Mod: Format files with black 23.3.0.

src/aac_metrics/functional/bleu.py

@@ -165,7 +165,7 @@ def __cook_references(
     for ref in refs:
         rl, counts = __cook_sentence(ref, n, tokenizer)
         reflen.append(rl)
-        for (ngram, count) in counts.items():
+        for ngram, count in counts.items():
             maxcounts[ngram] = max(maxcounts.get(ngram, 0), count)
 
     # Calculate effective reference sentence length.
@@ -203,7 +203,7 @@ def __cook_candidate(
     result["guess"] = [max(0, testlen - k + 1) for k in range(1, n + 1)]
     result["correct"] = [0] * n
 
-    for (ngram, count) in counts.items():
+    for ngram, count in counts.items():
         result["correct"][len(ngram) - 1] += min(refmaxcounts.get(ngram, 0), count)
 
     return result

src/aac_metrics/functional/cider_d.py

@@ -182,7 +182,7 @@ def __counter_to_vec(
     length = 0
     norm = np.zeros((n,))
 
-    for (ngram, term_freq) in counters.items():
+    for ngram, term_freq in counters.items():
         if isinstance(doc_frequencies, Mapping):
             count = doc_frequencies[ngram]
         else:
@@ -233,7 +233,7 @@ def __similarity(
 
     for ni in range(n):
         # ngram
-        for (ngram, count) in cand_vec[ni].items():
+        for ngram, count in cand_vec[ni].items():
             # vrama91 : added clipping
             similarities[ni] += min(count, ref_vec[ni][ngram]) * ref_vec[ni][ngram]
 
@@ -255,7 +255,6 @@ def __compute_cider(
     sigma: float,
     scale: float,
 ) -> tuple[np.ndarray, list[tuple[list, list]]]:
-
     scores = np.empty((len(cooked_cands),))
     tfidf_lst = []