"""Quantum circuit implementation for feature processing."""

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

import numpy as np
from numpy.typing import NDArray

# Try to import qiskit dependencies, with fallbacks
try:
    from qiskit import ClassicalRegister
    from qiskit import QuantumCircuit as QiskitCircuit
    from qiskit import QuantumRegister
    from qiskit.circuit.library import EfficientSU2, TwoLocal
    from qiskit.primitives import Sampler
    from qiskit.quantum_info import Statevector
    from qiskit_aer.noise import NoiseModel
    from qiskit_algorithms.optimizers import SPSA
    from qiskit_machine_learning.algorithms import NeuralNetworkClassifier
    from qiskit_machine_learning.neural_networks import SamplerQNN

    QISKIT_AVAILABLE = True
except ImportError:
    QISKIT_AVAILABLE = False
    ClassicalRegister = None
    QiskitCircuit = None
    QuantumRegister = None
    EfficientSU2 = None
    TwoLocal = None
    Sampler = None
    Statevector = None
    NoiseModel = None
    NeuralNetworkClassifier = None
    SamplerQNN = None

    # Provide a mock SPSA
    class SPSA:

        def __init__(self, maxiter=100):
            self.maxiter = maxiter

        def optimize(self, *args, **kwargs):
            return None


# MockRegister for use in all mocks
class MockRegister:
    def __init__(self, size, name):
        self.size = size
        self.name = name


# Constants
CIRCUIT_NOT_INITIALIZED_ERROR = "Circuit not initialized"

logger = logging.getLogger(__name__)


class CircuitError(Exception):
    """Custom exception for circuit-related errors."""


class QuantumCircuit:
    """Quantum circuit for feature processing and optimization"""

    def __init__(
        self,
        n_qubits: int = 4,
        n_layers: int = 2,
        entanglement: str = "full",
        use_advanced_circuits: bool = True,
        use_error_mitigation: bool = True,
        use_quantum_memory: bool = True,
        use_adaptive_entanglement: bool = True,
    ) -> None:
        """Initialize quantum circuit with specified parameters.

        Args:
            n_qubits: Number of qubits in the circuit
            n_layers: Number of layers in the circuit
            entanglement: Entanglement pattern ("full", "linear", or "circular")
            use_advanced_circuits: Whether to use advanced circuit components
            use_error_mitigation: Whether to use error mitigation
            use_quantum_memory: Whether to use quantum memory
            use_adaptive_entanglement: Whether to use adaptive entanglement
        """
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.entanglement = entanglement
        self.use_advanced_circuits = use_advanced_circuits
        self.use_error_mitigation = use_error_mitigation
        self.use_quantum_memory = use_quantum_memory
        self.use_adaptive_entanglement = use_adaptive_entanglement

        # Initialize quantum registers
        if QISKIT_AVAILABLE:
            self.qr = QuantumRegister(n_qubits, "q")
            self.cr = ClassicalRegister(n_qubits, "c")
        else:
            self.qr = MockRegister(n_qubits, "q")
            self.cr = MockRegister(n_qubits, "c")

        # Initialize circuit
        self._initialize_circuit()
        self.feature_map: Optional[TwoLocal] = None
        self.ansatz: Optional[EfficientSU2] = None
        self.qnn: Optional[SamplerQNN] = None
        self.classifier: Optional[NeuralNetworkClassifier] = None

        # Initialize quantum memory
        self.quantum_memory: Dict[str, Any] = {}

        # Initialize error mitigation
        self.noise_model: Optional[NoiseModel] = (
            self._get_noise_model() if use_error_mitigation else None
        )

        # Initialize optimizer
        if QISKIT_AVAILABLE:
            self.optimizer = SPSA(maxiter=100)
        else:

            class MockOptimizer:
                def __init__(self, maxiter=100):
                    self.maxiter = maxiter

                def optimize(self, *args, **kwargs):
                    return None

            self.optimizer = MockOptimizer(maxiter=100)

        # Initialize metrics
        self.metrics: Dict[str, float] = {
            "circuit_depth": 0.0,
            "circuit_size": 0.0,
            "circuit_width": 0.0,
            "num_parameters": 0.0,
            "optimization_score": 0.0,
            "error_rate": 0.0,
            "entanglement_quality": 0.0,
            "memory_usage": 0.0,
        }

        self.rng = np.random.default_rng(seed=42)  # Fixed seed for reproducibility

        # Build the circuit
        self.build_circuit()

    def _get_noise_model(self) -> NoiseModel:
        """Get noise model for error mitigation"""
        if not QISKIT_AVAILABLE:
            # Mock noise model
            class MockNoiseModel:
                def add_all_qubit_quantum_error(self, error, gates):
                    pass

                def add_all_qubit_readout_error(self, matrix):
                    pass

            return MockNoiseModel()

        noise_model = NoiseModel()
        # Add depolarizing noise
        noise_model.add_all_qubit_quantum_error(
            noise_model.depolarizing_error(0.01, 1), ["h", "x", "y", "z", "s", "t"]
        )
        # Add readout error
        noise_model.add_all_qubit_readout_error([[0.9, 0.1], [0.1, 0.9]])
        return noise_model

    def _select_quantum_features(self, features: np.ndarray) -> np.ndarray:
        """Select features using quantum feature selection"""
        # Calculate feature importance using quantum state
        importance_scores = np.zeros(features.shape[1])
        for i in range(features.shape[1]):
            # Create quantum state for feature
            state = np.zeros(2**self.n_qubits)
            state[0] = np.mean(features[:, i])
            state[1] = np.std(features[:, i])
            state = state / np.linalg.norm(state)

            # Measure entanglement
            importance_scores[i] = self._measure_entanglement(state)

        # Select top features
        n_selected = min(features.shape[1], self.n_qubits)
        selected_indices = np.argsort(importance_scores)[-n_selected:]
        return features[:, selected_indices]

    def _measure_entanglement(self, state: np.ndarray) -> float:
        """Measure entanglement of quantum state"""
        # Calculate von Neumann entropy
        density_matrix = np.outer(state, state.conj())
        eigenvalues = np.linalg.eigvals(density_matrix)
        eigenvalues = eigenvalues[eigenvalues > 0]
        return -np.sum(eigenvalues * np.log2(eigenvalues))

    def _update_entanglement_pattern(self, features: np.ndarray) -> None:
        """Update entanglement pattern based on feature correlations"""
        if not self.use_adaptive_entanglement or self.circuit is None:
            return

        # Calculate feature correlations
        correlations = np.corrcoef(features.T)

        # Update entanglement pattern
        for i in range(self.n_qubits):
            for j in range(i + 1, self.n_qubits):
                if abs(correlations[i, j]) > 0.5:  # Strong correlation threshold
                    self.circuit.cx(self.qr[i], self.qr[j])

    def build_circuit(self) -> bool:
        """Build the quantum circuit"""
        if QISKIT_AVAILABLE:
            try:
                self.circuit = QiskitCircuit(self.qr, self.cr)
                return True
            except Exception:
                return False
        else:
            # Use the mock circuit
            self.circuit = self.circuit or None  # Already set in _initialize_circuit
            return True

    def _build_advanced_circuit(self) -> None:
        """Build advanced quantum circuit with variational ansatz."""
        if self.circuit is None:
            raise RuntimeError(CIRCUIT_NOT_INITIALIZED_ERROR)

        # Create feature map
        self.feature_map = TwoLocal(
            self.n_qubits,
            "ry",
            "cz",
            entanglement=self.entanglement,
            reps=1,
            parameter_prefix="fm",
        )

        # Create variational ansatz
        self.ansatz = EfficientSU2(
            self.n_qubits,
            entanglement=self.entanglement,
            reps=self.n_layers,
            parameter_prefix="an",
        )

        # Combine circuits
        self.circuit.compose(self.feature_map, inplace=True)
        self.circuit.compose(self.ansatz, inplace=True)

        # Add measurements
        self.circuit.measure(self.qr, self.cr)

    def _build_basic_circuit(self) -> None:
        """Build basic quantum circuit."""
        if self.circuit is None:
            raise RuntimeError(CIRCUIT_NOT_INITIALIZED_ERROR)

        # Add basic gates
        for i in range(self.n_qubits):
            self.circuit.h(i)  # Hadamard gates for superposition
            if i < self.n_qubits - 1:
                self.circuit.cx(i, i + 1)  # CNOT gates for entanglement

        # Add measurements
        self.circuit.measure(self.qr, self.cr)

    def _update_circuit_metrics(self) -> None:
        """Update circuit metrics."""
        if self.circuit is None:
            raise RuntimeError(CIRCUIT_NOT_INITIALIZED_ERROR)

        self.metrics["circuit_depth"] = float(self.circuit.depth())
        self.metrics["circuit_size"] = float(self.circuit.size())
        self.metrics["circuit_width"] = float(self.circuit.width())
        self.metrics["num_parameters"] = float(len(self.circuit.parameters))

    async def process_features(
        self, features: NDArray[np.float64]
    ) -> Optional[NDArray[np.float64]]:
        """Process features using quantum circuit.

        Args:
            features: Input features to process

        Returns:
            Processed features or None if processing fails
        """
        try:
            if self.use_error_mitigation:
                processed = self._process_with_error_mitigation(features)
            else:
                processed = self._process_basic(features)

            if self.use_quantum_memory:
                self._update_quantum_memory(features, processed)

            return processed

        except Exception as e:
            logger.error(f"Error processing features: {str(e)}")
            return None

    def _process_with_error_mitigation(self, features: NDArray[np.float64]) -> float:
        """Process features with error mitigation.

        Args:
            features: Input features to process

        Returns:
            float: Processed result with error mitigation
        """
        try:
            result = self._run_circuit_with_mitigation(features)
            return float(result)  # Explicit cast to float
        except Exception as e:
            logger.error(f"Error in error mitigation processing: {e}")
            raise CircuitError("Failed to process with error mitigation") from e

    def _process_basic(self, features: NDArray[np.float64]) -> NDArray[np.float64]:
        """Process features without error mitigation.

        Args:
            features: Input features to process

        Returns:
            Processed features
        """
        if self.circuit is None:
            raise RuntimeError(CIRCUIT_NOT_INITIALIZED_ERROR)

        # Select quantum features
        quantum_features = self._select_quantum_features(features)

        # Process features
        sampler = Sampler()
        job = sampler.run(self.circuit, quantum_features)
        result = job.result()

        return result.quasi_dists[0]

    def _update_quantum_memory(
        self, features: NDArray[np.float64], processed: NDArray[np.float64]
    ) -> None:
        """Update quantum memory with processed features"""
        if self.quantum_memory is None:
            return

        # Store feature patterns
        pattern = np.concatenate([features.flatten(), processed.flatten()])
        self.quantum_memory[hash(str(pattern))] = {
            "features": features,
            "processed": processed,
            "timestamp": time.time(),
        }

    def _calculate_error_rate(self) -> float:
        """Calculate current error rate"""
        if not self.use_error_mitigation or self.noise_model is None:
            return 0.0

        # Use noise model to estimate error rate
        return self.noise_model.depolarizing_error(0.01, 1).probability

    def get_circuit_info(self) -> Dict[str, Any]:
        """Get circuit information and metrics"""
        if self.circuit is None:
            raise RuntimeError(CIRCUIT_NOT_INITIALIZED_ERROR)

        return {
            "n_qubits": self.n_qubits,
            "n_layers": self.n_layers,
            "entanglement": self.entanglement,
            "metrics": self.metrics.copy(),
            "circuit_draw": self.circuit.draw() if self.circuit else None,
            "error_mitigation": self.use_error_mitigation,
            "quantum_memory": bool(self.quantum_memory),
            "adaptive_entanglement": self.use_adaptive_entanglement,
        }

    def apply_random_gate(self, qubit: int) -> None:
        """Apply a random quantum gate to a qubit."""
        if self.circuit is None:
            raise RuntimeError(CIRCUIT_NOT_INITIALIZED_ERROR)

        if qubit < 0 or qubit >= self.n_qubits:
            raise ValueError(f"Invalid qubit index: {qubit}")

        gate_type = self.rng.choice(["H", "X", "Y", "Z", "S", "T"])

        # Apply the selected gate
        if gate_type == "H":
            self.circuit.h(self.qr[qubit])
        elif gate_type == "X":
            self.circuit.x(self.qr[qubit])
        elif gate_type == "Y":
            self.circuit.y(self.qr[qubit])
        elif gate_type == "Z":
            self.circuit.z(self.qr[qubit])
        elif gate_type == "S":
            self.circuit.s(self.qr[qubit])
        elif gate_type == "T":
            self.circuit.t(self.qr[qubit])

    def apply_random_rotation(self, qubit: int) -> None:
        """Apply a random rotation gate to a qubit."""
        if self.circuit is None:
            raise RuntimeError(CIRCUIT_NOT_INITIALIZED_ERROR)

        if qubit < 0 or qubit >= self.n_qubits:
            raise ValueError(f"Invalid qubit index: {qubit}")

        angle = self.rng.uniform(0, 2 * np.pi)
        self.circuit.ry(angle, self.qr[qubit])

    def _initialize_circuit(self) -> None:
        """Initialize the quantum circuit with registers."""
        if QISKIT_AVAILABLE:
            self.circuit = QiskitCircuit(self.qr, self.cr)
        else:
            # Mock circuit
            class MockCircuit:
                def __init__(self):
                    self.depth = lambda: 0
                    self.size = lambda: 0
                    self.width = lambda: 0
                    self.parameters = []
                    self.data = []

                def h(self, qubit):
                    pass

                def x(self, qubit):
                    pass

                def y(self, qubit):
                    pass

                def z(self, qubit):
                    pass

                def cx(self, control, target):
                    pass

                def rz(self, angle, qubit):
                    pass

                def ry(self, angle, qubit):
                    pass

                def measure(self, qubit, classical_bit):
                    pass

                def add_classical_register(self, size):
                    return MockRegister(size, "c")

            self.circuit = MockCircuit()

    def _update_metrics(self) -> None:
        self.metrics["circuit_depth"] = self.circuit.depth()
        self.metrics["circuit_size"] = self.circuit.size()
        self.metrics["circuit_width"] = self.circuit.width()
        self.metrics["num_parameters"] = len(self.circuit.parameters)
        self.metrics["error_rate"] = self._calculate_error_rate()

    def add_gate(
        self, gate_name: str, qubits: List[int], params: Optional[List[float]] = None
    ) -> None:
        if params is None:
            params = []

        self._validate_gate_parameters(gate_name, qubits, params)
        self._apply_gate(gate_name, qubits, params)
        self._update_metrics()

    def _validate_gate_parameters(
        self, gate_name: str, qubits: List[int], params: List[float]
    ) -> None:
        """Validate gate parameters before application."""
        if not qubits:
            raise ValueError("No qubits provided")

        single_qubit_gates = ["h", "x", "y", "z"]
        if gate_name in single_qubit_gates and len(qubits) < 1:
            raise ValueError(f"Gate {gate_name} requires at least 1 qubit")

        if gate_name == "cx" and len(qubits) < 2:
            raise ValueError("CX gate requires at least 2 qubits")

        if gate_name == "rz" and (len(qubits) < 1 or len(params) < 1):
            raise ValueError("RZ gate requires at least 1 qubit and 1 parameter")

    def _apply_gate(
        self, gate_name: str, qubits: List[int], params: List[float]
    ) -> None:
        """Apply the specified gate to the circuit."""
        gate_operations = {
            "h": lambda: self.circuit.h(qubits[0]),
            "x": lambda: self.circuit.x(qubits[0]),
            "y": lambda: self.circuit.y(qubits[0]),
            "z": lambda: self.circuit.z(qubits[0]),
            "cx": lambda: self.circuit.cx(qubits[0], qubits[1]),
            "rz": lambda: self.circuit.rz(params[0], qubits[0]),
        }

        if gate_name not in gate_operations:
            raise ValueError(f"Unsupported gate: {gate_name}")

        gate_operations[gate_name]()

    def measure(self, qubit: int) -> int:
        # Create a new classical register for measurement
        cr = self.circuit.add_classical_register(1)
        self.circuit.measure(qubit, cr[0])

        # Execute the circuit and get the measurement result
        from qiskit import Aer, execute

        backend = Aer.get_backend("qasm_simulator")
        job = execute(self.circuit, backend, shots=1)
        result = job.result().get_counts()

        # Extract the measurement outcome
        measured_state = list(result.keys())[0]
        return int(measured_state[-1])

    def get_state(self) -> NDArray[np.complex128]:
        return Statevector.from_instruction(self.circuit).data

    def optimize(self) -> None:
        # Perform circuit optimization
        self._remove_redundant_gates()
        self._merge_adjacent_gates()
        self._update_metrics()

    def _remove_redundant_gates(self) -> None:
        # Identify and remove redundant gates
        current_layer: List[Any] = []
        for instruction in self.circuit.data:
            gate = instruction[0]
            qubits = instruction[1]
            if self._are_gates_cancellable(current_layer, gate, qubits):
                current_layer.pop()
            else:
                current_layer.append(gate)

    def _merge_adjacent_gates(self) -> None:
        # Merge adjacent gates when possible
        current_layer: List[Any] = []
        next_layer: List[Any] = []

        for instruction in self.circuit.data:
            gate = instruction[0]
            qubits = instruction[1]

            if self._can_merge_gates(current_layer, gate):
                self._merge_gates(current_layer, gate, qubits)
            else:
                next_layer = [(gate, qubits)]
                current_layer = next_layer

    def _are_gates_cancellable(
        self,
        layer: List[Any],
        gate: Any,
        qubits: List[int],
    ) -> bool:
        """Check if gates cancel each other based on quantum circuit properties."""
        if not layer:  # Empty layer
            return False

        last_gate = layer[-1]
        # Check for self-cancelling gate pairs (e.g., two consecutive X gates)
        if (
            last_gate.name == gate.name
            and last_gate.name in ["x", "y", "z", "h"]
            and qubits == last_gate.qubits
        ):
            return True
        return False

    def _can_merge_gates(self, layer: List[Any], gate: Any) -> bool:
        """Check if gates can be merged based on quantum circuit optimization rules."""
        if not layer:  # Empty layer
            return False

        last_gate = layer[-1][0]  # Get gate from (gate, qubits) tuple
        # Check for mergeable rotation gates
        if (
            last_gate.name.startswith("r")
            and gate.name.startswith("r")
            and last_gate.name == gate.name
        ):
            return True
        return False

    def _merge_gates(self, layer: List[Any], gate: Any, qubits: List[int]) -> None:
        # Merge compatible gates
        pass  # Placeholder implementation

    def get_metrics(self) -> Dict[str, float]:
        return self.metrics.copy()

    def __str__(self) -> str:
        circuit_str = f"Circuit(num_qubits={self.n_qubits})\n"
        for i, instruction in enumerate(self.circuit.data):
            gate, qubits = instruction
            circuit_str += f"Gate {i}: {gate.name} on qubits {qubits}\n"
        return circuit_str

    def _run_circuit_with_mitigation(self, features: NDArray[np.float64]) -> float:
        """Run quantum circuit with error mitigation.

        This is a research placeholder for quantum error mitigation.
        In production, this would implement:
        - Zero-noise extrapolation (ZNE)
        - Probabilistic error cancellation (PEC)
        - Measurement error mitigation
        - Quantum error correction codes
        """
        # Research placeholder - returns processed features for experimentation
        processed_features = np.mean(features)  # Simple averaging as placeholder
        return float(processed_features)
