Automatic sleep staging is crucial for diagnosing sleep disorders, however, existing inter-epoch feature extraction schemes such as RNN-based networks or transformers often struggle with long sleep sequences due to overfitting. This study presents a novel automatic sleep staging method utilizing a pre-trained vision transformer with compression as a sequence encoder and a two-step attention to enhance the sleep-stage classification performance. In contrast to existing transformer-based methods, the pre-trained transformer with compression can handle long sequences covering a sleep cycle, leveraging robust feature extraction capabilities with substantially fewer parameters. Furthermore, an epoch encoder based on a bidirectional temporal convolutional network with a multi-head two-step attention mechanism is proposed to improve the efficiency of epoch-level feature extraction. The performance of the proposed method is evaluated using three publicly available datasets: SleepEDF-20, SleepEDF-78, and SHHS. Numerical experiments show notable performance enhancement of the proposed scheme in comparison with the state-of- the-art algorithms, particularly for small training datasets, which validates the resilience of the proposed method against overfitting. These results suggest that with appropriate regularization, transformer-based models can effectively capture long-term contextual information across a complete sleep cycle.