from datasets import load_dataset
from transformers import BertTokenizer

def load_and_tokenize_data(config):
    """
    Load and tokenize data based on the provided configuration.

    Args:
        config (dict): Configuration dictionary containing dataset and tokenizer details.

    Returns:
        tuple: A tuple containing the tokenized train and test datasets.
    """
    # Load the dataset
    dataset = load_dataset(config['dataset']['name'], split=config['dataset']['split'])
    dataset = dataset.train_test_split(test_size=0.2)
    train_dataset = dataset['train']
    test_dataset = dataset['test']

    # Initialize the tokenizer
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

    # Define the tokenization function
    def tokenize_function(examples):
        return tokenizer(examples['text'], padding='max_length', truncation=True)

    # Apply tokenization to the train and test datasets
    train_dataset = train_dataset.map(tokenize_function, batched=True)
    test_dataset = test_dataset.map(tokenize_function, batched=True)

    # Set the format to PyTorch tensors
    train_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])
    test_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])

    return train_dataset, test_dataset

# Example usage
if __name__ == "__main__":
    config = {
        'dataset': {
            'name': 'imdb',
            'split': 'train[:10%]'
        }
    }
    train_dataset, test_dataset = load_and_tokenize_data(config)
    print("Train dataset and Test dataset have been loaded and tokenized successfully.")