no need to iterate over results for greedy_until

lm_eval/api/task.py

@@ -1017,37 +1017,37 @@ class ConfigurableTask(Task):
             else:
                 gold = str(gold)
 
+            result, _ = results
             for metric in self._metric_fn_list.keys():
-                for result in results:
-                    if self.multiple_target:
-                        # in the case where we have multiple targets,
-                        # return true if any are true
-                        # TODO: this may break for multipLe_target, non zero-or-1 metrics
-                        scores = []
-                        for gold_option in gold:
-                            res = self._metric_fn_list[metric](
-                                references=[gold_option],
-                                predictions=[result],
-                                **self._metric_fn_kwargs[metric],
-                            )
-                            if isinstance(res, dict):
-                                # TODO: this handles the case where HF evaluate returns a dict.
-                                res = res[metric]
-                            scores.append(res)
-                        if any(scores):
-                            result_score = 1.0
-                        else:
-                            result_score = 0.0
-                    else:
-                        result_score = self._metric_fn_list[metric](
-                            references=[gold],
+                if self.multiple_target:
+                    # in the case where we have multiple targets,
+                    # return true if any are true
+                    # TODO: this may break for multipLe_target, non zero-or-1 metrics
+                    scores = []
+                    for gold_option in gold:
+                        res = self._metric_fn_list[metric](
+                            references=[gold_option],
                             predictions=[result],
                             **self._metric_fn_kwargs[metric],
                         )
-                        if isinstance(result_score, dict):
+                        if isinstance(res, dict):
                             # TODO: this handles the case where HF evaluate returns a dict.
-                            result_score = result_score[metric]
-                    result_dict[metric] = result_score
+                            res = res[metric]
+                        scores.append(res)
+                    if any(scores):
+                        result_score = 1.0
+                    else:
+                        result_score = 0.0
+                else:
+                    result_score = self._metric_fn_list[metric](
+                        references=[gold],
+                        predictions=[result],
+                        **self._metric_fn_kwargs[metric],
+                    )
+                    if isinstance(result_score, dict):
+                        # TODO: this handles the case where HF evaluate returns a dict.
+                        result_score = result_score[metric]
+                result_dict[metric] = result_score
         else:
             raise ValueError(
                 f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ",