"""
  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

  from config.tenant_config_loader import get_tenant_config_loader
  from indexer.document_transformer import SPUDocumentTransformer

  # 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

          translator = None
          translation_prompts = {}

          try:
              config_loader = ConfigLoader()
              config = config_loader.load_config()
              self.searchable_option_dimensions = config.spu_config.searchable_option_dimensions

              
              # Initialize translator if translation is enabled
              if config.query_config.enable_translation:
                  from query.translator import Translator
                  translator = Translator(
                      api_key=config.query_config.translation_api_key,
                      use_cache=True,  # 索引时使用缓存避免重复翻译
                      glossary_id=config.query_config.translation_glossary_id,
                      translation_context=config.query_config.translation_context
                  )
                  translation_prompts = config.query_config.translation_prompts

          except Exception as e:

              logger.warning(f"Failed to load config, using default: {e}")

              self.searchable_option_dimensions = ['option1', 'option2', 'option3']

          
          # Load category ID to name mapping
          self.category_id_to_name = self._load_category_mapping()

          
          # Load tenant config
          tenant_config_loader = get_tenant_config_loader()
          tenant_config = tenant_config_loader.get_tenant_config(tenant_id)
          
          # Initialize document transformer
          self.document_transformer = SPUDocumentTransformer(
              category_id_to_name=self.category_id_to_name,
              searchable_option_dimensions=self.searchable_option_dimensions,
              tenant_config=tenant_config,
              translator=translator,
              translation_prompts=translation_prompts
          )

      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,

                  fake_sales, display_fake_sales,

                  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.document_transformer.transform_spu_to_doc(
                      tenant_id=self.tenant_id,
                      spu_row=spu_row,
                      skus=skus,
                      options=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