"""
SPU文档转换器 - 公共转换逻辑。

提取全量和增量索引共用的文档转换逻辑，避免代码冗余。
"""

import pandas as pd
import logging
from typing import Dict, Any, Optional, List
from config import ConfigLoader

logger = logging.getLogger(__name__)

# Try to import translator (optional dependency)
try:
    from query.translator import Translator
    TRANSLATOR_AVAILABLE = True
except ImportError:
    TRANSLATOR_AVAILABLE = False
    Translator = None


class SPUDocumentTransformer:
    """SPU文档转换器，将SPU、SKU、Option数据转换为ES文档格式。"""

    def __init__(
        self,
        category_id_to_name: Dict[str, str],
        searchable_option_dimensions: List[str],
        tenant_config: Optional[Dict[str, Any]] = None,
        translator: Optional[Any] = None,
        translation_prompts: Optional[Dict[str, str]] = None,
        encoder: Optional[Any] = None,
        enable_title_embedding: bool = True
    ):
        """
        初始化文档转换器。

        Args:
            category_id_to_name: 分类ID到名称的映射
            searchable_option_dimensions: 可搜索的option维度列表
            tenant_config: 租户配置（包含主语言和翻译配置）
            translator: 翻译器实例（可选，如果提供则启用翻译功能）
            translation_prompts: 翻译提示词配置（可选）
            encoder: 文本编码器实例（可选，用于生成title_embedding）
            enable_title_embedding: 是否启用标题向量化（默认True）
        """
        self.category_id_to_name = category_id_to_name
        self.searchable_option_dimensions = searchable_option_dimensions
        self.tenant_config = tenant_config or {}
        self.translator = translator
        self.translation_prompts = translation_prompts or {}
        self.encoder = encoder
        self.enable_title_embedding = enable_title_embedding

    def transform_spu_to_doc(
        self,
        tenant_id: str,
        spu_row: pd.Series,
        skus: pd.DataFrame,
        options: pd.DataFrame
    ) -> Optional[Dict[str, Any]]:
        """
        将单个SPU行和其SKUs转换为ES文档。

        Args:
            tenant_id: 租户ID
            spu_row: SPU行数据
            skus: SKU数据DataFrame
            options: Option数据DataFrame

        Returns:
            ES文档字典
        """
        doc = {}

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

        # 获取租户配置
        primary_lang = self.tenant_config.get('primary_language', 'zh')

        # 文本字段处理（使用translator的内部逻辑自动处理多语言翻译）
        self._fill_text_fields(doc, spu_row, primary_lang)
        
        # 标题向量化处理（如果启用）
        if self.enable_title_embedding and self.encoder:
            self._fill_title_embedding(doc)

        # Tags
        if pd.notna(spu_row.get('tags')):
            tags_str = str(spu_row['tags'])
            doc['tags'] = [tag.strip() for tag in tags_str.split(',') if tag.strip()]

        # Category相关字段
        self._fill_category_fields(doc, spu_row)

        # Option名称（从option表获取）
        self._fill_option_names(doc, options)

        # Image URL
        self._fill_image_url(doc, spu_row)

        # Sales (fake_sales)
        if pd.notna(spu_row.get('fake_sales')):
            try:
                doc['sales'] = int(spu_row['fake_sales'])
            except (ValueError, TypeError):
                doc['sales'] = 0
        else:
            doc['sales'] = 0

        # Process SKUs and build specifications
        skus_list, prices, compare_prices, sku_prices, sku_weights, sku_weight_units, total_inventory, specifications = \
            self._process_skus(skus, options)

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

        # 提取option值（根据配置的searchable_option_dimensions）
        self._fill_option_values(doc, skus)

        # 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

        # 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 _fill_text_fields(
        self,
        doc: Dict[str, Any],
        spu_row: pd.Series,
        primary_lang: str
    ):
        """
        填充文本字段（根据主语言自动处理多语言翻译）。
        
        翻译逻辑在translator内部处理：
        - 如果店铺语言不等于zh，自动翻译成zh
        - 如果店铺语言不等于en，自动翻译成en
        """
        # Title
        if pd.notna(spu_row.get('title')):
            title_text = str(spu_row['title'])
            
            # 使用translator的translate_for_indexing方法，自动处理多语言翻译
            if self.translator:
                # 根据目标语言选择对应的提示词
                prompt_zh = self.translation_prompts.get('product_title_zh') or self.translation_prompts.get('default_zh')
                prompt_en = self.translation_prompts.get('product_title_en') or self.translation_prompts.get('default_en')
                
                # 调用translate_for_indexing，自动处理翻译逻辑
                translations = self.translator.translate_for_indexing(
                    title_text,
                    shop_language=primary_lang,
                    source_lang=primary_lang,
                    prompt=prompt_zh if primary_lang == 'zh' else prompt_en
                )
                
                # 填充翻译结果
                doc['title_zh'] = translations.get('zh') or (title_text if primary_lang == 'zh' else None)
                doc['title_en'] = translations.get('en') or (title_text if primary_lang == 'en' else None)
            else:
                # 无翻译器，只填充主语言字段
                if primary_lang == 'zh':
                    doc['title_zh'] = title_text
                    doc['title_en'] = None
                else:
                    doc['title_zh'] = None
                    doc['title_en'] = title_text
        else:
            doc['title_zh'] = None
            doc['title_en'] = None

        # Brief
        if pd.notna(spu_row.get('brief')):
            brief_text = str(spu_row['brief'])
            if self.translator:
                prompt = self.translation_prompts.get('default_zh') or self.translation_prompts.get('default_en')
                translations = self.translator.translate_for_indexing(
                    brief_text,
                    shop_language=primary_lang,
                    source_lang=primary_lang,
                    prompt=prompt
                )
                doc['brief_zh'] = translations.get('zh') or (brief_text if primary_lang == 'zh' else None)
                doc['brief_en'] = translations.get('en') or (brief_text if primary_lang == 'en' else None)
            else:
                if primary_lang == 'zh':
                    doc['brief_zh'] = brief_text
                    doc['brief_en'] = None
                else:
                    doc['brief_zh'] = None
                    doc['brief_en'] = brief_text
        else:
            doc['brief_zh'] = None
            doc['brief_en'] = None

        # Description
        if pd.notna(spu_row.get('description')):
            desc_text = str(spu_row['description'])
            if self.translator:
                prompt = self.translation_prompts.get('default_zh') or self.translation_prompts.get('default_en')
                translations = self.translator.translate_for_indexing(
                    desc_text,
                    shop_language=primary_lang,
                    source_lang=primary_lang,
                    prompt=prompt
                )
                doc['description_zh'] = translations.get('zh') or (desc_text if primary_lang == 'zh' else None)
                doc['description_en'] = translations.get('en') or (desc_text if primary_lang == 'en' else None)
            else:
                if primary_lang == 'zh':
                    doc['description_zh'] = desc_text
                    doc['description_en'] = None
                else:
                    doc['description_zh'] = None
                    doc['description_en'] = desc_text
        else:
            doc['description_zh'] = None
            doc['description_en'] = None

        # Vendor
        if pd.notna(spu_row.get('vendor')):
            vendor_text = str(spu_row['vendor'])
            if self.translator:
                prompt = self.translation_prompts.get('default_zh') or self.translation_prompts.get('default_en')
                translations = self.translator.translate_for_indexing(
                    vendor_text,
                    shop_language=primary_lang,
                    source_lang=primary_lang,
                    prompt=prompt
                )
                doc['vendor_zh'] = translations.get('zh') or (vendor_text if primary_lang == 'zh' else None)
                doc['vendor_en'] = translations.get('en') or (vendor_text if primary_lang == 'en' else None)
            else:
                if primary_lang == 'zh':
                    doc['vendor_zh'] = vendor_text
                    doc['vendor_en'] = None
                else:
                    doc['vendor_zh'] = None
                    doc['vendor_en'] = vendor_text
        else:
            doc['vendor_zh'] = None
            doc['vendor_en'] = None

    def _fill_category_fields(self, doc: Dict[str, Any], spu_row: pd.Series):
        """填充类目相关字段。"""
        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 = []
            for cid in category_ids:
                if cid in self.category_id_to_name:
                    category_names.append(self.category_id_to_name[cid])
                else:
                    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}")
                    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'])

    def _fill_option_names(self, doc: Dict[str, Any], options: pd.DataFrame):
        """填充Option名称字段。"""
        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'])

    def _fill_image_url(self, doc: Dict[str, Any], spu_row: pd.Series):
        """填充图片URL字段。"""
        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

    def _process_skus(
        self,
        skus: pd.DataFrame,
        options: pd.DataFrame
    ) -> tuple:
        """处理SKU数据，返回处理结果。"""
        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'])
                    })

        return skus_list, prices, compare_prices, sku_prices, sku_weights, sku_weight_units, total_inventory, specifications

    def _fill_option_values(self, doc: Dict[str, Any], skus: pd.DataFrame):
        """填充option值字段。"""
        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'] = []

    def _transform_sku_row(self, sku_row: pd.Series, option_name_map: Dict[int, str] = None) -> Optional[Dict[str, Any]]:
        """
        将SKU行转换为SKU对象。

        Args:
            sku_row: SKU行数据
            option_name_map: position到option名称的映射

        Returns:
            SKU字典
        """
        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
    
    def _fill_title_embedding(self, doc: Dict[str, Any]) -> None:
        """
        填充标题向量化字段。
        
        使用英文标题（title_en）生成embedding。如果title_en不存在，则使用title_zh。
        
        Args:
            doc: ES文档字典
        """
        # 优先使用英文标题，如果没有则使用中文标题
        title_text = doc.get('title_en') or doc.get('title_zh')
        
        if not title_text or not title_text.strip():
            logger.debug(f"No title text available for embedding, SPU: {doc.get('spu_id')}")
            return
        
        try:
            # 使用BgeEncoder生成embedding
            # encode方法返回numpy数组，形状为(n, 1024)
            embeddings = self.encoder.encode(title_text)
            
            if embeddings is not None and len(embeddings) > 0:
                # 取第一个embedding（因为只传了一个文本）
                embedding = embeddings[0]
                # 转换为列表格式（ES需要）
                doc['title_embedding'] = embedding.tolist()
                logger.debug(f"Generated title_embedding for SPU: {doc.get('spu_id')}, title: {title_text[:50]}...")
            else:
                logger.warning(f"Failed to generate embedding for title: {title_text[:50]}...")
        except Exception as e:
            logger.error(f"Error generating title_embedding for SPU {doc.get('spu_id')}: {e}", exc_info=True)