#!/usr/bin/env python3 """Audit pinned task datasets without assigning train/evaluation roles.""" from __future__ import annotations import argparse import csv import hashlib import json import re from pathlib import Path import pyarrow.parquet as pq def sha256(path: Path) -> str: digest = hashlib.sha256() with path.open("rb") as handle: for chunk in iter(lambda: handle.read(1024 * 1024), b""): digest.update(chunk) return digest.hexdigest() def write_csv(path: Path, rows: list[dict[str, object]]) -> None: path.parent.mkdir(parents=True, exist_ok=True) with path.open("w", newline="") as handle: writer = csv.DictWriter(handle, fieldnames=list(rows[0])) writer.writeheader() writer.writerows(rows) def audit_swe(path: Path, output: Path, revision: str) -> None: table = pq.read_table(path, columns=["language", "repo", "base_commit", "license"]) data = table.to_pydict() languages = sorted(set(data["language"])) rows = [] for language in languages: indices = [i for i, value in enumerate(data["language"]) if value == language] rows.append( { "language": language, "tasks": len(indices), "unique_repositories": len({data["repo"][i] for i in indices}), "unique_base_commits": len({data["base_commit"][i] for i in indices}), "license_values": len({data["license"][i] for i in indices}), "source_revision": revision, "source_sha256": sha256(path), } ) rows.sort(key=lambda row: (-int(row["tasks"]), str(row["language"]))) write_csv(output, rows) def audit_mceval_instruct(path: Path, output: Path, revision: str) -> None: records = json.loads(path.read_text()) grouped: dict[str, list[dict[str, str]]] = {} for record in records: grouped.setdefault(record["language"].casefold(), []).append(record) rows = [] for language, group in grouped.items(): rows.append( { "normalized_language": language, "rows": len(group), "unique_instructions": len({record["instruction"] for record in group}), "unique_outputs": len({record["output"] for record in group}), "output_utf8_bytes": sum(len(record["output"].encode()) for record in group), "original_labels": ";".join(sorted({record["language"] for record in group})), "source_revision": revision, "source_sha256": sha256(path), } ) rows.sort(key=lambda row: (-int(row["rows"]), str(row["normalized_language"]))) write_csv(output, rows) def audit_mceval_eval(root: Path, output: Path, revision: str) -> None: grouped: dict[tuple[str, str], dict[str, object]] = {} source_files = sorted(root.glob("generation/*.jsonl")) source_files += sorted(root.glob("explanation/*.jsonl")) source_files += sorted(root.glob("completion/*/*.jsonl")) for path in source_files: relative = path.relative_to(root) surface = "/".join(relative.parts[:-1]) language = path.stem group = grouped.setdefault( (surface, language), {"rows": 0, "base_ids": set(), "solutions": set(), "tests": set()}, ) with path.open() as handle: for line in handle: record = json.loads(line) group["rows"] = int(group["rows"]) + 1 match = re.match(r"^([^/]+)/(\d+)", record["task_id"]) group["base_ids"].add(match.group(2) if match else record["task_id"]) group["solutions"].add(record.get("canonical_solution", "")) group["tests"].add(record.get("test", "")) tree_hash = hashlib.sha256() for path in source_files: tree_hash.update(str(path.relative_to(root)).encode()) tree_hash.update(bytes.fromhex(sha256(path))) rows = [] for (surface, language), group in grouped.items(): rows.append( { "surface": surface, "language": language, "rows": group["rows"], "unique_base_problem_ids": len(group["base_ids"]), "unique_canonical_solutions": len(group["solutions"]), "unique_tests": len(group["tests"]), "source_revision": revision, "audited_tree_sha256": tree_hash.hexdigest(), } ) rows.sort(key=lambda row: (str(row["surface"]), str(row["language"]))) write_csv(output, rows) def audit_swe_leaderboard(root: Path, output: Path, revision: str) -> None: """Inventory every frozen leaderboard split without treating it as training data.""" source_files = sorted((root / "data").glob("*.parquet")) if not source_files: raise FileNotFoundError(f"no parquet files found under {root / 'data'}") rows = [] for path in source_files: table = pq.read_table(path, columns=["repo", "instance_id", "base_commit"]) data = table.to_pydict() rows.append( { "split": path.stem.split("-00000", 1)[0], "rows": table.num_rows, "unique_instances": len(set(data["instance_id"])), "unique_repositories": len(set(data["repo"])), "unique_base_commits": len(set(data["base_commit"])), "source_revision": revision, "file_sha256": sha256(path), } ) write_csv(output, rows) def main() -> None: parser = argparse.ArgumentParser() subparsers = parser.add_subparsers(dest="source", required=True) swe = subparsers.add_parser("swe-rebench-v2") swe.add_argument("--input", type=Path, required=True) swe.add_argument("--output", type=Path, required=True) swe.add_argument("--revision", required=True) mceval = subparsers.add_parser("mceval-instruct") mceval.add_argument("--input", type=Path, required=True) mceval.add_argument("--output", type=Path, required=True) mceval.add_argument("--revision", required=True) mceval_eval = subparsers.add_parser("mceval-eval") mceval_eval.add_argument("--input", type=Path, required=True) mceval_eval.add_argument("--output", type=Path, required=True) mceval_eval.add_argument("--revision", required=True) leaderboard = subparsers.add_parser("swe-rebench-leaderboard") leaderboard.add_argument("--input", type=Path, required=True) leaderboard.add_argument("--output", type=Path, required=True) leaderboard.add_argument("--revision", required=True) args = parser.parse_args() if args.source == "swe-rebench-v2": audit_swe(args.input, args.output, args.revision) elif args.source == "mceval-instruct": audit_mceval_instruct(args.input, args.output, args.revision) elif args.source == "mceval-eval": audit_mceval_eval(args.input, args.output, args.revision) else: audit_swe_leaderboard(args.input, args.output, args.revision) if __name__ == "__main__": main()