"""
Service for calculating ride rankings using the Internet Roller Coaster Poll algorithm.

This service implements a pairwise comparison system where each ride is compared
to every other ride based on mutual riders (users who have rated both rides).
Rankings are determined by winning percentage in these comparisons.
"""

import logging
from typing import Dict, List, Optional
from decimal import Decimal
from datetime import date

from django.db import transaction
from django.db.models import Avg, Count, Q
from django.utils import timezone

from apps.rides.models import (
    Ride,
    RideReview,
    RideRanking,
    RidePairComparison,
    RankingSnapshot,
)


logger = logging.getLogger(__name__)


class RideRankingService:
    """
    Calculates ride rankings using the Internet Roller Coaster Poll algorithm.

    Algorithm Overview:
    1. For each pair of rides, find users who have rated both
    2. Count how many users preferred each ride (higher rating)
    3. Calculate wins, losses, and ties for each ride
    4. Rank rides by winning percentage (ties count as 0.5 wins)
    5. Break ties by head-to-head comparison
    """

    def __init__(self):
        self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
        self.calculation_version = "1.0"

    def update_all_rankings(self, category: Optional[str] = None) -> Dict[str, any]:
        """
        Main entry point to update all ride rankings.

        Args:
            category: Optional ride category to filter ('RC' for roller coasters, etc.)
                     If None, ranks all rides.

        Returns:
            Dictionary with statistics about the ranking calculation
        """
        start_time = timezone.now()
        self.logger.info(
            f"Starting ranking calculation for category: {category or 'ALL'}"
        )

        try:
            with transaction.atomic():
                # Get rides to rank
                rides = self._get_eligible_rides(category)
                if not rides:
                    self.logger.warning("No eligible rides found for ranking")
                    return {
                        "status": "skipped",
                        "message": "No eligible rides found",
                        "duration": (timezone.now() - start_time).total_seconds(),
                    }

                self.logger.info(f"Found {len(rides)} rides to rank")

                # Calculate pairwise comparisons
                comparisons = self._calculate_all_comparisons(rides)

                # Calculate rankings from comparisons
                rankings = self._calculate_rankings_from_comparisons(rides, comparisons)

                # Save rankings
                self._save_rankings(rankings)

                # Save snapshots for historical tracking
                self._save_ranking_snapshots(rankings)

                # Clean up old data
                self._cleanup_old_data()

                duration = (timezone.now() - start_time).total_seconds()
                self.logger.info(
                    f"Ranking calculation completed in {duration:.2f} seconds"
                )

                return {
                    "status": "success",
                    "rides_ranked": len(rides),
                    "comparisons_made": len(comparisons),
                    "duration": duration,
                    "timestamp": timezone.now(),
                }

        except Exception as e:
            self.logger.error(f"Error updating rankings: {e}", exc_info=True)
            raise

    def _get_eligible_rides(self, category: Optional[str] = None) -> List[Ride]:
        """
        Get rides that are eligible for ranking.

        Only includes rides that:
        - Are currently operating
        - Have at least one review/rating
        """
        queryset = (
            Ride.objects.filter(status="OPERATING", reviews__is_published=True)
            .annotate(
                review_count=Count("reviews", filter=Q(reviews__is_published=True))
            )
            .filter(review_count__gt=0)
        )

        if category:
            queryset = queryset.filter(category=category)

        return list(queryset.distinct())

    def _calculate_all_comparisons(
        self, rides: List[Ride]
    ) -> Dict[tuple[int, int], RidePairComparison]:
        """
        Calculate pairwise comparisons for all ride pairs.

        Returns a dictionary keyed by (ride_a_id, ride_b_id) tuples.
        """
        comparisons = {}
        total_pairs = len(rides) * (len(rides) - 1) // 2
        processed = 0

        for i, ride_a in enumerate(rides):
            for ride_b in rides[i + 1:]:
                comparison = self._calculate_pairwise_comparison(ride_a, ride_b)
                if comparison:
                    # Store both directions for easy lookup
                    comparisons[(ride_a.id, ride_b.id)] = comparison
                    comparisons[(ride_b.id, ride_a.id)] = comparison

                processed += 1
                if processed % 100 == 0:
                    self.logger.debug(
                        f"Processed {processed}/{total_pairs} comparisons"
                    )

        return comparisons

    def _calculate_pairwise_comparison(
        self, ride_a: Ride, ride_b: Ride
    ) -> Optional[RidePairComparison]:
        """
        Calculate the pairwise comparison between two rides.

        Finds users who have rated both rides and determines which ride
        they preferred based on their ratings.
        """
        # Get mutual riders (users who have rated both rides)
        ride_a_reviewers = set(
            RideReview.objects.filter(ride=ride_a, is_published=True).values_list(
                "user_id", flat=True
            )
        )

        ride_b_reviewers = set(
            RideReview.objects.filter(ride=ride_b, is_published=True).values_list(
                "user_id", flat=True
            )
        )

        mutual_riders = ride_a_reviewers & ride_b_reviewers

        if not mutual_riders:
            # No mutual riders, no comparison possible
            return None

        # Get ratings from mutual riders
        ride_a_ratings = {
            review.user_id: review.rating
            for review in RideReview.objects.filter(
                ride=ride_a, user_id__in=mutual_riders, is_published=True
            )
        }

        ride_b_ratings = {
            review.user_id: review.rating
            for review in RideReview.objects.filter(
                ride=ride_b, user_id__in=mutual_riders, is_published=True
            )
        }

        # Count wins and ties
        ride_a_wins = 0
        ride_b_wins = 0
        ties = 0

        for user_id in mutual_riders:
            rating_a = ride_a_ratings.get(user_id, 0)
            rating_b = ride_b_ratings.get(user_id, 0)

            if rating_a > rating_b:
                ride_a_wins += 1
            elif rating_b > rating_a:
                ride_b_wins += 1
            else:
                ties += 1

        # Calculate average ratings from mutual riders
        ride_a_avg = (
            sum(ride_a_ratings.values()) / len(ride_a_ratings) if ride_a_ratings else 0
        )
        ride_b_avg = (
            sum(ride_b_ratings.values()) / len(ride_b_ratings) if ride_b_ratings else 0
        )

        # Create or update comparison record
        comparison, created = RidePairComparison.objects.update_or_create(
            ride_a=ride_a if ride_a.id < ride_b.id else ride_b,
            ride_b=ride_b if ride_a.id < ride_b.id else ride_a,
            defaults={
                "ride_a_wins": ride_a_wins if ride_a.id < ride_b.id else ride_b_wins,
                "ride_b_wins": ride_b_wins if ride_a.id < ride_b.id else ride_a_wins,
                "ties": ties,
                "mutual_riders_count": len(mutual_riders),
                "ride_a_avg_rating": (
                    Decimal(str(ride_a_avg))
                    if ride_a.id < ride_b.id
                    else Decimal(str(ride_b_avg))
                ),
                "ride_b_avg_rating": (
                    Decimal(str(ride_b_avg))
                    if ride_a.id < ride_b.id
                    else Decimal(str(ride_a_avg))
                ),
            },
        )

        return comparison

    def _calculate_rankings_from_comparisons(
        self, rides: List[Ride], comparisons: Dict[tuple[int, int], RidePairComparison]
    ) -> List[Dict]:
        """
        Calculate final rankings from pairwise comparisons.

        Returns a list of dictionaries containing ranking data for each ride.
        """
        rankings = []

        for ride in rides:
            wins = 0
            losses = 0
            ties = 0
            comparison_count = 0

            # Count wins, losses, and ties
            for other_ride in rides:
                if ride.id == other_ride.id:
                    continue

                comparison_key = (
                    min(ride.id, other_ride.id),
                    max(ride.id, other_ride.id),
                )
                comparison = comparisons.get(comparison_key)

                if not comparison:
                    continue

                comparison_count += 1

                # Determine win/loss/tie for this ride
                if comparison.ride_a_id == ride.id:
                    if comparison.ride_a_wins > comparison.ride_b_wins:
                        wins += 1
                    elif comparison.ride_a_wins < comparison.ride_b_wins:
                        losses += 1
                    else:
                        ties += 1
                else:  # ride_b_id == ride.id
                    if comparison.ride_b_wins > comparison.ride_a_wins:
                        wins += 1
                    elif comparison.ride_b_wins < comparison.ride_a_wins:
                        losses += 1
                    else:
                        ties += 1

            # Calculate winning percentage (ties count as 0.5)
            total_comparisons = wins + losses + ties
            if total_comparisons > 0:
                winning_percentage = Decimal(
                    str((wins + 0.5 * ties) / total_comparisons)
                )
            else:
                winning_percentage = Decimal("0.5")

            # Get average rating and reviewer count
            ride_stats = RideReview.objects.filter(
                ride=ride, is_published=True
            ).aggregate(
                avg_rating=Avg("rating"), reviewer_count=Count("user", distinct=True)
            )

            rankings.append(
                {
                    "ride": ride,
                    "wins": wins,
                    "losses": losses,
                    "ties": ties,
                    "winning_percentage": winning_percentage,
                    "comparison_count": comparison_count,
                    "average_rating": ride_stats["avg_rating"],
                    "mutual_riders_count": ride_stats["reviewer_count"] or 0,
                }
            )

        # Sort by winning percentage (descending), then by mutual riders count for ties
        rankings.sort(
            key=lambda x: (
                x["winning_percentage"],
                x["mutual_riders_count"],
                x["average_rating"] or 0,
            ),
            reverse=True,
        )

        # Handle tie-breaking with head-to-head comparisons
        rankings = self._apply_tiebreakers(rankings, comparisons)

        # Assign final ranks
        for i, ranking_data in enumerate(rankings, 1):
            ranking_data["rank"] = i

        return rankings

    def _apply_tiebreakers(
        self,
        rankings: List[Dict],
        comparisons: Dict[tuple[int, int], RidePairComparison],
    ) -> List[Dict]:
        """
        Apply head-to-head tiebreaker for rides with identical winning percentages.

        If two rides have the same winning percentage, the one that beat the other
        in their head-to-head comparison gets the higher rank.
        """
        i = 0
        while i < len(rankings) - 1:
            # Find rides with same winning percentage
            tied_group = [rankings[i]]
            j = i + 1

            while (
                j < len(rankings)
                and rankings[j]["winning_percentage"]
                == rankings[i]["winning_percentage"]
            ):
                tied_group.append(rankings[j])
                j += 1

            if len(tied_group) > 1:
                # Apply head-to-head tiebreaker within the group
                tied_group = self._sort_tied_group(tied_group, comparisons)

                # Replace the tied section with sorted group
                rankings[i:j] = tied_group

            i = j

        return rankings

    def _sort_tied_group(
        self,
        tied_group: List[Dict],
        comparisons: Dict[tuple[int, int], RidePairComparison],
    ) -> List[Dict]:
        """
        Sort a group of tied rides using head-to-head comparisons.
        """
        # Create mini-rankings within the tied group
        for ride_data in tied_group:
            mini_wins = 0
            mini_losses = 0

            for other_data in tied_group:
                if ride_data["ride"].id == other_data["ride"].id:
                    continue

                comparison_key = (
                    min(ride_data["ride"].id, other_data["ride"].id),
                    max(ride_data["ride"].id, other_data["ride"].id),
                )
                comparison = comparisons.get(comparison_key)

                if comparison:
                    if comparison.ride_a_id == ride_data["ride"].id:
                        if comparison.ride_a_wins > comparison.ride_b_wins:
                            mini_wins += 1
                        elif comparison.ride_a_wins < comparison.ride_b_wins:
                            mini_losses += 1
                    else:
                        if comparison.ride_b_wins > comparison.ride_a_wins:
                            mini_wins += 1
                        elif comparison.ride_b_wins < comparison.ride_a_wins:
                            mini_losses += 1

            ride_data["tiebreaker_score"] = mini_wins - mini_losses

        # Sort by tiebreaker score, then by mutual riders count, then by average rating
        tied_group.sort(
            key=lambda x: (
                x["tiebreaker_score"],
                x["mutual_riders_count"],
                x["average_rating"] or 0,
            ),
            reverse=True,
        )

        return tied_group

    def _save_rankings(self, rankings: List[Dict]):
        """Save calculated rankings to the database."""
        for ranking_data in rankings:
            RideRanking.objects.update_or_create(
                ride=ranking_data["ride"],
                defaults={
                    "rank": ranking_data["rank"],
                    "wins": ranking_data["wins"],
                    "losses": ranking_data["losses"],
                    "ties": ranking_data["ties"],
                    "winning_percentage": ranking_data["winning_percentage"],
                    "mutual_riders_count": ranking_data["mutual_riders_count"],
                    "comparison_count": ranking_data["comparison_count"],
                    "average_rating": ranking_data["average_rating"],
                    "last_calculated": timezone.now(),
                    "calculation_version": self.calculation_version,
                },
            )

    def _save_ranking_snapshots(self, rankings: List[Dict]):
        """Save ranking snapshots for historical tracking."""
        today = date.today()

        for ranking_data in rankings:
            RankingSnapshot.objects.update_or_create(
                ride=ranking_data["ride"],
                snapshot_date=today,
                defaults={
                    "rank": ranking_data["rank"],
                    "winning_percentage": ranking_data["winning_percentage"],
                },
            )

    def _cleanup_old_data(self, days_to_keep: int = 365):
        """Clean up old comparison and snapshot data."""
        cutoff_date = timezone.now() - timezone.timedelta(days=days_to_keep)

        # Delete old snapshots
        deleted_snapshots = RankingSnapshot.objects.filter(
            snapshot_date__lt=cutoff_date.date()
        ).delete()

        if deleted_snapshots[0] > 0:
            self.logger.info(f"Deleted {deleted_snapshots[0]} old ranking snapshots")

    def get_ride_ranking_details(self, ride: Ride) -> Optional[Dict]:
        """
        Get detailed ranking information for a specific ride.

        Returns dictionary with ranking details or None if not ranked.
        """
        try:
            ranking = RideRanking.objects.get(ride=ride)

            # Get recent head-to-head comparisons
            comparisons = (
                RidePairComparison.objects.filter(Q(ride_a=ride) | Q(ride_b=ride))
                .select_related("ride_a", "ride_b")
                .order_by("-mutual_riders_count")[:10]
            )

            # Get ranking history
            history = RankingSnapshot.objects.filter(ride=ride).order_by(
                "-snapshot_date"
            )[:30]

            return {
                "current_rank": ranking.rank,
                "winning_percentage": ranking.winning_percentage,
                "wins": ranking.wins,
                "losses": ranking.losses,
                "ties": ranking.ties,
                "average_rating": ranking.average_rating,
                "mutual_riders_count": ranking.mutual_riders_count,
                "last_calculated": ranking.last_calculated,
                "head_to_head": [
                    {
                        "opponent": (
                            comp.ride_b if comp.ride_a_id == ride.id else comp.ride_a
                        ),
                        "result": (
                            "win"
                            if (
                                (
                                    comp.ride_a_id == ride.id
                                    and comp.ride_a_wins > comp.ride_b_wins
                                )
                                or (
                                    comp.ride_b_id == ride.id
                                    and comp.ride_b_wins > comp.ride_a_wins
                                )
                            )
                            else (
                                "loss"
                                if (
                                    (
                                        comp.ride_a_id == ride.id
                                        and comp.ride_a_wins < comp.ride_b_wins
                                    )
                                    or (
                                        comp.ride_b_id == ride.id
                                        and comp.ride_b_wins < comp.ride_a_wins
                                    )
                                )
                                else "tie"
                            )
                        ),
                        "mutual_riders": comp.mutual_riders_count,
                    }
                    for comp in comparisons
                ],
                "ranking_history": [
                    {
                        "date": snapshot.snapshot_date,
                        "rank": snapshot.rank,
                        "winning_percentage": snapshot.winning_percentage,
                    }
                    for snapshot in history
                ],
            }
        except RideRanking.DoesNotExist:
            return None