Add head_mask/decoder_head_mask for TF BART models (#9639)

* Add head_mask/decoder_head_mask for TF BART models

* Add head_mask and decoder_head_mask input arguments for TF BART-based
models as a TF counterpart to the PR #9569

* Add test_headmasking functionality to tests/test_modeling_tf_common.py

* TODO: Add a test to verify that we can get a gradient back for
importance score computation

* Remove redundant #TODO note

Remove redundant #TODO note from tests/test_modeling_tf_common.py

* Fix assertions

* Make style

* Fix ...Model input args and adjust one new test

* Add back head_mask and decoder_head_mask to BART-based ...Model
after the last commit

* Remove head_mask ande decoder_head_mask from input_dict
in TF test_train_pipeline_custom_model as these two have different
shape than other input args (Necessary for passing this test)

* Revert adding global_rng in test_modeling_tf_common.py

tests/test_modeling_tf_t5.py

@@ -248,6 +248,7 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
     is_encoder_decoder = True
     all_model_classes = (TFT5Model, TFT5ForConditionalGeneration) if is_tf_available() else ()
     all_generative_model_classes = (TFT5ForConditionalGeneration,) if is_tf_available() else ()
+    test_head_masking = False
 
     def setUp(self):
         self.model_tester = TFT5ModelTester(self)
@@ -417,6 +418,7 @@ class TFT5EncoderOnlyModelTester:
 class TFT5EncoderOnlyModelTest(TFModelTesterMixin, unittest.TestCase):
     is_encoder_decoder = False
     all_model_classes = (TFT5EncoderModel,) if is_tf_available() else ()
+    test_head_masking = False
 
     def setUp(self):
         self.model_tester = TFT5EncoderOnlyModelTester(self)