All tests working ostensibly

tests/test_load_prompts.py

@@ -1,4 +1,4 @@
-from itertools import cycle, islice
+from itertools import islice
 from typing import Literal
 
 import pytest
@@ -10,26 +10,22 @@
 @pytest.mark.filterwarnings("ignore:Unable to find a decoding function")
 def test_load_prompts():
     def test_single_split(cfg: PromptConfig, split_type: Literal["train", "val"]):
-        prompt_ds = load_prompts(
-            *cfg.datasets,
-            split_type=split_type,
-        )
-        prompters = []
-
-        for ds in cfg.datasets:
-            ds_name, _, config_name = ds.partition(" ")
+        for cfg in cfg.explode():
+            ds_string = cfg.datasets[0]
+            prompt_ds = load_prompts(ds_string, split_type=split_type)
+
+            ds_name, _, config_name = ds_string.partition(" ")
             prompter = DatasetTemplates(ds_name, config_name or None)
-            prompters.append(prompter)
 
-        limit = cfg.max_examples[0 if split_type == "train" else 1]
-        for prompter, record in zip(cycle(prompters), islice(prompt_ds, limit)):
-            true_template_names = prompter.all_template_names
-            returned_template_names = record["template_names"]
+            limit = cfg.max_examples[0 if split_type == "train" else 1]
+            for record in islice(prompt_ds, limit):
+                true_template_names = prompter.all_template_names
+                returned_template_names = record["template_names"]
 
-            # check for using the same templates
-            assert set(true_template_names) == set(returned_template_names)
-            # check for them being in the same order
-            assert true_template_names == true_template_names
+                # check for using the same templates
+                assert set(true_template_names) == set(returned_template_names)
+                # check for them being in the same order
+                assert true_template_names == true_template_names
 
     # the case where the dataset has 2 classes
     # this dataset is small