🔥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/data2vec/test_modeling_tf_data2vec_vision.py

@@ -26,6 +26,7 @@ from transformers.testing_utils import require_tf, require_vision, 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():
@@ -172,7 +173,7 @@ class TFData2VecVisionModelTester:
 
 
 @require_tf
-class TFData2VecVisionModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFData2VecVisionModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -183,6 +184,11 @@ class TFData2VecVisionModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {"feature-extraction": TFData2VecVisionModel, "image-classification": TFData2VecVisionForImageClassification}
+        if is_tf_available()
+        else {}
+    )
 
     test_pruning = False
     test_onnx = False

tests/models/deberta/test_modeling_deberta.py

@@ -19,6 +19,7 @@ from transformers.testing_utils import require_sentencepiece, require_tokenizers
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -213,7 +214,7 @@ class DebertaModelTester(object):
 
 
 @require_torch
-class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
+class DebertaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             DebertaModel,
@@ -225,6 +226,18 @@ class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": DebertaModel,
+            "fill-mask": DebertaForMaskedLM,
+            "question-answering": DebertaForQuestionAnswering,
+            "text-classification": DebertaForSequenceClassification,
+            "token-classification": DebertaForTokenClassification,
+            "zero-shot": DebertaForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     fx_compatible = True
     test_torchscript = False

tests/models/deberta/test_modeling_tf_deberta.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():
@@ -207,7 +208,7 @@ class TFDebertaModelTester:
 
 
 @require_tf
-class TFDebertaModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFDebertaModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFDebertaModel,
@@ -219,6 +220,18 @@ class TFDebertaModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFDebertaModel,
+            "fill-mask": TFDebertaForMaskedLM,
+            "question-answering": TFDebertaForQuestionAnswering,
+            "text-classification": TFDebertaForSequenceClassification,
+            "token-classification": TFDebertaForTokenClassification,
+            "zero-shot": TFDebertaForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
 
     test_head_masking = False
     test_onnx = False

tests/models/deberta_v2/test_modeling_deberta_v2.py

@@ -19,6 +19,7 @@ from transformers.testing_utils import require_sentencepiece, require_tokenizers
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -226,7 +227,7 @@ class DebertaV2ModelTester(object):
 
 
 @require_torch
-class DebertaV2ModelTest(ModelTesterMixin, unittest.TestCase):
+class DebertaV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             DebertaV2Model,
@@ -239,6 +240,18 @@ class DebertaV2ModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": DebertaV2Model,
+            "fill-mask": DebertaV2ForMaskedLM,
+            "question-answering": DebertaV2ForQuestionAnswering,
+            "text-classification": DebertaV2ForSequenceClassification,
+            "token-classification": DebertaV2ForTokenClassification,
+            "zero-shot": DebertaV2ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     fx_compatible = True
     test_torchscript = False

tests/models/deberta_v2/test_modeling_tf_deberta_v2.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():
@@ -209,7 +210,7 @@ class TFDebertaV2ModelTester:
 
 
 @require_tf
-class TFDebertaModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFDebertaModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFDebertaV2Model,
@@ -221,6 +222,18 @@ class TFDebertaModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFDebertaV2Model,
+            "fill-mask": TFDebertaV2ForMaskedLM,
+            "question-answering": TFDebertaV2ForQuestionAnswering,
+            "text-classification": TFDebertaV2ForSequenceClassification,
+            "token-classification": TFDebertaV2ForTokenClassification,
+            "zero-shot": TFDebertaV2ForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
 
     test_head_masking = False
     test_onnx = False

tests/models/decision_transformer/test_modeling_decision_transformer.py

@@ -24,6 +24,7 @@ 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, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -131,9 +132,10 @@ class DecisionTransformerModelTester:
 
 
 @require_torch
-class DecisionTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class DecisionTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (DecisionTransformerModel,) if is_torch_available() else ()
     all_generative_model_classes = ()
+    pipeline_model_mapping = {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}
 
     # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
     test_generate_without_input_ids = False

tests/models/deformable_detr/test_modeling_deformable_detr.py

@@ -27,6 +27,7 @@ from transformers.testing_utils import require_timm, require_torch_gpu, require_
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_timm_available():
@@ -185,8 +186,13 @@ class DeformableDetrModelTester:
 
 
 @require_timm
-class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_timm_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection}
+        if is_timm_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_torchscript = False
     test_pruning = False

tests/models/deit/test_modeling_deit.py

@@ -33,6 +33,7 @@ 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():
@@ -187,7 +188,7 @@ class DeiTModelTester:
 
 
 @require_torch
-class DeiTModelTest(ModelTesterMixin, unittest.TestCase):
+class DeiTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as DeiT does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -203,6 +204,14 @@ class DeiTModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": DeiTModel,
+            "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
+        }
+        if is_torch_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/deit/test_modeling_tf_deit.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():
@@ -162,7 +163,7 @@ class TFDeiTModelTester:
 
 
 @require_tf
-class TFDeiTModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFDeiTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_tf_common.py, as DeiT does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -178,6 +179,14 @@ class TFDeiTModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFDeiTModel,
+            "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
+        }
+        if is_tf_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/deta/test_modeling_deta.py

@@ -26,6 +26,7 @@ from transformers.testing_utils import require_torchvision, require_vision, slow
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -169,8 +170,13 @@ class DetaModelTester:
 
 
 @require_torchvision
-class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (DetaModel, DetaForObjectDetection) if is_torchvision_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": DetaModel, "object-detection": DetaForObjectDetection}
+        if is_torchvision_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_torchscript = False
     test_pruning = False

tests/models/detr/test_modeling_detr.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, _config_zero_init, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_timm_available():
@@ -174,7 +175,7 @@ class DetrModelTester:
 
 
 @require_timm
-class DetrModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class DetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             DetrModel,
@@ -184,6 +185,15 @@ class DetrModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
         if is_timm_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": DetrModel,
+            "image-segmentation": DetrForSegmentation,
+            "object-detection": DetrForObjectDetection,
+        }
+        if is_timm_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_torchscript = False
     test_pruning = False

tests/models/dinat/test_modeling_dinat.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():
@@ -192,7 +193,7 @@ class DinatModelTester:
 
 @require_natten
 @require_torch
-class DinatModelTest(ModelTesterMixin, unittest.TestCase):
+class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             DinatModel,
@@ -202,6 +203,11 @@ class DinatModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
 
     test_torchscript = False

tests/models/distilbert/test_modeling_distilbert.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_torch, require_torch_gpu, slow, t
 
 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():
@@ -195,7 +196,7 @@ class DistilBertModelTester(object):
 
 
 @require_torch
-class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
+class DistilBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             DistilBertModel,
@@ -208,6 +209,18 @@ class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else None
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": DistilBertModel,
+            "fill-mask": DistilBertForMaskedLM,
+            "question-answering": DistilBertForQuestionAnswering,
+            "text-classification": DistilBertForSequenceClassification,
+            "token-classification": DistilBertForTokenClassification,
+            "zero-shot": DistilBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = True
     test_pruning = True
     test_resize_embeddings = True

tests/models/distilbert/test_modeling_tf_distilbert.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():
@@ -169,7 +170,7 @@ class TFDistilBertModelTester:
 
 
 @require_tf
-class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFDistilBertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFDistilBertModel,
@@ -182,6 +183,18 @@ class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else None
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFDistilBertModel,
+            "fill-mask": TFDistilBertForMaskedLM,
+            "question-answering": TFDistilBertForQuestionAnswering,
+            "text-classification": TFDistilBertForSequenceClassification,
+            "token-classification": TFDistilBertForTokenClassification,
+            "zero-shot": TFDistilBertForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/donut/test_modeling_donut_swin.py

@@ -24,6 +24,7 @@ from transformers.utils import is_torch_available
 
 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():
@@ -143,8 +144,9 @@ class DonutSwinModelTester:
 
 
 @require_torch
-class DonutSwinModelTest(ModelTesterMixin, unittest.TestCase):
+class DonutSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (DonutSwinModel,) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": DonutSwinModel} if is_torch_available() else {}
     fx_compatible = True
 
     test_pruning = False

tests/models/dpr/test_modeling_dpr.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():
@@ -178,7 +179,7 @@ class DPRModelTester:
 
 
 @require_torch
-class DPRModelTest(ModelTesterMixin, unittest.TestCase):
+class DPRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             DPRContextEncoder,
@@ -188,6 +189,7 @@ class DPRModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = {"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}
 
     test_resize_embeddings = False
     test_missing_keys = False  # why?

tests/models/dpr/test_modeling_tf_dpr.py

@@ -20,6 +20,7 @@ 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():
@@ -171,7 +172,7 @@ class TFDPRModelTester:
 
 
 @require_tf
-class TFDPRModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFDPRModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFDPRContextEncoder,
@@ -181,6 +182,7 @@ class TFDPRModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
 
     test_resize_embeddings = False
     test_missing_keys = False

tests/models/dpt/test_modeling_dpt.py

@@ -25,6 +25,7 @@ from transformers.testing_utils import require_torch, require_vision, slow, torc
 
 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():
@@ -149,13 +150,22 @@ class DPTModelTester:
 
 
 @require_torch
-class DPTModelTest(ModelTesterMixin, unittest.TestCase):
+class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as DPT does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
     """
 
     all_model_classes = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "depth-estimation": DPTForDepthEstimation,
+            "feature-extraction": DPTModel,
+            "image-segmentation": DPTForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/dpt/test_modeling_dpt_hybrid.py

@@ -25,6 +25,7 @@ from transformers.testing_utils import require_torch, require_vision, slow, torc
 
 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():
@@ -163,13 +164,22 @@ class DPTModelTester:
 
 
 @require_torch
-class DPTModelTest(ModelTesterMixin, unittest.TestCase):
+class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as DPT does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
     """
 
     all_model_classes = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "depth-estimation": DPTForDepthEstimation,
+            "feature-extraction": DPTModel,
+            "image-segmentation": DPTForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     test_pruning = False
     test_resize_embeddings = False

tests/models/efficientformer/test_modeling_efficientformer.py

@@ -26,6 +26,7 @@ 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():
@@ -157,7 +158,7 @@ class EfficientFormerModelTester:
 
 
 @require_torch
-class EfficientFormerModelTest(ModelTesterMixin, unittest.TestCase):
+class EfficientFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Here we also overwrite some of the tests of test_modeling_common.py, as EfficientFormer does not use input_ids, inputs_embeds,
     attention_mask and seq_length.
@@ -172,6 +173,17 @@ class EfficientFormerModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": EfficientFormerModel,
+            "image-classification": (
+                EfficientFormerForImageClassification,
+                EfficientFormerForImageClassificationWithTeacher,
+            ),
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
 
     test_pruning = False