Pathological　examination　of　nasopharyngeal　carcinoma　(NPC)　is　an　indispensable　factor　for　diagnosis,　guiding　clinical　treatment　and　judging　prognosis.　Traditional　and　fully　supervised　NPC　diagnosis　algorithms　require　manual　delineation　of　regions　of　interest　on　the　gigapixel　of　whole　slide　images　(WSIs),　which　however　is　laborious　and　often　biased.　In　this　paper,　we　propose　a　weakly　supervised　framework　based　on　Tokens-to-Token　Vision　Transformer　(WS-T2T-ViT)　for　accurate　NPC　classification　with　only　a　slide-level　label.　The　label　of　tile　images　is　inherited　from　their　slide-level　label.　Specifically,　WS-T2T-ViT　is　composed　of　the　multi-resolution　pyramid,　T2T-ViT　and　multi-scale　attention　module.　The　multi-resolution　pyramid　is　designed　for　imitating　the　coarse-to-fine　process　of　manual　pathological　analysis　to　learn　features　from　different　magnification　levels.　The　T2T　module　captures　the　local　and　global　features　to　overcome　the　lack　of　global　information.　The　multi-scale　attention　module　improves　classification　performance　by　weighting　the　contributions　of　different　granularity　levels.　Extensive　experiments　are　performed　on　the　802-patient　NPC　and　CAMELYON16　dataset.　WS-T2T-ViT　achieves　an　area　under　the　receiver　operating　characteristic　curve　(AUC)　of　0.989　for　NPC　classification　on　the　NPC　dataset.　The　experiment　results　of　CAMELYON16　dataset　demonstrate　the　robustness　and　generalizability　of　WS-T2T-ViT　in　WSI-level　classification.　IEEE