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Byebye test_batching_equivalence's flakiness (#35729)

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* fix

* fix

* skip

* better error message

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2025-01-21 13:11:33 +01:00
committed by GitHub
parent 78f5ee0217
commit fd8d61fdb2
18 changed files with 92 additions and 50 deletions
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6
tests/models/dpt/test_modeling_dpt_hybrid.py
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@@ -18,7 +18,7 @@ import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import is_flaky, require_torch, require_vision, slow, torch_device
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
@@ -304,10 +304,6 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
with self.assertRaises(ValueError):
_ = DPTForDepthEstimation(config)
@is_flaky(description="is_flaky https://github.com/huggingface/transformers/issues/29516")
def test_batching_equivalence(self):
super().test_batching_equivalence()
# We will verify our results on an image of cute cats
def prepare_img():