import jax
import jax.numpy as jnp
import flax.linen as nn
from typing import List, Dict, Any
from flax.training import train_state
import optax
import logging
import time

from NeuroFlex.ai_ethics.aif360_integration import AIF360Integration
from NeuroFlex.ai_ethics.ethical_framework import EthicalFramework, Guideline
from NeuroFlex.ai_ethics.self_fixing_algorithms import SelfCuringRLAgent, create_self_curing_rl_agent
from NeuroFlex.ai_ethics.rl_module import RLEnvironment
from NeuroFlex.ai_ethics.scikit_bio_integration import ScikitBioIntegration
from NeuroFlex.neuroflex_integration import NeuroFlexIntegrator

# Lazy imports to avoid circular dependencies
def lazy_import(module_name, class_name):
    def _import():
        module = __import__(module_name, fromlist=[class_name])
        return getattr(module, class_name)
    return _import

ThreatDetector = lazy_import('NeuroFlex.threat_detection', 'ThreatDetector')
from NeuroFlex.model_monitoring import ModelMonitor

class AdvancedSecurityAgent:
    def __init__(self, features: List[int], action_dim: int, update_frequency: int = 100):
        self.fairness_agent = AIF360Integration()
        self.ethical_framework = EthicalFramework()
        self.rl_agent = create_self_curing_rl_agent(features, action_dim)
        self.env = RLEnvironment("CartPole-v1")  # Example environment, replace with appropriate one
        self.threat_detector = ThreatDetector()()  # Initialize ThreatDetector
        self.threat_detector.setup()  # Set up the ThreatDetector with new features
        self.model_monitor = ModelMonitor()  # Initialize ModelMonitor
        self.model_monitor.setup()  # Set up the ModelMonitor
        self.scikit_bio = ScikitBioIntegration()
        self.neuroflex_integrator = NeuroFlexIntegrator()
        self.performance_history = []
        self.threat_history = []
        self.last_security_audit = time.time()
        self.update_frequency = update_frequency
        self.bio_sequences = []  # Store DNA sequences for analysis
        self.dna_sequences = []  # Initialize dna_sequences attribute
        self.anomaly_detector = self.threat_detector.anomaly_detector
        self.deep_learning_model = self.threat_detector.deep_learning_model

    def _get_latest_performance(self):
        return self.model_monitor.get_overall_performance() if hasattr(self.model_monitor, 'get_overall_performance') else {}

    def setup_threat_detection(self):
        from NeuroFlex.threat_detection import ThreatDetector
        self.threat_detector = ThreatDetector()

    def setup_model_monitoring(self):
        from NeuroFlex.model_monitoring import ModelMonitor
        self.model_monitor = ModelMonitor()

    def setup_fairness(self, df, label_name, favorable_classes, protected_attribute_names, privileged_classes):
        self.fairness_agent.load_dataset(df, label_name, favorable_classes, protected_attribute_names, privileged_classes)

    def setup_ethical_guidelines(self):
        def no_harm(action):
            # Implement logic to check if the action causes harm
            return True  # Placeholder

        self.ethical_framework.add_guideline(Guideline("Do no harm", no_harm))
        # Add more ethical guidelines as needed

    def train(self, num_episodes: int, max_steps: int):
        training_info = self.rl_agent.train(self.env, num_episodes, max_steps)
        logging.info(f"Training completed. Final reward: {training_info['final_reward']}")

    def evaluate_fairness(self):
        original_metrics = self.fairness_agent.compute_metrics()
        mitigated_dataset = self.fairness_agent.mitigate_bias()
        mitigated_metrics = self.fairness_agent.compute_metrics()
        evaluation = self.fairness_agent.evaluate_fairness(original_metrics, mitigated_metrics)
        return evaluation

    def make_decision(self, state):
        action = self.rl_agent.select_action(state)
        if self.ethical_framework.evaluate_action(action):
            return action
        else:
            logging.warning("Action rejected by ethical framework")
            return None  # Or implement a fallback action

    def self_diagnose(self):
        issues = self.rl_agent.diagnose()
        if issues:
            logging.info(f"Detected issues: {issues}")
            self.rl_agent.heal(self.env, num_episodes=500, max_steps=500)
            logging.info(f"Healing completed. New performance: {self.rl_agent.performance}")

    def run(self, num_episodes: int):
        for episode in range(num_episodes):
            state = self.env.reset()
            done = False
            episode_reward = 0
            step = 0
            while not done:
                action = self.make_decision(state)
                if action is not None:
                    next_state, reward, done, _ = self.env.step(action)
                    self.rl_agent.replay_buffer.add(state, action, reward, next_state, done)

                    # Perform enhanced threat detection
                    threat_detected = self.threat_detector.detect_threat(state, action, next_state)
                    is_adversarial = self.threat_detector.is_adversarial_pattern(state, action, next_state)

                    if threat_detected or is_adversarial:
                        logging.warning(f"Potential threat detected at episode {episode}, step {step}")
                        if is_adversarial:
                            logging.warning("Adversarial pattern detected")
                        self.mitigate_threat(state, action, next_state)
                        # Re-evaluate the state after mitigation
                        state, reward, done, _ = self.env.step(self.make_decision(next_state))
                    else:
                        state = next_state

                    episode_reward += reward

                    # Monitor model performance
                    self.model_monitor.update(state, action, reward, next_state, done)

                step += 1

            self.self_diagnose()
            fairness_eval = self.evaluate_fairness()
            logging.info(f"Episode {episode} - Fairness evaluation: {fairness_eval}")
            logging.info(f"Episode {episode} - Episode reward: {episode_reward}")

            # Perform periodic model updates and security checks
            if episode % self.update_frequency == 0:
                self.update_model()
                self.security_check()

            # Perform threat analysis after each episode
            threat_analysis = self.perform_threat_analysis()
            logging.info(f"Episode {episode} - Threat analysis: {threat_analysis}")

        # Final evaluation
        overall_performance = self.model_monitor.get_overall_performance()
        logging.info(f"Overall performance after {num_episodes} episodes: {overall_performance}")

        # Final comprehensive threat analysis
        final_threat_analysis = self.perform_threat_analysis()
        logging.info(f"Final threat analysis: {final_threat_analysis}")

    def mitigate_threat(self, state, action, next_state):
        # Implement threat mitigation strategy
        logging.info("Mitigating detected threat...")
        # Example: Adjust action to reduce potential harm
        safe_action = self.threat_detector.get_safe_action(state, action, next_state)
        self.env.step(safe_action)

    def update_model(self):
        logging.info("Updating model...")
        # Implement model update logic
        self.rl_agent.train(self.env, num_episodes=100, max_steps=500)
        self.last_update = time.time()

    def security_check(self):
        logging.info("Starting security check...")

        logging.debug("Scanning for vulnerabilities...")
        vulnerabilities = self.threat_detector.scan_for_vulnerabilities(self.rl_agent)
        logging.debug(f"Vulnerability scan complete. Found {len(vulnerabilities)} vulnerabilities.")

        logging.debug("Calling get_dna_sequences...")
        dna_sequences = self.get_dna_sequences()
        logging.info(f"Retrieved {len(dna_sequences)} DNA sequences for analysis")

        # Perform bioinformatics-specific security checks
        if dna_sequences:
            try:
                logging.info(f"Performing anomaly detection on {len(dna_sequences)} DNA sequences")
                logging.debug("Calling scikit_bio.detect_anomalies...")
                bio_anomalies = self.scikit_bio.detect_anomalies(dna_sequences)
                logging.info(f"Anomaly detection completed. Found {len(bio_anomalies)} anomalies.")
                if bio_anomalies:
                    logging.warning(f"Detected bioinformatics anomalies: {bio_anomalies}")
                    for i in bio_anomalies:
                        anomaly = f"Bio anomaly in sequence {i}"
                        if anomaly not in vulnerabilities:
                            vulnerabilities.append(anomaly)
                    logging.debug(f"Updated vulnerabilities list: {vulnerabilities}")
            except Exception as e:
                logging.error(f"Error in detecting bioinformatics anomalies: {str(e)}")
                logging.exception("Exception details:")
        else:
            logging.warning("No DNA sequences available for anomaly detection")

        if vulnerabilities:
            logging.warning(f"Detected vulnerabilities: {vulnerabilities}")
            logging.debug("Calling address_vulnerabilities...")
            self.address_vulnerabilities(vulnerabilities)
        else:
            logging.info("No vulnerabilities detected during security check")

        logging.info("Security check completed.")
        return vulnerabilities  # Return vulnerabilities as a list

    def get_dna_sequences(self):
        # Mock DNA sequences for testing purposes
        mock_sequences = [
            "ATCGATCGATCG",
            "GCTAGCTAGCTA",
            "TTTTAAAACCCC",
            "GGGGCCCCAAAA",
            "ATGCATGCATGC"
        ]
        return mock_sequences

    def address_vulnerabilities(self, vulnerabilities):
        for vulnerability in vulnerabilities:
            logging.info(f"Addressing vulnerability: {vulnerability}")
            # Implement specific mitigation strategies for each type of vulnerability
            # This could involve retraining, adjusting hyperparameters, or modifying the model architecture

    def generate_security_report(self):
        current_time = time.time()
        report = {
            'timestamp': current_time,
            'threats': self._get_threat_info(),
            'model_health': self._get_model_health_info(),
            'performance': self._get_latest_performance(),
            'last_security_audit': self.last_security_audit,
            'time_since_last_audit': current_time - self.last_security_audit,
            'bioinformatics_security': self._get_bioinformatics_security_info(),
            'ethical_evaluation': self._get_ethical_evaluation(),
            'fairness_metrics': self._get_fairness_metrics() if hasattr(self, '_get_fairness_metrics') else {},
            'last_model_update': getattr(self, 'last_update', None)
        }
        return report

    def _get_model_health_info(self):
        return {
            'accuracy': self.model_monitor.get_accuracy(),
            'loss': self.model_monitor.get_loss(),
            'performance_trend': self.model_monitor.get_performance_trend(),
            'last_update_time': self.last_update if hasattr(self, 'last_update') else None
        }

    def _get_threat_info(self):
        # Use a mock state and action for threat detection
        mock_state = [0] * 10  # Assuming a 10-dimensional state
        mock_action = [0] * 5  # Assuming a 5-dimensional action
        mock_next_state = [0] * 10  # Assuming a 10-dimensional next state
        threat_detected = self.threat_detector.detect_threat(mock_state, mock_action, mock_next_state)
        return {
            'detected_threats': [threat_detected],
            'threat_count': 1 if threat_detected else 0,
            'last_threat_detection_time': time.time()
        }

    def _get_bioinformatics_security_info(self):
        if hasattr(self, 'scikit_bio') and hasattr(self, 'dna_sequences') and self.dna_sequences:
            try:
                anomalies = self.scikit_bio.detect_anomalies(self.dna_sequences)
                return {
                    'anomalies_detected': anomalies,
                    'num_anomalies': len(anomalies),
                    'sequence_similarities': self.scikit_bio.calculate_sequence_similarity(self.dna_sequences[0], self.dna_sequences[1]) if len(self.dna_sequences) >= 2 else None,
                    'num_sequences_analyzed': len(self.dna_sequences)
                }
            except Exception as e:
                logging.error(f"Error in _get_bioinformatics_security_info: {str(e)}")
        return {}

    def _get_ethical_evaluation(self):
        return self.ethical_framework.evaluate_model(self.rl_agent) if hasattr(self, 'ethical_framework') else {}

    def _get_fairness_metrics(self):
        # Implement logic to get fairness metrics
        return {}

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    agent = AdvancedSecurityAgent([64, 64], 2)  # Example architecture
    agent.setup_ethical_guidelines()
    agent.setup_threat_detection()
    agent.setup_model_monitoring()
    agent.integrate_with_neuroflex()
    agent.train(num_episodes=1000, max_steps=500)
    agent.run(num_steps=100)

    # Demonstrate new features
    agent.perform_threat_analysis()
    agent.check_model_health()
    agent.generate_security_report()

def setup_threat_detection(self):
    self.threat_detector.setup()
    self.anomaly_detector = self.threat_detector.anomaly_detector
    self.deep_learning_model = self.threat_detector.deep_learning_model
    logging.info("Threat detection setup complete with anomaly detector and deep learning model.")

def setup_model_monitoring(self):
    self.model_monitor.setup()

def integrate_with_neuroflex(self):
    if hasattr(self, 'neuroflex_integrator'):
        self.neuroflex_integrator.setup()
    else:
        logging.warning("NeurFlexIntegrator not initialized. Skipping integration.")

def perform_threat_analysis(self):
    if hasattr(self, 'threat_detector'):
        analysis_results = self.threat_detector.analyze()
        logging.info("Threat analysis results:")
        logging.info(f"Total threats: {analysis_results['total_threats']}")
        logging.info(f"Recent threats: {analysis_results['recent_threats']}")
        logging.info(f"Vulnerability summary: {analysis_results['vulnerability_summary']}")
        logging.info(f"Anomaly detector performance: {analysis_results['anomaly_detector_performance']}")
        logging.info(f"Deep learning model performance: {analysis_results['deep_learning_model_performance']}")
        return analysis_results
    else:
        logging.warning("ThreatDetector not initialized. Skipping threat analysis.")
        return {}

def check_model_health(self):
    if hasattr(self, 'model_monitor'):
        health_status = self.model_monitor.check_health()
        logging.info(f"Model health status: {health_status}")
        return health_status
    else:
        logging.warning("ModelMonitor not initialized. Skipping health check.")
        return {}

def _get_threat_info(self):
    # Implement logic to get threat information
    threats = self.threat_detector.get_current_threats()
    return {
        'detected_threats': threats,
        'threat_count': len(threats),
        'last_threat_detection_time': self.threat_detector.last_detection_time
    }

def _get_model_health_info(self):
    # Implement logic to get model health information
    return {
        'accuracy': self.model_monitor.get_accuracy(),
        'loss': self.model_monitor.get_loss(),
        'performance_trend': self.model_monitor.get_performance_trend(),
        'last_update_time': self.last_update if hasattr(self, 'last_update') else None
    }

def _get_latest_performance(self):
    # Implement logic to get latest performance metrics
    return self.model_monitor.get_overall_performance() if hasattr(self, 'model_monitor') else {}

def _get_bioinformatics_security_info(self):
    # Implement logic to get bioinformatics security information
    return {}

def _get_fairness_metrics(self):
    # Implement logic to get fairness metrics
    return {}

def _calculate_overall_security_score(self, report):
    # Implement logic to calculate overall security score
    return 0.0

def _assess_threat_severity(self, threats):
    # Implement logic to assess threat severity
    return "Low"

def _assess_anomaly_severity(self, anomalies):
    # Implement logic to assess anomaly severity
    return "Low"

def _generate_recommended_actions(self, report):
    # Implement logic to generate recommended actions
    return []

def _calculate_sequence_similarity(self, seq1, seq2):
    return self.scikit_bio.calculate_sequence_similarity(seq1, seq2)

    # Ensure all required fields are present
    required_fields = ['timestamp', 'threats', 'model_health', 'performance', 'last_security_audit',
                       'time_since_last_audit', 'bioinformatics_security', 'ethical_evaluation',
                       'fairness_metrics', 'overall_security_score', 'threat_severity',
                       'anomaly_severity', 'recommended_actions']
    for field in required_fields:
        if field not in report:
            report[field] = None
            logging.warning(f"Required field '{field}' was not present in the security report.")

    logging.info(f"Security report generated: {report}")
    self.last_security_audit = current_time
    return report

def _get_threat_info(self):
    return self.threat_detector.get_threat_history() if hasattr(self, 'threat_detector') else []

def _get_model_health_info(self):
    return self.model_monitor.get_health_history() if hasattr(self, 'model_monitor') else {}

def _get_latest_performance(self):
    return self.performance_history[-10:] if self.performance_history else []

def _get_bioinformatics_security_info(self):
    if hasattr(self, 'scikit_bio') and hasattr(self, 'dna_sequences') and self.dna_sequences:
        try:
            anomalies = self.scikit_bio.detect_anomalies(self.dna_sequences)
            return {
                'anomalies_detected': anomalies,
                'num_anomalies': len(anomalies),
                'sequence_similarities': self.scikit_bio.calculate_sequence_similarity(self.dna_sequences[0], self.dna_sequences[1]) if len(self.dna_sequences) >= 2 else None,
                'num_sequences_analyzed': len(self.dna_sequences)
            }
        except Exception as e:
            logging.error(f"Error in _get_bioinformatics_security_info: {str(e)}")
    return {}

def _get_fairness_metrics(self):
    return self.fairness_agent.compute_metrics() if hasattr(self, 'fairness_agent') else {}

def _calculate_overall_security_score(self, report):
    score = 100  # Start with a perfect score
    if report['threats']:
        score -= len(report['threats']) * 5  # Deduct 5 points for each threat
    if report['bioinformatics_security'].get('anomalies_detected'):
        score -= len(report['bioinformatics_security']['anomalies_detected']) * 3  # Deduct 3 points for each anomaly
    if report['fairness_metrics']:
        fairness_score = sum(report['fairness_metrics'].values()) / len(report['fairness_metrics'])
        score += (fairness_score - 0.5) * 20  # Adjust score based on fairness (assuming fairness metrics are between 0 and 1)
    if report['ethical_evaluation']:
        ethical_score = sum(report['ethical_evaluation'].values()) / len(report['ethical_evaluation'])
        score += (ethical_score - 0.5) * 20  # Adjust score based on ethical evaluation
    return max(0, min(100, score))  # Ensure the score is between 0 and 100

def _assess_threat_severity(self, threats):
    # Placeholder implementation
    return 'HIGH' if len(threats) > 5 else 'MEDIUM' if len(threats) > 0 else 'LOW'

def _assess_anomaly_severity(self, anomalies):
    # Placeholder implementation
    return 'HIGH' if len(anomalies) > 3 else 'MEDIUM' if len(anomalies) > 0 else 'LOW'

def _generate_recommended_actions(self, report):
    actions = []
    if report['threat_severity'] != 'LOW':
        actions.append("Investigate and mitigate detected threats")
    if report['anomaly_severity'] != 'LOW':
        actions.append("Analyze and address bioinformatics anomalies")
    if report['overall_security_score'] < 70:
        actions.append("Conduct comprehensive security review")
    if report['time_since_last_audit'] > 86400:  # If more than a day has passed
        actions.append("Schedule regular security audits")
    return actions
