"""
SPU data transformer for Shoplazza products.

Transforms SPU and SKU data from MySQL into SPU-level ES documents with nested skus.
"""

import pandas as pd
import numpy as np
import logging
from typing import Dict, Any, List, Optional
from sqlalchemy import create_engine, text
from utils.db_connector import create_db_connection
from config import ConfigLoader

# Configure logger
logger = logging.getLogger(__name__)


class SPUTransformer:
    """Transform SPU and SKU data into SPU-level ES documents."""

    def __init__(
        self,
        db_engine: Any,
        tenant_id: str
    ):
        """
        Initialize SPU transformer.

        Args:
            db_engine: SQLAlchemy database engine
            tenant_id: Tenant ID for filtering data
        """
        self.db_engine = db_engine
        self.tenant_id = tenant_id
        
        # Load configuration to get searchable_option_dimensions
        try:
            config_loader = ConfigLoader()
            config = config_loader.load_config()
            self.searchable_option_dimensions = config.spu_config.searchable_option_dimensions
        except Exception as e:
            print(f"Warning: Failed to load config, using default searchable_option_dimensions: {e}")
            self.searchable_option_dimensions = ['option1', 'option2', 'option3']
        
        # Load category ID to name mapping
        self.category_id_to_name = self._load_category_mapping()

    def _load_category_mapping(self) -> Dict[str, str]:
        """
        Load category ID to name mapping from database.
        
        Returns:
            Dictionary mapping category_id to category_name
        """
        query = text("""
            SELECT DISTINCT
                category_id,
                category
            FROM shoplazza_product_spu
            WHERE deleted = 0 AND category_id IS NOT NULL
        """)
        
        mapping = {}
        with self.db_engine.connect() as conn:
            result = conn.execute(query)
            for row in result:
                category_id = str(int(row.category_id))
                category_name = row.category
                
                if not category_name or not category_name.strip():
                    logger.warning(f"Category ID {category_id} has empty name, skipping")
                    continue
                
                mapping[category_id] = category_name
        
        logger.info(f"Loaded {len(mapping)} category ID to name mappings")
        
        # Log all category mappings for debugging
        if mapping:
            logger.debug("Category ID mappings:")
            for cid, name in sorted(mapping.items()):
                logger.debug(f"  {cid} -> {name}")
        
        return mapping
    
    def load_spu_data(self) -> pd.DataFrame:
        """
        Load SPU data from MySQL.

        Returns:
            DataFrame with SPU data
        """
        query = text("""
            SELECT 
                id, shop_id, shoplazza_id, title, brief, description,
                spu, vendor, vendor_url,
                image_src, image_width, image_height, image_path, image_alt,
                tags, note, category, category_id, category_google_id,
                category_level, category_path,
                tenant_id, creator, create_time, updater, update_time, deleted
            FROM shoplazza_product_spu
            WHERE tenant_id = :tenant_id AND deleted = 0
        """)
        
        with self.db_engine.connect() as conn:
            df = pd.read_sql(query, conn, params={"tenant_id": self.tenant_id})
        
        logger.info(f"Loaded {len(df)} SPU records for tenant_id={self.tenant_id}")
        
        # Statistics
        if len(df) > 0:
            has_category_path = df['category_path'].notna().sum()
            has_category = df['category'].notna().sum()
            has_title = df['title'].notna().sum()
            
            logger.info(f"SPU data statistics:")
            logger.info(f"  - Has title: {has_title}/{len(df)} ({100*has_title/len(df):.1f}%)")
            logger.info(f"  - Has category_path: {has_category_path}/{len(df)} ({100*has_category_path/len(df):.1f}%)")
            logger.info(f"  - Has category: {has_category}/{len(df)} ({100*has_category/len(df):.1f}%)")
            
            # Warn if too many SPUs don't have category_path
            if has_category_path < len(df) * 0.5:
                logger.warning(f"Only {100*has_category_path/len(df):.1f}% of SPUs have category_path, data quality may be low")
        else:
            logger.warning(f"No SPU data found for tenant_id={self.tenant_id}")
            
            # Debug: Check if there's any data for this tenant_id
            debug_query = text("""
                SELECT 
                    COUNT(*) as total_count,
                    SUM(CASE WHEN deleted = 0 THEN 1 ELSE 0 END) as active_count,
                    SUM(CASE WHEN deleted = 1 THEN 1 ELSE 0 END) as deleted_count
                FROM shoplazza_product_spu
                WHERE tenant_id = :tenant_id
            """)
            with self.db_engine.connect() as conn:
                debug_df = pd.read_sql(debug_query, conn, params={"tenant_id": self.tenant_id})
            if not debug_df.empty:
                total = debug_df.iloc[0]['total_count']
                active = debug_df.iloc[0]['active_count']
                deleted = debug_df.iloc[0]['deleted_count']
                logger.debug(f"tenant_id={self.tenant_id}: total={total}, active={active}, deleted={deleted}")
            
            # Check what tenant_ids exist in the table
            tenant_check_query = text("""
                SELECT tenant_id, COUNT(*) as count, SUM(CASE WHEN deleted = 0 THEN 1 ELSE 0 END) as active
                FROM shoplazza_product_spu
                GROUP BY tenant_id
                ORDER BY tenant_id
                LIMIT 10
            """)
            with self.db_engine.connect() as conn:
                tenant_df = pd.read_sql(tenant_check_query, conn)
            if not tenant_df.empty:
                logger.debug(f"Available tenant_ids in shoplazza_product_spu:")
                for _, row in tenant_df.iterrows():
                    logger.debug(f"  tenant_id={row['tenant_id']}: total={row['count']}, active={row['active']}")
        
        return df

    def load_sku_data(self) -> pd.DataFrame:
        """
        Load SKU data from MySQL.

        Returns:
            DataFrame with SKU data
        """
        query = text("""
            SELECT 
                id, spu_id, shop_id, shoplazza_id, shoplazza_product_id,
                shoplazza_image_id, title, sku, barcode, position,
                price, compare_at_price, cost_price,
                option1, option2, option3,
                inventory_quantity, weight, weight_unit, image_src,
                wholesale_price, note, extend,
                shoplazza_created_at, shoplazza_updated_at, tenant_id,
                creator, create_time, updater, update_time, deleted
            FROM shoplazza_product_sku
            WHERE tenant_id = :tenant_id AND deleted = 0
        """)
        
        with self.db_engine.connect() as conn:
            df = pd.read_sql(query, conn, params={"tenant_id": self.tenant_id})
        
        logger.info(f"Loaded {len(df)} SKU records for tenant_id={self.tenant_id}")
        
        # Statistics
        if len(df) > 0:
            has_price = df['price'].notna().sum()
            has_inventory = df['inventory_quantity'].notna().sum()
            has_option1 = df['option1'].notna().sum()
            has_option2 = df['option2'].notna().sum()
            has_option3 = df['option3'].notna().sum()
            
            logger.info(f"SKU data statistics:")
            logger.info(f"  - Has price: {has_price}/{len(df)} ({100*has_price/len(df):.1f}%)")
            logger.info(f"  - Has inventory: {has_inventory}/{len(df)} ({100*has_inventory/len(df):.1f}%)")
            logger.info(f"  - Has option1: {has_option1}/{len(df)} ({100*has_option1/len(df):.1f}%)")
            logger.info(f"  - Has option2: {has_option2}/{len(df)} ({100*has_option2/len(df):.1f}%)")
            logger.info(f"  - Has option3: {has_option3}/{len(df)} ({100*has_option3/len(df):.1f}%)")
            
            # Warn about data quality issues
            if has_price < len(df) * 0.95:
                logger.warning(f"Only {100*has_price/len(df):.1f}% of SKUs have price")
        
        return df

    def load_option_data(self) -> pd.DataFrame:
        """
        Load option data from MySQL.

        Returns:
            DataFrame with option data (name, position for each SPU)
        """
        query = text("""
            SELECT 
                id, spu_id, shop_id, shoplazza_id, shoplazza_product_id,
                position, name, `values`, tenant_id,
                creator, create_time, updater, update_time, deleted
            FROM shoplazza_product_option
            WHERE tenant_id = :tenant_id AND deleted = 0
            ORDER BY spu_id, position
        """)
        
        with self.db_engine.connect() as conn:
            df = pd.read_sql(query, conn, params={"tenant_id": self.tenant_id})
        
        logger.info(f"Loaded {len(df)} option records for tenant_id={self.tenant_id}")
        
        # Statistics
        if len(df) > 0:
            unique_spus_with_options = df['spu_id'].nunique()
            has_name = df['name'].notna().sum()
            
            logger.info(f"Option data statistics:")
            logger.info(f"  - Unique SPUs with options: {unique_spus_with_options}")
            logger.info(f"  - Has name: {has_name}/{len(df)} ({100*has_name/len(df):.1f}%)")
            
            # Warn about missing option names
            if has_name < len(df):
                missing = len(df) - has_name
                logger.warning(f"{missing} option records are missing names")
        
        return df

    def transform_batch(self) -> List[Dict[str, Any]]:
        """
        Transform SPU and SKU data into ES documents.

        Returns:
            List of SPU-level ES documents
        """
        logger.info(f"Starting data transformation for tenant_id={self.tenant_id}")
        
        # Load data
        spu_df = self.load_spu_data()
        sku_df = self.load_sku_data()
        option_df = self.load_option_data()

        if spu_df.empty:
            logger.warning("No SPU data to transform")
            return []

        # Group SKUs by SPU
        sku_groups = sku_df.groupby('spu_id')
        logger.info(f"Grouped SKUs into {len(sku_groups)} SPU groups")
        
        # Group options by SPU
        option_groups = option_df.groupby('spu_id') if not option_df.empty else None
        if option_groups:
            logger.info(f"Grouped options into {len(option_groups)} SPU groups")

        documents = []
        skipped_count = 0
        error_count = 0
        
        for idx, spu_row in spu_df.iterrows():
            spu_id = spu_row['id']
            
            try:
                # Get SKUs for this SPU
                skus = sku_groups.get_group(spu_id) if spu_id in sku_groups.groups else pd.DataFrame()
                
                # Get options for this SPU
                options = option_groups.get_group(spu_id) if option_groups and spu_id in option_groups.groups else pd.DataFrame()
                
                # Warn if SPU has no SKUs
                if skus.empty:
                    logger.warning(f"SPU {spu_id} (title: {spu_row.get('title', 'N/A')}) has no SKUs")
                
                # Transform to ES document
                doc = self._transform_spu_to_doc(spu_row, skus, options)
                if doc:
                    documents.append(doc)
                else:
                    skipped_count += 1
                    logger.warning(f"SPU {spu_id} transformation returned None, skipped")
            except Exception as e:
                error_count += 1
                logger.error(f"Error transforming SPU {spu_id}: {e}", exc_info=True)

        logger.info(f"Transformation complete:")
        logger.info(f"  - Total SPUs: {len(spu_df)}")
        logger.info(f"  - Successfully transformed: {len(documents)}")
        logger.info(f"  - Skipped: {skipped_count}")
        logger.info(f"  - Errors: {error_count}")
        
        return documents

    def _transform_spu_to_doc(
        self,
        spu_row: pd.Series,
        skus: pd.DataFrame,
        options: pd.DataFrame
    ) -> Optional[Dict[str, Any]]:
        """
        Transform a single SPU row and its SKUs into an ES document.

        Args:
            spu_row: SPU row from database
            skus: DataFrame with SKUs for this SPU
            options: DataFrame with options for this SPU

        Returns:
            ES document or None if transformation fails
        """
        doc = {}

        # Tenant ID (required)
        doc['tenant_id'] = str(self.tenant_id)

        # SPU ID
        spu_id = spu_row['id']
        doc['spu_id'] = str(spu_id)
        
        # Validate required fields
        if pd.isna(spu_row.get('title')) or not str(spu_row['title']).strip():
            logger.error(f"SPU {spu_id} has no title, this may cause search issues")

        # 文本相关性相关字段（中英文双语，暂时只填充中文）
        if pd.notna(spu_row.get('title')):
            doc['title_zh'] = str(spu_row['title'])
        doc['title_en'] = None  # 暂时设为空

        if pd.notna(spu_row.get('brief')):
            doc['brief_zh'] = str(spu_row['brief'])
        doc['brief_en'] = None

        if pd.notna(spu_row.get('description')):
            doc['description_zh'] = str(spu_row['description'])
        doc['description_en'] = None

        if pd.notna(spu_row.get('vendor')):
            doc['vendor_zh'] = str(spu_row['vendor'])
        doc['vendor_en'] = None

        # Tags
        if pd.notna(spu_row.get('tags')):
            # Tags是逗号分隔的字符串，需要转换为数组
            tags_str = str(spu_row['tags'])
            doc['tags'] = [tag.strip() for tag in tags_str.split(',') if tag.strip()]

        # Category相关字段
        if pd.notna(spu_row.get('category_path')):
            category_path = str(spu_row['category_path'])
            
            # 解析category_path - 这是逗号分隔的类目ID列表
            category_ids = [cid.strip() for cid in category_path.split(',') if cid.strip()]
            
            # 将ID映射为名称
            category_names = []
            missing_category_ids = []
            for cid in category_ids:
                if cid in self.category_id_to_name:
                    category_names.append(self.category_id_to_name[cid])
                else:
                    # 如果找不到映射，记录错误并使用ID作为备选
                    logger.error(f"Category ID {cid} not found in mapping for SPU {spu_row['id']} (title: {spu_row.get('title', 'N/A')}), category_path={category_path}")
                    missing_category_ids.append(cid)
                    category_names.append(cid)  # 使用ID作为备选
            
            # 构建类目路径字符串（用于搜索）
            if category_names:
                category_path_str = '/'.join(category_names)
                doc['category_path_zh'] = category_path_str
                doc['category_path_en'] = None  # 暂时设为空
                
                # 填充分层类目名称
                if len(category_names) > 0:
                    doc['category1_name'] = category_names[0]
                if len(category_names) > 1:
                    doc['category2_name'] = category_names[1]
                if len(category_names) > 2:
                    doc['category3_name'] = category_names[2]
        elif pd.notna(spu_row.get('category')):
            # 如果category_path为空，使用category字段作为category1_name的备选
            category = str(spu_row['category'])
            doc['category_name_zh'] = category
            doc['category_name_en'] = None
            doc['category_name'] = category
            
            # 尝试从category字段解析多级分类
            if '/' in category:
                path_parts = category.split('/')
                if len(path_parts) > 0:
                    doc['category1_name'] = path_parts[0].strip()
                if len(path_parts) > 1:
                    doc['category2_name'] = path_parts[1].strip()
                if len(path_parts) > 2:
                    doc['category3_name'] = path_parts[2].strip()
            else:
                # 如果category不包含"/"，直接作为category1_name
                doc['category1_name'] = category.strip()

        if pd.notna(spu_row.get('category')):
            # 确保category相关字段都被设置（如果前面没有设置）
            category_name = str(spu_row['category'])
            if 'category_name_zh' not in doc:
                doc['category_name_zh'] = category_name
            if 'category_name_en' not in doc:
                doc['category_name_en'] = None
            if 'category_name' not in doc:
                doc['category_name'] = category_name

        if pd.notna(spu_row.get('category_id')):
            doc['category_id'] = str(int(spu_row['category_id']))

        if pd.notna(spu_row.get('category_level')):
            doc['category_level'] = int(spu_row['category_level'])

        # Option名称（从option表获取）
        if not options.empty:
            # 按position排序获取option名称
            sorted_options = options.sort_values('position')
            if len(sorted_options) > 0 and pd.notna(sorted_options.iloc[0].get('name')):
                doc['option1_name'] = str(sorted_options.iloc[0]['name'])
            if len(sorted_options) > 1 and pd.notna(sorted_options.iloc[1].get('name')):
                doc['option2_name'] = str(sorted_options.iloc[1]['name'])
            if len(sorted_options) > 2 and pd.notna(sorted_options.iloc[2].get('name')):
                doc['option3_name'] = str(sorted_options.iloc[2]['name'])

        # Image URL
        if pd.notna(spu_row.get('image_src')):
            image_src = str(spu_row['image_src'])
            if not image_src.startswith('http'):
                image_src = f"//{image_src}" if image_src.startswith('//') else image_src
            doc['image_url'] = image_src

        # Process SKUs and build specifications
        skus_list = []
        prices = []
        compare_prices = []
        sku_prices = []
        sku_weights = []
        sku_weight_units = []
        total_inventory = 0
        specifications = []

        # 构建option名称映射（position -> name）
        option_name_map = {}
        if not options.empty:
            for _, opt_row in options.iterrows():
                position = opt_row.get('position')
                name = opt_row.get('name')
                if pd.notna(position) and pd.notna(name):
                    option_name_map[int(position)] = str(name)

        for _, sku_row in skus.iterrows():
            sku_data = self._transform_sku_row(sku_row, option_name_map)
            if sku_data:
                skus_list.append(sku_data)
                
                # 收集价格信息
                if 'price' in sku_data and sku_data['price'] is not None:
                    try:
                        price_val = float(sku_data['price'])
                        prices.append(price_val)
                        sku_prices.append(price_val)
                    except (ValueError, TypeError):
                        pass
                
                if 'compare_at_price' in sku_data and sku_data['compare_at_price'] is not None:
                    try:
                        compare_prices.append(float(sku_data['compare_at_price']))
                    except (ValueError, TypeError):
                        pass
                
                # 收集重量信息
                if 'weight' in sku_data and sku_data['weight'] is not None:
                    try:
                        sku_weights.append(int(float(sku_data['weight'])))
                    except (ValueError, TypeError):
                        pass
                
                if 'weight_unit' in sku_data and sku_data['weight_unit']:
                    sku_weight_units.append(str(sku_data['weight_unit']))
                
                # 收集库存信息
                if 'stock' in sku_data and sku_data['stock'] is not None:
                    try:
                        total_inventory += int(sku_data['stock'])
                    except (ValueError, TypeError):
                        pass
                
                # 构建specifications（从SKU的option值和option表的name）
                sku_id = str(sku_row['id'])
                if pd.notna(sku_row.get('option1')) and 1 in option_name_map:
                    specifications.append({
                        'sku_id': sku_id,
                        'name': option_name_map[1],
                        'value': str(sku_row['option1'])
                    })
                if pd.notna(sku_row.get('option2')) and 2 in option_name_map:
                    specifications.append({
                        'sku_id': sku_id,
                        'name': option_name_map[2],
                        'value': str(sku_row['option2'])
                    })
                if pd.notna(sku_row.get('option3')) and 3 in option_name_map:
                    specifications.append({
                        'sku_id': sku_id,
                        'name': option_name_map[3],
                        'value': str(sku_row['option3'])
                    })

        doc['skus'] = skus_list
        doc['specifications'] = specifications

        # 提取option值（根据配置的searchable_option_dimensions）
        # 从子SKU的option1_value, option2_value, option3_value中提取去重后的值
        option1_values = []
        option2_values = []
        option3_values = []
        
        for _, sku_row in skus.iterrows():
            if pd.notna(sku_row.get('option1')):
                option1_values.append(str(sku_row['option1']))
            if pd.notna(sku_row.get('option2')):
                option2_values.append(str(sku_row['option2']))
            if pd.notna(sku_row.get('option3')):
                option3_values.append(str(sku_row['option3']))
        
        # 去重并根据配置决定是否写入索引
        if 'option1' in self.searchable_option_dimensions:
            doc['option1_values'] = list(set(option1_values)) if option1_values else []
        else:
            doc['option1_values'] = []
        
        if 'option2' in self.searchable_option_dimensions:
            doc['option2_values'] = list(set(option2_values)) if option2_values else []
        else:
            doc['option2_values'] = []
        
        if 'option3' in self.searchable_option_dimensions:
            doc['option3_values'] = list(set(option3_values)) if option3_values else []
        else:
            doc['option3_values'] = []

        # Calculate price ranges
        if prices:
            doc['min_price'] = float(min(prices))
            doc['max_price'] = float(max(prices))
        else:
            doc['min_price'] = 0.0
            doc['max_price'] = 0.0

        if compare_prices:
            doc['compare_at_price'] = float(max(compare_prices))
        else:
            doc['compare_at_price'] = None

        # SKU扁平化字段
        doc['sku_prices'] = sku_prices
        doc['sku_weights'] = sku_weights
        doc['sku_weight_units'] = list(set(sku_weight_units))  # 去重
        doc['total_inventory'] = total_inventory

        # Image URL
        if pd.notna(spu_row.get('image_src')):
            image_src = str(spu_row['image_src'])
            if not image_src.startswith('http'):
                image_src = f"//{image_src}" if image_src.startswith('//') else image_src
            doc['image_url'] = image_src

        # Time fields - convert datetime to ISO format string for ES DATE type
        if pd.notna(spu_row.get('create_time')):
            create_time = spu_row['create_time']
            if hasattr(create_time, 'isoformat'):
                doc['create_time'] = create_time.isoformat()
            else:
                doc['create_time'] = str(create_time)
        
        if pd.notna(spu_row.get('update_time')):
            update_time = spu_row['update_time']
            if hasattr(update_time, 'isoformat'):
                doc['update_time'] = update_time.isoformat()
            else:
                doc['update_time'] = str(update_time)

        return doc

    def _transform_sku_row(self, sku_row: pd.Series, option_name_map: Dict[int, str] = None) -> Optional[Dict[str, Any]]:
        """
        Transform a SKU row into a SKU object.

        Args:
            sku_row: SKU row from database
            option_name_map: Mapping from position to option name

        Returns:
            SKU dictionary or None
        """
        sku_data = {}

        # SKU ID
        sku_data['sku_id'] = str(sku_row['id'])

        # Price
        if pd.notna(sku_row.get('price')):
            try:
                sku_data['price'] = float(sku_row['price'])
            except (ValueError, TypeError):
                sku_data['price'] = None
        else:
            sku_data['price'] = None

        # Compare at price
        if pd.notna(sku_row.get('compare_at_price')):
            try:
                sku_data['compare_at_price'] = float(sku_row['compare_at_price'])
            except (ValueError, TypeError):
                sku_data['compare_at_price'] = None
        else:
            sku_data['compare_at_price'] = None

        # SKU Code
        if pd.notna(sku_row.get('sku')):
            sku_data['sku_code'] = str(sku_row['sku'])

        # Stock
        if pd.notna(sku_row.get('inventory_quantity')):
            try:
                sku_data['stock'] = int(sku_row['inventory_quantity'])
            except (ValueError, TypeError):
                sku_data['stock'] = 0
        else:
            sku_data['stock'] = 0

        # Weight
        if pd.notna(sku_row.get('weight')):
            try:
                sku_data['weight'] = float(sku_row['weight'])
            except (ValueError, TypeError):
                sku_data['weight'] = None
        else:
            sku_data['weight'] = None

        # Weight unit
        if pd.notna(sku_row.get('weight_unit')):
            sku_data['weight_unit'] = str(sku_row['weight_unit'])

        # Option values
        if pd.notna(sku_row.get('option1')):
            sku_data['option1_value'] = str(sku_row['option1'])
        if pd.notna(sku_row.get('option2')):
            sku_data['option2_value'] = str(sku_row['option2'])
        if pd.notna(sku_row.get('option3')):
            sku_data['option3_value'] = str(sku_row['option3'])
        
        # Image src
        if pd.notna(sku_row.get('image_src')):
            sku_data['image_src'] = str(sku_row['image_src'])

        return sku_data