Comment out code

visual.py

@@ -102,33 +102,33 @@ class Transformer(nn.Module):
     def forward(self, hidden_states):
 
         print("Shape of hidden states before CLIP:", hidden_states.shape)
-        torch.save(hidden_states, "hidden_states_before_clip.pt")
+        # torch.save(hidden_states, "hidden_states_before_clip.pt")
 
-        from huggingface_hub import HfApi
+        # from huggingface_hub import HfApi
 
-        api = HfApi()
-        api.upload_file(
-            path_or_fileobj="hidden_states_before_clip.pt",
-            path_in_repo="hidden_states_before_clip.pt",
-            repo_id="nielsr/test-cogvlm",
-            repo_type="dataset",
-        )
+        # api = HfApi()
+        # api.upload_file(
+        #     path_or_fileobj="hidden_states_before_clip.pt",
+        #     path_in_repo="hidden_states_before_clip.pt",
+        #     repo_id="nielsr/test-cogvlm",
+        #     repo_type="dataset",
+        # )
 
         for idx, layer_module in enumerate(self.layers):
             hidden_states = layer_module(hidden_states, print_values=idx==0)
 
         print("Shape of hidden states after CLIP:", hidden_states.shape)
-        torch.save(hidden_states, "hidden_states_after_clip.pt")
+        # torch.save(hidden_states, "hidden_states_after_clip.pt")
 
-        from huggingface_hub import HfApi
+        # from huggingface_hub import HfApi
 
-        api = HfApi()
-        api.upload_file(
-            path_or_fileobj="hidden_states_after_clip.pt",
-            path_in_repo="hidden_states_after_clip.pt",
-            repo_id="nielsr/test-cogvlm",
-            repo_type="dataset",
-        )
+        # api = HfApi()
+        # api.upload_file(
+        #     path_or_fileobj="hidden_states_after_clip.pt",
+        #     path_in_repo="hidden_states_after_clip.pt",
+        #     repo_id="nielsr/test-cogvlm",
+        #     repo_type="dataset",
+        # )
 
         return hidden_states