🔥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>
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@@ -29,6 +29,7 @@ from transformers.utils import cached_property, is_torch_available, is_vision_av
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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@@ -152,13 +153,18 @@ class TimesformerModelTester:
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@require_torch
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class TimesformerModelTest(ModelTesterMixin, unittest.TestCase):
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class TimesformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as TimeSformer does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
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if is_torch_available()
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else {}
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)
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test_pruning = False
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test_torchscript = False
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