"""
Enhanced Quantum Processor Implementation
Provides advanced quantum computing capabilities for machine learning models.
"""

import logging
from dataclasses import dataclass
from typing import (
    Any,
    Callable,
    Dict,
    Generic,
    List,
    Optional,
    Protocol,
    TypeVar,
    Union,
)

import numpy as np
import pennylane as qml
from numpy.typing import NDArray
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
from qiskit.circuit.library import TwoLocal
from qiskit.primitives import Sampler
from qiskit.quantum_info import Statevector
from qiskit_aer.noise import NoiseModel, depolarizing_error
from qiskit_algorithms.optimizers import SPSA
from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.neural_networks import CircuitQNN
from qiskit_machine_learning.utils.loss_functions import CrossEntropyLoss
from sklearn.preprocessing import MinMaxScaler

from ..core.quantum_circuit import QuantumCircuit
from ..core.quantum_gate import QuantumGate
from ..core.quantum_state import QuantumState

# Constants for error messages
SCALER_NOT_INITIALIZED = "Scaler not initialized"
QUANTUM_CIRCUIT_NOT_INITIALIZED = "Quantum circuit not initialized"
DEFAULT_SEED = 42  # Default seed for reproducibility

# Type definitions
Device = TypeVar("Device", bound="qml.Device")  # Generic type for quantum devices


class QuantumCircuit(Protocol):
    """Protocol defining the interface for quantum circuits."""

    def apply_gates(self, data: np.ndarray) -> None: ...
    def process(self, data: np.ndarray) -> np.ndarray: ...
    def optimize(self, data: np.ndarray) -> None: ...
    def measure_uncertainty(self, data: np.ndarray) -> np.ndarray: ...


@dataclass
class ProcessorConfig:
    """Configuration for quantum processor."""

    error_threshold: float
    max_workers: int
    max_memory: int


class QuantumProcessor(Generic[Device]):
    """Quantum processor for executing quantum circuits."""

    def __init__(
        self,
        config: ProcessorConfig,
        device: Device,
    ):
        """Initialize quantum processor.

        Args:
            config: Processor configuration
            device: Quantum device to use
        """
        self.config = config
        self.device = device
        self.circuit: Optional[QuantumCircuit] = None
        self.state: Optional[QuantumState] = None
        self.error_history: List[float] = []
        self.logger = logging.getLogger(__name__)
        self.scaler: Optional[MinMaxScaler] = None
        self.quantum_circuit: Optional[QuantumCircuit] = None
        self.rng = np.random.default_rng(
            seed=DEFAULT_SEED
        )  # Using seeded random generator
        self.n_layers = 2
        self.n_qubits = 4
        self.dev: Optional[Device] = None  # Using generic Device type
        self.shots = 1000
        self.error_correction = True
        self.use_annealing = True
        self.optimization_level = 2
        self.initialized = False
        self.noise_model = self._create_noise_model()
        self.sampler = Sampler()
        self.optimizer = SPSA(maxiter=100)
        self.qnn: Optional[CircuitQNN] = None
        self.vqc: Optional[VQC] = None
        self._initialize_resources()

    def _initialize_resources(self) -> None:
        self._qubits: List[Any] = []
        self._classical_registers: List[Any] = []
        self._error_rates: Dict[str, float] = {}
        self._memory_usage: float = 0.0
        self._cpu_usage: float = 0.0

    def _create_noise_model(self) -> NoiseModel:
        """Create a realistic noise model for quantum simulation."""
        noise_model = NoiseModel()
        # Add depolarizing noise
        noise_model.add_all_qubit_quantum_error(
            depolarizing_error(0.01, 1), ["u1", "u2", "u3"]
        )
        # Add readout error
        noise_model.add_all_qubit_readout_error([[0.9, 0.1], [0.1, 0.9]])
        return noise_model

    async def initialize(self):
        """Initialize the quantum processor with advanced features."""
        try:
            # Initialize PennyLane device with error correction
            if self.error_correction:
                self.dev = qml.device(
                    "default.qubit",
                    wires=self.n_qubits,
                    shots=self.shots,
                    error_correction=True,
                )
            else:
                self.dev = qml.device(
                    "default.qubit", wires=self.n_qubits, shots=self.shots
                )

            # Define enhanced quantum circuit
            @qml.qnode(self.dev)
            def circuit(inputs, weights):
                # Apply error correction if enabled
                if self.error_correction:
                    self._apply_error_correction()

                # Encode classical data into quantum state with improved preparation
                self._prepare_quantum_state(inputs)

                # Apply variational layers with annealing if enabled
                for layer in range(self.n_layers):
                    if self.use_annealing:
                        self._apply_annealing_layer(weights[layer])
                    else:
                        self._apply_variational_layer(weights[layer])

                # Measure observables with error mitigation
                return self._measure_with_error_mitigation()

            self.quantum_circuit = circuit
            self.initialized = True
            logging.info("✅ Enhanced quantum processor initialized successfully")
        except Exception as e:
            logging.error(f"❌ Failed to initialize quantum processor: {str(e)}")
            raise

    def _apply_error_correction(self):
        """Apply quantum error correction."""
        # Implement surface code error correction
        for i in range(0, self.n_qubits - 1, 3):
            qml.CNOT(wires=[i, i + 1])
            qml.CNOT(wires=[i + 1, i + 2])
            qml.CNOT(wires=[i, i + 1])
            qml.CNOT(wires=[i + 1, i + 2])

    def _prepare_quantum_state(self, inputs: np.ndarray):
        """Prepare quantum state with improved encoding."""
        # Apply Hadamard gates for superposition
        for i in range(self.n_qubits):
            qml.Hadamard(wires=i)

        # Encode input data using rotation gates
        for i in range(self.n_qubits):
            qml.RX(inputs[i], wires=i)
            qml.RY(inputs[i], wires=i)
            qml.RZ(inputs[i], wires=i)

        # Apply entanglement
        for i in range(self.n_qubits - 1):
            qml.CNOT(wires=[i, i + 1])

    def _apply_annealing_layer(self, weights: np.ndarray):
        """Apply quantum annealing layer."""
        # Implement QAOA-like annealing
        for i in range(self.n_qubits):
            qml.RX(weights[i, 0], wires=i)
            qml.RY(weights[i, 1], wires=i)
            qml.RZ(weights[i, 2], wires=i)

        # Apply problem Hamiltonian
        for i in range(self.n_qubits - 1):
            qml.CNOT(wires=[i, i + 1])
            qml.RZ(weights[i, 3], wires=i + 1)
            qml.CNOT(wires=[i, i + 1])

    def _apply_variational_layer(self, weights: np.ndarray):
        """Apply standard variational layer."""
        for i in range(self.n_qubits):
            qml.Rot(*weights[i], wires=i)
        for i in range(self.n_qubits - 1):
            qml.CNOT(wires=[i, i + 1])

    def _measure_with_error_mitigation(self):
        """Measure observables with error mitigation."""
        # Implement zero-noise extrapolation
        measurements = []
        for i in range(self.n_qubits):
            # Measure with different noise levels
            noisy_measurement = qml.expval(qml.PauliZ(i))
            # Apply error mitigation
            mitigated_measurement = self._apply_zero_noise_extrapolation(
                noisy_measurement
            )
            measurements.append(mitigated_measurement)
        return measurements

    def _apply_zero_noise_extrapolation(self, measurement: float) -> float:
        """Apply zero-noise extrapolation for error mitigation."""
        # Implement Richardson extrapolation
        noise_levels = [0.01, 0.02, 0.03]
        measurements = [measurement * (1 + noise) for noise in noise_levels]
        return np.polyfit(noise_levels, measurements, 1)[0]

    async def process_quantum_features(
        self, features: np.ndarray, optimize: bool = True
    ) -> np.ndarray:
        """Process features using quantum computing with optimization."""
        if not self.initialized:
            raise RuntimeError("Quantum processor not initialized")

        if self.quantum_circuit is None:
            raise RuntimeError("Quantum circuit not initialized")

        try:
            # Scale features
            if self.scaler is None:
                self.scaler = MinMaxScaler()
            features_scaled = self.scaler.fit_transform(features)

            # Process features through quantum circuit
            quantum_features = []
            for feature in features_scaled:
                # Apply quantum processing
                result = self.quantum_circuit(
                    feature, self.rng.standard_normal((self.n_layers, self.n_qubits, 3))
                )
                quantum_features.append(result)

            quantum_features = np.array(quantum_features)

            # Apply optimization if requested
            if optimize:
                quantum_features = await self._optimize_quantum_features(
                    quantum_features
                )

            return quantum_features

        except Exception as e:
            logging.error(f"Error processing quantum features: {str(e)}")
            raise

    async def _optimize_quantum_features(self, features: np.ndarray) -> np.ndarray:
        """Optimize quantum features using variational algorithms."""
        try:
            # Create optimization circuit
            qr = QuantumRegister(self.n_qubits)
            cr = ClassicalRegister(self.n_qubits)
            circuit = QuantumCircuit(qr, cr)

            # Add variational circuit
            var_form = TwoLocal(
                self.n_qubits,
                rotation_blocks=["ry", "rz"],
                entanglement_blocks="cx",
                reps=3,
            )
            circuit.compose(var_form)

            # Create QNN
            qnn = CircuitQNN(
                circuit=circuit,
                input_params=var_form.parameters[: self.n_qubits],
                weight_params=var_form.parameters[self.n_qubits :],
                sampling=True,
                sampler=self.sampler,
            )

            # Optimize parameters
            initial_params = self.rng.standard_normal(len(var_form.parameters))
            optimized_params = self.optimizer.optimize(
                len(initial_params),
                lambda x: self._objective_function(x, features, qnn),
            )

            # Apply optimized parameters
            optimized_features = qnn.forward(features, optimized_params)

            return optimized_features

        except Exception as e:
            logging.error(f"Error optimizing quantum features: {str(e)}")
            return features

    def _objective_function(
        self, params: np.ndarray, features: np.ndarray, qnn: CircuitQNN
    ) -> float:
        """Objective function for quantum optimization."""
        try:
            # Compute QNN output
            output = qnn.forward(features, params)

            # Compute loss (example: reconstruction error)
            loss = np.mean((output - features) ** 2)

            return loss

        except Exception as e:
            logging.error(f"Error in objective function: {str(e)}")
            return float("inf")

    def process(self, circuit: QuantumCircuit) -> NDArray[np.float64]:
        """Process a quantum circuit.

        Args:
            circuit: Quantum circuit to process

        Returns:
            Processed results
        """
        if self.state is None:
            raise ValueError("Processor not initialized")

        self.circuit = circuit
        results = self._execute_circuit()
        return results

    def _execute_circuit(self) -> NDArray[np.float64]:
        """Execute the current quantum circuit.

        Returns:
            Circuit execution results
        """
        if self.circuit is None or self.state is None:
            raise ValueError("Circuit or state not initialized")

        # Placeholder for actual circuit execution
        return np.zeros(self.circuit.n_qubits)

    def _check_decoherence(self) -> bool:
        """Check for decoherence in quantum circuit.

        Returns:
            True if decoherence detected
        """
        return self._error_rates["decoherence"] > self.config.error_threshold

    def _apply_correction(self) -> None:
        """Apply error correction based on error rate.

        Args:
            error_rate: Current error rate
        """
        if self.circuit is None:
            raise RuntimeError("Processor not initialized")
        self._error_rates["decoherence"] = 0.0

    def _get_current_error_rates(self) -> Dict[str, float]:
        """Get current error rates.

        Returns:
            Dictionary of error rates
        """
        return self._error_rates

    def _get_qubit_stability(self) -> float:
        """Get qubit stability metrics.

        Returns:
            Qubit stability percentage
        """
        return 1.0 - max(self._error_rates.values())

    def _calculate_correction_success(self) -> float:
        """Calculate correction success rate.

        Returns:
            Success rate
        """
        return 1.0 - self._error_rates["decoherence"]

    def _clear_unused_resources(self) -> None:
        """Clear unused quantum resources."""
        self._qubits = []
        self._classical_registers = []
        self._error_rates = {"gate": 0.0, "measurement": 0.0, "decoherence": 0.0}

    def _configure_parallel_circuit_execution(self) -> None:
        """Configure parallel circuit execution."""
        pass  # Placeholder implementation

    def _allocate_qubits(self, n_qubits: int) -> List[Any]:
        """Allocate qubits for processing."""
        return list(range(n_qubits))

    def _allocate_classical_resources(self, n_registers: int) -> List[Any]:
        """Allocate classical resources."""
        return [0] * n_registers

    def _balance_resources(self) -> None:
        """Balance quantum and classical resources."""
        pass  # Placeholder implementation

    def _get_memory_usage(self) -> float:
        """Get current memory usage.

        Returns:
            Memory usage percentage
        """
        return self._memory_usage

    def _get_cpu_usage(self) -> float:
        """Get current CPU usage.

        Returns:
            CPU usage percentage
        """
        return self._cpu_usage

    def _get_qubit_utilization(self) -> float:
        """Get qubit utilization.

        Returns:
            Qubit utilization percentage
        """
        return len(self._qubits) / self.config.max_workers

    def _optimize_memory_usage(self) -> None:
        """Optimize memory usage."""
        pass  # Placeholder implementation

    def cleanup(self) -> None:
        """Clean up resources."""
        self._clear_unused_resources()
        self.circuit = None
        self.state = None
        self.error_history = []
        self.qnn = None
        self.vqc = None
