# mypy: disable-error-code="no-any-return,arg-type"
"""Use-Case → Controls retrieval — the SHARED layer the document agents AND the
CRA finding-mapper query to pull the controls that belong to a topic.

Read-only over the existing ``mc_use_case_mappings`` seed (no schema change).
The seed is recall-oriented ("this MC comes from a law about the topic"); the
ranking here is a deterministic *precision proxy* — is_primary + mapping
confidence + cluster size, plus a keyword-relevance score derived from the
use-case registry. The LLM precision pass (Phase B) refines this later; the
ranking field stays the same so consumers do not change.
"""

from __future__ import annotations

from typing import Any, Optional

from sqlalchemy import text
from sqlalchemy.orm import Session

from compliance.data.use_case_registry import REGISTRY, is_valid_use_case
from compliance.domain import NotFoundError


def relevance_score(
    title: Optional[str],
    objective: Optional[str],
    keyword_tokens: tuple[str, ...],
    is_primary: Optional[bool],
    confidence: Optional[float],
) -> float:
    """Deterministic precision proxy in [0, 1]. Pure → unit-testable.

    Combines the recall signals already on the mapping (primary flag, mapping
    confidence) with a content signal: how many of the use-case's registry
    keyword tokens appear in the control's own representative text. The content
    term is what separates "actually about this topic" from "merely from a
    related law" — the core of the precision problem.
    """
    haystack = f"{title or ''} {objective or ''}".lower()
    hits = sum(1 for kw in keyword_tokens if kw and kw in haystack)
    kw_score = min(hits / 3.0, 1.0) if keyword_tokens else 0.0
    score = (0.5 if is_primary else 0.0) + 0.3 * float(confidence or 0.0) + 0.2 * kw_score
    return round(min(score, 1.0), 3)


# Representative member (most severe, then lowest control_id) carries the
# human-readable title/objective — master_controls.canonical_name is only the
# merge token, so we surface a real member control per master.
_LIST_SQL = text("""
    SELECT mc.id, mc.master_control_id, mc.canonical_name, mc.total_controls,
           m.is_primary, m.confidence,
           (SELECT r.source_regulation FROM mc_regulations r
              WHERE r.master_control_uuid = mc.id AND r.is_primary LIMIT 1)
              AS primary_regulation,
           rep.title, rep.objective, rep.severity, rep.category
    FROM master_controls mc
    JOIN mc_use_case_mappings m
      ON m.master_control_uuid = mc.id AND m.use_case = :uc
    LEFT JOIN LATERAL (
        SELECT cc.title, cc.objective, cc.severity, cc.category
        FROM master_control_members mcm
        JOIN canonical_controls cc ON cc.id = mcm.control_uuid
        WHERE mcm.master_control_uuid = mc.id
        ORDER BY CASE cc.severity WHEN 'critical' THEN 0 WHEN 'high' THEN 1
                                  WHEN 'medium' THEN 2 ELSE 3 END, cc.control_id
        LIMIT 1
    ) rep ON true
    WHERE (:primary_only = false OR m.is_primary)
    ORDER BY m.is_primary DESC, m.confidence DESC NULLS LAST,
             mc.total_controls DESC
    LIMIT :lim OFFSET :off
""")


# Atom-grain path: the one-time Haiku classification (atom_classification) gives
# per-atom relevance + sub-topic. Far more precise + organized than the master
# seed. Preferred whenever the use-case has been processed.
_ATOM_LIST_SQL = text("""
    SELECT ac.control_uuid, ac.sub_topic, ac.canonical_obligation,
           cc.control_id, cc.title, cc.objective, cc.severity,
           cpl.source_regulation, cpl.source_article
    FROM atom_classification ac
    JOIN canonical_controls cc ON cc.id = ac.control_uuid
    LEFT JOIN LATERAL (
        SELECT cpl.source_regulation, cpl.source_article
        FROM control_parent_links cpl
        WHERE cpl.control_uuid = ac.control_uuid LIMIT 1
    ) cpl ON true
    WHERE ac.use_case = :uc AND ac.relevant = true
      AND (:sub IS NULL OR ac.sub_topic = :sub)
    ORDER BY ac.sub_topic NULLS LAST,
             CASE cc.severity WHEN 'critical' THEN 0 WHEN 'high' THEN 1
                              WHEN 'medium' THEN 2 ELSE 3 END, cc.title
    LIMIT :lim OFFSET :off
""")


class UseCaseControlsService:
    """Topic → controls retrieval over the seeded use-case mappings."""

    def __init__(self, db: Session) -> None:
        self.db = db

    def list_use_cases(self) -> list[dict[str, Any]]:
        """Registry use-cases with live counts — atom-grain (Haiku classification)
        plus the legacy master seed. Backs the coverage overview so every topic is
        visible with how many obligations it actually carries."""
        counts = {
            row[0]: int(row[1])
            for row in self.db.execute(text(
                "SELECT use_case, count(*) FROM mc_use_case_mappings "
                "GROUP BY use_case"
            )).fetchall()
        }
        atom: dict[str, tuple[int, int]] = {}
        if self.db.execute(text(
            "SELECT to_regclass('compliance.atom_classification')"
        )).scalar() is not None:
            atom = {
                row[0]: (int(row[1]), int(row[2]))
                for row in self.db.execute(text(
                    "SELECT use_case, count(*), count(*) FILTER (WHERE relevant) "
                    "FROM atom_classification GROUP BY use_case"
                )).fetchall()
            }
        out = [
            {
                "key": uc.key,
                "label": uc.label,
                "group": uc.group,
                "regulations": list(uc.regulations),
                "verification_methods": list(uc.verification_methods),
                "mapped_controls": counts.get(uc.key, 0),
                "atom_total": atom.get(uc.key, (0, 0))[0],
                "atom_relevant": atom.get(uc.key, (0, 0))[1],
            }
            for uc in REGISTRY.values() if uc.enabled
        ]
        out.sort(key=lambda x: (x["atom_relevant"], x["mapped_controls"]),
                 reverse=True)
        return out

    def controls_for_use_case(
        self,
        use_case: str,
        primary_only: bool = False,
        limit: int = 50,
        offset: int = 0,
        sub_topic: Optional[str] = None,
    ) -> dict[str, Any]:
        """Controls for ``use_case``. Prefers the atom-grain Haiku classification
        (precise + sub-topic-organized) when present; falls back to the
        master-grain seed otherwise."""
        if not is_valid_use_case(use_case):
            raise NotFoundError(f"Unknown use_case '{use_case}'")
        uc = REGISTRY[use_case]
        lim = min(max(int(limit), 1), 200)
        off = max(int(offset), 0)

        if self._has_atom_grain(use_case):
            return self._atom_grain(uc, lim, off, sub_topic)

        # --- master-grain fallback (recall seed) ---
        count_sql = (
            "SELECT count(*) FROM mc_use_case_mappings WHERE use_case = :uc"
            + (" AND is_primary" if primary_only else "")
        )
        total = self.db.execute(text(count_sql), {"uc": use_case}).scalar() or 0
        rows = self.db.execute(_LIST_SQL, {
            "uc": use_case, "primary_only": bool(primary_only), "lim": lim, "off": off,
        }).fetchall()
        controls = [
            {
                "id": str(r.id),
                "master_control_id": r.master_control_id,
                "title": r.title or r.canonical_name,
                "objective": r.objective,
                "severity": r.severity,
                "category": r.category,
                "member_count": r.total_controls,
                "is_primary": bool(r.is_primary),
                "confidence": float(r.confidence) if r.confidence is not None else None,
                "primary_regulation": r.primary_regulation,
                "relevance": relevance_score(
                    r.title, r.objective, uc.keyword_tokens, r.is_primary, r.confidence,
                ),
            }
            for r in rows
        ]
        return {
            "use_case": uc.key, "label": uc.label, "group": uc.group,
            "granularity": "master", "total": int(total), "limit": lim, "offset": off,
            "primary_only": bool(primary_only), "controls": controls,
        }

    def _has_atom_grain(self, use_case: str) -> bool:
        if self.db.execute(
            text("SELECT to_regclass('compliance.atom_classification')")
        ).scalar() is None:
            return False
        return (self.db.execute(
            text("SELECT count(*) FROM atom_classification WHERE use_case = :uc"),
            {"uc": use_case},
        ).scalar() or 0) > 0

    def _atom_grain(
        self, uc, lim: int, off: int, sub_topic: Optional[str],
    ) -> dict[str, Any]:
        total = self.db.execute(text(
            "SELECT count(*) FROM atom_classification "
            "WHERE use_case = :uc AND relevant = true "
            "AND (:sub IS NULL OR sub_topic = :sub)"
        ), {"uc": uc.key, "sub": sub_topic}).scalar() or 0
        facet = {
            row[0]: int(row[1])
            for row in self.db.execute(text(
                "SELECT COALESCE(sub_topic, '(none)'), count(*) "
                "FROM atom_classification WHERE use_case = :uc AND relevant = true "
                "GROUP BY 1 ORDER BY 2 DESC"
            ), {"uc": uc.key}).fetchall()
        }
        rows = self.db.execute(_ATOM_LIST_SQL, {
            "uc": uc.key, "sub": sub_topic, "lim": lim, "off": off,
        }).fetchall()
        controls = [
            {
                "id": str(r.control_uuid),
                "control_id": r.control_id,
                "title": r.title,
                "objective": r.objective,
                "severity": r.severity,
                "sub_topic": r.sub_topic,
                "canonical_obligation": r.canonical_obligation,
                "source_regulation": r.source_regulation,
                "source_article": r.source_article,
            }
            for r in rows
        ]
        return {
            "use_case": uc.key, "label": uc.label, "group": uc.group,
            "granularity": "atom", "total": int(total), "limit": lim, "offset": off,
            "sub_topic": sub_topic, "subtopic_counts": facet, "controls": controls,
        }