Fix `test_tf_encode_plus_sent_to_model` for `LayoutLMv3` (#18898)

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/layoutlmv3/test_tokenization_layoutlmv3.py

@@ -31,7 +31,14 @@ from transformers import (
     logging,
 )
 from transformers.models.layoutlmv3.tokenization_layoutlmv3 import VOCAB_FILES_NAMES, LayoutLMv3Tokenizer
-from transformers.testing_utils import is_pt_tf_cross_test, require_pandas, require_tokenizers, require_torch, slow
+from transformers.testing_utils import (
+    is_pt_tf_cross_test,
+    require_pandas,
+    require_tf,
+    require_tokenizers,
+    require_torch,
+    slow,
+)
 
 from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, merge_model_tokenizer_mappings
 
@@ -2400,3 +2407,39 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     @unittest.skip("Doesn't support another framework than PyTorch")
     def test_np_encode_plus_sent_to_model(self):
         pass
+
+    @require_tf
+    @slow
+    def test_tf_encode_plus_sent_to_model(self):
+        from transformers import TF_MODEL_MAPPING, TOKENIZER_MAPPING
+
+        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)
+
+        tokenizers = self.get_tokenizers(do_lower_case=False)
+        for tokenizer in tokenizers:
+            with self.subTest(f"{tokenizer.__class__.__name__}"):
+                if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
+                    return
+
+                config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
+                config = config_class()
+
+                if config.is_encoder_decoder or config.pad_token_id is None:
+                    return
+
+                model = model_class(config)
+
+                # Make sure the model contains at least the full vocabulary size in its embedding matrix
+                self.assertGreaterEqual(model.config.vocab_size, len(tokenizer))
+
+                # Build sequence
+                first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
+                boxes = [[1000, 1000, 1000, 1000] for _ in range(len(first_ten_tokens))]
+                encoded_sequence = tokenizer.encode_plus(first_ten_tokens, boxes=boxes, return_tensors="tf")
+                batch_encoded_sequence = tokenizer.batch_encode_plus(
+                    [first_ten_tokens, first_ten_tokens], boxes=[boxes, boxes], return_tensors="tf"
+                )
+
+                # This should not fail
+                model(encoded_sequence)
+                model(batch_encoded_sequence)