Fixing tests for perceiver (texts) (#14719)

* Fixing tests for perceiver (texts)

* For MaskedLM

src/transformers/models/perceiver/modeling_perceiver.py

@@ -915,6 +915,7 @@ class PerceiverForMaskedLM(PerceiverPreTrainedModel):
         output_hidden_states=None,
         labels=None,
         return_dict=None,
+        input_ids=None,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -922,6 +923,10 @@ class PerceiverForMaskedLM(PerceiverPreTrainedModel):
             config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
             (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
         """
+        if inputs is not None and input_ids is not None:
+            raise ValueError("You cannot use both `inputs` and `input_ids`")
+        elif inputs is None and input_ids is not None:
+            inputs = input_ids
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -994,6 +999,7 @@ class PerceiverForSequenceClassification(PerceiverPreTrainedModel):
         output_hidden_states=None,
         labels=None,
         return_dict=None,
+        input_ids=None,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1015,6 +1021,10 @@ class PerceiverForSequenceClassification(PerceiverPreTrainedModel):
             >>> outputs = model(inputs=inputs)
             >>> logits = outputs.logits
         """
+        if inputs is not None and input_ids is not None:
+            raise ValueError("You cannot use both `inputs` and `input_ids`")
+        elif inputs is None and input_ids is not None:
+            inputs = input_ids
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1121,6 +1131,7 @@ class PerceiverForImageClassificationLearned(PerceiverPreTrainedModel):
         output_hidden_states=None,
         labels=None,
         return_dict=None,
+        pixel_values=None,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1149,6 +1160,11 @@ class PerceiverForImageClassificationLearned(PerceiverPreTrainedModel):
             >>> predicted_class_idx = logits.argmax(-1).item()
             >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
         """
+        if inputs is not None and pixel_values is not None:
+            raise ValueError("You cannot use both `inputs` and `pixel_values`")
+        elif inputs is None and pixel_values is not None:
+            inputs = pixel_values
+
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         outputs = self.perceiver(

tests/test_modeling_perceiver.py

@@ -196,6 +196,13 @@ class PerceiverModelTester:
             num_labels=self.num_labels,
         )
 
+    def get_pipeline_config(self):
+        config = self.get_config()
+        # Byte level vocab
+        config.vocab_size = 261
+        config.max_position_embeddings = 40
+        return config
+
     def create_and_check_for_masked_lm(self, config, inputs, input_mask, sequence_labels, token_labels):
         model = PerceiverForMaskedLM(config=config)
         model.to(torch_device)