benchmarks/edit/build_six_method_manifest.py

#!/usr/bin/env python3
"""Build manifests for the six val20/val100 edit-result folders."""

from __future__ import annotations

import argparse
from collections import defaultdict
import json
from pathlib import Path
from typing import Any


METHODS = ("wan_only", "ditto_global", "full", "text", "vace_hint", "vace_context")


def read_jsonl(path: Path) -> list[dict]:
    rows: list[dict] = []
    with path.open("r", encoding="utf-8") as handle:
        for line in handle:
            text = line.strip()
            if text:
                rows.append(json.loads(text))
    return rows


def load_source_maps(paths: list[Path], repo_root: Path) -> tuple[dict[str, Path], dict[str, Path]]:
    """Load optional source maps keyed by sample_id and by original path/basename."""
    by_id: dict[str, Path] = {}
    by_key: dict[str, Path] = {}
    for path in paths:
        path = path if path.is_absolute() else repo_root / path
        rows = read_jsonl(path)
        for row in rows:
            video = (
                row.get("path")
                or row.get("video")
                or row.get("source_video")
                or row.get("source_path")
                or row.get("control_video")
            )
            if not video:
                continue
            video_path = Path(str(video))
            if not video_path.is_absolute():
                video_path = repo_root / video_path
            for key_name in ("sample_id", "id"):
                if row.get(key_name) is not None:
                    by_id[str(row[key_name])] = video_path
            for key_name in ("old_path", "old_video", "control_video", "source_video"):
                if row.get(key_name) is not None:
                    key = str(row[key_name])
                    by_key[key] = video_path
                    by_key[Path(key).name] = video_path
            by_key[video_path.name] = video_path
    return by_id, by_key


def dataset_name_from_path(path: str) -> str:
    parts = Path(path).parts
    for index, part in enumerate(parts):
        if part == "datas" and index + 1 < len(parts):
            return parts[index + 1]
    return ""


def target_bucket_from_path(path: str) -> str:
    stem = Path(path).stem
    if "__" in stem:
        return stem.split("__", 1)[0]
    if "/" in path:
        parts = Path(path).parts
        if "high" in parts:
            index = parts.index("high")
            if index + 1 < len(parts):
                return parts[index + 1]
    return ""


def edit_index_from_path(path: str) -> str:
    stem = Path(path).stem
    if "_" not in stem:
        return ""
    tail = stem.rsplit("_", 1)[-1]
    return tail if tail.isdigit() else ""


def load_dataset_manifests(paths: list[Path], repo_root: Path) -> dict[tuple[str, str, str], list[dict[str, Any]]]:
    by_dataset_bucket_prompt: dict[tuple[str, str, str], list[dict[str, Any]]] = defaultdict(list)
    for path in paths:
        path = path if path.is_absolute() else repo_root / path
        if not path.exists():
            continue
        dataset = path.parent.name
        rows = json.loads(path.read_text(encoding="utf-8"))
        for row in rows:
            bucket = str(row.get("target_bucket", ""))
            prompt = str(row.get("prompt", ""))
            if dataset and bucket and prompt:
                by_dataset_bucket_prompt[(dataset, bucket, prompt)].append(row)
    return by_dataset_bucket_prompt


def resolve_source_video(
    repo_root: Path,
    sample_id: str,
    raw_path: str,
    target_path: str,
    prompt: str,
    source_roots: list[Path],
    source_by_id: dict[str, Path],
    source_by_key: dict[str, Path],
    dataset_rows: dict[tuple[str, str, str], list[dict[str, Any]]],
) -> Path:
    if sample_id in source_by_id:
        return source_by_id[sample_id]
    if raw_path in source_by_key:
        return source_by_key[raw_path]
    raw_name = Path(raw_path).name
    if raw_name in source_by_key:
        return source_by_key[raw_name]

    dataset = dataset_name_from_path(target_path) or dataset_name_from_path(raw_path)
    bucket = target_bucket_from_path(target_path)
    rows = dataset_rows.get((dataset, bucket, prompt), [])
    if rows:
        edit_index = edit_index_from_path(target_path)
        if edit_index:
            indexed_rows = [
                row
                for row in rows
                if Path(str(row.get("high_materialized") or row.get("high_video_path") or row.get("high_rel") or "")).stem.endswith(
                    f"_{edit_index}"
                )
            ]
            if len(indexed_rows) == 1:
                rows = indexed_rows
        if len(rows) == 1:
            manifest_source = rows[0].get("low_materialized") or rows[0].get("low_video_path")
            if manifest_source:
                source_from_manifest = Path(str(manifest_source))
                if not source_from_manifest.is_absolute():
                    source_from_manifest = repo_root / source_from_manifest
                return source_from_manifest

    source_video = Path(raw_path)
    if not source_video.is_absolute():
        source_video = repo_root / source_video
    if source_video.exists():
        return source_video

    # Some machines only have downloaded/evaluated outputs, not the original
    # absolute data tree. Let callers provide roots that contain source mp4s.
    for root in source_roots:
        root = root if root.is_absolute() else repo_root / root
        candidate = root / source_video.name
        if candidate.exists():
            return candidate
    for root in source_roots:
        root = root if root.is_absolute() else repo_root / root
        if not root.exists():
            continue
        matches = list(root.rglob(source_video.name))
        if matches:
            return matches[0]
    return source_video


def build_split(
    repo_root: Path,
    split: str,
    samples_path: Path,
    outputs_root: Path,
    output_path: Path,
    source_roots: list[Path],
    source_by_id: dict[str, Path],
    source_by_key: dict[str, Path],
    dataset_rows: dict[tuple[str, str, str], list[dict[str, Any]]],
) -> None:
    samples = read_jsonl(samples_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    count = 0
    missing_sources: set[str] = set()
    missing_edits: list[str] = []
    with output_path.open("w", encoding="utf-8") as handle:
        for sample in samples:
            sample_id = str(sample["id"])
            source_video = resolve_source_video(
                repo_root,
                sample_id,
                str(sample["control_video"]),
                str(sample.get("target_video", "")),
                str(sample["prompt"]),
                source_roots,
                source_by_id,
                source_by_key,
                dataset_rows,
            )
            if not source_video.exists():
                missing_sources.add(str(source_video))
            for method in METHODS:
                edited_video = outputs_root / method / f"{sample_id}.mp4"
                if not edited_video.exists():
                    missing_edits.append(str(edited_video))
                row = {
                    "split": split,
                    "sample_id": sample_id,
                    "method": method,
                    "instruction": sample["prompt"],
                    "source_video": str(source_video),
                    "edited_video": str(edited_video),
                    "source_prompt": sample.get("source_prompt", ""),
                    "target_prompt": sample.get("target_prompt", sample["prompt"]),
                }
                handle.write(json.dumps(row, ensure_ascii=False) + "\n")
                count += 1
    print(f"{split}: wrote {count} rows -> {output_path}")
    if missing_sources:
        print(f"{split}: warning missing source videos: {len(missing_sources)} unique paths")
        for path in sorted(missing_sources)[:20]:
            print(f"  missing source: {path}")
    if missing_edits:
        print(f"{split}: warning missing edited videos: {len(missing_edits)} rows")
        for path in missing_edits[:20]:
            print(f"  missing edited: {path}")


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--repo-root", type=Path, default=Path.cwd())
    parser.add_argument("--output-dir", type=Path, default=Path("out/edit_model_face_stage1/traditional_eval_manifests"))
    parser.add_argument(
        "--source-root",
        type=Path,
        action="append",
        default=[],
        help="Optional directory to search for original source mp4s by basename when eval_samples control_video is missing.",
    )
    parser.add_argument(
        "--source-map",
        type=Path,
        action="append",
        default=[],
        help=(
            "Optional JSONL map. Each row may contain sample_id/id and path/video/source_video/source_path. "
            "It can also contain old_path/old_video/control_video to map old names to current hashed paths."
        ),
    )
    parser.add_argument(
        "--dataset-manifest",
        type=Path,
        action="append",
        default=[],
        help="Dataset manifest.json used to map eval_samples target_video/prompt back to hash low_materialized paths.",
    )
    args = parser.parse_args()

    repo_root = args.repo_root.resolve()
    base = repo_root / "out/edit_model_face_stage1"
    output_dir = args.output_dir if args.output_dir.is_absolute() else repo_root / args.output_dir
    source_by_id, source_by_key = load_source_maps(args.source_map, repo_root)
    dataset_manifest_paths = args.dataset_manifest or [
        Path("datas/ditto_face/manifest.json"),
        Path("datas/ditto_face2/manifest.json"),
    ]
    dataset_rows = load_dataset_manifests(dataset_manifest_paths, repo_root)

    jobs = [
        (
            "val20",
            base / "eval_samples/val_20.jsonl",
            base / "eval_outputs",
            output_dir / "val20.jsonl",
        ),
        (
            "val100",
            base / "eval_samples/val_100.jsonl",
            base / "eval_outputs_val100",
            output_dir / "val100.jsonl",
        ),
    ]
    for split, samples_path, outputs_root, output_path in jobs:
        if not samples_path.exists():
            raise FileNotFoundError(samples_path)
        build_split(
            repo_root,
            split,
            samples_path,
            outputs_root,
            output_path,
            args.source_root,
            source_by_id,
            source_by_key,
            dataset_rows,
        )


if __name__ == "__main__":
    main()