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Pass datasets trust_remote_code (#31406)

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* Pass datasets trust_remote_code

* Pass trust_remote_code in more tests

* Add trust_remote_dataset_code arg to some tests

* Revert "Temporarily pin datasets upper version to fix CI"

This reverts commit b7672826ca.

* Pass trust_remote_code in librispeech_asr_dummy docstrings

* Revert "Pin datasets<2.20.0 for examples"

This reverts commit 833fc17a3e.

* Pass trust_remote_code to all examples

* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects

* Pass trust_remote_code to tests

* Pass trust_remote_code to docstrings

* Fix flax examples tests requirements

* Pass trust_remote_dataset_code arg to tests

* Replace trust_remote_dataset_code with trust_remote_code in one example

* Fix duplicate trust_remote_code

* Replace args.trust_remote_dataset_code with args.trust_remote_code

* Replace trust_remote_dataset_code with trust_remote_code in parser

* Replace trust_remote_dataset_code with trust_remote_code in dataclasses

* Replace trust_remote_dataset_code with trust_remote_code arg
This commit is contained in:
Albert Villanova del Moral
2024-06-17 18:29:13 +02:00
committed by GitHub
parent 485fd81471
commit a14b055b65
168 changed files with 804 additions and 410 deletions
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6
tests/pipelines/test_pipelines_audio_classification.py
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@@ -69,7 +69,9 @@ class AudioClassificationPipelineTests(unittest.TestCase):
import datasets
# test with a local file
dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = datasets.load_dataset(
"hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
)
audio = dataset[0]["audio"]["array"]
output = audio_classifier(audio)
self.assertEqual(
@@ -115,7 +117,7 @@ class AudioClassificationPipelineTests(unittest.TestCase):
model = "superb/wav2vec2-base-superb-ks"
audio_classifier = pipeline("audio-classification", model=model)
dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test")
dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test", trust_remote_code=True)
audio = np.array(dataset[3]["speech"], dtype=np.float32)
output = audio_classifier(audio, top_k=4)