#!/usr/bin/env python

# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
SLURM-distributed recomputation of a LeRobotDataset's ``meta/stats.json``.

Modified copy of lerobot's examples/dataset/slurm_recompute_stats.py
(feat/recompute-stats-readonly-and-visual branch) with cluster-friendly additions:

  1. --qos              : pass a SLURM QoS through to every worker's sbatch.
  2. --venv-path        : activate a venv on each worker before the python step.
  3. --env-command      : raw shell snippet injected before the python step (e.g. to
                          export HF_LEROBOT_HOME). Runs in addition to --venv-path.
  4. --chain-aggregate  : submit ``aggregate`` with an afterok dependency on
                          ``compute`` so it only runs once all shards exist
                          (no manual squeue-wait, no gap/overlap race).
  5. --update-episode-stats : in ``aggregate``, also rewrite the per-episode stats in the
                          episodes parquet so they stay consistent with meta/stats.json
                          (default: only stats.json is written).

Data access: no filesystem mount. Point HF_LEROBOT_HOME at a node-visible shared
cache (e.g. /fsx/$USER/.cache) so the dataset downloads once and all workers read
it. This is the download route; the source dataset is fetched from the Hub on the
CPU workers.

IMPORTANT — how to run (do NOT sbatch this file):
  Run it as a normal python process on the LOGIN node. datatrove submits the
  workers for you. The reference copy (--new-root) is built on the login node and
  references the shared HF cache, so /fsx must be visible there (it is).

Requires: pip install 'lerobot[dataset]' datatrove

Example (single command, compute then dependent aggregate):

    export HF_LEROBOT_HOME=/fsx/$USER/.cache

    python slurm_recompute_stats_patched.py compute \
        --repo-id behavior-1k/2026-challenge-demos \
        --new-root /fsx/$USER/behavior-1k_recomputed \
        --shard-dir /fsx/$USER/behavior-1k_recomputed/stats_shards \
        --logs-dir /fsx/$USER/logs/recompute \
        --skip-image-video 0 \
        --workers 250 \
        --partition hopper-cpu \
        --qos normal \
        --cpus-per-task 8 --mem-per-cpu 4G \
        --venv-path /fsx/$USER/venvs/lerobot/bin/activate \
        --env-command 'export HF_LEROBOT_HOME=/fsx/'"$USER"'/.cache' \
        --chain-aggregate

REHEARSE FIRST with --workers 2 --skip-image-video 1 and inspect one worker's log
under --logs-dir to confirm QoS was accepted and a numeric stats.json is written.
"""

import argparse
from pathlib import Path

from datatrove.executor import LocalPipelineExecutor
from datatrove.executor.slurm import SlurmPipelineExecutor
from datatrove.pipeline.base import PipelineStep

class ComputeEpisodeStatsShards(PipelineStep):
    """Each worker computes per-episode stats for its ``episodes[rank::world_size]`` shard."""

    def __init__(self, repo_id, root, new_root, skip_image_video, shard_dir, video_backend=None):
        super().__init__()
        self.repo_id = repo_id
        self.root = root
        self.new_root = new_root
        self.skip_image_video = skip_image_video
        self.shard_dir = shard_dir
        self.video_backend = video_backend

    def run(self, data=None, rank: int = 0, world_size: int = 1):
        # NOTE: this method is pickled and executed on a worker, where this script's module
        # globals are NOT available. Keep it self-contained: import locally and don't reference
        # module-level helpers/constants.
        import logging
        import pickle
        from pathlib import Path

        from lerobot.datasets import LeRobotDataset, compute_dataset_episode_stats
        from lerobot.utils.utils import init_logging

        init_logging()
        load_kwargs = {"video_backend": self.video_backend} if self.video_backend else {}
        root = self.new_root if self.new_root and Path(self.new_root).exists() else self.root
        dataset = LeRobotDataset(self.repo_id, root=root, **load_kwargs)

        my_episodes = list(range(dataset.meta.total_episodes))[rank::world_size]
        if not my_episodes:
            logging.info(f"Rank {rank}: no episodes assigned")
            return
        logging.info(f"Rank {rank}: {len(my_episodes)} / {dataset.meta.total_episodes} episodes")

        episode_stats = compute_dataset_episode_stats(
            dataset,
            episode_indices=my_episodes,
            skip_image_video=self.skip_image_video,
        )

        shard_dir = Path(self.shard_dir)
        shard_dir.mkdir(parents=True, exist_ok=True)
        out = shard_dir / f"episode_stats_{rank:05d}.pkl"
        with open(out, "wb") as f:
            pickle.dump(episode_stats, f)
        logging.info(f"Rank {rank}: saved {len(episode_stats)} episode stats to {out}")


class AggregateEpisodeStats(PipelineStep):
    """Merge all per-episode stat shards into meta/stats.json."""

    def __init__(
        self,
        repo_id,
        root,
        new_root,
        shard_dir,
        push_to_hub=False,
        video_backend=None,
        update_episode_stats=False,
    ):
        super().__init__()
        self.repo_id = repo_id
        self.root = root
        self.new_root = new_root
        self.shard_dir = shard_dir
        self.push_to_hub = push_to_hub
        self.video_backend = video_backend
        self.update_episode_stats = update_episode_stats

    def run(self, data=None, rank: int = 0, world_size: int = 1):
        # NOTE: pickled and executed on a worker; keep self-contained (see ComputeEpisodeStatsShards.run).
        import logging
        import pickle
        from pathlib import Path

        from lerobot.datasets import LeRobotDataset, aggregate_episode_stats
        from lerobot.utils.utils import init_logging

        init_logging()
        if rank != 0:
            return

        shard_dir = Path(self.shard_dir)
        shards = sorted(shard_dir.glob("episode_stats_*.pkl"))
        if not shards:
            raise FileNotFoundError(f"No episode stat shards found in {shard_dir}")

        # Shards map episode_index -> stats; merging by key makes a dropped shard show up as a
        # missing episode and a re-run shard overwrite rather than double-count.
        all_episode_stats = {}
        for shard in shards:
            with open(shard, "rb") as f:
                all_episode_stats.update(pickle.load(f))
        logging.info(f"Aggregating {len(all_episode_stats)} episode stats from {len(shards)} shards")

        load_kwargs = {"video_backend": self.video_backend} if self.video_backend else {}
        root = self.new_root if self.new_root and Path(self.new_root).exists() else self.root
        dataset = LeRobotDataset(self.repo_id, root=root, **load_kwargs)

        # Aggregation is order-independent, so the only way sharding changes the result is a
        # gap (dropped shard) or an overlap (episode counted twice). Verify the shards cover
        # every episode exactly once before writing stats.json.
        expected_episodes = dataset.meta.total_episodes
        if len(all_episode_stats) != expected_episodes:
            raise ValueError(
                f"Expected {expected_episodes} per-episode stats (one per episode) but got "
                f"{len(all_episode_stats)} across {len(shards)} shards. A compute shard is likely "
                "missing or was written more than once; re-run the failed shards before aggregating."
            )

        # Frame-count check catches the case where a duplicate and a gap cancel out in the
        # episode count: summed per-episode frame counts must equal the dataset's total frames.
        stats_values = list(all_episode_stats.values())
        numeric_key = next(
            (
                k
                for k, v in dataset.meta.features.items()
                if v["dtype"] not in ("image", "video", "string") and stats_values and k in stats_values[0]
            ),
            None,
        )
        if numeric_key is not None:
            total_frames = sum(int(s[numeric_key]["count"][0]) for s in stats_values)
            if total_frames != dataset.meta.total_frames:
                raise ValueError(
                    f"Summed frame count from shards ({total_frames}) != dataset total_frames "
                    f"({dataset.meta.total_frames}); episodes are double-counted or missing."
                )

        new_stats = aggregate_episode_stats(
            dataset, all_episode_stats, update_episode_stats=self.update_episode_stats
        )
        if new_stats is None:
            raise RuntimeError("Aggregation produced no stats")
        logging.info(f"Wrote stats for features: {list(new_stats.keys())} to {dataset.root}")

        if self.push_to_hub:
            logging.info(f"Pushing {self.repo_id} to hub")
            dataset.push_to_hub()


def _mem_gb(mem: str) -> int:
    """Parse '4G' / '4GB' / '4' into an int number of GB for datatrove's mem_per_cpu_gb."""
    s = str(mem).strip().lower().rstrip("b").rstrip("g")
    return int(float(s))


def _make_executor(
    pipeline,
    logs_dir,
    job_name,
    slurm,
    workers,
    tasks,
    time,
    partition,
    cpus,
    mem,
    qos=None,
    env_command=None,
    venv_path=None,
    depends=None,
):
    kwargs = {"pipeline": pipeline, "logging_dir": str(Path(logs_dir) / job_name)}
    if slurm:
        kwargs.update(
            {
                "job_name": job_name,
                "tasks": tasks,
                "workers": workers,
                "time": time,
                "partition": partition,
                "cpus_per_task": cpus,
                "mem_per_cpu_gb": _mem_gb(mem),  # datatrove's native field (int GB)
                "sbatch_args": {},
            }
        )
        if qos:
            kwargs["qos"] = qos  # -> "#SBATCH --qos=<qos>" on every worker
        if venv_path:
            kwargs["venv_path"] = venv_path  # datatrove sources this before the python step
        if env_command:
            kwargs["env_command"] = env_command  # extra raw snippet before python (composes with venv_path)
        if depends is not None:
            kwargs["depends"] = depends  # chains --dependency=afterok:<compute jobid>
        return SlurmPipelineExecutor(**kwargs)
    kwargs.update({"tasks": tasks, "workers": 1})
    return LocalPipelineExecutor(**kwargs)


def _maybe_reference_copy(repo_id, root, new_root, download_videos):
    """Create the read-only-safe reference copy once, before submitting workers.

    Loads metadata only (to resolve the source root and revision) instead of a full
    ``LeRobotDataset``, which would also memory-map the entire frame index just to read a
    path. Fetches the source into the shared cache so the copy's symlinks point at real
    files and workers don't each re-download, pulling videos only when the run needs them
    (i.e. when image/video stats are being recomputed).
    """
    if not new_root:
        return
    from huggingface_hub import snapshot_download

    from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
    from lerobot.scripts.lerobot_edit_dataset import _reference_copy_dataset
    from lerobot.utils.constants import HF_LEROBOT_HUB_CACHE

    new_root_path = Path(new_root)
    if new_root_path.exists():
        return

    meta = LeRobotDatasetMetadata(repo_id, root=Path(root) if root else None)
    ignore_patterns = None if download_videos else "videos/"
    if root:
        snapshot_download(
            repo_id,
            repo_type="dataset",
            revision=meta.revision,
            local_dir=meta.root,
            ignore_patterns=ignore_patterns,
        )
        src_root = Path(meta.root)
    else:
        src_root = Path(
            snapshot_download(
                repo_id,
                repo_type="dataset",
                revision=meta.revision,
                cache_dir=HF_LEROBOT_HUB_CACHE,
                ignore_patterns=ignore_patterns,
            )
        )
    _reference_copy_dataset(src_root, new_root_path)


def _add_shared_args(p):
    p.add_argument("--repo-id", type=str, required=True, help="Dataset identifier, e.g. 'user/dataset'.")
    p.add_argument("--root", type=str, default=None, help="Source dataset root (defaults to the Hub cache).")
    p.add_argument(
        "--new-root",
        type=str,
        default=None,
        help="Writable output root; a read-only-safe reference copy of --root. If omitted, stats "
        "are written in place at --root.",
    )
    p.add_argument("--shard-dir", type=Path, default=Path("stats_shards"), help="Per-rank shard dir.")
    p.add_argument("--logs-dir", type=Path, default=Path("logs"), help="datatrove logs dir.")
    p.add_argument("--job-name", type=str, default=None, help="SLURM job name.")
    p.add_argument("--slurm", type=int, default=1, help="1 = submit via SLURM; 0 = run locally.")
    p.add_argument("--partition", type=str, default=None, help="SLURM partition, e.g. 'hopper-cpu'.")
    p.add_argument("--qos", type=str, default=None, help="SLURM QoS, e.g. 'normal'. Passed to every worker.")
    p.add_argument("--cpus-per-task", type=int, default=4, help="CPUs per SLURM task.")
    p.add_argument("--mem-per-cpu", type=str, default="4G", help="Memory per CPU, e.g. '4G'.")
    p.add_argument(
        "--video-backend",
        type=str,
        default=None,
        help="Video decoding backend (e.g. 'pyav', 'torchcodec'). Defaults to the dataset's default; "
        "use 'pyav' if torchcodec fails to load locally.",
    )
    p.add_argument("--venv-path", type=str, default=None, help="venv activate script sourced on each worker.")
    p.add_argument(
        "--env-command",
        type=str,
        default=None,
        help="Raw shell snippet injected into each worker's sbatch before the python step "
        "(e.g. to export HF_LEROBOT_HOME). Runs in addition to --venv-path.",
    )


def main():
    parser = argparse.ArgumentParser(
        description="PATCHED SLURM-distributed LeRobotDataset stats recomputation",
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    sub = parser.add_subparsers(dest="command", required=True)

    cp = sub.add_parser("compute", help="Distribute per-episode stats across SLURM workers.")
    _add_shared_args(cp)
    cp.add_argument("--workers", type=int, default=50, help="Number of parallel SLURM tasks.")
    cp.add_argument(
        "--skip-image-video",
        type=int,
        default=1,
        help="1 = numeric features only (fast); 0 = also recompute image/video stats (decodes frames).",
    )
    cp.add_argument(
        "--chain-aggregate",
        action="store_true",
        help="After building compute, submit aggregate with an afterok dependency (single command).",
    )
    cp.add_argument("--push-to-hub", action="store_true", help="For the chained aggregate: push after done.")
    cp.add_argument(
        "--update-episode-stats",
        action="store_true",
        help="For the chained aggregate: also rewrite per-episode stats in the episodes parquet.",
    )

    ap = sub.add_parser("aggregate", help="Merge shards into meta/stats.json.")
    _add_shared_args(ap)
    ap.add_argument("--push-to-hub", action="store_true", help="Push the dataset after aggregation.")
    ap.add_argument(
        "--update-episode-stats",
        action="store_true",
        help="Also rewrite per-episode stats in the episodes parquet to match stats.json.",
    )
    ap.add_argument(
        "--depends-job-id",
        type=str,
        default=None,
        help="Optional SLURM job id; aggregate waits for it (afterok) before running.",
    )

    args = parser.parse_args()
    slurm = args.slurm == 1

    if args.command == "compute":
        # The reference copy (if any) is created once on the submitting node so workers
        # can all load --new-root without racing to build it. Videos are only fetched when
        # image/video stats are being recomputed.
        _maybe_reference_copy(
            args.repo_id, args.root, args.new_root, download_videos=not bool(args.skip_image_video)
        )

        compute_exec = _make_executor(
            pipeline=[
                ComputeEpisodeStatsShards(
                    args.repo_id,
                    args.root,
                    args.new_root,
                    bool(args.skip_image_video),
                    str(args.shard_dir),
                    args.video_backend,
                )
            ],
            logs_dir=args.logs_dir,
            job_name=args.job_name or "recompute_stats_compute",
            slurm=slurm,
            workers=args.workers,
            tasks=args.workers,
            time="24:00:00",
            partition=args.partition,
            cpus=args.cpus_per_task,
            mem=args.mem_per_cpu,
            qos=args.qos,
            env_command=args.env_command,
            venv_path=args.venv_path,
        )

        if args.chain_aggregate and slurm:
            # Build aggregate depending on compute. datatrove launches the dependency
            # (compute) first, then submits aggregate with --dependency=afterok:<jobid>.
            aggregate_exec = _make_executor(
                pipeline=[
                    AggregateEpisodeStats(
                        args.repo_id,
                        args.root,
                        args.new_root,
                        str(args.shard_dir),
                        args.push_to_hub,
                        args.video_backend,
                        args.update_episode_stats,
                    )
                ],
                logs_dir=args.logs_dir,
                job_name="recompute_stats_aggregate",
                slurm=slurm,
                workers=1,
                tasks=1,
                time="02:00:00",
                partition=args.partition,
                cpus=args.cpus_per_task,
                mem=args.mem_per_cpu,
                qos=args.qos,
                env_command=args.env_command,
                venv_path=args.venv_path,
                depends=compute_exec,
            )
            aggregate_exec.run()
        else:
            compute_exec.run()
    else:
        aggregate_exec = _make_executor(
            pipeline=[
                AggregateEpisodeStats(
                    args.repo_id,
                    args.root,
                    args.new_root,
                    str(args.shard_dir),
                    args.push_to_hub,
                    args.video_backend,
                    args.update_episode_stats,
                )
            ],
            logs_dir=args.logs_dir,
            job_name=args.job_name or "recompute_stats_aggregate",
            slurm=slurm,
            workers=1,
            tasks=1,
            time="02:00:00",
            partition=args.partition,
            cpus=args.cpus_per_task,
            mem=args.mem_per_cpu,
            qos=args.qos,
            env_command=args.env_command,
            venv_path=args.venv_path,
        )
        if args.depends_job_id is not None:
            aggregate_exec.depends_job_id = args.depends_job_id
        aggregate_exec.run()


if __name__ == "__main__":
    main()