"""Rebuild the published viewer files from the pinned upstream JSON snapshot. The original source files remain in source/. This script adds uniform-family sampling weights and a deterministic, 200-per-family reference view; it does not generate new acting challenges or claim the source families are independent. """ from collections import Counter, defaultdict import hashlib import json from pathlib import Path ROOT = Path(__file__).resolve().parent SOURCE = ROOT / "source" DATA = ROOT / "data" SOURCE_COMMIT = "e3b0d5281558f7f73e1118c6a9b4c149bb8c0621" EXPECTED = { "laions_got_talent": 278, "haiku_batch": 1000, "voicenet_extension": 200, "eric_morris_inspired": 4030, "existing_challenge_inspired": 7390, "situation_inspired": 5749, "extreme_physical": 600, } def load(name): return json.loads((SOURCE / name).read_text(encoding="utf-8")) def rank(row): key = f'{row["source"]}:{row["id"]}' return hashlib.sha256(key.encode()).hexdigest() def grouped_reference(rows, key_name, count=200): if key_name is None: return sorted(rows, key=rank)[:count] by_group = defaultdict(list) for row in rows: by_group[row.get(key_name) or "unlabelled"].append(row) for group in by_group: by_group[group].sort(key=rank) keys = sorted(by_group) chosen = [] while len(chosen) < count: advanced = False for key in keys: if by_group[key] and len(chosen) < count: chosen.append(by_group[key].pop(0)) advanced = True if not advanced: raise ValueError("Insufficient examples for balanced reference") return chosen master = load("all_acting_challenges.json") assert len(master) == 19247 assert Counter(row["source"] for row in master) == EXPECTED assert len({row["id"] for row in master}) == len(master) extreme = load("acting_challenges_extreme_physical.json") extreme_by_text = {(row["title"], row["instruction"]): row for row in extreme} assert len(extreme_by_text) == 600 rows = [] for raw in master: row = dict(raw) if row["source"] == "extreme_physical": details = extreme_by_text[(row["title"], row["instruction"])] for key in ("category_code", "category_name", "subcategory", "scenario", "emotions_sampled", "speaker_gender", "speaker_age", "item_id"): row[key] = details.get(key) row["family_sampling_weight"] = 1 / (len(EXPECTED) * EXPECTED[row["source"]]) rows.append(row) DATA.mkdir(exist_ok=True) with (DATA / "all_challenges.jsonl").open("w", encoding="utf-8") as out: for row in rows: out.write(json.dumps(row, ensure_ascii=False) + "\n") reference = [] for source in EXPECTED: group = [row for row in rows if row["source"] == source] key_name = { "eric_morris_inspired": "inspired_by", "existing_challenge_inspired": "inspired_by", "situation_inspired": "situation_dim", "extreme_physical": "category_code", }.get(source) reference.extend(grouped_reference(group, key_name)) reference.sort(key=rank) assert len(reference) == 1400 assert Counter(row["source"] for row in reference) == {key: 200 for key in EXPECTED} with (DATA / "family_balanced_reference.jsonl").open("w", encoding="utf-8") as out: for row in reference: out.write(json.dumps(row, ensure_ascii=False) + "\n") manifest = { "source_commit": SOURCE_COMMIT, "source_master_sha256": hashlib.sha256((SOURCE / "all_acting_challenges.json").read_bytes()).hexdigest(), "full_count": len(rows), "source_counts": EXPECTED, "reference_count": len(reference), "reference_counts": dict(Counter(row["source"] for row in reference)), "reference_selection": "deterministic SHA-256 order; 200 per family, round-robin over available source groups", "source_title_uniqueness": len({row["title"].strip().casefold() for row in rows}), "arena_tasks_included": False, } (ROOT / "MANIFEST.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") print(json.dumps(manifest, indent=2))