update type hints

lm_eval/api/metrics.py

@@ -21,36 +21,36 @@ def bypass_agg(arr):
 
 
 @register_aggregation("nanmean")
-def nanmean(arr):
+def nanmean(arr: list[float]) -> float:
     if len(arr) == 0 or all(np.isnan(arr)):
         return np.nan
     return np.nanmean(arr)
 
 
 @register_aggregation("mean")
-def mean(arr):
+def mean(arr: list[float]) -> float:
     return sum(arr) / len(arr)
 
 
 @register_aggregation("median")
-def median(arr):
+def median(arr: list[float]) -> float:
     return arr[len(arr) // 2]
 
 
 # Certain metrics must be calculated across all documents in a benchmark.
 # We use them as aggregation metrics, paired with no-op passthrough metric fns.
 @register_aggregation("perplexity")
-def perplexity(items):
+def perplexity(items: list[float]) -> float:
     return math.exp(-mean(items))
 
 
 @register_aggregation("weighted_perplexity")
-def weighted_perplexity(items):
+def weighted_perplexity(items: list[tuple[float, float]]) -> float:
     return math.exp(-weighted_mean(items))
 
 
 @register_aggregation("bits_per_byte")
-def bits_per_byte(items):
+def bits_per_byte(items: list[tuple[float, float]]) -> float:
     return -weighted_mean(items) / math.log(2)
 
 
@@ -413,7 +413,7 @@ def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
     return max(scores_for_ground_truths)
 
 
-def weighted_mean(items):
+def weighted_mean(items: List[tuple[float, float]]) -> float:
     a, b = zip(*items)
     return sum(a) / sum(b)