Optional layers (#8961)
* 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 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 * Fix wrong model name * Fix BART * 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 * Address Patrick's comments * Address Patrick's comments * Add boolean processing for the inputs * Take into account the optional layers * Add missing/unexpected weights in the other models * Apply style * rename parameters * Apply style * Remove useless * Remove useless * Remove useless * Update num parameters * Fix tests * Address Patrick's comment * Remove useless attribute
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@@ -335,7 +335,7 @@ class TFBertModelTest(TFModelTesterMixin, unittest.TestCase):
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model, output_loading_info = TFBertForTokenClassification.from_pretrained(
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"jplu/tiny-tf-bert-random", output_loading_info=True
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)
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self.assertEqual(sorted(output_loading_info["unexpected_keys"]), ["mlm___cls", "nsp___cls"])
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self.assertEqual(sorted(output_loading_info["unexpected_keys"]), [])
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for layer in output_loading_info["missing_keys"]:
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self.assertTrue(layer.split("_")[0] in ["dropout", "classifier"])
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