from typing import Dict, Any, List, Tuple
import numpy as np
try:
    from skbio import TreeNode
    from skbio.sequence import DNA
    BIO_AVAILABLE = True
except ImportError:
    BIO_AVAILABLE = False

class BioRenderer:
    """
    Handles bio-inspired SVG rendering and transformations.
    """
    
    def __init__(self):
        """Initialize bio-rendering system."""
        self.cuda_enabled = False
        self._check_requirements()
        self.patterns = {}
        self.current_sequence = None
        
    def _check_requirements(self) -> None:
        """Verify bio-algorithm requirements are met."""
        if not BIO_AVAILABLE:
            raise ImportError(
                "Bio features require scikit-bio. "
                "Install with: pip install scikit-bio"
            )
            
    def apply_bio_transforms(self, attrs: Dict[str, Any]) -> Dict[str, Any]:
        """
        Apply bio-inspired transformations to SVG attributes.
        
        Args:
            attrs: Original SVG attributes
            
        Returns:
            Processed attributes with bio transformations
        """
        processed_attrs = attrs.copy()
        
        if 'bio-sequence' in attrs:
            sequence = attrs['bio-sequence']
            self.current_sequence = DNA(sequence)
            
            if 'd' in attrs:  # Transform path data using DNA sequence
                processed_attrs['d'] = self._dna_path_transform(
                    attrs['d'],
                    self.current_sequence
                )
                
        if 'bio-pattern' in attrs:
            pattern_type = attrs['bio-pattern']
            if pattern_type == 'cellular':
                processed_attrs = self._apply_cellular_automaton(
                    processed_attrs
                )
            elif pattern_type == 'fractal':
                processed_attrs = self._apply_l_system(processed_attrs)
                
        return processed_attrs
        
    def _dna_path_transform(self, path: str, sequence: 'DNA') -> str:
        """
        Transform SVG path using DNA sequence patterns.
        
        Args:
            path: Original SVG path
            sequence: DNA sequence to base transformation on
            
        Returns:
            Transformed path
        """
        # Convert DNA sequence to numerical parameters
        params = self._sequence_to_params(sequence)
        
        # Parse path into commands and coordinates
        commands = self._parse_path(path)
        
        # Apply DNA-based transformations
        transformed = self._apply_dna_transforms(commands, params)
        
        return self._commands_to_path(transformed)
        
    def _sequence_to_params(self, sequence: 'DNA') -> Dict[str, float]:
        """Convert DNA sequence to transformation parameters."""
        # Count nucleotide frequencies
        counts = sequence.frequencies()
        total = sum(counts.values())
        
        # Convert to parameters
        params = {
            'scale_x': 1.0 + (counts.get('A', 0) / total),
            'scale_y': 1.0 + (counts.get('T', 0) / total),
            'rotation': 360 * (counts.get('G', 0) / total),
            'complexity': counts.get('C', 0) / total
        }
        
        return params
        
    def _parse_path(self, path: str) -> List[Tuple[str, List[float]]]:
        """Parse SVG path into command-coordinate pairs."""
        commands = []
        current_cmd = None
        coords = []
        
        tokens = path.split()
        for token in tokens:
            if token[0].isalpha():
                if current_cmd:
                    commands.append((current_cmd, coords))
                    coords = []
                current_cmd = token
            else:
                try:
                    coords.append(float(token))
                except ValueError:
                    continue
                    
        if current_cmd:
            commands.append((current_cmd, coords))
            
        return commands
        
    def _apply_dna_transforms(
        self,
        commands: List[Tuple[str, List[float]]],
        params: Dict[str, float]
    ) -> List[Tuple[str, List[float]]]:
        """Apply DNA-based transformations to path commands."""
        transformed = []
        
        for cmd, coords in commands:
            if cmd in {'M', 'L', 'H', 'V'}:  # Position commands
                new_coords = self._transform_coordinates(
                    coords,
                    params
                )
                transformed.append((cmd, new_coords))
            elif cmd in {'C', 'S', 'Q', 'T'}:  # Curve commands
                new_coords = self._transform_curve(coords, params)
                transformed.append((cmd, new_coords))
            else:
                transformed.append((cmd, coords))
                
        return transformed
        
    def _transform_coordinates(
        self,
        coords: List[float],
        params: Dict[str, float]
    ) -> List[float]:
        """Transform coordinates using DNA parameters."""
        transformed = []
        for i in range(0, len(coords), 2):
            if i + 1 >= len(coords):
                break
                
            x, y = coords[i], coords[i + 1]
            
            # Apply scaling
            x *= params['scale_x']
            y *= params['scale_y']
            
            # Apply rotation
            angle = np.radians(params['rotation'])
            new_x = x * np.cos(angle) - y * np.sin(angle)
            new_y = x * np.sin(angle) + y * np.cos(angle)
            
            transformed.extend([new_x, new_y])
            
        return transformed
        
    def _transform_curve(
        self,
        coords: List[float],
        params: Dict[str, float]
    ) -> List[float]:
        """Transform curve control points using DNA parameters."""
        # Add complexity to curves based on DNA parameters
        complexity = params['complexity']
        
        transformed = []
        for i in range(0, len(coords), 2):
            if i + 1 >= len(coords):
                break
                
            x, y = coords[i], coords[i + 1]
            
            # Add controlled variation to control points
            variation = np.sin(complexity * np.pi * i/len(coords))
            x += variation * complexity * 10
            y += variation * complexity * 10
            
            transformed.extend([x, y])
            
        return transformed
        
    def _commands_to_path(
        self,
        commands: List[Tuple[str, List[float]]]
    ) -> str:
        """Convert commands back to SVG path string."""
        path_parts = []
        
        for cmd, coords in commands:
            path_parts.append(cmd)
            coord_strs = [f"{c:.2f}" for c in coords]
            path_parts.extend(coord_strs)
            
        return ' '.join(path_parts)
        
    def _apply_cellular_automaton(
        self,
        attrs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply cellular automaton patterns to attributes."""
        if 'width' in attrs and 'height' in attrs:
            width = float(attrs['width'])
            height = float(attrs['height'])
            
            # Generate cellular automaton pattern
            pattern = self._generate_ca_pattern(int(width), int(height))
            
            # Convert pattern to path data
            attrs['d'] = self._pattern_to_path(pattern)
            
        return attrs
        
    def _apply_l_system(self, attrs: Dict[str, Any]) -> Dict[str, Any]:
        """Apply L-system fractal patterns to attributes."""
        if 'd' in attrs:
            # Use original path as base for L-system
            base_path = attrs['d']
            # Generate fractal pattern
            attrs['d'] = self._generate_l_system(base_path)
            
        return attrs
        
    def enable_cuda(self) -> None:
        """Enable CUDA acceleration for bio-computations."""
        try:
            import numba.cuda
            self.cuda_enabled = True
        except ImportError:
            raise RuntimeError("CUDA acceleration requires numba")

    def _generate_ca_pattern(self, width: int, height: int) -> np.ndarray:
        """Generate cellular automaton pattern."""
        # Simple implementation of Conway's Game of Life
        grid = np.random.choice([0, 1], size=(height, width))
        
        if self.cuda_enabled:
            # Use CUDA for parallel processing
            return self._generate_ca_pattern_cuda(grid)
        
        # CPU implementation
        new_grid = grid.copy()
        for _ in range(5):  # 5 generations
            for i in range(1, height-1):
                for j in range(1, width-1):
                    neighbors = np.sum(grid[i-1:i+2, j-1:j+2]) - grid[i,j]
                    if grid[i,j] == 1:
                        new_grid[i,j] = 1 if 2 <= neighbors <= 3 else 0
                    else:
                        new_grid[i,j] = 1 if neighbors == 3 else 0
            grid = new_grid.copy()
            
        return grid
        
    def _pattern_to_path(self, pattern: np.ndarray) -> str:
        """Convert binary pattern to SVG path."""
        height, width = pattern.shape
        path_parts = []
        
        for i in range(height):
            for j in range(width):
                if pattern[i,j]:
                    path_parts.append(
                        f"M {j} {i} h 1 v 1 h -1 z"
                    )
                    
        return ' '.join(path_parts)
        
    def _generate_l_system(self, base_path: str) -> str:
        """Generate L-system fractal pattern."""
        # Simple L-system implementation
        commands = self._parse_path(base_path)
        
        # Apply fractal rules
        new_commands = []
        for cmd, coords in commands:
            if cmd in {'M', 'L'}:
                # Add fractal detail to line segments
                new_commands.extend(
                    self._fractal_segment(cmd, coords)
                )
            else:
                new_commands.append((cmd, coords))
                
        return self._commands_to_path(new_commands)
        
    def _fractal_segment(
        self,
        cmd: str,
        coords: List[float]
    ) -> List[Tuple[str, List[float]]]:
        """Generate fractal pattern for line segment."""
        if len(coords) < 4:
            return [(cmd, coords)]
            
        # Koch curve-like fractal
        x1, y1 = coords[0], coords[1]
        x2, y2 = coords[2], coords[3]
        
        # Calculate intermediate points
        dx = x2 - x1
        dy = y2 - y1
        
        # Create fractal pattern
        return [
            (cmd, [x1, y1]),
            ('L', [x1 + dx/3, y1 + dy/3]),
            ('L', [x1 + dx/2 - dy/6, y1 + dy/2 + dx/6]),
            ('L', [x1 + 2*dx/3, y1 + 2*dy/3]),
            ('L', [x2, y2])
        ]
