Mark pipeline tests to skip them easily (#21887)

* Mark pipeline tests to skip them easily

* Mark the mixin as pipeline test

* Update src/transformers/testing_utils.py

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

---------

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

tests/pipelines/test_pipelines_audio_classification.py

@@ -18,11 +18,19 @@ import numpy as np
 
 from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 from transformers.pipelines import AudioClassificationPipeline, pipeline
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torchaudio, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_torchaudio,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 @require_torch
 class AudioClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -33,6 +33,7 @@ from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
 from transformers.pipelines.audio_utils import chunk_bytes_iter
 from transformers.pipelines.automatic_speech_recognition import _find_timestamp_sequence, chunk_iter
 from transformers.testing_utils import (
+    is_pipeline_test,
     is_torch_available,
     nested_simplify,
     require_pyctcdecode,
@@ -53,6 +54,7 @@ if is_torch_available():
 # from .test_pipelines_common import CustomInputPipelineCommonMixin
 
 
+@is_pipeline_test
 class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
     model_mapping = {
         k: v

tests/pipelines/test_pipelines_common.py

@@ -39,6 +39,7 @@ from transformers.testing_utils import (
     USER,
     CaptureLogger,
     RequestCounter,
+    is_pipeline_test,
     is_staging_test,
     nested_simplify,
     require_tensorflow_probability,
@@ -77,6 +78,7 @@ class ANY:
         return f"ANY({', '.join(_type.__name__ for _type in self._types)})"
 
 
+@is_pipeline_test
 class CommonPipelineTest(unittest.TestCase):
     @require_torch
     def test_pipeline_iteration(self):
@@ -194,6 +196,7 @@ class CommonPipelineTest(unittest.TestCase):
         self.assertEqual(len(outputs), 20)
 
 
+@is_pipeline_test
 class PipelineScikitCompatTest(unittest.TestCase):
     @require_torch
     def test_pipeline_predict_pt(self):
@@ -244,6 +247,7 @@ class PipelineScikitCompatTest(unittest.TestCase):
         self.assertEqual(expected_output, actual_output)
 
 
+@is_pipeline_test
 class PipelinePadTest(unittest.TestCase):
     @require_torch
     def test_pipeline_padding(self):
@@ -325,6 +329,7 @@ class PipelinePadTest(unittest.TestCase):
         )
 
 
+@is_pipeline_test
 class PipelineUtilsTest(unittest.TestCase):
     @require_torch
     def test_pipeline_dataset(self):
@@ -620,6 +625,7 @@ class CustomPipeline(Pipeline):
         return model_outputs["logits"].softmax(-1).numpy()
 
 
+@is_pipeline_test
 class CustomPipelineTest(unittest.TestCase):
     def test_warning_logs(self):
         transformers_logging.set_verbosity_debug()

tests/pipelines/test_pipelines_conversational.py

@@ -29,7 +29,7 @@ from transformers import (
     TFAutoModelForCausalLM,
     pipeline,
 )
-from transformers.testing_utils import require_tf, require_torch, slow, torch_device
+from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow, torch_device
 
 from .test_pipelines_common import ANY
 
@@ -37,6 +37,7 @@ from .test_pipelines_common import ANY
 DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
 
 
+@is_pipeline_test
 class ConversationalPipelineTests(unittest.TestCase):
     model_mapping = dict(
         list(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())

tests/pipelines/test_pipelines_depth_estimation.py

@@ -17,7 +17,15 @@ import unittest
 
 from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
 from transformers.pipelines import DepthEstimationPipeline, pipeline
-from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_timm,
+    require_torch,
+    require_vision,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
@@ -40,6 +48,7 @@ def hashimage(image: Image) -> str:
     return m.hexdigest()
 
 
+@is_pipeline_test
 @require_vision
 @require_timm
 @require_torch

tests/pipelines/test_pipelines_document_question_answering.py

@@ -18,6 +18,7 @@ from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoToke
 from transformers.pipelines import pipeline
 from transformers.pipelines.document_question_answering import apply_tesseract
 from transformers.testing_utils import (
+    is_pipeline_test,
     nested_simplify,
     require_detectron2,
     require_pytesseract,
@@ -52,6 +53,7 @@ INVOICE_URL = (
 )
 
 
+@is_pipeline_test
 @require_torch
 @require_vision
 class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):

tests/pipelines/test_pipelines_feature_extraction.py

@@ -27,7 +27,7 @@ from transformers import (
     is_torch_available,
     pipeline,
 )
-from transformers.testing_utils import nested_simplify, require_tf, require_torch
+from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
 
 
 if is_torch_available():
@@ -37,6 +37,7 @@ if is_tf_available():
     import tensorflow as tf
 
 
+@is_pipeline_test
 class FeatureExtractionPipelineTests(unittest.TestCase):
     model_mapping = MODEL_MAPPING
     tf_model_mapping = TF_MODEL_MAPPING

tests/pipelines/test_pipelines_fill_mask.py

@@ -16,11 +16,19 @@ import unittest
 
 from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
 from transformers.pipelines import PipelineException
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_gpu, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_torch_gpu,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 class FillMaskPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_MASKED_LM_MAPPING
     tf_model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING

tests/pipelines/test_pipelines_image_classification.py

@@ -22,6 +22,7 @@ from transformers import (
 )
 from transformers.pipelines import ImageClassificationPipeline, pipeline
 from transformers.testing_utils import (
+    is_pipeline_test,
     nested_simplify,
     require_tf,
     require_torch,
@@ -43,6 +44,7 @@ else:
             pass
 
 
+@is_pipeline_test
 @require_torch_or_tf
 @require_vision
 class ImageClassificationPipelineTests(unittest.TestCase):

tests/pipelines/test_pipelines_image_segmentation.py

@@ -34,7 +34,15 @@ from transformers import (
     is_vision_available,
     pipeline,
 )
-from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_timm,
+    require_torch,
+    require_vision,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
@@ -67,6 +75,7 @@ def mask_to_test_readable_only_shape(mask: Image) -> Dict:
     return {"shape": shape}
 
 
+@is_pipeline_test
 @require_vision
 @require_timm
 @require_torch

tests/pipelines/test_pipelines_image_to_text.py

@@ -16,7 +16,7 @@ import unittest
 
 from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
 from transformers.pipelines import pipeline
-from transformers.testing_utils import require_tf, require_torch, require_vision, slow
+from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, require_vision, slow
 
 from .test_pipelines_common import ANY
 
@@ -31,6 +31,7 @@ else:
             pass
 
 
+@is_pipeline_test
 @require_vision
 class ImageToTextPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_VISION_2_SEQ_MAPPING

tests/pipelines/test_pipelines_object_detection.py

@@ -23,6 +23,7 @@ from transformers import (
     pipeline,
 )
 from transformers.testing_utils import (
+    is_pipeline_test,
     nested_simplify,
     require_pytesseract,
     require_tf,
@@ -45,6 +46,7 @@ else:
             pass
 
 
+@is_pipeline_test
 @require_vision
 @require_timm
 @require_torch

tests/pipelines/test_pipelines_question_answering.py

@@ -22,11 +22,19 @@ from transformers import (
 )
 from transformers.data.processors.squad import SquadExample
 from transformers.pipelines import QuestionAnsweringArgumentHandler, pipeline
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_or_tf, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_torch_or_tf,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 class QAPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING
     tf_model_mapping = TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING

tests/pipelines/test_pipelines_summarization.py

@@ -21,7 +21,7 @@ from transformers import (
     TFPreTrainedModel,
     pipeline,
 )
-from transformers.testing_utils import get_gpu_count, require_tf, require_torch, slow, torch_device
+from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
 from transformers.tokenization_utils import TruncationStrategy
 
 from .test_pipelines_common import ANY
@@ -30,6 +30,7 @@ from .test_pipelines_common import ANY
 DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
 
 
+@is_pipeline_test
 class SummarizationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

tests/pipelines/test_pipelines_table_question_answering.py

@@ -22,9 +22,17 @@ from transformers import (
     TFAutoModelForTableQuestionAnswering,
     pipeline,
 )
-from transformers.testing_utils import require_pandas, require_tensorflow_probability, require_tf, require_torch, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    require_pandas,
+    require_tensorflow_probability,
+    require_tf,
+    require_torch,
+    slow,
+)
 
 
+@is_pipeline_test
 class TQAPipelineTests(unittest.TestCase):
     # Putting it there for consistency, but TQA do not have fast tokenizer
     # which are needed to generate automatic tests

tests/pipelines/test_pipelines_text2text_generation.py

@@ -20,7 +20,7 @@ from transformers import (
     Text2TextGenerationPipeline,
     pipeline,
 )
-from transformers.testing_utils import require_tf, require_torch
+from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
 from transformers.utils import is_torch_available
 
 from .test_pipelines_common import ANY
@@ -30,6 +30,7 @@ if is_torch_available():
     import torch
 
 
+@is_pipeline_test
 class Text2TextGenerationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

tests/pipelines/test_pipelines_text_classification.py

@@ -20,11 +20,12 @@ from transformers import (
     TextClassificationPipeline,
     pipeline,
 )
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, slow
+from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
 
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 class TextClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

tests/pipelines/test_pipelines_text_generation.py

@@ -16,6 +16,7 @@ import unittest
 
 from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TextGenerationPipeline, pipeline
 from transformers.testing_utils import (
+    is_pipeline_test,
     require_accelerate,
     require_tf,
     require_torch,
@@ -26,6 +27,7 @@ from transformers.testing_utils import (
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 @require_torch_or_tf
 class TextGenerationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING

tests/pipelines/test_pipelines_token_classification.py

@@ -25,7 +25,14 @@ from transformers import (
     pipeline,
 )
 from transformers.pipelines import AggregationStrategy, TokenClassificationArgumentHandler
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_gpu, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_torch_gpu,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
@@ -33,6 +40,7 @@ from .test_pipelines_common import ANY
 VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
 
 
+@is_pipeline_test
 class TokenClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
     tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

tests/pipelines/test_pipelines_translation.py

@@ -25,11 +25,12 @@ from transformers import (
     TranslationPipeline,
     pipeline,
 )
-from transformers.testing_utils import require_tf, require_torch, slow
+from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow
 
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 class TranslationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

tests/pipelines/test_pipelines_video_classification.py

@@ -19,6 +19,7 @@ from huggingface_hub import hf_hub_download
 from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
 from transformers.pipelines import VideoClassificationPipeline, pipeline
 from transformers.testing_utils import (
+    is_pipeline_test,
     nested_simplify,
     require_decord,
     require_tf,
@@ -30,6 +31,7 @@ from transformers.testing_utils import (
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 @require_torch_or_tf
 @require_vision
 @require_decord

tests/pipelines/test_pipelines_visual_question_answering.py

@@ -16,7 +16,14 @@ import unittest
 
 from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
 from transformers.pipelines import pipeline
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_vision,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
@@ -31,6 +38,7 @@ else:
             pass
 
 
+@is_pipeline_test
 @require_torch
 @require_vision
 class VisualQuestionAnsweringPipelineTests(unittest.TestCase):

tests/pipelines/test_pipelines_zero_shot.py

@@ -21,11 +21,12 @@ from transformers import (
     ZeroShotClassificationPipeline,
     pipeline,
 )
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, slow
+from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
 
 from .test_pipelines_common import ANY
 
 
+@is_pipeline_test
 class ZeroShotClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

tests/pipelines/test_pipelines_zero_shot_audio_classification.py

@@ -17,9 +17,10 @@ import unittest
 from datasets import load_dataset
 
 from transformers.pipelines import pipeline
-from transformers.testing_utils import nested_simplify, require_torch, slow
+from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
 
 
+@is_pipeline_test
 @require_torch
 class ZeroShotAudioClassificationPipelineTests(unittest.TestCase):
     # Deactivating auto tests since we don't have a good MODEL_FOR_XX mapping,

tests/pipelines/test_pipelines_zero_shot_image_classification.py

@@ -16,7 +16,14 @@ import unittest
 
 from transformers import is_vision_available
 from transformers.pipelines import pipeline
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_vision,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
@@ -31,6 +38,7 @@ else:
             pass
 
 
+@is_pipeline_test
 @require_vision
 class ZeroShotImageClassificationPipelineTests(unittest.TestCase):
     # Deactivating auto tests since we don't have a good MODEL_FOR_XX mapping,

tests/pipelines/test_pipelines_zero_shot_object_detection.py

@@ -15,7 +15,14 @@
 import unittest
 
 from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
-from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_vision,
+    slow,
+)
 
 from .test_pipelines_common import ANY
 
@@ -30,6 +37,7 @@ else:
             pass
 
 
+@is_pipeline_test
 @require_vision
 @require_torch
 class ZeroShotObjectDetectionPipelineTests(unittest.TestCase):