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Fix multi-line asserts in Captum tests #1655

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Fix multi-line asserts in Captum tests #1655

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3 changes: 2 additions & 1 deletion tests/attr/test_approximation_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,10 @@ def __init__(self, methodName: str = "runTest") -> None:
super().__init__(methodName)

def test_riemann_0(self) -> None:
step_sizes, alphas = riemann_builders()
with self.assertRaises(AssertionError):
step_sizes, alphas = riemann_builders()
step_sizes(0)
with self.assertRaises(AssertionError):
alphas(0)

def test_riemann_2(self) -> None:
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4 changes: 2 additions & 2 deletions tests/attr/test_llm_attr.py
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Expand Up @@ -904,16 +904,16 @@ def test_get_logprobs_successful(self) -> None:

def test_get_logprobs_missing_tokenizer(self) -> None:
"""Test get_logprobs with missing tokenizer."""
provider = VLLMProvider(api_url=self.api_url, model_name=self.model_name)
with self.assertRaises(ValueError) as context:
provider = VLLMProvider(api_url=self.api_url, model_name=self.model_name)
provider.get_logprobs(self.input_prompt, self.target_str, None)

self.assertIn("Tokenizer is required", str(context.exception))

def test_get_logprobs_empty_target(self) -> None:
"""Test get_logprobs with empty target string."""
provider = VLLMProvider(api_url=self.api_url, model_name=self.model_name)
with self.assertRaises(ValueError) as context:
provider = VLLMProvider(api_url=self.api_url, model_name=self.model_name)
provider.get_logprobs(self.input_prompt, "", self.tokenizer)

self.assertIn("Target string cannot be empty", str(context.exception))
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4 changes: 2 additions & 2 deletions tests/attr/test_targets.py
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Expand Up @@ -220,15 +220,15 @@ class TestTargets(BaseTest, metaclass=TargetsMeta):
def test_simple_target_missing_error(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.zeros((1, 3))
attr = IntegratedGradients(net)
with self.assertRaises(AssertionError):
attr = IntegratedGradients(net)
attr.attribute(inp) # type: ignore[has-type]

def test_multi_target_error(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.zeros((1, 3))
attr = IntegratedGradients(net)
with self.assertRaises(AssertionError):
attr = IntegratedGradients(net)
attr.attribute( # type: ignore[has-type]
inp, additional_forward_args=(None, True), target=(1, 0)
)
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