import os
import torch
import torchvision
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
import toml

batch_size = 8
num_epochs = 10
learning_rate = 0.001

class LoRAModel(torch.nn.Module):
    def __init__(self):
        super(LoRAModel, self).__init__()

    def forward(self, x):
        pass

custom_dataset = """
[[datasets]]

[[datasets.subsets]]
image_dir = "/path/to/directory"
num_repeats = 10

[[datasets.subsets]]
image_dir = "/path/to/directory"
is_reg = true
num_repeats = 1
"""

dataset_config = toml.loads(custom_dataset)
datasets = dataset_config.get("datasets", [])
transform = transforms.Compose([
    transforms.Resize((512, 512)),
    transforms.ToTensor(),
])

train_datasets = []
for dataset in datasets:
    subsets = dataset.get("subsets", [])
    for subset in subsets:
        image_dir = subset.get("image_dir")
        num_repeats = subset.get("num_repeats", 1)
        is_reg = subset.get("is_reg", False)

        dataset = torchvision.datasets.ImageFolder(root=image_dir, transform=transform)
        train_datasets.extend([dataset] * num_repeats)

train_dataset = torch.utils.data.ConcatDataset(train_datasets)

dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

model = LoRAModel()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

total_step = len(dataloader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(dataloader):
        outputs = model(images)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i + 1) % 100 == 0:
            print(f"Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{total_step}], Loss: {loss.item()}")

save_path = "/path/to/directory/model.pth"
torch.save(model.state_dict(), save_path)