diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py
index 93381bae..55052815 100644
--- a/modules/sub_quadratic_attention.py
+++ b/modules/sub_quadratic_attention.py
@@ -15,14 +15,9 @@ import torch
 from torch import Tensor
 from torch.utils.checkpoint import checkpoint
 import math
-
-try:
-    from typing import Protocol
-except:
-    from typing_extensions import Protocol
-    
 from typing import Optional, NamedTuple, List
 
+
 def narrow_trunc(
     input: Tensor,
     dim: int,
@@ -31,12 +26,14 @@ def narrow_trunc(
 ) -> Tensor:
     return torch.narrow(input, dim, start, length if input.shape[dim] >= start + length else input.shape[dim] - start)
 
+
 class AttnChunk(NamedTuple):
     exp_values: Tensor
     exp_weights_sum: Tensor
     max_score: Tensor
 
-class SummarizeChunk(Protocol):
+
+class SummarizeChunk:
     @staticmethod
     def __call__(
         query: Tensor,
@@ -44,7 +41,8 @@ class SummarizeChunk(Protocol):
         value: Tensor,
     ) -> AttnChunk: ...
 
-class ComputeQueryChunkAttn(Protocol):
+
+class ComputeQueryChunkAttn:
     @staticmethod
     def __call__(
         query: Tensor,
@@ -52,6 +50,7 @@ class ComputeQueryChunkAttn(Protocol):
         value: Tensor,
     ) -> Tensor: ...
 
+
 def _summarize_chunk(
     query: Tensor,
     key: Tensor,
@@ -72,6 +71,7 @@ def _summarize_chunk(
     max_score = max_score.squeeze(-1)
     return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
 
+
 def _query_chunk_attention(
     query: Tensor,
     key: Tensor,
@@ -112,6 +112,7 @@ def _query_chunk_attention(
     all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
     return all_values / all_weights
 
+
 # TODO: refactor CrossAttention#get_attention_scores to share code with this
 def _get_attention_scores_no_kv_chunking(
     query: Tensor,
@@ -131,10 +132,12 @@ def _get_attention_scores_no_kv_chunking(
     hidden_states_slice = torch.bmm(attn_probs, value)
     return hidden_states_slice
 
+
 class ScannedChunk(NamedTuple):
     chunk_idx: int
     attn_chunk: AttnChunk
 
+
 def efficient_dot_product_attention(
     query: Tensor,
     key: Tensor,