Fix functional TF Whisper and modernize tests (#24301)

* Revert whisper change and modify the test_compile_tf_model test

* make fixup

* Tweak test slightly

* Add functional model saving to test

* Ensure TF can infer shapes for data2vec

* Add override for efficientformer

* Mark test as slow

tests/models/pegasus/test_modeling_tf_pegasus.py

@@ -15,7 +15,6 @@
 
 from __future__ import annotations
 
-import tempfile
 import unittest
 
 from transformers import AutoTokenizer, PegasusConfig, is_tf_available
@@ -207,37 +206,6 @@ class TFPegasusModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestC
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
 
-    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])
-
     @tooslow
     def test_saved_model_creation(self):
         pass