[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -2,25 +2,25 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v6.0.0
     hooks:
     -   id: trailing-whitespace
     -   id: end-of-file-fixer
     -   id: check-yaml
         # The promptsource templates spuriously fail without this
         args: ["--unsafe"]
     -   id: check-added-large-files
--   repo: https://github.com/psf/black
-    rev: 23.7.0
+-   repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 26.3.1
     hooks:
     -   id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 'v0.0.278'
+    rev: 'v0.15.11'
     hooks:
         - id: ruff
           args: [--fix, --exit-non-zero-on-fix]
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.4.2
     hooks:
     -   id: codespell
         # The promptsource templates spuriously get flagged without this

elk/extraction/extraction.py

@@ -1,4 +1,5 @@
 """Functions for extracting the hidden states of a model."""
+
 import os
 from collections import defaultdict
 from dataclasses import InitVar, dataclass, replace
@@ -202,7 +203,7 @@
                     a_id = tokenizer.encode(" " + choice, add_special_tokens=False)
 
                     # the Llama tokenizer splits off leading spaces
                     if tokenizer.decode(a_id[0]).strip() == "":
                         a_id_without_space = tokenizer.encode(
                             choice, add_special_tokens=False
                         )

elk/metrics/eval.py

@@ -73,7 +73,7 @@ def evaluate_preds(
     Returns:
         dict: A dictionary containing the accuracy, AUROC, and ECE.
     """
-    (n, v) = y_logits.shape
+    n, v = y_logits.shape
     assert y_true.shape == (n,)
 
     if ensembling == "full":

elk/plotting/visualize.py

@@ -78,9 +78,9 @@ def render(
                         y=dataset_data["auroc_estimate"],
                         mode="lines",
                         name=ensemble,
-                        showlegend=False
-                        if dataset_name != unique_datasets[0]
-                        else True,
+                        showlegend=(
+                            False if dataset_name != unique_datasets[0] else True
+                        ),
                         line=dict(color=color_map[ensemble]),
                     ),
                     row=row,

elk/promptsource/templates.py

@@ -215,9 +215,11 @@ def _escape_pipe(cls, example):
         # Replaces any occurrences of the "|||" separator in the example, which
         # which will be replaced back after splitting
         protected_example = {
-            key: value.replace("|||", cls.pipe_protector)
-            if isinstance(value, str)
-            else value
+            key: (
+                value.replace("|||", cls.pipe_protector)
+                if isinstance(value, str)
+                else value
+            )
             for key, value in example.items()
         }
         return protected_example

elk/training/platt_scaling.py

@@ -12,8 +12,7 @@ class PlattMixin(ABC):
     scale: nn.Parameter
 
     @abstractmethod
-    def __call__(self, *args: Any, **kwds: Any) -> Any:
-        ...
+    def __call__(self, *args: Any, **kwds: Any) -> Any: ...
 
     def platt_scale(self, labels: Tensor, hiddens: Tensor, max_iter: int = 100):
         """Fit the scale and bias terms to data with LBFGS.

elk/training/supervised.py

@@ -21,7 +21,7 @@ def train_supervised(
     leace = None
 
     for train_data in data.values():
-        (n, v, d) = train_data.hiddens.shape
+        n, v, d = train_data.hiddens.shape
         train_h = rearrange(train_data.hiddens, "n v d -> (n v) d")
 
         if erase_paraphrases and v > 1:

elk/training/train.py

@@ -64,7 +64,7 @@ def apply_to_layer(
         val_dict = self.prepare_data(device, layer, "val")
 
         first_train_data, *rest = train_dict.values()
-        (_, v, d) = first_train_data.hiddens.shape
+        _, v, d = first_train_data.hiddens.shape
         if not all(other_data.hiddens.shape[-1] == d for other_data in rest):
             raise ValueError("All datasets must have the same hidden state size")