tests/transforms/croppad/test_pad_nd_dtypes.py (1)

49-58: Consider testing additional modes.

Current tests only cover "constant" mode. The updated code supports {"reflect", "edge", "replicate", "wrap", "circular"} via PyTorch. Testing dtype preservation across these modes would strengthen coverage.

Optional enhancement

@pytest.mark.parametrize(
    "mode", ["constant", "reflect", "replicate"]
)
@pytest.mark.parametrize(
    "dtype", [torch.bool, torch.int8, torch.float32]
)
def test_pad_modes_with_dtypes(mode, dtype):
    """Test that pad_nd handles various modes and dtypes correctly."""
    img = torch.ones((1, 4, 4), dtype=dtype)
    to_pad = [(0, 0), (1, 1), (2, 2)]
    out = pad_nd(img, to_pad, mode=mode, value=0)
    
    assert out.shape == (1, 6, 8)
    assert out.dtype == img.dtype

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tests/transforms/croppad/test_pad_nd_dtypes.py (4)

1-20: Module docstring and imports look good.

Past review comments about missing module docstring have been addressed.

---

22-31: Test correctly validates PyTorch backend usage and dtype preservation.

Past docstring feedback addressed.

---

33-45: Fallback test is well-structured.

Mocking _pt_pad to raise NotImplementedError and verifying _np_pad is called covers the new fallback path. Past docstring feedback addressed.

---

47-57: Good dtype coverage.

Tests bool, int8/16/32/64, uint8, float32. Past docstring feedback addressed.

monai/transforms/croppad/functional.py (1)

99-112: Minor formatting: double spaces before **call_kwargs.

Lines 107 and 112 have two spaces before **call_kwargs.

🔎 Fix spacing

     except NotImplementedError:
-        return _np_pad(img, pad_width=to_pad, mode=mode,  **call_kwargs)
+        return _np_pad(img, pad_width=to_pad, mode=mode, **call_kwargs)
     except (ValueError, TypeError, RuntimeError) as err:
         if any(
             k in str(err) for k in ("supported", "unexpected keyword", "implemented", "value")
         ):
-            return _np_pad(img, pad_width=to_pad, mode=mode,  **call_kwargs)
+            return _np_pad(img, pad_width=to_pad, mode=mode, **call_kwargs)

tests/transforms/croppad/test_pad_nd_dtypes.py (1)

59-69: Consider omitting value=0 for non-constant modes.

Line 66 passes value=0 for all modes, but "reflect" and "replicate" don't use this parameter. While the implementation strips it, omitting it in tests makes the intent clearer.

🔎 Suggested change

-    out = pad_nd(img, to_pad, mode=mode, value=0)
+    kwargs = {"value": 0} if mode == "constant" else {}
+    out = pad_nd(img, to_pad, mode=mode, **kwargs)

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tests/transforms/croppad/test_pad_nd_dtypes.py (4)

15-20: Import ordering could follow PEP8 more strictly.

Group imports: standard library, third-party, local. Consider:

 from __future__ import annotations
+
 from unittest.mock import Mock, patch
+
 import pytest
 import torch
+
 import monai.transforms.croppad.functional as F
 from monai.transforms.croppad.functional import pad_nd

---

22-31: Consider verifying output shape and values.

Test confirms backend selection and dtype preservation but doesn't check padding correctness. Add assertions:

assert out.shape == (1, 6, 8)
assert out[0, 1, 2].item() == True  # verify padded region if needed

---

33-45: Expand fallback testing to cover other exception types.

The implementation also catches ValueError, TypeError, and RuntimeError with message matching. Test these paths:

🔎 Additional test cases

@pytest.mark.parametrize("error_type,message", [
    (ValueError, "not supported"),
    (TypeError, "unexpected keyword argument"),
    (RuntimeError, "not implemented"),
])
def test_pad_falls_back_on_other_errors(error_type, message):
    """Test fallback when PyTorch raises ValueError/TypeError/RuntimeError."""
    img = torch.ones((1, 4, 4), dtype=torch.bool)
    to_pad = [(0, 0), (1, 1), (2, 2)]
    with (
        patch.object(F, "_pt_pad", new=Mock(side_effect=error_type(message))),
        patch.object(F, "_np_pad", wraps=F._np_pad) as mock_np,
    ):
        out = pad_nd(img, to_pad, mode="constant", value=0)
    assert mock_np.called
    assert out.dtype == img.dtype

---

59-70: LGTM. Consider expanding mode coverage.

Correctly handles value kwarg for constant mode only. For more comprehensive testing, add modes like "edge", "wrap", "circular".

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