"""
Quantum computing features for Bleu.js
Requires: pip install 'bleu-js[quantum]'
"""

import logging
from typing import Any, Optional

import numpy as np

logger = logging.getLogger(__name__)


class QuantumFeatureExtractor:
    """
    Extract quantum-enhanced features from data.

    Args:
        num_qubits (int): Number of qubits to use
        entanglement_type (str): Type of entanglement ('full', 'linear', 'circular')

    Example:
        >>> extractor = QuantumFeatureExtractor(num_qubits=4)
        >>> features = extractor.extract(data)
    """

    def __init__(self, num_qubits: int = 4, entanglement_type: str = "full", **kwargs):
        """Initialize quantum feature extractor."""
        self.num_qubits = num_qubits
        self.entanglement_type = entanglement_type
        self.config = kwargs

        logger.info(f"Initialized QuantumFeatureExtractor with {num_qubits} qubits")

        # Try to import quantum libraries
        self.qiskit_available = self._check_qiskit()
        self.pennylane_available = self._check_pennylane()

        if not (self.qiskit_available or self.pennylane_available):
            logger.warning(
                "⚠️ No quantum libraries found. "
                "Install with: pip install 'bleu-js[quantum]'"
            )

    def _check_qiskit(self) -> bool:
        """Check if Qiskit is available."""
        try:
            import qiskit

            logger.info("✅ Qiskit available")
            return True
        except ImportError:
            return False

    def _check_pennylane(self) -> bool:
        """Check if PennyLane is available."""
        try:
            import pennylane

            logger.info("✅ PennyLane available")
            return True
        except ImportError:
            return False

    def extract(
        self, data: np.ndarray, use_entanglement: bool = True, **kwargs
    ) -> np.ndarray:
        """
        Extract quantum features from input data.

        Args:
            data: Input data array
            use_entanglement: Enable quantum entanglement
            **kwargs: Additional options

        Returns:
            Quantum-enhanced feature array
        """
        if isinstance(data, list):
            data = np.array(data)

        if not isinstance(data, np.ndarray):
            data = np.array([data])

        # If quantum libraries available, use them
        if self.qiskit_available:
            return self._extract_with_qiskit(data, use_entanglement)
        elif self.pennylane_available:
            return self._extract_with_pennylane(data, use_entanglement)
        else:
            # Fallback to classical simulation
            logger.info("Using classical simulation of quantum features")
            return self._classical_simulation(data)

    def _extract_with_qiskit(
        self, data: np.ndarray, use_entanglement: bool
    ) -> np.ndarray:
        """Extract features using Qiskit."""
        try:
            from qiskit import QuantumCircuit
            from qiskit.quantum_info import Statevector

            # Create quantum circuit
            qc = QuantumCircuit(self.num_qubits)

            # Encode data
            for i in range(min(len(data.flatten()), self.num_qubits)):
                angle = float(data.flatten()[i]) * np.pi / 2
                qc.ry(angle, i)

            # Add entanglement if requested
            if use_entanglement and self.entanglement_type == "full":
                for i in range(self.num_qubits - 1):
                    qc.cx(i, i + 1)

            # Get statevector
            sv = Statevector.from_instruction(qc)
            quantum_features = np.abs(sv.data) ** 2

            logger.info("✅ Extracted quantum features using Qiskit")
            return quantum_features

        except Exception as e:
            logger.warning(f"Qiskit extraction failed: {e}, falling back to classical")
            return self._classical_simulation(data)

    def _extract_with_pennylane(
        self, data: np.ndarray, use_entanglement: bool
    ) -> np.ndarray:
        """Extract features using PennyLane."""
        try:
            import pennylane as qml

            dev = qml.device("default.qubit", wires=self.num_qubits)

            @qml.qnode(dev)
            def quantum_circuit(inputs):
                # Encode data
                for i in range(min(len(inputs), self.num_qubits)):
                    qml.RY(inputs[i] * np.pi / 2, wires=i)

                # Add entanglement
                if use_entanglement:
                    for i in range(self.num_qubits - 1):
                        qml.CNOT(wires=[i, i + 1])

                return [qml.expval(qml.PauliZ(i)) for i in range(self.num_qubits)]

            quantum_features = quantum_circuit(data.flatten()[: self.num_qubits])

            logger.info("✅ Extracted quantum features using PennyLane")
            return np.array(quantum_features)

        except Exception as e:
            logger.warning(
                f"PennyLane extraction failed: {e}, falling back to classical"
            )
            return self._classical_simulation(data)

    def _classical_simulation(self, data: np.ndarray) -> np.ndarray:
        """
        Classical simulation of quantum feature extraction.
        Uses mathematical transformations that approximate quantum behavior.
        """
        # Flatten and normalize data
        flat_data = data.flatten()
        normalized = (flat_data - flat_data.mean()) / (flat_data.std() + 1e-8)

        # Simulate quantum-inspired transformations
        # This is a simplified approximation
        quantum_like = np.zeros(2**self.num_qubits)

        for i in range(min(len(normalized), self.num_qubits)):
            angle = normalized[i] * np.pi / 2
            quantum_like[i] = np.cos(angle) ** 2
            quantum_like[i + self.num_qubits] = np.sin(angle) ** 2

        # Normalize
        quantum_like = quantum_like / (np.linalg.norm(quantum_like) + 1e-8)

        logger.info("✅ Generated quantum-inspired features (classical simulation)")
        return quantum_like


class QuantumAttention:
    """
    Attention-style layer with a classical (simulated) implementation.

    This is a classical simulation only: it uses random projections and
    softmax-style weighting. It does not run quantum circuits. The name
    is kept for API compatibility; for real quantum attention, use
    QuantumFeatureExtractor with Qiskit/PennyLane.

    Args:
        num_heads (int): Number of attention heads
        dim (int): Input dimension
        dropout (float): Dropout rate

    Example:
        >>> attention = QuantumAttention(num_heads=8, dim=512)
        >>> output = attention.process(input_data)
    """

    def __init__(
        self, num_heads: int = 8, dim: int = 512, dropout: float = 0.1, **kwargs
    ):
        """Initialize quantum attention mechanism."""
        self.num_heads = num_heads
        self.dim = dim
        self.dropout = dropout
        self.config = kwargs

        logger.info(f"Initialized QuantumAttention (heads={num_heads}, dim={dim})")

    def process(
        self, input_data: Any, quantum_enhanced: bool = True, **kwargs
    ) -> np.ndarray:
        """
        Process input with attention-style weighting (classical simulation).

        Uses a simple embedding and softmax-style weights. No quantum circuit
        is executed. The quantum_enhanced flag is accepted for API compatibility
        but does not change behavior.

        Args:
            input_data: Input data
            quantum_enhanced: Ignored; kept for API compatibility
            **kwargs: Additional options

        Returns:
            Attention-weighted output array
        """
        # Convert to array
        if isinstance(input_data, (list, str)):
            # For text data, create simple embedding
            data = np.random.randn(len(str(input_data)), self.dim)
        elif isinstance(input_data, np.ndarray):
            data = input_data
        else:
            data = np.array([input_data])

        # Apply attention (simplified)
        attention_weights = np.exp(data) / np.exp(data).sum(axis=-1, keepdims=True)
        output = data * attention_weights

        logger.info("✅ Applied quantum attention")
        return output


# Convenience exports
__all__ = [
    "QuantumFeatureExtractor",
    "QuantumAttention",
]
