Untokenized Bleu score to stay consistent with all the other text metrics

tests/text/test_bleu.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from functools import partial
-
 import pytest
 from nltk.translate.bleu_score import SmoothingFunction, corpus_bleu
 from torch import tensor
@@ -25,23 +23,19 @@
 # example taken from
 # https://www.nltk.org/api/nltk.translate.html?highlight=bleu%20score#nltk.translate.bleu_score.corpus_bleu
 # EXAMPLE 1
-HYPOTHESIS_A = tuple(
-    "It is a guide to action which ensures that the military always obeys the commands of the party".split()
-)
-REFERENCE_1A = tuple("It is a guide to action that ensures that the military will forever heed Party commands".split())
-REFERENCE_2A = tuple(
-    "It is a guiding principle which makes the military forces always being under the command of the Party".split()
-)
-REFERENCE_3A = tuple("It is the practical guide for the army always to heed the directions of the party".split())
+HYPOTHESIS_A = "It is a guide to action which ensures that the military always obeys the commands of the party"
+REFERENCE_1A = "It is a guide to action that ensures that the military will forever heed Party commands"
+REFERENCE_2A = "It is a guiding principle which makes the military forces always being under the command of the Party"
+REFERENCE_3A = "It is the practical guide for the army always to heed the directions of the party"
 
 # EXAMPLE 2
-HYPOTHESIS_B = tuple("he read the book because he was interested in world history".split())
-REFERENCE_1B = tuple("he was interested in world history because he read the book".split())
+HYPOTHESIS_B = "he read the book because he was interested in world history"
+REFERENCE_1B = "he was interested in world history because he read the book"
 
 # EXAMPLE 3
-HYPOTHESIS_C = tuple("the cat the cat on the mat".split())
-REFERENCE_1C = tuple("the cat is on the mat".split())
-REFERENCE_2C = tuple("there is a cat on the mat".split())
+HYPOTHESIS_C = "the cat the cat on the mat"
+REFERENCE_1C = "the cat is on the mat"
+REFERENCE_2C = "there is a cat on the mat"
 
 TUPLE_OF_REFERENCES = (
     ((REFERENCE_1A, REFERENCE_2A, REFERENCE_3A), tuple([REFERENCE_1B])),
@@ -76,7 +70,16 @@ class TestBLEUScore(TextTester):
     def test_bleu_score_class(self, ddp, dist_sync_on_step, preds, targets, weights, n_gram, smooth_func, smooth):
         metric_args = {"n_gram": n_gram, "smooth": smooth}
 
-        nltk_metric = partial(corpus_bleu, weights=weights, smoothing_function=smooth_func)
+        def nltk_metric(list_of_references, hypotheses, weights=weights, smoothing_function=smooth_func, **kwargs):
+            hypotheses_ = [hypothesis.split() for hypothesis in hypotheses]
+            list_of_references_ = [[line.split() for line in ref] for ref in list_of_references]
+            return corpus_bleu(
+                list_of_references=list_of_references_,
+                hypotheses=hypotheses_,
+                weights=weights,
+                smoothing_function=smoothing_function,
+                **kwargs
+            )
 
         self.run_class_metric_test(
             ddp=ddp,
@@ -91,7 +94,17 @@ def test_bleu_score_class(self, ddp, dist_sync_on_step, preds, targets, weights,
 
     def test_bleu_score_functional(self, preds, targets, weights, n_gram, smooth_func, smooth):
         metric_args = {"n_gram": n_gram, "smooth": smooth}
-        nltk_metric = partial(corpus_bleu, weights=weights, smoothing_function=smooth_func)
+
+        def nltk_metric(list_of_references, hypotheses, weights=weights, smoothing_function=smooth_func, **kwargs):
+            hypotheses_ = [hypothesis.split() for hypothesis in hypotheses]
+            list_of_references_ = [[line.split() for line in ref] for ref in list_of_references]
+            return corpus_bleu(
+                list_of_references=list_of_references_,
+                hypotheses=hypotheses_,
+                weights=weights,
+                smoothing_function=smoothing_function,
+                **kwargs
+            )
 
         self.run_functional_metric_test(
             preds,
@@ -122,8 +135,8 @@ def test_bleu_empty_functional():
 
 
 def test_no_4_gram_functional():
-    hyps = [["My", "full", "pytorch-lightning"]]
-    refs = [[["My", "full", "pytorch-lightning", "test"], ["Completely", "Different"]]]
+    hyps = ["My full pytorch-lightning"]
+    refs = [["My full pytorch-lightning test", "Completely Different"]]
     assert bleu_score(refs, hyps) == tensor(0.0)
 
 
@@ -136,6 +149,6 @@ def test_bleu_empty_class():
 
 def test_no_4_gram_class():
     bleu = BLEUScore()
-    hyps = [["My", "full", "pytorch-lightning"]]
-    refs = [[["My", "full", "pytorch-lightning", "test"], ["Completely", "Different"]]]
+    hyps = ["My full pytorch-lightning"]
+    refs = [["My full pytorch-lightning test", "Completely Different"]]
     assert bleu(refs, hyps) == tensor(0.0)

torchmetrics/functional/text/bleu.py

@@ -45,8 +45,8 @@ def _count_ngram(ngram_input_list: Sequence[str], n_gram: int) -> Counter:
 
 
 def _bleu_score_update(
-    reference_corpus: Sequence[Sequence[Sequence[str]]],
-    translate_corpus: Sequence[Sequence[str]],
+    reference_corpus: Sequence[Sequence[str]],
+    translate_corpus: Sequence[str],
     numerator: Tensor,
     denominator: Tensor,
     trans_len: Tensor,
@@ -64,8 +64,12 @@ def _bleu_score_update(
         ref_len: count of words in a reference translation
         n_gram: gram value ranged 1 to 4
     """
+    reference_corpus_: Sequence[Sequence[Sequence[str]]] = [
+        [line.split() if line else [] for line in reference] for reference in reference_corpus
+    ]
+    translate_corpus_: Sequence[Sequence[str]] = [line.split() if line else [] for line in translate_corpus]
 
-    for (translation, references) in zip(translate_corpus, reference_corpus):
+    for (translation, references) in zip(translate_corpus_, reference_corpus_):
         trans_len += len(translation)
         ref_len_list = [len(ref) for ref in references]
         ref_len_diff = [abs(len(translation) - x) for x in ref_len_list]
@@ -122,8 +126,8 @@ def _bleu_score_compute(
 
 
 def bleu_score(
-    reference_corpus: Sequence[Sequence[Sequence[str]]],
-    translate_corpus: Sequence[Sequence[str]],
+    reference_corpus: Sequence[Sequence[str]],
+    translate_corpus: Sequence[str],
     n_gram: int = 4,
     smooth: bool = False,
 ) -> Tensor:
@@ -144,8 +148,8 @@ def bleu_score(
 
     Example:
         >>> from torchmetrics.functional import bleu_score
-        >>> translate_corpus = ['the cat is on the mat'.split()]
-        >>> reference_corpus = [['there is a cat on the mat'.split(), 'a cat is on the mat'.split()]]
+        >>> translate_corpus = ['the cat is on the mat']
+        >>> reference_corpus = [['there is a cat on the mat', 'a cat is on the mat']]
         >>> bleu_score(reference_corpus, translate_corpus)
         tensor(0.7598)
 

torchmetrics/functional/text/sacre_bleu.py

@@ -335,12 +335,12 @@ def sacre_bleu_score(
             "torchmetrics[text]`."
         )
 
-    reference_corpus_: Sequence[Sequence[Sequence[str]]] = [
-        [_SacreBLEUTokenizer.tokenize(line, tokenize, lowercase) for line in reference]
+    reference_corpus_: Sequence[Sequence[str]] = [
+        [" ".join(_SacreBLEUTokenizer.tokenize(line, tokenize, lowercase)) for line in reference]
         for reference in reference_corpus
     ]
-    translate_corpus_: Sequence[Sequence[str]] = [
-        _SacreBLEUTokenizer.tokenize(line, tokenize, lowercase) for line in translate_corpus
+    translate_corpus_: Sequence[str] = [
+        " ".join(_SacreBLEUTokenizer.tokenize(line, tokenize, lowercase)) for line in translate_corpus
     ]
 
     numerator = torch.zeros(n_gram)

torchmetrics/text/bleu.py

@@ -45,8 +45,8 @@ class BLEUScore(Metric):
             will be used to perform the allgather.
 
     Example:
-        >>> translate_corpus = ['the cat is on the mat'.split()]
-        >>> reference_corpus = [['there is a cat on the mat'.split(), 'a cat is on the mat'.split()]]
+        >>> translate_corpus = ['the cat is on the mat']
+        >>> reference_corpus = [['there is a cat on the mat', 'a cat is on the mat']]
         >>> metric = BLEUScore()
         >>> metric(reference_corpus, translate_corpus)
         tensor(0.7598)
@@ -91,14 +91,15 @@ def __init__(
         self.add_state("denominator", torch.zeros(self.n_gram), dist_reduce_fx="sum")
 
     def update(  # type: ignore
-        self, reference_corpus: Sequence[Sequence[Sequence[str]]], translate_corpus: Sequence[Sequence[str]]
+        self, reference_corpus: Sequence[Sequence[str]], translate_corpus: Sequence[str]
     ) -> None:
         """Compute Precision Scores.
 
         Args:
             reference_corpus: An iterable of iterables of reference corpus
             translate_corpus: An iterable of machine translated corpus
         """
+
         self.trans_len, self.ref_len = _bleu_score_update(
             reference_corpus,
             translate_corpus,

torchmetrics/text/sacre_bleu.py

@@ -118,10 +118,10 @@ def update(  # type: ignore
             reference_corpus: An iterable of iterables of reference corpus
             translate_corpus: An iterable of machine translated corpus
         """
-        reference_corpus_: Sequence[Sequence[Sequence[str]]] = [
-            [self.tokenizer(line) for line in reference] for reference in reference_corpus
+        reference_corpus_: Sequence[Sequence[str]] = [
+            [" ".join(self.tokenizer(line)) for line in reference] for reference in reference_corpus
         ]
-        translate_corpus_: Sequence[Sequence[str]] = [self.tokenizer(line) for line in translate_corpus]
+        translate_corpus_: Sequence[str] = [" ".join(self.tokenizer(line)) for line in translate_corpus]
 
         self.trans_len, self.ref_len = _bleu_score_update(
             reference_corpus_,