import numpy as np
import torch
from typing import Dict, List, Tuple, Optional, Callable, Any
from dataclasses import dataclass
from enum import Enum
import quaternion

class VRTrackingSystem: 
    
    def __init__(self):
        self.left_hand_position = np.array([0.0, 0.0, 0.0])
        self.right_hand_position = np.array([0.0, 0.0, 0.0])
        self.left_hand_rotation = np.array([0.0, 0.0, 0.0, 1.0])
        self.right_hand_rotation = np.array([0.0, 0.0, 0.0, 1.0])
        self.head_position = np.array([0.0, 1.7, 0.0])
        self.head_rotation = np.array([0.0, 0.0, 0.0, 1.0])
        self.grip_left = 0.0
        self.grip_right = 0.0
        self.tracking_history = []
    
    def update_hand_position(self, hand:  str, position: np.ndarray):
        if hand == 'left':
            self.left_hand_position = position. copy()
        elif hand == 'right':
            self.right_hand_position = position.copy()
        
        self.tracking_history.append({
            'timestamp': len(self.tracking_history),
            'hand': hand,
            'position':  position.copy()
        })
    
    def update_hand_rotation(self, hand: str, quaternion_rot: np.ndarray):
        if hand == 'left':
            self.left_hand_rotation = quaternion_rot.copy()
        elif hand == 'right': 
            self.right_hand_rotation = quaternion_rot.copy()
    
    def update_head_position(self, position: np.ndarray):
        self.head_position = position. copy()
    
    def update_head_rotation(self, quaternion_rot: np.ndarray):
        self.head_rotation = quaternion_rot.copy()
    
    def update_grip_strength(self, hand: str, strength: float):
        strength = np.clip(strength, 0.0, 1.0)
        if hand == 'left':
            self.grip_left = strength
        elif hand == 'right': 
            self.grip_right = strength
    
    def get_hand_velocity(self, hand: str, window_size: int = 5) -> np.ndarray:
        if len(self.tracking_history) < window_size:
            return np.zeros(3)
        
        relevant_history = [h for h in self.tracking_history[-window_size:] if h['hand'] == hand]
        
        if len(relevant_history) < 2:
            return np.zeros(3)
        
        positions = np.array([h['position'] for h in relevant_history])
        velocity = np.diff(positions, axis=0).mean(axis=0)
        
        return velocity
    
    def get_hand_angular_velocity(self, hand: str) -> np.ndarray:
        if hand == 'left': 
            quat = self.left_hand_rotation
        else:
            quat = self. right_hand_rotation
        
        w = quat[3]
        xyz = quat[: 3]
        
        if w == 0:
            angular_vel = xyz
        else:
            angular_vel = 2 * np.arccos(np.clip(w, -1, 1)) * (xyz / (np.linalg.norm(xyz) + 1e-10))
        
        return angular_vel


class AROverlaySystem:
    
    def __init__(self, camera_resolution: Tuple[int, int] = (1920, 1080)):
        self.camera_resolution = camera_resolution
        self. overlay_objects = []
        self.anchor_points = {}
        self.plane_detection_enabled = True
        self.lighting_estimation_enabled = True
        self. current_lighting = {
            'intensity': 1.0,
            'direction': np.array([0.0, 1.0, 0.0]),
            'color': np.array([1.0, 1.0, 1.0])
        }
    
    def add_overlay_object(self, object_id: str, position_4d: np.ndarray,
                          shape: str = 'tesseract', scale: float = 1.0):
        self.overlay_objects. append({
            'id': object_id,
            'position_4d': position_4d. copy(),
            'shape': shape,
            'scale': scale,
            'visible': True,
            'interaction_enabled': True
        })
    
    def create_spatial_anchor(self, anchor_id: str, position_3d: np.ndarray,
                            rotation:  np.ndarray = None):
        if rotation is None:
            rotation = np.array([0, 0, 0, 1])
        
        self.anchor_points[anchor_id] = {
            'position':  position_3d.copy(),
            'rotation': rotation.copy(),
            'children': []
        }
    
    def project_4d_to_ar(self, point_4d: np.ndarray, camera_matrix: np.ndarray,
                        projection_method: str = 'perspective') -> np.ndarray:
        
        if projection_method == 'perspective':
            distance = 2. 0
            w = point_4d[3]
            scale = distance / (distance + w + 1e-10)
            point_3d = point_4d[: 3] * scale
        elif projection_method == 'stereographic':
            pole = 1.0
            if abs(point_4d[3] - pole) < 1e-10:
                point_3d = point_4d[: 3] * 1e6
            else:
                scale = 2 / (pole - point_4d[3])
                point_3d = point_4d[:3] * scale
        else:
            point_3d = point_4d[: 3]
        
        point_homogeneous = np.concatenate([point_3d, [1.0]])
        projected_2d = camera_matrix @ point_homogeneous
        
        if projected_2d[2] > 0:
            return projected_2d[: 2] / projected_2d[2]
        else:
            return np.array([-9999, -9999])
    
    def estimate_lighting_from_environment(self) -> Dict[str, Any]:
        self.current_lighting = {
            'intensity': np.random.uniform(0.7, 1.3),
            'direction': self._random_direction(),
            'color':  self._random_color(),
            'ambient': np.random.uniform(0.2, 0.5)
        }
        return self. current_lighting
    
    def _random_direction(self) -> np.ndarray:
        theta = np.random.uniform(0, 2 * np.pi)
        phi = np.random.uniform(0, np.pi)
        return np.array([
            np.sin(phi) * np.cos(theta),
            np.cos(phi),
            np.sin(phi) * np.sin(theta)
        ])
    
    def _random_color(self) -> np.ndarray:
        return np.array([
            np. random.uniform(0.8, 1.0),
            np.random.uniform(0.8, 1.0),
            np.random.uniform(0.8, 1.0)
        ])
    
    def detect_planes(self) -> List[Dict[str, Any]]:
        detected_planes = []
        
        for i in range(np.random.randint(1, 5)):
            plane = {
                'id': f'plane_{i}',
                'center': np.random.uniform(-5, 5, 3),
                'normal': self._random_direction(),
                'extent': np.random.uniform(0.5, 3. 0, 2)
            }
            detected_planes.append(plane)
        
        return detected_planes


class Advanced4DPhysicsEngine:
    
    def __init__(self, use_gpu: bool = True):
        self.use_gpu = use_gpu and torch.cuda.is_available()
        self.device = torch. device('cuda' if self.use_gpu else 'cpu')
        self.particles = []
        self.time = 0.0
        self.gravity_4d = torch.tensor([0.0, -9.81, 0.0, 0.0], device=self.device)
        self.dark_energy = 0.0001
        self.spacetime_curvature = np.zeros((4, 4, 4, 4))
    
    def add_4d_particle(self, position_4d: np.ndarray, velocity_4d: np.ndarray,
                       mass: float, charge: float = 0.0, spin: float = 0.5):
        
        particle = {
            'position': torch.tensor(position_4d, dtype=torch.float32, device=self.device),
            'velocity': torch.tensor(velocity_4d, dtype=torch. float32, device=self.device),
            'acceleration': torch.zeros(4, device=self.device),
            'mass': mass,
            'charge': charge,
            'spin':  spin,
            'energy': self._calculate_energy(velocity_4d, mass),
            'momentum': torch.tensor(mass * velocity_4d, dtype=torch.float32, device=self.device),
            'angular_momentum': torch.zeros(6, device=self.device)
        }
        
        self.particles.append(particle)
    
    def _calculate_energy(self, velocity_4d:  np.ndarray, mass: float) -> float:
        kinetic = 0.5 * mass * np.dot(velocity_4d, velocity_4d)
        potential = mass * 9.81 * velocity_4d[1]
        return kinetic + potential
    
    def apply_dark_energy_force(self):
        
        for particle in self.particles:
            dark_energy_accel = self.dark_energy * particle['position']
            particle['acceleration'] += dark_energy_accel
    
    def apply_gravitational_force_4d(self):
        
        for i, particle_i in enumerate(self.particles):
            for j, particle_j in enumerate(self.particles):
                if i == j:
                    continue
                
                diff = particle_j['position'] - particle_i['position']
                distance = torch.norm(diff) + 1e-10
                
                G = 6.674e-11
                force_magnitude = G * particle_i['mass'] * particle_j['mass'] / (distance ** 2)
                force = force_magnitude * (diff / distance)
                
                particle_i['acceleration'] += force / particle_i['mass']
    
    def apply_electromagnetic_force_4d(self):
        
        k = 8.988e9
        
        for i, particle_i in enumerate(self.particles):
            if particle_i['charge'] == 0:
                continue
            
            for j, particle_j in enumerate(self.particles):
                if i == j or particle_j['charge'] == 0:
                    continue
                
                diff = particle_j['position'] - particle_i['position']
                distance = torch.norm(diff) + 1e-10
                
                force_magnitude = k * particle_i['charge'] * particle_j['charge'] / (distance ** 2)
                
                cross_product = torch.cross(particle_i['velocity'][:3], particle_j['velocity'][:3])
                magnetic_force = torch.tensor([cross_product[0], cross_product[1], cross_product[2], 0.0],
                                            device=self.device) * 1e-10
                
                total_force = force_magnitude * (diff / distance) + magnetic_force
                particle_i['acceleration'] += total_force / particle_i['mass']
    
    def apply_relativistic_effects(self):
        
        c = 3e8
        
        for particle in self.particles:
            v_magnitude = torch.norm(particle['velocity'])
            
            if v_magnitude > 0:
                gamma = 1.0 / torch.sqrt(1.0 - (v_magnitude ** 2) / (c ** 2) + 1e-10)
                
                particle['relativistic_mass'] = particle['mass'] * gamma
                particle['momentum'] = gamma * particle['mass'] * particle['velocity']
    
    def apply_quantum_tunneling(self, probability: float = 0.001):
        
        for particle in self.particles:
            if np.random.random() < probability:
                tunnel_offset = np.random.normal(0, 0.1, 4)
                particle['position'] += torch.tensor(tunnel_offset, dtype=torch.float32, device=self.device)
    
    def calculate_spacetime_curvature(self):
        
        for i in range(4):
            for j in range(4):
                for k in range(4):
                    for l in range(4):
                        total_mass_energy = sum(p['mass'] for p in self. particles)
                        self.spacetime_curvature[i, j, k, l] = total_mass_energy / (8 * np.pi)
    
    def apply_spacetime_curvature_effects(self):
        
        for particle in self.particles:
            curvature_force = np.zeros(4)
            
            for i in range(4):
                for j in range(4):
                    curvature_force[i] += self.spacetime_curvature[i, j, j, i] * particle['position'][j]. cpu().numpy()
            
            particle['acceleration'] += torch.tensor(curvature_force * 0.001, dtype=torch.float32, device=self.device)
    
    def simulate_wormhole_traversal(self, particle_idx: int, wormhole_entry:  np.ndarray,
                                   wormhole_exit: np.ndarray, traversal_time: float = 1.0):
        
        particle = self.particles[particle_idx]
        
        steps = int(traversal_time * 60)
        
        for step in range(steps):
            t = step / steps
            
            throat_position = (1 - t) * wormhole_entry + t * wormhole_exit
            
            particle['position'] = torch.tensor(throat_position, dtype=torch.float32, device=self.device)
            
            tidal_force = 100.0 * (1 - np.abs(2 * t - 1))
            
            radial_direction = (particle['position'][:3] / (torch.norm(particle['position'][:3]) + 1e-10))
            tidal_acceleration = tidal_force * radial_direction
            
            particle['acceleration'][:3] += tidal_acceleration
    
    def update(self, dt: float = 0.016):
        
        self.apply_gravitational_force_4d()
        self.apply_electromagnetic_force_4d()
        self.apply_dark_energy_force()
        self.apply_relativistic_effects()
        self.apply_quantum_tunneling(probability=0.0001)
        self.calculate_spacetime_curvature()
        self.apply_spacetime_curvature_effects()
        
        for particle in self.particles:
            particle['velocity'] += particle['acceleration'] * dt
            particle['position'] += particle['velocity'] * dt
            particle['acceleration'] *= 0.0
            
            particle['momentum'] = torch.tensor(particle['mass'], device=self.device) * particle['velocity']
            
            particle['energy'] = 0.5 * particle['mass'] * torch.dot(particle['velocity'], particle['velocity'])
        
        self.time += dt
    
    def get_total_energy(self) -> float:
        total = 0.0
        for particle in self.particles:
            total += float(particle['energy'])
        return total
    
    def get_total_momentum(self) -> np.ndarray:
        total = np.zeros(4)
        for particle in self. particles:
            total += particle['momentum']. cpu().numpy()
        return total
    
    def detect_black_hole_formation(self, threshold_density: float = 1e20) -> List[Dict[str, Any]]: 
        
        black_holes = []
        
        for i, particle_i in enumerate(self.particles):
            mass_concentration = particle_i['mass']
            volume_estimate = (4/3) * np.pi * (0.1 ** 3)
            density = mass_concentration / (volume_estimate + 1e-20)
            
            if density > threshold_density:
                schwarzschild_radius = 2 * 6.674e-11 * particle_i['mass'] / (3e8 ** 2)
                
                black_holes.append({
                    'center': particle_i['position']. cpu().numpy(),
                    'mass': particle_i['mass'],
                    'schwarzschild_radius': schwarzschild_radius,
                    'particle_index': i
                })
        
        return black_holes
    
    def apply_hawking_radiation(self):
        
        black_holes = self.detect_black_hole_formation()
        
        for bh in black_holes:
            temp = (1. 227e23) / bh['schwarzschild_radius']
            
            radiation_power = (6.172e21) / (bh['schwarzschild_radius'] ** 2)
            
            evaporation_time = (5.367e67) * (bh['schwarzschild_radius'] ** 3)
            
            mass_loss = radiation_power / (3e8 ** 2)
            self.particles[bh['particle_index']]['mass'] -= mass_loss * 0.000001