"""Quantum intelligence implementation."""

import logging
from typing import Any, Dict, List, Optional

import numpy as np
from numpy.typing import NDArray

from ..ml.enhanced_xgboost import EnhancedXGBoost
from ..quantum.circuit import QuantumCircuit
from ..quantum.processor import QuantumProcessor

logger = logging.getLogger(__name__)


class QuantumIntelligence:
    """Quantum intelligence for market analysis."""

    def __init__(
        self,
        n_qubits: int = 4,
        n_layers: int = 2,
        use_advanced_circuits: bool = True,
        use_error_mitigation: bool = True,
    ):
        """Initialize quantum intelligence.

        Args:
            n_qubits: Number of qubits to use
            n_layers: Number of layers in the quantum circuit
            use_advanced_circuits: Whether to use advanced quantum circuits
            use_error_mitigation: Whether to use error mitigation
        """
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.use_advanced_circuits = use_advanced_circuits
        self.use_error_mitigation = use_error_mitigation

        # Initialize quantum components
        self.circuit = QuantumCircuit(
            n_qubits=n_qubits,
            n_layers=n_layers,
            use_advanced_circuits=use_advanced_circuits,
            use_error_mitigation=use_error_mitigation,
        )
        self.processor = QuantumProcessor()
        self.ml_model = EnhancedXGBoost()

        # Initialize metrics
        self.metrics: Dict[str, float] = {
            "quantum_accuracy": 0.0,
            "ml_accuracy": 0.0,
            "hybrid_accuracy": 0.0,
            "processing_time": 0.0,
        }
        self.quantum_processor: Optional[Any] = (
            None  # Replace Any with actual processor type
        )
        self.adaptation_history: List[Dict[str, Any]] = []
        self.optimization_history: List[Dict[str, Any]] = []

    async def analyze_market_data(
        self, market_data: NDArray[np.float64]
    ) -> Dict[str, Any]:
        """Analyze market data using quantum and classical methods.

        Args:
            market_data: Market data array to analyze

        Returns:
            Dict containing analysis results
        """
        try:
            # Process quantum features
            quantum_features = await self._process_quantum_features(market_data)

            # Get ML predictions
            if self.ml_model is None:
                raise ValueError("ML model not initialized")

            ml_predictions = await self.ml_model.predict(market_data)

            # Combine results
            hybrid_predictions = self._combine_predictions(
                quantum_features, ml_predictions
            )

            # Update metrics
            self._update_metrics(hybrid_predictions)

            return {
                "quantum_features": quantum_features,
                "ml_predictions": ml_predictions,
                "hybrid_predictions": hybrid_predictions,
                "metrics": self.metrics,
            }

        except Exception as e:
            logger.error(f"Error analyzing market data: {str(e)}")
            raise

    def _combine_predictions(
        self, quantum_features: NDArray[np.float64], ml_predictions: NDArray[np.float64]
    ) -> NDArray[np.float64]:
        """Combine quantum and classical predictions.

        Args:
            quantum_features: Features from quantum processing
            ml_predictions: Predictions from classical ML

        Returns:
            Combined predictions
        """
        # Simple weighted average for now
        weights = np.array([0.5, 0.5])  # Equal weights
        return weights[0] * quantum_features + weights[1] * ml_predictions

    def _update_metrics(self, predictions: NDArray[np.float64]) -> None:
        """Update performance metrics.

        Args:
            predictions: Model predictions
        """
        # Placeholder for actual metric calculation
        self.metrics["hybrid_accuracy"] = 0.8
        self.metrics["processing_time"] = 0.1

    async def _process_quantum_features(
        self, data: NDArray[np.float64]
    ) -> NDArray[np.float64]:
        """Process data using quantum methods.

        Args:
            data: Input data to process

        Returns:
            Processed quantum features
        """
        if self.quantum_processor is None:
            raise ValueError("Quantum processor not initialized")

        # Placeholder for actual quantum processing
        return np.array(data)  # Replace with actual quantum processing

    def _query_quantum_memory(self, key: str) -> Optional[NDArray[np.float64]]:
        """Query quantum memory for stored patterns.

        Args:
            key: Key to query in quantum memory

        Returns:
            Retrieved pattern or None if not found
        """
        return None  # Placeholder implementation

    def _apply_quantum_transformations(
        self, data: NDArray[np.float64]
    ) -> NDArray[np.float64]:
        """Apply quantum transformations to data.

        Args:
            data: Input data to transform

        Returns:
            Transformed data
        """
        return data  # Placeholder implementation

    def _update_attention_weights(self, weights: NDArray[np.float64]) -> None:
        """Update attention weights for quantum processing.

        Args:
            weights: New attention weights
        """
        pass  # Placeholder implementation

    def _calculate_optimization_score(self, metrics: Dict[str, float]) -> float:
        """Calculate optimization score based on metrics.

        Args:
            metrics: Performance metrics

        Returns:
            Optimization score
        """
        return 0.0  # Placeholder implementation
