#!/usr/bin/env python3
"""Migrate the prebuilt copernicus-rag-core Qdrant indexes into a Qdrant SERVER.

Downloads indexes/*.tar.gz from the HF dataset (or uses --source-dir if you
already have them), opens each embedded index locally, and streams every
collection into the target server: identical vectors (dense 768 + sparse BM25),
identical payloads (incl. the relinked publication<->dataset fields), identical
payload indexes. No embedding model or API key of any kind is needed.

Usage:
    python load_all.py --url http://localhost:6333
    python load_all.py --url http://localhost:6333 --collections publications
    python load_all.py --url https://<cluster>.cloud.qdrant.io --api-key <key>
    python load_all.py --url ... --source-dir ./indexes_untarred   # skip download

Resumable: a collection already on the server with the full point count is
skipped; pass --recreate to force a clean re-copy.
"""
import argparse
import os
import sys
import tarfile
import tempfile
import time
from pathlib import Path

from qdrant_client import QdrantClient, models

REPO = "dmpantiu/copernicus-rag-core"
TARBALLS = {
    "qdrant_marine_and_cards.tar.gz": ["marine_docs", "copernicus_docs"],
    "qdrant_cds_docs.tar.gz": ["cds_docs"],
    "qdrant_eqc_qa.tar.gz": ["eqc_qa"],
    "qdrant_publications.tar.gz": ["publications"],
}
BATCH = 512

# The embedded (local-mode) indexes cannot persist payload indexes, so they are
# re-created here exactly as the original loaders defined them.
K, I, B = "keyword", "integer", "bool"
PAYLOAD_INDEXES = {
    "marine_docs": {"product_id": K, "doc_type": K, "chunk_type": K, "section_path": K},
    "copernicus_docs": {"product_id": K, "doc_type": K, "store": K},
    "cds_docs": {"dataset_ids": K, "store": K, "doc_type": K, "doc_url": K},
    "eqc_qa": {"dataset_id": K, "store": K, "doc_type": K, "aspect": K},
    "publications": {"doi": K, "paper_id": K, "journal": K, "year": I,
                     "domains": K, "orphan": B, "linked_products": K, "chunk_type": K},
}


def log(msg):
    print(f"[load_all] {msg}", flush=True)


def fetch_and_untar(work: Path, only: set[str] | None) -> dict[str, Path]:
    """Download needed tarballs from HF and untar. Returns {tarball: qdrant_db dir}."""
    from huggingface_hub import hf_hub_download
    token = os.environ.get("HF_TOKEN")
    if not token:
        sys.exit("HF_TOKEN env var required to download the private dataset "
                 "(or pre-download and use --source-dir).")
    out = {}
    for tb, colls in TARBALLS.items():
        if only and not (set(colls) & only):
            continue
        dest = work / tb.replace(".tar.gz", "")
        if (dest / "qdrant_db").exists():
            log(f"{tb}: already untarred, reusing")
        else:
            log(f"downloading {tb} ...")
            p = hf_hub_download(REPO, f"indexes/{tb}", repo_type="dataset",
                                token=token, local_dir=work / "_dl")
            dest.mkdir(parents=True, exist_ok=True)
            log(f"untarring {tb} ...")
            with tarfile.open(p) as t:
                t.extractall(dest)
        out[tb] = dest / "qdrant_db"
    return out


def source_dirs(src: Path, only: set[str] | None) -> dict[str, Path]:
    """Use pre-untarred dirs: <src>/<tarball-stem>/qdrant_db."""
    out = {}
    for tb, colls in TARBALLS.items():
        if only and not (set(colls) & only):
            continue
        d = src / tb.replace(".tar.gz", "") / "qdrant_db"
        if not d.exists():
            sys.exit(f"missing {d} — untar indexes/{tb} there, "
                     f"or drop --source-dir to auto-download")
        out[tb] = d
    return out


def migrate_collection(local: QdrantClient, remote: QdrantClient,
                       coll: str, recreate: bool):
    info = local.get_collection(coll)
    total = local.count(coll).count

    skip_first = 0
    if remote.collection_exists(coll):
        have = remote.count(coll).count
        if have == total and not recreate:
            log(f"{coll}: server already has {have}/{total} points — skip "
                f"(--recreate to force)")
            return
        if recreate or have == 0 or have > total:
            log(f"{coll}: server has {have}/{total} — recreating")
            remote.delete_collection(coll)
        else:
            # Interrupted copy: local scroll order is deterministic and
            # upserts are idempotent, so resume with an overlap margin.
            skip_first = max(0, have - 8 * BATCH)
            log(f"{coll}: server has {have}/{total} — resuming from "
                f"~{skip_first} (with overlap)")

    if not remote.collection_exists(coll):
        remote.create_collection(
            collection_name=coll,
            vectors_config=info.config.params.vectors,
            sparse_vectors_config=info.config.params.sparse_vectors,
            on_disk_payload=True,
        )
        indexes = {f: s.data_type for f, s in (info.payload_schema or {}).items()}
        if not indexes:
            indexes = PAYLOAD_INDEXES.get(coll, {})
        for field, schema in indexes.items():
            remote.create_payload_index(collection_name=coll, field_name=field,
                                        field_schema=schema)
        log(f"{coll}: created (vectors={list(info.config.params.vectors)}, "
            f"sparse={list(info.config.params.sparse_vectors or {})}, "
            f"payload indexes={list(indexes)})")

    done, seen, offset, t0 = 0, 0, None, time.time()
    while True:
        points, offset = local.scroll(coll, limit=BATCH, offset=offset,
                                      with_payload=True, with_vectors=True)
        if not points:
            break
        seen += len(points)
        if seen > skip_first:
            batch = points if seen - len(points) >= skip_first else \
                points[-(seen - skip_first):]
            remote.upsert(coll, wait=False, points=[
                models.PointStruct(id=p.id, vector=p.vector, payload=p.payload)
                for p in batch])
            done += len(batch)
        if seen % (BATCH * 40) == 0 or offset is None:
            rate = done / max(time.time() - t0, 1e-9)
            log(f"{coll}: {skip_first + done}/{total} ({rate:.0f} pts/s live)")
        if offset is None:
            break
    time.sleep(2)  # let async upserts settle before the count check

    got = remote.count(coll).count
    status = "OK" if got == total else "MISMATCH"
    log(f"{coll}: {status} — server {got} / source {total}")
    if got != total:
        sys.exit(f"{coll}: point count mismatch, aborting")


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--url", required=True, help="Qdrant server URL, e.g. http://localhost:6333")
    ap.add_argument("--api-key", default=None)
    ap.add_argument("--source-dir", default=None,
                    help="dir with pre-untarred indexes (skips HF download)")
    ap.add_argument("--collections", nargs="*", default=None,
                    help="subset, e.g. --collections publications eqc_qa")
    ap.add_argument("--recreate", action="store_true")
    a = ap.parse_args()

    only = set(a.collections) if a.collections else None
    remote = QdrantClient(url=a.url, api_key=a.api_key, timeout=120)
    remote.get_collections()  # fail fast if unreachable

    if a.source_dir:
        dirs = source_dirs(Path(a.source_dir), only)
    else:
        work = Path(tempfile.gettempdir()) / "copernicus_rag_indexes"
        work.mkdir(parents=True, exist_ok=True)
        log(f"workdir: {work}")
        dirs = fetch_and_untar(work, only)

    for tb, db_dir in dirs.items():
        log(f"opening embedded index {db_dir}")
        local = QdrantClient(path=str(db_dir))
        try:
            for coll in TARBALLS[tb]:
                if only and coll not in only:
                    continue
                migrate_collection(local, remote, coll, a.recreate)
        finally:
            local.close()

    log("ALL DONE. Server collections:")
    for c in remote.get_collections().collections:
        log(f"  {c.name}: {remote.count(c.name).count} points")


if __name__ == "__main__":
    main()