"""
Quantum-Enhanced Attention Mechanism
"""

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

import cirq
import numpy as np
import tensorflow as tf
from cirq.circuits.circuit import Circuit
from cirq.devices.line_qubit import LineQubit
from cirq.ops.common_gates import CNOT, H, Ry, Rz
from cirq.sim.sparse_simulator import Simulator
from cirq.study.result import Result


@dataclass
class QuantumAttentionConfig:
    """Configuration for quantum attention mechanism."""

    num_qubits: int = 4
    num_layers: int = 2
    rotation_angle: float = np.pi / 4
    use_attention: bool = True


class QuantumAttention:
    """Quantum-enhanced attention mechanism with advanced features."""

    def __init__(self, config: Optional[QuantumAttentionConfig] = None):
        self.config = config or QuantumAttentionConfig()
        self.logger = logging.getLogger(__name__)
        self.quantum_circuit = None
        self._build_quantum_circuit()

    def _build_quantum_circuit(self) -> None:
        """Build quantum circuit for attention computation."""
        try:
            # Create quantum registers
            qubits = LineQubit.range(self.config.num_qubits)
            self.quantum_circuit = Circuit()

            # Apply quantum gates for attention computation
            self._apply_quantum_gates(qubits)

        except Exception as e:
            self.logger.error(f"Failed to build quantum circuit: {str(e)}")
            raise

    def _apply_quantum_gates(self, qubits: List[LineQubit]) -> None:
        """Apply quantum gates for attention computation."""
        if self.quantum_circuit is None:
            raise RuntimeError("Quantum circuit not initialized")

        try:
            # Apply Hadamard gates for superposition
            for qubit in qubits:
                self.quantum_circuit.append(H(qubit))

            # Apply CNOT gates for entanglement
            for i in range(len(qubits) - 1):
                self.quantum_circuit.append(CNOT(qubits[i], qubits[i + 1]))

            # Apply rotation gates for attention weights
            for qubit in qubits:
                self.quantum_circuit.append(Rz(self.config.rotation_angle)(qubit))

        except Exception as e:
            self.logger.error(f"Failed to apply quantum gates: {str(e)}")
            raise

    def compute_attention(self, features: np.ndarray) -> np.ndarray:
        """Compute quantum attention weights."""
        if features is None or len(features) == 0:
            raise ValueError("Features cannot be None or empty")

        try:
            # Build and execute quantum circuit
            circuit = self._build_quantum_circuit_for_features(features)

            # Simulate circuit
            simulator = Simulator()
            result = simulator.simulate(circuit)

            # Process results
            state_vector = result.final_state_vector
            attention_weights = np.abs(state_vector) ** 2
            attention_weights = attention_weights / np.sum(attention_weights)

            return attention_weights
        except Exception as e:
            self.logger.error(f"Error computing attention: {str(e)}")
            raise

    def _initialize_quantum_state(
        self, circuit: Circuit, qubits: List[LineQubit], features: np.ndarray
    ) -> None:
        """Initialize quantum state with feature values."""
        for i, qubit in enumerate(qubits):
            if i < len(features):
                circuit.append(Ry(features[i])(qubit))

    def _apply_attention_layer(self, circuit: Circuit, qubits: List[LineQubit]) -> None:
        """Apply a single layer of attention gates."""
        for j in range(self.config.num_qubits):
            circuit.append(H(qubits[j]))
            for k in range(j + 1, self.config.num_qubits):
                circuit.append(CNOT(qubits[j], qubits[k]))
                circuit.append(Rz(self.config.rotation_angle)(qubits[k]))
                circuit.append(CNOT(qubits[j], qubits[k]))

    def _build_quantum_circuit_for_features(self, features: np.ndarray) -> Circuit:
        """Build a quantum circuit for attention computation."""
        if features is None or len(features) == 0:
            raise ValueError("Features cannot be None or empty")

        try:
            qubits = LineQubit.range(self.config.num_qubits)
            circuit = Circuit()

            # Initialize quantum state
            self._initialize_quantum_state(circuit, qubits, features)

            # Apply attention gates
            if self.config.use_attention:
                for _ in range(self.config.num_layers):
                    self._apply_attention_layer(circuit, qubits)

            return circuit
        except Exception as e:
            self.logger.error(f"Error building quantum circuit: {str(e)}")
            raise

    def get_config(self) -> Dict:
        """Get configuration dictionary."""
        return {
            "num_qubits": self.config.num_qubits,
            "num_layers": self.config.num_layers,
            "rotation_angle": self.config.rotation_angle,
            "use_attention": self.config.use_attention,
        }

    @classmethod
    def from_config(cls, config: Dict) -> "QuantumAttention":
        """Create instance from configuration dictionary."""
        return cls(QuantumAttentionConfig(**config))

    def _apply_quantum_attention(self, data: np.ndarray) -> np.ndarray:
        """Apply quantum attention to the input data."""
        if not self.initialized:
            raise RuntimeError("QuantumAttention must be initialized before use")

        # Normalize input data
        normalized_data = self._normalize_data(data)

        # Apply quantum operations
        quantum_state = self._prepare_quantum_state(normalized_data)
        quantum_state = self._apply_attention_gates(quantum_state)

        # Measure and process results
        measurements = self._measure_quantum_state(quantum_state)
        return self._process_measurements(measurements)
