source_metadata/build_hf_hardneg_dataset.py

#!/usr/bin/env python3
from __future__ import annotations

import argparse
import hashlib
import json
import random
import shutil
from pathlib import Path
from typing import Any

import pyarrow as pa
import pyarrow.parquet as pq


NEGATIVE_COUNT = 5
SOURCE_COLUMNS = ("query_id", "case_id", "raw_text", "parsed_text", "video")
OUTPUT_COLUMNS = ("text", "video", "hard_negative_texts", "hard_negative_videos")

HF_FEATURES = {
    "text": {"dtype": "string", "_type": "Value"},
    "video": {"dtype": "string", "_type": "Value"},
    "hard_negative_texts": {
        "feature": {"dtype": "string", "_type": "Value"},
        "length": -1,
        "_type": "List",
    },
    "hard_negative_videos": {
        "feature": {"dtype": "string", "_type": "Value"},
        "length": -1,
        "_type": "List",
    },
}

OUTPUT_SCHEMA = pa.schema(
    [
        pa.field("text", pa.string()),
        pa.field("video", pa.string()),
        pa.field("hard_negative_texts", pa.list_(pa.string())),
        pa.field("hard_negative_videos", pa.list_(pa.string())),
    ],
    metadata={b"huggingface": json.dumps({"info": {"features": HF_FEATURES}}, separators=(",", ":")).encode()},
)


def _write_json(path: Path, payload: Any) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")


def _source_files(source_dir: Path) -> list[tuple[str, Path]]:
    return sorted(
        ((path.parent.name, path) for path in source_dir.glob("*/train-*.parquet")),
        key=lambda item: item[0],
    )


def _read_family(path: Path) -> list[dict[str, Any]]:
    table = pq.read_table(path)
    if tuple(table.column_names) != SOURCE_COLUMNS:
        raise ValueError(f"Unexpected source columns in {path}: {table.column_names}")
    return sorted(table.to_pylist(), key=lambda row: str(row["case_id"]))


def _sample_assignments(
    family_rows: dict[str, list[dict[str, Any]]],
    *,
    seed: int,
) -> dict[str, dict[str, list[str]]]:
    rng = random.Random(seed)
    assignments: dict[str, dict[str, list[str]]] = {}
    for family in sorted(family_rows):
        rows = sorted(family_rows[family], key=lambda row: str(row["case_id"]))
        family_assignments: dict[str, list[str]] = {}
        for row in rows:
            case_id = str(row["case_id"])
            candidates = [str(candidate["case_id"]) for candidate in rows if str(candidate["case_id"]) != case_id]
            family_assignments[case_id] = rng.sample(candidates, NEGATIVE_COUNT)
        assignments[family] = family_assignments
    return assignments


def _assignment_digest(assignments: dict[str, dict[str, list[str]]]) -> str:
    payload = [
        {"family": family, "case_id": case_id, "negative_case_ids": assignments[family][case_id]}
        for family in sorted(assignments)
        for case_id in sorted(assignments[family])
    ]
    return hashlib.sha256(json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()).hexdigest()


def _video_relpath(family: str, case_id: str) -> str:
    return f"videos/{family}/{case_id}.mp4"


def _update_video_digest(digest: Any, relpath: str, data: bytes) -> None:
    digest.update(relpath.encode())
    digest.update(b"\0")
    digest.update(data)
    digest.update(b"\n")


def _feature_yaml(indent: str = "  ") -> list[str]:
    return [
        f"{indent}- name: text",
        f"{indent}  dtype: string",
        f"{indent}- name: video",
        f"{indent}  dtype: string",
        f"{indent}- name: hard_negative_texts",
        f"{indent}  list: string",
        f"{indent}- name: hard_negative_videos",
        f"{indent}  list: string",
    ]


def _readme(
    *,
    repo_id: str,
    source_repo: str,
    seed: int,
    total_rows: int,
    metadata_size: int,
    video_size: int,
) -> str:
    total_size = metadata_size + video_size
    lines = [
        "---",
        "dataset_info:",
        "  features:",
        *_feature_yaml("  "),
        "  splits:",
        "  - name: train",
        f"    num_bytes: {total_size}",
        f"    num_examples: {total_rows}",
        f"  download_size: {total_size}",
        f"  dataset_size: {total_size}",
        "configs:",
        "- config_name: default",
        "  data_files:",
        "  - split: train",
        "    path: metadata.parquet",
        "---",
        "",
        "# Physics Bench Optics With Hard Negatives",
        "",
        f"Repository: `{repo_id}`",
        "",
        f"Source dataset: `{source_repo}`",
        "",
        "This repository uses a path-based VideoFolder-style layout for direct positive and hard-negative loading.",
        "",
        f"- rows: {total_rows}",
        "- metadata columns: `text`, `video`, `hard_negative_texts`, `hard_negative_videos`",
        "- positive text: query 1 (`__full__00`) from the source row",
        f"- hard negatives per row: {NEGATIVE_COUNT}",
        "- video paths: repository-relative `videos/<family>/<case_id>.mp4`",
        "- list alignment: `hard_negative_texts[i]` and `hard_negative_videos[i]` come from the same case",
        "- candidate pool: only the other 99 cases in the positive case's family",
        "",
        "## Reproducibility",
        "",
        f"- global seed: {seed}",
        "- traversal: family alphabetical order, then case_id order",
        "- sampling: one `random.Random(seed)` stream for all 2700 rows",
        "- selection: `random.sample(sorted(other_99_case_ids), 5)`",
        "",
        "## Loading",
        "",
        "```python",
        "from pathlib import Path",
        "import pyarrow.parquet as pq",
        "from huggingface_hub import snapshot_download",
        "",
        f"root = Path(snapshot_download(\"{repo_id}\", repo_type=\"dataset\"))",
        "rows = pq.read_table(root / \"metadata.parquet\").to_pylist()",
        "row = rows[0]",
        "positive_video = root / row[\"video\"]",
        "negative_videos = [root / path for path in row[\"hard_negative_videos\"]]",
        "```",
        "",
        "The training metadata intentionally contains only the four requested columns. Case IDs and query IDs are retained in `source_metadata/sampling_manifest.json` for auditing.",
        "",
    ]
    return "\n".join(lines)


def _validate_output(
    *,
    out_dir: Path,
    metadata_rows: list[dict[str, Any]],
    family_rows: dict[str, list[dict[str, Any]]],
    expected_total_rows: int,
    source_video_hash: str,
) -> dict[str, Any]:
    errors: list[str] = []
    table = pq.read_table(out_dir / "metadata.parquet")
    if tuple(table.column_names) != OUTPUT_COLUMNS:
        errors.append(f"metadata columns are {table.column_names}, expected {OUTPUT_COLUMNS}")
    written_rows = table.to_pylist()
    if len(written_rows) != expected_total_rows:
        errors.append(f"metadata has {len(written_rows)} rows, expected {expected_total_rows}")
    if written_rows != metadata_rows:
        errors.append("metadata changed during parquet serialization")

    source_by_path: dict[str, dict[str, Any]] = {}
    for family, rows in family_rows.items():
        for row in rows:
            source_by_path[_video_relpath(family, str(row["case_id"]))] = row

    output_digest = hashlib.sha256()
    seen_positive_paths: set[str] = set()
    hard_negative_pairs = 0
    cross_family_count = 0
    self_negative_count = 0
    for row in written_rows:
        video = str(row["video"])
        source = source_by_path.get(video)
        if source is None:
            errors.append(f"positive video path is unknown: {video}")
            continue
        family = Path(video).parts[1]
        if video in seen_positive_paths:
            errors.append(f"duplicate positive video path: {video}")
        seen_positive_paths.add(video)
        if str(row["text"]) != str(source["raw_text"]):
            errors.append(f"positive text mismatch: {video}")
        neg_texts = list(row["hard_negative_texts"])
        neg_videos = [str(path) for path in row["hard_negative_videos"]]
        if len(neg_texts) != NEGATIVE_COUNT or len(neg_videos) != NEGATIVE_COUNT:
            errors.append(f"{video}: expected five hard negatives")
        if len(set(neg_videos)) != NEGATIVE_COUNT:
            errors.append(f"{video}: hard-negative videos are not unique")
        for neg_text, neg_video in zip(neg_texts, neg_videos):
            hard_negative_pairs += 1
            neg_source = source_by_path.get(neg_video)
            if neg_source is None:
                errors.append(f"{video}: unknown hard-negative path {neg_video}")
                continue
            if Path(neg_video).parts[1] != family:
                cross_family_count += 1
            if neg_video == video:
                self_negative_count += 1
            if str(neg_text) != str(neg_source["raw_text"]):
                errors.append(f"{video}: hard-negative text/path mismatch for {neg_video}")

    if cross_family_count:
        errors.append(f"found {cross_family_count} cross-family hard negatives")
    if self_negative_count:
        errors.append(f"found {self_negative_count} self hard negatives")

    missing_videos = []
    for relpath in sorted(source_by_path):
        path = out_dir / relpath
        if not path.is_file() or path.stat().st_size == 0:
            missing_videos.append(relpath)
            continue
        _update_video_digest(output_digest, relpath, path.read_bytes())
    output_video_hash = output_digest.hexdigest()
    if output_video_hash != source_video_hash:
        errors.append("video bytes changed while creating the VideoFolder")

    return {
        "passed": not errors,
        "errors": errors[:100],
        "metadata_rows": len(written_rows),
        "metadata_columns": table.column_names,
        "video_files": len(source_by_path) - len(missing_videos),
        "missing_videos": missing_videos[:100],
        "hard_negative_pairs": hard_negative_pairs,
        "cross_family_hard_negatives": cross_family_count,
        "self_hard_negatives": self_negative_count,
        "source_video_sha256": source_video_hash,
        "output_video_sha256": output_video_hash,
        "video_bytes_preserved": source_video_hash == output_video_hash,
    }


def build_dataset(
    *,
    source_dir: Path,
    out_dir: Path,
    repo_id: str,
    source_repo: str,
    seed: int,
    expected_rows_per_family: int,
    expected_total_rows: int,
) -> dict[str, Any]:
    source_files = _source_files(source_dir)
    if not source_files:
        raise FileNotFoundError(f"No source family parquet files found under {source_dir}")
    family_rows = {family: _read_family(path) for family, path in source_files}
    bad_counts = {family: len(rows) for family, rows in family_rows.items() if len(rows) != expected_rows_per_family}
    if bad_counts:
        raise ValueError(f"Expected {expected_rows_per_family} rows per family: {bad_counts}")
    if sum(map(len, family_rows.values())) != expected_total_rows:
        raise ValueError(f"Expected {expected_total_rows} rows in total")

    assignments = _sample_assignments(family_rows, seed=seed)
    repeated = _sample_assignments(family_rows, seed=seed)
    if assignments != repeated:
        raise AssertionError("Hard-negative sampling is not reproducible")

    if out_dir.exists():
        shutil.rmtree(out_dir)
    out_dir.mkdir(parents=True)

    metadata_rows: list[dict[str, Any]] = []
    manifest_rows: list[dict[str, Any]] = []
    source_video_digest = hashlib.sha256()
    video_size = 0
    for family in sorted(family_rows):
        rows = family_rows[family]
        by_case = {str(row["case_id"]): row for row in rows}
        for source_row in rows:
            case_id = str(source_row["case_id"])
            query_id = str(source_row["query_id"])
            if not query_id.endswith("__full__00"):
                raise ValueError(f"Source row does not contain query 1: {query_id}")
            relpath = _video_relpath(family, case_id)
            video_value = source_row["video"]
            video_bytes = bytes(video_value["bytes"])
            video_path = out_dir / relpath
            video_path.parent.mkdir(parents=True, exist_ok=True)
            video_path.write_bytes(video_bytes)
            video_size += len(video_bytes)
            _update_video_digest(source_video_digest, relpath, video_bytes)

            negative_case_ids = assignments[family][case_id]
            negative_rows = [by_case[negative_id] for negative_id in negative_case_ids]
            negative_paths = [_video_relpath(family, negative_id) for negative_id in negative_case_ids]
            metadata_rows.append(
                {
                    "text": str(source_row["raw_text"]),
                    "video": relpath,
                    "hard_negative_texts": [str(row["raw_text"]) for row in negative_rows],
                    "hard_negative_videos": negative_paths,
                }
            )
            manifest_rows.append(
                {
                    "family": family,
                    "case_id": case_id,
                    "query_id": query_id,
                    "video": relpath,
                    "negative_case_ids": negative_case_ids,
                    "negative_query_ids": [str(row["query_id"]) for row in negative_rows],
                    "hard_negative_videos": negative_paths,
                }
            )

    metadata_path = out_dir / "metadata.parquet"
    table = pa.Table.from_pylist(metadata_rows, schema=OUTPUT_SCHEMA)
    pq.write_table(table, metadata_path, compression="snappy", row_group_size=100, write_page_index=True)
    source_video_hash = source_video_digest.hexdigest()
    validation = _validate_output(
        out_dir=out_dir,
        metadata_rows=metadata_rows,
        family_rows=family_rows,
        expected_total_rows=expected_total_rows,
        source_video_hash=source_video_hash,
    )
    validation.update(
        {
            "repo_id": repo_id,
            "source_repo": source_repo,
            "seed": seed,
            "family_count": len(family_rows),
            "rows_per_family": {family: len(rows) for family, rows in family_rows.items()},
            "assignment_sha256": _assignment_digest(assignments),
            "second_pass_assignment_sha256": _assignment_digest(repeated),
            "reproducibility_verified": assignments == repeated,
            "single_rng_stream": True,
            "traversal_order": "family alphabetical, then case_id",
        }
    )
    _write_json(out_dir / "quality" / "validation.json", validation)
    _write_json(
        out_dir / "generation_manifest.json",
        {
            "repo_id": repo_id,
            "source_repo": source_repo,
            "seed": seed,
            "algorithm": "one random.Random(seed) stream; sorted families; sorted case_id; random.sample(other_99, 5)",
            "family_count": len(family_rows),
            "total_rows": len(metadata_rows),
            "assignment_sha256": _assignment_digest(assignments),
        },
    )
    _write_json(out_dir / "source_metadata" / "sampling_manifest.json", manifest_rows)
    shutil.copy2(Path(__file__).resolve(), out_dir / "source_metadata" / "build_hf_hardneg_dataset.py")
    (out_dir / ".gitattributes").write_text("*.mp4 filter=lfs diff=lfs merge=lfs -text\n", encoding="utf-8")
    (out_dir / "README.md").write_text(
        _readme(
            repo_id=repo_id,
            source_repo=source_repo,
            seed=seed,
            total_rows=len(metadata_rows),
            metadata_size=metadata_path.stat().st_size,
            video_size=video_size,
        ),
        encoding="utf-8",
    )
    if not validation["passed"]:
        raise ValueError(f"Validation failed: {validation['errors'][:10]}")
    return validation


def main() -> None:
    parser = argparse.ArgumentParser(description="Build path-based optics hard-negative VideoFolder data.")
    parser.add_argument("--source-dir", type=Path, required=True)
    parser.add_argument("--out-dir", type=Path, required=True)
    parser.add_argument("--repo-id", default="gowitheflowlab/physics-bench-optics-w-hardnegs")
    parser.add_argument("--source-repo", default="gowitheflowlab/physics-bench-optics-train-2700")
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--expected-rows-per-family", type=int, default=100)
    parser.add_argument("--expected-total-rows", type=int, default=2700)
    args = parser.parse_args()
    result = build_dataset(
        source_dir=args.source_dir.resolve(),
        out_dir=args.out_dir.resolve(),
        repo_id=args.repo_id,
        source_repo=args.source_repo,
        seed=args.seed,
        expected_rows_per_family=args.expected_rows_per_family,
        expected_total_rows=args.expected_total_rows,
    )
    print(json.dumps(result, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()