import jax
import jax.numpy as jnp
import flax.linen as nn
from typing import List, Tuple, Dict, Any
import pennylane as qml
import time
import logging
from ..cognitive_architectures import (
    PERFORMANCE_THRESHOLD,
    UPDATE_INTERVAL,
    LEARNING_RATE_ADJUSTMENT,
    MAX_HEALING_ATTEMPTS
)

class QuantumModel(nn.Module):
    num_qubits: int = 4
    num_layers: int = 2
    learning_rate: float = 0.001

    def setup(self):
        self.device = qml.device("default.qubit", wires=self.num_qubits)
        self.qlayer = qml.QNode(self.quantum_circuit, self.device, interface="jax")
        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 quantum_circuit(self, inputs, weights):
        qml.AngleEmbedding(inputs, wires=range(self.num_qubits))
        for l in range(self.num_layers):
            qml.StronglyEntanglingLayers(weights[l], wires=range(self.num_qubits))
        return [qml.expval(qml.PauliZ(i)) for i in range(self.num_qubits)]

    def __call__(self, x):
        weights = self.param('weights', nn.initializers.uniform(scale=0.1), (self.num_layers, self.num_qubits, 3))
        return jax.vmap(self.qlayer, in_axes=(0, None))(x, weights)

    def get_params(self):
        return {'num_qubits': self.num_qubits, 'num_layers': self.num_layers}

    def diagnose(self) -> List[str]:
        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):
        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, x):
        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(x, 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 self.get_params()['weights']

    def evaluate_performance(self, x, weights):
        outputs = self(x)
        performance = jnp.mean(jnp.abs(outputs))
        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):
        return nn.initializers.uniform(scale=0.1)((self.num_layers, self.num_qubits, 3))

def create_quantum_model(num_qubits: int = 4, num_layers: int = 2) -> QuantumModel:
    return QuantumModel(num_qubits=num_qubits, num_layers=num_layers)
