import torch
from torchvision import datasets, transforms
import numpy as np
from matplotlib import pyplot as plt
from utils import plot_tsne
import numpy as np
import random
import argparse

NUM_CLASSES = 10

def freeze_seeds(seed=0):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
def get_args():   
    parser = argparse.ArgumentParser()
    parser.add_argument('--seed', default=0, type=int, help='Seed for random number generators')
    parser.add_argument('--data-path', default="/datasets/cv_datasets/data", type=str, help='Path to dataset')
    parser.add_argument('--batch-size', default=8, type=int, help='Size of each batch')
    parser.add_argument('--latent-dim', default=128, type=int, help='encoding dimension')
    parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help='Default device to use')
    parser.add_argument('--mnist', action='store_true', default=False,
                        help='Whether to use MNIST (True) or CIFAR10 (False) data')
    parser.add_argument('--self-supervised', action='store_true', default=False,
                        help='Whether train self-supervised with reconstruction objective, or jointly with classifier for classification objective.')
    return parser.parse_args()
    

if __name__ == "__main__":
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  #one possible convenient normalization. You don't have to use it.
    ])

    args = get_args()
    freeze_seeds(args.seed)
                
                                           
    if args.mnist:
        train_dataset = datasets.MNIST(root=args.data_path, train=True, download=False, transform=transform)
        test_dataset = datasets.MNIST(root=args.data_path, train=False, download=False, transform=transform)
    else:
        train_dataset = datasets.CIFAR10(root=args.data_path, train=True, download=True, transform=transform)
        test_dataset = datasets.CIFAR10(root=args.data_path, train=False, download=True, transform=transform)
        
    # When you create your dataloader you should split train_dataset or test_dataset to leave some aside for validation

    #this is just for the example. Simple flattening of the image is probably not the best idea                                        
    encoder_model = torch.nn.Linear(32*32*3,args.latent_dim).to(args.device)
    decoder_model = torch.nn.Linear(args.latent_dim,32*32*3 if args.self_supervised else NUM_CLASSES).to(args.device) 

    sample = train_dataset[0][0][None].to(args.device) #This is just for the example - you should use a dataloader
    output = decoder_model(encoder_model(sample.flatten()))
    print(output.shape)