diff --git a/src/transformers/modeling_tf_utils.py b/src/transformers/modeling_tf_utils.py
index 5e37a1818df..428af92b2b3 100644
--- a/src/transformers/modeling_tf_utils.py
+++ b/src/transformers/modeling_tf_utils.py
@@ -1135,6 +1135,11 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         return model_embeds
 
     def _get_word_embedding_weight(model, embedding_layer):
+        # If the variable holds the weights themselves, return them
+        if isinstance(embedding_layer, tf.Tensor):
+            return embedding_layer
+        # Otherwise, try to get them from the layer's attributes
+
         embeds = getattr(embedding_layer, "weight", None)
         if embeds is not None:
             return embeds
diff --git a/tests/test_modeling_tf_mt5.py b/tests/test_modeling_tf_mt5.py
index 9b23e05f752..1ab1a635b39 100644
--- a/tests/test_modeling_tf_mt5.py
+++ b/tests/test_modeling_tf_mt5.py
@@ -22,7 +22,24 @@ from transformers.testing_utils import require_sentencepiece, require_tf, requir
 if is_tf_available():
     import tensorflow as tf
 
-    from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
+    from transformers import AutoTokenizer, T5Tokenizer, TFAutoModelForSeq2SeqLM, TFMT5ForConditionalGeneration
+
+
+@require_tf
+class TFMT5ModelTest(unittest.TestCase):  # no mixin with common tests -> most cases are already covered in the TF T5
+    @slow
+    def test_resize_embeddings(self):
+        model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+        original_vocab_size = model.get_input_embeddings().weight.shape[0]
+        # the vocab size is defined in the model config
+        self.assertEqual(original_vocab_size, model.config.vocab_size)
+
+        tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
+        tokenizer.add_special_tokens({"bos_token": "", "eos_token": ""})
+        model._resize_token_embeddings(len(tokenizer))
+        # the vocab size is now resized to the length of the tokenizer, which is different from the original size
+        self.assertEqual(model.get_input_embeddings().weight.shape[0], len(tokenizer))
+        self.assertNotEqual(model.get_input_embeddings().weight.shape[0], original_vocab_size)
 
 
 @require_tf
diff --git a/tests/test_modeling_tf_t5.py b/tests/test_modeling_tf_t5.py
index 59ee70c53ec..67e780f24c2 100644
--- a/tests/test_modeling_tf_t5.py
+++ b/tests/test_modeling_tf_t5.py
@@ -314,6 +314,20 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
         # TODO: Fix head-masking according to PyTorch T5 model
         pass
 
+    @slow
+    def test_resize_embeddings(self):
+        model = TFT5ForConditionalGeneration.from_pretrained("t5-small")
+        original_vocab_size = model.get_input_embeddings().weight.shape[0]
+        # the vocab size is defined in the model config
+        self.assertEqual(original_vocab_size, model.config.vocab_size)
+
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+        tokenizer.add_special_tokens({"bos_token": "", "eos_token": ""})
+        model._resize_token_embeddings(len(tokenizer))
+        # the vocab size is now resized to the length of the tokenizer, which is different from the original size
+        self.assertEqual(model.get_input_embeddings().weight.shape[0], len(tokenizer))
+        self.assertNotEqual(model.get_input_embeddings().weight.shape[0], original_vocab_size)
+
 
 class TFT5EncoderOnlyModelTester:
     def __init__(