from typing import Dict, List, Any

class ColumnMapper:
    """
    Maps dynamic source column names (e.g., 'Item Code', 'Selling Price')
    to standard system target fields (e.g., 'sku', 'price').
    Provides heuristic / AI auto-mapping suggestions.
    """

    STANDARD_FIELDS = {
        "sku": ["sku", "item_code", "item code", "product_code", "part_number", "model_number"],
        "name": ["product_name", "name", "title", "product name", "item_name"],
        "parent_name": ["parent_name", "base_product", "product_group", "parent_product", "model_series", "parent_title"],
        "price": ["price", "selling_price", "mrp", "unit_price", "rate"],
        "cost_price": ["cost_price", "purchase_price", "buying_price", "cost price"],
        "stock": ["stock", "quantity", "qty", "inventory", "stock_count", "balance"],
        "brand": ["brand", "brand_name", "manufacturer", "make"],
        "category": ["category", "category_name", "department", "group"],
        "parent_category": ["parent_category", "parent_category_name", "parent category"],
        "is_parent_feature": ["is_parent_feature", "parent_feature", "is parent feature", "featured_category"],
        "parent_media_key": ["parent_media_key", "parent_image_key", "parent media key"],
        "media_key": ["media_key", "media key", "image_key", "folder_key", "media_group", "variant_media_key"],
        "barcode": ["barcode", "upc", "ean", "isbn"],
        "description": ["description", "details", "specifications", "summary"],
        "device_series": ["device_series", "series", "device series", "series_name"],
        "device_model": ["device_model", "model", "device model", "target_model", "compatibility_model"],
        "device_type": ["device_type", "device type", "type", "device_category"]
    }

    @classmethod
    def suggest_mappings(cls, headers: List[str]) -> Dict[str, str]:
        """
        Suggests mapping of source header -> standard target field.
        """
        suggestions = {}
        for header in headers:
            normalized = header.lower().strip().replace("-", "_")
            matched = False
            for target_field, aliases in cls.STANDARD_FIELDS.items():
                if normalized == target_field or normalized in aliases:
                    suggestions[header] = target_field
                    matched = True
                    break
            if not matched:
                # Fuzzy keyword match fallback
                for target_field, aliases in cls.STANDARD_FIELDS.items():
                    for alias in aliases:
                        if alias in normalized:
                            # Avoid matching 'rate' in non-price contexts like 'refresh_rate' or 'heart_rate'
                            if alias == 'rate' and ('refresh' in normalized or 'heart' in normalized):
                                continue
                            suggestions[header] = target_field
                            matched = True
                            break
                    if matched:
                        break
            if not matched:
                suggestions[header] = header  # Keep as custom dynamic attribute
        return suggestions

    @classmethod
    def apply_mapping(cls, raw_row: Dict[str, Any], column_maps: Dict[str, str]) -> Dict[str, Any]:
        """
        Transforms a raw row using the given column_maps dictionary.
        Auto-generates mapping suggestions if column_maps is empty or missing.
        """
        if not column_maps or column_maps == {}:
            column_maps = cls.suggest_mappings(list(raw_row.keys()))

        mapped_row = {}
        attributes = {}
        for source_col, val in raw_row.items():
            target_field = column_maps.get(source_col, source_col)
            if target_field in cls.STANDARD_FIELDS:
                mapped_row[target_field] = val
            else:
                attributes[target_field] = val

        if attributes:
            mapped_row["attributes"] = attributes
        return mapped_row