Merge branch 'master' into req/mecab-ko

src/torchmetrics/functional/text/chrf.py

@@ -188,7 +188,7 @@ def _get_total_ngrams(n_grams_counts: Dict[int, Dict[Tuple[str, ...], Tensor]])
         """Get total sum of n-grams over n-grams w.r.t n."""
         total_n_grams: Dict[int, Tensor] = defaultdict(lambda: tensor(0.0))
         for n in n_grams_counts:
-            total_n_grams[n] = tensor(sum(n_grams_counts[n].values()))
+            total_n_grams[n] = sum(n_grams_counts[n].values()).detach().clone()  # type: ignore
         return total_n_grams
 
     char_n_grams_counts, word_n_grams_counts = _char_and_word_ngrams_counts(
@@ -216,12 +216,10 @@ def _get_ngram_matches(
     """
     matching_n_grams: Dict[int, Tensor] = defaultdict(lambda: tensor(0.0))
     for n in hyp_n_grams_counts:
-        matching_n_grams[n] = tensor(
-            sum(
-                torch.min(ref_n_grams_counts[n][n_gram], hyp_n_grams_counts[n][n_gram])
-                for n_gram in hyp_n_grams_counts[n]
-            )
-        )
+        min_n_grams = [
+            torch.min(ref_n_grams_counts[n][n_gram], hyp_n_grams_counts[n][n_gram]) for n_gram in hyp_n_grams_counts[n]
+        ]
+        matching_n_grams[n] = sum(min_n_grams).detach().clone()  # type: ignore
     return matching_n_grams
 
 

tests/unittests/text/test_sacre_bleu.py

@@ -54,6 +54,8 @@ def test_bleu_score_class(self, ddp, preds, targets, tokenize, lowercase):
         """Test class implementation of metric."""
         if _should_skip_tokenizer(tokenize):
             pytest.skip(reason="`ko-mecab` tokenizer requires  `mecab-ko` package to be installed")
+        if tokenize == "flores200":
+            pytest.skip("flores200 tests are flaky")  # TODO: figure out why
 
         metric_args = {"tokenize": tokenize, "lowercase": lowercase}
         original_sacrebleu = partial(_reference_sacre_bleu, tokenize=tokenize, lowercase=lowercase)

tests/unittests/text/test_ter.py

@@ -64,7 +64,7 @@ class TestTER(TextTester):
     """Test class for `TranslationEditRate` metric."""
 
     @pytest.mark.parametrize("ddp", [pytest.param(True, marks=pytest.mark.DDP), False])
-    def test_chrf_score_class(self, ddp, preds, targets, normalize, no_punctuation, asian_support, lowercase):
+    def test_ter_class(self, ddp, preds, targets, normalize, no_punctuation, asian_support, lowercase):
         """Test class implementation of metric."""
         metric_args = {
             "normalize": normalize,
@@ -113,7 +113,7 @@ def test_ter_score_functional(self, preds, targets, normalize, no_punctuation, a
             metric_args=metric_args,
         )
 
-    def test_chrf_score_differentiability(self, preds, targets, normalize, no_punctuation, asian_support, lowercase):
+    def test_ter_differentiability(self, preds, targets, normalize, no_punctuation, asian_support, lowercase):
         """Test the differentiability of the metric, according to its `is_differentiable` attribute."""
         metric_args = {
             "normalize": normalize,