"""Scoring service — granular 0-100 scoring for techniques, tactics, actors, and org.

Uses configurable weights from Settings to compute coverage scores with
detailed breakdowns.

Bulk helpers (``bulk_technique_scores``) pre-fetch all scoring data in a
fixed number of aggregated queries so that organisation-wide calculations
never produce N+1 traffic.
"""

from datetime import datetime, timedelta
from typing import Optional

from sqlalchemy import case, func
from sqlalchemy.orm import Session

from app.config import settings
from app.models.technique import Technique
from app.models.test import Test
from app.models.detection_rule import DetectionRule
from app.models.test_detection_result import TestDetectionResult
from app.models.defensive_technique import DefensiveTechniqueMapping
from app.models.threat_actor import ThreatActor, ThreatActorTechnique
from app.models.enums import TestState, TestResult


# ── Bulk scoring helpers (5 queries for ALL techniques) ───────────────


def _build_empty_stats():
    return {
        "validated": 0,
        "detected": 0,
        "platforms": set(),
        "latest_validated_at": None,
    }


def bulk_technique_scores(db: Session) -> dict:
    """Pre-fetch all scoring data and compute per-technique scores in memory.

    Executes exactly 5 queries regardless of technique count:
      Q1 — Test aggregates per technique (validated / detected / platforms / freshness)
      Q2 — Detection rules per mitre_id
      Q3 — Triggered rules per mitre_id
      Q4 — D3FEND mapping counts per technique
      Q5 — All techniques

    Returns ``{technique_id: {"total_score": float, "breakdown": dict}}``.
    """
    w_tests = settings.SCORING_WEIGHT_TESTS
    w_detection = settings.SCORING_WEIGHT_DETECTION_RULES
    w_d3fend = settings.SCORING_WEIGHT_D3FEND
    w_freshness = settings.SCORING_WEIGHT_FRESHNESS
    w_diversity = settings.SCORING_WEIGHT_PLATFORM_DIVERSITY

    # Q1: test stats grouped by technique_id
    test_rows = (
        db.query(
            Test.technique_id,
            func.count(Test.id).label("validated_count"),
            func.count(
                case((Test.detection_result == TestResult.detected, Test.id))
            ).label("detected_count"),
            func.max(Test.red_validated_at).label("latest_validated_at"),
            func.count(func.distinct(Test.platform)).label("platform_count"),
        )
        .filter(Test.state == TestState.validated)
        .group_by(Test.technique_id)
        .all()
    )

    test_stats: dict = {}
    for row in test_rows:
        test_stats[row.technique_id] = {
            "validated": row.validated_count,
            "detected": row.detected_count,
            "latest_validated_at": row.latest_validated_at,
            "platform_count": row.platform_count,
        }

    # Q2: active detection rules per mitre_id
    rule_rows = (
        db.query(
            DetectionRule.mitre_technique_id,
            func.count(DetectionRule.id).label("total"),
        )
        .filter(DetectionRule.is_active == True)  # noqa: E712
        .group_by(DetectionRule.mitre_technique_id)
        .all()
    )
    rules_by_mitre: dict[str, int] = {r.mitre_technique_id: r.total for r in rule_rows}

    # Q3: triggered rules per mitre_id
    triggered_rows = (
        db.query(
            DetectionRule.mitre_technique_id,
            func.count(TestDetectionResult.id).label("triggered"),
        )
        .join(DetectionRule, DetectionRule.id == TestDetectionResult.detection_rule_id)
        .filter(TestDetectionResult.triggered == True)  # noqa: E712
        .group_by(DetectionRule.mitre_technique_id)
        .all()
    )
    triggered_by_mitre: dict[str, int] = {
        r.mitre_technique_id: r.triggered for r in triggered_rows
    }

    # Q4: D3FEND mapping counts per technique
    d3fend_rows = (
        db.query(
            DefensiveTechniqueMapping.attack_technique_id,
            func.count(DefensiveTechniqueMapping.id).label("total"),
        )
        .group_by(DefensiveTechniqueMapping.attack_technique_id)
        .all()
    )
    d3fend_by_tech: dict = {r.attack_technique_id: r.total for r in d3fend_rows}

    # Q5: all techniques
    techniques = db.query(Technique).all()

    now = datetime.utcnow()
    results: dict = {}

    for tech in techniques:
        ts = test_stats.get(tech.id, {})
        validated = ts.get("validated", 0)
        detected = ts.get("detected", 0)
        latest_at = ts.get("latest_validated_at")
        plat_count = ts.get("platform_count", 0)

        breakdown = {}

        # 1. Tests validated with detection
        if validated > 0:
            test_ratio = detected / validated
            test_score = round(test_ratio * w_tests, 1)
        else:
            test_ratio = 0
            test_score = 0
        breakdown["tests_validated"] = {
            "score": test_score,
            "max": w_tests,
            "detail": (
                f"{detected}/{validated} tests detected"
                if validated else "No validated tests"
            ),
        }

        # 2. Detection rules
        total_rules = rules_by_mitre.get(tech.mitre_id, 0)
        triggered_rules = triggered_by_mitre.get(tech.mitre_id, 0)
        if total_rules > 0:
            detection_ratio = min(triggered_rules / total_rules, 1.0)
            detection_score = round(detection_ratio * w_detection, 1)
        else:
            detection_ratio = 0
            detection_score = 0
        breakdown["detection_rules"] = {
            "score": detection_score,
            "max": w_detection,
            "detail": (
                f"{triggered_rules}/{total_rules} rules triggered"
                if total_rules > 0 else "No detection rules available"
            ),
        }

        # 3. D3FEND coverage
        total_cm = d3fend_by_tech.get(tech.id, 0)
        if total_cm > 0 and detected > 0:
            verified_cm = min(detected, total_cm)
            d3fend_score = round((verified_cm / total_cm) * w_d3fend, 1)
        else:
            verified_cm = 0
            d3fend_score = 0
        breakdown["d3fend_coverage"] = {
            "score": d3fend_score,
            "max": w_d3fend,
            "detail": (
                f"{verified_cm}/{total_cm} countermeasures"
                if total_cm > 0 else "No D3FEND mappings"
            ),
        }

        # 4. Freshness
        if latest_at:
            days_ago = (now - latest_at).days
            if days_ago < 90:
                freshness_pct = 1.0
            elif days_ago < 180:
                freshness_pct = 0.5
            else:
                freshness_pct = 0.0
            freshness_score = round(freshness_pct * w_freshness, 1)
            freshness_detail = f"Last test {days_ago} days ago"
        else:
            freshness_score = 0
            freshness_detail = "No validated tests"
        breakdown["freshness"] = {
            "score": freshness_score,
            "max": w_freshness,
            "detail": freshness_detail,
        }

        # 5. Platform diversity
        available = tech.platforms or []
        total_platforms = len(available) if available else 3
        if total_platforms > 0 and plat_count > 0:
            diversity_score = round(
                min(plat_count / total_platforms, 1.0) * w_diversity, 1,
            )
        else:
            diversity_score = 0
        breakdown["platform_diversity"] = {
            "score": diversity_score,
            "max": w_diversity,
            "detail": (
                f"{plat_count}/{total_platforms} platforms covered"
                if plat_count > 0 else "No platforms tested"
            ),
        }

        total = min(
            test_score + detection_score + d3fend_score
            + freshness_score + diversity_score,
            100,
        )
        results[tech.id] = {
            "total_score": round(total, 1),
            "breakdown": breakdown,
            "mitre_id": tech.mitre_id,
            "tactic": tech.tactic,
        }

    return results


# ── Technique-level scoring (single technique — preserved API) ────────


def calculate_technique_score(technique: Technique, db: Session) -> dict:
    """Calculate a 0-100 score for a technique with detailed breakdown.

    Weights (configurable via settings):
    - tests_validated:       weight from SCORING_WEIGHT_TESTS
    - detection_rules:       weight from SCORING_WEIGHT_DETECTION_RULES
    - d3fend_coverage:       weight from SCORING_WEIGHT_D3FEND
    - freshness:             weight from SCORING_WEIGHT_FRESHNESS
    - platform_diversity:    weight from SCORING_WEIGHT_PLATFORM_DIVERSITY
    """
    w_tests = settings.SCORING_WEIGHT_TESTS
    w_detection = settings.SCORING_WEIGHT_DETECTION_RULES
    w_d3fend = settings.SCORING_WEIGHT_D3FEND
    w_freshness = settings.SCORING_WEIGHT_FRESHNESS
    w_diversity = settings.SCORING_WEIGHT_PLATFORM_DIVERSITY

    breakdown = {}

    # ── 1. Tests validated with detection ──────────────────────────
    all_tests = (
        db.query(Test)
        .filter(Test.technique_id == technique.id)
        .all()
    )
    validated_tests = [t for t in all_tests if t.state == TestState.validated]
    detected_tests = [
        t for t in validated_tests
        if t.detection_result == TestResult.detected
    ]

    if validated_tests:
        test_ratio = len(detected_tests) / len(validated_tests)
        test_score = round(test_ratio * w_tests, 1)
    else:
        test_ratio = 0
        test_score = 0

    breakdown["tests_validated"] = {
        "score": test_score,
        "max": w_tests,
        "detail": f"{len(detected_tests)}/{len(validated_tests)} tests detected"
        if validated_tests
        else "No validated tests",
    }

    # ── 2. Detection rules coverage ───────────────────────────────
    total_rules = (
        db.query(func.count(DetectionRule.id))
        .filter(
            DetectionRule.mitre_technique_id == technique.mitre_id,
            DetectionRule.is_active == True,  # noqa: E712
        )
        .scalar()
    ) or 0

    triggered_rules = 0
    if total_rules > 0:
        triggered_rules = (
            db.query(func.count(TestDetectionResult.id))
            .join(
                DetectionRule,
                DetectionRule.id == TestDetectionResult.detection_rule_id,
            )
            .filter(
                DetectionRule.mitre_technique_id == technique.mitre_id,
                TestDetectionResult.triggered == True,  # noqa: E712
            )
            .scalar()
        ) or 0

        detection_ratio = min(triggered_rules / total_rules, 1.0)
        detection_score = round(detection_ratio * w_detection, 1)
    else:
        detection_ratio = 0
        detection_score = 0

    breakdown["detection_rules"] = {
        "score": detection_score,
        "max": w_detection,
        "detail": f"{triggered_rules}/{total_rules} rules triggered"
        if total_rules > 0
        else "No detection rules available",
    }

    # ── 3. D3FEND coverage ────────────────────────────────────────
    total_countermeasures = (
        db.query(func.count(DefensiveTechniqueMapping.id))
        .filter(DefensiveTechniqueMapping.attack_technique_id == technique.id)
        .scalar()
    ) or 0

    verified_countermeasures = 0
    if total_countermeasures > 0 and len(detected_tests) > 0:
        verified_countermeasures = min(len(detected_tests), total_countermeasures)
        d3fend_ratio = verified_countermeasures / total_countermeasures
        d3fend_score = round(d3fend_ratio * w_d3fend, 1)
    else:
        d3fend_ratio = 0
        d3fend_score = 0

    breakdown["d3fend_coverage"] = {
        "score": d3fend_score,
        "max": w_d3fend,
        "detail": f"{verified_countermeasures}/{total_countermeasures} countermeasures"
        if total_countermeasures > 0
        else "No D3FEND mappings",
    }

    # ── 4. Freshness ──────────────────────────────────────────────
    most_recent_test = (
        db.query(func.max(Test.red_validated_at))
        .filter(
            Test.technique_id == technique.id,
            Test.state == TestState.validated,
        )
        .scalar()
    )

    now = datetime.utcnow()
    if most_recent_test:
        days_ago = (now - most_recent_test).days
        if days_ago < 90:
            freshness_pct = 1.0
        elif days_ago < 180:
            freshness_pct = 0.5
        else:
            freshness_pct = 0.0
        freshness_score = round(freshness_pct * w_freshness, 1)
        freshness_detail = f"Last test {days_ago} days ago"
    else:
        freshness_pct = 0
        freshness_score = 0
        freshness_detail = "No validated tests"

    breakdown["freshness"] = {
        "score": freshness_score,
        "max": w_freshness,
        "detail": freshness_detail,
    }

    # ── 5. Platform diversity ─────────────────────────────────────
    available_platforms = technique.platforms or []
    total_platforms = len(available_platforms) if available_platforms else 3

    tested_platforms = set()
    for t in validated_tests:
        if t.platform:
            tested_platforms.add(t.platform.lower())

    if total_platforms > 0 and tested_platforms:
        diversity_ratio = min(len(tested_platforms) / total_platforms, 1.0)
        diversity_score = round(diversity_ratio * w_diversity, 1)
    else:
        diversity_ratio = 0
        diversity_score = 0

    breakdown["platform_diversity"] = {
        "score": diversity_score,
        "max": w_diversity,
        "detail": f"{len(tested_platforms)}/{total_platforms} platforms covered"
        if tested_platforms
        else "No platforms tested",
    }

    # ── Total ─────────────────────────────────────────────────────
    total = min(
        test_score + detection_score + d3fend_score + freshness_score + diversity_score,
        100,
    )

    return {
        "total_score": round(total, 1),
        "breakdown": breakdown,
    }


# ── Tactic-level scoring ─────────────────────────────────────────────


def calculate_tactic_score(tactic: str, db: Session) -> dict:
    """Calculate average score for all techniques in a tactic."""
    scores_map = bulk_technique_scores(db)

    matching = [
        v["total_score"]
        for v in scores_map.values()
        if v.get("tactic") and tactic.lower() in v["tactic"].lower()
    ]

    return {
        "tactic": tactic,
        "average_score": round(sum(matching) / len(matching), 1) if matching else 0,
        "techniques_count": len(matching),
        "techniques_scored": len([s for s in matching if s > 0]),
    }


# ── Threat actor scoring ─────────────────────────────────────────────


def calculate_actor_coverage_score(actor_id: str, db: Session) -> dict:
    """Calculate coverage score for a specific threat actor's techniques."""
    actor = db.query(ThreatActor).filter(ThreatActor.id == actor_id).first()
    if not actor:
        return {"total_score": 0, "techniques_count": 0, "techniques_covered": 0}

    actor_techniques = (
        db.query(ThreatActorTechnique)
        .filter(ThreatActorTechnique.threat_actor_id == actor.id)
        .all()
    )

    technique_ids = {at.technique_id for at in actor_techniques}
    if not technique_ids:
        return {
            "actor_id": str(actor.id),
            "actor_name": actor.name,
            "total_score": 0,
            "techniques_count": 0,
            "techniques_covered": 0,
            "techniques_detail": [],
        }

    scores_map = bulk_technique_scores(db)

    scores = []
    details = []
    for tid in technique_ids:
        entry = scores_map.get(tid)
        if not entry:
            continue
        score = entry["total_score"]
        scores.append(score)
        details.append({
            "mitre_id": entry["mitre_id"],
            "name": entry.get("name", ""),
            "score": score,
            "breakdown": entry["breakdown"],
        })

    avg_score = round(sum(scores) / len(scores), 1) if scores else 0

    return {
        "actor_id": str(actor.id),
        "actor_name": actor.name,
        "total_score": avg_score,
        "techniques_count": len(technique_ids),
        "techniques_covered": len([s for s in scores if s > 50]),
        "techniques_detail": details,
    }


# ── Organization-level scoring ────────────────────────────────────────


def calculate_organization_score(db: Session) -> dict:
    """Calculate the overall organization security score.

    Uses ``bulk_technique_scores`` to compute all technique scores in
    5 aggregated queries instead of N*5.
    """
    scores_map = bulk_technique_scores(db)
    total_count = len(scores_map)

    if total_count == 0:
        return {
            "overall_score": 0,
            "total_coverage": 0,
            "critical_coverage": 0,
            "detection_maturity": 0,
            "response_readiness": 0,
            "techniques_evaluated": 0,
            "techniques_total": 0,
        }

    all_scores = [v["total_score"] for v in scores_map.values()]
    evaluated_scores = [s for s in all_scores if s > 0]
    evaluated_count = len(evaluated_scores)

    total_coverage = (
        round(sum(evaluated_scores) / len(evaluated_scores), 1)
        if evaluated_scores else 0
    )

    # Critical coverage: techniques with high/critical severity templates
    from app.models.test_template import TestTemplate

    critical_mitre_ids = set(
        row[0]
        for row in db.query(TestTemplate.mitre_technique_id)
        .filter(TestTemplate.severity.in_(["high", "critical"]))
        .distinct()
        .all()
    )

    critical_scores = [
        v["total_score"]
        for v in scores_map.values()
        if v.get("mitre_id") in critical_mitre_ids
    ]
    critical_coverage = (
        round(sum(critical_scores) / len(critical_scores), 1)
        if critical_scores else 0
    )

    # Detection maturity (2 scalar queries — already efficient)
    total_rules = (
        db.query(func.count(DetectionRule.id))
        .filter(DetectionRule.is_active == True)  # noqa: E712
        .scalar()
    ) or 0
    triggered_total = (
        db.query(func.count(TestDetectionResult.id))
        .filter(TestDetectionResult.triggered == True)  # noqa: E712
        .scalar()
    ) or 0

    detection_maturity = (
        round((triggered_total / total_rules) * 100, 1)
        if total_rules > 0 else 0
    )
    detection_maturity = min(detection_maturity, 100)

    # Response readiness (2 scalar queries — already efficient)
    remediation_total = (
        db.query(func.count(Test.id))
        .filter(Test.remediation_status.isnot(None))
        .scalar()
    ) or 0
    remediation_completed = (
        db.query(func.count(Test.id))
        .filter(Test.remediation_status == "completed")
        .scalar()
    ) or 0

    response_readiness = (
        round((remediation_completed / remediation_total) * 100, 1)
        if remediation_total > 0 else 0
    )

    overall = round(
        total_coverage * 0.4
        + critical_coverage * 0.25
        + detection_maturity * 0.2
        + response_readiness * 0.15,
        1,
    )

    return {
        "overall_score": overall,
        "total_coverage": total_coverage,
        "critical_coverage": critical_coverage,
        "detection_maturity": detection_maturity,
        "response_readiness": response_readiness,
        "techniques_evaluated": evaluated_count,
        "techniques_total": total_count,
    }


# ── Score history ────────────────────────────────────────────────────


def get_score_history(db: Session, period: str = "90d") -> list:
    """Get historical score snapshots.

    Since we don't have a dedicated history table, we approximate by
    computing scores based on test dates within time windows.
    Returns a list of weekly data points.
    """
    from app.models.audit import AuditLog

    now = datetime.utcnow()
    if period == "30d":
        start = now - timedelta(days=30)
    elif period == "1y":
        start = now - timedelta(days=365)
    else:  # 90d default
        start = now - timedelta(days=90)

    # Group validated tests by week
    weeks = []
    current = start
    while current < now:
        week_end = min(current + timedelta(days=7), now)

        # Count validated tests up to this week
        validated_up_to = (
            db.query(func.count(Test.id))
            .filter(
                Test.state == TestState.validated,
                Test.red_validated_at <= week_end,
            )
            .scalar()
        ) or 0

        total_techniques = (
            db.query(func.count(Technique.id)).scalar()
        ) or 1

        # Simple approximation: coverage percentage as score proxy
        score_approx = round((validated_up_to / total_techniques) * 100, 1)

        weeks.append({
            "date": current.strftime("%Y-%m-%d"),
            "score": min(score_approx, 100),
            "validated_tests": validated_up_to,
        })

        current = week_end

    return weeks