"""Wiki package flow orchestration; all remote writes use WeKnora APIs."""

from __future__ import annotations

import asyncio

from starlette.requests import Request


async def sync_to_global(app, mapping):
    mapping_id = str(mapping["id"])
    # Multiple local-index triggers (manual, mutation debounce and periodic
    # reconciliation) may finish close together.  Coalesce the check/create/
    # import sequence per mapping inside this Gateway process; the repository's
    # database base-row lock remains the cross-process authority.
    locks = getattr(app.state, "knowledge_transfer_locks", None)
    if locks is None:
        locks = {}
        app.state.knowledge_transfer_locks = locks
    lock = locks.get(mapping_id)
    if lock is None:
        lock = asyncio.Lock()
        locks[mapping_id] = lock
    async with lock:
        return await _sync_to_global_locked(app, mapping, mapping_id=mapping_id)


async def _sync_to_global_locked(app, mapping, *, mapping_id: str):
    from app.gateway.routers.assistant_knowledge import _export_weknora_then_import_for_job

    if mapping.get("is_conversation_deposit") or mapping.get("owner_user_id") == "system":
        return {"status": "excluded"}
    store = app.state.assistant_knowledge_store
    revision, fingerprint, _ = await app.state.llmwiki_index_store.get_index_revision(mapping_id)
    base = await store.initialize(created_by=None)
    for source in await store.list_sources(base_id=base["id"]):
        meta = source.get("metadata") or {}
        if source.get("source_key") == mapping_id and meta.get("index_revision") == revision:
            job = await store.get_job(source.get("last_import_job_id")) if source.get("last_import_job_id") else None
            if job and job.get("status") == "completed":
                return {"status": "unchanged", "job_id": job["id"], "base_id": base["id"]}
    source = {"provider": "weknora", "type": "weknora", "knowledge_base_id": mapping_id, "knowledge_base_name": mapping["name"], "index_revision": revision, "embedding_fingerprint": fingerprint}
    job = await store.create_queued_import_job(source_type="weknora", source_key=mapping_id, source_name=mapping["name"], trigger="vectorization_completed", created_by=None, metadata=source, mapping_id=mapping_id, base_id=base["id"])
    await _export_weknora_then_import_for_job(request=Request({"type": "http", "app": app}), job_id=job["id"], mapping=mapping, remote_id=mapping["weknora_id"], source=source, actor=None, index_fresh=True)
    result = await store.get_job(job["id"])
    if not result or result.get("status") != "completed":
        raise RuntimeError(f"向量化已完成，但知识梳理同步失败：{(result or {}).get('last_error_message') or job['id']}")
    return {"status": "completed", "job_id": job["id"], "base_id": base["id"], "counts": result.get("counts")}