const { outputProgress, formatElapsedTime, estimateRemaining, calculateRate, logError } = require('./utils/progress');
const { getConnection } = require('./utils/db');

async function calculateSalesForecasts(startTime, totalProducts, processedCount, isCancelled = false) {
    const connection = await getConnection();
    try {
        if (isCancelled) {
            outputProgress({
                status: 'cancelled',
                operation: 'Sales forecasts calculation cancelled',
                current: processedCount,
                total: totalProducts,
                elapsed: formatElapsedTime(startTime),
                remaining: null,
                rate: calculateRate(startTime, processedCount),
                percentage: ((processedCount / totalProducts) * 100).toFixed(1),
                timing: {
                    start_time: new Date(startTime).toISOString(),
                    end_time: new Date().toISOString(),
                    elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
                }
            });
            return processedCount;
        }

        outputProgress({
            status: 'running',
            operation: 'Starting sales forecasts calculation',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        // First, create a temporary table for forecast dates
        await connection.query(`
            CREATE TEMPORARY TABLE IF NOT EXISTS temp_forecast_dates (
                forecast_date DATE,
                day_of_week INT,
                month INT,
                PRIMARY KEY (forecast_date)
            )
        `);

        await connection.query(`
            INSERT INTO temp_forecast_dates
            SELECT 
                DATE_ADD(CURRENT_DATE, INTERVAL n DAY) as forecast_date,
                DAYOFWEEK(DATE_ADD(CURRENT_DATE, INTERVAL n DAY)) as day_of_week,
                MONTH(DATE_ADD(CURRENT_DATE, INTERVAL n DAY)) as month
            FROM (
                SELECT a.N + b.N * 10 as n 
                FROM 
                    (SELECT 0 as N UNION SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION
                     SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9) a,
                    (SELECT 0 as N UNION SELECT 1 UNION SELECT 2) b
                ORDER BY n
                LIMIT 31
            ) numbers
        `);

        processedCount = Math.floor(totalProducts * 0.92);
        outputProgress({
            status: 'running',
            operation: 'Forecast dates prepared, calculating daily sales stats',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Create temporary table for daily sales stats
        await connection.query(`
            CREATE TEMPORARY TABLE IF NOT EXISTS temp_daily_sales AS
            SELECT 
                o.pid,
                DAYOFWEEK(o.date) as day_of_week,
                SUM(o.quantity) as daily_quantity,
                SUM(o.price * o.quantity) as daily_revenue,
                COUNT(DISTINCT DATE(o.date)) as day_count
            FROM orders o
            WHERE o.canceled = false
                AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
            GROUP BY o.pid, DAYOFWEEK(o.date)
        `);

        processedCount = Math.floor(totalProducts * 0.94);
        outputProgress({
            status: 'running',
            operation: 'Daily sales stats calculated, preparing product stats',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Create temporary table for product stats
        await connection.query(`
            CREATE TEMPORARY TABLE IF NOT EXISTS temp_product_stats AS
            SELECT 
                pid,
                AVG(daily_revenue) as overall_avg_revenue,
                SUM(day_count) as total_days
            FROM temp_daily_sales
            GROUP BY pid
        `);

        processedCount = Math.floor(totalProducts * 0.96);
        outputProgress({
            status: 'running',
            operation: 'Product stats prepared, calculating product-level forecasts',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Calculate product-level forecasts
        await connection.query(`
            INSERT INTO sales_forecasts (
                pid,
                forecast_date,
                forecast_units,
                forecast_revenue,
                confidence_level,
                last_calculated_at
            )
            WITH daily_stats AS (
                SELECT 
                    ds.pid,
                    AVG(ds.daily_quantity) as avg_daily_qty,
                    STDDEV(ds.daily_quantity) as std_daily_qty,
                    COUNT(DISTINCT ds.day_count) as data_points,
                    SUM(ds.day_count) as total_days,
                    AVG(ds.daily_revenue) as avg_daily_revenue,
                    STDDEV(ds.daily_revenue) as std_daily_revenue,
                    MIN(ds.daily_quantity) as min_daily_qty,
                    MAX(ds.daily_quantity) as max_daily_qty,
                    AVG(ABS(ds.daily_quantity - LAG(ds.daily_quantity) OVER (PARTITION BY ds.pid ORDER BY ds.day_of_week))) as avg_daily_variance
                FROM temp_daily_sales ds
                GROUP BY ds.pid
                HAVING AVG(ds.daily_quantity) > 0
            )
            SELECT 
                ds.pid,
                fd.forecast_date,
                GREATEST(0, 
                    ROUND(
                        ds.avg_daily_qty * 
                        (1 + COALESCE(sf.seasonality_factor, 0)) *
                        CASE
                            WHEN ds.std_daily_qty / NULLIF(ds.avg_daily_qty, 0) > 1.5 THEN 0.85
                            WHEN ds.std_daily_qty / NULLIF(ds.avg_daily_qty, 0) > 1.0 THEN 0.9
                            WHEN ds.std_daily_qty / NULLIF(ds.avg_daily_qty, 0) > 0.5 THEN 0.95
                            ELSE 1.0
                        END,
                        2
                    )
                ) as forecast_units,
                GREATEST(0, 
                    ROUND(
                        COALESCE(
                            CASE 
                                WHEN ds.data_points >= 4 THEN ds.avg_daily_revenue
                                ELSE ps.overall_avg_revenue
                            END * 
                            (1 + COALESCE(sf.seasonality_factor, 0)) *
                            CASE
                                WHEN ds.std_daily_revenue / NULLIF(ds.avg_daily_revenue, 0) > 1.5 THEN 0.85
                                WHEN ds.std_daily_revenue / NULLIF(ds.avg_daily_revenue, 0) > 1.0 THEN 0.9
                                WHEN ds.std_daily_revenue / NULLIF(ds.avg_daily_revenue, 0) > 0.5 THEN 0.95
                                ELSE 1.0
                            END,
                            0
                        ),
                        2
                    )
                ) as forecast_revenue,
                CASE
                    WHEN ds.total_days >= 60 AND ds.avg_daily_variance / NULLIF(ds.avg_daily_qty, 0) < 0.5 THEN 90
                    WHEN ds.total_days >= 60 THEN 85
                    WHEN ds.total_days >= 30 AND ds.avg_daily_variance / NULLIF(ds.avg_daily_qty, 0) < 0.5 THEN 80
                    WHEN ds.total_days >= 30 THEN 75
                    WHEN ds.total_days >= 14 AND ds.avg_daily_variance / NULLIF(ds.avg_daily_qty, 0) < 0.5 THEN 70
                    WHEN ds.total_days >= 14 THEN 65
                    ELSE 60
                END as confidence_level,
                NOW() as last_calculated_at
            FROM daily_stats ds
            JOIN temp_product_stats ps ON ds.pid = ps.pid
            CROSS JOIN temp_forecast_dates fd
            LEFT JOIN sales_seasonality sf ON fd.month = sf.month
            GROUP BY ds.pid, fd.forecast_date, ps.overall_avg_revenue, sf.seasonality_factor
            ON DUPLICATE KEY UPDATE
                forecast_units = VALUES(forecast_units),
                forecast_revenue = VALUES(forecast_revenue),
                confidence_level = VALUES(confidence_level),
                last_calculated_at = NOW()
        `);

        processedCount = Math.floor(totalProducts * 0.98);
        outputProgress({
            status: 'running',
            operation: 'Product forecasts calculated, preparing category stats',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Create temporary table for category stats
        await connection.query(`
            CREATE TEMPORARY TABLE IF NOT EXISTS temp_category_sales AS
            SELECT 
                pc.cat_id,
                DAYOFWEEK(o.date) as day_of_week,
                SUM(o.quantity) as daily_quantity,
                SUM(o.price * o.quantity) as daily_revenue,
                COUNT(DISTINCT DATE(o.date)) as day_count
            FROM orders o
            JOIN product_categories pc ON o.pid = pc.pid
            WHERE o.canceled = false
                AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
            GROUP BY pc.cat_id, DAYOFWEEK(o.date)
        `);

        await connection.query(`
            CREATE TEMPORARY TABLE IF NOT EXISTS temp_category_stats AS
            SELECT 
                cat_id,
                AVG(daily_revenue) as overall_avg_revenue,
                SUM(day_count) as total_days
            FROM temp_category_sales
            GROUP BY cat_id
        `);

        processedCount = Math.floor(totalProducts * 0.99);
        outputProgress({
            status: 'running',
            operation: 'Category stats prepared, calculating category-level forecasts',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Calculate category-level forecasts
        await connection.query(`
            INSERT INTO category_forecasts (
                category_id,
                forecast_date,
                forecast_units,
                forecast_revenue,
                confidence_level,
                last_calculated_at
            )
            SELECT 
                cs.cat_id as category_id,
                fd.forecast_date,
                GREATEST(0, 
                    AVG(cs.daily_quantity) * 
                    (1 + COALESCE(sf.seasonality_factor, 0))
                ) as forecast_units,
                GREATEST(0, 
                    COALESCE(
                        CASE 
                            WHEN SUM(cs.day_count) >= 4 THEN AVG(cs.daily_revenue)
                            ELSE ct.overall_avg_revenue
                        END * 
                        (1 + COALESCE(sf.seasonality_factor, 0)) *
                        (0.95 + (RAND() * 0.1)),
                        0
                    )
                ) as forecast_revenue,
                CASE
                    WHEN ct.total_days >= 60 THEN 90
                    WHEN ct.total_days >= 30 THEN 80
                    WHEN ct.total_days >= 14 THEN 70
                    ELSE 60
                END as confidence_level,
                NOW() as last_calculated_at
            FROM temp_category_sales cs
            JOIN temp_category_stats ct ON cs.cat_id = ct.cat_id
            CROSS JOIN temp_forecast_dates fd
            LEFT JOIN sales_seasonality sf ON fd.month = sf.month
            GROUP BY cs.cat_id, fd.forecast_date, ct.overall_avg_revenue, ct.total_days, sf.seasonality_factor
            HAVING AVG(cs.daily_quantity) > 0
            ON DUPLICATE KEY UPDATE
                forecast_units = VALUES(forecast_units),
                forecast_revenue = VALUES(forecast_revenue),
                confidence_level = VALUES(confidence_level),
                last_calculated_at = NOW()
        `);

        // Clean up temporary tables
        await connection.query(`
            DROP TEMPORARY TABLE IF EXISTS temp_forecast_dates;
            DROP TEMPORARY TABLE IF EXISTS temp_daily_sales;
            DROP TEMPORARY TABLE IF EXISTS temp_product_stats;
            DROP TEMPORARY TABLE IF EXISTS temp_category_sales;
            DROP TEMPORARY TABLE IF EXISTS temp_category_stats;
        `);

        processedCount = Math.floor(totalProducts * 1.0);
        outputProgress({
            status: 'running',
            operation: 'Category forecasts calculated and temporary tables cleaned up',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        return processedCount;
    } catch (error) {
        logError(error, 'Error calculating sales forecasts');
        throw error;
    } finally {
        if (connection) {
            connection.release();
        }
    }
}

module.exports = calculateSalesForecasts;