diff --git a/vllm/model_executor/models/whisper.py b/vllm/model_executor/models/whisper.py
index d2318ebc8bfca..e8b8c456aeae7 100644
--- a/vllm/model_executor/models/whisper.py
+++ b/vllm/model_executor/models/whisper.py
@@ -1,6 +1,6 @@
 import math
-from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
-                    TypedDict, Union)
+from typing import (Iterable, List, Mapping, Optional, Set, Tuple, TypedDict,
+                    Union)
 
 import numpy as np
 import torch
@@ -589,7 +589,6 @@ def dummy_encoder_data_for_whisper(ctx: InputContext, seq_len: int,
 
 
 def input_processor_for_whisper(ctx: InputContext, inputs):
-    
     multi_modal_data = inputs["encoder"]["multi_modal_data"]
     if isinstance(multi_modal_data["audio"], list):
         assert len(multi_modal_data["audio"]) == 1
@@ -710,7 +709,7 @@ def _parse_and_validate_audio_input(
         if input_features is not None:
             if not isinstance(input_features, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of audio features. "
-                                f"Got type: {type(input_features)}")
+                                 f"Got type: {type(input_features)}")
             input_features = [feat.to(self.dtype) for feat in input_features]
 
         return WhisperAudioInputs(input_features=input_features)
diff --git a/vllm/multimodal/processing.py b/vllm/multimodal/processing.py
index c09b4bfbc34d4..f75b835e8464b 100644
--- a/vllm/multimodal/processing.py
+++ b/vllm/multimodal/processing.py
@@ -62,7 +62,8 @@ def _cached_encode(
     *,
     add_special_tokens: bool = False,
 ) -> list[int]:
-    return encode_tokens(tokenizer, text,
+    return encode_tokens(tokenizer,
+                         text,
                          add_special_tokens=add_special_tokens)
 
 
@@ -746,7 +747,8 @@ def _apply_prompt_replacements(
                 mm_item_counts,
             )
 
-            token_ids = encode_tokens(tokenizer, text,
+            token_ids = encode_tokens(tokenizer,
+                                      text,
                                       add_special_tokens=False)
             matched_repls = [match.prompt_repl for match in text_matches]
 
diff --git a/vllm/transformers_utils/tokenizer_group/tokenizer_group.py b/vllm/transformers_utils/tokenizer_group/tokenizer_group.py
index 0b20be2ac071f..6dc2f90561873 100644
--- a/vllm/transformers_utils/tokenizer_group/tokenizer_group.py
+++ b/vllm/transformers_utils/tokenizer_group/tokenizer_group.py
@@ -2,8 +2,7 @@
 
 from vllm.config import TokenizerPoolConfig
 from vllm.lora.request import LoRARequest
-from vllm.transformers_utils.tokenizer import (AnyTokenizer,
-                                               encode_tokens,
+from vllm.transformers_utils.tokenizer import (AnyTokenizer, encode_tokens,
                                                get_lora_tokenizer,
                                                get_lora_tokenizer_async,
                                                get_tokenizer)
@@ -59,7 +58,8 @@ def encode(self,
                lora_request: Optional[LoRARequest] = None,
                add_special_tokens: Optional[bool] = None) -> List[int]:
         tokenizer = self.get_lora_tokenizer(lora_request)
-        ret = encode_tokens(tokenizer, prompt,
+        ret = encode_tokens(tokenizer,
+                            prompt,
                             add_special_tokens=add_special_tokens)
         self._raise_if_input_too_long(ret, lora_request)
         return ret
@@ -71,7 +71,8 @@ async def encode_async(
             lora_request: Optional[LoRARequest] = None,
             add_special_tokens: Optional[bool] = None) -> List[int]:
         tokenizer = await self.get_lora_tokenizer_async(lora_request)
-        ret = encode_tokens(tokenizer, prompt,
+        ret = encode_tokens(tokenizer,
+                            prompt,
                             add_special_tokens=add_special_tokens)
         self._raise_if_input_too_long(ret, lora_request)
         return ret