mport torch
import numpy as np
from sklearn.metrics import accuracy_score  # Example metric

# Load your hidden test set (adjust path and format to your data)
TEST_DATA_PATH = "test_data.pt"  # Replace with the actual path
TEST_LABELS_PATH = "test_labels.pt"

test_data = torch.load(TEST_DATA_PATH)
test_labels = torch.load(TEST_LABELS_PATH)

# Evaluation script entry point
def evaluate_submission(model_checkpoint_path: str):
    """
    Evaluates the submitted model on the hidden test set.
    Args:
        model_checkpoint_path (str): Path to the submitted model checkpoint.
    
    Returns:
        dict: A dictionary containing the evaluation metrics.
    """
    # Load the participant's model
    model = torch.load(model_checkpoint_path)
    model.eval()

    # Move model and data to the appropriate device (e.g., GPU if available)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = model.to(device)
    test_data_tensor = test_data.to(device)

    # Perform inference
    with torch.no_grad():
        predictions = model(test_data_tensor)
        predictions = torch.argmax(predictions, axis=1).cpu().numpy()

    # Calculate evaluation metric (e.g., accuracy)
    accuracy = accuracy_score(test_labels, predictions)

    return {"accuracy": accuracy}  # Replace with other metrics as needed

if __name__ == "__main__":
    # For local testing, you can pass a sample model path here
    sample_model_path = "sample_submission.pt"  # Replace with a test checkpoint
    result = evaluate_submission(sample_model_path)
    print(result)