"""
HTML parsing module for extracting oil price data from web pages.
"""
import logging
import re
from bs4 import BeautifulSoup


def parse_zone_slug_to_int(zone_slug_str: str) -> int | None:
    """
    Extract the numeric part of a zone slug.
    
    Examples:
        "zone1" -> 1
        "zonema5" -> 5
        
    Args:
        zone_slug_str: Zone slug string like "zone1", "zonema5"
        
    Returns:
        Integer zone number or None if parsing fails
    """
    if not zone_slug_str:
        return None
    match = re.search(r'\d+$', zone_slug_str)
    if match:
        return int(match.group(0))
    logging.warning(f"Could not parse numeric zone from slug: '{zone_slug_str}'")
    return None


def _find_price_table_columns(thead) -> dict | None:
    """
    Find column indices for company, price, and date in a table header.
    
    Args:
        thead: BeautifulSoup thead element
        
    Returns:
        Dictionary with column indices or None if not a price table
    """
    headers_lower = [th.get_text(strip=True).lower() for th in thead.find_all('th')]
    column_indices = {}
    
    try:
        column_indices['company'] = headers_lower.index('company name')
        price_col_name_part = 'price'
        column_indices['price'] = next(
            i for i, header in enumerate(headers_lower) if price_col_name_part in header
        )
        column_indices['date'] = headers_lower.index('date')
        return column_indices
    except (ValueError, StopIteration):
        return None


def _parse_row(cells: list, column_indices: dict, state_name: str, zone: int) -> dict | None:
    """
    Parse a single table row into a price record.
    
    Args:
        cells: List of td elements
        column_indices: Dictionary mapping column names to indices
        state_name: State name string
        zone: Zone number
        
    Returns:
        Dictionary with parsed data or None if parsing fails
    """
    max_required_index = max(column_indices.values())
    
    if len(cells) <= max_required_index:
        return None
    
    # Extract company name (prefer link text if available)
    company_cell = cells[column_indices['company']]
    company_name = company_cell.get_text(strip=True)
    company_link = company_cell.find('a')
    if company_link:
        company_name = company_link.get_text(strip=True)
    
    # Extract and parse price
    price_str = cells[column_indices['price']].get_text(strip=True)
    price_float = None
    try:
        cleaned_price_str = ''.join(filter(lambda x: x.isdigit() or x == '.', price_str))
        if cleaned_price_str:
            price_float = float(cleaned_price_str)
    except ValueError:
        logging.warning(f"Could not parse price: '{price_str}' for {company_name} in {state_name}/zone{zone}.")
    except Exception as e:
        logging.error(f"Unexpected error parsing price: '{price_str}' for {company_name}. Error: {e}")
    
    # Extract date
    date_posted_str = cells[column_indices['date']].get_text(strip=True)
    
    return {
        "state": state_name.capitalize(),
        "zone": zone,
        "name": company_name,
        "price": price_float,
        "date": date_posted_str,
    }


def parse_price_table(soup: BeautifulSoup, state_name_key: str, zone_slug_str: str) -> list[dict]:
    """
    Parse price tables from a BeautifulSoup page.
    
    Args:
        soup: BeautifulSoup object of the page
        state_name_key: State key like "connecticut", "maine"
        zone_slug_str: Zone slug like "zone1", "zonema5"
        
    Returns:
        List of dictionaries containing price data
    """
    data_dicts = []
    all_tables = soup.find_all('table')
    logging.info(f"Found {len(all_tables)} table(s) on page for {state_name_key} - {zone_slug_str}.")
    
    if not all_tables:
        logging.warning(f"No HTML tables found at all for {state_name_key} - {zone_slug_str}.")
        return data_dicts
    
    # Parse zone number from slug
    zone_int = parse_zone_slug_to_int(zone_slug_str)
    if zone_int is None:
        logging.error(f"Cannot parse zone number for {state_name_key} - {zone_slug_str}. Skipping.")
        return data_dicts
    
    candidate_tables_found = 0
    
    for table_index, table in enumerate(all_tables):
        thead = table.find('thead')
        if not thead:
            logging.debug(f"Table {table_index} has no thead.")
            continue
        
        # Check if this is a price table
        column_indices = _find_price_table_columns(thead)
        if column_indices is None:
            logging.debug(f"Table {table_index} headers do not contain all key columns.")
            continue
        
        logging.debug(f"Table {table_index} identified as price table. Indices: {column_indices}")
        candidate_tables_found += 1
        
        # Parse table body
        tbody = table.find('tbody')
        if not tbody:
            logging.warning(f"Price table identified by headers has no tbody. Skipping. State: {state_name_key}, Zone: {zone_slug_str}")
            continue
        
        rows = tbody.find_all('tr')
        if not rows:
            logging.debug(f"No rows found in tbody for price table in {state_name_key}/{zone_slug_str}")
            continue
        
        # Parse each row
        for row_index, row in enumerate(rows):
            cells = row.find_all('td')
            record = _parse_row(cells, column_indices, state_name_key, zone_int)
            
            if record:
                data_dicts.append(record)
            elif len(cells) > 0:
                max_required = max(column_indices.values()) + 1
                logging.warning(
                    f"Skipping row {row_index+1} with insufficient cells ({len(cells)}, need {max_required}) "
                    f"in {state_name_key}/{zone_slug_str}"
                )
    
    if candidate_tables_found == 0:
        logging.warning(f"No tables matching expected price table structure found for {state_name_key} - {zone_slug_str}.")
    
    return data_dicts