[CI-doc-daily] Remove RobertaPreLayernorm random tests (#20992)

* Remove random output

* remove values

* fix copy statements

src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py

@@ -1042,12 +1042,12 @@ class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel):
 @add_start_docstrings(
     """RoBERTa-PreLayerNorm Model with a `language modeling` head on top.""", ROBERTA_PRELAYERNORM_START_DOCSTRING
 )
-# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
 class RobertaPreLayerNormForMaskedLM(RobertaPreLayerNormPreTrainedModel):
     _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
     _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
     _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
+    # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.__init__ with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
     def __init__(self, config):
         super().__init__(config)
 
@@ -1078,10 +1078,8 @@ class RobertaPreLayerNormForMaskedLM(RobertaPreLayerNormPreTrainedModel):
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=MaskedLMOutput,
         config_class=_CONFIG_FOR_DOC,
-        mask="<mask>",
-        expected_output="' Paris'",
-        expected_loss=0.1,
     )
+    # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.forward with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
@@ -1199,8 +1197,6 @@ class RobertaPreLayerNormForSequenceClassification(RobertaPreLayerNormPreTrained
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
-        expected_output="'optimism'",
-        expected_loss=0.08,
     )
     # Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.forward with roberta->roberta_prelayernorm
     def forward(
@@ -1400,8 +1396,6 @@ class RobertaPreLayerNormForTokenClassification(RobertaPreLayerNormPreTrainedMod
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=TokenClassifierOutput,
         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_roberta.RobertaForTokenClassification.forward with roberta->roberta_prelayernorm
     def forward(
@@ -1507,8 +1501,6 @@ class RobertaPreLayerNormForQuestionAnswering(RobertaPreLayerNormPreTrainedModel
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=QuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
-        expected_output="' puppet'",
-        expected_loss=0.86,
     )
     # Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.forward with roberta->roberta_prelayernorm
     def forward(