Fix for slow the bug tokenizer adding spaces to single id decodes (#32564)

* _decode signature change and quick return * added bunch of decoding tests * signature match and return * added tests for decoding * merged decoding test * more tests for special tokens * cosmetics * fixed param * ruffed the file * refinement for single special tokens * added test for single special tokens * slight change to test name Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com> * minor change test name for skip tokens Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com> * killed already defined var Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com> * minor update with vars Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com> * killed already defined var once more Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com> --------- Co-authored-by: Ita Zaporozhets <31893021+itazap@users.noreply.github.com>
2024-09-18 13:32:02 +03:00
parent e6d9f39dd7
commit 52e22cbf67
2 changed files with 71 additions and 2 deletions
--- a/tests/tokenization/test_tokenization_utils.py
+++ b/tests/tokenization/test_tokenization_utils.py
@@ -253,6 +253,71 @@ class TokenizerUtilsTest(unittest.TestCase):
        self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
        self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])

+    @require_tokenizers
+    def test_decoding_single_token(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
+
+                token_id = 2300
+                decoded_flat = tokenizer.decode(token_id)
+                decoded_list = tokenizer.decode([token_id])
+
+                self.assertEqual(decoded_flat, "Force")
+                self.assertEqual(decoded_list, "Force")
+
+                token_id = 0
+                decoded_flat = tokenizer.decode(token_id)
+                decoded_list = tokenizer.decode([token_id])
+
+                self.assertEqual(decoded_flat, "[PAD]")
+                self.assertEqual(decoded_list, "[PAD]")
+
+                last_item_id = tokenizer.vocab_size - 1
+                decoded_flat = tokenizer.decode(last_item_id)
+                decoded_list = tokenizer.decode([last_item_id])
+
+                self.assertEqual(decoded_flat, "##：")
+                self.assertEqual(decoded_list, "##：")
+
+    @require_tokenizers
+    def test_decoding_skip_special_tokens(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
+                tokenizer.add_tokens(["ஐ"], special_tokens=True)
+
+                # test special token with other tokens, skip the special tokens
+                sentence = "This is a beautiful flower ஐ"
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "This is a beautiful flower")
+
+                # test special token with other tokens, do not skip the special tokens
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[CLS] This is a beautiful flower ஐ [SEP]")
+
+                # test special token stand alone, skip the special tokens
+                sentence = "ஐ"
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "")
+
+                # test special token stand alone, do not skip the special tokens
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[CLS] ஐ [SEP]")
+
+                # test single special token alone, skip
+                pad_id = 0
+                decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "")
+
+                # test single special token alone, do not skip
+                decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[PAD]")
+
    @require_torch
    def test_padding_accepts_tensors_pt(self):
        import torch