# utils/csv_processor.py
"""CSV processor utilities ....

This module provides classes and functions for:
- ....
- ....
- ....
"""

import logging
from datetime import datetime
from io import StringIO
from typing import Any, Dict

import pandas as pd
import requests
from sqlalchemy import literal
from sqlalchemy.orm import Session

from models.database_models import Product

logger = logging.getLogger(__name__)


class CSVProcessor:
    """Handles CSV import with robust parsing and batching."""

    ENCODINGS = ["utf-8", "latin-1", "iso-8859-1", "cp1252", "utf-8-sig"]

    PARSING_CONFIGS = [
        # Try auto-detection first
        {"sep": None, "engine": "python"},
        # Try semicolon (common in European CSVs)
        {"sep": ";", "engine": "python"},
        # Try comma
        {"sep": ",", "engine": "python"},
        # Try tab
        {"sep": "\t", "engine": "python"},
    ]

    COLUMN_MAPPING = {
        # Standard variations
        "id": "product_id",
        "ID": "product_id",
        "Product ID": "product_id",
        "name": "title",
        "Name": "title",
        "product_name": "title",
        "Product Name": "title",
        # Google Shopping feed standard
        "g:id": "product_id",
        "g:title": "title",
        "g:description": "description",
        "g:link": "link",
        "g:image_link": "image_link",
        "g:availability": "availability",
        "g:price": "price",
        "g:brand": "brand",
        "g:gtin": "gtin",
        "g:mpn": "mpn",
        "g:condition": "condition",
        "g:adult": "adult",
        "g:multipack": "multipack",
        "g:is_bundle": "is_bundle",
        "g:age_group": "age_group",
        "g:color": "color",
        "g:gender": "gender",
        "g:material": "material",
        "g:pattern": "pattern",
        "g:size": "size",
        "g:size_type": "size_type",
        "g:size_system": "size_system",
        "g:item_group_id": "item_group_id",
        "g:google_product_category": "google_product_category",
        "g:product_type": "product_type",
        "g:custom_label_0": "custom_label_0",
        "g:custom_label_1": "custom_label_1",
        "g:custom_label_2": "custom_label_2",
        "g:custom_label_3": "custom_label_3",
        "g:custom_label_4": "custom_label_4",
        # Handle complex shipping column
        "shipping(country:price:max_handling_time:min_transit_time:max_transit_time)": "shipping",
    }

    def __init__(self):
        """Class constructor."""
        from utils.data_processing import GTINProcessor, PriceProcessor

        self.gtin_processor = GTINProcessor()
        self.price_processor = PriceProcessor()

    def download_csv(self, url: str) -> str:
        """Download and decode CSV with multiple encoding attempts."""
        try:
            response = requests.get(url, timeout=30)
            response.raise_for_status()

            content = response.content

            # Try different encodings
            for encoding in self.ENCODINGS:
                try:
                    decoded_content = content.decode(encoding)
                    logger.info(f"Successfully decoded CSV with encoding: {encoding}")
                    return decoded_content
                except UnicodeDecodeError:
                    continue

            # Fallback with error ignoring
            decoded_content = content.decode("utf-8", errors="ignore")
            logger.warning("Used UTF-8 with error ignoring for CSV decoding")
            return decoded_content

        except requests.RequestException as e:
            logger.error(f"Error downloading CSV: {e}")
            raise

    def parse_csv(self, csv_content: str) -> pd.DataFrame:
        """Parse CSV with multiple separator attempts."""
        for config in self.PARSING_CONFIGS:
            try:
                df = pd.read_csv(
                    StringIO(csv_content),
                    on_bad_lines="skip",
                    quotechar='"',
                    skip_blank_lines=True,
                    skipinitialspace=True,
                    **config,
                )
                logger.info(f"Successfully parsed CSV with config: {config}")
                return df
            except pd.errors.ParserError:
                continue

        raise pd.errors.ParserError("Could not parse CSV with any configuration")

    def normalize_columns(self, df: pd.DataFrame) -> pd.DataFrame:
        """Normalize column names using mapping."""
        # Clean column names
        df.columns = df.columns.str.strip()

        # Apply mapping
        df = df.rename(columns=self.COLUMN_MAPPING)

        logger.info(f"Normalized columns: {list(df.columns)}")
        return df

    def _clean_row_data(self, row_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process a single row with data normalization."""
        # Handle NaN values
        processed_data = {k: (v if pd.notna(v) else None) for k, v in row_data.items()}

        # Process GTIN
        if processed_data.get("gtin"):
            processed_data["gtin"] = self.gtin_processor.normalize(
                processed_data["gtin"]
            )

        # Process price and currency
        if processed_data.get("price"):
            parsed_price, currency = self.price_processor.parse_price_currency(
                processed_data["price"]
            )
            processed_data["price"] = parsed_price
            processed_data["currency"] = currency

        # Process sale_price
        if processed_data.get("sale_price"):
            parsed_sale_price, _ = self.price_processor.parse_price_currency(
                processed_data["sale_price"]
            )
            processed_data["sale_price"] = parsed_sale_price

        # Clean MPN (remove .0 endings)
        if processed_data.get("mpn"):
            mpn_str = str(processed_data["mpn"]).strip()
            if mpn_str.endswith(".0"):
                processed_data["mpn"] = mpn_str[:-2]

        # Handle multipack type conversion
        if processed_data.get("multipack") is not None:
            try:
                processed_data["multipack"] = int(float(processed_data["multipack"]))
            except (ValueError, TypeError):
                processed_data["multipack"] = None

        return processed_data

    async def process_marketplace_csv_from_url(
        self, url: str, marketplace: str, shop_name: str, batch_size: int, db: Session
    ) -> Dict[str, Any]:
        """
        Process CSV from URL with marketplace and shop information.

        Args:
            url: URL to the CSV file
            marketplace: Name of the marketplace (e.g., 'Letzshop', 'Amazon')
            shop_name: Name of the shop
            batch_size: Number of rows to process in each batch
            db: Database session

        Returns:
            Dictionary with processing results
        """
        logger.info(
            f"Starting marketplace CSV import from {url} for {marketplace} -> {shop_name}"
        )
        # Download and parse CSV
        csv_content = self.download_csv(url)
        df = self.parse_csv(csv_content)
        df = self.normalize_columns(df)

        logger.info(f"Processing CSV with {len(df)} rows and {len(df.columns)} columns")

        imported = 0
        updated = 0
        errors = 0

        # Process in batches
        for i in range(0, len(df), batch_size):
            batch_df = df.iloc[i : i + batch_size]
            batch_result = await self._process_marketplace_batch(
                batch_df, marketplace, shop_name, db, i // batch_size + 1
            )

            imported += batch_result["imported"]
            updated += batch_result["updated"]
            errors += batch_result["errors"]

            logger.info(f"Processed batch {i // batch_size + 1}: {batch_result}")

        return {
            "total_processed": imported + updated + errors,
            "imported": imported,
            "updated": updated,
            "errors": errors,
            "marketplace": marketplace,
            "shop_name": shop_name,
        }

    async def _process_marketplace_batch(
        self,
        batch_df: pd.DataFrame,
        marketplace: str,
        shop_name: str,
        db: Session,
        batch_num: int,
    ) -> Dict[str, int]:
        """Process a batch of CSV rows with marketplace information."""
        imported = 0
        updated = 0
        errors = 0

        logger.info(
            f"Processing batch {batch_num} with {len(batch_df)} rows for "
            f"{marketplace} -> {shop_name}"
        )

        for index, row in batch_df.iterrows():
            try:
                # Convert row to dictionary and clean up
                product_data = self._clean_row_data(row.to_dict())

                # Add marketplace and shop information
                product_data["marketplace"] = marketplace
                product_data["shop_name"] = shop_name

                # Validate required fields
                if not product_data.get("product_id"):
                    logger.warning(f"Row {index}: Missing product_id, skipping")
                    errors += 1
                    continue

                if not product_data.get("title"):
                    logger.warning(f"Row {index}: Missing title, skipping")
                    errors += 1
                    continue

                # Check if product exists
                existing_product = (
                    db.query(Product)
                    .filter(Product.product_id == literal(product_data["product_id"]))
                    .first()
                )

                if existing_product:
                    # Update existing product
                    for key, value in product_data.items():
                        if key not in ["id", "created_at"] and hasattr(
                            existing_product, key
                        ):
                            setattr(existing_product, key, value)
                    existing_product.updated_at = datetime.utcnow()
                    updated += 1
                    logger.debug(
                        f"Updated product {product_data['product_id']} for "
                        f"{marketplace} and shop {shop_name}"
                    )
                else:
                    # Create new product
                    filtered_data = {
                        k: v
                        for k, v in product_data.items()
                        if k not in ["id", "created_at", "updated_at"]
                        and hasattr(Product, k)
                    }
                    new_product = Product(**filtered_data)
                    db.add(new_product)
                    imported += 1
                    logger.debug(
                        f"Imported new product {product_data['product_id']} "
                        f"for {marketplace} and shop {shop_name}"
                    )

            except Exception as e:
                logger.error(f"Error processing row: {e}")
                errors += 1
                continue

        # Commit the batch
        try:
            db.commit()
            logger.info(f"Batch {batch_num} committed successfully")
        except Exception as e:
            logger.error(f"Failed to commit batch {batch_num}: {e}")
            db.rollback()
            # Count all rows in this batch as errors
            errors = len(batch_df)
            imported = 0
            updated = 0

        return {"imported": imported, "updated": updated, "errors": errors}