, top_p=TOP_P)
    else:
        gkw.update(do_sample=False)
    if max_time and max_time > 0:
        gkw["max_time"] = float(max_time)              # MaxTimeCriteria stops the decode
    with torch.no_grad():
        gen = model.generate(ids, **gkw)
    return tok.decode(gen[0][ids.shape[-1]:], skip_special_tokens=True).strip()


def build_refine_messages(row, answers):
    """A 'check and correct' pass: show the draft answers and ask the model to re-derive
    the rule from the data, verify each answer, and output a corrected list (same format)."""
    context = (row.get("context") or "").strip()
    query = (row.get("query") or "").strip()
    draft = "\n".join("%d. %s" % (i + 1, a) for i, a in enumerate(answers))
    system = (
        "You are re-checking a DRAFT solution to an International Linguistics Olympiad "
        "problem. Re-derive the rules STRICTLY from the data, test them on every example, "
        "then verify each draft answer and FIX any that are wrong (keep the right ones). "
        "Output the corrected answers in EXACTLY this format:\n"
        f"{ANSWER_MARKER}\n1. <answer 1>\n2. <answer 2>\n(one per sub-question, in order)\n"
        f"{WHY_MARKER}\n- <the rule you used>\n- <what you changed, if anything>\n\n"
        "Copy exact characters/diacritics; give only the requested form; never leave a blank."
    )
    user = "%s\n\n%s\n\nDRAFT ANSWERS:\n%s" % (context, query, draft)
    return [{"role": "system", "content": system}, {"role": "user", "content": user}]


def _well_formed(raw, answer_list, min_count):
    """A refine/sample result is trustworthy enough to vote only if it hit the marker
    and yielded at least min_count real (non-'?') answers."""
    real = sum(1 for a in answer_list if a and a != "?")
    return (ANSWER_MARKER in raw) and real >= min_count


def solve_row(tok, model, row, row_deadline):
    """Adaptive per-row compute: greedy anchor + sampled self-consistency (early stop on
    consensus) + an optional refine vote, all bounded by row_deadline and the wall phases.
    Returns (answers, explanation, mode)."""
    context = (row.get("context") or "").strip()
    query = (row.get("query") or "").strip()
    min_count = detect_count(context, query)
    messages = build_messages(row)

    def budget():                                   # time left for THIS row's next decode
        by_row = row_deadline - time.time()
        by_wall = SOFT_BUDGET_S - (time.time() - SCRIPT_START)
        return min(by_row, by_wall)

    elapsed = time.time() - SCRIPT_START
    if elapsed > HARD_BUDGET_S:                      # no time: safe placeholder, keep file whole
        return ["?"] * min_count, default_explanation(row), "placeholder"
    if elapsed > SOFT_BUDGET_S:                      # low time: one greedy pass only
        raw = generate_one(tok, model, messages, False, max_time=max(MIN_DECODE_S, budget()))
        return (postprocess(row, parse_answers(raw, min_count), raw),
                parse_explanation(raw) or default_explanation(row), "single")

    # --- full pipeline: greedy anchor, then sampled self-consistency ---
    anchor = generate_one(tok, model, messages, False, max_time=max(MIN_DECODE_S, budget()))
    texts = [anchor]
    lists = [postprocess(row, parse_answers(anchor, min_count), anchor)]
    while len(texts) < MAX_SAMPLES and budget() > MIN_DECODE_S:
        t = generate_one(tok, model, messages, True, max_time=budget())
        texts.append(t)
        lists.append(postprocess(row, parse_answers(t, min_count), t))
        if len(lists) >= 3 and _agreement(lists, vote_lists(lists, min_count)) >= CONSENSUS_FRAC:
            break

    voted = vote_lists(lists, min_count)
    explanation = parse_explanation(anchor) or default_explanation(row)
    mode = "vote%d" % len(texts)

    # --- optional refine pass, folded in as one more vote (only if well-formed) ---
    if DO_REFINE and budget() > MIN_DECODE_S:
        rtext = generate_one(tok, model, build_refine_messages(row, voted), False,
                             max_time=budget())
        rlist = postprocess(row, parse_answers(rtext, min_count), rtext)
        if _well_formed(rtext, rlist, min_count):
            voted = vote_lists(lists + [rlist], min_count)
            explanation = parse_explanation(rtext) or explanation
            mode += "+refine"

    return voted, explanation, mode


def main():
    tok, model = load_model()

    with open(TEST_CSV, newline="", encoding="utf-8") as f:
        rows = list(csv.DictReader(f))

    # Write incrementally so a hard 30-min kill still leaves a valid partial file.
    # 'explanation' column opts into the Human-Eval jury track (not auto-scored).
    fout = open(OUT_CSV, "w", newline="", encoding="utf-8")
    writer = csv.DictWriter(fout, fieldnames=["id", "pred", "explanation"])
    writer.writeheader()
    fout.flush()

    n = len(rows)
    for k, r in enumerate(rows):
        rows_left = n - k
        soft_left = SOFT_BUDGET_S - (time.time() - SCRIPT_START)
        # Even share of the remaining soft budget; rows that finish early hand their
        # slack to later rows (next iteration's soft_left is larger).
        row_deadline = time.time() + max(0.0, soft_left) / max(1, rows_left)
        try:
            answers, explanation, mode = solve_row(tok, model, r, row_deadline)
        except Exception as e:      # one row must never zero the whole submission
            print("row %s failed: %r" % (r.get("id"), e), flush=True)
            mc = detect_count((r.get("context") or ""), (r.get("query") or ""))
            answers, explanation, mode = ["?"] * mc, default_explanation(r), "error"
        writer.writerow({"id": r["id"],
                         "pred": json.dumps(answers, ensure_ascii=False),
                         "explanation": explanation})
        fout.flush()                                    # survive a hard timeout
        print("%d/%d [%s] elapsed=%ds" % (k + 1, n, mode, time.time() - SCRIPT_START),
              flush=True)

    fout.close()
    print("wrote %s (%d rows)" % (OUT_CSV, n), flush=True)


if __name__ == "__main__":
    main()