New TF model inputs (#8602)

* Apply on BERT and ALBERT

* Update TF Bart

* Add input processing to TF BART

* Add input processing for TF CTRL

* Add input processing to TF Distilbert

* Add input processing to TF DPR

* Add input processing to TF Electra

* Add input processing for TF Flaubert

* Add deprecated arguments

* Add input processing to TF XLM

* remove unused imports

* Add input processing to TF Funnel

* Add input processing to TF GPT2

* Add input processing to TF Longformer

* Add input processing to TF Lxmert

* Apply style

* Add input processing to TF Mobilebert

* Add input processing to TF GPT

* Add input processing to TF Roberta

* Add input processing to TF T5

* Add input processing to TF TransfoXL

* Apply style

* Rebase on master

* Bug fix

* Retry to bugfix

* Retry bug fix

* Fix wrong model name

* Try another fix

* Fix BART

* Fix input precessing

* Apply style

* Put the deprecated warnings in the input processing function

* Remove the unused imports

* Raise an error when len(kwargs)>0

* test ModelOutput instead of TFBaseModelOutput

* Bug fix

* Address Patrick's comments

* Address Patrick's comments

* Address Sylvain's comments

* Add the new inputs in new Longformer models

* Update the template with the new input processing

* Remove useless assert

* Apply style

* Trigger CI

tests/test_modeling_tf_blenderbot.py

@@ -12,24 +12,23 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import tempfile
 import unittest
 
 from tests.test_configuration_common import ConfigTester
 from tests.test_modeling_tf_bart import TFBartModelTester
 from tests.test_modeling_tf_common import TFModelTesterMixin
-from transformers import BlenderbotConfig, BlenderbotSmallTokenizer, is_tf_available
+from transformers import (
+    BlenderbotConfig,
+    BlenderbotSmallTokenizer,
+    TFAutoModelForSeq2SeqLM,
+    TFBlenderbotForConditionalGeneration,
+    is_tf_available,
+)
 from transformers.file_utils import cached_property
 from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_tokenizers, slow
 
 
-if is_tf_available():
-    import tensorflow as tf
-
-    from transformers import TFAutoModelForSeq2SeqLM, TFBlenderbotForConditionalGeneration
-
-
-class ModelTester(TFBartModelTester):
+class TFBlenderbotModelTester(TFBartModelTester):
     config_updates = dict(
         normalize_before=True,
         static_position_embeddings=True,
@@ -40,15 +39,14 @@ class ModelTester(TFBartModelTester):
 
 
 @require_tf
-class TestTFBlenderbotCommon(TFModelTesterMixin, unittest.TestCase):
+class TFBlenderbotModelTest(TFModelTesterMixin, unittest.TestCase):
     all_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
-    model_tester_cls = ModelTester
     is_encoder_decoder = True
     test_pruning = False
 
     def setUp(self):
-        self.model_tester = self.model_tester_cls(self)
+        self.model_tester = TFBlenderbotModelTester(self)
         self.config_tester = ConfigTester(self, config_class=BlenderbotConfig)
 
     def test_config(self):
@@ -66,37 +64,6 @@ class TestTFBlenderbotCommon(TFModelTesterMixin, unittest.TestCase):
         # Should be uncommented during patrick TF refactor
         pass
 
-    def test_compile_tf_model(self):
-        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-
-        optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
-        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-        metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
-
-        model_class = self.all_generative_model_classes[0]
-        input_ids = {
-            "decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"),
-            "input_ids": tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32"),
-        }
-
-        # Prepare our model
-        model = model_class(config)
-        model(self._prepare_for_class(inputs_dict, model_class))  # Model must be called before saving.
-        # Let's load it from the disk to be sure we can use pretrained weights
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_pretrained(tmpdirname)
-            model = model_class.from_pretrained(tmpdirname)
-
-        outputs_dict = model(input_ids)
-        hidden_states = outputs_dict[0]
-
-        # Add a dense layer on top to test integration with other keras modules
-        outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
-
-        # Compile extended model
-        extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
-        extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
-
 
 @is_pt_tf_cross_test
 @require_tokenizers