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docs: fix return type annotation of get_default_model_revision (#35982)

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Marco Edward Gorelli
2025-02-13 10:59:15 +00:00
committed by GitHub
parent 6a1ab634b6
commit 3c912c9089
2 changed files with 8 additions and 4 deletions
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6
src/transformers/pipelines/base.py
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@@ -384,7 +384,7 @@ def get_framework(model, revision: Optional[str] = None):
def get_default_model_and_revision( def get_default_model_and_revision(
targeted_task: Dict, framework: Optional[str], task_options: Optional[Any] targeted_task: Dict, framework: Optional[str], task_options: Optional[Any]
) -> Union[str, Tuple[str, str]]: ) -> Tuple[str, str]:
""" """
Select a default model to use for a given task. Defaults to pytorch if ambiguous. Select a default model to use for a given task. Defaults to pytorch if ambiguous.
@@ -401,7 +401,9 @@ def get_default_model_and_revision(
Returns Returns
`str` The model string representing the default model for this pipeline Tuple:
- `str` The model string representing the default model for this pipeline.
- `str` The revision of the model.
""" """
if is_torch_available() and not is_tf_available(): if is_torch_available() and not is_tf_available():
framework = "pt" framework = "pt"

6
tests/pipelines/test_pipelines_common.py
View File

@@ -796,7 +796,7 @@ class CustomPipelineTest(unittest.TestCase):
pipeline_class=PairClassificationPipeline, pipeline_class=PairClassificationPipeline,
pt_model=AutoModelForSequenceClassification if is_torch_available() else None, pt_model=AutoModelForSequenceClassification if is_torch_available() else None,
tf_model=TFAutoModelForSequenceClassification if is_tf_available() else None, tf_model=TFAutoModelForSequenceClassification if is_tf_available() else None,
default={"pt": "hf-internal-testing/tiny-random-distilbert"}, default={"pt": ("hf-internal-testing/tiny-random-distilbert", "2ef615d")},
type="text", type="text",
) )
assert "custom-text-classification" in PIPELINE_REGISTRY.get_supported_tasks() assert "custom-text-classification" in PIPELINE_REGISTRY.get_supported_tasks()
@@ -806,7 +806,9 @@ class CustomPipelineTest(unittest.TestCase):
self.assertEqual(task_def["tf"], (TFAutoModelForSequenceClassification,) if is_tf_available() else ()) self.assertEqual(task_def["tf"], (TFAutoModelForSequenceClassification,) if is_tf_available() else ())
self.assertEqual(task_def["type"], "text") self.assertEqual(task_def["type"], "text")
self.assertEqual(task_def["impl"], PairClassificationPipeline) self.assertEqual(task_def["impl"], PairClassificationPipeline)
self.assertEqual(task_def["default"], {"model": {"pt": "hf-internal-testing/tiny-random-distilbert"}}) self.assertEqual(
task_def["default"], {"model": {"pt": ("hf-internal-testing/tiny-random-distilbert", "2ef615d")}}
)
# Clean registry for next tests. # Clean registry for next tests.
del PIPELINE_REGISTRY.supported_tasks["custom-text-classification"] del PIPELINE_REGISTRY.supported_tasks["custom-text-classification"]