Fix `RobertaPreLayerNorm` doctest (#21337)

* add mask="<mask>"

* update

* update

* fix
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py

@@ -1075,6 +1075,9 @@ class RobertaPreLayerNormForMaskedLM(RobertaPreLayerNormPreTrainedModel):
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=MaskedLMOutput,
         config_class=_CONFIG_FOR_DOC,
+        mask="<mask>",
+        expected_output="' Paris'",
+        expected_loss=0.69,
     )
     # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.forward with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
     def forward(

src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py

@@ -1067,11 +1067,11 @@ class TFRobertaPreLayerNormLMHead(tf.keras.layers.Layer):
 @add_start_docstrings(
     """RoBERTa-PreLayerNorm Model with a `language modeling` head on top.""", ROBERTA_PRELAYERNORM_START_DOCSTRING
 )
-# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMaskedLM with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
 class TFRobertaPreLayerNormForMaskedLM(TFRobertaPreLayerNormPreTrainedModel, TFMaskedLanguageModelingLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
     _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]
 
+    # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMaskedLM.__init__ with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
 
@@ -1095,8 +1095,9 @@ class TFRobertaPreLayerNormForMaskedLM(TFRobertaPreLayerNormPreTrainedModel, TFM
         config_class=_CONFIG_FOR_DOC,
         mask="<mask>",
         expected_output="' Paris'",
-        expected_loss=0.1,
+        expected_loss=0.69,
     )
+    # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForMaskedLM.call with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
     def call(
         self,
         input_ids: Optional[TFModelInputType] = None,
@@ -1354,8 +1355,6 @@ class TFRobertaPreLayerNormForSequenceClassification(
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=TFSequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
-        expected_output="'optimism'",
-        expected_loss=0.08,
     )
     # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForSequenceClassification.call with roberta->roberta_prelayernorm
     def call(
@@ -1570,8 +1569,6 @@ class TFRobertaPreLayerNormForTokenClassification(TFRobertaPreLayerNormPreTraine
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=TFTokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
-        expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']",
-        expected_loss=0.01,
     )
     # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForTokenClassification.call with roberta->roberta_prelayernorm
     def call(
@@ -1658,8 +1655,6 @@ class TFRobertaPreLayerNormForQuestionAnswering(TFRobertaPreLayerNormPreTrainedM
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=TFQuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
-        expected_output="' puppet'",
-        expected_loss=0.86,
     )
     # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForQuestionAnswering.call with roberta->roberta_prelayernorm
     def call(