import logging
import time

import torch


def evaluate(model, data_loader, device):
    """Performs evaluation on the dataset for one epoch.

    Args:
        model (torch.nn.Module): The model to evaluate.
        data_loader (torch.utils.data.DataLoader): DataLoader for the evaluation data.
        device (torch.device): The device to run evaluation on.

    Returns:
        dict: A dictionary containing evaluation metrics (e.g., average loss).
    """
    model.eval()  # Set model to evaluation mode
    total_loss = 0.0
    num_batches = len(data_loader)
    eval_start_time = time.time()
    status_interval = max(1, num_batches // 10)  # Log status roughly 10 times

    logging.info("--- Starting Evaluation --- ")

    with torch.no_grad():  # Disable gradient calculations
        for i, (images, targets) in enumerate(data_loader):
            images = list(image.to(device) for image in images)
            targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

            # In eval mode with targets, Mask R-CNN should still return losses
            # If it returned predictions, logic here would change to process predictions
            loss_dict = model(images, targets)
            losses = sum(loss for loss in loss_dict.values())
            loss_value = losses.item()
            total_loss += loss_value

            if (i + 1) % status_interval == 0:
                logging.info(f"  Evaluated batch {i + 1}/{num_batches}")

    avg_loss = total_loss / num_batches if num_batches > 0 else 0
    eval_duration = time.time() - eval_start_time

    logging.info("--- Evaluation Finished ---")
    logging.info(f"  Average Evaluation Loss: {avg_loss:.4f}")
    logging.info(f"  Evaluation Duration: {eval_duration:.2f}s")

    # Return metrics (currently just average loss)
    metrics = {"average_loss": avg_loss}
    return metrics


# Example usage (can be removed or kept for testing):
if __name__ == "__main__":
    # This is a dummy test and requires a model, dataloader, device
    print(
        "This script contains the evaluate function and cannot be run directly for testing without setup."
    )
    # Example:
    # device = torch.device('cpu')
    # # Create dummy model and dataloader
    # model = ...
    # data_loader = ...
    # metrics = evaluate(model, data_loader, device)
    # print(f"Dummy evaluation metrics: {metrics}")