Repository: huggingface/transformers
Pull request: Fix `TrainingArguments.torch_empty_cache_steps` post_init check

# What does this PR do?

PR #31546 introduced the `torch_empty_cache_steps` attribute to `TrainingArguments`, requiring it to be either a positive integer or `None`. This constraint is enforced via an explicit check in `TrainingArguments.__post_init__()`.

However, the current condition incorrectly uses:

`not ([is int] OR [> 0])`

instead of:

`not ([is int] AND [> 0])`

as intended to trigger a `ValueError`. As a result, the following invalid values are erroneously accepted:

```python
# Accepted because 1.0 > 0, but should raise an error
TrainingArguments(torch_empty_cache_steps=1.0)

# Accepted because 0 is an int, but should raise an error
TrainingArguments(torch_empty_cache_steps=0)
```

This PR fixes the condition to correctly enforce the “positive int or None” requirement and adds a corresponding test to verify the fix.

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Changed files:
filename: src/transformers/training_args.py
patch: @@ -1641,7 +1641,7 @@ def __post_init__(self):
             self.do_eval = True
 
         if self.torch_empty_cache_steps is not None:
-            if not (isinstance(self.torch_empty_cache_steps, int) or self.torch_empty_cache_steps > 0):
+            if not (isinstance(self.torch_empty_cache_steps, int) and self.torch_empty_cache_steps > 0):
                 raise ValueError(
                     f"`torch_empty_cache_steps` must be an integer bigger than 0, got {self.torch_empty_cache_steps}."
                 )

filename: tests/test_training_args.py
patch: @@ -40,3 +40,28 @@ def test_output_dir_creation(self):
             self.assertFalse(os.path.exists(output_dir))  # Still shouldn't exist
 
             # Directory should be created when actually needed (e.g. in Trainer)
+
+    def test_torch_empty_cache_steps_requirements(self):
+        """Test that torch_empty_cache_steps is a positive integer or None."""
+
+        # None is acceptable (feature is disabled):
+        args = TrainingArguments(torch_empty_cache_steps=None)
+        self.assertIsNone(args.torch_empty_cache_steps)
+
+        # non-int is unacceptable:
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps=1.0)
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps="none")
+
+        # negative int is unacceptable:
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps=-1)
+
+        # zero is unacceptable:
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps=0)
+
+        # positive int is acceptable:
+        args = TrainingArguments(torch_empty_cache_steps=1)
+        self.assertEqual(args.torch_empty_cache_steps, 1)