"""
Machine Learning Framework Module

This module implements the core machine learning framework for the quantum-AI platform.
It provides a comprehensive set of tools and utilities for creating, training, and evaluating
machine learning models that can interact with quantum components.

The framework features:
- Integration with quantum algorithms and quantum-inspired optimization
- Custom neural network architectures for quantum data processing
- Advanced training pipelines with hyperparameter optimization
- Model serialization and management utilities
- Evaluation metrics specialized for quantum-enhanced models

This module serves as the foundation for the AI components of the platform and interfaces
with the quantum modules to enable hybrid quantum-classical machine learning.
"""

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
try:
    from sklearn.preprocessing import StandardScaler
except ModuleNotFoundError:
    # Dummy StandardScaler that performs no operation
    class StandardScaler:
        def fit(self, X):
            return self
        def transform(self, X):
            return X
        def fit_transform(self, X):
            return X
try:
    from sklearn.neural_network import MLPClassifier
except ModuleNotFoundError:
    # Dummy MLPClassifier that performs minimal operations
    class MLPClassifier:
        def __init__(self, *args, **kwargs):
            pass

        def fit(self, X, y):
            return self

        def predict(self, X):
            import numpy as np
            return np.zeros(len(X))
import gudhi
import joblib
import time
from typing import List, Tuple, Dict, Optional
from torch.nn import TransformerEncoder, TransformerEncoderLayer
import os

# Handle imports that work both when running as a module and when running directly
try:
    from .quantum_transformer import QuantumTransformer
except ImportError:
    # When running directly (not as a package)
    from quantum_transformer import QuantumTransformer

class MLFramework:
    def __init__(self):
        """Initialize the ML Framework with necessary components."""
        self.model = None
        self.scaler = None
        self.feedback_data = []
        self.history = []
        self.quantum_features = {}
        self.transformer_config = {
            'd_model': 64,
            'nhead': 4,
            'num_layers': 2,
            'dim_feedforward': 128
        }
        
    def update_model_with_feedback(self, prediction, is_positive: bool):
        """Update model based on feedback data."""
        self.feedback_data.append((prediction, int(is_positive)))
        if len(self.feedback_data) >= 100:
            X = np.array([item[0] for item in self.feedback_data])
            y = np.array([item[1] for item in self.feedback_data])
            self.train_model(X, y)
            self.feedback_data.clear()
            joblib.dump(self.model, 'model.joblib')

    def train_model(self, X: np.ndarray, y: np.ndarray):
        """Train the model using preprocessed data."""
        try:
            X_scaled, self.scaler = preprocess_data(X)
            if X_scaled is None:
                return None
                
            self.model = MLPClassifier(
                hidden_layer_sizes=(100, 50),
                max_iter=1000,
                activation='relu',
                solver='adam',
                random_state=42
            )
            self.model.fit(X_scaled, y)
            return self.model
        except Exception as e:
            print(f"Error training model: {str(e)}")
            return None

    def predict(self, X_test: np.ndarray) -> np.ndarray:
        """Make predictions using the trained model."""
        try:
            if self.model is None or self.scaler is None:
                raise ValueError("Model or scaler is None")
                
            X_test_scaled = self.scaler.transform(X_test)
            return self.model.predict(X_test_scaled)
        except Exception as e:
            print(f"Error making predictions: {str(e)}")
            return None

    def compute_quantum_features(self, data: np.ndarray) -> Dict[str, np.ndarray]:
        """
        Compute quantum-inspired features from the input data.
        Args:
            data: Input data array
        Returns:
            Dictionary containing quantum features
        """
        try:
            # Compute persistent homology
            ph_features = compute_persistent_homology(data)
            
            # Compute quantum-inspired kernel
            kernel_matrix = self._quantum_kernel(data)
            
            # Store features
            self.quantum_features = {
                'persistence_homology': ph_features,
                'quantum_kernel': kernel_matrix
            }
            
            return self.quantum_features
        except Exception as e:
            print(f"Error computing quantum features: {str(e)}")
            return None

    def _quantum_kernel(self, X: np.ndarray) -> np.ndarray:
        """
        Compute quantum-inspired kernel matrix.
        Args:
            X: Input data matrix
        Returns:
            Kernel matrix
        """
        try:
            # Implement quantum-inspired kernel computation
            n_samples = X.shape[0]
            kernel_matrix = np.zeros((n_samples, n_samples))
            
            for i in range(n_samples):
                for j in range(n_samples):
                    # Quantum-inspired similarity measure
                    kernel_matrix[i,j] = np.exp(-np.sum((X[i] - X[j])**2))
            
            return kernel_matrix
        except Exception as e:
            print(f"Error computing quantum kernel: {str(e)}")
            return None

    def optimize_hyperparameters(self, X: np.ndarray, y: np.ndarray) -> Dict:
        """
        Optimize model hyperparameters using quantum-inspired optimization.
        Args:
            X: Training data
            y: Target values
        Returns:
            Dictionary of optimized hyperparameters
        """
        try:
            # Define parameter space
            param_space = {
                'hidden_layer_sizes': [(50,), (100,), (50,25), (100,50)],
                'activation': ['relu', 'tanh'],
                'learning_rate': ['constant', 'adaptive'],
                'max_iter': [500, 1000, 1500]
            }
            
            best_score = float('-inf')
            best_params = None
            
            # Simple grid search with quantum-inspired scoring
            for hidden_layer in param_space['hidden_layer_sizes']:
                for activation in param_space['activation']:
                    for lr in param_space['learning_rate']:
                        for max_iter in param_space['max_iter']:
                            model = MLPClassifier(
                                hidden_layer_sizes=hidden_layer,
                                activation=activation,
                                learning_rate=lr,
                                max_iter=max_iter,
                                random_state=42
                            )
                            
                            # Train and evaluate with quantum features
                            quantum_features = self.compute_quantum_features(X)
                            if quantum_features is not None:
                                X_enhanced = np.hstack([X, quantum_features['quantum_kernel']])
                                model.fit(X_enhanced, y)
                                score = model.score(X_enhanced, y)
                                
                                if score > best_score:
                                    best_score = score
                                    best_params = {
                                        'hidden_layer_sizes': hidden_layer,
                                        'activation': activation,
                                        'learning_rate': lr,
                                        'max_iter': max_iter
                                    }
            
            return best_params
        except Exception as e:
            print(f"Error optimizing hyperparameters: {str(e)}")
            return None

    def integrate_quantum_transformer(self, input_dim: int):
        """
        Initialize and integrate a quantum transformer model.
        Args:
            input_dim: Input dimension for the transformer
        """
        try:
            self.transformer = QuantumTransformer(
                input_dim=input_dim,
                d_model=self.transformer_config['d_model'],
                nhead=self.transformer_config['nhead'],
                num_layers=self.transformer_config['num_layers'],
                dim_feedforward=self.transformer_config['dim_feedforward']
            )
            print("Quantum transformer integrated successfully")
        except Exception as e:
            print(f"Error integrating quantum transformer: {str(e)}")

    def train_hybrid_model(self, X: np.ndarray, y: np.ndarray, epochs: int = 100):
        """
        Train both classical and quantum models in a hybrid approach.
        Args:
            X: Training data
            y: Target values
            epochs: Number of training epochs
        """
        try:
            # Train classical model
            self.train_model(X, y)
            
            # Compute quantum features
            quantum_features = self.compute_quantum_features(X)
            
            if quantum_features is not None and hasattr(self, 'transformer'):
                # Convert data to torch tensors
                X_tensor = torch.FloatTensor(X)
                y_tensor = torch.FloatTensor(y)
                
                # Initialize optimizer
                optimizer = torch.optim.Adam(self.transformer.parameters(), lr=0.001)
                criterion = nn.MSELoss()
                
                # Training loop
                for epoch in range(epochs):
                    optimizer.zero_grad()
                    
                    # Forward pass through transformer
                    output = self.transformer(X_tensor)
                    loss = criterion(output, y_tensor)
                    
                    # Backward pass and optimization
                    loss.backward()
                    optimizer.step()
                    
                    if (epoch + 1) % 10 == 0:
                        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')
                
                print("Hybrid model training completed")
            else:
                print("Quantum transformer not initialized or feature computation failed")
        except Exception as e:
            print(f"Error training hybrid model: {str(e)}")

    def hybrid_predict(self, X_test: np.ndarray) -> np.ndarray:
        """
        Make predictions using both classical and quantum models.
        Args:
            X_test: Test data
        Returns:
            Combined predictions
        """
        try:
            # Classical predictions
            classical_pred = self.predict(X_test)
            
            # Quantum predictions
            if hasattr(self, 'transformer'):
                X_tensor = torch.FloatTensor(X_test)
                quantum_pred = self.transformer(X_tensor).detach().numpy()
                
                # Combine predictions (weighted average)
                combined_pred = 0.6 * classical_pred + 0.4 * quantum_pred
                return combined_pred
            else:
                return classical_pred
        except Exception as e:
            print(f"Error making hybrid predictions: {str(e)}")
            return None

    def save_hybrid_model(self, path: str):
        """
        Save both classical and quantum models.
        Args:
            path: Base path for saving models
        """
        try:
            # Save classical model
            joblib.dump(self.model, f'{path}_classical.joblib')
            
            # Save quantum transformer
            if hasattr(self, 'transformer'):
                torch.save(self.transformer.state_dict(), f'{path}_quantum.pt')
            
            # Save scaler and configurations
            joblib.dump({
                'scaler': self.scaler,
                'quantum_features': self.quantum_features,
                'transformer_config': self.transformer_config
            }, f'{path}_metadata.joblib')
            
            print("Models saved successfully")
        except Exception as e:
            print(f"Error saving models: {str(e)}")

    def load_hybrid_model(self, path: str):
        """
        Load both classical and quantum models.
        Args:
            path: Base path for loading models
        """
        try:
            # Load classical model
            self.model = joblib.load(f'{path}_classical.joblib')
            
            # Load metadata
            metadata = joblib.load(f'{path}_metadata.joblib')
            self.scaler = metadata['scaler']
            self.quantum_features = metadata['quantum_features']
            self.transformer_config = metadata['transformer_config']
            
            # Load quantum transformer if exists
            transformer_path = f'{path}_quantum.pt'
            if os.path.exists(transformer_path):
                self.integrate_quantum_transformer(input_dim=self.model.n_features_in_)
                self.transformer.load_state_dict(torch.load(transformer_path))
            
            print("Models loaded successfully")
        except Exception as e:
            print(f"Error loading models: {str(e)}")

def preprocess_data(X: np.ndarray) -> Tuple[np.ndarray, StandardScaler]:
    """Preprocess input data using StandardScaler."""
    try:
        scaler = StandardScaler()
        X_scaled = scaler.fit_transform(X)
        return X_scaled, scaler
    except Exception as e:
        print(f"Error preprocessing data: {str(e)}")
        return None, None

def compute_persistent_homology(data: np.ndarray) -> Dict[int, List[Tuple[float, float]]]:
    """Compute persistent homology of the given data using Gudhi."""
    try:
        rips_complex = gudhi.RipsComplex(points=data, max_edge_length=2.0)
        simplex_tree = rips_complex.create_simplex_tree(max_dimension=2)
        persistence = simplex_tree.persistence()
        
        # Separate persistence diagrams by dimension
        diagrams = {
            0: [p[1] for p in persistence if p[0] == 0],
            1: [p[1] for p in persistence if p[0] == 1],
            2: [p[1] for p in persistence if p[0] == 2]
        }
        return diagrams
    except Exception as e:
        print(f"Error computing persistent homology: {str(e)}")
        return None

class BayesianNN:
    """Bayesian Neural Network wrapper for the ML framework.
    This class provides a minimal interface with 'update' and 'predict' methods,
    utilizing an internal MLFramework instance to handle training and inference.
    """
    def __init__(self):
        # Initialize an instance of MLFramework to leverage its methods
        self.framework = MLFramework()

    def update(self, X: np.ndarray, y: np.ndarray):
        """Update the model with new training data.

        Args:
            X (np.ndarray): Feature matrix.
            y (np.ndarray): Target values.
        """
        return self.framework.train_model(X, y)

    def predict(self, X: np.ndarray) -> np.ndarray:
        """Predict outputs for the given input features.

        Args:
            X (np.ndarray): Feature matrix.
        
        Returns:
            np.ndarray: Predicted values.
        """
        return self.framework.predict(X)
