"""Advanced self-learning module for quantum-enhanced machine learning."""

import logging
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional

import aiofiles
import numpy as np


# Mock Qiskit algorithms for compatibility
class QAOA:
    def __init__(self, *args, **kwargs):
        pass


class VQE:
    def __init__(self, *args, **kwargs):
        pass


class SPSA:
    def __init__(self, *args, **kwargs):
        pass


# Mock Qiskit classes for compatibility
class TwoLocal:
    def __init__(self, *args, **kwargs):
        pass


@dataclass
class LearningMetrics:
    """Metrics for tracking learning progress"""

    timestamp: datetime
    performance_score: float
    quantum_speedup: float
    coherence_time: float
    entanglement_quality: float
    error_rate: float
    learning_rate: float
    model_complexity: float
    adaptation_speed: float


class SelfLearningSystem:
    """Advanced self-learning system for quantum-enhanced machine learning"""

    def __init__(
        self,
        learning_rate: float = 0.01,
        adaptation_threshold: float = 0.1,
        max_complexity: float = 1.0,
        save_path: str = "learning_history",
        use_advanced_learning: bool = True,
        use_quantum_optimization: bool = True,
        use_meta_learning: bool = True,
    ):
        self.learning_rate = learning_rate
        self.adaptation_threshold = adaptation_threshold
        self.max_complexity = max_complexity
        self.save_path = Path(save_path)
        self.use_advanced_learning = use_advanced_learning
        self.use_quantum_optimization = use_quantum_optimization
        self.use_meta_learning = use_meta_learning

        # Initialize learning components
        self.learning_history: List[LearningMetrics] = []
        self.performance_history: List[float] = []
        self.adaptation_history: List[Dict] = []
        self.complexity_history: List[float] = []

        # Initialize quantum components
        self.quantum_optimizer: Optional[Dict] = None
        self.meta_learner: Optional[Dict] = None

        # Setup logging
        self.logger = logging.getLogger(__name__)
        self.logger.setLevel(logging.INFO)

        # Create save directory
        self.save_path.mkdir(parents=True, exist_ok=True)

    async def initialize(self):
        """Initialize the self-learning system"""
        try:
            # Initialize quantum optimizer
            if self.use_quantum_optimization:
                self.quantum_optimizer = self._initialize_quantum_optimizer()

            # Initialize meta learner
            if self.use_meta_learning:
                self.meta_learner = self._initialize_meta_learner()

            # Load learning history if exists
            await self._load_learning_history()

            self.logger.info("Self-learning system initialized successfully")

        except Exception as e:
            self.logger.error(f"Error initializing self-learning system: {str(e)}")
            raise

    def _initialize_quantum_optimizer(self) -> Dict:
        """Initialize quantum optimizer for self-improvement"""
        return {
            "vqe": VQE(
                ansatz=TwoLocal(
                    4, rotation_blocks=["ry", "rz"], entanglement_blocks="cz"
                ),
                optimizer=SPSA(maxiter=100),
            ),
            "qaoa": QAOA(optimizer=SPSA(maxiter=100), reps=2),
        }

    def _initialize_meta_learner(self) -> Dict:
        """Initialize meta-learning system"""
        return {
            "learning_rate": self.learning_rate,
            "adaptation_speed": 0.01,
            "complexity_factor": 0.1,
            "optimization_strategy": "adaptive",
        }

    async def learn(self, features: np.ndarray, labels: np.ndarray) -> None:
        """Learn from features and labels"""
        try:
            # Create current metrics
            current_metrics = {
                "performance_score": np.mean(labels),
                "quantum_speedup": 1.0,
                "coherence_time": 0.0,
                "entanglement_quality": 0.0,
                "error_rate": 0.0,
                "model_complexity": features.shape[1] / 100.0,
                "adaptation_speed": 1.0 / len(features),
            }

            # Record current state
            learning_metrics = self._create_learning_metrics(current_metrics)
            self.learning_history.append(learning_metrics)

            # Analyze performance
            performance_analysis = self._analyze_performance(learning_metrics)

            # Determine adaptation needs
            adaptation_needs = self._determine_adaptation_needs(performance_analysis)

            # Apply quantum optimization if needed
            if self.use_quantum_optimization and self.quantum_optimizer is not None:
                if adaptation_needs["needs_optimization"]:
                    optimized_state = await self._apply_quantum_optimization(
                        {"features": features, "labels": labels},
                        {"coherence_time": current_metrics["coherence_time"]},
                    )
                    features = optimized_state.get("features", features)

            # Apply meta-learning if needed
            if self.use_meta_learning and self.meta_learner is not None:
                if adaptation_needs["needs_meta_learning"]:
                    meta_learning_results = await self._apply_meta_learning(
                        {"features": features, "labels": labels}, performance_analysis
                    )
                    self.learning_rate = meta_learning_results.get(
                        "learning_rate", self.learning_rate
                    )

            # Update learning parameters
            self._update_learning_parameters(adaptation_needs)

            # Save learning history
            await self._save_learning_history()

        except Exception as e:
            self.logger.error(f"Error during learning: {str(e)}")
            raise

    def _create_learning_metrics(self, current_metrics: Dict) -> LearningMetrics:
        """Create learning metrics from current performance"""
        return LearningMetrics(
            timestamp=datetime.now(),
            performance_score=current_metrics.get("performance_score", 0.0),
            quantum_speedup=current_metrics.get("quantum_speedup", 1.0),
            coherence_time=current_metrics.get("coherence_time", 0.0),
            entanglement_quality=current_metrics.get("entanglement_quality", 0.0),
            error_rate=current_metrics.get("error_rate", 0.0),
            learning_rate=self.learning_rate,
            model_complexity=current_metrics.get("model_complexity", 0.0),
            adaptation_speed=current_metrics.get("adaptation_speed", 0.0),
        )

    def _analyze_performance(self, metrics: LearningMetrics) -> Dict:
        """Analyze current performance and identify areas for improvement"""
        analysis = {
            "performance_trend": self._calculate_performance_trend(),
            "quantum_efficiency": self._calculate_quantum_efficiency(metrics),
            "learning_effectiveness": self._calculate_learning_effectiveness(),
            "adaptation_needs": self._calculate_adaptation_needs(metrics),
        }

        return analysis

    def _calculate_performance_trend(self) -> float:
        """Calculate the trend in performance over time"""
        if len(self.performance_history) < 2:
            return 0.0

        recent_performance = [
            m.performance_score for m in self.performance_history[-5:]
        ]
        return np.polyfit(range(len(recent_performance)), recent_performance, 1)[0]

    def _calculate_quantum_efficiency(self, metrics: LearningMetrics) -> float:
        """Calculate quantum computing efficiency"""
        return metrics.quantum_speedup * (1 - metrics.error_rate)

    def _calculate_learning_effectiveness(self) -> float:
        """Calculate learning effectiveness"""
        if len(self.learning_history) < 2:
            return 0.0

        recent_learning = [m.learning_rate for m in self.learning_history[-5:]]
        return np.mean(recent_learning)

    def _calculate_adaptation_needs(self, metrics: LearningMetrics) -> Dict:
        """Calculate adaptation needs based on current metrics"""
        return {
            "needs_optimization": metrics.performance_score < 0.8,
            "needs_meta_learning": metrics.adaptation_speed < 0.1,
            "needs_complexity_reduction": metrics.model_complexity > 0.8,
            "needs_error_reduction": metrics.error_rate > 0.1,
        }

    def _determine_adaptation_needs(self, analysis: Dict) -> Dict:
        """Determine adaptation needs based on performance analysis"""
        needs = {
            "needs_optimization": False,
            "needs_meta_learning": False,
            "needs_complexity_reduction": False,
            "needs_error_reduction": False,
        }

        # Check performance trend
        if analysis["performance_trend"] < 0:
            needs["needs_optimization"] = True

        # Check quantum efficiency
        if analysis["quantum_efficiency"] < 0.7:
            needs["needs_meta_learning"] = True

        # Check learning effectiveness
        if analysis["learning_effectiveness"] < 0.5:
            needs["needs_complexity_reduction"] = True

        # Check adaptation needs
        if analysis["adaptation_needs"] > self.adaptation_threshold:
            needs["needs_error_reduction"] = True

        return needs

    async def _apply_quantum_optimization(
        self, model_state: Dict, quantum_state: Dict
    ) -> Dict:
        """Apply quantum optimization to improve model state"""
        if self.quantum_optimizer is None:
            raise RuntimeError("Quantum optimizer not initialized")

        try:
            # Extract features and labels
            features = model_state["features"]
            labels = model_state["labels"]

            # Apply VQE optimization
            vqe_result = self.quantum_optimizer["vqe"].compute_minimum_eigenvalue(
                features.reshape(1, -1)
            )

            # Apply QAOA optimization
            qaoa_result = self.quantum_optimizer["qaoa"].compute_minimum_eigenvalue(
                features.reshape(1, -1)
            )

            # Combine results
            optimized_features = 0.5 * (vqe_result.eigenstate + qaoa_result.eigenstate)

            return {
                "features": optimized_features,
                "labels": labels,
                "vqe_energy": vqe_result.eigenvalue,
                "qaoa_energy": qaoa_result.eigenvalue,
            }

        except Exception as e:
            self.logger.error(f"Error during quantum optimization: {str(e)}")
            return model_state

    async def _apply_meta_learning(
        self, model_state: Dict, performance_analysis: Dict
    ) -> Dict:
        """Apply meta-learning to improve learning parameters"""
        if self.meta_learner is None:
            raise RuntimeError("Meta learner not initialized")

        try:
            # Extract current parameters
            current_lr = self.meta_learner["learning_rate"]
            current_speed = self.meta_learner["adaptation_speed"]
            current_complexity = self.meta_learner["complexity_factor"]

            # Calculate performance metrics
            performance_trend = performance_analysis["performance_trend"]
            quantum_efficiency = performance_analysis["quantum_efficiency"]
            learning_effectiveness = performance_analysis["learning_effectiveness"]

            # Update learning rate based on performance
            if performance_trend < 0:
                new_lr = current_lr * 0.9
            else:
                new_lr = current_lr * 1.1

            # Update adaptation speed based on quantum efficiency
            if quantum_efficiency < 0.5:
                new_speed = current_speed * 1.2
            else:
                new_speed = current_speed * 0.8

            # Update complexity factor based on learning effectiveness
            if learning_effectiveness < 0.3:
                new_complexity = current_complexity * 0.9
            else:
                new_complexity = current_complexity * 1.1

            # Update meta learner parameters
            self.meta_learner.update(
                {
                    "learning_rate": new_lr,
                    "adaptation_speed": new_speed,
                    "complexity_factor": new_complexity,
                }
            )

            return {
                "learning_rate": new_lr,
                "adaptation_speed": new_speed,
                "complexity_factor": new_complexity,
            }

        except Exception as e:
            self.logger.error(f"Error during meta-learning: {str(e)}")
            return {
                "learning_rate": self.meta_learner["learning_rate"],
                "adaptation_speed": self.meta_learner["adaptation_speed"],
                "complexity_factor": self.meta_learner["complexity_factor"],
            }

    def _update_learning_parameters(self, adaptation_needs: Dict):
        """Update learning parameters based on adaptation needs"""
        if adaptation_needs["needs_optimization"]:
            self.learning_rate *= 1.1

        if adaptation_needs["needs_meta_learning"]:
            self.learning_rate *= 1.2

        if adaptation_needs["needs_complexity_reduction"]:
            self.learning_rate *= 0.9

        if adaptation_needs["needs_error_reduction"]:
            self.learning_rate *= 0.8

    async def _save_learning_history(self):
        """Save learning history to disk"""
        try:
            history_file = self.save_path / "learning_history.json"
            history_data = {
                "metrics": [
                    {
                        "timestamp": m.timestamp.isoformat(),
                        "performance_score": m.performance_score,
                        "quantum_speedup": m.quantum_speedup,
                        "coherence_time": m.coherence_time,
                        "entanglement_quality": m.entanglement_quality,
                        "error_rate": m.error_rate,
                        "learning_rate": m.learning_rate,
                        "model_complexity": m.model_complexity,
                        "adaptation_speed": m.adaptation_speed,
                    }
                    for m in self.learning_history
                ]
            }

            import json

            async with aiofiles.open(history_file, "w") as f:
                await f.write(json.dumps(history_data))

        except Exception as e:
            self.logger.error(f"Error saving learning history: {str(e)}")

    async def _load_learning_history(self):
        """Load learning history from disk"""
        try:
            history_file = self.save_path / "learning_history.json"
            if history_file.exists():
                import json

                async with aiofiles.open(history_file, "r") as f:
                    data = await f.read()
                    history_data = json.loads(data)

                self.learning_history = [
                    LearningMetrics(
                        timestamp=datetime.fromisoformat(m["timestamp"]),
                        performance_score=m["performance_score"],
                        quantum_speedup=m["quantum_speedup"],
                        coherence_time=m["coherence_time"],
                        entanglement_quality=m["entanglement_quality"],
                        error_rate=m["error_rate"],
                        learning_rate=m["learning_rate"],
                        model_complexity=m["model_complexity"],
                        adaptation_speed=m["adaptation_speed"],
                    )
                    for m in history_data["metrics"]
                ]

        except Exception as e:
            self.logger.error(f"Error loading learning history: {str(e)}")

    def get_learning_metrics(self) -> Dict:
        """Get current learning metrics"""
        if not self.learning_history:
            return {}

        latest_metrics = self.learning_history[-1]
        return {
            "performance_score": latest_metrics.performance_score,
            "quantum_speedup": latest_metrics.quantum_speedup,
            "coherence_time": latest_metrics.coherence_time,
            "entanglement_quality": latest_metrics.entanglement_quality,
            "error_rate": latest_metrics.error_rate,
            "learning_rate": latest_metrics.learning_rate,
            "model_complexity": latest_metrics.model_complexity,
            "adaptation_speed": latest_metrics.adaptation_speed,
        }

    def get_learning_history(self) -> List[LearningMetrics]:
        """Get complete learning history"""
        return self.learning_history


class QuantumSelfLearning:
    """Quantum Self-Learning System for advanced quantum-enhanced machine learning"""

    def __init__(self, learning_rate: float = 0.01, max_iterations: int = 100):
        self.learning_rate = learning_rate
        self.max_iterations = max_iterations
        self.learning_system = SelfLearningSystem(
            learning_rate=learning_rate,
            use_quantum_optimization=True,
            use_meta_learning=True,
        )

    async def initialize(self):
        """Initialize the quantum self-learning system"""
        await self.learning_system.initialize()

    async def learn(self, features: np.ndarray, labels: np.ndarray):
        """Learn from data using quantum-enhanced methods"""
        await self.learning_system.learn(features, labels)

    def get_metrics(self) -> Dict:
        """Get current learning metrics"""
        return self.learning_system.get_learning_metrics()

    def get_history(self) -> List[LearningMetrics]:
        """Get learning history"""
        return self.learning_system.get_learning_history()
