diff --git a/server/text_generation_server/utils/tokens.py b/server/text_generation_server/utils/tokens.py
index c7888f6f..5460ccfd 100644
--- a/server/text_generation_server/utils/tokens.py
+++ b/server/text_generation_server/utils/tokens.py
@@ -48,10 +48,10 @@ class NextTokenChooser:
         ) if logit_bias and any([logit_bias[k] != 0.0 for k in logit_bias]) else None
 
         has_warpers = (
-                (temperature is not None and temperature != 1.0)
-                or (top_k is not None and top_k != 0)
-                or (top_p is not None and top_p < 1.0)
-                or (typical_p is not None and typical_p < 1.0)
+            (temperature is not None and temperature != 1.0)
+            or (top_k is not None and top_k != 0)
+            or (top_p is not None and top_p < 1.0)
+            or (typical_p is not None and typical_p < 1.0)
         )
         if has_warpers:
             self.static_warper = static_warper(
@@ -82,10 +82,10 @@ class NextTokenChooser:
 
     @classmethod
     def from_pb(
-            cls,
-            pb: generate_pb2.NextTokenChooserParameters,
-            device: torch.device,
-            tokenizer: PreTrainedTokenizerBase,
+        cls,
+        pb: generate_pb2.NextTokenChooserParameters,
+        device: torch.device,
+        tokenizer: PreTrainedTokenizerBase,
     ) -> "NextTokenChooser":
         return NextTokenChooser(
             watermark=pb.watermark,
@@ -116,11 +116,11 @@ class StopSequenceCriteria:
 
 class StoppingCriteria:
     def __init__(
-            self,
-            eos_token_id: int,
-            stop_sequence_criterias: List[StopSequenceCriteria],
-            max_new_tokens: int = 20,
-            ignore_eos_token: bool = False,
+        self,
+        eos_token_id: int,
+        stop_sequence_criterias: List[StopSequenceCriteria],
+        max_new_tokens: int = 20,
+        ignore_eos_token: bool = False,
     ):
         self.eos_token_id = eos_token_id
         self.stop_sequence_criterias = stop_sequence_criterias
@@ -146,9 +146,9 @@ class StoppingCriteria:
 
     @classmethod
     def from_pb(
-            cls,
-            pb: generate_pb2.StoppingCriteriaParameters,
-            tokenizer: PreTrainedTokenizerBase,
+        cls,
+        pb: generate_pb2.StoppingCriteriaParameters,
+        tokenizer: PreTrainedTokenizerBase,
     ) -> "StoppingCriteria":
         stop_sequence_criterias = [
             StopSequenceCriteria(sequence) for sequence in pb.stop_sequences
@@ -163,18 +163,18 @@ class StoppingCriteria:
 
 class HeterogeneousNextTokenChooser:
     def __init__(
-            self,
-            dtype: torch.dtype,
-            device: torch.device,
-            watermark: List[bool],
-            temperature: List[float],
-            repetition_penalty: List[float],
-            top_k: List[int],
-            top_p: List[float],
-            typical_p: List[float],
-            do_sample: List[bool],
-            seeds: List[int],
-            logit_bias: List[Dict[Tuple[int], float]],
+        self,
+        dtype: torch.dtype,
+        device: torch.device,
+        watermark: List[bool],
+        temperature: List[float],
+        repetition_penalty: List[float],
+        top_k: List[int],
+        top_p: List[float],
+        typical_p: List[float],
+        do_sample: List[bool],
+        seeds: List[int],
+        logit_bias: List[Dict[Tuple[int], float]],
     ):
         warpers = []
 
@@ -284,11 +284,11 @@ class HeterogeneousNextTokenChooser:
 
     @classmethod
     def from_pb(
-            cls,
-            pb: List[generate_pb2.NextTokenChooserParameters],
-            dtype: torch.dtype,
-            device: torch.device,
-            tokenizer: PreTrainedTokenizerBase,
+        cls,
+        pb: List[generate_pb2.NextTokenChooserParameters],
+        dtype: torch.dtype,
+        device: torch.device,
+        tokenizer: PreTrainedTokenizerBase,
     ) -> "HeterogeneousNextTokenChooser":
         return HeterogeneousNextTokenChooser(
             watermark=[pb_.watermark for pb_ in pb],