Use tiny models for get_pretrained_model in TFEncoderDecoderModelTest (#15989)

* Use tiny model for TFRembertEncoderDecoderModelTest.get_pretrained_model()
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/encoder_decoder/test_modeling_tf_encoder_decoder.py

@@ -509,8 +509,7 @@ class TFEncoderDecoderMixin:
         model = TFEncoderDecoderModel(encoder_decoder_config)
         model(**inputs_dict)
 
-    @slow
-    def test_real_model_save_load_from_pretrained(self):
+    def test_model_save_load_from_pretrained(self):
         model_2 = self.get_pretrained_model()
         input_ids = ids_tensor([13, 5], model_2.config.encoder.vocab_size)
         decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size)
@@ -542,7 +541,10 @@ class TFEncoderDecoderMixin:
 @require_tf
 class TFBertEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
     def get_pretrained_model(self):
-        return TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
+        return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
+            "hf-internal-testing/tiny-random-bert",
+            "hf-internal-testing/tiny-random-bert",
+        )
 
     def get_encoder_decoder_model(self, config, decoder_config):
         encoder_model = TFBertModel(config, name="encoder")
@@ -637,7 +639,10 @@ class TFBertEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
 @require_tf
 class TFGPT2EncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
     def get_pretrained_model(self):
-        return TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-cased", "../gpt2")
+        return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
+            "hf-internal-testing/tiny-random-bert",
+            "hf-internal-testing/tiny-random-gpt2",
+        )
 
     def get_encoder_decoder_model(self, config, decoder_config):
         encoder_model = TFBertModel(config, name="encoder")
@@ -726,7 +731,10 @@ class TFGPT2EncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
 @require_tf
 class TFRoBertaEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
     def get_pretrained_model(self):
-        return TFEncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base")
+        return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
+            "hf-internal-testing/tiny-random-roberta",
+            "hf-internal-testing/tiny-random-roberta",
+        )
 
     def get_encoder_decoder_model(self, config, decoder_config):
         encoder_model = TFRobertaModel(config, name="encoder")
@@ -782,7 +790,10 @@ class TFRoBertaEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase)
 @require_tf
 class TFRembertEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
     def get_pretrained_model(self):
-        return TFEncoderDecoderModel.from_encoder_decoder_pretrained("google/rembert", "google/rembert")
+        return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
+            "hf-internal-testing/tiny-random-rembert",
+            "hf-internal-testing/tiny-random-rembert",
+        )
 
     def get_encoder_decoder_model(self, config, decoder_config):
         encoder_model = TFRemBertModel(config, name="encoder")