scripts/hf_sync.py

#!/usr/bin/env python3
"""
hf_sync.py — Sync project to/from a HuggingFace dataset repository.

BOOTSTRAP (brand new VM, no scripts yet):
    # Option A — wget this script directly from HuggingFace raw:
    wget https://huggingface.co/datasets/your-org/your-project/resolve/main/scripts/hf_sync.py
    python hf_sync.py pull --profile full

    # Option B — use huggingface-cli, no script needed at all:
    pip install huggingface_hub
    huggingface-cli download your-org/your-project --repo-type dataset --local-dir .

PUSH (upload changes):
    python scripts/hf_sync.py push
    python scripts/hf_sync.py push --message "added kl_v2"

PULL PROFILES:
    # Everything
    python scripts/hf_sync.py pull --profile full

    # Inference on one task only (deployment + its model weights)
    python scripts/hf_sync.py pull --profile inference --task dist_to_main_street

    # Train a new task from scratch (scripts + configs + data, no weights)
    python scripts/hf_sync.py pull --profile core

WHAT EACH PROFILE DOWNLOADS:
    full       — everything except logs
    inference  — scripts/ + tasks/<task>/experiments/*/deployment/
                 + the specific model weight dirs listed in deployment_config.json
                 + tasks/<task>/configs/
    core       — scripts/ + configs/ (all tasks) + data/ (no weights, no experiments)

IGNORED ON PUSH (never uploaded):
    *.log, ensemble_log_*.txt    training logs
    **/__pycache__/, *.pyc       Python cache
    **/inference_cache*/         inference resume caches (all variants)
    **/val_cache/                validation-harness cache
    **/_token_cache/             MLM pretraining token arrows
    .git/, .hf_config.json       local-only files
"""

import argparse
import json
import os
import re
import sys
import time
from pathlib import Path

HF_CONFIG_FILE = ".hf_config.json"

DEFAULT_PUSH_IGNORE = [
    "*.log",
    "ensemble_log_*.txt",
    "**/__pycache__",
    "*.pyc",
    "*.pyo",
    "**/inference_cache*",
    "**/.cache",
    "**/.ipynb_checkpoints",
    "**/val_cache",
    "**/_token_cache",
    ".git",
    HF_CONFIG_FILE,
]


# =============================================================================
# PROJECT ROOT DETECTION
# =============================================================================
def get_project_root() -> Path:
    """
    Always the parent of the 'scripts' directory containing this file.
    Works whether called as 'python scripts/hf_sync.py' or 'python hf_sync.py'.
    """
    here = Path(__file__).parent.resolve()
    return here.parent if here.name == "scripts" else here


# =============================================================================
# CONFIG
# =============================================================================
def load_config(root: Path) -> dict:
    p = root / HF_CONFIG_FILE
    return json.load(open(p)) if p.exists() else {}


def save_config(root: Path, cfg: dict):
    with open(root / HF_CONFIG_FILE, "w") as f:
        json.dump(cfg, f, indent=2)


# =============================================================================
# PUSH
# =============================================================================
def cmd_push(args):
    from huggingface_hub import HfApi, create_repo

    root    = get_project_root()
    cfg     = load_config(root)
    api     = HfApi()
    repo_id = args.repo or cfg.get("repo_id")

    if not repo_id:
        print("ERROR: No repo set. Use --repo your-org/repo-name (saved after first use).")
        sys.exit(1)

    if args.create:
        create_repo(repo_id=repo_id, repo_type="dataset",
                    private=False, exist_ok=True)
        print("Repo created (or already exists): {}".format(repo_id))

    ignore = list(DEFAULT_PUSH_IGNORE) + (args.exclude or [])

    print("\nPushing to https://huggingface.co/datasets/{}".format(repo_id))
    print("Local root  : {}".format(root))
    print("Ignore      : {}".format(ignore))

    if args.dry_run:
        _dry_run_list(root, ignore)
        return

    t0 = time.perf_counter()

    # Use upload_large_folder for repos > ~20GB — it uploads in parallel chunks
    # with automatic retry and resumability. Falls back to upload_folder for
    # smaller repos where the overhead isn't worth it.
    total_mb = sum(f.stat().st_size for f in root.rglob("*") if f.is_file()) / 1e6

    if total_mb > 20_000 or args.large:
        print("Using upload_large_folder ({:.1f} GB)...".format(total_mb / 1024))
        print("Progress is printed per-shard. Safe to Ctrl+C and resume.")
        api.upload_large_folder(
            folder_path     = str(root),
            repo_id         = repo_id,
            repo_type       = "dataset",
            ignore_patterns = ignore,
        )
        commit_url = "https://huggingface.co/datasets/{}".format(repo_id)
    else:
        info = api.upload_folder(
            folder_path     = str(root),
            repo_id         = repo_id,
            repo_type       = "dataset",
            ignore_patterns = ignore,
            commit_message  = args.message or "sync: {}".format(
                                  time.strftime("%Y-%m-%d %H:%M")),
        )
        commit_url = getattr(info, "commit_url", str(info))

    elapsed = time.perf_counter() - t0
    print("\nDone in {:.1f}s — {}".format(elapsed, commit_url))

    cfg.update({"repo_id": repo_id,
                "last_push": time.strftime("%Y-%m-%d %H:%M:%S"),
                "last_commit": commit_url})
    save_config(root, cfg)
    print("Config saved to {}. Future pushes: python scripts/hf_sync.py push".format(
          HF_CONFIG_FILE))


def _dry_run_list(root: Path, ignore_patterns: list):
    import fnmatch

    def _ignored(rel: str) -> bool:
        parts = Path(rel).parts
        for pat in ignore_patterns:
            pat_clean = pat.lstrip("**/").rstrip("/")
            if fnmatch.fnmatch(rel, pat):
                return True
            if any(fnmatch.fnmatch(p, pat_clean) for p in parts):
                return True
        return False

    files = [str(p.relative_to(root)) for p in root.rglob("*") if p.is_file()]
    to_up = [f for f in files if not _ignored(f)]
    total = sum((root / f).stat().st_size for f in to_up)
    print("\n[DRY RUN] {} files, {:.1f} MB".format(len(to_up), total / 1e6))
    for f in sorted(to_up)[:60]:
        print("  {:>8.1f} MB  {}".format((root / f).stat().st_size / 1e6, f))
    if len(to_up) > 60:
        print("  ... and {} more".format(len(to_up) - 60))


# =============================================================================
# PULL — profile resolution
# =============================================================================
def _allow_patterns_for_profile(profile: str, task: str,
                                  root: Path, repo_id: str) -> list | None:
    """
    Return an allow-list of glob patterns for snapshot_download.
    None means download everything (full profile).

    Profile: full | core | inference
    """
    if profile == "full":
        return None   # no filter — download everything

    if profile == "core":
        # Scripts + all task configs + data. No experiment artifacts or weights.
        return [
            "scripts/**",
            "configs/**",
            "data/**",
            "tasks/*/configs/**",
            ".gitignore",
            "README.md",
        ]

    if profile == "inference":
        if not task:
            print("ERROR: --task is required for --profile inference")
            sys.exit(1)

        # Base: scripts + this task's configs + deployment artifacts
        patterns = [
            "scripts/**",
            "tasks/{}/configs/**".format(task),
            "tasks/{}/experiments/**/deployment/**".format(task),
            "tasks/{}/experiments/**/embeddings/**".format(task),
        ]

        # Read deployment_config.json from the local copy if it exists,
        # otherwise we can't know which model weights are needed yet —
        # in that case include all experiment artifacts for this task.
        dep_cfg = _find_deployment_config(root, task)
        if dep_cfg:
            weight_patterns = _weight_patterns_from_config(dep_cfg, task)
            patterns.extend(weight_patterns)
            print("  Deployment config found — downloading {} model weight pattern(s).".format(
                  len(weight_patterns)))
        else:
            # No local config yet — download full task experiments
            print("  No local deployment_config.json found for task '{}'.".format(task))
            print("  Downloading all experiment artifacts for this task.")
            patterns.append("tasks/{}/experiments/**".format(task))

        return patterns

    print("ERROR: Unknown profile '{}'. Use full / core / inference.".format(profile))
    sys.exit(1)


def _find_deployment_config(root: Path, task: str) -> dict | None:
    """
    Look for deployment_config.json under tasks/<task>/experiments/*/deployment/.
    Returns the first one found, or None.
    """
    task_dir = root / "tasks" / task / "experiments"
    if not task_dir.exists():
        # Also check legacy flat structure: experiments/<task>/
        task_dir = root / "experiments"
    for p in task_dir.rglob("deployment_config.json"):
        try:
            return json.load(open(p))
        except Exception:
            pass
    return None


def _weight_patterns_from_config(dep_cfg: dict, task: str) -> list:
    """
    Extract HuggingFace glob patterns for the model weight directories
    referenced in a deployment_config.json.

    We match by clean_name inside the known artifacts directory structure.
    Each model needs: pretrained_checkpoints__<safe_name>/ (all folds).
    """
    patterns = []
    for m in dep_cfg.get("models", []):
        art_dir  = m.get("artifacts_dir", "")
        mname    = m.get("model_name", "")
        split_unk = m.get("split_unknown_stage", False)

        # Derive the safe artifact directory name used on disk
        if split_unk:
            for stage in ["stage1", "stage2"]:
                safe = (mname + "__" + stage).replace("/", "__")
                # Match relative to project root — strip absolute prefix
                pat = _make_relative_glob(art_dir, safe, task)
                if pat:
                    patterns.append(pat)
        else:
            safe = mname.replace("/", "__")
            pat  = _make_relative_glob(art_dir, safe, task)
            if pat:
                patterns.append(pat)

    return patterns


def _make_relative_glob(art_dir: str, safe_model_name: str, task: str) -> str | None:
    """
    Convert an absolute artifacts_dir + safe model name into a glob pattern
    relative to the project root.

    Examples:
      /home/user/tasks/dist_to_main_street/experiments/ce_v1/artifacts
      + pretrained_checkpoints__MikeGreen2710__mlm_listing__stage1
      ->  tasks/dist_to_main_street/experiments/ce_v1/artifacts/pretrained_checkpoints__MikeGreen2710__mlm_listing__stage1/**

    Falls back to a task-scoped glob if the absolute path can't be parsed.
    """
    if not art_dir:
        return None

    art_path = Path(art_dir)
    # Try to find 'tasks' or 'experiments' anchor in the path parts
    parts = art_path.parts
    for anchor in ("tasks", "experiments"):
        if anchor in parts:
            idx     = list(parts).index(anchor)
            rel_dir = Path(*parts[idx:])
            return str(rel_dir / safe_model_name) + "/**"

    # Fallback: just use task-scoped wildcard
    return "tasks/{}/**/{}/{safe}/**".format(task, safe_model_name)


# =============================================================================
# PULL COMMAND
# =============================================================================
def cmd_pull(args):
    from huggingface_hub import snapshot_download

    root    = Path(args.dest).resolve() if args.dest else get_project_root()
    cfg     = load_config(root)
    repo_id = args.repo or cfg.get("repo_id")

    if not repo_id:
        print("ERROR: No repo specified. Use --repo your-org/repo-name")
        print("\nBootstrap from scratch:")
        print("  pip install huggingface_hub && huggingface-cli login")
        print("  huggingface-cli download your-org/repo --repo-type dataset --local-dir .")
        sys.exit(1)

    profile  = args.profile
    task     = args.task
    patterns = _allow_patterns_for_profile(profile, task, root, repo_id)

    print("Pulling from https://huggingface.co/datasets/{}".format(repo_id))
    print("Profile     : {}".format(profile) +
          (" (task={})".format(task) if task else ""))
    print("Destination : {}".format(root))
    if patterns is not None:
        print("Patterns    : {} allow pattern(s)".format(len(patterns)))
        for p in patterns:
            print("  {}".format(p))
    else:
        print("Patterns    : all files")

    if not args.yes:
        confirm = input("\nContinue? [y/N] ").strip().lower()
        if confirm != "y":
            print("Aborted.")
            return

    root.mkdir(parents=True, exist_ok=True)
    t0 = time.perf_counter()

    local_dir = snapshot_download(
        repo_id         = repo_id,
        repo_type       = "dataset",
        local_dir       = str(root),
        allow_patterns  = patterns,
        ignore_patterns = [HF_CONFIG_FILE],
    )

    elapsed = time.perf_counter() - t0
    print("\nDownloaded in {:.1f}s -> {}".format(elapsed, local_dir))

    cfg.update({"repo_id": repo_id,
                "last_pull": time.strftime("%Y-%m-%d %H:%M:%S"),
                "last_pull_profile": profile})
    save_config(root, cfg)

    # Print next-step hints
    print("\nNext steps:")
    if profile == "inference":
        print("  python scripts/meta_learner_inference.py \\")
        print("      --config tasks/{}/experiments/.../deployment/deployment_config.json \\".format(
              task or "<task>"))
        print("      --data_path data/<new_data>.parquet \\")
        print("      --text_col text --output_path data/predictions.parquet --device cuda")
    elif profile == "core":
        print("  # Train a new task:")
        print("  python scripts/ensemble_distillation_generator.py \\")
        print("      --ensemble_config_path tasks/<new_task>/configs/ce_ensemble.json \\")
        print("      --artifacts_dir tasks/<new_task>/experiments/ce_v1/artifacts \\")
        print("      --data_path data/<labelled_data>.parquet ...")
    else:
        print("  python scripts/hf_sync.py push   # to sync changes back")


# =============================================================================
# STATUS
# =============================================================================
def cmd_status(args):
    root = get_project_root()
    cfg  = load_config(root)
    if not cfg:
        print("No HF config. Run: python scripts/hf_sync.py push --repo your-org/repo --create")
        return
    print("HuggingFace Sync Status")
    print("  Project root : {}".format(root))
    print("  Repo         : {}".format(cfg.get("repo_id", "not set")))
    print("  Last push    : {}".format(cfg.get("last_push", "never")))
    print("  Last pull    : {}".format(cfg.get("last_pull", "never")))
    if cfg.get("repo_id"):
        print("  URL          : https://huggingface.co/datasets/{}".format(cfg["repo_id"]))


# =============================================================================
# PARSE ARGS
# =============================================================================
def parse_args():
    p = argparse.ArgumentParser(
        description="Sync project to/from HuggingFace dataset repo.",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=__doc__,
    )
    sub = p.add_subparsers(dest="command", required=True)

    # push
    push = sub.add_parser("push", help="Upload changed files to HuggingFace.")
    push.add_argument("--repo",    type=str, default=None)
    push.add_argument("--create",  action="store_true",
                      help="Create the repo if it doesn't exist.")
    push.add_argument("--public",  action="store_true",
                      help="Make the repo public (default: private).")
    push.add_argument("--message", type=str, default=None,
                      help="Commit message.")
    push.add_argument("--exclude", type=str, nargs="*", default=[],
                      help="Extra glob patterns to exclude.")
    push.add_argument("--dry_run", action="store_true",
                      help="Print what would be uploaded without uploading.")
    push.add_argument("--large",   action="store_true",
                      help="Force upload_large_folder even for small repos. "
                           "Auto-selected for repos > 20GB.")

    # pull
    pull = sub.add_parser("pull", help="Download from HuggingFace.")
    pull.add_argument("--repo",    type=str, default=None)
    pull.add_argument("--dest",    type=str, default=None,
                      help="Destination directory (default: project root).")
    pull.add_argument("--profile", type=str, default="full",
                      choices=["full", "core", "inference"],
                      help="What to download: full / core / inference.")
    pull.add_argument("--task",    type=str, default=None,
                      help="Task name for --profile inference, "
                           "e.g. dist_to_main_street.")
    pull.add_argument("--yes",     action="store_true",
                      help="Skip confirmation.")

    # status
    sub.add_parser("status", help="Show sync status.")

    return p.parse_args()


# =============================================================================
# MAIN
# =============================================================================
def main():
    args = parse_args()
    try:
        import huggingface_hub  # noqa
    except ImportError:
        print("ERROR: pip install huggingface_hub")
        sys.exit(1)

    {"push": cmd_push, "pull": cmd_pull, "status": cmd_status}[args.command](args)


if __name__ == "__main__":
    main()