"""Orchestrator for the External Precedent Library.

Ingest pipeline (one upload):
    file → extract_text → proofread → INSERT case_law (source_kind='external_upload')
        → chunk → embed → store precedent_chunks
        → halacha_extractor.extract → embed halachot → store halachot
        → set extraction_status='completed'

Progress is reported via a caller-supplied async callback so the
web layer can pipe updates into the existing Redis ProgressStore /
SSE plumbing without this module knowing about Redis.
"""

from __future__ import annotations

import asyncio
import logging
import os
from pathlib import Path
from typing import Awaitable, Callable
from uuid import UUID

from legal_mcp import config
from legal_mcp.services import db, embeddings, hybrid_search, ingest  # noqa: F401

# Note: halacha_extractor and precedent_metadata_extractor are NOT imported
# at module load. They are imported lazily inside the dedicated re-extract
# entry points so that `ingest_precedent` (called from the FastAPI container,
# where `claude` CLI is unavailable) cannot accidentally pull them in. See
# the architectural rule in services/claude_session.py.

logger = logging.getLogger(__name__)


ProgressCb = Callable[[str, int, str], Awaitable[None]]


PRECEDENT_LIBRARY_DIR = Path(config.DATA_DIR) / "precedent-library"


_VALID_PRACTICE_AREAS = frozenset({"", "rishuy_uvniya", "betterment_levy", "compensation_197"})
_VALID_SOURCE_TYPES = frozenset({"", "court_ruling", "appeals_committee"})
_VALID_PRECEDENT_LEVELS = {
    "", "עליון", "מנהלי", "ועדת_ערר_ארצית", "ועדת_ערר_מחוזית",
    "supreme", "administrative", "national_appeals_committee", "district_appeals_committee",
}


async def _noop_progress(_status: str, _percent: int, _msg: str) -> None:
    return None


def _external_validate(inputs: dict) -> None:
    citation = (inputs.get("citation") or "").strip()
    if not citation:
        raise ValueError("citation is required")
    if citation.startswith(("ערר ", "ערר(", 'בל"מ ', 'בל"מ(', "ARAR ")):
        raise ValueError(
            "ציטוט שמתחיל ב-'ערר' או 'בל\"מ' הוא החלטת ועדת ערר. "
            "השתמש ב-internal_decision_upload (דורש chair_name + district), "
            "לא ב-precedent_library_upload."
        )


def _external_staging_subdir(inputs: dict) -> str:
    st = inputs.get("source_type") or ""
    return st if st in {"court_ruling", "appeals_committee"} else "other"


async def _create_external_record(**kw) -> dict:
    """Adapter: maps canonical inputs (citation) to create_external_case_law(case_number)."""
    return await db.create_external_case_law(
        case_number=kw["citation"].strip(),
        case_name=kw["case_name"],
        full_text=kw["full_text"],
        court=(kw.get("court") or "").strip(),
        decision_date=kw.get("decision_date"),
        practice_area=kw.get("practice_area", ""),
        appeal_subtype=(kw.get("appeal_subtype") or "").strip(),
        subject_tags=list(kw.get("subject_tags") or []),
        summary=(kw.get("summary") or "").strip(),
        headnote=(kw.get("headnote") or "").strip(),
        source_type=kw.get("source_type", ""),
        precedent_level=kw.get("precedent_level", ""),
        is_binding=kw.get("is_binding", True),
        document_id=kw.get("document_id"),
    )


_EXTERNAL_SPEC = ingest.IntakeSpec(
    source_kind="external_upload",
    id_field="citation",
    staging_root=PRECEDENT_LIBRARY_DIR,
    staging_subdir=_external_staging_subdir,
    validate=_external_validate,
    enum_fields={"practice_area": _VALID_PRACTICE_AREAS, "source_type": _VALID_SOURCE_TYPES},
    derive=lambda inputs: {},
    display_name_fallback="citation",
    create_record=_create_external_record,
)


async def ingest_precedent(
    *,
    file_path: str | Path,
    citation: str,
    case_name: str = "",
    court: str = "",
    decision_date=None,
    source_type: str = "",
    precedent_level: str = "",
    practice_area: str = "",
    appeal_subtype: str = "",
    subject_tags: list[str] | None = None,
    is_binding: bool = True,
    headnote: str = "",
    summary: str = "",
    document_id: UUID | None = None,
    progress: ingest.ProgressCb | None = None,
) -> dict:
    """Ingest one external precedent. Thin wrapper over the canonical pipeline."""
    inputs = {
        "citation": citation, "case_name": case_name, "court": court,
        "decision_date": decision_date, "source_type": source_type,
        "precedent_level": precedent_level, "practice_area": practice_area,
        "appeal_subtype": appeal_subtype, "subject_tags": subject_tags,
        "is_binding": is_binding, "headnote": headnote, "summary": summary,
    }
    return await ingest.ingest_document(
        _EXTERNAL_SPEC, inputs=inputs, file_path=file_path,
        document_id=document_id, progress=progress,
    )


async def reextract_halachot(
    case_law_id: UUID | str,
    progress: ProgressCb | None = None,
) -> dict:
    """Re-run the halacha extractor on an existing precedent. Idempotent.

    Chair-approved / published halachot are PRESERVED across the re-extract
    (INV-G10) — only un-reviewed rows are replaced. See
    ``db.reset_halacha_extraction`` / TaskMaster #108.

    **MCP-tool-only path.** This function calls into ``halacha_extractor``,
    which calls ``claude_session`` — the local CLI is required. Invoking
    this from the FastAPI container will raise ``Claude CLI not found``.
    See the architectural rule in ``services/claude_session.py``.
    """
    from legal_mcp.services import halacha_extractor

    progress = progress or _noop_progress
    if isinstance(case_law_id, str):
        case_law_id = UUID(case_law_id)

    record = await db.get_case_law(case_law_id)
    if not record:
        raise ValueError("precedent not found")
    # Was restricted to source_kind='external_upload'; opened 2026-05-06 so
    # internal_committee rows can also be re-extracted when ingest produced
    # bad data. See note in db.request_metadata_extraction.

    await progress("extracting_halachot", 50, "מחלץ הלכות מחדש")
    # Explicit re-extraction = clean slate (force): drop un-reviewed halachot +
    # clear per-chunk checkpoints and redo all, but PRESERVE chair-approved /
    # published rows (INV-G10; dedup-on-insert avoids duplicating them). (Queue
    # draining / resume uses force=False so an interrupted run continues.)
    result = await halacha_extractor.extract(case_law_id, force=True)
    # Clear the queue timestamp on completion so the UI badge / worker queue
    # don't keep showing this row. The queue worker (process_pending_extractions)
    # already does this; mirror it here so per-record extraction drains too.
    if result.get("status") in ("completed", "no_halachot"):
        await db.clear_extraction_request(case_law_id, kind="halacha")
    await progress(
        "completed",
        100,
        f"הופקו {result.get('stored', 0)} הלכות (ממתינות לאישור)",
    )
    return result


# Wait this many seconds between precedents in a multi-precedent run.
# Anthropic rate-limits across the org, so back-to-back extractions of large
# rulings (e.g. 129 chunks for one, then 79 for another) can spill the second
# precedent into a 429 storm. Observed 2026-05-03: 1110/20 succeeded with 9
# halachot, 317/10 immediately after returned silent no_halachot.
INTER_PRECEDENT_COOLDOWN_SEC = 30
# Metadata extraction is on Gemini (fast, high rate limits) — a brief spacer is
# enough; the 30s above is for the Claude-backed halacha path.
METADATA_COOLDOWN_SEC = float(os.environ.get("METADATA_COOLDOWN_SEC", "2"))

# How many times to retry a precedent that came back as 'extraction_failed'
# (i.e. >50% chunks crashed). Each retry uses a longer cooldown.
PRECEDENT_RETRY_ATTEMPTS = 1
PRECEDENT_RETRY_COOLDOWN_SEC = 60


async def process_pending_extractions(kind: str = "metadata", limit: int = 20) -> dict:
    """Drain the extraction queue (UI-button-stamped requests).

    The button in the web UI cannot run claude_session itself (it lives in
    the container, no CLI). It just stamps ``metadata_extraction_requested_at``
    on the row. This function — called from local Claude Code via the MCP
    tool — picks each stamped row up, runs the extractor, and clears the
    timestamp.

    Sequencing: precedents are processed serially (never in parallel) and
    each is followed by a short cooldown so the Anthropic rate-limit
    counter has time to drain before the next big precedent starts. If
    halacha extraction comes back as ``extraction_failed`` we retry the
    same precedent once with a longer cooldown — matching the empirical
    pattern where the second precedent in a back-to-back run gets
    rate-limited but recovers after a brief pause.

    Args:
        kind: 'metadata' or 'halacha'.
        limit: max rows to process this run.
    """
    from legal_mcp.services import halacha_extractor, precedent_metadata_extractor

    if kind not in {"metadata", "halacha"}:
        raise ValueError("kind must be 'metadata' or 'halacha'")

    # Self-heal stale 'processing' rows (fully unattended): a drain that crashed
    # mid-extraction can leave a row status='processing' with its requested_at
    # cleared — orphaned, so it would never be re-picked. Re-stamp it so it
    # re-drains (the halacha extractor uses force=False → resumes from chunk
    # checkpoints, no duplicates). Safe under the global advisory lock (only one
    # drain runs at a time). Mirrors the digests-drain self-heal.
    healed = await db.requeue_stale_processing_extractions(kind=kind)
    if healed:
        logger.warning("self-healed %d stale '%s' processing row(s)", healed, kind)

    pending = await db.list_pending_extraction_requests(kind=kind, limit=limit)
    if not pending:
        return {"status": "no_pending", "kind": kind, "processed": 0, "results": []}

    async def _run_once(cid: UUID) -> dict:
        if kind == "metadata":
            return await precedent_metadata_extractor.extract_and_apply(cid)
        # Bulk queue-drain → lighter effort (config.HALACHA_BULK_EXTRACT_EFFORT,
        # default 'high') to cut wall-clock at scale. Resume (force=False) so an
        # interrupted drain continues per-chunk. Single re-extract stays xhigh.
        return await halacha_extractor.extract(
            cid, effort=config.HALACHA_BULK_EXTRACT_EFFORT,
        )

    # Metadata extraction runs on Gemini (high rate limits, fast) — the long
    # cooldown is only needed for halacha (Claude/Anthropic rate limits).
    cooldown = METADATA_COOLDOWN_SEC if kind == "metadata" else INTER_PRECEDENT_COOLDOWN_SEC
    results: list[dict] = []
    processed = 0
    for idx, row in enumerate(pending):
        if idx > 0:
            await asyncio.sleep(cooldown)
        cid = UUID(str(row["id"]))
        attempts = 0
        result: dict = {}
        try:
            # Flip to 'processing' so the UI badge shows live progress while
            # this row is being worked (metadata has no per-chunk status of
            # its own — this is the only signal). Halacha already sets its own
            # 'processing' inside the extractor.
            if kind == "metadata":
                await db.set_case_law_metadata_status(cid, "processing")
            result = await _run_once(cid)
            # Retry only on systematic extraction failure (rate-limit storm).
            # Don't retry on 'no_halachot' — that means Claude looked and
            # genuinely found nothing.
            while (
                result.get("status") == "extraction_failed"
                and attempts < PRECEDENT_RETRY_ATTEMPTS
            ):
                attempts += 1
                logger.warning(
                    "process_pending_extractions: %s returned extraction_failed "
                    "(%d/%d chunks crashed), retry %d/%d after %ds cooldown",
                    cid,
                    result.get("failed_chunks", 0),
                    result.get("total_chunks", 0),
                    attempts, PRECEDENT_RETRY_ATTEMPTS,
                    PRECEDENT_RETRY_COOLDOWN_SEC,
                )
                await asyncio.sleep(PRECEDENT_RETRY_COOLDOWN_SEC)
                result = await _run_once(cid)

            # Finalise: success or terminal failure both clear the request
            # so the queue moves on. (Use 'failed' DB state for terminal
            # extraction_failed so the UI shows the warning chip.)
            if kind == "halacha":
                if result.get("status") == "extraction_failed":
                    await db.set_case_law_halacha_status(cid, "failed")
                await db.clear_extraction_request(cid, kind=kind)
            else:
                # metadata — set terminal 'completed' status (also clears the
                # request timestamp) so the UI badge settles instead of
                # lingering on 'processing'.
                await db.set_case_law_metadata_status(cid, "completed")
            processed += 1
            results.append({
                "case_law_id": str(cid),
                "case_number": row.get("case_number", ""),
                "status": result.get("status", "unknown"),
                "fields": result.get("fields", []),
                "stored": result.get("stored", 0),
                "retry_attempts": attempts,
            })
        except Exception as e:
            logger.exception("process_pending_extractions failed for %s: %s", cid, e)
            # Don't clear the request — it stays for the next run. But for
            # metadata, revert the badge from 'processing' back to 'pending'
            # (the timestamp is preserved) so the row shows "בתור" rather than
            # a stuck "מחלץ" until the retry picks it up.
            if kind == "metadata":
                try:
                    await db.set_case_law_metadata_status(cid, "pending")
                except Exception:
                    logger.exception("failed to revert metadata status for %s", cid)
            results.append({
                "case_law_id": str(cid),
                "case_number": row.get("case_number", ""),
                "status": "failed",
                "error": str(e),
                "retry_attempts": attempts,
            })

    return {
        "status": "completed",
        "kind": kind,
        "processed": processed,
        "total_pending": len(pending),
        "results": results,
    }


async def reextract_metadata(
    case_law_id: UUID | str,
    progress: ProgressCb | None = None,
) -> dict:
    """Re-run metadata extraction on an existing precedent.

    Only fills empty fields (subject_tags, summary, headnote, key_quote,
    appeal_subtype, and case_name when it equals the citation). User
    values are preserved.

    **MCP-tool-only path** — same constraint as :func:`reextract_halachot`.
    """
    from legal_mcp.services import precedent_metadata_extractor

    progress = progress or _noop_progress
    if isinstance(case_law_id, str):
        case_law_id = UUID(case_law_id)

    record = await db.get_case_law(case_law_id)
    if not record:
        raise ValueError("precedent not found")
    # See note in db.request_metadata_extraction — opened to all source kinds.

    # Mark 'processing' so a concurrent UI poll shows the live badge.
    await db.set_case_law_metadata_status(case_law_id, "processing")
    await progress("extracting_metadata", 40, "מחלץ מטא-דאטה (תקציר, תגיות)")
    result = await precedent_metadata_extractor.extract_and_apply(case_law_id)
    # Settle to terminal 'completed' (also NULLs the queue timestamp) so the
    # UI / worker stop showing this row. See note in reextract_halachot.
    if result.get("status") in ("completed", "no_changes"):
        await db.set_case_law_metadata_status(case_law_id, "completed")
    else:
        # e.g. 'no_metadata' (no full_text) — don't leave the badge stuck on
        # 'processing'; revert to 'pending' (preserves any queue timestamp).
        await db.set_case_law_metadata_status(case_law_id, "pending")
    fields = result.get("fields") or []
    msg = (
        f"מולאו {len(fields)} שדות: {', '.join(fields)}"
        if fields
        else "לא נמצא מה למלא (כל השדות מאוכלסים או לא ניתן לחלץ)"
    )
    await progress("completed", 100, msg)
    return result


async def delete_precedent(case_law_id: UUID | str) -> bool:
    """Delete a precedent and cascade chunks + halachot."""
    if isinstance(case_law_id, str):
        case_law_id = UUID(case_law_id)
    return await db.delete_case_law(case_law_id)


async def get_precedent(case_law_id: UUID | str) -> dict | None:
    """Get a precedent with its halachot and related cases attached."""
    if isinstance(case_law_id, str):
        case_law_id = UUID(case_law_id)
    record = await db.get_case_law(case_law_id)
    if not record:
        return None
    record["halachot"] = await db.list_halachot(case_law_id=case_law_id, limit=500)
    record["related_cases"] = await db.get_case_law_relations(case_law_id)
    return record


async def list_precedents(
    practice_area: str = "",
    court: str = "",
    precedent_level: str = "",
    source_type: str = "",
    search: str = "",
    source_kind: str = "external_upload",
    limit: int = 100,
    offset: int = 0,
) -> list[dict]:
    return await db.list_external_case_law(
        practice_area=practice_area,
        court=court,
        precedent_level=precedent_level,
        source_type=source_type,
        search=search,
        source_kind=source_kind,
        limit=limit,
        offset=offset,
    )


async def search_library(
    query: str,
    practice_area: str = "",
    court: str = "",
    precedent_level: str = "",
    appeal_subtype: str = "",
    is_binding: bool | None = None,
    subject_tag: str = "",
    limit: int = 10,
    include_halachot: bool = True,
) -> list[dict]:
    """Semantic search merging halachot (rule-level) and chunks (passage-level).

    Only ``approved`` / ``published`` halachot are returned, per chair-review
    policy. Chunks are returned regardless of halacha review status.

    When ``VOYAGE_RERANK_ENABLED`` is set, results are passed through
    voyage rerank-2 (cross-encoder). The +0.05 halacha boost from
    ``search_precedent_library_semantic`` is preserved before rerank
    but the rerank scores ultimately decide the order.
    """
    if not query.strip():
        return []
    query_vec = await embeddings.embed_query(query)

    return await hybrid_search.search_precedent_library_hybrid(
        query=query,
        query_text_embedding=query_vec,
        limit=limit,
        practice_area=practice_area,
        court=court,
        precedent_level=precedent_level,
        appeal_subtype=appeal_subtype,
        is_binding=is_binding,
        subject_tag=subject_tag,
        include_halachot=include_halachot,
    )