🔥Rework pipeline testing by removing `PipelineTestCaseMeta` 🚀 (#21516)

* Add PipelineTesterMixin

* remove class PipelineTestCaseMeta

* move validate_test_components

* Add for ViT

* Add to SPECIAL_MODULE_TO_TEST_MAP

* style and quality

* Add feature-extraction

* update

* raise instead of skip

* add tiny_model_summary.json

* more explicit

* skip tasks not in mapping

* add availability check

* Add Copyright

* A way to diable irrelevant tests

* update with main

* remove disable_irrelevant_tests

* skip tests

* better skip message

* better skip message

* Add all pipeline task tests

* revert

* Import PipelineTesterMixin

* subclass test classes with PipelineTesterMixin

* Add pipieline_model_mapping

* Fix import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix one more import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix test issues

* Fix import requirements

* Fix mapping for MobileViTModelTest

* Update

* Better skip message

* pipieline_model_mapping could not be None

* Remove some PipelineTesterMixin

* Fix typo

* revert tests_fetcher.py

* update

* rename

* revert

* Remove PipelineTestCaseMeta from ZeroShotAudioClassificationPipelineTests

* style and quality

* test fetcher for all pipeline/model tests

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

tests/models/mobilevit/test_modeling_mobilevit.py

@@ -24,6 +24,7 @@ from transformers.utils import cached_property, is_torch_available, is_vision_av
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -173,7 +174,7 @@ class MobileViTModelTester:
 
 
 @require_torch
-class MobileViTModelTest(ModelTesterMixin, unittest.TestCase):
+class MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -184,6 +185,15 @@ class MobileViTModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": MobileViTModel,
+            "image-classification": MobileViTForImageClassification,
+            "image-segmentation": MobileViTForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/mobilevit/test_modeling_tf_mobilevit.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_tf, slow
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -154,7 +155,7 @@ class TFMobileViTModelTester:
 
 
 @require_tf
-class TFMobileViTModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFMobileViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -165,6 +166,11 @@ class TFMobileViTModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
+        if is_tf_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/mpnet/test_modeling_mpnet.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -190,7 +191,7 @@ class MPNetModelTester:
 
 
 @require_torch
-class MPNetModelTest(ModelTesterMixin, unittest.TestCase):
+class MPNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             MPNetForMaskedLM,
@@ -203,6 +204,18 @@ class MPNetModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": MPNetModel,
+            "fill-mask": MPNetForMaskedLM,
+            "question-answering": MPNetForQuestionAnswering,
+            "text-classification": MPNetForSequenceClassification,
+            "token-classification": MPNetForTokenClassification,
+            "zero-shot": MPNetForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_pruning = False
     test_resize_embeddings = True
 

tests/models/mpnet/test_modeling_tf_mpnet.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_tf, slow
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -184,7 +185,7 @@ class TFMPNetModelTester:
 
 
 @require_tf
-class TFMPNetModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFMPNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFMPNetForMaskedLM,
@@ -197,6 +198,18 @@ class TFMPNetModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFMPNetModel,
+            "fill-mask": TFMPNetForMaskedLM,
+            "question-answering": TFMPNetForQuestionAnswering,
+            "text-classification": TFMPNetForSequenceClassification,
+            "token-classification": TFMPNetForTokenClassification,
+            "zero-shot": TFMPNetForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/mvp/test_modeling_mvp.py

@@ -28,6 +28,7 @@ from transformers.utils import cached_property
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -405,13 +406,28 @@ class MvpHeadTests(unittest.TestCase):
 
 
 @require_torch
-class MvpModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class MvpModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (MvpModel, MvpForConditionalGeneration, MvpForSequenceClassification, MvpForQuestionAnswering)
         if is_torch_available()
         else ()
     )
     all_generative_model_classes = (MvpForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": MvpForConditionalGeneration,
+            "feature-extraction": MvpModel,
+            "fill-mask": MvpForConditionalGeneration,
+            "question-answering": MvpForQuestionAnswering,
+            "summarization": MvpForConditionalGeneration,
+            "text2text-generation": MvpForConditionalGeneration,
+            "text-classification": MvpForSequenceClassification,
+            "text-generation": MvpForCausalLM,
+            "zero-shot": MvpForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     fx_compatible = False
     test_pruning = False

tests/models/nat/test_modeling_nat.py

@@ -24,6 +24,7 @@ from transformers.utils import cached_property, is_torch_available, is_vision_av
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -189,7 +190,7 @@ class NatModelTester:
 
 @require_natten
 @require_torch
-class NatModelTest(ModelTesterMixin, unittest.TestCase):
+class NatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             NatModel,
@@ -199,6 +200,11 @@ class NatModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
 
     test_torchscript = False

tests/models/nezha/test_modeling_nezha.py

@@ -23,6 +23,7 @@ from transformers.testing_utils import require_torch, require_torch_gpu, slow, t
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -315,7 +316,7 @@ class NezhaModelTester:
 
 
 @require_torch
-class NezhaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class NezhaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             NezhaModel,
@@ -330,6 +331,18 @@ class NezhaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase)
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": NezhaModel,
+            "fill-mask": NezhaForMaskedLM,
+            "question-answering": NezhaForQuestionAnswering,
+            "text-classification": NezhaForSequenceClassification,
+            "token-classification": NezhaForTokenClassification,
+            "zero-shot": NezhaForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = True
 
     # special case for ForPreTraining model

tests/models/nystromformer/test_modeling_nystromformer.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -216,7 +217,7 @@ class NystromformerModelTester:
 
 
 @require_torch
-class NystromformerModelTest(ModelTesterMixin, unittest.TestCase):
+class NystromformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             NystromformerModel,
@@ -229,6 +230,18 @@ class NystromformerModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": NystromformerModel,
+            "fill-mask": NystromformerForMaskedLM,
+            "question-answering": NystromformerForQuestionAnswering,
+            "text-classification": NystromformerForSequenceClassification,
+            "token-classification": NystromformerForTokenClassification,
+            "zero-shot": NystromformerForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_pruning = False
     test_headmasking = False
 

tests/models/oneformer/test_modeling_oneformer.py

@@ -27,6 +27,7 @@ from transformers.utils import cached_property
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -214,8 +215,9 @@ class OneFormerModelTester:
 
 
 @require_torch
-class OneFormerModelTest(ModelTesterMixin, unittest.TestCase):
+class OneFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (OneFormerModel, OneFormerForUniversalSegmentation) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": OneFormerModel} if is_torch_available() else {}
 
     is_encoder_decoder = False
     test_pruning = False

tests/models/openai/test_modeling_openai.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -189,7 +190,7 @@ class OpenAIGPTModelTester:
 
 
 @require_torch
-class OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
         if is_torch_available()
@@ -198,6 +199,16 @@ class OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestC
     all_generative_model_classes = (
         (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": OpenAIGPTModel,
+            "text-classification": OpenAIGPTForSequenceClassification,
+            "text-generation": OpenAIGPTLMHeadModel,
+            "zero-shot": OpenAIGPTForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     # special case for DoubleHeads model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

tests/models/openai/test_modeling_tf_openai.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_tf, slow
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -191,7 +192,7 @@ class TFOpenAIGPTModelTester:
 
 
 @require_tf
-class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFOpenAIGPTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
         if is_tf_available()
@@ -200,6 +201,16 @@ class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
     all_generative_model_classes = (
         (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFOpenAIGPTModel,
+            "text-classification": TFOpenAIGPTForSequenceClassification,
+            "text-generation": TFOpenAIGPTLMHeadModel,
+            "zero-shot": TFOpenAIGPTForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/opt/test_modeling_opt.py

@@ -27,6 +27,7 @@ from transformers.testing_utils import require_torch, require_torch_gpu, slow, t
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -183,13 +184,24 @@ class OPTModelTester:
 
 
 @require_torch
-class OPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class OPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (OPTModel, OPTForCausalLM, OPTForSequenceClassification, OPTForQuestionAnswering)
         if is_torch_available()
         else ()
     )
     all_generative_model_classes = (OPTForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": OPTModel,
+            "question-answering": OPTForQuestionAnswering,
+            "text-classification": OPTForSequenceClassification,
+            "text-generation": OPTForCausalLM,
+            "zero-shot": OPTForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = False
     fx_compatible = True
     test_pruning = False

tests/models/opt/test_modeling_tf_opt.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_sentencepiece, require_tf, slow,
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -146,9 +147,12 @@ class TFOPTModelTester:
 
 
 @require_tf
-class TFOPTModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFOPTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
     all_generative_model_classes = (TFOPTForCausalLM,) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
+    )
     is_encoder_decoder = False
     test_pruning = False
     test_onnx = False

tests/models/owlvit/test_modeling_owlvit.py

@@ -35,6 +35,7 @@ from ...test_modeling_common import (
     ids_tensor,
     random_attention_mask,
 )
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -393,8 +394,13 @@ class OwlViTModelTester:
 
 
 @require_torch
-class OwlViTModelTest(ModelTesterMixin, unittest.TestCase):
+class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (OwlViTModel,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": OwlViTModel, "zero-shot-object-detection": OwlViTForObjectDetection}
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
     test_head_masking = False
     test_pruning = False

tests/models/pegasus/test_modeling_pegasus.py

@@ -24,6 +24,7 @@ from transformers.utils import cached_property
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 from ..mbart.test_modeling_mbart import AbstractSeq2SeqIntegrationTest
 
 
@@ -233,9 +234,20 @@ class PegasusModelTester:
 
 
 @require_torch
-class PegasusModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class PegasusModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (PegasusModel, PegasusForConditionalGeneration) if is_torch_available() else ()
     all_generative_model_classes = (PegasusForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": PegasusForConditionalGeneration,
+            "feature-extraction": PegasusModel,
+            "summarization": PegasusForConditionalGeneration,
+            "text2text-generation": PegasusForConditionalGeneration,
+            "text-generation": PegasusForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     fx_compatible = True
     test_resize_position_embeddings = True

tests/models/pegasus/test_modeling_tf_pegasus.py

@@ -22,6 +22,7 @@ from transformers.utils import cached_property
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -175,9 +176,19 @@ def prepare_pegasus_inputs_dict(
 
 
 @require_tf
-class TFPegasusModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFPegasusModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
     all_generative_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": TFPegasusForConditionalGeneration,
+            "feature-extraction": TFPegasusModel,
+            "summarization": TFPegasusForConditionalGeneration,
+            "text2text-generation": TFPegasusForConditionalGeneration,
+        }
+        if is_tf_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_pruning = False
     test_onnx = False

tests/models/pegasus_x/test_modeling_pegasus_x.py

@@ -27,6 +27,7 @@ from transformers.utils import cached_property
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -194,9 +195,19 @@ class PegasusXModelTester:
 
 
 @require_torch
-class PegasusXModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class PegasusXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (PegasusXModel, PegasusXForConditionalGeneration) if is_torch_available() else ()
     all_generative_model_classes = (PegasusXForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": PegasusXForConditionalGeneration,
+            "feature-extraction": PegasusXModel,
+            "summarization": PegasusXForConditionalGeneration,
+            "text2text-generation": PegasusXForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_pruning = False
     test_head_masking = False

tests/models/perceiver/test_modeling_perceiver.py

@@ -32,6 +32,7 @@ from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -264,7 +265,7 @@ class PerceiverModelTester:
 
 
 @require_torch
-class PerceiverModelTest(ModelTesterMixin, unittest.TestCase):
+class PerceiverModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             PerceiverModel,
@@ -279,6 +280,21 @@ class PerceiverModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": PerceiverModel,
+            "fill-mask": PerceiverForMaskedLM,
+            "image-classification": (
+                PerceiverForImageClassificationConvProcessing,
+                PerceiverForImageClassificationFourier,
+                PerceiverForImageClassificationLearned,
+            ),
+            "text-classification": PerceiverForSequenceClassification,
+            "zero-shot": PerceiverForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_pruning = False
     test_head_masking = False
     test_torchscript = False

tests/models/plbart/test_modeling_plbart.py

@@ -26,6 +26,7 @@ from transformers.utils import cached_property
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -213,11 +214,24 @@ class PLBartModelTester:
 
 
 @require_torch
-class PLBartModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class PLBartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (PLBartModel, PLBartForConditionalGeneration, PLBartForSequenceClassification) if is_torch_available() else ()
     )
     all_generative_model_classes = (PLBartForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": PLBartForConditionalGeneration,
+            "feature-extraction": PLBartModel,
+            "summarization": PLBartForConditionalGeneration,
+            "text2text-generation": PLBartForConditionalGeneration,
+            "text-classification": PLBartForSequenceClassification,
+            "text-generation": PLBartForCausalLM,
+            "zero-shot": PLBartForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     fx_compatible = False  # Fix me Michael
     test_pruning = False

tests/models/poolformer/test_modeling_poolformer.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -121,8 +122,13 @@ class PoolFormerModelTester:
 
 
 @require_torch
-class PoolFormerModelTest(ModelTesterMixin, unittest.TestCase):
+class PoolFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
+        if is_torch_available()
+        else {}
+    )
 
     test_head_masking = False
     test_pruning = False