import time
import logging
import numpy as np
import torch
import tensorflow as tf
from Bio.Seq import Seq
from NeuroFlex.utils.utils import tokenize_text
from NeuroFlex.utils.descriptive_statistics import preprocess_data
from NeuroFlex.utils.logging_config import setup_logging
from .jax.pytorch_module_converted import PyTorchModel
from .tensorflow.tensorflow_module import TensorFlowModel
from .pytorch.pytorch_module import PyTorchModel as OriginalPyTorchModel
from NeuroFlex.quantum_neural_networks.quantum_nn_module import QuantumNeuralNetwork
from NeuroFlex.scientific_domains.bioinformatics.bioinformatics_integration import BioinformaticsIntegration
from NeuroFlex.ai_ethics.scikit_bio_integration import ScikitBioIntegration
from NeuroFlex.scientific_domains.bioinformatics.ete_integration import ETEIntegration
from NeuroFlex.scientific_domains.bioinformatics.alphafold_integration import AlphaFoldIntegration
from NeuroFlex.scientific_domains.xarray_integration import XarrayIntegration
from NeuroFlex.generative_models.ddpm import DDPM
from NeuroFlex.ai_ethics.advanced_security_agent import AdvancedSecurityAgent

# Set up logging
logger = setup_logging()

def load_bioinformatics_data(file_path, skip_visualization=False):
    """
    Load and process bioinformatics data from a file.

    Args:
        file_path (str): Path to the sequence file.
        skip_visualization (bool): If True, skip tree visualization. Default is False.

    Returns:
        dict: A dictionary containing processed bioinformatics data.
    """
    try:
        logging.info("Initializing integration classes...")
        bio_integration = BioinformaticsIntegration()
        scikit_bio_integration = ScikitBioIntegration()
        ete_integration = ETEIntegration()
        alphafold_integration = AlphaFoldIntegration()
        xarray_integration = XarrayIntegration()

        logging.info(f"Reading sequence file: {file_path}")
        sequences = bio_integration.read_sequence_file(file_path)
        processed_sequences = bio_integration.process_sequences(sequences)
        sequence_summaries = bio_integration.sequence_summary(processed_sequences)

        # Prepare ScikitBio data
        logging.info("Preparing DNA sequences for ScikitBio processing...")
        dna_sequences = [str(seq.seq) for seq in sequences if isinstance(seq.seq, Seq) and set(seq.seq.upper()).issubset({'A', 'C', 'G', 'T', 'N'})]
        logging.info(f"Number of DNA sequences: {len(dna_sequences)}")

        alignments = []
        for i in range(len(dna_sequences)):
            for j in range(i+1, len(dna_sequences)):
                try:
                    aligned_seq1, aligned_seq2, score = scikit_bio_integration.align_dna_sequences(dna_sequences[i], dna_sequences[j])
                    if aligned_seq1 is not None and aligned_seq2 is not None:
                        alignments.append((aligned_seq1, aligned_seq2, score))
                except Exception as e:
                    logging.warning(f"Error aligning sequences {i} and {j}: {str(e)}")

        try:
            msa = scikit_bio_integration.msa_maker(dna_sequences)
            logging.info("Multiple sequence alignment completed successfully")
        except Exception as e:
            logging.error(f"Error in multiple sequence alignment: {str(e)}")
            msa = None

        logging.info("Calculating GC contents...")
        gc_contents = [scikit_bio_integration.dna_gc_content(seq) for seq in dna_sequences]

        # Prepare ETE data
        logging.info("Preparing ETE data...")
        newick_string = "(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);"
        tree = ete_integration.create_tree(newick_string)
        tree_stats = ete_integration.get_tree_statistics(tree)

        if not skip_visualization:
            try:
                ete_integration.visualize_tree(tree, "output_tree.png")
                logging.info("Tree visualization completed successfully")
            except Exception as e:
                logging.warning(f"Tree visualization failed: {str(e)}")

        # Prepare AlphaFold data
        logging.info("Preparing AlphaFold data...")
        alphafold_structures, alphafold_plddt_scores, alphafold_pae_scores = [], [], []
        try:
            alphafold_integration.setup_model({'max_recycling': 3})
            protein_sequences = [seq for seq in processed_sequences if not isinstance(seq.seq, Seq) or not set(seq.seq.upper()).issubset({'A', 'C', 'G', 'T', 'N'})]
            for seq in protein_sequences:
                alphafold_integration.prepare_features(str(seq.seq))
                structure = alphafold_integration.predict_structure()
                alphafold_structures.append(structure)
                plddt_scores = alphafold_integration.get_plddt_scores()
                pae_scores = alphafold_integration.get_predicted_aligned_error()
                alphafold_plddt_scores.append(plddt_scores)
                alphafold_pae_scores.append(pae_scores)
            logging.info("AlphaFold predictions completed successfully")
        except Exception as e:
            logging.error(f"Error in AlphaFold integration: {str(e)}")

        # Print average scores
        if alphafold_plddt_scores and alphafold_pae_scores:
            logging.info(f"Average pLDDT score: {np.mean([np.mean(scores) for scores in alphafold_plddt_scores])}")
            logging.info(f"Average predicted aligned error: {np.mean([np.mean(scores) for scores in alphafold_pae_scores])}")

        # Create Xarray datasets
        logging.info("Creating Xarray datasets...")
        try:
            gc_content_dataset = xarray_integration.create_dataset('gc_content',
                                              {'gc': np.array(gc_contents)},
                                              {'sequence': np.arange(len(gc_contents))})
            logging.info("Successfully created 'gc_content' dataset")

            tree_stats_dataset = xarray_integration.create_dataset('tree_stats',
                                              tree_stats,
                                              {'stat': list(tree_stats.keys())})
            logging.info("Successfully created 'tree_stats' dataset")

            # Perform operations on datasets
            gc_mean = xarray_integration.apply_operation('gc_content', 'mean')
            tree_stats_max = xarray_integration.apply_operation('tree_stats', 'max')
            logging.info(f"Dataset operations completed. GC mean: {gc_mean}, Tree stats max: {tree_stats_max}")

            # Merge datasets
            merged_dataset = xarray_integration.merge_datasets(['gc_content', 'tree_stats'])
            if merged_dataset is None:
                raise ValueError("Failed to merge datasets")
            logging.info("Successfully merged datasets")

            # Register the merged dataset
            xarray_integration.datasets['merged_bio_data'] = merged_dataset
            logging.info("Successfully registered merged dataset")

            # Save merged dataset
            import os
            save_dir = os.path.dirname(file_path)
            save_path = os.path.join(save_dir, 'merged_bio_data.nc')

            # Ensure the directory exists
            os.makedirs(save_dir, exist_ok=True)

            try:
                xarray_integration.save_dataset('merged_bio_data', save_path)
                logging.info(f"Successfully saved merged dataset to {save_path}")
            except Exception as save_error:
                logging.error(f"Failed to save merged dataset: {str(save_error)}")
                logging.debug(f"Save directory: {save_dir}")
                logging.debug(f"Save path: {save_path}")
                logging.debug(f"Directory exists: {os.path.exists(save_dir)}")
                logging.debug(f"Directory is writable: {os.access(save_dir, os.W_OK)}")
                raise IOError(f"Failed to save merged dataset to {save_path}: {str(save_error)}")

            # Verify that the file was actually created
            if not os.path.exists(save_path):
                logging.error(f"File not found at {save_path} after saving attempt")
                logging.debug(f"Directory contents: {os.listdir(save_dir)}")
                raise IOError(f"Failed to create file at {save_path}")

            # Attempt to load the saved dataset to ensure it's valid
            try:
                loaded_dataset = xarray_integration.load_dataset(save_path)
                if loaded_dataset is None:
                    logging.error("Loaded dataset is None")
                    raise ValueError("Loaded dataset is None")
                logging.info("Successfully verified the saved dataset")
                # Ensure the dataset is registered in memory
                xarray_integration.datasets['merged_bio_data'] = loaded_dataset
            except Exception as load_error:
                logging.error(f"Failed to load the saved dataset: {str(load_error)}")
                logging.debug(f"File size: {os.path.getsize(save_path) if os.path.exists(save_path) else 'File does not exist'}")
                raise IOError(f"Failed to load the saved dataset from {save_path}: {str(load_error)}")

        except Exception as e:
            logging.error(f"Error in Xarray operations: {str(e)}")
            raise

        # Verify that the merged dataset exists in memory
        if 'merged_bio_data' not in xarray_integration.datasets:
            raise ValueError("'merged_bio_data' dataset not found in memory after merging and saving")

        logging.info("Bioinformatics data processing completed successfully")
        return {
            'sequences': sequences,
            'processed_sequences': processed_sequences,
            'sequence_summaries': sequence_summaries,
            'alignments': alignments,
            'msa': msa,
            'gc_contents': gc_contents,
            'phylogenetic_tree': tree,
            'tree_statistics': tree_stats,
            'alphafold_structures': alphafold_structures,
            'alphafold_plddt_scores': alphafold_plddt_scores,
            'alphafold_pae_scores': alphafold_pae_scores,
            'bio_integration': bio_integration,
            'scikit_bio_integration': scikit_bio_integration,
            'ete_integration': ete_integration,
            'alphafold_integration': alphafold_integration,
            'xarray_integration': xarray_integration,
            'merged_dataset': merged_dataset
        }
    except Exception as e:
        logging.error(f"Critical error in load_bioinformatics_data: {str(e)}")
        raise

# Define your model
class SelfCuringAlgorithm:
    def __init__(self, model):
        self.model = model
        self.learning_rate = 0.001
        self.performance_history = []

    def diagnose(self):
        issues = []
        if not hasattr(self.model, 'is_trained') or not self.model.is_trained:
            issues.append("Model is not trained")
        if not hasattr(self.model, 'performance') or self.model.performance < 0.8:
            issues.append("Model performance is below threshold")
        if not hasattr(self.model, 'last_update') or (time.time() - self.model.last_update > 86400):
            issues.append("Model hasn't been updated in 24 hours")
        if hasattr(self.model, 'gradient_norm') and self.model.gradient_norm > 10:
            issues.append("Gradient explosion detected")
        if len(self.performance_history) > 5 and all(p < 0.01 for p in self.performance_history[-5:]):
            issues.append("Model is stuck in local minimum")
        return issues

    def heal(self, issues):
        for issue in issues:
            if issue == "Model is not trained":
                self.train_model()
            elif issue == "Model performance is below threshold":
                self.improve_model()
            elif issue == "Model hasn't been updated in 24 hours":
                self.update_model()
            elif issue == "Gradient explosion detected":
                self.handle_gradient_explosion()
            elif issue == "Model is stuck in local minimum":
                self.escape_local_minimum()

    def train_model(self):
        print("Training model...")
        # Actual training logic would go here
        self.model.is_trained = True
        self.model.last_update = time.time()
        self.update_performance()

    def improve_model(self):
        print("Improving model performance...")
        # Logic to improve model performance would go here
        self.model.performance = min(self.model.performance * 1.1, 1.0)  # Increase performance by 10%, max 1.0
        self.update_performance()

    def update_model(self):
        print("Updating model...")
        # Logic to update the model with new data would go here
        self.model.last_update = time.time()
        self.update_performance()

    def handle_gradient_explosion(self):
        print("Handling gradient explosion...")
        self.learning_rate *= 0.5  # Reduce learning rate
        # Additional logic to handle gradient explosion (e.g., gradient clipping)

    def escape_local_minimum(self):
        print("Attempting to escape local minimum...")
        self.learning_rate *= 2  # Increase learning rate
        # Additional logic to escape local minimum (e.g., add noise to parameters)

    def update_performance(self):
        # Update performance history
        self.performance_history.append(self.model.performance)
        if len(self.performance_history) > 100:
            self.performance_history.pop(0)

    def adjust_learning_rate(self):
        # Implement adaptive learning rate adjustment
        if len(self.performance_history) > 10:
            recent_performance = self.performance_history[-10:]
            if all(x < y for x, y in zip(recent_performance, recent_performance[1:])):
                self.learning_rate *= 1.1  # Increase learning rate if consistently improving
            elif all(x > y for x, y in zip(recent_performance, recent_performance[1:])):
                self.learning_rate *= 0.9  # Decrease learning rate if consistently worsening
        return self.learning_rate

class NeuroFlex:
    def __init__(self, config):
        self.config = config
        self.logger = setup_logging()
        self.features = config.get('CORE_MODEL_FEATURES', [])
        self.use_cnn = config.get('USE_CNN', False)
        self.use_rnn = config.get('USE_RNN', False)
        self.use_gan = config.get('USE_GAN', False)
        self.fairness_constraint = config.get('FAIRNESS_CONSTRAINT', None)
        self.use_quantum = config.get('USE_QUANTUM', False)
        self.use_alphafold = config.get('USE_ALPHAFOLD', False)
        self.backend = config.get('BACKEND', 'pytorch')
        self.bioinformatics_data = None
        self.core_model = None
        self.quantum_model = None
        self.ethical_framework = None
        self.explainable_ai = None
        self.bci_processor = None
        self.consciousness_sim = None
        self.alphafold = None
        self.math_solver = None
        self.edge_optimizer = None
        self.security_agent = None
        self.optimizer = None
        self.loss_fn = None

    def _setup_core_model(self):
        input_shape = self.config.get('INPUT_SHAPE', (28, 28, 1))
        input_dim = int(np.prod(input_shape))
        if self.backend == 'pytorch':
            self.core_model = PyTorchModel(
                input_dim=input_dim,
                output_dim=self.config.get('OUTPUT_DIM', 10),
                hidden_layers=self.config.get('HIDDEN_LAYERS', [64, 32])
            )
            self.optimizer = torch.optim.Adam(self.core_model.parameters(), lr=self.config.get('LEARNING_RATE', 0.001))
            self.loss_fn = torch.nn.CrossEntropyLoss()
        elif self.backend == 'tensorflow':
            self.core_model = TensorFlowModel(
                input_shape=input_shape,
                output_dim=self.config.get('OUTPUT_DIM', 10),
                hidden_layers=self.config.get('HIDDEN_LAYERS', [64, 32]),
                use_cnn=self.use_cnn,
                use_rnn=self.use_rnn
            )
            self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.config.get('LEARNING_RATE', 0.001))
            self.loss_fn = tf.keras.losses.CategoricalCrossentropy()
        else:
            raise ValueError(f"Unsupported backend: {self.backend}")

        self.logger.info(f"Core model set up with backend: {self.backend}")

    def _setup_quantum_model(self):
        if self.use_quantum:
            self.quantum_model = QuantumNeuralNetwork(
                n_qubits=self.config.get('QUANTUM_N_QUBITS', 4),
                n_layers=self.config.get('QUANTUM_N_LAYERS', 2)
            )
            self.logger.info("Quantum model set up")

    def _setup_ethical_framework(self):
        from NeuroFlex.ai_ethics.ethical_framework import EthicalFramework
        self.ethical_framework = EthicalFramework()
        self.logger.info("Ethical framework set up")

    def _setup_explainable_ai(self):
        from NeuroFlex.ai_ethics.explainable_ai import ExplainableAI
        self.explainable_ai = ExplainableAI()
        self.explainable_ai.set_model(self.core_model)
        self.logger.info("Explainable AI set up")

    def _setup_bci_processor(self):
        if self.config.get('USE_BCI', False):
            from NeuroFlex.bci_integration.bci_processing import BCIProcessor
            self.bci_processor = BCIProcessor(
                sampling_rate=self.config.get('BCI_SAMPLING_RATE', 250),
                num_channels=self.config.get('BCI_NUM_CHANNELS', 32)
            )
            self.logger.info("BCI processor set up")

    def _setup_consciousness_sim(self):
        if self.config.get('USE_CONSCIOUSNESS_SIM', False):
            from NeuroFlex.cognitive_architectures.consciousness_simulation import ConsciousnessSimulation
            self.consciousness_sim = ConsciousnessSimulation(
                features=self.config.get('CONSCIOUSNESS_SIM_FEATURES', [64, 32])
            )
            self.logger.info("Consciousness simulation set up")

    def _setup_alphafold(self):
        if self.use_alphafold:
            from NeuroFlex.scientific_domains.alphafold_integration import AlphaFoldIntegration
            self.alphafold = AlphaFoldIntegration()
            self.logger.info("AlphaFold integration set up")

    def _setup_math_solver(self):
        from NeuroFlex.scientific_domains.math_solvers import MathSolver
        self.math_solver = MathSolver()
        self.logger.info("Math solver set up")

    def _setup_edge_optimizer(self):
        if self.config.get('USE_EDGE_OPTIMIZATION', False):
            from NeuroFlex.edge_ai.edge_ai_optimization import EdgeAIOptimization
            self.edge_optimizer = EdgeAIOptimization()
            self.logger.info("Edge AI optimizer set up")

    def _setup_security_agent(self):
        from NeuroFlex.ai_ethics.advanced_security_agent import AdvancedSecurityAgent
        self.security_agent = AdvancedSecurityAgent(
            features=self.features,
            action_dim=self.config.get('ACTION_DIM', 2),
            update_frequency=self.config.get('SECURITY_UPDATE_FREQUENCY', 100)
        )
        self.security_agent.setup_ethical_guidelines()
        self.security_agent.setup_threat_detection()
        self.security_agent.setup_model_monitoring()
        self.security_agent.integrate_with_neuroflex()
        self.logger.info("Advanced Security Agent set up")

    def load_bioinformatics_data(self, file_path, skip_visualization=False):
        """
        Load bioinformatics data from a file and store it in the instance.

        Args:
            file_path (str): Path to the sequence file.
            skip_visualization (bool): If True, skip tree visualization. Default is False.
        """
        self.bioinformatics_data = load_bioinformatics_data(file_path, skip_visualization=skip_visualization)

    def process_text(self, text):
        """
        Process the input text by tokenizing.

        Args:
            text (str): The input text to be processed.

        Returns:
            List[str]: A list of tokens from the processed text.
        """
        tokens = tokenize_text(text)
        return tokens

    def dnn_block(self, x, deterministic):
        """Apply DNN layers to the input."""
        for layer in self.dense_layers:
            x = layer(x)
            if not deterministic:
                x = self.dropout(x)
        return x

    def evaluate_ethics(self, action):
        """
        Evaluate the ethical implications of an action.

        Args:
            action (dict): A dictionary representing the action to be evaluated.

        Returns:
            bool: True if the action is ethical, False otherwise.
        """
        if self.ethical_framework is None:
            self._setup_ethical_framework()
        return self.ethical_framework.evaluate_action(action)

    def explain_prediction(self, data):
        """
        Generate an explanation for a model prediction.

        Args:
            data: The input data for which the prediction is to be made and explained.

        Returns:
            dict: An explanation of the prediction.
        """
        if self.explainable_ai is None:
            self._setup_explainable_ai()
        return self.explainable_ai.explain_prediction(data)

    def solve_math_problem(self, problem):
        """
        Solve a mathematical problem.

        Args:
            problem (str): A string representing the mathematical problem.

        Returns:
            str: The solution to the problem.
        """
        if self.math_solver is None:
            self._setup_math_solver()
        return self.math_solver.solve(problem)

    def process_bci_data(self, raw_data):
        """
        Process raw BCI data.

        Args:
            raw_data (numpy.ndarray): Raw BCI data.

        Returns:
            numpy.ndarray: Processed BCI data.
        """
        if self.bci_processor is None:
            self._setup_bci_processor()
        return self.bci_processor.process(raw_data) if self.bci_processor else raw_data

    def simulate_consciousness(self, input_data):
        """
        Simulate consciousness based on input data.

        Args:
            input_data (numpy.ndarray): Input data for consciousness simulation.

        Returns:
            dict: Result of consciousness simulation.
        """
        if self.consciousness_sim is None:
            self._setup_consciousness_sim()
        return self.consciousness_sim.simulate_consciousness(input_data) if self.consciousness_sim else None

    def predict_protein_structure(self, sequence):
        """
        Predict protein structure from a given sequence.

        Args:
            sequence (str): Protein sequence.

        Returns:
            dict: Predicted protein structure.
        """
        if self.alphafold is None:
            self._setup_alphafold()
        return self.alphafold.predict_structure(sequence) if self.alphafold else None

    def optimize_for_edge(self, model):
        """
        Optimize the given model for edge deployment.

        Args:
            model: The model to be optimized.

        Returns:
            The optimized model.
        """
        if self.edge_optimizer is None:
            self._setup_edge_optimizer()
        return self.edge_optimizer.optimize(model) if self.edge_optimizer else model

    def secure_action(self, state):
        """
        Get a secure action based on the current state.

        Args:
            state: The current state of the environment.

        Returns:
            The secure action to take.
        """
        if self.security_agent is None:
            self._setup_security_agent()
        return self.security_agent.make_decision(state)

    def perform_security_check(self):
        """
        Perform a security check on the model.

        Returns:
            dict: A report of the security check results.
        """
        if self.security_agent is None:
            self._setup_security_agent()
        self.security_agent.security_check()
        return self.security_agent.generate_security_report()

    def update(self, batch_data):
        """
        Update the model with a batch of data.

        Args:
            batch_data (tuple): A tuple containing at least (inputs, targets),
                                and potentially additional elements.

        Returns:
            float: The loss value for this batch.
        """
        if self.core_model is None:
            self._setup_core_model()

        if self.security_agent is None:
            self._setup_security_agent()

        # Perform security check before processing the batch
        self.security_agent.security_check()

        if len(batch_data) < 2:
            raise ValueError("batch_data must contain at least inputs and targets")

        inputs, targets, *additional_data = batch_data
        if additional_data:
            self.logger.warning(f"Additional {len(additional_data)} element(s) in batch_data were ignored")

        # Convert inputs to NumPy array and reshape to match the expected input dimensions
        input_shape = self.config.get('INPUT_SHAPE', (28, 28, 1))
        inputs = np.array(inputs)
        batch_size = inputs.shape[0]
        inputs = inputs.reshape(batch_size, -1)  # Flatten the input
        if inputs.shape[1] != np.prod(input_shape):
            raise ValueError(f"Input shape mismatch. Expected {np.prod(input_shape)} features, got {inputs.shape[1]}")

        if self.backend == 'pytorch':
            inputs = torch.tensor(inputs, dtype=torch.float32)
            targets = torch.tensor(targets, dtype=torch.long)
            self.core_model.train()
            self.optimizer.zero_grad()
            outputs = self.core_model(inputs)
            loss = self.loss_fn(outputs, targets)
            loss.backward()
            self.optimizer.step()
        elif self.backend == 'tensorflow':
            inputs = tf.convert_to_tensor(inputs, dtype=tf.float32)
            targets = tf.convert_to_tensor(targets, dtype=tf.int64)
            with tf.GradientTape() as tape:
                outputs = self.core_model(inputs, training=True)
                loss = self.loss_fn(targets, outputs)
            gradients = tape.gradient(loss, self.core_model.trainable_variables)
            self.optimizer.apply_gradients(zip(gradients, self.core_model.trainable_variables))
        else:
            raise ValueError(f"Unsupported backend: {self.backend}")

        # Update the security agent with the new state
        self.security_agent.update(inputs.cpu().numpy() if self.backend == 'pytorch' else inputs.numpy(),
                                   outputs.detach().cpu().numpy() if self.backend == 'pytorch' else outputs.numpy(),
                                   loss.item())

        return loss.item()

config = {
    'CORE_MODEL_FEATURES': [64, 32, 10],
    'USE_CNN': True,
    'USE_RNN': True,
    'USE_GAN': True,
    'FAIRNESS_CONSTRAINT': 0.1,
    'USE_QUANTUM': True,
    'USE_ALPHAFOLD': True,
    'BACKEND': 'pytorch',
    'TENSORFLOW_MODEL': TensorFlowModel,
    'PYTORCH_MODEL': PyTorchModel,
    'QUANTUM_MODEL': QuantumNeuralNetwork,
    'BIOINFORMATICS_INTEGRATION': BioinformaticsIntegration(),
    'SCIKIT_BIO_INTEGRATION': ScikitBioIntegration(),  # This now refers to the correct ScikitBioIntegration class
    'ETE_INTEGRATION': ETEIntegration(),
    'ALPHAFOLD_INTEGRATION': AlphaFoldIntegration(),
    'ALPHAFOLD_PARAMS': {'max_recycling': 3}
}

model = NeuroFlex(config)

# Initialize self-curing algorithm
self_curing_algorithm = SelfCuringAlgorithm(model)

# Prepare training data (placeholder)
train_data = None  # Replace with actual training data
val_data = None    # Replace with actual validation data

# Train your model
def train_neuroflex_model(model, train_data, val_data):
    if model.bioinformatics_data is None:
        raise ValueError("Bioinformatics data not loaded. Call load_bioinformatics_data() first.")

    def train_model(model, train_data, val_data, num_epochs, batch_size, learning_rate, **kwargs):
        print("Training model...")

        # Initialize AdvancedSecurityAgent
        security_agent = model.security_agent

        for epoch in range(num_epochs):
            total_loss = 0
            num_batches = 0
            for batch_start in range(0, len(train_data), batch_size):
                # Perform security check before processing each batch
                security_agent.security_check()

                # Process batch
                batch_end = min(batch_start + batch_size, len(train_data))
                batch_data = train_data[batch_start:batch_end]

                # Prepare inputs and targets
                inputs = np.array([x[0] for x in batch_data])
                targets = np.array([x[1] for x in batch_data])

                # Reshape inputs to match the expected input shape
                input_shape = model.config.get('INPUT_SHAPE', (28, 28, 1))
                inputs = inputs.reshape((-1,) + input_shape)

                # Detect and mitigate threats
                if security_agent.threat_detector.detect_threat(inputs):
                    inputs = security_agent.mitigate_threat(inputs)

                # Update model
                loss = model.update((inputs, targets))
                total_loss += loss
                num_batches += 1

            # Calculate average loss for the epoch
            avg_loss = total_loss / num_batches
            print(f"Epoch {epoch + 1}/{num_epochs} - Average Loss: {avg_loss:.4f}")

            # Evaluate fairness after each epoch
            fairness_eval = security_agent.evaluate_fairness()
            print(f"Epoch {epoch + 1}/{num_epochs} - Fairness evaluation: {fairness_eval}")

        # Final security check and model health assessment
        security_agent.security_check()
        health_status = security_agent.check_model_health()
        print(f"Final model health status: {health_status}")

        trained_state = model.core_model.state_dict() if hasattr(model.core_model, 'state_dict') else None
        trained_model = model
        return trained_state, trained_model

    trained_state, trained_model = train_model(
        model, train_data, val_data,
        num_epochs=10, batch_size=32, learning_rate=1e-3,
        bioinformatics_data=model.bioinformatics_data,
        use_alphafold=model.use_alphafold,
        use_quantum=model.use_quantum,
        alphafold_structures=model.bioinformatics_data['alphafold_structures'],
        quantum_params=model.quantum_model.get_params() if model.quantum_model else None
    )
    return trained_state, trained_model

# Note: Call model.load_bioinformatics_data() before training
# trained_state, trained_model = train_neuroflex_model(model, train_data, val_data)
