🔥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/longformer/test_modeling_tf_longformer.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, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -270,7 +271,7 @@ class TFLongformerModelTester:
 
 
 @require_tf
-class TFLongformerModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFLongformerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFLongformerModel,
@@ -283,6 +284,18 @@ class TFLongformerModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFLongformerModel,
+            "fill-mask": TFLongformerForMaskedLM,
+            "question-answering": TFLongformerForQuestionAnswering,
+            "text-classification": TFLongformerForSequenceClassification,
+            "token-classification": TFLongformerForTokenClassification,
+            "zero-shot": TFLongformerForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/longt5/test_modeling_longt5.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():
@@ -500,9 +501,19 @@ class LongT5ModelTester:
 
 
 @require_torch
-class LongT5ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class LongT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (LongT5Model, LongT5ForConditionalGeneration) if is_torch_available() else ()
     all_generative_model_classes = (LongT5ForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": LongT5ForConditionalGeneration,
+            "feature-extraction": LongT5Model,
+            "summarization": LongT5ForConditionalGeneration,
+            "text2text-generation": LongT5ForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
     test_pruning = False
     test_torchscript = True

tests/models/luke/test_modeling_luke.py

@@ -20,6 +20,7 @@ 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():
@@ -585,7 +586,7 @@ class LukeModelTester:
 
 
 @require_torch
-class LukeModelTest(ModelTesterMixin, unittest.TestCase):
+class LukeModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             LukeModel,
@@ -601,6 +602,18 @@ class LukeModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": LukeModel,
+            "fill-mask": LukeForMaskedLM,
+            "question-answering": LukeForQuestionAnswering,
+            "text-classification": LukeForSequenceClassification,
+            "token-classification": LukeForTokenClassification,
+            "zero-shot": LukeForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_pruning = False
     test_torchscript = False
     test_resize_embeddings = True

tests/models/lxmert/test_modeling_lxmert.py

@@ -25,6 +25,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -529,8 +530,13 @@ class LxmertModelTester:
 
 
 @require_torch
-class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
+class LxmertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (LxmertModel, LxmertForPreTraining, LxmertForQuestionAnswering) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": LxmertModel, "question-answering": LxmertForQuestionAnswering}
+        if is_torch_available()
+        else {}
+    )
 
     fx_compatible = True
     test_head_masking = False

tests/models/lxmert/test_modeling_tf_lxmert.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_tf, slow, tooslow
 
 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():
@@ -363,8 +364,9 @@ class TFLxmertModelTester(object):
 
 
 @require_tf
-class TFLxmertModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFLxmertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (TFLxmertModel, TFLxmertForPreTraining) if is_tf_available() else ()
+    pipeline_model_mapping = {"feature-extraction": TFLxmertModel} if is_tf_available() else {}
     test_head_masking = False
     test_onnx = False
 

tests/models/m2m_100/test_modeling_m2m_100.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():
@@ -220,7 +221,7 @@ class M2M100ModelTester:
 
 
 @require_torch
-class M2M100ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class M2M100ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             M2M100Model,
@@ -230,6 +231,16 @@ class M2M100ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase
         else ()
     )
     all_generative_model_classes = (M2M100ForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": M2M100ForConditionalGeneration,
+            "feature-extraction": M2M100Model,
+            "summarization": M2M100ForConditionalGeneration,
+            "text2text-generation": M2M100ForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     fx_compatible = True
     test_pruning = False

tests/models/marian/test_modeling_marian.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():
@@ -235,9 +236,20 @@ class MarianModelTester:
 
 
 @require_torch
-class MarianModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class MarianModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (MarianModel, MarianMTModel) if is_torch_available() else ()
     all_generative_model_classes = (MarianMTModel,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": MarianMTModel,
+            "feature-extraction": MarianModel,
+            "summarization": MarianMTModel,
+            "text2text-generation": MarianMTModel,
+            "text-generation": MarianForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     fx_compatible = True
     test_pruning = False

tests/models/marian/test_modeling_tf_marian.py

@@ -24,6 +24,7 @@ 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():
@@ -177,9 +178,19 @@ def prepare_marian_inputs_dict(
 
 
 @require_tf
-class TFMarianModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFMarianModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     all_generative_model_classes = (TFMarianMTModel,) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": TFMarianMTModel,
+            "feature-extraction": TFMarianModel,
+            "summarization": TFMarianMTModel,
+            "text2text-generation": TFMarianMTModel,
+        }
+        if is_tf_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_pruning = False
     test_onnx = False

tests/models/markuplm/test_modeling_markuplm.py

@@ -22,6 +22,7 @@ from transformers.utils import cached_property
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -275,7 +276,7 @@ class MarkupLMModelTester:
 
 
 @require_torch
-class MarkupLMModelTest(ModelTesterMixin, unittest.TestCase):
+class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             MarkupLMModel,
@@ -286,6 +287,17 @@ class MarkupLMModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else None
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": MarkupLMModel,
+            "question-answering": MarkupLMForQuestionAnswering,
+            "text-classification": MarkupLMForSequenceClassification,
+            "token-classification": MarkupLMForTokenClassification,
+            "zero-shot": MarkupLMForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     def setUp(self):
         self.model_tester = MarkupLMModelTester(self)

tests/models/mask2former/test_modeling_mask2former.py

@@ -26,6 +26,7 @@ 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():
@@ -172,8 +173,9 @@ class Mask2FormerModelTester:
 
 
 @require_torch
-class Mask2FormerModelTest(ModelTesterMixin, unittest.TestCase):
+class Mask2FormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (Mask2FormerModel, Mask2FormerForUniversalSegmentation) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": Mask2FormerModel} if is_torch_available() else {}
 
     is_encoder_decoder = False
     test_pruning = False

tests/models/maskformer/test_modeling_maskformer.py

@@ -26,6 +26,7 @@ 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():
@@ -172,8 +173,13 @@ class MaskFormerModelTester:
 
 
 @require_torch
-class MaskFormerModelTest(ModelTesterMixin, unittest.TestCase):
+class MaskFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
+        if is_torch_available()
+        else {}
+    )
 
     is_encoder_decoder = False
     test_pruning = False

tests/models/maskformer/test_modeling_maskformer_swin.py

@@ -25,6 +25,7 @@ from transformers.utils import is_torch_available
 
 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():
@@ -162,7 +163,7 @@ class MaskFormerSwinModelTester:
 
 
 @require_torch
-class MaskFormerSwinModelTest(ModelTesterMixin, unittest.TestCase):
+class MaskFormerSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             MaskFormerSwinModel,
@@ -171,6 +172,7 @@ class MaskFormerSwinModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
     fx_compatible = False
     test_torchscript = False
     test_pruning = False

tests/models/mbart/test_modeling_mbart.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():
@@ -224,13 +225,28 @@ class MBartModelTester:
 
 
 @require_torch
-class MBartModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class MBartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (MBartModel, MBartForConditionalGeneration, MBartForSequenceClassification, MBartForQuestionAnswering)
         if is_torch_available()
         else ()
     )
     all_generative_model_classes = (MBartForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": MBartForConditionalGeneration,
+            "feature-extraction": MBartModel,
+            "fill-mask": MBartForConditionalGeneration,
+            "question-answering": MBartForQuestionAnswering,
+            "summarization": MBartForConditionalGeneration,
+            "text2text-generation": MBartForConditionalGeneration,
+            "text-classification": MBartForSequenceClassification,
+            "text-generation": MBartForCausalLM,
+            "zero-shot": MBartForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     fx_compatible = False  # Fix me Michael
     test_pruning = False

tests/models/mbart/test_modeling_tf_mbart.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():
@@ -180,9 +181,19 @@ def prepare_mbart_inputs_dict(
 
 
 @require_tf
-class TFMBartModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFMBartModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
     all_generative_model_classes = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": TFMBartForConditionalGeneration,
+            "feature-extraction": TFMBartModel,
+            "summarization": TFMBartForConditionalGeneration,
+            "text2text-generation": TFMBartForConditionalGeneration,
+        }
+        if is_tf_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_pruning = False
     test_onnx = False

tests/models/mctct/test_modeling_mctct.py

@@ -25,6 +25,7 @@ from transformers.testing_utils import require_soundfile, require_torch, slow, t
 
 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():
@@ -265,8 +266,11 @@ class MCTCTModelTester:
 
 @require_torch
 @unittest.skipIf(is_torch_less_than_1_9, "MCTCT is only available in torch v1.9+")
-class MCTCTModelTest(ModelTesterMixin, unittest.TestCase):
+class MCTCTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (MCTCTForCTC, MCTCTModel) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"automatic-speech-recognition": MCTCTForCTC, "feature-extraction": MCTCTModel} if is_torch_available() else {}
+    )
     test_pruning = False
     test_headmasking = False
     test_torchscript = False

tests/models/megatron_bert/test_modeling_megatron_bert.py

@@ -25,6 +25,7 @@ from transformers.testing_utils import require_sentencepiece, require_tokenizers
 
 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():
@@ -266,7 +267,7 @@ class MegatronBertModelTester:
 
 
 @require_torch
-class MegatronBertModelTest(ModelTesterMixin, unittest.TestCase):
+class MegatronBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             MegatronBertModel,
@@ -282,6 +283,19 @@ class MegatronBertModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": MegatronBertModel,
+            "fill-mask": MegatronBertForMaskedLM,
+            "question-answering": MegatronBertForQuestionAnswering,
+            "text-classification": MegatronBertForSequenceClassification,
+            "text-generation": MegatronBertForCausalLM,
+            "token-classification": MegatronBertForTokenClassification,
+            "zero-shot": MegatronBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = True
     # test_resize_embeddings = False
     test_head_masking = False

tests/models/mobilebert/test_modeling_mobilebert.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_sentencepiece, require_tokenizers
 
 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():
@@ -253,7 +254,7 @@ class MobileBertModelTester:
 
 
 @require_torch
-class MobileBertModelTest(ModelTesterMixin, unittest.TestCase):
+class MobileBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             MobileBertModel,
@@ -268,6 +269,18 @@ class MobileBertModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": MobileBertModel,
+            "fill-mask": MobileBertForMaskedLM,
+            "question-answering": MobileBertForQuestionAnswering,
+            "text-classification": MobileBertForSequenceClassification,
+            "token-classification": MobileBertForTokenClassification,
+            "zero-shot": MobileBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = True
 
     # special case for ForPreTraining model

tests/models/mobilebert/test_modeling_tf_mobilebert.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_tf, slow, tooslow
 
 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():
@@ -41,7 +42,7 @@ if is_tf_available():
 
 
 @require_tf
-class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFMobileBertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFMobileBertModel,
@@ -56,6 +57,18 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFMobileBertModel,
+            "fill-mask": TFMobileBertForMaskedLM,
+            "question-answering": TFMobileBertForQuestionAnswering,
+            "text-classification": TFMobileBertForSequenceClassification,
+            "token-classification": TFMobileBertForTokenClassification,
+            "zero-shot": TFMobileBertForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/mobilenet_v1/test_modeling_mobilenet_v1.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():
@@ -139,13 +140,18 @@ class MobileNetV1ModelTester:
 
 
 @require_torch
-class MobileNetV1ModelTest(ModelTesterMixin, unittest.TestCase):
+class MobileNetV1ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as MobileNetV1 does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
     """
 
     all_model_classes = (MobileNetV1Model, MobileNetV1ForImageClassification) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification}
+        if is_torch_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/mobilenet_v2/test_modeling_mobilenet_v2.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():
@@ -182,7 +183,7 @@ class MobileNetV2ModelTester:
 
 
 @require_torch
-class MobileNetV2ModelTest(ModelTesterMixin, unittest.TestCase):
+class MobileNetV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as MobileNetV2 does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -193,6 +194,15 @@ class MobileNetV2ModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": MobileNetV2Model,
+            "image-classification": MobileNetV2ForImageClassification,
+            "image-segmentation": MobileNetV2ForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False