"""
Integration module for advanced features in AutoPipelineDoctor.

This module provides integration for all advanced modules, making them accessible
through the main Doctor class when enable_advanced=True is passed.
"""

import logging
from typing import Dict, List, Optional, Union, Any, Tuple, Callable, Set

import torch
import torch.nn as nn

from autopd.core.doctor import Doctor
from autopd.advanced.neuro_behavioral_pattern_clustering import NeuroBehavioralPatternClustering
from autopd.advanced.autonomous_optimization_loop_injection import AutonomousOptimizationLoopInjection
from autopd.advanced.causal_fault_tree_analysis import CausalFaultTreeAnalysis
from autopd.advanced.latent_loss_surface_mapping import LatentLossSurfaceMapping
from autopd.advanced.synthetic_model_shadowing import SyntheticModelShadowing
from autopd.advanced.hardware_aware_learning_curve_forecasting import HardwareAwareLearningCurveForecasting
from autopd.advanced.anomaly_activated_alert_system import AnomalyActivatedAlertSystem
from autopd.advanced.llm_connected_think_tank import LLMConnectedThinkTank
from autopd.advanced.dna_tracker import DNATracker
from autopd.advanced.real_time_model_cognition import RealTimeModelCognition
from autopd.advanced.quantum_inspired_optimization_pathfinder import QuantumInspiredOptimizationPathfinder
from autopd.advanced.federated_training_synchronization_monitor import FederatedTrainingSynchronizationMonitor
from autopd.advanced.adversarial_robustness_analyzer import AdversarialRobustnessAnalyzer

logger = logging.getLogger(__name__)


def integrate_advanced_modules(doctor_instance: Doctor) -> None:
    """
    Integrate advanced modules into the Doctor instance.
    
    Args:
        doctor_instance: The Doctor instance to integrate advanced modules into
    """
    if not hasattr(doctor_instance, 'enable_advanced') or not doctor_instance.enable_advanced:
        return
    
    logger.info("Integrating advanced modules into AutoPipelineDoctor")
    
    # Initialize advanced modules
    _initialize_advanced_modules(doctor_instance)
    
    # Patch Doctor methods to include advanced functionality
    _patch_doctor_methods(doctor_instance)
    
    # Register advanced module hooks
    _register_advanced_hooks(doctor_instance)
    
    logger.info("Advanced modules integration complete")


def _initialize_advanced_modules(doctor_instance: Doctor) -> None:
    """
    Initialize all advanced modules.
    
    Args:
        doctor_instance: The Doctor instance to initialize modules for
    """
    # Create advanced modules container
    doctor_instance.advanced_modules = {}
    
    # Get model, device, and other common parameters
    model = doctor_instance.model
    device = doctor_instance.device
    output_dir = doctor_instance.output_dir
    
    # Initialize Neuro-Behavioral Pattern Clustering
    doctor_instance.advanced_modules['nbpc'] = NeuroBehavioralPatternClustering(
        model=model,
        device=device,
        output_dir=output_dir,
        history_length=doctor_instance.config.get('nbpc_history_length', 100),
        cluster_threshold=doctor_instance.config.get('nbpc_cluster_threshold', 0.7),
        warning_threshold=doctor_instance.config.get('nbpc_warning_threshold', 0.8)
    )
    
    # Initialize Autonomous Optimization Loop Injection
    doctor_instance.advanced_modules['aoli'] = AutonomousOptimizationLoopInjection(
        model=model,
        optimizer=doctor_instance.optimizer,
        dataloader=doctor_instance.dataloader,
        device=device,
        auto_apply=doctor_instance.config.get('aoli_auto_apply', False),
        risk_level=doctor_instance.config.get('aoli_risk_level', 'medium')
    )
    
    # Initialize Causal Fault Tree Analysis
    doctor_instance.advanced_modules['cfta'] = CausalFaultTreeAnalysis(
        model=model,
        device=device,
        output_dir=output_dir,
        max_depth=doctor_instance.config.get('cfta_max_depth', 5),
        min_confidence=doctor_instance.config.get('cfta_min_confidence', 0.7)
    )
    
    # Initialize Latent Loss Surface Mapping
    doctor_instance.advanced_modules['llsm'] = LatentLossSurfaceMapping(
        model=model,
        optimizer=doctor_instance.optimizer,
        device=device,
        output_dir=output_dir,
        resolution=doctor_instance.config.get('llsm_resolution', 20),
        update_frequency=doctor_instance.config.get('llsm_update_frequency', 100)
    )
    
    # Initialize Synthetic Model Shadowing
    doctor_instance.advanced_modules['sms'] = SyntheticModelShadowing(
        model=model,
        device=device,
        output_dir=output_dir,
        shadow_type=doctor_instance.config.get('sms_shadow_type', 'distilled'),
        update_frequency=doctor_instance.config.get('sms_update_frequency', 50)
    )
    
    # Initialize Hardware-Aware Learning Curve Forecasting
    doctor_instance.advanced_modules['halcf'] = HardwareAwareLearningCurveForecasting(
        model=model,
        device=device,
        output_dir=output_dir,
        hardware_info=doctor_instance.hardware_info,
        dataset_size=doctor_instance.config.get('dataset_size', None),
        batch_size=doctor_instance.config.get('batch_size', None)
    )
    
    # Initialize Anomaly-Activated Alert System
    doctor_instance.advanced_modules['aaas'] = AnomalyActivatedAlertSystem(
        model=model,
        device=device,
        output_dir=output_dir,
        alert_channels=doctor_instance.config.get('aaas_alert_channels', ['cli']),
        sensitivity=doctor_instance.config.get('aaas_sensitivity', 'medium'),
        webhook_url=doctor_instance.config.get('aaas_webhook_url', None),
        email_config=doctor_instance.config.get('aaas_email_config', None)
    )
    
    # Initialize LLM-Connected Think Tank
    doctor_instance.advanced_modules['llmtt'] = LLMConnectedThinkTank(
        model=model,
        device=device,
        output_dir=output_dir,
        api_key=doctor_instance.config.get('llmtt_api_key', None),
        provider=doctor_instance.config.get('llmtt_provider', 'openai'),
        num_agents=doctor_instance.config.get('llmtt_num_agents', 3),
        max_tokens=doctor_instance.config.get('llmtt_max_tokens', 1000)
    )
    
    # Initialize DNA Tracker
    doctor_instance.advanced_modules['dna'] = DNATracker(
        model=model,
        device=device,
        output_dir=output_dir,
        track_checkpoints=doctor_instance.config.get('dna_track_checkpoints', True),
        track_code=doctor_instance.config.get('dna_track_code', True),
        track_data=doctor_instance.config.get('dna_track_data', False)
    )
    
    # Initialize Real-Time Model Cognition Visualization
    doctor_instance.advanced_modules['rtmcv'] = RealTimeModelCognition(
        model=model,
        device=device,
        output_dir=output_dir,
        update_frequency=doctor_instance.config.get('rtmcv_update_frequency', 10),
        visualization_type=doctor_instance.config.get('rtmcv_visualization_type', 'all')
    )
    
    # Initialize Quantum-Inspired Optimization Pathfinder
    doctor_instance.advanced_modules['qiop'] = QuantumInspiredOptimizationPathfinder(
        model=model,
        optimizer=doctor_instance.optimizer,
        device=device,
        output_dir=output_dir,
        num_particles=doctor_instance.config.get('qiop_num_particles', 20),
        search_space=doctor_instance.config.get('qiop_search_space', 'hyperparameters'),
        optimization_target=doctor_instance.config.get('qiop_optimization_target', 'performance')
    )
    
    # Initialize Federated Training Synchronization Monitor
    doctor_instance.advanced_modules['ftsm'] = FederatedTrainingSynchronizationMonitor(
        model=model,
        device=device,
        output_dir=output_dir,
        num_nodes=doctor_instance.config.get('ftsm_num_nodes', 1),
        sync_strategy=doctor_instance.config.get('ftsm_sync_strategy', 'adaptive'),
        drift_threshold=doctor_instance.config.get('ftsm_drift_threshold', 0.1)
    )
    
    # Initialize Adversarial Robustness Analyzer
    doctor_instance.advanced_modules['ara'] = AdversarialRobustnessAnalyzer(
        model=model,
        device=device,
        attack_methods=doctor_instance.config.get('ara_attack_methods', ['fgsm', 'pgd']),
        defense_methods=doctor_instance.config.get('ara_defense_methods', []),
        robustness_metrics=doctor_instance.config.get('ara_robustness_metrics', ['empirical_robustness']),
        auto_enhance=doctor_instance.config.get('ara_auto_enhance', False),
        output_dir=output_dir
    )


def _patch_doctor_methods(doctor_instance: Doctor) -> None:
    """
    Patch Doctor methods to include advanced functionality.
    
    Args:
        doctor_instance: The Doctor instance to patch methods for
    """
    # Store original methods
    original_watch = doctor_instance.watch
    original_auto_patch = doctor_instance.auto_patch
    original_on_batch_complete = doctor_instance.on_batch_complete
    original_on_epoch_complete = doctor_instance.on_epoch_complete
    original_get_recommendations = doctor_instance.get_recommendations
    original_visualize = doctor_instance.visualize
    original_generate_report = doctor_instance.generate_report
    
    # Patch watch method
    def patched_watch(train_func):
        # Call original method
        result = original_watch(train_func)
        
        # Add advanced monitoring
        for module_name, module in doctor_instance.advanced_modules.items():
            if hasattr(module, 'start'):
                module.start()
        
        return result
    
    # Patch auto_patch method
    def patched_auto_patch():
        # Call original method
        result = original_auto_patch()
        
        # Add advanced auto-patching
        if 'aoli' in doctor_instance.advanced_modules:
            doctor_instance.advanced_modules['aoli'].auto_patch()
        
        return result
    
    # Patch on_batch_complete method
    def patched_on_batch_complete(iteration, batch=None, loss=None, outputs=None, batch_time=None):
        # Call original method
        result = original_on_batch_complete(iteration, batch, loss, outputs, batch_time)
        
        # Update advanced modules
        for module_name, module in doctor_instance.advanced_modules.items():
            if hasattr(module, 'on_batch_complete'):
                module.on_batch_complete(iteration, batch, loss, outputs, batch_time)
            elif hasattr(module, 'on_iteration_complete'):
                module.on_iteration_complete(iteration, batch)
        
        return result
    
    # Patch on_epoch_complete method
    def patched_on_epoch_complete(epoch, metrics=None):
        # Call original method
        result = original_on_epoch_complete(epoch, metrics)
        
        # Update advanced modules
        for module_name, module in doctor_instance.advanced_modules.items():
            if hasattr(module, 'on_epoch_complete'):
                module.on_epoch_complete(epoch, metrics)
        
        return result
    
    # Patch get_recommendations method
    def patched_get_recommendations():
        # Get original recommendations
        recommendations = original_get_recommendations()
        
        # Add advanced recommendations
        advanced_recommendations = []
        
        for module_name, module in doctor_instance.advanced_modules.items():
            if hasattr(module, 'get_recommendations'):
                module_recommendations = module.get_recommendations()
                if module_recommendations:
                    for rec in module_recommendations:
                        rec['source'] = module_name
                    advanced_recommendations.extend(module_recommendations)
        
        # Combine recommendations
        all_recommendations = recommendations + advanced_recommendations
        
        # Sort by priority
        priority_map = {'critical': 0, 'high': 1, 'medium': 2, 'low': 3}
        all_recommendations.sort(key=lambda x: priority_map.get(x.get('priority', 'low'), 3))
        
        return all_recommendations
    
    # Patch visualize method
    def patched_visualize(output_file=None, visualization_type='all'):
        # Get original visualization
        visualization = original_visualize(output_file, visualization_type)
        
        # Add advanced visualizations
        if visualization_type in ['all', 'advanced']:
            advanced_visualizations = {}
            
            for module_name, module in doctor_instance.advanced_modules.items():
                if hasattr(module, 'visualize'):
                    module_visualization = module.visualize()
                    if module_visualization:
                        advanced_visualizations[module_name] = module_visualization
            
            # Add to visualization
            visualization['advanced'] = advanced_visualizations
        
        return visualization
    
    # Patch generate_report method
    def patched_generate_report(output_file=None, report_type='all'):
        # Get original report
        report = original_generate_report(output_file, report_type)
        
        # Add advanced report sections
        if report_type in ['all', 'advanced']:
            advanced_reports = {}
            
            for module_name, module in doctor_instance.advanced_modules.items():
                if hasattr(module, 'generate_report'):
                    module_report = module.generate_report()
                    if module_report:
                        advanced_reports[module_name] = module_report
            
            # Add to report
            report['advanced'] = advanced_reports
        
        return report
    
    # Apply patches
    doctor_instance.watch = patched_watch
    doctor_instance.auto_patch = patched_auto_patch
    doctor_instance.on_batch_complete = patched_on_batch_complete
    doctor_instance.on_epoch_complete = patched_on_epoch_complete
    doctor_instance.get_recommendations = patched_get_recommendations
    doctor_instance.visualize = patched_visualize
    doctor_instance.generate_report = patched_generate_report
    
    # Add advanced-specific methods
    def analyze_with_think_tank(query):
        """
        Analyze the current training state using the LLM Think Tank.
        
        Args:
            query: Question or issue to analyze
            
        Returns:
            Analysis results from the think tank
        """
        if 'llmtt' in doctor_instance.advanced_modules:
            return doctor_instance.advanced_modules['llmtt'].analyze(query)
        else:
            logger.warning("LLM Think Tank module not enabled")
            return None
    
    def visualize_model_cognition(layer_name=None, neuron_indices=None):
        """
        Visualize what the model is "thinking" during training.
        
        Args:
            layer_name: Specific layer to visualize (optional)
            neuron_indices: Specific neurons to visualize (optional)
            
        Returns:
            Visualization data
        """
        if 'rtmcv' in doctor_instance.advanced_modules:
            return doctor_instance.advanced_modules['rtmcv'].visualize_layer(layer_name, neuron_indices)
        else:
            logger.warning("Real-Time Model Cognition module not enabled")
            return None
    
    def get_model_dna():
        """
        Get the DNA fingerprint and lineage of the current model.
        
        Returns:
            DNA information and lineage visualization
        """
        if 'dna' in doctor_instance.advanced_modules:
            return doctor_instance.advanced_modules['dna'].get_dna_report()
        else:
            logger.warning("DNA Tracker module not enabled")
            return None
    
    def analyze_adversarial_robustness():
        """
        Analyze the adversarial robustness of the model.
        
        Returns:
            Adversarial robustness analysis report
        """
        if 'ara' in doctor_instance.advanced_modules:
            return doctor_instance.advanced_modules['ara'].generate_report()
        else:
            logger.warning("Adversarial Robustness Analyzer module not enabled")
            return None
    
    def optimize_with_quantum_pathfinder(target='performance', iterations=10):
        """
        Optimize model hyperparameters using quantum-inspired algorithms.
        
        Args:
            target: Optimization target ('performance', 'memory', 'convergence')
            iterations: Number of optimization iterations
            
        Returns:
            Optimization results
        """
        if 'qiop' in doctor_instance.advanced_modules:
            return doctor_instance.advanced_modules['qiop'].optimize(target, iterations)
        else:
            logger.warning("Quantum-Inspired Optimization Pathfinder module not enabled")
            return None
    
    def monitor_federated_training(node_id=None):
        """
        Monitor federated training synchronization.
        
        Args:
            node_id: Specific node to monitor (optional)
            
        Returns:
            Federated training monitoring report
        """
        if 'ftsm' in doctor_instance.advanced_modules:
            return doctor_instance.advanced_modules['ftsm'].get_monitoring_report(node_id)
        else:
            logger.warning("Federated Training Synchronization Monitor module not enabled")
            return None
    
    # Add methods to Doctor instance
    doctor_instance.analyze_with_think_tank = analyze_with_think_tank
    doctor_instance.visualize_model_cognition = visualize_model_cognition
    doctor_instance.get_model_dna = get_model_dna
    doctor_instance.analyze_adversarial_robustness = analyze_adversarial_robustness
    doctor_instance.optimize_with_quantum_pathfinder = optimize_with_quantum_pathfinder
    doctor_instance.monitor_federated_training = monitor_federated_training


def _register_advanced_hooks(doctor_instance: Doctor) -> None:
    """
    Register hooks for advanced modules.
    
    Args:
        doctor_instance: The Doctor instance to register hooks for
    """
    # Register hooks for each module that supports them
    for module_name, module in doctor_instance.advanced_modules.items():
        if hasattr(module, 'register_hooks') and callable(module.register_hooks):
            module.register_hooks(doctor_instance.model)
    
    # Register AOLI optimization hooks if available
    if 'aoli' in doctor_instance.advanced_modules:
        doctor_instance.advanced_modules['aoli'].register_optimization_hooks(
            doctor_instance.model, 
            doctor_instance.optimizer
        )
    
    # Register model cognition hooks if available
    if 'rtmcv' in doctor_instance.advanced_modules:
        doctor_instance.advanced_modules['rtmcv'].register_visualization_hooks(
            doctor_instance.model
        )
    
    # Register adversarial robustness hooks if available
    if 'ara' in doctor_instance.advanced_modules:
        doctor_instance.advanced_modules['ara'].start()


# Patch the Doctor class to support advanced modules
original_doctor_init = Doctor.__init__

def patched_doctor_init(self, model, optimizer=None, dataloader=None, device=None, output_dir=None, config=None, enable_advanced=False):
    # Call original init
    original_doctor_init(self, model, optimizer, dataloader, device, output_dir, config)
    
    # Store advanced flag
    self.enable_advanced = enable_advanced
    
    # Initialize hardware info
    self.hardware_info = {
        'device_type': 'cuda' if torch.cuda.is_available() else 'cpu',
        'device_name': torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU',
        'device_count': torch.cuda.device_count() if torch.cuda.is_available() else 1,
        'memory_allocated': torch.cuda.memory_allocated() if torch.cuda.is_available() else 0,
        'memory_reserved': torch.cuda.memory_reserved() if torch.cuda.is_available() else 0,
    }
    
    # Integrate advanced modules if enabled
    if enable_advanced:
        integrate_advanced_modules(self)

# Apply the patch
Doctor.__init__ = patched_doctor_init
