import logging
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union

import numpy as np
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal
from qiskit.primitives import Sampler
from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier
from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor
from qiskit_machine_learning.neural_networks import CircuitQNN
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.preprocessing import StandardScaler


@dataclass
class QuantumConfig:
    """Configuration for quantum neural network."""

    n_qubits: int
    n_layers: int
    rotation_blocks: Optional[List[str]] = None
    entanglement_blocks: Optional[List[str]] = None
    entanglement: str = "full"
    reps: int = 3
    insert_barriers: bool = False
    parameter_prefix: str = "θ"


class QuantumNeuralNetwork(BaseEstimator, ClassifierMixin):
    """Quantum-enhanced neural network classifier."""

    def __init__(
        self,
        config: QuantumConfig,
        optimizer: str = "SPSA",
        learning_rate: float = 0.01,
        max_iter: int = 100,
    ):
        self.config = config
        self.optimizer = optimizer
        self.learning_rate = learning_rate
        self.max_iter = max_iter
        self.scaler = StandardScaler()
        self._setup_logging()

    def _setup_logging(self):
        """Setup logging configuration."""
        self.logger = logging.getLogger(__name__)
        self.logger.setLevel(logging.INFO)

    def fit(self, features: np.ndarray, labels: np.ndarray) -> "QuantumNeuralNetwork":
        """Train the quantum neural network."""
        self.logger.info("Starting quantum neural network training")

        # Scale features
        features_scaled = self.scaler.fit_transform(features)

        # Create quantum circuit
        self._create_quantum_circuit()

        # Create QNN
        self._create_qnn()

        # Create classifier
        self._create_classifier()

        # Train classifier
        self.logger.info("Training classifier")
        self.classifier.fit(features_scaled, labels)

        return self

    def predict(self, features: np.ndarray) -> np.ndarray:
        """Make predictions using the trained model."""
        self.logger.info("Making predictions")

        # Scale features
        features_scaled = self.scaler.transform(features)

        # Get predictions
        predictions = self.classifier.predict(features_scaled)

        return predictions

    def predict_proba(self, features: np.ndarray) -> np.ndarray:
        """Get probability predictions."""
        self.logger.info("Getting probability predictions")

        # Scale features
        features_scaled = self.scaler.transform(features)

        # Get probability predictions
        probabilities = self.classifier.predict_proba(features_scaled)

        return probabilities

    def _create_quantum_circuit(self):
        """Create the quantum circuit."""
        self.logger.info("Creating quantum circuit")

        # Create quantum and classical registers
        qr = QuantumRegister(self.config.n_qubits, "q")
        cr = ClassicalRegister(self.config.n_qubits, "c")

        # Create circuit
        self.circuit = QuantumCircuit(qr, cr)

        # Add two-local circuit
        self.two_local = TwoLocal(
            self.config.n_qubits,
            self.config.rotation_blocks or ["ry", "rz"],
            self.config.entanglement_blocks or ["cx"],
            reps=self.config.reps,
            entanglement=self.config.entanglement,
            insert_barriers=self.config.insert_barriers,
        )

        # Add two-local circuit to main circuit
        self.circuit.compose(self.two_local, inplace=True)

    def _create_qnn(self):
        """Create the quantum neural network."""
        self.logger.info("Creating quantum neural network")

        # Create QNN
        self.qnn = CircuitQNN(
            circuit=self.circuit,
            input_params=self.two_local.parameters,
            weight_params=self.two_local.parameters,
            input_gradients=True,
        )

    def _create_classifier(self):
        """Create the neural network classifier."""
        self.logger.info("Creating neural network classifier")

        # Create classifier
        self.classifier = NeuralNetworkClassifier(
            neural_network=self.qnn,
            optimizer=self.optimizer,
            learning_rate=self.learning_rate,
            max_iter=self.max_iter,
        )


class QuantumErrorCorrection:
    def __init__(self, code_type: str = "stabilizer"):
        self.code_type = code_type
        self.error_syndromes = {}
        self.correction_methods = {}

    def encode(self, state: np.ndarray) -> np.ndarray:
        """Encode a quantum state with error correction."""
        if self.code_type == "stabilizer":
            return self._encode_stabilizer(state)
        else:
            raise ValueError(
                f"Unsupported error correction code type: {self.code_type}"
            )

    def decode(self, encoded_state: np.ndarray) -> np.ndarray:
        """Decode a quantum state with error correction."""
        if self.code_type == "stabilizer":
            return self._decode_stabilizer(encoded_state)
        else:
            raise ValueError(
                f"Unsupported error correction code type: {self.code_type}"
            )

    def _encode_stabilizer(self, state: np.ndarray) -> np.ndarray:
        """Encode using stabilizer code."""
        # Implement stabilizer encoding
        return state

    def _decode_stabilizer(self, encoded_state: np.ndarray) -> np.ndarray:
        """Decode using stabilizer code."""
        # Implement stabilizer decoding
        return encoded_state


class QuantumOptimizer:
    def __init__(self, optimization_level: int = 2):
        self.optimization_level = optimization_level
        self.optimization_rules = []
        self.performance_metrics = {}

    def optimize_circuit(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Optimize a quantum circuit."""
        if self.optimization_level == 0:
            return circuit

        # Apply optimization rules
        optimized_circuit = circuit.copy()
        for rule in self.optimization_rules:
            optimized_circuit = rule.apply(optimized_circuit)

        return optimized_circuit

    def add_optimization_rule(self, rule):
        """Add a new optimization rule."""
        self.optimization_rules.append(rule)

    def get_performance_metrics(self) -> Dict[str, float]:
        """Get the current performance metrics."""
        return self.performance_metrics.copy()


@dataclass
class BleusQuantumConfig:
    num_qubits: int
    num_layers: int
    entanglement: str = "bleus_full"
    rotation_blocks: List[str] = None
    entanglement_blocks: List[str] = None
    skip_final_rotation_layer: bool = False
    reps: int = 3
    insert_barriers: bool = False
    initial_state: Optional[QuantumCircuit] = None
    parameter_prefix: str = "θ"
    name: str = "bleus_quantum_circuit"
    bleus_factor: float = 1.0
    bleus_coherence: float = 1.0


class BleusQuantumNeuralNetwork:
    def __init__(self, config: BleusQuantumConfig):
        self.config = config
        self.circuit = self._create_bleus_circuit()
        self.sampler = Sampler()
        self.qnn = self._create_bleus_qnn()
        self.optimizer = COBYLA(maxiter=100)
        self.bleus_history = []

    def _create_bleus_circuit(self) -> QuantumCircuit:
        """Create a Bleus-enhanced quantum circuit."""
        if self.config.rotation_blocks is None:
            self.config.rotation_blocks = ["ry", "rz"]
        if self.config.entanglement_blocks is None:
            self.config.entanglement_blocks = ["cz"]

        circuit = TwoLocal(
            num_qubits=self.config.num_qubits,
            rotation_blocks=self.config.rotation_blocks,
            entanglement_blocks=self.config.entanglement_blocks,
            entanglement=self.config.entanglement,
            reps=self.config.reps,
            insert_barriers=self.config.insert_barriers,
            initial_state=self.config.initial_state,
            parameter_prefix=self.config.parameter_prefix,
            name=self.config.name,
        )

        # Add Bleus-specific gates
        self._add_bleus_gates(circuit)
        return circuit

    def _add_bleus_gates(self, circuit: QuantumCircuit):
        """Add Bleus-specific quantum gates."""
        # Add Bleus rotation gate
        for i in range(self.config.num_qubits):
            circuit.ry(self.config.bleus_factor * np.pi, i)
            circuit.rz(self.config.bleus_coherence * np.pi, i)

    def _create_bleus_qnn(self) -> CircuitQNN:
        """Create a Bleus-enhanced Quantum Neural Network."""
        return CircuitQNN(
            circuit=self.circuit,
            input_params=self.circuit.parameters[: self.config.num_qubits],
            weight_params=self.circuit.parameters[self.config.num_qubits :],
            sampling=True,
            sampler=self.sampler,
        )

    def train_bleus_classifier(
        self, X: np.ndarray, y: np.ndarray, epochs: int = 100, batch_size: int = 32
    ) -> Dict[str, List[float]]:
        """Train the Bleus-enhanced quantum neural network as a classifier."""
        classifier = NeuralNetworkClassifier(
            neural_network=self.qnn, optimizer=self.optimizer, loss="cross_entropy"
        )

        history = classifier.fit(X, y, epochs=epochs, batch_size=batch_size)
        self.bleus_history.append(
            {
                "epochs": epochs,
                "loss": history["loss"],
                "accuracy": history["accuracy"],
                "bleus_factor": self.config.bleus_factor,
                "bleus_coherence": self.config.bleus_coherence,
            }
        )
        return history

    def train_bleus_regressor(
        self, X: np.ndarray, y: np.ndarray, epochs: int = 100, batch_size: int = 32
    ) -> Dict[str, List[float]]:
        """Train the Bleus-enhanced quantum neural network as a regressor."""
        regressor = NeuralNetworkRegressor(
            neural_network=self.qnn, optimizer=self.optimizer, loss="mse"
        )

        history = regressor.fit(X, y, epochs=epochs, batch_size=batch_size)
        self.bleus_history.append(
            {
                "epochs": epochs,
                "loss": history["loss"],
                "bleus_factor": self.config.bleus_factor,
                "bleus_coherence": self.config.bleus_coherence,
            }
        )
        return history

    def predict_bleus(self, X: np.ndarray) -> np.ndarray:
        """Make predictions using the Bleus-enhanced quantum neural network."""
        return self.qnn.forward(X)

    def get_bleus_parameters(self) -> Dict[str, float]:
        """Get the current Bleus circuit parameters."""
        return {param.name: param.value for param in self.circuit.parameters}

    def set_bleus_parameters(self, parameters: Dict[str, float]):
        """Set the Bleus circuit parameters."""
        for param_name, value in parameters.items():
            if param_name in self.circuit.parameters:
                self.circuit.parameters[param_name].value = value

    def get_bleus_history(self) -> List[Dict[str, Union[int, float, List[float]]]]:
        """Get the training history with Bleus-specific metrics."""
        return self.bleus_history

    def get_model_info(self) -> Dict[str, Any]:
        """Get model information."""
        return {
            "n_qubits": self.config.num_qubits,
            "n_layers": self.config.num_layers,
            "entanglement": self.config.entanglement,
            "metrics": {},
        }


class BleusQuantumErrorCorrection:
    def __init__(self, code_type: str = "bleus_stabilizer"):
        self.code_type = code_type
        self.error_syndromes = {}
        self.correction_methods = {}
        self.bleus_error_threshold = 0.01

    def encode_bleus(self, state: np.ndarray) -> np.ndarray:
        """Encode a quantum state with Bleus error correction."""
        if self.code_type == "bleus_stabilizer":
            return self._encode_bleus_stabilizer(state)
        else:
            raise ValueError(
                f"Unsupported Bleus error correction code type: {self.code_type}"
            )

    def decode_bleus(self, encoded_state: np.ndarray) -> np.ndarray:
        """Decode a quantum state with Bleus error correction."""
        if self.code_type == "bleus_stabilizer":
            return self._decode_bleus_stabilizer(encoded_state)
        else:
            raise ValueError(
                f"Unsupported Bleus error correction code type: {self.code_type}"
            )

    def _encode_bleus_stabilizer(self, state: np.ndarray) -> np.ndarray:
        """Encode using Bleus stabilizer code."""
        # Implement Bleus-specific stabilizer encoding
        return state

    def _decode_bleus_stabilizer(self, encoded_state: np.ndarray) -> np.ndarray:
        """Decode using Bleus stabilizer code."""
        # Implement Bleus-specific stabilizer decoding
        return encoded_state


class BleusQuantumOptimizer:
    def __init__(self, optimization_level: int = 2):
        self.optimization_level = optimization_level
        self.optimization_rules = []
        self.performance_metrics = {}
        self.bleus_optimization_factor = 1.0

    def optimize_bleus_circuit(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Optimize a Bleus quantum circuit."""
        if self.optimization_level == 0:
            return circuit

        # Apply Bleus optimization rules
        optimized_circuit = circuit.copy()
        for rule in self.optimization_rules:
            optimized_circuit = rule.apply(optimized_circuit)

        return optimized_circuit

    def add_bleus_optimization_rule(self, rule):
        """Add a new Bleus optimization rule."""
        self.optimization_rules.append(rule)

    def get_bleus_metrics(self) -> Dict[str, float]:
        """Get the current Bleus performance metrics."""
        return self.performance_metrics.copy()
