diff --git a/setup.py b/setup.py
index d438cbe..a3881ab 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ with open('README.md') as f:
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.11.3',
+  version = '1.11.4',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description = long_description,
diff --git a/vit_pytorch/accept_video_wrapper.py b/vit_pytorch/accept_video_wrapper.py
index c5ef059..ee10f0e 100644
--- a/vit_pytorch/accept_video_wrapper.py
+++ b/vit_pytorch/accept_video_wrapper.py
@@ -91,7 +91,7 @@ class AcceptVideoWrapper(Module):
 
             pos_emb = pos_emb.reshape(*pos_emb.shape[:2], *((1,) * dims_to_unsqueeze) , pos_emb.shape[-1])
 
-            embed = embed + pos_emb
+            embed = embed + pos_emb[:, :embed.shape[1]]
 
             outputs[self.output_pos_add_pos_emb] = embed
 
@@ -114,16 +114,16 @@ if __name__ == '__main__':
         emb_dropout = 0.1
     )
 
-    videos = torch.randn(1, 3, 10, 256, 256)
+    videos = torch.randn(1, 3, 7, 256, 256)
 
     # step up the difficulty and return embeddings for robotics
 
     from vit_pytorch.extractor import Extractor
     v = Extractor(v)
 
-    video_acceptor = AcceptVideoWrapper(v, add_time_pos_emb = True, output_pos_add_pos_emb = 1, time_seq_len = 10, dim_emb = 1024)
+    video_acceptor = AcceptVideoWrapper(v, add_time_pos_emb = True, output_pos_add_pos_emb = 1, time_seq_len = 12, dim_emb = 1024)
 
     logits, embeddings = video_acceptor(videos, eval_with_no_grad = True) # always (batch, channels, time, height, width) - time is always dimension 2
 
-    assert logits.shape == (1, 10, 1000)
-    assert embeddings.shape == (1, 10, 65, 1024)
+    assert logits.shape == (1, 7, 1000)
+    assert embeddings.shape == (1, 7, 65, 1024)