import os
from PIL import Image

from train_mm_moco import evaluate_and_plot, compute_tsne, MultiModalMoCo
import matplotlib.pyplot as plt

import torch
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import random_split
from torch.utils.data import DataLoader, Dataset
from torch.utils.tensorboard import SummaryWriter

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

writer = SummaryWriter('runs/mmssl')

# Custom dataset
class CustomMultiModalDataset(Dataset):
    def __init__(self, vision_folder, tactile_folder, transform=None):
        self.vision_folder = vision_folder
        self.tactile_folder = tactile_folder
        self.transform = transform

        self.vision_files = sorted(os.listdir(vision_folder))
        self.tactile_files = sorted(os.listdir(tactile_folder))

    def __len__(self):
        return len(self.vision_files)

    def __getitem__(self, idx):
        vision_path = os.path.join(self.vision_folder, self.vision_files[idx])
        tactile_path = os.path.join(self.tactile_folder, self.tactile_files[idx])

        vision_image = Image.open(vision_path).convert("RGB")
        tactile_image = Image.open(tactile_path).convert("RGB")

        if self.transform:
            vision_image = self.transform(vision_image)
            tactile_image = self.transform(tactile_image)

        return vision_image, tactile_image

# Initialize augmentation
simple_transforms = transforms.Compose([
    transforms.CenterCrop(500),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

data_transforms = transforms.Compose([
    transforms.RandomApply([transforms.RandomRotation(150)], p=0.50),
    transforms.RandomResizedCrop(224, scale=(0.2, 1.0)),
    transforms.RandomApply([transforms.RandomHorizontalFlip()], p=0.50),
    transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
    transforms.RandomGrayscale(p=0.2),
    transforms.RandomApply([transforms.GaussianBlur(3, sigma=(0.1, 2.0))], p=0.5),
])

# Initialize dataset and dataloader
vision_folder = "/home/vedant/Downloads/ssvtp_data/images_rgb"
tactile_folder = "/home/vedant/Downloads/ssvtp_data/images_tac"
dataset = CustomMultiModalDataset(vision_folder, tactile_folder, transform=simple_transforms)
#dataloader = DataLoader(dataset, batch_size=128, shuffle=True)

# Split the dataset into 80-20
train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])

# Initialize dataloaders for train and test
train_dataloader = DataLoader(train_dataset, batch_size=96, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)


# Initialize model
model = MultiModalMoCo(writer, K=4096, m=0.999, T=0.07).to(device)

# Initialize optimizer
vision_module = list(model.vision_base_q.parameters()) + list(model.vision_head_intra_q.parameters()) + list(model.vision_head_inter_q.parameters())
tactile_module = list(model.tactile_base_q.parameters()) + list(model.tactile_head_intra_q.parameters()) + list(model.tactile_head_inter_q.parameters())
optim_vision = optim.Adam(vision_module, lr=0.0001)
optim_tactile = optim.Adam(tactile_module, lr=0.0001)

# Training loop
n_epochs = 250  # Number of epochs
for epoch in range(n_epochs):
    for i, (x_vision, x_tactile) in enumerate(train_dataloader):
        
        # Augment images
        x_vision_q = data_transforms(x_vision).to(device)
        x_vision_k = data_transforms(x_vision).to(device)

        x_tactile_q = data_transforms(x_tactile).to(device)
        x_tactile_k = data_transforms(x_tactile).to(device)

        # Forward pass to get the loss
        loss = model(x_vision_q, x_vision_k, x_tactile_q, x_tactile_k, epoch, i, len(train_dataloader))

        # Backward pass and optimization
        optim_vision.zero_grad()
        optim_tactile.zero_grad()
        loss.backward()
        optim_vision.step()
        optim_tactile.step()

        # Logging
        if i % 10 == 0:
            print(f"Epoch [{epoch+1}/{n_epochs}], Step [{i+1}/{len(train_dataloader)}], Loss: {loss.item():.4f}")
            writer.add_scalar('training loss', loss.item(), epoch * len(train_dataloader) + i)

    # Evaluate and plot
    compute_tsne(model, test_dataloader, writer, epoch)
    evaluate_and_plot(model, test_dataloader, epoch, writer, device)
    if epoch % 10 == 0:
        torch.save(model.state_dict(), 'models/model.pth')

    
plt.show()