"""Heatmap service — ATT&CK Navigator-compatible layer generation.

Builds multiple layer types (coverage, threat actor, detection rules,
campaign) as plain dictionaries ready for JSON serialisation.

This module is framework-agnostic: no FastAPI imports, no HTTPException,
no ``db.commit()``.
"""

from __future__ import annotations

import json
from typing import Optional

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

from app.models.campaign import Campaign, CampaignTest
from app.models.detection_rule import DetectionRule
from app.models.defensive_technique import DefensiveTechniqueMapping
from app.models.enums import TechniqueStatus, TestState
from app.models.technique import Technique
from app.models.test import Test
from app.models.test_detection_result import TestDetectionResult
from app.models.threat_actor import ThreatActor, ThreatActorTechnique
from app.utils import escape_like

# ── Constants ─────────────────────────────────────────────────────────

ATTACK_VERSION = "15"
NAVIGATOR_VERSION = "5.0"
LAYER_VERSION = "4.5"
DOMAIN = "enterprise-attack"

STATUS_SCORE_MAP: dict[TechniqueStatus, int] = {
    TechniqueStatus.validated: 100,
    TechniqueStatus.partial: 60,
    TechniqueStatus.in_progress: 30,
    TechniqueStatus.not_covered: 10,
    TechniqueStatus.not_evaluated: 0,
    TechniqueStatus.review_required: 10,
}

TEST_STATE_SCORE: dict[TestState, int] = {
    TestState.validated: 100,
    TestState.in_review: 70,
    TestState.blue_evaluating: 50,
    TestState.red_executing: 30,
    TestState.draft: 10,
    TestState.rejected: 5,
}


# ── Internal helpers ──────────────────────────────────────────────────


def _score_to_color(score: int) -> str:
    """Map a 0-100 score to a red-yellow-green colour hex."""
    if score <= 0:
        return "#d3d3d3"
    if score <= 25:
        return "#ff6666"
    if score <= 50:
        return "#ff9933"
    if score <= 75:
        return "#ffff66"
    return "#66ff66"


def _build_layer_skeleton(
    name: str,
    description: str,
    gradient_colors: list[str] | None = None,
) -> dict:
    """Return a base layer dict compatible with ATT&CK Navigator."""
    return {
        "name": name,
        "versions": {
            "attack": ATTACK_VERSION,
            "navigator": NAVIGATOR_VERSION,
            "layer": LAYER_VERSION,
        },
        "domain": DOMAIN,
        "description": description,
        "filters": {"platforms": ["windows", "linux", "macos"]},
        "gradient": {
            "colors": gradient_colors or ["#ff6666", "#ffff66", "#66ff66"],
            "minValue": 0,
            "maxValue": 100,
        },
        "techniques": [],
    }


def _apply_filters(
    query,
    model,
    platforms: list[str] | None = None,
    tactics: list[str] | None = None,
):
    """Apply common platform and tactic filters to a technique query."""
    if platforms:
        platform_filters = [
            model.platforms.op("@>")(json.dumps([p])) for p in platforms
        ]
        query = query.filter(or_(*platform_filters))
    if tactics:
        tactic_filters = [
            model.tactic.ilike(f"%{escape_like(t)}%") for t in tactics
        ]
        query = query.filter(or_(*tactic_filters))
    return query


def _format_tactic(tactic_str: str | None) -> str:
    """Normalize tactic string to ATT&CK Navigator format (kebab-case)."""
    if not tactic_str:
        return ""
    return tactic_str.split(",")[0].strip().lower()


def _parse_csv(value: str | None) -> list[str] | None:
    """Split a comma-separated string into a trimmed list, or ``None``."""
    if not value:
        return None
    return [v.strip() for v in value.split(",") if v.strip()]


# ── Public API ────────────────────────────────────────────────────────


def build_coverage_layer(
    db: Session,
    *,
    platforms: str | None = None,
    tactics: str | None = None,
    min_score: int = 0,
) -> dict:
    """Coverage layer -- score based on ``status_global`` of each technique."""
    layer = _build_layer_skeleton("Aegis Coverage", "Coverage layer generated by Aegis")

    query = _apply_filters(
        db.query(Technique), Technique,
        _parse_csv(platforms), _parse_csv(tactics),
    )
    techniques = query.all()

    # Bulk-fetch test counts and rule counts to avoid N+1
    tech_ids = [t.id for t in techniques]
    mitre_ids = [t.mitre_id for t in techniques]

    test_counts = dict(
        db.query(Test.technique_id, func.count(Test.id))
        .filter(Test.technique_id.in_(tech_ids), Test.state == TestState.validated)
        .group_by(Test.technique_id)
        .all()
    ) if tech_ids else {}

    rule_counts = dict(
        db.query(DetectionRule.mitre_technique_id, func.count(DetectionRule.id))
        .filter(DetectionRule.mitre_technique_id.in_(mitre_ids))
        .group_by(DetectionRule.mitre_technique_id)
        .all()
    ) if mitre_ids else {}

    for tech in techniques:
        score = STATUS_SCORE_MAP.get(tech.status_global, 0)
        if score < min_score:
            continue

        tc = test_counts.get(tech.id, 0)
        rc = rule_counts.get(tech.mitre_id, 0)

        metadata = [
            {"name": "tests_count", "value": str(tc)},
            {"name": "detection_rules", "value": str(rc)},
        ]
        if tech.last_review_date:
            metadata.append(
                {"name": "last_validated", "value": tech.last_review_date.strftime("%Y-%m-%d")}
            )

        comment_parts = [
            f"Status: {tech.status_global.value}",
            f"{tc} tests validated",
            f"{rc} detection rules",
        ]

        layer["techniques"].append({
            "techniqueID": tech.mitre_id,
            "tactic": _format_tactic(tech.tactic),
            "color": _score_to_color(score),
            "score": score,
            "comment": " - ".join(comment_parts),
            "enabled": True,
            "metadata": metadata,
        })

    return layer


def build_threat_actor_layer(
    db: Session,
    actor_id: str,
    *,
    platforms: str | None = None,
    tactics: str | None = None,
    min_score: int = 0,
) -> dict | None:
    """Threat actor layer -- techniques used by an actor with coverage colour.

    Returns ``None`` if the actor does not exist.
    """
    actor = db.query(ThreatActor).filter(ThreatActor.id == actor_id).first()
    if not actor:
        return None

    layer = _build_layer_skeleton(
        f"Threat Actor: {actor.name}",
        f"Techniques used by {actor.name} with coverage overlay",
        gradient_colors=["#808080", "#ff6666", "#66ff66"],
    )

    actor_technique_ids = {
        row.technique_id
        for row in db.query(ThreatActorTechnique.technique_id)
        .filter(ThreatActorTechnique.threat_actor_id == actor.id)
        .all()
    }
    if not actor_technique_ids:
        return layer

    query = _apply_filters(
        db.query(Technique), Technique,
        _parse_csv(platforms), _parse_csv(tactics),
    )
    techniques = query.all()

    # Bulk-fetch metadata for actor techniques only
    test_counts = dict(
        db.query(Test.technique_id, func.count(Test.id))
        .filter(Test.technique_id.in_(actor_technique_ids), Test.state == TestState.validated)
        .group_by(Test.technique_id)
        .all()
    )
    actor_mitre_ids = [t.mitre_id for t in techniques if t.id in actor_technique_ids]
    rule_counts = dict(
        db.query(DetectionRule.mitre_technique_id, func.count(DetectionRule.id))
        .filter(DetectionRule.mitre_technique_id.in_(actor_mitre_ids))
        .group_by(DetectionRule.mitre_technique_id)
        .all()
    ) if actor_mitre_ids else {}

    for tech in techniques:
        is_actor_technique = tech.id in actor_technique_ids
        score = STATUS_SCORE_MAP.get(tech.status_global, 0) if is_actor_technique else 0

        if is_actor_technique and score < min_score:
            continue

        if is_actor_technique:
            tc = test_counts.get(tech.id, 0)
            rc = rule_counts.get(tech.mitre_id, 0)
            metadata = [
                {"name": "tests_count", "value": str(tc)},
                {"name": "detection_rules", "value": str(rc)},
            ]
            if tech.last_review_date:
                metadata.append(
                    {"name": "last_validated", "value": tech.last_review_date.strftime("%Y-%m-%d")}
                )
            layer["techniques"].append({
                "techniqueID": tech.mitre_id,
                "tactic": _format_tactic(tech.tactic),
                "color": _score_to_color(score),
                "score": score,
                "comment": f"Used by {actor.name} - Coverage: {tech.status_global.value}",
                "enabled": True,
                "metadata": metadata,
            })
        else:
            layer["techniques"].append({
                "techniqueID": tech.mitre_id,
                "tactic": _format_tactic(tech.tactic),
                "color": "",
                "score": 0,
                "comment": "",
                "enabled": False,
                "metadata": [],
            })

    return layer


def build_detection_rules_layer(
    db: Session,
    *,
    platforms: str | None = None,
    tactics: str | None = None,
    min_score: int = 0,
) -> dict:
    """Detection rules layer -- score based on rule availability and evaluation ratio."""
    layer = _build_layer_skeleton(
        "Detection Rules Coverage",
        "Coverage of detection rules per technique",
    )

    query = _apply_filters(
        db.query(Technique), Technique,
        _parse_csv(platforms), _parse_csv(tactics),
    )
    techniques = query.all()

    rule_counts = dict(
        db.query(DetectionRule.mitre_technique_id, func.count(DetectionRule.id))
        .filter(DetectionRule.is_active == True)  # noqa: E712
        .group_by(DetectionRule.mitre_technique_id)
        .all()
    )
    max_rules = max(rule_counts.values()) if rule_counts else 1

    evaluated_counts = dict(
        db.query(DetectionRule.mitre_technique_id, func.count(TestDetectionResult.id))
        .join(TestDetectionResult, TestDetectionResult.detection_rule_id == DetectionRule.id)
        .filter(TestDetectionResult.triggered.isnot(None))
        .group_by(DetectionRule.mitre_technique_id)
        .all()
    )

    for tech in techniques:
        total_rules = rule_counts.get(tech.mitre_id, 0)
        evaluated_rules = evaluated_counts.get(tech.mitre_id, 0)

        if total_rules > 0:
            availability_score = min((total_rules / max_rules) * 50, 50)
            evaluation_score = (evaluated_rules / total_rules) * 50
            score = int(min(availability_score + evaluation_score, 100))
        else:
            score = 0

        if score < min_score:
            continue

        layer["techniques"].append({
            "techniqueID": tech.mitre_id,
            "tactic": _format_tactic(tech.tactic),
            "color": _score_to_color(score),
            "score": score,
            "comment": f"{total_rules} rules available, {evaluated_rules} evaluated",
            "enabled": True,
            "metadata": [
                {"name": "total_rules", "value": str(total_rules)},
                {"name": "evaluated_rules", "value": str(evaluated_rules)},
            ],
        })

    return layer


def build_campaign_layer(
    db: Session,
    campaign_id: str,
    *,
    platforms: str | None = None,
    tactics: str | None = None,
    min_score: int = 0,
) -> dict | None:
    """Campaign layer -- techniques in a campaign, coloured by test state.

    Returns ``None`` if the campaign does not exist.
    """
    campaign = db.query(Campaign).filter(Campaign.id == campaign_id).first()
    if not campaign:
        return None

    layer = _build_layer_skeleton(
        f"Campaign: {campaign.name}",
        f"Progress of campaign '{campaign.name}'",
    )

    campaign_tests = (
        db.query(CampaignTest)
        .filter(CampaignTest.campaign_id == campaign.id)
        .all()
    )
    if not campaign_tests:
        return layer

    test_ids = [ct.test_id for ct in campaign_tests]
    tests = db.query(Test).filter(Test.id.in_(test_ids)).all()
    test_map = {t.id: t for t in tests}

    technique_ids = {t.technique_id for t in tests if t.technique_id}
    techniques = db.query(Technique).filter(Technique.id.in_(technique_ids)).all()
    tech_map = {t.id: t for t in techniques}

    # Group tests by technique, keeping the best state score
    tech_scores: dict = {}
    for ct in campaign_tests:
        test = test_map.get(ct.test_id)
        if not test:
            continue
        tech = tech_map.get(test.technique_id)
        if not tech:
            continue

        state_score = TEST_STATE_SCORE.get(test.state, 0)
        if tech.mitre_id not in tech_scores:
            tech_scores[tech.mitre_id] = {
                "technique": tech,
                "max_score": state_score,
                "tests": [],
            }
        else:
            tech_scores[tech.mitre_id]["max_score"] = max(
                tech_scores[tech.mitre_id]["max_score"], state_score,
            )
        tech_scores[tech.mitre_id]["tests"].append(test)

    platform_list = _parse_csv(platforms)
    tactic_list = _parse_csv(tactics)

    for mitre_id, info in tech_scores.items():
        tech = info["technique"]
        score = info["max_score"]

        if platform_list:
            tech_platforms = tech.platforms or []
            if not any(p in tech_platforms for p in platform_list):
                continue
        if tactic_list:
            tech_tactics = [t.strip() for t in (tech.tactic or "").lower().split(",")]
            if not any(t in tech_tactics for t in tactic_list):
                continue
        if score < min_score:
            continue

        test_states = [t.state.value for t in info["tests"]]
        layer["techniques"].append({
            "techniqueID": mitre_id,
            "tactic": _format_tactic(tech.tactic),
            "color": _score_to_color(score),
            "score": score,
            "comment": f"Campaign tests: {', '.join(test_states)}",
            "enabled": True,
            "metadata": [
                {"name": "campaign_tests", "value": str(len(info["tests"]))},
                {"name": "best_state", "value": max(test_states) if test_states else "none"},
            ],
        })

    return layer