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

async function calculateSalesForecasts(startTime, totalProducts, processedCount = 0, isCancelled = false) {
    const connection = await getConnection();
    let success = false;
    let processedOrders = 0;
    
    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 {
                processedProducts: processedCount,
                processedOrders: 0,
                processedPurchaseOrders: 0,
                success
            };
        }

        // Get order count that will be processed
        const orderCount = await connection.query(`
            SELECT COUNT(*) as count 
            FROM orders o 
            WHERE o.canceled = false
                AND o.date >= CURRENT_DATE - INTERVAL '90 days'
        `);
        processedOrders = parseInt(orderCount.rows[0].count);

        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 
                CURRENT_DATE + (n || ' days')::INTERVAL as forecast_date,
                EXTRACT(DOW FROM CURRENT_DATE + (n || ' days')::INTERVAL) + 1 as day_of_week,
                EXTRACT(MONTH FROM CURRENT_DATE + (n || ' days')::INTERVAL) 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 {
            processedProducts: processedCount,
            processedOrders,
            processedPurchaseOrders: 0,
            success
        };

        // Create temporary table for daily sales stats
        await connection.query(`
            CREATE TEMPORARY TABLE temp_daily_sales AS
            SELECT 
                o.pid,
                EXTRACT(DOW FROM o.date) + 1 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 >= CURRENT_DATE - INTERVAL '90 days'
            GROUP BY o.pid, EXTRACT(DOW FROM o.date) + 1
        `);

        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 {
            processedProducts: processedCount,
            processedOrders,
            processedPurchaseOrders: 0,
            success
        };

        // Create temporary table for product stats
        await connection.query(`
            CREATE TEMPORARY TABLE 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 {
            processedProducts: processedCount,
            processedOrders,
            processedPurchaseOrders: 0,
            success
        };

        // Calculate product-level forecasts
        await connection.query(`
            INSERT INTO sales_forecasts (
                pid,
                forecast_date,
                forecast_quantity,
                confidence_level,
                created_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,
                    -- Calculate variance without using LAG
                    COALESCE(
                        STDDEV(ds.daily_quantity) / NULLIF(AVG(ds.daily_quantity), 0),
                        0
                    ) as daily_variance_ratio
                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))
                    )
                ) as forecast_quantity,
                CASE
                    WHEN ds.total_days >= 60 AND ds.daily_variance_ratio < 0.5 THEN 90
                    WHEN ds.total_days >= 60 THEN 85
                    WHEN ds.total_days >= 30 AND ds.daily_variance_ratio < 0.5 THEN 80
                    WHEN ds.total_days >= 30 THEN 75
                    WHEN ds.total_days >= 14 AND ds.daily_variance_ratio < 0.5 THEN 70
                    WHEN ds.total_days >= 14 THEN 65
                    ELSE 60
                END as confidence_level,
                NOW() as created_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, 
                    ds.avg_daily_qty, ds.std_daily_qty, ds.avg_daily_qty, ds.total_days, ds.daily_variance_ratio
            ON CONFLICT (pid, forecast_date) DO UPDATE
            SET
                forecast_quantity = EXCLUDED.forecast_quantity,
                confidence_level = EXCLUDED.confidence_level,
                created_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 {
            processedProducts: processedCount,
            processedOrders,
            processedPurchaseOrders: 0,
            success
        };

        // Create temporary table for category stats
        await connection.query(`
            CREATE TEMPORARY TABLE temp_category_sales AS
            SELECT 
                pc.cat_id,
                EXTRACT(DOW FROM o.date) + 1 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 >= CURRENT_DATE - INTERVAL '90 days'
            GROUP BY pc.cat_id, EXTRACT(DOW FROM o.date) + 1
        `);

        await connection.query(`
            CREATE TEMPORARY TABLE 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 {
            processedProducts: processedCount,
            processedOrders,
            processedPurchaseOrders: 0,
            success
        };

        // Calculate category-level forecasts
        await connection.query(`
            INSERT INTO category_forecasts (
                category_id,
                forecast_date,
                forecast_units,
                forecast_revenue,
                confidence_level,
                created_at
            )
            SELECT 
                cs.cat_id::bigint as category_id,
                fd.forecast_date,
                GREATEST(0, 
                    ROUND(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
                    )
                ) 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 created_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,
                sf.month
            HAVING AVG(cs.daily_quantity) > 0
            ON CONFLICT (category_id, forecast_date) DO UPDATE
            SET
                forecast_units = EXCLUDED.forecast_units,
                forecast_revenue = EXCLUDED.forecast_revenue,
                confidence_level = EXCLUDED.confidence_level,
                created_at = NOW()
        `);

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

        // If we get here, everything completed successfully
        success = true;
        
        // Update calculate_status
        await connection.query(`
            INSERT INTO calculate_status (module_name, last_calculation_timestamp)
            VALUES ('sales_forecasts', NOW())
            ON CONFLICT (module_name) DO UPDATE
            SET last_calculation_timestamp = NOW()
        `);

        return {
            processedProducts: processedCount,
            processedOrders,
            processedPurchaseOrders: 0,
            success
        };

    } catch (error) {
        success = false;
        logError(error, 'Error calculating sales forecasts');
        throw error;
    } finally {
        if (connection) {
            try {
                // Ensure temporary tables are cleaned up
                await connection.query(`
                    DROP TABLE IF EXISTS temp_forecast_dates;
                    DROP TABLE IF EXISTS temp_daily_sales;
                    DROP TABLE IF EXISTS temp_product_stats;
                    DROP TABLE IF EXISTS temp_category_sales;
                    DROP TABLE IF EXISTS temp_category_stats;
                `);
            } catch (err) {
                console.error('Error cleaning up temporary tables:', err);
            }
            connection.release();
        }
    }
}

module.exports = calculateSalesForecasts;