import datetime
import time

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch


def getDevice():
    if torch.cuda.is_available():    
        device = torch.device("cuda")

        print('There are %d GPU(s) available.' % torch.cuda.device_count())
        print('We will use the GPU:', torch.cuda.get_device_name(0))
    else:
        print('No GPU available, using the CPU instead.')
        device = torch.device("cpu")
    
    return device


def flatAccuracy(preds, labels):
        pred_flat = np.argmax(preds, axis=1).flatten()
        labels_flat = labels.flatten()
        return np.sum(pred_flat == labels_flat) / len(labels_flat)
    

def formatTime(elapsed):
    elapsed_rounded = int(round((elapsed)))
    
    # Format as hh:mm:ss
    return str(datetime.timedelta(seconds=elapsed_rounded))


def plotTrainingLoss(lossValues):
    sns.set(style='darkgrid')
    sns.set(font_scale=1.5)
    plt.rcParams["figure.figsize"] = (12,6)

    plt.plot(lossValues, 'b-o')

    plt.title("Training loss")
    plt.xlabel("Epoch")
    plt.ylabel("Loss")

    plt.show()