fix retribert's test_torch_encode_plus_sent_to_model (#17231)

tests/models/retribert/test_tokenization_retribert.py

@@ -27,9 +27,9 @@ from transformers.models.bert.tokenization_bert import (
     _is_punctuation,
     _is_whitespace,
 )
-from transformers.testing_utils import require_tokenizers, slow
+from transformers.testing_utils import require_tokenizers, require_torch, slow
 
-from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
+from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english, merge_model_tokenizer_mappings
 
 
 # Copied from transformers.tests.bert.test_modeling_bert.py with Bert->RetriBert
@@ -338,3 +338,47 @@ class RetriBertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
                 ]
                 self.assertListEqual(tokens_without_spe_char_p, expected_tokens)
                 self.assertListEqual(tokens_without_spe_char_r, expected_tokens)
+
+    # RetriBertModel doesn't define `get_input_embeddings` and it's forward method doesn't take only the output of the tokenizer as input
+    @require_torch
+    @slow
+    def test_torch_encode_plus_sent_to_model(self):
+        import torch
+
+        from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
+
+        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(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)
+
+                # The following test is different from the common's one
+                self.assertGreaterEqual(model.bert_query.get_input_embeddings().weight.shape[0], len(tokenizer))
+
+                # Build sequence
+                first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
+                sequence = " ".join(first_ten_tokens)
+                encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="pt")
+
+                # Ensure that the BatchEncoding.to() method works.
+                encoded_sequence.to(model.device)
+
+                batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
+                # This should not fail
+
+                with torch.no_grad():  # saves some time
+                    # The following lines are different from the common's ones
+                    model.embed_questions(**encoded_sequence)
+                    model.embed_questions(**batch_encoded_sequence)