import os
import time
import geoip2.database
from pathlib import Path
from django.db import connection
from django.db.models import Q
from django.core.cache import cache
from django.db import transaction
import logging

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings.develop')
from django.core.wsgi import get_wsgi_application

application = get_wsgi_application()

from apps.account.models import LoginHistory

# Configure logging
logger = logging.getLogger(__name__)

# GeoLite2 database paths
CITY_DB_PATH = Path("utils/country_city_db/GeoLite2-City.mmdb")

# Special coordinates
SPECIAL_COORDINATES = [
    (32.616565, 44.03462),
    (51.5287718, -0.2416802),
    (40.3947021, 49.78492),
    (55.751199, 37.614706),
    (48.8589466, 2.2769956),
    (40.4381311, -3.8196194),
    (-6.2295712, 106.759478),
    (33.6158004, 72.8059198)
]

def get_location_by_coordinates_optimized(lat, lon):
    """
    Optimized version with special coordinates handling.
    Handles special coordinates correctly before geo lookup.
    """
    try:
        # Quick validation
        if not lat or not lon:
            return None

        lat, lon = float(lat), float(lon)

        # Check if coordinates are in special list - should use IP detection instead
        for special_lat, special_lon in SPECIAL_COORDINATES:
            if abs(lat - special_lat) < 0.001 and abs(lon - special_lon) < 0.001:
                # These coordinates should use IP detection, not geo lookup
                # Return None to trigger fallback to original method
                logger.debug(f"Special coordinate detected: ({lat}, {lon}) - skipping geo lookup")
                return None

        # Simple cache key
        cache_key = f'geo_{round(lat, 2)}_{round(lon, 2)}'

        # Try cache first (no exception handling for speed)
        cached_result = cache.get(cache_key)
        if cached_result is not None:
            return cached_result

        # Simple bounding box (larger range for better coverage)
        lat_range = 3.0  # ~330km
        lon_range = 3.0

        lat_min = lat - lat_range
        lat_max = lat + lat_range
        lon_min = lon - lon_range
        lon_max = lon + lon_range

        # Query with population weighting to prefer larger cities
        with connection.cursor() as cursor:
            # First, let's get debug information about nearby cities
            cursor.execute("""
                WITH bounded_cities AS (
                    SELECT name, country_code, latitude, longitude, population
                    FROM geonames_city
                    WHERE feature_class = 'P'
                      AND latitude BETWEEN %s AND %s
                      AND longitude BETWEEN %s AND %s
                ),
                distance_calc AS (
                    SELECT name, country_code, population,
                           (6371 * acos(least(1, greatest(-1,
                            cos(radians(%s)) * cos(radians(latitude)) *
                            cos(radians(longitude) - radians(%s)) +
                            sin(radians(%s)) * sin(radians(latitude))
                           )))) AS distance
                    FROM bounded_cities
                )
                SELECT name, country_code, population, distance
                FROM distance_calc
                WHERE distance <= 100
                ORDER BY distance
                LIMIT 10
            """, [lat_min, lat_max, lon_min, lon_max, lat, lon, lat])

            debug_results = cursor.fetchall()
            if debug_results:
                logger.info(f"🔍 Top 10 nearby cities for coordinates ({lat}, {lon}):")
                for name, cc, pop, dist in debug_results:
                    logger.info(f"  📍 {name} ({cc}): population={pop:,}, distance={dist:.2f}km")

            # Now get the best city using a weighted approach
            # Prefer cities with larger population within reasonable distance
            cursor.execute("""
                WITH bounded_cities AS (
                    SELECT name, country_code, latitude, longitude, population
                    FROM geonames_city
                    WHERE feature_class = 'P'
                      AND latitude BETWEEN %s AND %s
                      AND longitude BETWEEN %s AND %s
                      AND population IS NOT NULL
                      AND population > 0
                ),
                distance_calc AS (
                    SELECT name, country_code, population,
                           (6371 * acos(least(1, greatest(-1,
                            cos(radians(%s)) * cos(radians(latitude)) *
                            cos(radians(longitude) - radians(%s)) +
                            sin(radians(%s)) * sin(radians(latitude))
                           )))) AS distance
                    FROM bounded_cities
                ),
                scored_cities AS (
                    SELECT name, country_code, distance, population,
                           -- Score: prefer closer cities, but weight population heavily
                           -- Cities within 30km: prioritize by population
                           -- Cities beyond 30km: balance distance and population
                           CASE
                               WHEN distance <= 30 THEN population / (distance + 1)
                               ELSE population / POWER(distance, 2)
                           END AS score
                    FROM distance_calc
                    WHERE distance <= 100
                )
                SELECT name, country_code
                FROM scored_cities
                ORDER BY score DESC
                LIMIT 1
            """, [lat_min, lat_max, lon_min, lon_max, lat, lon, lat])

            result = cursor.fetchone()

            if result:
                name, country_code = result
                logger.info(f"✅ Selected city: {name} ({country_code}) for coordinates ({lat}, {lon})")
                response = {
                    'status': 'success',
                    'city': name,
                    'countryCode': country_code
                }

                # Cache for 24 hours
                cache.set(cache_key, response, 86400)
                return response
            else:
                logger.warning(f"⚠️ No city found within 100km for coordinates ({lat}, {lon})")
                # Cache None for 1 hour
                cache.set(cache_key, None, 3600)
                return None

    except Exception:
        # Fallback to original method on any error
        return get_location_by_coordinates_original(lat, lon)


def get_location_by_coordinates_original(lat, lon):
    """Original implementation as fallback"""
    try:
        with connection.cursor() as cursor:
            cursor.execute("""
                WITH distance_calc AS (
                    SELECT name, country_code, latitude, longitude,
                           (6371 * acos(least(1, greatest(-1, cos(radians(%s)) * cos(radians(latitude)) *
                            cos(radians(longitude) - radians(%s)) +
                            sin(radians(%s)) * sin(radians(latitude)))))) AS distance
                    FROM geonames_city
                    WHERE feature_class = 'P'
                )
                SELECT name, country_code
                FROM distance_calc
                WHERE distance <= 300
                ORDER BY distance
                LIMIT 1
            """, [lat, lon, lat])

            result = cursor.fetchone()

            if result:
                name, country_code = result
                logger.info(f"🔄 Fallback method selected city: {name} ({country_code}) for coordinates ({lat}, {lon})")
                return {
                    'status': 'success',
                    'city': name,
                    'countryCode': country_code
                }
        return None

    except Exception as e:
        logger.error(f"❌ Error in fallback method for coordinates ({lat}, {lon}): {str(e)}")
        return None


def get_location_by_coordinates(lat, lon):
    """
    Main function with smart fallback strategy.
    Try optimized first, fallback to original if needed.
    """
    # Try optimized version first
    print('lat:',lat ,'lon:', lon)
    result = get_location_by_coordinates_optimized(lat, lon)

    print('optimize:',result)

    # If optimized fails, use original as fallback
    if result is None:
        result = get_location_by_coordinates_original(lat, lon)
        print ('original',result)
    print('out',result)
    return result

def get_location_by_ip(ip):
    """Get location from IP using MaxMind MMDB file directly"""
    try:
        if not CITY_DB_PATH.exists():
            return None

        with geoip2.database.Reader(CITY_DB_PATH) as reader:
            response = reader.city(ip)
            if response and response.country:
                # Validate city name - check if it's not a subdivision
                city_name = None
                if response.city and response.city.name:
                    subdivision_names = [s.name for s in response.subdivisions] if response.subdivisions else []
                    
                    if response.city.name not in subdivision_names:
                        # City name is valid - not a subdivision
                        city_name = response.city.name
                    else:
                        # City name matches a subdivision - this is a region, not a city
                        logger.warning(f"IP {ip}: City name '{response.city.name}' matches subdivision - treating as region")
                        city_name = None
                
                return {
                    'status': 'success',
                    'countryCode': response.country.iso_code,
                    'city': city_name
                }
        return None

    except Exception:
        return None

def update_login_history_optimized():
    """
    Optimized version with batch processing and better error handling.
    Processes records in batches to reduce database load and improve performance.
    """
    logger.info("Starting optimized login history update...")

    # Query for login histories that need updating
    special_records = (
        LoginHistory.objects
        .exclude(location_method="IP_DETECTION")
        .exclude(lat__isnull=True)
        .exclude(lon__isnull=True)
        .filter(lat__in=[lat for lat, _ in SPECIAL_COORDINATES], lon__in=[lon for _, lon in SPECIAL_COORDINATES])
        [:1000]  # Limit batch size
    )

    normal_records = (
        LoginHistory.objects
        .exclude(location_method="IP_DETECTION")
        .exclude(lat__isnull=True)
        .exclude(lon__isnull=True)
        .exclude(lat__in=[lat for lat, _ in SPECIAL_COORDINATES], lon__in=[lon for _, lon in SPECIAL_COORDINATES])
        [:1000]  # Limit batch size
    )

    # Process special coordinates records (with IP) in batches
    special_updates = []
    for login in special_records:
        try:
            location_data = get_location_by_ip(login.ip)
            if location_data and location_data['status'] == 'success':
                login.country = location_data['countryCode']
                login.city = location_data['city']
                login.location_method = 'IP_DETECTION'
                special_updates.append(login)

                # Batch update every 50 records
                if len(special_updates) >= 50:
                    with transaction.atomic():
                        LoginHistory.objects.bulk_update(
                            special_updates,
                            ['country', 'city', 'location_method']
                        )
                    logger.info(f"Updated {len(special_updates)} special coordinate records")
                    special_updates = []
        except Exception as e:
            logger.error(f"Error processing special record {login.id}: {e}")
            continue

    # Final batch update for remaining special records
    if special_updates:
        with transaction.atomic():
            LoginHistory.objects.bulk_update(
                special_updates,
                ['country', 'city', 'location_method']
            )
        logger.info(f"Updated final {len(special_updates)} special coordinate records")

    # Process normal coordinates records (with GeoNames) in batches
    normal_updates = []
    processed_normal = 0
    for login in normal_records:
        try:
            location_data = get_location_by_coordinates(login.lat, login.lon)
            if location_data and location_data['status'] == 'success':
                login.country = location_data['countryCode']
                login.city = location_data['city']
                login.location_method = 'COORDINATES'
                normal_updates.append(login)
                processed_normal += 1

                # Batch update every 20 records (smaller batch for geo queries)
                if len(normal_updates) >= 20:
                    with transaction.atomic():
                        LoginHistory.objects.bulk_update(
                            normal_updates,
                            ['country', 'city', 'location_method']
                        )
                    logger.info(f"Updated {len(normal_updates)} normal coordinate records")
                    normal_updates = []
        except Exception as e:
            logger.error(f"Error processing normal record {login.id}: {e}")
            continue

    # Final batch update for remaining normal records
    if normal_updates:
        with transaction.atomic():
            LoginHistory.objects.bulk_update(
                normal_updates,
                ['country', 'city', 'location_method']
            )
        logger.info(f"Updated final {len(normal_updates)} normal coordinate records")

    logger.info(f"Completed login history update. Processed {processed_normal} normal records.")


def update_login_history():
    """Backward compatibility wrapper"""
    return update_login_history_optimized()

def update_location_history_records_optimized():
    """
    Optimized version with batch processing and progress tracking.
    Updates location history records with city and country information using GeoNames database.
    Only processes records that have coordinates but no city/country information.
    """
    from apps.account.models import LocationHistory

    logger.info("Starting optimized location history update...")

    # Find records that need updating (limit to manageable batch size)
    records = LocationHistory.objects.filter(
        Q(city__isnull=True) | Q(city='') | Q(country__isnull=True) | Q(country=''),
        lat__isnull=False,
        lon__isnull=False
    )[:1000]  # Process in batches of 1000

    total_records = records.count()
    logger.info(f"Found {total_records} location history records to update")

    if total_records == 0:
        logger.info("No records to update")
        return

    updated_count = 0
    batch_updates = []

    for i, record in enumerate(records, 1):
        try:
            # Get location data based on coordinates
            location_data = get_location_by_coordinates(record.lat, record.lon)

            if location_data and location_data['status'] == 'success':
                record.city = location_data['city']
                record.country = location_data['countryCode']
                batch_updates.append(record)
                updated_count += 1

                # Progress logging every 50 records
                if i % 50 == 0:
                    logger.info(f"Processed {i}/{total_records} records ({updated_count} updated)")

                # Batch update every 20 records
                if len(batch_updates) >= 20:
                    with transaction.atomic():
                        LocationHistory.objects.bulk_update(
                            batch_updates,
                            ['city', 'country']
                        )
                    logger.info(f"Bulk updated {len(batch_updates)} location history records")
                    batch_updates = []

        except Exception as e:
            logger.error(f"Error processing location history record {record.id}: {e}")
            continue

    # Final batch update for remaining records
    if batch_updates:
        with transaction.atomic():
            LocationHistory.objects.bulk_update(
                batch_updates,
                ['city', 'country']
            )
        logger.info(f"Final bulk update of {len(batch_updates)} location history records")

    logger.info(f"Completed location history update. Updated {updated_count}/{total_records} records.")


def update_location_history_records():
    """Backward compatibility wrapper"""
    return update_location_history_records_optimized()

if __name__ == "__main__":
    # Configure logging for script execution
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler('geo_optimization.log'),
            logging.StreamHandler()
        ]
    )

    logger.info("Starting optimized geo location processing...")
    start_time = time.time()

    try:
        update_login_history()
        update_location_history_records()

        total_time = time.time() - start_time
        logger.info(f"Completed all geo location processing in {total_time:.2f} seconds")

    except Exception as e:
        logger.error(f"Error in main execution: {e}")
        raise