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

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

import numpy as np
from qiskit.algorithms import QAOA, VQE
from qiskit.algorithms.optimizers import SPSA
from qiskit.circuit.library import TwoLocal


@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
                ]
            }

            with open(history_file, "w") as f:
                json.dump(history_data, f)

        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():
                with open(history_file, "r") as f:
                    history_data = json.load(f)

                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
