"""Schema creation and column migrations for the chat and custom-bot tables."""

from __future__ import annotations

import logging
import sqlite3

import numpy as np

from vibe_bot.db.connection import connect

logger = logging.getLogger(__name__)


def initialize_chat_tables(db_path: str) -> None:
    """Create (and migrate) the chat history and embedding tables."""
    logger.info("Initializing SQLite database at %s", db_path)
    conn = connect(db_path)
    cursor = conn.cursor()

    logger.info("Creating chat_messages table if not exists")
    cursor.execute(
        """
        CREATE TABLE IF NOT EXISTS chat_messages (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            message_id TEXT UNIQUE,
            user_id TEXT,
            username TEXT,
            content TEXT,
            timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
            channel_id TEXT,
            guild_id TEXT
        )
    """,
    )
    logger.info("chat_messages table initialized successfully")
    _migrate_chat_messages(cursor)

    logger.info("Creating message_embeddings table if not exists")
    cursor.execute(
        """
        CREATE TABLE IF NOT EXISTS message_embeddings (
            message_id TEXT PRIMARY KEY,
            embedding BLOB,
            norm REAL,
            FOREIGN KEY (message_id) REFERENCES chat_messages(message_id)
        )
    """,
    )
    logger.info("message_embeddings table initialized successfully")
    _migrate_message_embeddings(cursor)

    logger.info("Creating idx_timestamp index if not exists")
    cursor.execute(
        """
        CREATE INDEX IF NOT EXISTS idx_timestamp ON chat_messages(timestamp)
    """,
    )
    logger.info("idx_timestamp index created successfully")

    logger.info("Creating idx_user_id index if not exists")
    cursor.execute(
        """
        CREATE INDEX IF NOT EXISTS idx_user_id ON chat_messages(user_id)
    """,
    )
    logger.info("idx_user_id index created successfully")

    logger.info("Creating image_generation_times table if not exists")
    cursor.execute(
        """
        CREATE TABLE IF NOT EXISTS image_generation_times (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            duration_seconds REAL NOT NULL,
            generated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
        )
    """,
    )
    logger.info("image_generation_times table initialized successfully")

    conn.commit()
    logger.info("Database initialization completed successfully")
    conn.close()


def _migrate_chat_messages(cursor: sqlite3.Cursor) -> None:
    """Add the bot_name and role columns to pre-existing databases."""
    logger.info("Checking for chat_messages column migrations")
    cursor.execute("PRAGMA table_info(chat_messages)")
    columns = {row[1] for row in cursor.fetchall()}

    if "bot_name" not in columns:
        logger.info("Adding bot_name column to chat_messages table")
        cursor.execute("ALTER TABLE chat_messages ADD COLUMN bot_name TEXT")
        logger.info("bot_name column added successfully")

    # role replaces the old convention of identifying bot responses by a
    # hard-coded bot username.
    if "role" not in columns:
        logger.info("Adding role column to chat_messages table")
        cursor.execute("ALTER TABLE chat_messages ADD COLUMN role TEXT")
        cursor.execute(
            "UPDATE chat_messages SET role = 'assistant' "
            "WHERE message_id LIKE '%_response' AND role IS NULL",
        )
        cursor.execute(
            "UPDATE chat_messages SET role = 'user' WHERE role IS NULL",
        )
        logger.info("role column added and backfilled")


# Backfill in batches so a large legacy table does not build one huge
# executemany parameter list in memory.
NORM_BACKFILL_BATCH = 500


def _migrate_message_embeddings(cursor: sqlite3.Cursor) -> None:
    """Add the norm column to pre-existing databases and backfill it.

    The norm is the L2 norm of the stored float32 blob, so search can score
    candidates with one matrix multiply and no per-vector renormalization.
    """
    logger.info("Checking for message_embeddings column migrations")
    cursor.execute("PRAGMA table_info(message_embeddings)")
    columns = {row[1] for row in cursor.fetchall()}

    if "norm" in columns:
        return

    logger.info("Adding norm column to message_embeddings table")
    cursor.execute("ALTER TABLE message_embeddings ADD COLUMN norm REAL")

    cursor.execute(
        "SELECT message_id, embedding FROM message_embeddings "
        "WHERE embedding IS NOT NULL",
    )
    rows = cursor.fetchall()
    for start in range(0, len(rows), NORM_BACKFILL_BATCH):
        cursor.executemany(
            "UPDATE message_embeddings SET norm = ? WHERE message_id = ?",
            [
                (
                    float(np.linalg.norm(np.frombuffer(blob, dtype=np.float32))),
                    message_id,
                )
                for message_id, blob in rows[start : start + NORM_BACKFILL_BATCH]
            ],
        )
    logger.info("norm column added and backfilled for %d rows", len(rows))


def initialize_custom_bots_table(db_path: str) -> None:
    """Create the custom bots table in SQLite."""
    conn = connect(db_path)
    conn.execute(
        """
        CREATE TABLE IF NOT EXISTS custom_bots (
            bot_name TEXT PRIMARY KEY,
            system_prompt TEXT NOT NULL,
            created_by TEXT NOT NULL,
            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
            is_active INTEGER DEFAULT 1
        )
    """,
    )
    conn.commit()
    conn.close()