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

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

import numpy as np
from numpy.typing import NDArray
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

# Constants
CIRCUIT_NOT_INITIALIZED_ERROR = "Circuit not initialized"

logger = logging.getLogger(__name__)


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

    pass


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
        self.qr = QuantumRegister(n_qubits, "q")
        self.cr = ClassicalRegister(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
        self.optimizer = SPSA(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"""
        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"""
        try:
            # Create base circuit
            self.circuit = QiskitCircuit(self.qr, self.cr)

            if self.use_advanced_circuits:
                # Use advanced circuit components
                self._build_advanced_circuit()
            else:
                # Use basic circuit
                self._build_basic_circuit()

            # Update metrics
            self._update_circuit_metrics()

            return True

        except RuntimeError as e:
            print(f"Error building circuit: {str(e)}")
            return False
        except ValueError as e:
            print(f"Invalid circuit parameters: {str(e)}")
            return False
        except ImportError as e:
            print(f"Failed to import required dependencies: {str(e)}")
            return False

    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: np.ndarray, processed: np.ndarray
    ) -> 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."""
        self.circuit = QiskitCircuit(self.qr, self.cr)

    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 = []

        if gate_name == "h":
            self.circuit.h(qubits[0])
        elif gate_name == "x":
            self.circuit.x(qubits[0])
        elif gate_name == "y":
            self.circuit.y(qubits[0])
        elif gate_name == "z":
            self.circuit.z(qubits[0])
        elif gate_name == "cx":
            self.circuit.cx(qubits[0], qubits[1])
        elif gate_name == "rz":
            self.circuit.rz(params[0], qubits[0])
        else:
            raise ValueError(f"Unsupported gate: {gate_name}")

        self._update_metrics()

    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:
        # Placeholder for the actual implementation of _run_circuit_with_mitigation
        # This method should return the processed result with error mitigation
        raise NotImplementedError("Method _run_circuit_with_mitigation not implemented")

    def _process_quantum_features(
        self, features: NDArray[np.float64]
    ) -> NDArray[np.float64]:
        """Process quantum features through the circuit.

        Args:
            features: Input features array

        Returns:
            NDArray[np.float64]: Processed quantum features
        """
        try:
            result = self._quantum_processor.process_features(features)
            return np.array(result, dtype=np.float64)  # Explicit cast to float64 array
        except Exception as e:
            logger.error(f"Error processing quantum features: {e}")
            raise CircuitError("Failed to process quantum features") from e

    def _get_circuit_output(self) -> NDArray[np.float64]:
        """Get the circuit output as a float64 array.

        Returns:
            NDArray[np.float64]: Circuit output array
        """
        try:
            result = self._quantum_processor.get_output()
            return np.array(result, dtype=np.float64)
        except Exception as e:
            logger.error(f"Error getting circuit output: {e}")
            raise CircuitError("Failed to get circuit output") from e

    def _update_metrics(self, key: str, value: float) -> None:
        """Update metrics dictionary with float values.

        Args:
            key: Metric name
            value: Metric value
        """
        if not isinstance(key, str):
            raise TypeError("Metric key must be a string")
        self._metrics[key] = float(value)  # Ensure value is float

    def _get_complex_amplitudes(self) -> NDArray[np.complex128]:
        """Get complex amplitudes from the circuit.

        Returns:
            NDArray[np.complex128]: Complex amplitudes array
        """
        try:
            result = self._quantum_processor.get_amplitudes()
            return np.array(result, dtype=np.complex128)
        except Exception as e:
            logger.error(f"Error getting complex amplitudes: {e}")
            raise CircuitError("Failed to get complex amplitudes") from e
