Fix RandomWeightedCrop for Integer Weightmap Handling (#8097)

Fixes #7949 .

### Description
Regardless of the type of `weight map`, random numbers should be kept as
floating-point numbers for calculating the sampling location. However,
`searchsorted` requires matching data structures. I have modified
`convert_to_dst_type` to control converting only the data structure
while maintaining the original data type. Additionally, I have included
an example with integer weight maps in the test file.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Han123su <popsmall212@gmail.com>
Signed-off-by: Han123su <107395380+Han123su@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

monai/transforms/utils.py

@@ -582,7 +582,8 @@ def weighted_patch_samples(
     if not v[-1] or not isfinite(v[-1]) or v[-1] < 0:  # uniform sampling
         idx = r_state.randint(0, len(v), size=n_samples)
     else:
-        r, *_ = convert_to_dst_type(r_state.random(n_samples), v)
+        r_samples = r_state.random(n_samples)
+        r, *_ = convert_to_dst_type(r_samples, v, dtype=r_samples.dtype)
         idx = searchsorted(v, r * v[-1], right=True)  # type: ignore
     idx, *_ = convert_to_dst_type(idx, v, dtype=torch.int)  # type: ignore
     # compensate 'valid' mode

tests/test_rand_weighted_crop.py

@@ -90,6 +90,21 @@ def get_data(ndim):
                 [[63, 37], [31, 43], [66, 20]],
             ]
         )
+        im = SEG1_2D
+        weight_map = np.zeros_like(im, dtype=np.int32)
+        weight_map[0, 30, 20] = 3
+        weight_map[0, 45, 44] = 1
+        weight_map[0, 60, 50] = 2
+        TESTS.append(
+            [
+                "int w 2d",
+                dict(spatial_size=(10, 12), num_samples=3),
+                p(im),
+                q(weight_map),
+                (1, 10, 12),
+                [[60, 50], [30, 20], [45, 44]],
+            ]
+        )
         im = SEG1_3D
         weight = np.zeros_like(im)
         weight[0, 5, 30, 17] = 1.1
@@ -149,6 +164,21 @@ def get_data(ndim):
                 [[32, 24, 40], [32, 24, 40], [32, 24, 40]],
             ]
         )
+        im = SEG1_3D
+        weight_map = np.zeros_like(im, dtype=np.int32)
+        weight_map[0, 6, 22, 19] = 4
+        weight_map[0, 8, 40, 31] = 2
+        weight_map[0, 13, 20, 24] = 3
+        TESTS.append(
+            [
+                "int w 3d",
+                dict(spatial_size=(8, 10, 12), num_samples=3),
+                p(im),
+                q(weight_map),
+                (1, 8, 10, 12),
+                [[13, 20, 24], [6, 22, 19], [8, 40, 31]],
+            ]
+        )
 
 
 class TestRandWeightedCrop(CropTest):