"""
  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
  from typing import Dict, Any, List, Optional
  from sqlalchemy import create_engine, text
  from utils.db_connector import create_db_connection
  
  
  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
  
      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})
          

          # Debug: Check if there's any data for this tenant_id
          if len(df) == 0:
              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']
                  print(f"DEBUG: 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:
                  print(f"DEBUG: Available tenant_ids in shoplazza_product_spu:")
                  for _, row in tenant_df.iterrows():
                      print(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})
          

          print(f"DEBUG: Loaded {len(df)} SKU records for tenant_id={self.tenant_id}")
          

          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})
          
          print(f"DEBUG: Loaded {len(df)} option records for tenant_id={self.tenant_id}")
          
          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
          """
          # Load data
          spu_df = self.load_spu_data()
          sku_df = self.load_sku_data()

          option_df = self.load_option_data()

  
          if spu_df.empty:
              return []
  
          # Group SKUs by SPU
          sku_groups = sku_df.groupby('spu_id')

          
          # Group options by SPU
          option_groups = option_df.groupby('spu_id') if not option_df.empty else None

  
          documents = []
          for _, spu_row in spu_df.iterrows():
              spu_id = spu_row['id']
              
              # 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()
              

              # Transform to ES document

              doc = self._transform_spu_to_doc(spu_row, skus, options)

              if doc:
                  documents.append(doc)
  
          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
          doc['spu_id'] = str(spu_row['id'])

          # 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'])
              doc['category_path_zh'] = category_path
              doc['category_path_en'] = None  # 暂时设为空
              
              # 解析category_path获取多层级分类名称
              path_parts = category_path.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()

          if pd.notna(spu_row.get('category')):

              category_name = str(spu_row['category'])
              doc['category_name_zh'] = category_name
              doc['category_name_en'] = None
              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

  
          # 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