#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Demo Retail Sales Seeding Script for Sales Service
Creates realistic historical sales data for child retail outlets

This script runs as a Kubernetes init job inside the sales-service container.
It populates child retail tenants with 30 days of sales history.

Usage:
    python /app/scripts/demo/seed_demo_sales_retail.py

Environment Variables Required:
    SALES_DATABASE_URL - PostgreSQL connection string for sales database
    DEMO_MODE - Set to 'production' for production seeding
    LOG_LEVEL - Logging level (default: INFO)
"""

import asyncio
import uuid
import sys
import os
from datetime import datetime, timezone, timedelta
from pathlib import Path
import random
from decimal import Decimal

# Add app to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
# Add shared to path for demo utilities
sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent.parent))

from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy import select
import structlog

from shared.utils.demo_dates import BASE_REFERENCE_DATE

from app.models.sales import SalesData

# Configure logging
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.dev.ConsoleRenderer()
    ]
)

logger = structlog.get_logger()

# Fixed Demo Tenant IDs (must match tenant service)
DEMO_TENANT_CHILD_1 = uuid.UUID("d4e5f6a7-b8c9-40d1-e2f3-a4b5c6d7e8f9")  # Madrid Centro
DEMO_TENANT_CHILD_2 = uuid.UUID("e5f6a7b8-c9d0-41e2-f3a4-b5c6d7e8f9a0")  # Barcelona Gràcia
DEMO_TENANT_CHILD_3 = uuid.UUID("f6a7b8c9-d0e1-42f3-a4b5-c6d7e8f9a0b1")  # Valencia Ruzafa

# Hardcoded product IDs from ingredientes_es.json (finished products)
PRODUCT_IDS = {
    "PRO-BAG-001": "20000000-0000-0000-0000-000000000001",  # Baguette Tradicional
    "PRO-CRO-001": "20000000-0000-0000-0000-000000000002",  # Croissant de Mantequilla
    "PRO-PUE-001": "20000000-0000-0000-0000-000000000003",  # Pan de Pueblo
    "PRO-NAP-001": "20000000-0000-0000-0000-000000000004",  # Napolitana de Chocolate
}

# Retail sales patterns for each store
# Madrid Centro - Large urban store, high traffic
MADRID_CENTRO_PRODUCTS = [
    {"sku": "PRO-BAG-001", "name": "Baguette Tradicional", "avg_qty": 120, "variance": 20, "price": 1.30},
    {"sku": "PRO-CRO-001", "name": "Croissant de Mantequilla", "avg_qty": 80, "variance": 15, "price": 1.60},
    {"sku": "PRO-PUE-001", "name": "Pan de Pueblo", "avg_qty": 35, "variance": 8, "price": 3.80},
    {"sku": "PRO-NAP-001", "name": "Napolitana de Chocolate", "avg_qty": 60, "variance": 12, "price": 1.90},
]

# Barcelona Gràcia - Medium neighborhood store
BARCELONA_GRACIA_PRODUCTS = [
    {"sku": "PRO-BAG-001", "name": "Baguette Tradicional", "avg_qty": 90, "variance": 15, "price": 1.25},
    {"sku": "PRO-CRO-001", "name": "Croissant de Mantequilla", "avg_qty": 60, "variance": 12, "price": 1.55},
    {"sku": "PRO-PUE-001", "name": "Pan de Pueblo", "avg_qty": 25, "variance": 6, "price": 3.70},
    {"sku": "PRO-NAP-001", "name": "Napolitana de Chocolate", "avg_qty": 45, "variance": 10, "price": 1.85},
]

# Valencia Ruzafa - Smaller boutique store
VALENCIA_RUZAFA_PRODUCTS = [
    {"sku": "PRO-BAG-001", "name": "Baguette Tradicional", "avg_qty": 70, "variance": 12, "price": 1.20},
    {"sku": "PRO-CRO-001", "name": "Croissant de Mantequilla", "avg_qty": 45, "variance": 10, "price": 1.50},
    {"sku": "PRO-PUE-001", "name": "Pan de Pueblo", "avg_qty": 20, "variance": 5, "price": 3.60},
    {"sku": "PRO-NAP-001", "name": "Napolitana de Chocolate", "avg_qty": 35, "variance": 8, "price": 1.80},
]

# Child tenant configurations
CHILD_TENANTS = [
    (DEMO_TENANT_CHILD_1, "Madrid Centro", MADRID_CENTRO_PRODUCTS),
    (DEMO_TENANT_CHILD_2, "Barcelona Gràcia", BARCELONA_GRACIA_PRODUCTS),
    (DEMO_TENANT_CHILD_3, "Valencia Ruzafa", VALENCIA_RUZAFA_PRODUCTS)
]


def get_product_by_sku(tenant_id: uuid.UUID, sku: str, product_name: str):
    """
    Get tenant-specific product ID using XOR transformation

    Args:
        tenant_id: Tenant UUID
        sku: Product SKU code
        product_name: Product name

    Returns:
        Tuple of (product_id, product_name) or (None, None) if not found
    """
    if sku not in PRODUCT_IDS:
        return None, None

    # Generate tenant-specific product ID using XOR (same as inventory seed script)
    base_product_id = uuid.UUID(PRODUCT_IDS[sku])
    tenant_int = int(tenant_id.hex, 16)
    product_id = uuid.UUID(int=tenant_int ^ int(base_product_id.hex, 16))

    return product_id, product_name


async def seed_retail_sales_for_tenant(
    db: AsyncSession,
    tenant_id: uuid.UUID,
    tenant_name: str,
    product_patterns: list,
    days_of_history: int = 30
) -> dict:
    """
    Seed retail sales data for a specific child tenant

    Args:
        db: Database session
        tenant_id: UUID of the child tenant
        tenant_name: Name of the tenant (for logging)
        product_patterns: List of product sales patterns
        days_of_history: Number of days of historical data to generate (default: 30)

    Returns:
        Dict with seeding statistics
    """
    logger.info("─" * 80)
    logger.info(f"Seeding retail sales data for: {tenant_name}")
    logger.info(f"Tenant ID: {tenant_id}")
    logger.info(f"Days of history: {days_of_history}")
    logger.info("─" * 80)

    created_sales = 0
    skipped_sales = 0

    # Generate sales data for each day (working backwards from BASE_REFERENCE_DATE)
    for days_ago in range(days_of_history, 0, -1):
        sale_date = BASE_REFERENCE_DATE - timedelta(days=days_ago)

        # Skip some random days to simulate closures/holidays (3% chance)
        if random.random() < 0.03:
            continue

        # For each product, generate sales
        for product_pattern in product_patterns:
            sku = product_pattern["sku"]
            product_name = product_pattern["name"]

            # Get tenant-specific product ID using XOR transformation
            product_id, product_name = get_product_by_sku(tenant_id, sku, product_name)

            if not product_id:
                logger.warning(f"      ⚠️  Product not found: {sku}")
                continue

            # Check if sales record already exists
            result = await db.execute(
                select(SalesData).where(
                    SalesData.tenant_id == tenant_id,
                    SalesData.inventory_product_id == product_id,
                    SalesData.date == sale_date
                )
            )
            existing = result.scalars().first()

            if existing:
                skipped_sales += 1
                continue

            # Calculate sales quantity with realistic variance
            avg_qty = product_pattern["avg_qty"]
            variance = product_pattern["variance"]

            # Add weekly patterns (weekends sell more for bakeries)
            weekday = sale_date.weekday()
            if weekday in [5, 6]:  # Saturday, Sunday
                multiplier = random.uniform(1.3, 1.6)  # 30-60% more sales on weekends
            elif weekday == 4:  # Friday
                multiplier = random.uniform(1.1, 1.3)  # 10-30% more on Fridays
            else:  # Weekdays
                multiplier = random.uniform(0.85, 1.15)

            quantity = max(0, int((avg_qty + random.uniform(-variance, variance)) * multiplier))

            if quantity == 0:
                continue

            # Calculate revenue
            unit_price = Decimal(str(product_pattern["price"]))
            revenue = Decimal(str(quantity)) * unit_price

            # Determine if weekend
            is_weekend = weekday in [5, 6]

            # Create sales record
            sales_record = SalesData(
                id=uuid.uuid4(),
                tenant_id=tenant_id,
                inventory_product_id=product_id,
                date=sale_date,
                quantity_sold=quantity,
                revenue=revenue,
                unit_price=unit_price,
                sales_channel="in_store",  # Retail outlets primarily use in-store sales
                location_id="main",  # Single location per retail outlet
                source="demo_seed",
                is_weekend=is_weekend,
                created_at=sale_date,
                updated_at=sale_date
            )

            db.add(sales_record)
            created_sales += 1

            logger.debug(
                f"      ✅ {sale_date.strftime('%Y-%m-%d')}: {product_name} - "
                f"{quantity} units @ €{unit_price} = €{revenue:.2f}"
            )

    # Commit all changes for this tenant
    await db.commit()

    logger.info(f"   📊 Sales records created: {created_sales}, Skipped: {skipped_sales}")
    logger.info("")

    return {
        "tenant_id": str(tenant_id),
        "tenant_name": tenant_name,
        "sales_created": created_sales,
        "sales_skipped": skipped_sales,
        "days_of_history": days_of_history
    }


async def seed_retail_sales(db: AsyncSession):
    """
    Seed retail sales for all child tenant templates

    Args:
        db: Database session

    Returns:
        Dict with overall seeding statistics
    """
    logger.info("=" * 80)
    logger.info("💰 Starting Demo Retail Sales Seeding")
    logger.info("=" * 80)
    logger.info("Creating 30 days of sales history for retail outlets")
    logger.info("")

    results = []

    # Seed for each child retail outlet
    for child_tenant_id, child_tenant_name, product_patterns in CHILD_TENANTS:
        logger.info("")
        result = await seed_retail_sales_for_tenant(
            db,
            child_tenant_id,
            f"{child_tenant_name} (Retail Outlet)",
            product_patterns,
            days_of_history=30  # 30 days of sales history
        )
        results.append(result)

    # Calculate totals
    total_sales = sum(r["sales_created"] for r in results)
    total_skipped = sum(r["sales_skipped"] for r in results)

    logger.info("=" * 80)
    logger.info("✅ Demo Retail Sales Seeding Completed")
    logger.info("=" * 80)

    return {
        "service": "sales_retail",
        "tenants_seeded": len(results),
        "total_sales_created": total_sales,
        "total_skipped": total_skipped,
        "results": results
    }


async def main():
    """Main execution function"""

    logger.info("Demo Retail Sales Seeding Script Starting")
    logger.info("Mode: %s", os.getenv("DEMO_MODE", "development"))
    logger.info("Log Level: %s", os.getenv("LOG_LEVEL", "INFO"))

    # Get database URL from environment
    database_url = os.getenv("SALES_DATABASE_URL") or os.getenv("DATABASE_URL")
    if not database_url:
        logger.error("❌ SALES_DATABASE_URL or DATABASE_URL environment variable must be set")
        return 1

    # Convert to async URL if needed
    if database_url.startswith("postgresql://"):
        database_url = database_url.replace("postgresql://", "postgresql+asyncpg://", 1)

    logger.info("Connecting to sales database")

    # Create engine and session
    engine = create_async_engine(
        database_url,
        echo=False,
        pool_pre_ping=True,
        pool_size=5,
        max_overflow=10
    )

    async_session = sessionmaker(
        engine,
        class_=AsyncSession,
        expire_on_commit=False
    )

    try:
        async with async_session() as session:
            result = await seed_retail_sales(session)

            logger.info("")
            logger.info("📊 Retail Sales Seeding Summary:")
            logger.info(f"   ✅ Retail outlets seeded: {result['tenants_seeded']}")
            logger.info(f"   ✅ Total sales records: {result['total_sales_created']}")
            logger.info(f"   ⏭️  Total skipped: {result['total_skipped']}")
            logger.info("")

            # Print per-tenant details
            for tenant_result in result['results']:
                logger.info(
                    f"   {tenant_result['tenant_name']}: "
                    f"{tenant_result['sales_created']} sales records"
                )

            logger.info("")
            logger.info("🎉 Success! Retail sales history is ready for cloning.")
            logger.info("")
            logger.info("Sales characteristics:")
            logger.info("  ✓ 30 days of historical data")
            logger.info("  ✓ Weekend sales boost (30-60% higher)")
            logger.info("  ✓ Friday pre-weekend surge (10-30% higher)")
            logger.info("  ✓ Realistic variance per product")
            logger.info("  ✓ Store-specific pricing and volumes")
            logger.info("")
            logger.info("Next steps:")
            logger.info("  1. Seed customer data")
            logger.info("  2. Seed retail orders (internal transfers from parent)")
            logger.info("  3. Test forecasting with retail sales data")
            logger.info("")

            return 0

    except Exception as e:
        logger.error("=" * 80)
        logger.error("❌ Demo Retail Sales Seeding Failed")
        logger.error("=" * 80)
        logger.error("Error: %s", str(e))
        logger.error("", exc_info=True)
        return 1

    finally:
        await engine.dispose()


if __name__ == "__main__":
    exit_code = asyncio.run(main())
    sys.exit(exit_code)