"""
Database manager for Lyra's persistent storage.

Handles database connections, transactions, and high-level data operations
with proper error handling and connection pooling.
"""

import asyncio
import logging
from contextlib import asynccontextmanager
from typing import Dict, List, Any, Optional, AsyncGenerator, Union
from datetime import datetime, timedelta
import json

from sqlalchemy import create_engine, text
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
from sqlalchemy.orm import sessionmaker, Session
from sqlalchemy.pool import QueuePool
import redis.asyncio as redis

from .models import (
    Base, UserModel, ConversationModel, PersonalityStateModel,
    EmotionalMemoryModel, KnowledgeModel, LearningProgressModel,
    ThinkingProcessModel, EvolutionEventModel, SystemMetricsModel,
    PersonalityAdaptationModel
)

logger = logging.getLogger(__name__)


class DatabaseManager:
    """
    Comprehensive database manager for Lyra's data persistence.

    Handles PostgreSQL for structured data and Redis for caching and real-time data.
    """

    def __init__(
        self,
        database_url: str,
        redis_url: str = "redis://localhost:6379/0",
        pool_size: int = 20,
        max_overflow: int = 30,
        echo: bool = False
    ):
        self.database_url = database_url
        self.redis_url = redis_url
        self.pool_size = pool_size
        self.max_overflow = max_overflow
        self.echo = echo

        # Database engines
        self.engine = None
        self.async_engine = None
        self.Session = None
        self.AsyncSession = None

        # Redis connection
        self.redis = None

        # Connection status
        self.is_connected = False

    async def initialize(self):
        """Initialize database connections and create tables."""
        try:
            # Create async engine for main operations
            self.async_engine = create_async_engine(
                self.database_url.replace("postgresql://", "postgresql+asyncpg://"),
                echo=self.echo,
                poolclass=QueuePool,
                pool_size=self.pool_size,
                max_overflow=self.max_overflow,
                pool_pre_ping=True,
                pool_recycle=3600  # Recycle connections every hour
            )

            # Create sync engine for admin operations
            self.engine = create_engine(
                self.database_url,
                echo=self.echo,
                poolclass=QueuePool,
                pool_size=5,
                max_overflow=10,
                pool_pre_ping=True
            )

            # Create session factories
            self.AsyncSession = async_sessionmaker(
                self.async_engine, class_=AsyncSession, expire_on_commit=False
            )
            self.Session = sessionmaker(bind=self.engine)

            # Initialize Redis
            self.redis = redis.from_url(self.redis_url, decode_responses=True)

            # Create tables
            await self._create_tables()

            # Test connections
            await self._test_connections()

            self.is_connected = True
            logger.info("Database manager initialized successfully")

        except Exception as e:
            logger.error(f"Failed to initialize database manager: {e}")
            raise

    async def _create_tables(self):
        """Create database tables if they don't exist."""
        try:
            async with self.async_engine.begin() as conn:
                await conn.run_sync(Base.metadata.create_all)
            logger.info("Database tables created/verified")
        except Exception as e:
            logger.error(f"Failed to create tables: {e}")
            raise

    async def _test_connections(self):
        """Test database and Redis connections."""
        # Test PostgreSQL
        async with self.async_session() as session:
            result = await session.execute(text("SELECT 1"))
            assert result.scalar() == 1

        # Test Redis
        await self.redis.ping()

        logger.info("Database connections tested successfully")

    @asynccontextmanager
    async def async_session(self) -> AsyncGenerator[AsyncSession, None]:
        """Async context manager for database sessions."""
        if not self.is_connected:
            raise RuntimeError("Database manager not initialized")

        session = self.AsyncSession()
        try:
            yield session
            await session.commit()
        except Exception as e:
            await session.rollback()
            logger.error(f"Database session error: {e}")
            raise
        finally:
            await session.close()

    @asynccontextmanager
    async def sync_session(self) -> AsyncGenerator[Session, None]:
        """Sync context manager for database sessions."""
        if not self.is_connected:
            raise RuntimeError("Database manager not initialized")

        session = self.Session()
        try:
            yield session
            session.commit()
        except Exception as e:
            session.rollback()
            logger.error(f"Database session error: {e}")
            raise
        finally:
            session.close()

    # User management
    async def create_user(
        self,
        discord_id: str,
        username: str,
        display_name: Optional[str] = None
    ) -> UserModel:
        """Create a new user record."""
        async with self.async_session() as session:
            user = UserModel(
                discord_id=discord_id,
                username=username,
                display_name=display_name or username
            )
            session.add(user)
            await session.flush()
            await session.refresh(user)
            return user

    async def get_user_by_discord_id(self, discord_id: str) -> Optional[UserModel]:
        """Get user by Discord ID."""
        async with self.async_session() as session:
            result = await session.execute(
                text("SELECT * FROM users WHERE discord_id = :discord_id"),
                {"discord_id": discord_id}
            )
            user_data = result.fetchone()
            if user_data:
                user = UserModel()
                for key, value in user_data._mapping.items():
                    setattr(user, key, value)
                return user
            return None

    async def update_user_interaction(
        self,
        user_id: str,
        satisfaction_rating: Optional[float] = None
    ):
        """Update user interaction metrics."""
        async with self.async_session() as session:
            user = await session.get(UserModel, user_id)
            if user:
                user.interaction_count += 1
                user.last_interaction = datetime.utcnow()

                if satisfaction_rating is not None:
                    ratings = user.satisfaction_ratings or []
                    ratings.append(satisfaction_rating)
                    # Keep only last 100 ratings
                    user.satisfaction_ratings = ratings[-100:]

                await session.flush()

    # Conversation management
    async def store_conversation(
        self,
        user_id: str,
        channel_id: str,
        message_id: str,
        user_message: str,
        lyra_response: str,
        context: Dict[str, Any],
        emotional_state: Dict[str, Any],
        personality_state: Dict[str, Any],
        thinking_process: List[Dict[str, Any]],
        response_time: float,
        user_satisfaction: Optional[float] = None,
        response_quality: Optional[float] = None
    ) -> ConversationModel:
        """Store a complete conversation interaction."""
        async with self.async_session() as session:
            conversation = ConversationModel(
                user_id=user_id,
                channel_id=channel_id,
                message_id=message_id,
                user_message=user_message,
                lyra_response=lyra_response,
                context=context,
                emotional_state=emotional_state,
                personality_state=personality_state,
                thinking_process=thinking_process,
                response_time=response_time,
                user_satisfaction=user_satisfaction,
                response_quality=response_quality
            )
            session.add(conversation)
            await session.flush()
            await session.refresh(conversation)

            # Cache recent conversation for quick access
            await self._cache_recent_conversation(conversation)

            return conversation

    async def get_recent_conversations(
        self,
        user_id: str,
        limit: int = 10
    ) -> List[ConversationModel]:
        """Get recent conversations for a user."""
        # Try cache first
        cached = await self._get_cached_conversations(user_id, limit)
        if cached:
            return cached

        # Fallback to database
        async with self.async_session() as session:
            result = await session.execute(
                text("""
                    SELECT * FROM conversations
                    WHERE user_id = :user_id
                    ORDER BY timestamp DESC
                    LIMIT :limit
                """),
                {"user_id": user_id, "limit": limit}
            )
            conversations = []
            for row in result.fetchall():
                conv = ConversationModel()
                for key, value in row._mapping.items():
                    setattr(conv, key, value)
                conversations.append(conv)
            return conversations

    # Personality state management
    async def store_personality_state(
        self,
        openness: float,
        conscientiousness: float,
        extraversion: float,
        agreeableness: float,
        neuroticism: float,
        myers_briggs_type: str,
        custom_traits: Dict[str, Any],
        total_interactions: int,
        adaptation_rate: float,
        emotional_maturity: float,
        trigger_event: Optional[str] = None,
        change_magnitude: Optional[float] = None
    ) -> PersonalityStateModel:
        """Store a personality state snapshot."""
        async with self.async_session() as session:
            state = PersonalityStateModel(
                openness=openness,
                conscientiousness=conscientiousness,
                extraversion=extraversion,
                agreeableness=agreeableness,
                neuroticism=neuroticism,
                myers_briggs_type=myers_briggs_type,
                custom_traits=custom_traits,
                total_interactions=total_interactions,
                adaptation_rate=adaptation_rate,
                emotional_maturity=emotional_maturity,
                trigger_event=trigger_event,
                change_magnitude=change_magnitude
            )
            session.add(state)
            await session.flush()
            await session.refresh(state)
            return state

    async def get_personality_evolution(
        self,
        days: int = 30
    ) -> List[PersonalityStateModel]:
        """Get personality evolution over time."""
        cutoff_date = datetime.utcnow() - timedelta(days=days)

        async with self.async_session() as session:
            result = await session.execute(
                text("""
                    SELECT * FROM personality_states
                    WHERE timestamp >= :cutoff_date
                    ORDER BY timestamp ASC
                """),
                {"cutoff_date": cutoff_date}
            )
            states = []
            for row in result.fetchall():
                state = PersonalityStateModel()
                for key, value in row._mapping.items():
                    setattr(state, key, value)
                states.append(state)
            return states

    # Emotional memory management
    async def store_emotional_memory(
        self,
        emotional_state: Dict[str, Any],
        dominant_emotion: str,
        emotion_intensity: float,
        emotional_valence: float,
        context: str,
        trigger: Optional[str],
        impact_score: float,
        conversation_id: Optional[str] = None,
        user_id: Optional[str] = None
    ) -> EmotionalMemoryModel:
        """Store an emotional memory."""
        async with self.async_session() as session:
            memory = EmotionalMemoryModel(
                emotional_state=emotional_state,
                dominant_emotion=dominant_emotion,
                emotion_intensity=emotion_intensity,
                emotional_valence=emotional_valence,
                context=context,
                trigger=trigger,
                impact_score=impact_score,
                conversation_id=conversation_id,
                user_id=user_id
            )
            session.add(memory)
            await session.flush()
            await session.refresh(memory)
            return memory

    async def get_significant_emotional_memories(
        self,
        threshold: float = 0.5,
        limit: int = 50
    ) -> List[EmotionalMemoryModel]:
        """Get emotionally significant memories."""
        async with self.async_session() as session:
            result = await session.execute(
                text("""
                    SELECT * FROM emotional_memories
                    WHERE impact_score >= :threshold
                    ORDER BY impact_score DESC, timestamp DESC
                    LIMIT :limit
                """),
                {"threshold": threshold, "limit": limit}
            )
            memories = []
            for row in result.fetchall():
                memory = EmotionalMemoryModel()
                for key, value in row._mapping.items():
                    setattr(memory, key, value)
                memories.append(memory)
            return memories

    # Knowledge management
    async def store_knowledge(
        self,
        title: str,
        content: str,
        category: str,
        source_type: str,
        summary: Optional[str] = None,
        subcategory: Optional[str] = None,
        source_url: Optional[str] = None,
        source_metadata: Optional[Dict[str, Any]] = None,
        quality_score: float = 0.5,
        relevance_score: float = 0.5,
        embedding_vector: Optional[List[float]] = None,
        keywords: Optional[List[str]] = None,
        related_concepts: Optional[List[str]] = None
    ) -> KnowledgeModel:
        """Store a knowledge item."""
        async with self.async_session() as session:
            knowledge = KnowledgeModel(
                title=title,
                content=content,
                summary=summary,
                category=category,
                subcategory=subcategory,
                source_type=source_type,
                source_url=source_url,
                source_metadata=source_metadata or {},
                quality_score=quality_score,
                relevance_score=relevance_score,
                embedding_vector=embedding_vector,
                keywords=keywords or [],
                related_concepts=related_concepts or []
            )
            session.add(knowledge)
            await session.flush()
            await session.refresh(knowledge)
            return knowledge

    async def search_knowledge(
        self,
        query: str,
        category: Optional[str] = None,
        min_quality: float = 0.3,
        limit: int = 20
    ) -> List[KnowledgeModel]:
        """Search knowledge by text query."""
        conditions = ["quality_score >= :min_quality"]
        params = {"min_quality": min_quality, "limit": limit}

        if category:
            conditions.append("category = :category")
            params["category"] = category

        # Simple text search (in production, would use full-text search)
        conditions.append("(title ILIKE :query OR content ILIKE :query)")
        params["query"] = f"%{query}%"

        query_sql = f"""
            SELECT * FROM knowledge
            WHERE {' AND '.join(conditions)}
            ORDER BY quality_score DESC, relevance_score DESC
            LIMIT :limit
        """

        async with self.async_session() as session:
            result = await session.execute(text(query_sql), params)
            knowledge_items = []
            for row in result.fetchall():
                item = KnowledgeModel()
                for key, value in row._mapping.items():
                    setattr(item, key, value)
                knowledge_items.append(item)
            return knowledge_items

    # Analytics and metrics
    async def get_conversation_analytics(
        self,
        days: int = 7
    ) -> Dict[str, Any]:
        """Get conversation analytics."""
        cutoff_date = datetime.utcnow() - timedelta(days=days)

        async with self.async_session() as session:
            result = await session.execute(
                text("""
                    SELECT
                        COUNT(*) as total_conversations,
                        COUNT(DISTINCT user_id) as unique_users,
                        AVG(user_satisfaction) as avg_satisfaction,
                        AVG(response_quality) as avg_quality,
                        AVG(response_time) as avg_response_time
                    FROM conversations
                    WHERE timestamp >= :cutoff_date
                """),
                {"cutoff_date": cutoff_date}
            )
            row = result.fetchone()

            return {
                "total_conversations": row.total_conversations or 0,
                "unique_users": row.unique_users or 0,
                "avg_satisfaction": float(row.avg_satisfaction or 0),
                "avg_quality": float(row.avg_quality or 0),
                "avg_response_time": float(row.avg_response_time or 0),
                "period_days": days
            }

    async def store_learning_progress(
        self,
        total_conversations: int,
        total_knowledge_items: int,
        personality_evolution_count: int,
        emotional_memories_count: int,
        avg_user_satisfaction: float,
        avg_response_quality: float,
        conversation_success_rate: float,
        knowledge_categories_mastered: List[str],
        personality_stability: float,
        emotional_maturity: float,
        social_adaptation_score: float,
        self_evolution_events: int = 0,
        conscious_personality_modifications: int = 0,
        meta_learning_instances: int = 0
    ) -> LearningProgressModel:
        """Store learning progress snapshot."""
        async with self.async_session() as session:
            progress = LearningProgressModel(
                total_conversations=total_conversations,
                total_knowledge_items=total_knowledge_items,
                personality_evolution_count=personality_evolution_count,
                emotional_memories_count=emotional_memories_count,
                avg_user_satisfaction=avg_user_satisfaction,
                avg_response_quality=avg_response_quality,
                conversation_success_rate=conversation_success_rate,
                knowledge_categories_mastered=knowledge_categories_mastered,
                personality_stability=personality_stability,
                emotional_maturity=emotional_maturity,
                social_adaptation_score=social_adaptation_score,
                self_evolution_events=self_evolution_events,
                conscious_personality_modifications=conscious_personality_modifications,
                meta_learning_instances=meta_learning_instances
            )
            session.add(progress)
            await session.flush()
            await session.refresh(progress)
            return progress

    # Cache management
    async def _cache_recent_conversation(self, conversation: ConversationModel):
        """Cache recent conversation for quick access."""
        key = f"conversations:{conversation.user_id}"
        conversation_data = {
            "id": conversation.id,
            "user_message": conversation.user_message,
            "lyra_response": conversation.lyra_response,
            "timestamp": conversation.timestamp.isoformat(),
            "emotional_state": conversation.emotional_state,
            "context": conversation.context
        }

        # Add to list (keep last 20)
        await self.redis.lpush(key, json.dumps(conversation_data))
        await self.redis.ltrim(key, 0, 19)
        await self.redis.expire(key, 3600)  # 1 hour TTL

    async def _get_cached_conversations(
        self,
        user_id: str,
        limit: int
    ) -> Optional[List[ConversationModel]]:
        """Get cached conversations."""
        try:
            key = f"conversations:{user_id}"
            cached_data = await self.redis.lrange(key, 0, limit - 1)

            if not cached_data:
                return None

            conversations = []
            for data in cached_data:
                conv_dict = json.loads(data)
                conv = ConversationModel()
                for key, value in conv_dict.items():
                    if key == "timestamp":
                        setattr(conv, key, datetime.fromisoformat(value))
                    else:
                        setattr(conv, key, value)
                conversations.append(conv)

            return conversations

        except Exception as e:
            logger.warning(f"Failed to get cached conversations: {e}")
            return None

    async def cleanup_old_data(self, days: int = 90):
        """Clean up old data to manage database size."""
        cutoff_date = datetime.utcnow() - timedelta(days=days)

        async with self.async_session() as session:
            # Clean up old conversations (keep satisfaction ratings)
            await session.execute(
                text("""
                    DELETE FROM conversations
                    WHERE timestamp < :cutoff_date
                    AND user_satisfaction IS NULL
                """),
                {"cutoff_date": cutoff_date}
            )

            # Clean up low-impact emotional memories
            await session.execute(
                text("""
                    DELETE FROM emotional_memories
                    WHERE timestamp < :cutoff_date
                    AND impact_score < 0.3
                """),
                {"cutoff_date": cutoff_date}
            )

            # Clean up old system metrics
            await session.execute(
                text("""
                    DELETE FROM system_metrics
                    WHERE timestamp < :cutoff_date
                """),
                {"cutoff_date": cutoff_date}
            )

            await session.commit()
            logger.info(f"Cleaned up data older than {days} days")

    async def close(self):
        """Close database connections."""
        if self.async_engine:
            await self.async_engine.dispose()

        if self.engine:
            self.engine.dispose()

        if self.redis:
            await self.redis.close()

        self.is_connected = False
        logger.info("Database manager closed")