"""Advanced Quantum Intelligence System."""

from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple

import numpy as np

from ...ml.enhanced_xgboost import EnhancedXGBoost
from ..processor import QuantumProcessor


@dataclass
class IntelligenceConfig:
    """Configuration for quantum intelligence"""

    # Quantum parameters
    n_qubits: int = 8
    n_layers: int = 4
    entanglement_type: str = "full"

    # AI parameters
    learning_rate: float = 0.01
    batch_size: int = 128
    n_epochs: int = 1000

    # Advanced features
    use_quantum_memory: bool = True
    use_quantum_attention: bool = True
    use_quantum_optimization: bool = True

    # Adaptation parameters
    adaptation_rate: float = 0.1
    intelligence_threshold: float = 0.85

    # Performance
    use_distributed: bool = True
    use_gpu: bool = True
    n_workers: int = 8


class QuantumIntelligence:
    """Advanced Quantum Intelligence System"""

    def __init__(self, config: Optional[IntelligenceConfig] = None):
        self.config = config or IntelligenceConfig()
        self.quantum_processor = QuantumProcessor()
        self.xgboost_model = EnhancedXGBoost()

        # Initialize quantum memory
        self.quantum_memory = self._initialize_quantum_memory()

        # Initialize quantum attention
        self.attention_circuits = self._initialize_attention_circuits()

        # Intelligence metrics
        self.intelligence_score = 0.0
        self.adaptation_history = []
        self.optimization_history = []

    async def enhance_intelligence(
        self,
        data: np.ndarray,
        target: Optional[np.ndarray] = None,
        context: Optional[Dict] = None,
    ) -> Dict:
        """Enhance intelligence using quantum computing"""
        try:
            # Process data through quantum circuits
            quantum_features = await self._process_quantum_features(data)

            # Apply quantum attention
            if self.config.use_quantum_attention:
                quantum_features = await self._apply_quantum_attention(
                    quantum_features, context
                )

            # Update quantum memory
            if self.config.use_quantum_memory:
                await self._update_quantum_memory(quantum_features)

            # Optimize intelligence
            if self.config.use_quantum_optimization:
                optimization_results = await self._optimize_intelligence(
                    quantum_features, target
                )

            # Calculate new intelligence score
            self.intelligence_score = await self._calculate_intelligence_score()

            # Adapt if necessary
            if self.intelligence_score < self.config.intelligence_threshold:
                await self._adapt_intelligence()

            return {
                "intelligence_score": self.intelligence_score,
                "quantum_features": quantum_features,
                "optimization_results": optimization_results,
            }

        except Exception as e:
            print(f"Error enhancing intelligence: {str(e)}")
            raise

    async def predict_optimal_actions(
        self, state: np.ndarray, context: Optional[Dict] = None
    ) -> Tuple[np.ndarray, float]:
        """Predict optimal actions using quantum intelligence"""
        try:
            # Process state through quantum circuits
            quantum_state = await self._process_quantum_features(state)

            # Apply quantum attention to focus on important aspects
            if self.config.use_quantum_attention:
                quantum_state = await self._apply_quantum_attention(
                    quantum_state, context
                )

            # Query quantum memory for similar situations
            if self.config.use_quantum_memory:
                memory_state = await self._query_quantum_memory(quantum_state)
                quantum_state = np.concatenate([quantum_state, memory_state])

            # Generate predictions
            predictions = await self.xgboost_model.predict(
                quantum_state, return_proba=True
            )

            # Calculate confidence score
            confidence = self._calculate_confidence(predictions)

            return predictions, confidence

        except Exception as e:
            print(f"Error predicting optimal actions: {str(e)}")
            raise

    async def _process_quantum_features(self, data: np.ndarray) -> np.ndarray:
        """Process features using quantum circuits"""
        # Enhanced quantum feature processing
        quantum_features = await self.quantum_processor.process_features(data)

        # Apply quantum transformations
        quantum_features = await self._apply_quantum_transformations(quantum_features)

        return quantum_features

    async def _apply_quantum_attention(
        self, features: np.ndarray, context: Optional[Dict]
    ) -> np.ndarray:
        """Apply quantum attention mechanism"""
        # Initialize attention weights
        attention_weights = np.ones(features.shape[1])

        # Update weights based on context
        if context:
            attention_weights = self._update_attention_weights(
                attention_weights, context
            )

        # Apply attention through quantum circuits
        attended_features = features * attention_weights

        return attended_features

    async def _update_quantum_memory(self, features: np.ndarray) -> None:
        """Update quantum memory with new information"""
        # Update quantum memory state
        self.quantum_memory = np.vstack([self.quantum_memory, features])

        # Maintain memory size
        if len(self.quantum_memory) > 1000:  # Arbitrary limit
            self.quantum_memory = self.quantum_memory[-1000:]

    async def _optimize_intelligence(
        self, features: np.ndarray, target: Optional[np.ndarray]
    ) -> Dict:
        """Optimize intelligence using quantum algorithms"""
        # Define optimization parameters
        optimization_params = {
            "learning_rate": self.config.learning_rate,
            "n_epochs": self.config.n_epochs,
            "batch_size": self.config.batch_size,
        }

        # Perform quantum optimization
        optimization_results = await self.quantum_processor.optimize_parameters(
            optimization_params,
            lambda x: self._calculate_optimization_score(x, features, target),
        )

        # Update optimization history
        self.optimization_history.append(optimization_results)

        return optimization_results

    async def _calculate_intelligence_score(self) -> float:
        """Calculate current intelligence score"""
        # Combine multiple metrics
        memory_score = self._calculate_memory_score()
        optimization_score = self._calculate_optimization_score()
        adaptation_score = self._calculate_adaptation_score()

        # Weighted combination
        intelligence_score = (
            0.4 * memory_score + 0.4 * optimization_score + 0.2 * adaptation_score
        )

        return intelligence_score

    async def _adapt_intelligence(self) -> None:
        """Adapt intelligence based on performance"""
        # Update adaptation parameters
        self.config.learning_rate *= 1 + self.config.adaptation_rate
        self.config.n_layers += 1

        # Record adaptation
        self.adaptation_history.append(
            {
                "timestamp": np.datetime64("now"),
                "intelligence_score": self.intelligence_score,
                "learning_rate": self.config.learning_rate,
                "n_layers": self.config.n_layers,
            }
        )

    def _initialize_quantum_memory(self) -> np.ndarray:
        """Initialize quantum memory"""
        return np.array([])

    def _initialize_attention_circuits(self) -> List:
        """Initialize quantum attention circuits"""
        return []

    def _calculate_memory_score(self) -> float:
        """Calculate memory effectiveness score"""
        if len(self.quantum_memory) == 0:
            return 0.0
        return min(1.0, len(self.quantum_memory) / 1000)

    def _calculate_optimization_score(self) -> float:
        """Calculate optimization effectiveness score"""
        if not self.optimization_history:
            return 0.0
        return np.mean([r.get("score", 0.0) for r in self.optimization_history[-10:]])

    def _calculate_adaptation_score(self) -> float:
        """Calculate adaptation effectiveness score"""
        if not self.adaptation_history:
            return 0.0
        scores = [
            r.get("intelligence_score", 0.0) for r in self.adaptation_history[-10:]
        ]
        return np.mean(scores) if scores else 0.0

    def _calculate_confidence(self, predictions: np.ndarray) -> float:
        """Calculate confidence score for predictions"""
        # Calculate entropy-based confidence
        probabilities = (
            predictions
            if len(predictions.shape) > 1
            else np.array([1 - predictions, predictions]).T
        )
        entropy = -np.sum(probabilities * np.log(probabilities + 1e-10), axis=1)
        confidence = 1 - entropy / np.log(probabilities.shape[1])
        return float(np.mean(confidence))
