from torch.utils.data import DataLoader
from transformers import AdamW
from datasets import load_dataset

from robocat import robo_cat
import torch

# Step 1: Load the dataset
dataset = load_dataset("your_dataset_name")

# Step 2: Preprocess the dataset
def preprocess(example):
    video = torch.tensor(example["video"])  # assuming "video" key in the dataset
    instructions = example["instructions"]  # assuming "instructions" key in the dataset
    # further preprocessing steps here...
    return video, instructions

dataset = dataset.map(preprocess)

# Step 3: Create a DataLoader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# Initialize the optimizer
optimizer = AdamW(robo_cat.parameters(), lr=1e-4)

# Step 4: Write the training loop
for epoch in range(epochs):
    for video, instructions in dataloader:
        # Forward pass
        logits = robo_cat(video, instructions)

        # Compute the loss
        loss = torch.nn.CrossEntropy()# ... compute the loss based on your task

        # Backpropagation
        loss.backward()

        # Update weights
        optimizer.step()

        # Zero gradients
        optimizer.zero_grad()

    # Step 5: Validation loop...
