import jax
import jax.numpy as jnp
import pennylane as qml
from typing import List, Tuple, Callable, Optional
import time
import logging
import importlib.metadata

from ..constants import (
    PERFORMANCE_THRESHOLD,
    UPDATE_INTERVAL,
    LEARNING_RATE_ADJUSTMENT,
    MAX_HEALING_ATTEMPTS
)

# Check PennyLane version
try:
    pennylane_version = importlib.metadata.version("pennylane")
    if pennylane_version != "0.37.0":
        logging.warning(f"This module was tested with PennyLane 0.37.0. You are using version {pennylane_version}. Some features may not work as expected.")
except importlib.metadata.PackageNotFoundError:
    logging.warning("Unable to determine PennyLane version. Make sure it's installed correctly.")

class QuantumNeuralNetwork:
    """
    An enhanced Quantum Neural Network implementation using PennyLane and JAX.

    This class represents a variational quantum circuit that can be used as a quantum neural network.
    It supports advanced input encoding, variational layers, and measurement operations.

    Attributes:
        n_qubits (int): The number of qubits in the quantum circuit.
        n_layers (int): The number of variational layers in the circuit.
        dev (qml.Device): The PennyLane quantum device used for computation.
        quantum_circuit (qml.QNode): The quantum circuit as a QNode.
        encoding_method (Callable): The method used for encoding classical data into quantum states.
    """

    def __init__(self, n_qubits: int, n_layers: int, encoding_method: Optional[Callable] = None,
                 learning_rate: float = 0.001):
        """
        Initialize the enhanced Quantum Neural Network with self-healing capabilities.

        Args:
            n_qubits (int): The number of qubits to use in the quantum circuit.
            n_layers (int): The number of variational layers in the circuit.
            encoding_method (Optional[Callable]): Custom encoding method for input data.
            learning_rate (float): Initial learning rate for optimization.
        """
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.dev = qml.device("default.qubit", wires=n_qubits)
        self.quantum_circuit = qml.QNode(self.circuit, self.dev)
        self.encoding_method = encoding_method or self.amplitude_encoding
        self.learning_rate = learning_rate
        self.performance = 0.0
        self.last_update = time.time()
        self.performance_history = []
        self.performance_threshold = PERFORMANCE_THRESHOLD
        self.update_interval = UPDATE_INTERVAL
        self.max_healing_attempts = MAX_HEALING_ATTEMPTS

    def circuit(self, inputs: jnp.ndarray, weights: jnp.ndarray) -> List[float]:
        """
        Define the enhanced quantum circuit structure.

        This method sets up the structure of the quantum circuit, including advanced input encoding,
        variational layers with more quantum operations, and measurement operations.

        Args:
            inputs (jnp.ndarray): Input data to be encoded into the quantum circuit.
            weights (jnp.ndarray): Weights for the variational layers.

        Returns:
            List[float]: Expectation values of observables.
        """
        # Encode input data using the specified method
        self.encoding_method(inputs)

        # Apply enhanced variational quantum layers
        for layer in range(self.n_layers):
            self.variational_layer(weights[layer])

        # Apply entangling layer
        self.entangling_layer()

        # Measure the output using different observables
        return [qml.expval(qml.PauliZ(i)) for i in range(self.n_qubits)] + \
               [qml.expval(qml.PauliX(i)) for i in range(self.n_qubits)]

    def amplitude_encoding(self, inputs: jnp.ndarray) -> None:
        """
        Encode classical input data into quantum amplitudes.

        Args:
            inputs (jnp.ndarray): Input data to be encoded. Should have length equal to 2^n_qubits.
        """
        qml.QubitStateVector(inputs, wires=range(self.n_qubits))

    def angle_encoding(self, inputs: jnp.ndarray) -> None:
        """
        Encode classical input data into qubit rotation angles.

        Args:
            inputs (jnp.ndarray): Input data to be encoded. Should have length equal to n_qubits.
        """
        for i, inp in enumerate(inputs):
            qml.RY(inp, wires=i)

    def variational_layer(self, weights: jnp.ndarray) -> None:
        """
        Apply an enhanced variational layer to the quantum circuit.

        This method applies rotation gates to each qubit, CNOT gates between adjacent qubits,
        and additional quantum operations for increased expressivity.

        Args:
            weights (jnp.ndarray): Weights for the quantum operations in this layer.
        """
        for i in range(self.n_qubits):
            qml.Rot(*weights[i, :3], wires=i)
            qml.RZ(weights[i, 3], wires=i)
        for i in range(self.n_qubits - 1):
            qml.CNOT(wires=[i, i + 1])
        qml.CRZ(weights[0, 4], wires=[0, self.n_qubits - 1])

    def entangling_layer(self) -> None:
        """
        Apply an entangling layer to increase qubit interactions.
        """
        for i in range(self.n_qubits):
            qml.Hadamard(wires=i)
        for i in range(self.n_qubits):
            qml.CZ(wires=[i, (i + 1) % self.n_qubits])

    def forward(self, inputs: jnp.ndarray, weights: jnp.ndarray) -> jnp.ndarray:
        """
        Perform a forward pass through the quantum circuit.

        Args:
            inputs (jnp.ndarray): Input data to be processed by the quantum circuit.
            weights (jnp.ndarray): Weights for all layers of the quantum circuit.

        Returns:
            jnp.ndarray: The output of the quantum circuit as a JAX numpy array.
        """
        return jnp.array(self.quantum_circuit(inputs, weights))

    def initialize_weights(self) -> jnp.ndarray:
        """
        Initialize random weights for the quantum circuit.

        Returns:
            jnp.ndarray: Randomly initialized weights for all layers of the quantum circuit.
        """
        key = jax.random.PRNGKey(0)
        return jax.random.uniform(key, shape=(self.n_layers, self.n_qubits, 5), minval=0, maxval=2*jnp.pi)

    def quantum_classical_hybrid(self, inputs: jnp.ndarray, weights: jnp.ndarray, classical_layer: Callable) -> jnp.ndarray:
        """
        Perform a quantum-classical hybrid computation.

        Args:
            inputs (jnp.ndarray): Input data to be processed.
            weights (jnp.ndarray): Weights for the quantum circuit.
            classical_layer (Callable): A classical neural network layer.

        Returns:
            jnp.ndarray: The output of the hybrid computation.
        """
        quantum_output = self.forward(inputs, weights)
        return classical_layer(quantum_output)

    def diagnose(self) -> List[str]:
        """
        Diagnose potential issues with the quantum neural network.

        Returns:
            List[str]: A list of diagnosed issues.
        """
        issues = []
        if self.performance < self.performance_threshold:
            issues.append(f"Low performance: {self.performance:.4f}")
        if (time.time() - self.last_update) > self.update_interval:
            issues.append(f"Long time since last update: {(time.time() - self.last_update) / 3600:.2f} hours")
        if len(self.performance_history) > 5 and all(p < self.performance_threshold for p in self.performance_history[-5:]):
            issues.append("Consistently low performance")
        return issues

    def adjust_learning_rate(self):
        """
        Adjust the learning rate based on recent performance history.
        """
        if len(self.performance_history) >= 2:
            if self.performance_history[-1] > self.performance_history[-2]:
                self.learning_rate *= 1.05
            else:
                self.learning_rate *= 0.95
        self.learning_rate = max(min(self.learning_rate, 0.1), 1e-5)
        logging.info(f"Adjusted learning rate to {self.learning_rate:.6f}")

    def self_heal(self, inputs: jnp.ndarray, weights: jnp.ndarray):
        """
        Perform self-healing on the quantum neural network.

        Args:
            inputs (jnp.ndarray): Input data for testing performance.
            weights (jnp.ndarray): Current weights of the quantum circuit.
        """
        issues = self.diagnose()
        if issues:
            logging.info(f"Self-healing triggered. Issues: {issues}")
            for attempt in range(self.max_healing_attempts):
                self.adjust_learning_rate()
                new_weights = self.reinitialize_weights()
                new_performance = self.evaluate_performance(inputs, new_weights)
                if new_performance > self.performance_threshold:
                    logging.info(f"Self-healing successful. New performance: {new_performance:.4f}")
                    return new_weights
            logging.warning("Self-healing unsuccessful after maximum attempts")
        return weights

    def evaluate_performance(self, inputs: jnp.ndarray, weights: jnp.ndarray) -> float:
        """
        Evaluate the performance of the quantum neural network.

        Args:
            inputs (jnp.ndarray): Input data for evaluation.
            weights (jnp.ndarray): Weights of the quantum circuit.

        Returns:
            float: Performance metric (e.g., accuracy or loss).
        """
        outputs = self.forward(inputs, weights)
        performance = jnp.mean(jnp.abs(outputs))  # Example metric, adjust as needed
        self.performance = performance
        self.performance_history.append(performance)
        if len(self.performance_history) > 100:
            self.performance_history.pop(0)
        self.last_update = time.time()
        return performance

    def reinitialize_weights(self) -> jnp.ndarray:
        """
        Reinitialize the weights of the quantum circuit.

        Returns:
            jnp.ndarray: Newly initialized weights.
        """
        return self.initialize_weights()
