🔥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/prophetnet/test_modeling_prophetnet.py

@@ -23,6 +23,7 @@ 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, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -885,9 +886,20 @@ class ProphetNetStandaloneEncoderModelTester:
 
 
 @require_torch
-class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (ProphetNetModel, ProphetNetForConditionalGeneration) if is_torch_available() else ()
     all_generative_model_classes = (ProphetNetForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": ProphetNetForConditionalGeneration,
+            "feature-extraction": ProphetNetModel,
+            "summarization": ProphetNetForConditionalGeneration,
+            "text2text-generation": ProphetNetForConditionalGeneration,
+            "text-generation": ProphetNetForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_pruning = False
     test_resize_embeddings = False
     is_encoder_decoder = True

tests/models/qdqbert/test_modeling_qdqbert.py

@@ -23,6 +23,7 @@ from transformers.testing_utils import require_pytorch_quantization, require_tor
 
 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():
@@ -419,7 +420,7 @@ class QDQBertModelTester:
 
 @require_torch
 @require_pytorch_quantization
-class QDQBertModelTest(ModelTesterMixin, unittest.TestCase):
+class QDQBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             QDQBertModel,
@@ -435,6 +436,19 @@ class QDQBertModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = (QDQBertLMHeadModel,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": QDQBertModel,
+            "fill-mask": QDQBertForMaskedLM,
+            "question-answering": QDQBertForQuestionAnswering,
+            "text-classification": QDQBertForSequenceClassification,
+            "text-generation": QDQBertLMHeadModel,
+            "token-classification": QDQBertForTokenClassification,
+            "zero-shot": QDQBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     def setUp(self):
         self.model_tester = QDQBertModelTester(self)

tests/models/realm/test_modeling_realm.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, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -303,7 +304,7 @@ class RealmModelTester:
 
 
 @require_torch
-class RealmModelTest(ModelTesterMixin, unittest.TestCase):
+class RealmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             RealmEmbedder,
@@ -316,6 +317,7 @@ class RealmModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = ()
+    pipeline_model_mapping = {} if is_torch_available() else {}
 
     # disable these tests because there is no base_model in Realm
     test_save_load_fast_init_from_base = False

tests/models/reformer/test_modeling_reformer.py

@@ -28,6 +28,7 @@ from transformers.testing_utils import (
 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():
@@ -683,13 +684,27 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, Mod
 
 
 @require_torch
-class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class ReformerLSHAttnModelTest(
+    ReformerTesterMixin, ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
+):
     all_model_classes = (
         (ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
         if is_torch_available()
         else ()
     )
     all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ReformerModel,
+            "fill-mask": ReformerForMaskedLM,
+            "question-answering": ReformerForQuestionAnswering,
+            "text-classification": ReformerForSequenceClassification,
+            "text-generation": ReformerModelWithLMHead,
+            "zero-shot": ReformerForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_pruning = False
     test_headmasking = False
     test_torchscript = False

tests/models/regnet/test_modeling_regnet.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_torch, require_vision, slow, torc
 
 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():
@@ -118,13 +119,18 @@ class RegNetModelTester:
 
 
 @require_torch
-class RegNetModelTest(ModelTesterMixin, unittest.TestCase):
+class RegNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as RegNet does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
     """
 
     all_model_classes = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
+        if is_torch_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/regnet/test_modeling_tf_regnet.py

@@ -24,6 +24,7 @@ from transformers.utils import cached_property, is_tf_available, is_vision_avail
 
 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():
@@ -111,13 +112,18 @@ class TFRegNetModelTester:
 
 
 @require_tf
-class TFRegNetModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFRegNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as RegNet does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
     """
 
     all_model_classes = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
+        if is_tf_available()
+        else {}
+    )
 
     test_pruning = False
     test_onnx = False

tests/models/rembert/test_modeling_rembert.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, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -360,7 +361,7 @@ class RemBertModelTester:
 
 
 @require_torch
-class RemBertModelTest(ModelTesterMixin, unittest.TestCase):
+class RemBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             RemBertModel,
@@ -375,6 +376,19 @@ class RemBertModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = (RemBertForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": RemBertModel,
+            "fill-mask": RemBertForMaskedLM,
+            "question-answering": RemBertForQuestionAnswering,
+            "text-classification": RemBertForSequenceClassification,
+            "text-generation": RemBertForCausalLM,
+            "token-classification": RemBertForTokenClassification,
+            "zero-shot": RemBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     def setUp(self):
         self.model_tester = RemBertModelTester(self)

tests/models/rembert/test_modeling_tf_rembert.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, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -570,7 +571,7 @@ class TFRemBertModelTester:
 
 
 @require_tf
-class TFRemBertModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFRemBertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFRemBertModel,
@@ -584,6 +585,19 @@ class TFRemBertModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFRemBertModel,
+            "fill-mask": TFRemBertForMaskedLM,
+            "question-answering": TFRemBertForQuestionAnswering,
+            "text-classification": TFRemBertForSequenceClassification,
+            "text-generation": TFRemBertForCausalLM,
+            "token-classification": TFRemBertForTokenClassification,
+            "zero-shot": TFRemBertForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
 
     test_head_masking = False
     test_onnx = False

tests/models/resnet/test_modeling_resnet.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():
@@ -150,7 +151,7 @@ class ResNetModelTester:
 
 
 @require_torch
-class ResNetModelTest(ModelTesterMixin, unittest.TestCase):
+class ResNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as ResNet does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -165,6 +166,11 @@ class ResNetModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
+        if is_torch_available()
+        else {}
+    )
 
     fx_compatible = True
     test_pruning = False

tests/models/resnet/test_modeling_tf_resnet.py

@@ -26,6 +26,7 @@ from transformers.utils import cached_property, is_tf_available, is_vision_avail
 
 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():
@@ -116,13 +117,18 @@ class TFResNetModelTester:
 
 
 @require_tf
-class TFResNetModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFResNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as ResNet does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
     """
 
     all_model_classes = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
+        if is_tf_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/roberta/test_modeling_roberta.py

@@ -23,6 +23,7 @@ from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_
 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():
@@ -366,7 +367,7 @@ class RobertaModelTester:
 
 
 @require_torch
-class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             RobertaForCausalLM,
@@ -381,6 +382,19 @@ class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCas
         else ()
     )
     all_generative_model_classes = (RobertaForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": RobertaModel,
+            "fill-mask": RobertaForMaskedLM,
+            "question-answering": RobertaForQuestionAnswering,
+            "text-classification": RobertaForSequenceClassification,
+            "text-generation": RobertaForCausalLM,
+            "token-classification": RobertaForTokenClassification,
+            "zero-shot": RobertaForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = True
 
     def setUp(self):

tests/models/roberta/test_modeling_tf_roberta.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_sentencepiece, require_tf, requir
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -547,7 +548,7 @@ class TFRobertaModelTester:
 
 
 @require_tf
-class TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFRobertaModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFRobertaModel,
@@ -560,6 +561,19 @@ class TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFRobertaModel,
+            "fill-mask": TFRobertaForMaskedLM,
+            "question-answering": TFRobertaForQuestionAnswering,
+            "text-classification": TFRobertaForSequenceClassification,
+            "text-generation": TFRobertaForCausalLM,
+            "token-classification": TFRobertaForTokenClassification,
+            "zero-shot": TFRobertaForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/roberta_prelayernorm/test_modeling_roberta_prelayernorm.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_
 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():
@@ -365,7 +366,7 @@ class RobertaPreLayerNormModelTester:
 
 @require_torch
 # Copied from tests.models.roberta.test_modelling_roberta.RobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm
-class RobertaPreLayerNormModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class RobertaPreLayerNormModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             RobertaPreLayerNormForCausalLM,
@@ -380,6 +381,19 @@ class RobertaPreLayerNormModelTest(ModelTesterMixin, GenerationTesterMixin, unit
         else ()
     )
     all_generative_model_classes = (RobertaPreLayerNormForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": RobertaPreLayerNormModel,
+            "fill-mask": RobertaPreLayerNormForMaskedLM,
+            "question-answering": RobertaPreLayerNormForQuestionAnswering,
+            "text-classification": RobertaPreLayerNormForSequenceClassification,
+            "text-generation": RobertaPreLayerNormForCausalLM,
+            "token-classification": RobertaPreLayerNormForTokenClassification,
+            "zero-shot": RobertaPreLayerNormForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
 
     def setUp(self):

tests/models/roberta_prelayernorm/test_modeling_tf_roberta_prelayernorm.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_sentencepiece, require_tf, requir
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -549,7 +550,7 @@ class TFRobertaPreLayerNormModelTester:
 
 @require_tf
 # Copied from tests.models.roberta.test_modelling_tf_roberta.TFRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm
-class TFRobertaPreLayerNormModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFRobertaPreLayerNormModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFRobertaPreLayerNormModel,
@@ -562,6 +563,19 @@ class TFRobertaPreLayerNormModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFRobertaPreLayerNormModel,
+            "fill-mask": TFRobertaPreLayerNormForMaskedLM,
+            "question-answering": TFRobertaPreLayerNormForQuestionAnswering,
+            "text-classification": TFRobertaPreLayerNormForSequenceClassification,
+            "text-generation": TFRobertaPreLayerNormForCausalLM,
+            "token-classification": TFRobertaPreLayerNormForTokenClassification,
+            "zero-shot": TFRobertaPreLayerNormForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/roc_bert/test_modeling_roc_bert.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, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -555,7 +556,7 @@ class RoCBertModelTester:
 
 
 @require_torch
-class RoCBertModelTest(ModelTesterMixin, unittest.TestCase):
+class RoCBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             RoCBertModel,
@@ -571,6 +572,19 @@ class RoCBertModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = (RoCBertForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": RoCBertModel,
+            "fill-mask": RoCBertForMaskedLM,
+            "question-answering": RoCBertForQuestionAnswering,
+            "text-classification": RoCBertForSequenceClassification,
+            "text-generation": RoCBertForCausalLM,
+            "token-classification": RoCBertForTokenClassification,
+            "zero-shot": RoCBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

tests/models/roformer/test_modeling_roformer.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, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -360,7 +361,7 @@ class RoFormerModelTester:
 
 
 @require_torch
-class RoFormerModelTest(ModelTesterMixin, unittest.TestCase):
+class RoFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             RoFormerModel,
@@ -375,6 +376,19 @@ class RoFormerModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = (RoFormerForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": RoFormerModel,
+            "fill-mask": RoFormerForMaskedLM,
+            "question-answering": RoFormerForQuestionAnswering,
+            "text-classification": RoFormerForSequenceClassification,
+            "text-generation": RoFormerForCausalLM,
+            "token-classification": RoFormerForTokenClassification,
+            "zero-shot": RoFormerForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     def setUp(self):
         self.model_tester = RoFormerModelTester(self)

tests/models/roformer/test_modeling_tf_roformer.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():
@@ -239,7 +240,7 @@ class TFRoFormerModelTester:
 
 
 @require_tf
-class TFRoFormerModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFRoFormerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFRoFormerModel,
@@ -253,6 +254,19 @@ class TFRoFormerModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFRoFormerModel,
+            "fill-mask": TFRoFormerForMaskedLM,
+            "question-answering": TFRoFormerForQuestionAnswering,
+            "text-classification": TFRoFormerForSequenceClassification,
+            "text-generation": TFRoFormerForCausalLM,
+            "token-classification": TFRoFormerForTokenClassification,
+            "zero-shot": TFRoFormerForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
 
     test_head_masking = False
     test_onnx = False

tests/models/segformer/test_modeling_segformer.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():
@@ -159,7 +160,7 @@ class SegformerModelTester:
 
 
 @require_torch
-class SegformerModelTest(ModelTesterMixin, unittest.TestCase):
+class SegformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             SegformerModel,
@@ -169,6 +170,15 @@ class SegformerModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": SegformerModel,
+            "image-classification": SegformerForImageClassification,
+            "image-segmentation": SegformerForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     fx_compatible = True
     test_head_masking = False

tests/models/segformer/test_modeling_tf_segformer.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():
@@ -150,12 +151,17 @@ class TFSegformerModelTester:
 
 
 @require_tf
-class TFSegformerModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFSegformerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (TFSegformerModel, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {"feature-extraction": TFSegformerModel, "image-classification": TFSegformerForImageClassification}
+        if is_tf_available()
+        else {}
+    )
 
     test_head_masking = False
     test_onnx = False

tests/models/sew/test_modeling_sew.py

@@ -31,6 +31,7 @@ from ...test_modeling_common import (
     ids_tensor,
     random_attention_mask,
 )
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -299,8 +300,17 @@ class SEWModelTester:
 
 
 @require_torch
-class SEWModelTest(ModelTesterMixin, unittest.TestCase):
+class SEWModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (SEWForCTC, SEWModel, SEWForSequenceClassification) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "audio-classification": SEWForSequenceClassification,
+            "automatic-speech-recognition": SEWForCTC,
+            "feature-extraction": SEWModel,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_pruning = False
     test_headmasking = False