Rework pipeline tests (#19366)

* Rework pipeline tests

* Try to fix Flax tests

* Try to put it before

* Use a new decorator instead

* Remove ignore marker since it doesn't work

* Filter pipeline tests

* Woopsie

* Use the fitlered list

* Clean up and fake modif

* Remove init

* Revert fake modif

tests/pipelines/test_pipelines_audio_classification.py

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

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -31,7 +31,6 @@ from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
 from transformers.pipelines.audio_utils import chunk_bytes_iter
 from transformers.pipelines.automatic_speech_recognition import chunk_iter
 from transformers.testing_utils import (
-    is_pipeline_test,
     is_torch_available,
     nested_simplify,
     require_pyctcdecode,
@@ -52,7 +51,6 @@ if is_torch_available():
 # from .test_pipelines_common import CustomInputPipelineCommonMixin
 
 
-@is_pipeline_test
 class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = {
         k: v

tests/pipelines/test_pipelines_common.py

@@ -48,13 +48,13 @@ from transformers.testing_utils import (
     USER,
     CaptureLogger,
     RequestCounter,
-    is_pipeline_test,
     is_staging_test,
     nested_simplify,
     require_scatter,
     require_tensorflow_probability,
     require_tf,
     require_torch,
+    require_torch_or_tf,
     slow,
 )
 from transformers.utils import is_tf_available, is_torch_available
@@ -307,7 +307,6 @@ class PipelineTestCaseMeta(type):
         return type.__new__(mcs, name, bases, dct)
 
 
-@is_pipeline_test
 class CommonPipelineTest(unittest.TestCase):
     @require_torch
     def test_pipeline_iteration(self):
@@ -416,7 +415,6 @@ class CommonPipelineTest(unittest.TestCase):
         self.assertEqual(len(outputs), 20)
 
 
-@is_pipeline_test
 class PipelinePadTest(unittest.TestCase):
     @require_torch
     def test_pipeline_padding(self):
@@ -498,7 +496,6 @@ class PipelinePadTest(unittest.TestCase):
         )
 
 
-@is_pipeline_test
 class PipelineUtilsTest(unittest.TestCase):
     @require_torch
     def test_pipeline_dataset(self):
@@ -795,7 +792,6 @@ 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()
@@ -835,6 +831,7 @@ class CustomPipelineTest(unittest.TestCase):
         # Clean registry for next tests.
         del PIPELINE_REGISTRY.supported_tasks["custom-text-classification"]
 
+    @require_torch_or_tf
     def test_dynamic_pipeline(self):
         PIPELINE_REGISTRY.register_pipeline(
             "pair-classification",
@@ -886,6 +883,7 @@ class CustomPipelineTest(unittest.TestCase):
             [{"label": "LABEL_0", "score": 0.505}],
         )
 
+    @require_torch_or_tf
     def test_cached_pipeline_has_minimum_calls_to_head(self):
         # Make sure we have cached the pipeline.
         _ = pipeline("text-classification", model="hf-internal-testing/tiny-random-bert")

tests/pipelines/test_pipelines_conversational.py

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

tests/pipelines/test_pipelines_document_question_answering.py

@@ -18,7 +18,6 @@ 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,
@@ -53,7 +52,6 @@ INVOICE_URL = (
 )
 
 
-@is_pipeline_test
 @require_torch
 @require_vision
 class DocumentQuestionAnsweringPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):

tests/pipelines/test_pipelines_feature_extraction.py

@@ -22,12 +22,11 @@ from transformers import (
     LxmertConfig,
     pipeline,
 )
-from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
+from transformers.testing_utils import nested_simplify, require_tf, require_torch
 
 from .test_pipelines_common import PipelineTestCaseMeta
 
 
-@is_pipeline_test
 class FeatureExtractionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_MAPPING
     tf_model_mapping = TF_MODEL_MAPPING

tests/pipelines/test_pipelines_fill_mask.py

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

tests/pipelines/test_pipelines_image_classification.py

@@ -22,10 +22,10 @@ from transformers import (
 )
 from transformers.pipelines import ImageClassificationPipeline, pipeline
 from transformers.testing_utils import (
-    is_pipeline_test,
     nested_simplify,
     require_tf,
     require_torch,
+    require_torch_or_tf,
     require_vision,
     slow,
 )
@@ -43,7 +43,7 @@ else:
             pass
 
 
-@is_pipeline_test
+@require_torch_or_tf
 @require_vision
 class ImageClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

tests/pipelines/test_pipelines_image_segmentation.py

@@ -31,15 +31,7 @@ from transformers import (
     is_vision_available,
     pipeline,
 )
-from transformers.testing_utils import (
-    is_pipeline_test,
-    nested_simplify,
-    require_tf,
-    require_timm,
-    require_torch,
-    require_vision,
-    slow,
-)
+from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
@@ -62,7 +54,6 @@ def hashimage(image: Image) -> str:
 @require_vision
 @require_timm
 @require_torch
-@is_pipeline_test
 class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = {
         k: v

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

tests/pipelines/test_pipelines_object_detection.py

@@ -22,15 +22,7 @@ from transformers import (
     is_vision_available,
     pipeline,
 )
-from transformers.testing_utils import (
-    is_pipeline_test,
-    nested_simplify,
-    require_tf,
-    require_timm,
-    require_torch,
-    require_vision,
-    slow,
-)
+from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
@@ -48,7 +40,6 @@ else:
 @require_vision
 @require_timm
 @require_torch
-@is_pipeline_test
 class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING
 

tests/pipelines/test_pipelines_question_answering.py

@@ -22,12 +22,11 @@ from transformers import (
 )
 from transformers.data.processors.squad import SquadExample
 from transformers.pipelines import QuestionAnsweringArgumentHandler, pipeline
-from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
+from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_or_tf, slow
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
 
-@is_pipeline_test
 class QAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING
     tf_model_mapping = TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING
@@ -345,7 +344,7 @@ between them. It's straightforward to train your models with one before loading
         self.assertEqual(nested_simplify(outputs), {"score": 0.979, "start": 27, "end": 32, "answer": "Paris"})
 
 
-@is_pipeline_test
+@require_torch_or_tf
 class QuestionAnsweringArgumentHandlerTests(unittest.TestCase):
     def test_argument_handler(self):
         qa = QuestionAnsweringArgumentHandler()

tests/pipelines/test_pipelines_summarization.py

@@ -23,7 +23,7 @@ from transformers import (
     T5Config,
     pipeline,
 )
-from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow, torch_device
+from transformers.testing_utils import require_tf, require_torch, slow, torch_device
 from transformers.tokenization_utils import TruncationStrategy
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
@@ -32,7 +32,6 @@ from .test_pipelines_common import ANY, PipelineTestCaseMeta
 DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
 
 
-@is_pipeline_test
 class SummarizationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     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

@@ -23,7 +23,6 @@ from transformers import (
     pipeline,
 )
 from transformers.testing_utils import (
-    is_pipeline_test,
     require_pandas,
     require_tensorflow_probability,
     require_tf,
@@ -35,7 +34,6 @@ from transformers.testing_utils import (
 from .test_pipelines_common import PipelineTestCaseMeta
 
 
-@is_pipeline_test
 class TQAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     # 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 is_pipeline_test, require_tf, require_torch
+from transformers.testing_utils import require_tf, require_torch
 from transformers.utils import is_torch_available
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
@@ -30,7 +30,6 @@ if is_torch_available():
     import torch
 
 
-@is_pipeline_test
 class Text2TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     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

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

tests/pipelines/test_pipelines_text_generation.py

@@ -16,17 +16,17 @@ 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,
     require_torch_gpu,
+    require_torch_or_tf,
 )
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
 
-@is_pipeline_test
+@require_torch_or_tf
 class TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING
     tf_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING

tests/pipelines/test_pipelines_token_classification.py

@@ -25,14 +25,7 @@ from transformers import (
     pipeline,
 )
 from transformers.pipelines import AggregationStrategy, TokenClassificationArgumentHandler
-from transformers.testing_utils import (
-    is_pipeline_test,
-    nested_simplify,
-    require_tf,
-    require_torch,
-    require_torch_gpu,
-    slow,
-)
+from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_gpu, slow
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
@@ -40,7 +33,6 @@ from .test_pipelines_common import ANY, PipelineTestCaseMeta
 VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
 
 
-@is_pipeline_test
 class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
     tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
@@ -770,7 +762,6 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         )
 
 
-@is_pipeline_test
 class TokenClassificationArgumentHandlerTestCase(unittest.TestCase):
     def setUp(self):
         self.args_parser = TokenClassificationArgumentHandler()

tests/pipelines/test_pipelines_translation.py

@@ -25,12 +25,11 @@ from transformers import (
     TranslationPipeline,
     pipeline,
 )
-from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow
+from transformers.testing_utils import require_tf, require_torch, slow
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
 
-@is_pipeline_test
 class TranslationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
@@ -118,7 +117,6 @@ class TranslationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta
         )
 
 
-@is_pipeline_test
 class TranslationNewFormatPipelineTests(unittest.TestCase):
     @require_torch
     @slow

tests/pipelines/test_pipelines_visual_question_answering.py

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

tests/pipelines/test_pipelines_zero_shot.py

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

tests/pipelines/test_pipelines_zero_shot_image_classification.py

@@ -16,14 +16,7 @@ import unittest
 
 from transformers import is_vision_available
 from transformers.pipelines import pipeline
-from transformers.testing_utils import (
-    is_pipeline_test,
-    nested_simplify,
-    require_tf,
-    require_torch,
-    require_vision,
-    slow,
-)
+from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
@@ -39,7 +32,6 @@ else:
 
 
 @require_vision
-@is_pipeline_test
 class ZeroShotImageClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     # Deactivating auto tests since we don't have a good MODEL_FOR_XX mapping,
     # and only CLIP would be there for now.