import pandas as pd
import re
import logging

from typing import Dict, List, Optional, Set, Union
from time import perf_counter

import settings

def pluralize(word: str) -> str:
    """Convert a word to its plural form using basic English pluralization rules.

    Args:
        word: The singular word to pluralize

    Returns:
        The pluralized word
    """
    if word.endswith('y'):
        return word[:-1] + 'ies'
    elif word.endswith(('s', 'sh', 'ch', 'x', 'z')):
        return word + 'es'
    elif word.endswith(('f')):
        return word[:-1] + 'ves'
    else:
        return word + 's'

def sort_list(items: Union[List, pd.Series]) -> Union[List, pd.Series]:
    """Sort a list or pandas Series in ascending order.

    Args:
        items: List or Series to sort

    Returns:
        Sorted list or Series
    """
    if isinstance(items, (list, pd.Series)):
        return sorted(items) if isinstance(items, list) else items.sort_values()
    return items

def create_type_mask(df: pd.DataFrame, type_text: Union[str, List[str]], regex: bool = True) -> pd.Series:
    """Create a boolean mask for rows where type matches one or more patterns.

    Args:
        df: DataFrame to search
        type_text: Type text pattern(s) to match. Can be a single string or list of strings.
        regex: Whether to treat patterns as regex expressions (default: True)

    Returns:
        Boolean Series indicating matching rows

    Raises:
        ValueError: If type_text is empty or None
        TypeError: If type_text is not a string or list of strings
    """
    if not type_text:
        raise ValueError("type_text cannot be empty or None")

    if isinstance(type_text, str):
        type_text = [type_text]
    elif not isinstance(type_text, list):
        raise TypeError("type_text must be a string or list of strings")

    if regex:
        pattern = '|'.join(f'{p}' for p in type_text)
        return df['type'].str.contains(pattern, case=False, na=False, regex=True)
    else:
        masks = [df['type'].str.contains(p, case=False, na=False, regex=False) for p in type_text]
        return pd.concat(masks, axis=1).any(axis=1)

def create_text_mask(df: pd.DataFrame, type_text: Union[str, List[str]], regex: bool = True, combine_with_or: bool = True) -> pd.Series:
    """Create a boolean mask for rows where text matches one or more patterns.

    Args:
        df: DataFrame to search
        type_text: Type text pattern(s) to match. Can be a single string or list of strings.
        regex: Whether to treat patterns as regex expressions (default: True)
        combine_with_or: Whether to combine multiple patterns with OR (True) or AND (False)

    Returns:
        Boolean Series indicating matching rows

    Raises:
        ValueError: If type_text is empty or None
        TypeError: If type_text is not a string or list of strings
    """
    if not type_text:
        raise ValueError("type_text cannot be empty or None")

    if isinstance(type_text, str):
        type_text = [type_text]
    elif not isinstance(type_text, list):
        raise TypeError("type_text must be a string or list of strings")

    if regex:
        pattern = '|'.join(f'{p}' for p in type_text)
        return df['text'].str.contains(pattern, case=False, na=False, regex=True)
    else:
        masks = [df['text'].str.contains(p, case=False, na=False, regex=False) for p in type_text]
        if combine_with_or:
            return pd.concat(masks, axis=1).any(axis=1)
        else:
            return pd.concat(masks, axis=1).all(axis=1)

def create_keyword_mask(df: pd.DataFrame, type_text: Union[str, List[str]], regex: bool = True) -> pd.Series:
    """Create a boolean mask for rows where keyword text matches one or more patterns.

    Args:
        df: DataFrame to search
        type_text: Type text pattern(s) to match. Can be a single string or list of strings.
        regex: Whether to treat patterns as regex expressions (default: True)

    Returns:
        Boolean Series indicating matching rows. For rows with empty/null keywords,
        returns False.

    Raises:
        ValueError: If type_text is empty or None
        TypeError: If type_text is not a string or list of strings
        ValueError: If required 'keywords' column is missing from DataFrame
    """
    # Validate required columns
    validate_dataframe_columns(df, {'keywords'})

    # Handle empty DataFrame case
    if len(df) == 0:
        return pd.Series([], dtype=bool)

    if not type_text:
        raise ValueError("type_text cannot be empty or None")

    if isinstance(type_text, str):
        type_text = [type_text]
    elif not isinstance(type_text, list):
        raise TypeError("type_text must be a string or list of strings")

    # Create default mask for null values
    # Handle null values and convert to string
    keywords = df['keywords'].fillna('')
    # Convert non-string values to strings
    keywords = keywords.astype(str)

    if regex:
        pattern = '|'.join(f'{p}' for p in type_text)
        return keywords.str.contains(pattern, case=False, na=False, regex=True)
    else:
        masks = [keywords.str.contains(p, case=False, na=False, regex=False) for p in type_text]
        return pd.concat(masks, axis=1).any(axis=1)
def create_name_mask(df: pd.DataFrame, type_text: Union[str, List[str]], regex: bool = True) -> pd.Series:
    """Create a boolean mask for rows where name matches one or more patterns.

    Args:
        df: DataFrame to search
        type_text: Type text pattern(s) to match. Can be a single string or list of strings.
        regex: Whether to treat patterns as regex expressions (default: True)

    Returns:
        Boolean Series indicating matching rows

    Raises:
        ValueError: If type_text is empty or None
        TypeError: If type_text is not a string or list of strings
    """
    if not type_text:
        raise ValueError("type_text cannot be empty or None")

    if isinstance(type_text, str):
        type_text = [type_text]
    elif not isinstance(type_text, list):
        raise TypeError("type_text must be a string or list of strings")

    if regex:
        pattern = '|'.join(f'{p}' for p in type_text)
        return df['name'].str.contains(pattern, case=False, na=False, regex=True)
    else:
        masks = [df['name'].str.contains(p, case=False, na=False, regex=False) for p in type_text]
        return pd.concat(masks, axis=1).any(axis=1)

def extract_creature_types(type_text: str, creature_types: List[str], non_creature_types: List[str]) -> List[str]:
    """Extract creature types from a type text string.

    Args:
        type_text: The type line text to parse
        creature_types: List of valid creature types
        non_creature_types: List of non-creature types to exclude

    Returns:
        List of extracted creature types
    """
    types = [t.strip() for t in type_text.split()]
    return [t for t in types if t in creature_types and t not in non_creature_types]

def find_types_in_text(text: str, name: str, creature_types: List[str]) -> List[str]:
    """Find creature types mentioned in card text.

    Args:
        text: Card text to search
        name: Card name to exclude from search
        creature_types: List of valid creature types

    Returns:
        List of found creature types
    """
    if pd.isna(text):
        return []
        
    found_types = []
    words = text.split()
    
    for word in words:
        clean_word = re.sub(r'[^a-zA-Z-]', '', word)
        if clean_word in creature_types:
            if clean_word not in name:
                found_types.append(clean_word)
                
    return list(set(found_types))

def add_outlaw_type(types: List[str], outlaw_types: List[str]) -> List[str]:
    """Add Outlaw type if card has an outlaw-related type.

    Args:
        types: List of current types
        outlaw_types: List of types that qualify for Outlaw

    Returns:
        Updated list of types
    """
    if any(t in outlaw_types for t in types) and 'Outlaw' not in types:
        return types + ['Outlaw']
    return types

def create_tag_mask(df: pd.DataFrame, tag_patterns: Union[str, List[str]], column: str = 'themeTags') -> pd.Series:
    """Create a boolean mask for rows where tags match specified patterns.

    Args:
        df: DataFrame to search
        tag_patterns: String or list of strings to match against tags
        column: Column containing tags to search (default: 'themeTags')

    Returns:
        Boolean Series indicating matching rows

    Examples:
        # Match cards with draw-related tags
        >>> mask = create_tag_mask(df, ['Card Draw', 'Conditional Draw'])
        >>> mask = create_tag_mask(df, 'Unconditional Draw')
    """
    if isinstance(tag_patterns, str):
        tag_patterns = [tag_patterns]

    # Handle empty DataFrame case
    if len(df) == 0:
        return pd.Series([], dtype=bool)

    # Create mask for each pattern
    masks = [df[column].apply(lambda x: any(pattern in tag for tag in x)) for pattern in tag_patterns]
    
    # Combine masks with OR
    return pd.concat(masks, axis=1).any(axis=1)

def validate_dataframe_columns(df: pd.DataFrame, required_columns: Set[str]) -> None:
    """Validate that DataFrame contains all required columns.

    Args:
        df: DataFrame to validate
        required_columns: Set of column names that must be present

    Raises:
        ValueError: If any required columns are missing
    """
    missing = required_columns - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {missing}")
    
def apply_tag_vectorized(df: pd.DataFrame, mask: pd.Series, tags: List[str]) -> None:
    """Apply tags to rows in a dataframe based on a boolean mask.
    
    Args:
        df: The dataframe to modify
        mask: Boolean series indicating which rows to tag
        tags: List of tags to apply
    """
    if not isinstance(tags, list):
        tags = [tags]
        
    # Get current tags for masked rows
    current_tags = df.loc[mask, 'themeTags']
    
    # Add new tags
    df.loc[mask, 'themeTags'] = current_tags.apply(lambda x: sorted(list(set(x + tags))))
def create_mass_effect_mask(df: pd.DataFrame, effect_type: str) -> pd.Series:
    """Create a boolean mask for cards with mass removal effects of a specific type.

    Args:
        df: DataFrame to search
        effect_type: Type of mass effect to match ('destruction', 'exile', 'bounce', 'sacrifice', 'damage')

    Returns:
        Boolean Series indicating which cards have mass effects of the specified type

    Raises:
        ValueError: If effect_type is not recognized
    """
    if effect_type not in settings.BOARD_WIPE_TEXT_PATTERNS:
        raise ValueError(f"Unknown effect type: {effect_type}")

    patterns = settings.BOARD_WIPE_TEXT_PATTERNS[effect_type]
    return create_text_mask(df, patterns)
def create_damage_pattern(number: Union[int, str]) -> str:
    """Create a pattern for matching X damage effects.

    Args:
        number: Number or variable (X) for damage amount

    Returns:
        Pattern string for matching damage effects
    """
    return f'deals {number} damage'
def create_mass_damage_mask(df: pd.DataFrame) -> pd.Series:
    """Create a boolean mask for cards with mass damage effects.

    Args:
        df: DataFrame to search

    Returns:
        Boolean Series indicating which cards have mass damage effects
    """
    # Create patterns for numeric damage
    number_patterns = [create_damage_pattern(i) for i in range(1, 21)]
    
    # Add X damage pattern
    number_patterns.append(create_damage_pattern('X'))
    
    # Add patterns for damage targets
    target_patterns = [
        'to each creature',
        'to all creatures',
        'to each player',
        'to each opponent',
        'to everything'
    ]
    
    # Create masks
    damage_mask = create_text_mask(df, number_patterns)
    target_mask = create_text_mask(df, target_patterns)
    
    return damage_mask & target_mask