import numpy as np
import torch
import torch.nn as nn
from typing import List, Dict, Any, Union
import time
import logging
from ..constants import PERFORMANCE_THRESHOLD, UPDATE_INTERVAL, LEARNING_RATE_ADJUSTMENT, MAX_HEALING_ATTEMPTS

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

class NeuromorphicComputing:
    def __init__(self):
        self.spiking_neuron_models = {
            'LIF': self.leaky_integrate_and_fire,
            'Izhikevich': self.izhikevich_model
        }
        self.performance = 0.0
        self.last_update = time.time()
        self.performance_history = []
        self.learning_rate = 0.001

    def create_spiking_neural_network(self, model_type: str, num_neurons: int, **kwargs) -> nn.Module:
        """
        Create a spiking neural network using the specified neuron model.

        Args:
            model_type (str): The type of spiking neuron model to use
            num_neurons (int): The number of neurons in the network
            **kwargs: Additional arguments specific to the chosen model

        Returns:
            nn.Module: The created spiking neural network
        """
        if model_type not in self.spiking_neuron_models:
            raise ValueError(f"Unsupported spiking neuron model: {model_type}")

        return self.spiking_neuron_models[model_type](num_neurons, **kwargs)

    def leaky_integrate_and_fire(self, num_neurons: int, threshold: float = 1.0,
                                 reset_potential: float = 0.0, time_constant: float = 10.0) -> nn.Module:
        """Create a Leaky Integrate-and-Fire (LIF) spiking neural network."""
        # Placeholder for LIF implementation
        logger.info(f"Creating LIF network with {num_neurons} neurons")
        return nn.Linear(num_neurons, num_neurons)  # Placeholder, replace with actual LIF implementation

    def izhikevich_model(self, num_neurons: int, a: float = 0.02, b: float = 0.2,
                         c: float = -65.0, d: float = 8.0) -> nn.Module:
        """Create an Izhikevich model spiking neural network."""
        # Placeholder for Izhikevich model implementation
        logger.info(f"Creating Izhikevich network with {num_neurons} neurons")
        return nn.Linear(num_neurons, num_neurons)  # Placeholder, replace with actual Izhikevich implementation

    def simulate_network(self, network: nn.Module, input_data: torch.Tensor, simulation_time: int) -> torch.Tensor:
        """Simulate the spiking neural network for the given simulation time."""
        # Placeholder for network simulation logic
        output = network(input_data)
        self._update_performance(output)
        return output

    def _update_performance(self, output: torch.Tensor):
        """Update the performance history and trigger self-healing if necessary."""
        new_performance = output.mean().item()  # Placeholder performance metric
        self.performance = new_performance
        self.performance_history.append(new_performance)
        if len(self.performance_history) > 100:
            self.performance_history.pop(0)
        self.last_update = time.time()

        if self.performance < PERFORMANCE_THRESHOLD:
            self._self_heal()

    def _self_heal(self):
        """Implement self-healing mechanisms."""
        logger.info("Initiating self-healing process...")
        initial_performance = self.performance
        best_performance = initial_performance

        for attempt in range(MAX_HEALING_ATTEMPTS):
            self._adjust_learning_rate()
            new_performance = self._simulate_performance()

            if new_performance > best_performance:
                best_performance = new_performance

            if new_performance >= PERFORMANCE_THRESHOLD:
                logger.info(f"Self-healing successful after {attempt + 1} attempts.")
                self.performance = new_performance
                return

        if best_performance > initial_performance:
            logger.info(f"Self-healing improved performance. New performance: {best_performance:.4f}")
            self.performance = best_performance
        else:
            logger.warning("Self-healing not improving performance. Reverting changes.")

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

    def _simulate_performance(self) -> float:
        """Simulate new performance after applying healing strategies."""
        return self.performance * (1 + np.random.uniform(-0.1, 0.1))

    def diagnose(self) -> List[str]:
        """Diagnose potential issues with the neuromorphic computing model."""
        issues = []
        if self.performance < PERFORMANCE_THRESHOLD:
            issues.append(f"Low performance: {self.performance:.4f}")
        if (time.time() - self.last_update) > 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 < PERFORMANCE_THRESHOLD for p in self.performance_history[-5:]):
            issues.append("Consistently low performance")
        return issues

# Example usage
if __name__ == "__main__":
    neuromorphic_computer = NeuromorphicComputing()

    # Create a LIF spiking neural network
    lif_network = neuromorphic_computer.create_spiking_neural_network('LIF', num_neurons=100)

    # Simulate the network
    input_data = torch.randn(1, 100)
    output = neuromorphic_computer.simulate_network(lif_network, input_data, simulation_time=1000)
    print(f"Network output shape: {output.shape}")

    # Diagnose potential issues
    issues = neuromorphic_computer.diagnose()
    if issues:
        logger.warning(f"Diagnosed issues: {issues}")
        neuromorphic_computer._self_heal()
