[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

delphi/scorers/classifier/detection.py

@@ -1,7 +1,7 @@
 from ...clients.client import Client
 from ...latents import LatentRecord
-from .classifier import Classifier
 from ...scorers.scorer import Scorer
+from .classifier import Classifier
 from .prompts.detection_prompt import prompt as detection_prompt
 from .sample import Sample, examples_to_samples
 

delphi/scorers/simulator/oai_autointerp/activations/activations.py

@@ -13,7 +13,7 @@ class ActivationRecord(Serializable):
 
     tokens: list[str]
     """Tokens in the text sequence, represented as strings."""
-    
+
     activations: list[int | float]
     """Raw activation values for the neuron on each token in the text sequence."""
 

delphi/scorers/simulator/oai_simulator.py

@@ -1,7 +1,8 @@
-import torch
 from typing import Union
 
-from delphi.latents.latents import NonActivatingExample, ActivatingExample
+import torch
+
+from delphi.latents.latents import ActivatingExample, NonActivatingExample
 
 from ..scorer import Scorer, ScorerResult
 from .oai_autointerp import (
@@ -56,24 +57,28 @@ async def __call__(self, record):  # type: ignore
             score=result,
         )
 
-    def to_activation_records(self, examples: list[Union[ActivatingExample, NonActivatingExample]]) -> list[ActivationRecord]:
+    def to_activation_records(
+        self, examples: list[Union[ActivatingExample, NonActivatingExample]]
+    ) -> list[ActivationRecord]:
         # Filter Nones
         result = []
         for example in examples:
-            if example is None: 
+            if example is None:
                 continue
-            
+
             if example.normalized_activations is None:
                 # Use zeros for non-activating examples
-                example.normalized_activations = torch.zeros_like(
-                    example.activations
-                )
-            
+                example.normalized_activations = torch.zeros_like(example.activations)
+
             result.append(
                 ActivationRecord(
                     self.tokenizer.batch_decode(example.tokens),
                     example.normalized_activations.half(),
-                    quantile=example.quantile if isinstance(example, ActivatingExample) else None,
+                    quantile=(
+                        example.quantile
+                        if isinstance(example, ActivatingExample)
+                        else None
+                    ),
                 )
             )