"""Anomaly scoring pipeline — batch-score log_entries with a HF classifier.

Designed to run after each glean cycle (or standalone).  When no model is
configured the scorer is a no-op and returns immediately, so it is always
safe to wire into the glean pipeline.

Model: any HuggingFace text-classification model.  The existing Hybrid-BERT
label map (from diagnose/classifier.py) is reused when the model produces
NORMAL/SECURITY_ANOMALY/… outputs; other models get a generic severity map.

Scoring strategy
----------------
- Query unscored rows in batches (WHERE anomaly_scored_at IS NULL)
- Run each entry text through the HF pipeline
- Write anomaly_score + anomaly_label + anomaly_scored_at back
- INSERT high-confidence hits (score >= threshold) into detections table,
  skipping duplicates so the scorer is safe to re-run
"""
from __future__ import annotations

import logging
import os
import uuid
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

from app.db import get_conn, resolve_tenant_id
from app.db.dialect import q

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Label maps — reuse Hybrid-BERT vocabulary from diagnose/classifier.py
# ---------------------------------------------------------------------------

_HYBRID_BERT_SEVERITY: dict[str, str] = {
    "NORMAL": "INFO",
    "SECURITY_ANOMALY": "ERROR",
    "SYSTEM_FAILURE": "CRITICAL",
    "PERFORMANCE_ISSUE": "WARN",
    "NETWORK_ANOMALY": "WARN",
    "CONFIG_ERROR": "ERROR",
    "HARDWARE_ISSUE": "CRITICAL",
}

_GENERIC_SEVERITY: dict[str, str] = {
    "CRITICAL": "CRITICAL",
    "ERROR": "ERROR",
    "WARNING": "WARN",
    "WARN": "WARN",
    "INFO": "INFO",
    "DEBUG": "DEBUG",
}

_ANOMALOUS_LABELS: frozenset[str] = frozenset(
    {
        "SECURITY_ANOMALY",
        "SYSTEM_FAILURE",
        "PERFORMANCE_ISSUE",
        "NETWORK_ANOMALY",
        "CONFIG_ERROR",
        "HARDWARE_ISSUE",
        "CRITICAL",
        "ERROR",
    }
)

_DEFAULT_THRESHOLD = float(os.environ.get("TURNSTONE_ANOMALY_THRESHOLD", "0.75"))
_DEFAULT_MODEL = os.environ.get("TURNSTONE_ANOMALY_MODEL", "")
_DEFAULT_DEVICE = os.environ.get("TURNSTONE_ANOMALY_DEVICE", "cpu")
_DEFAULT_BATCH = int(os.environ.get("TURNSTONE_ANOMALY_BATCH", "256"))

# ---------------------------------------------------------------------------
# ML singleton
# ---------------------------------------------------------------------------

_pipeline: Any | None = None


def _get_pipeline(model_id: str, device: str) -> Any:
    global _pipeline  # noqa: PLW0603
    if _pipeline is None:
        from transformers import pipeline as hf_pipeline  # type: ignore[import-untyped]
        _pipeline = hf_pipeline("text-classification", model=model_id, device=device)
    return _pipeline


def reset_pipeline() -> None:
    """Reset the cached pipeline singleton (test helper)."""
    global _pipeline  # noqa: PLW0603
    _pipeline = None


# ---------------------------------------------------------------------------
# Result types
# ---------------------------------------------------------------------------


@dataclass
class ScoringResult:
    scored: int = 0
    detections: int = 0
    skipped: bool = False
    error: str | None = None


# ---------------------------------------------------------------------------
# Internal helpers
# ---------------------------------------------------------------------------


def _map_label(raw_label: str, score: float) -> tuple[str, str]:
    """Return (normalised_label, severity) for a raw model output label."""
    upper = raw_label.upper()
    if upper in _HYBRID_BERT_SEVERITY:
        return upper, _HYBRID_BERT_SEVERITY[upper]
    sev = _GENERIC_SEVERITY.get(upper, "WARN")
    return upper, sev


def _fetch_unscored(conn: Any, tenant_id: str, limit: int) -> list[dict]:
    rows = conn.execute(
        q("""
        SELECT id, source_id, text, timestamp_iso, severity
        FROM log_entries
        WHERE anomaly_scored_at IS NULL
          AND (tenant_id = ? OR tenant_id = '')
        ORDER BY ingest_time DESC
        LIMIT ?
        """),
        (tenant_id, limit),
    ).fetchall()
    return [dict(r) for r in rows]


def _write_scores(
    conn: Any,
    rows: list[dict],
    scored_at: str,
) -> None:
    conn.executemany(
        q("UPDATE log_entries SET anomaly_score = ?, anomaly_label = ?, anomaly_scored_at = ? WHERE id = ?"),
        [(r["anomaly_score"], r["anomaly_label"], scored_at, r["id"]) for r in rows],
    )


def _insert_detections(conn: Any, rows: list[dict], tenant_id: str, detected_at: str) -> int:
    inserted = 0
    for r in rows:
        try:
            conn.execute(
                q("""
                INSERT INTO detections
                    (id, tenant_id, entry_id, source_id, anomaly_label, anomaly_score,
                     severity, text, timestamp_iso, detected_at)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                """),
                (
                    str(uuid.uuid4()),
                    tenant_id,
                    r["id"],
                    r["source_id"],
                    r["anomaly_label"],
                    r["anomaly_score"],
                    r["severity"],
                    r["text"][:2000],
                    r.get("timestamp_iso"),
                    detected_at,
                ),
            )
            inserted += 1
        except Exception:  # noqa: BLE001
            pass  # duplicate entry_id or constraint violation — skip
    return inserted


# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------


def score_unscored(
    db_path: Path,
    model_id: str = _DEFAULT_MODEL,
    device: str = _DEFAULT_DEVICE,
    batch_size: int = _DEFAULT_BATCH,
    threshold: float = _DEFAULT_THRESHOLD,
) -> ScoringResult:
    """Score all unscored log_entries in batches.

    Returns immediately (skipped=True) when model_id is empty — allows
    unconditional wiring without requiring the model to be configured.
    """
    if not model_id:
        return ScoringResult(skipped=True)

    try:
        pipe = _get_pipeline(model_id, device)
    except Exception as exc:
        logger.error("Failed to load anomaly model %r: %s", model_id, exc)
        return ScoringResult(error=str(exc))

    tenant_id = resolve_tenant_id()
    total_scored = 0
    total_detections = 0

    while True:
        with get_conn(db_path) as conn:
            batch = _fetch_unscored(conn, tenant_id, batch_size)
        if not batch:
            break

        texts = [r["text"][:512] for r in batch]
        try:
            predictions = pipe(texts, truncation=True, max_length=512)
        except Exception as exc:
            logger.error("Inference error on batch of %d: %s", len(batch), exc)
            return ScoringResult(scored=total_scored, detections=total_detections, error=str(exc))

        scored_at = datetime.now(tz=timezone.utc).isoformat()
        scored_rows: list[dict] = []
        detection_rows: list[dict] = []

        for row, pred in zip(batch, predictions):
            label, severity = _map_label(pred["label"], pred["score"])
            enriched = {**row, "anomaly_score": pred["score"], "anomaly_label": label, "severity": severity}
            scored_rows.append(enriched)
            if label in _ANOMALOUS_LABELS and pred["score"] >= threshold:
                detection_rows.append(enriched)

        with get_conn(db_path) as conn:
            _write_scores(conn, scored_rows, scored_at)
            det_count = _insert_detections(conn, detection_rows, tenant_id, scored_at)
            conn.commit()

        total_scored += len(scored_rows)
        total_detections += det_count
        logger.info(
            "Scored %d entries, %d detections (threshold=%.2f)",
            len(scored_rows), det_count, threshold,
        )

        if len(batch) < batch_size:
            break

    return ScoringResult(scored=total_scored, detections=total_detections)


def list_detections(
    db_path: Path,
    limit: int = 100,
    unacked_only: bool = False,
    label: str | None = None,
    scorer: str | None = None,
) -> list[dict]:
    """Return detections ordered by detected_at DESC."""
    tenant_id = resolve_tenant_id()
    conditions = ["(tenant_id = ? OR tenant_id = '')"]
    params: list[Any] = [tenant_id]

    if unacked_only:
        conditions.append("acknowledged = 0")
    if label:
        conditions.append(q("anomaly_label = ?"))
        params.append(label.upper())
    if scorer:
        conditions.append(q("scorer = ?"))
        params.append(scorer.lower())

    where = " AND ".join(conditions)
    with get_conn(db_path) as conn:
        rows = conn.execute(
            q(f"SELECT * FROM detections WHERE {where} ORDER BY detected_at DESC LIMIT ?"),  # noqa: S608
            (*params, limit),
        ).fetchall()
    return [dict(r) for r in rows]


def acknowledge_detection(db_path: Path, detection_id: str, notes: str = "") -> bool:
    """Mark a detection as acknowledged. Returns True if a row was updated."""
    tenant_id = resolve_tenant_id()
    acked_at = datetime.now(tz=timezone.utc).isoformat()
    with get_conn(db_path) as conn:
        cur = conn.execute(
            q("""
            UPDATE detections
            SET acknowledged = 1, acknowledged_at = ?, notes = ?
            WHERE id = ? AND (tenant_id = ? OR tenant_id = '')
            """),
            (acked_at, notes, detection_id, tenant_id),
        )
        conn.commit()
        return cur.rowcount > 0