"""
  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, handle, title, brief, description,
                  spu, vendor, vendor_url, seo_title, seo_description, seo_keywords,
                  image_src, image_width, image_height, image_path, image_alt,
                  tags, note, category,
                  shoplazza_created_at, shoplazza_updated_at, 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 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()
  
          if spu_df.empty:
              return []
  
          # Group SKUs by SPU
          sku_groups = sku_df.groupby('spu_id')
  
          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()
              
              # Transform to ES document
              doc = self._transform_spu_to_doc(spu_row, skus)
              if doc:
                  documents.append(doc)
  
          return documents
  
      def _transform_spu_to_doc(
          self,
          spu_row: pd.Series,
          skus: 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
  
          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'])

  
          # Handle
          if pd.notna(spu_row.get('handle')):
              doc['handle'] = str(spu_row['handle'])
  
          # Title
          if pd.notna(spu_row.get('title')):
              doc['title'] = str(spu_row['title'])
  
          # Brief
          if pd.notna(spu_row.get('brief')):
              doc['brief'] = str(spu_row['brief'])
  
          # Description
          if pd.notna(spu_row.get('description')):
              doc['description'] = str(spu_row['description'])
  
          # SEO fields
          if pd.notna(spu_row.get('seo_title')):
              doc['seo_title'] = str(spu_row['seo_title'])
          if pd.notna(spu_row.get('seo_description')):
              doc['seo_description'] = str(spu_row['seo_description'])
          if pd.notna(spu_row.get('seo_keywords')):
              doc['seo_keywords'] = str(spu_row['seo_keywords'])
  
          # Vendor
          if pd.notna(spu_row.get('vendor')):
              doc['vendor'] = str(spu_row['vendor'])

  
          # Tags
          if pd.notna(spu_row.get('tags')):

              doc['tags'] = str(spu_row['tags'])

  
          # Category
          if pd.notna(spu_row.get('category')):
              doc['category'] = str(spu_row['category'])

  
          # 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
          skus_list = []

          prices = []
          compare_prices = []
  
          for _, sku_row in skus.iterrows():

              sku_data = self._transform_sku_row(sku_row)
              if sku_data:
                  skus_list.append(sku_data)
                  if 'price' in sku_data and sku_data['price'] is not None:

                      try:

                          prices.append(float(sku_data['price']))

                      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
  

          doc['skus'] = skus_list

  
          # 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
  

          # 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)
          
          if pd.notna(spu_row.get('shoplazza_created_at')):
              shoplazza_created_at = spu_row['shoplazza_created_at']
              if hasattr(shoplazza_created_at, 'isoformat'):
                  doc['shoplazza_created_at'] = shoplazza_created_at.isoformat()
              else:
                  doc['shoplazza_created_at'] = str(shoplazza_created_at)
          
          if pd.notna(spu_row.get('shoplazza_updated_at')):
              shoplazza_updated_at = spu_row['shoplazza_updated_at']
              if hasattr(shoplazza_updated_at, 'isoformat'):
                  doc['shoplazza_updated_at'] = shoplazza_updated_at.isoformat()
              else:
                  doc['shoplazza_updated_at'] = str(shoplazza_updated_at)
  

          return doc
  

      def _transform_sku_row(self, sku_row: pd.Series) -> Optional[Dict[str, Any]]:

          """

          Transform a SKU row into a SKU object.

  
          Args:
              sku_row: SKU row from database
  
          Returns:

              SKU dictionary or None

          """

          sku_data = {}

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

  
          # Title
          if pd.notna(sku_row.get('title')):

              sku_data['title'] = str(sku_row['title'])

  
          # 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
          if pd.notna(sku_row.get('sku')):

              sku_data['sku'] = 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

  
          # Options (from option1, option2, option3)
          options = {}
          if pd.notna(sku_row.get('option1')):
              options['option1'] = str(sku_row['option1'])
          if pd.notna(sku_row.get('option2')):
              options['option2'] = str(sku_row['option2'])
          if pd.notna(sku_row.get('option3')):
              options['option3'] = str(sku_row['option3'])
          
          if options:

              sku_data['options'] = options

          return sku_data