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

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

elk/extraction/prompt_loading.py

@@ -1,8 +1,8 @@
-from os.path import exists
 from collections import Counter
 from copy import deepcopy
 from dataclasses import dataclass
 from itertools import zip_longest
+from os.path import exists
 from random import Random
 from typing import Any, Iterator, Literal, Optional
 
@@ -48,7 +48,7 @@ class PromptConfig(Serializable):
  -1.
  seed: The seed to use for prompt randomization. Defaults to 42.
  stream: Whether to stream the dataset from the Internet. Defaults to False.
- combined_template_output_path: Path to save a combined template file to, when 
+ combined_template_output_path: Path to save a combined template file to, when
  applying prompt invariance across multiple datasets. Interpreted as a
  subpath of `combined_paths` in the templates dir. Defaults to empty string.
  """
@@ -127,11 +127,11 @@ def combine_templates(self):
  "Saved to promptsource/templates/combined_templates/"
  + f"{self.combined_template_output_path}.yaml"
  )
- 
+
  def verify_cols(self, ds_builder, ref_ds_builder) -> bool:
- '''Verify that number of features and number of classes for ClassLabel
-  match the expected values.
- '''
+ """Verify that number of features and number of classes for ClassLabel
+ match the expected values.
+ """
  expected_features = len(ref_ds_builder.info.features)
  expected_classes = ref_ds_builder.info.features["label"].num_classes
  num_features = len(ds_builder.info.features)
@@ -198,15 +198,13 @@ def load_prompts(
  An iterable of prompt dictionaries.
  """
  ds_name, _, config_name = ds_string.partition(" ")
- 
+
  prompter = None
  if combined_template_output_path and exists(combined_template_output_path):
- prompter = DatasetTemplates(
- "combined_templates", combined_template_output_path
- )
+ prompter = DatasetTemplates("combined_templates", combined_template_output_path)
  else:
  prompter = DatasetTemplates(ds_name, config_name)
- 
+
  ds_dict = assert_type(
  dict, load_dataset(ds_name, config_name or None, streaming=stream)
  )