import numpy as np
import torch
import torch.nn as nn
from typing import Tuple, List, Optional, Callable, Dict, Any
from sklearn.neural_network import MLPRegressor
from scipy.integrate import odeint

class PredictiveAI4D: 
    
    def __init__(self, use_gpu: bool = False):
        self.device = torch.device('cuda' if use_gpu and torch.cuda.is_available() else 'cpu')
        self.model = None
        self.history = []
    
    def build_neural_network(self, input_dim: int = 8, hidden_dims: List[int] = None,
                            output_dim: int = 4):
        if hidden_dims is None:
            hidden_dims = [256, 512, 256]
        
        class Net4D(nn.Module):
            def __init__(self, input_dim, hidden_dims, output_dim):
                super().__init__()
                layers = []
                prev_dim = input_dim
                
                for hidden_dim in hidden_dims: 
                    layers.append(nn. Linear(prev_dim, hidden_dim))
                    layers.append(nn.ReLU())
                    layers.append(nn.BatchNorm1d(hidden_dim))
                    prev_dim = hidden_dim
                
                layers.append(nn.Linear(prev_dim, output_dim))
                
                self.network = nn.Sequential(*layers)
            
            def forward(self, x):
                return self.network(x)
        
        self.model = Net4D(input_dim, hidden_dims, output_dim).to(self.device)
        return self.model
    
    def predict_trajectory(self, initial_state: np.ndarray,
                          time_steps: int = 100) -> List[np.ndarray]:
        
        trajectory = [initial_state.copy()]
        current_state = torch.tensor(initial_state, dtype=torch.float32).to(self.device)
        
        for _ in range(time_steps):
            with torch.no_grad():
                next_state = self.model(current_state. unsqueeze(0)).squeeze(0)
            
            trajectory.append(next_state.cpu().numpy())
            current_state = next_state
        
        return trajectory
    
    def suggest_optimal_transformation(self, current_shape: np.ndarray,
                                      target_shape: np.ndarray) -> Tuple[np.ndarray, float]: 
        
        def shape_distance(transform):
            transformed = self._apply_transform(current_shape, transform)
            return np.linalg.norm(transformed - target_shape)
        
        from scipy.optimize import minimize
        initial_guess = np.zeros(7)
        
        result = minimize(shape_distance, initial_guess, method='BFGS')
        
        return result.x, float(result.fun)
    
    def _apply_transform(self, shape: np.ndarray, transform: np.ndarray) -> np.ndarray:
        angles = transform[:4]
        translation = transform[4:7]
        
        rotated = shape.copy()
        for angle in angles:
            matrix = self._rotation_matrix_4d(angle)
            rotated = rotated @ matrix.T
        
        return rotated + translation


class TimeWarpVisualizer: 
    
    def __init__(self):
        self.time_parameter = 0.0
    
    def generate_time_evolved_shape(self, base_vertices:  np.ndarray,
                                   time:  float,
                                   evolution_function: Callable) -> np.ndarray:
        
        evolved = []
        for vertex in base_vertices:
            new_vertex = evolution_function(vertex, time)
            evolved.append(new_vertex)
        
        return np.array(evolved)
    
    def hyperbolic_evolution(self, vertex: np.ndarray, time: float) -> np.ndarray:
        factor = np.cosh(time / 10. 0)
        return vertex * factor
    
    def spiral_evolution(self, vertex: np.ndarray, time: float) -> np.ndarray:
        angle = time * 0.5
        x, y, z, w = vertex
        
        new_x = x * np.cos(angle) - y * np.sin(angle)
        new_y = x * np.sin(angle) + y * np.cos(angle)
        new_z = z + time * 0.1
        new_w = w * np.cos(angle * 0.5)
        
        return np.array([new_x, new_y, new_z, new_w])
    
    def wave_evolution(self, vertex: np.ndarray, time: float) -> np.ndarray:
        x, y, z, w = vertex
        
        wave = 0.5 * np.sin(x * np.pi + time) * np.cos(y * np. pi + time)
        
        return np.array([
            x + wave * 0.1,
            y + wave * 0.1,
            z + wave * 0.1,
            w + wave * 0.2
        ])


class PatternRecognition4D:
    
    def __init__(self):
        self.classifier = None
    
    def detect_symmetry(self, vertices: np.ndarray,
                       tolerance: float = 1e-6) -> Dict[str, bool]:
        
        symmetries = {
            'mirror_xy': self._check_mirror_symmetry(vertices, [0, 1], tolerance),
            'mirror_xz': self._check_mirror_symmetry(vertices, [0, 2], tolerance),
            'mirror_xw': self._check_mirror_symmetry(vertices, [0, 3], tolerance),
            'mirror_yz': self._check_mirror_symmetry(vertices, [1, 2], tolerance),
            'mirror_yw': self._check_mirror_symmetry(vertices, [1, 3], tolerance),
            'mirror_zw': self._check_mirror_symmetry(vertices, [2, 3], tolerance),
            'rotational': self._check_rotational_symmetry(vertices, tolerance),
        }
        
        return symmetries
    
    def _check_mirror_symmetry(self, vertices: np.ndarray,
                              axes: List[int],
                              tolerance: float) -> bool:
        
        reflected = vertices.copy()
        for axis in axes:
            reflected[: , axis] = -reflected[:, axis]
        
        distances = np.min(np.linalg.norm(vertices[: , np.newaxis] - reflected[np.newaxis, :], axis=2),
                          axis=1)
        
        return np.all(distances < tolerance)
    
    def _check_rotational_symmetry(self, vertices: np.ndarray,
                                  tolerance: float) -> bool:
        
        center = np.mean(vertices, axis=0)
        centered = vertices - center
        
        angle = np.pi / 2
        cos_a, sin_a = np.cos(angle), np.sin(angle)
        
        rotation = np.array([
            [cos_a, -sin_a, 0, 0],
            [sin_a, cos_a, 0, 0],
            [0, 0, cos_a, -sin_a],
            [0, 0, sin_a, cos_a]
        ])
        
        rotated = centered @ rotation. T
        
        distances = np.min(np.linalg.norm(centered[: , np.newaxis] - rotated[np.newaxis, : ], axis=2),
                          axis=1)
        
        return np.all(distances < tolerance)