Exclude code from coverage (#2272)

This PR continues #2268 and suggests excluding some lines of code from
coverage using `pragma:: no cover` to increase the coverage statistic

dpnp/__init__.py

@@ -42,7 +42,7 @@
 # are not installed under any of default paths where Python is searching.
 from platform import system
 
-if system() == "Windows":
+if system() == "Windows":  # pragma: no cover
     if hasattr(os, "add_dll_directory"):
         os.add_dll_directory(mypath)
         os.add_dll_directory(dpctlpath)

dpnp/dpnp_iface.py

@@ -712,7 +712,7 @@ def is_cuda_backend(obj=None):
     if (
         sycl_device is not None
         and sycl_device.backend == dpctl.backend_type.cuda
-    ):
+    ):  # pragma: no cover
         return True
     return False
 

dpnp/dpnp_iface_histograms.py

@@ -91,7 +91,7 @@ def _align_dtypes(a_dtype, bins_dtype, ntype, supported_types, device):
             return sample_type, hist_type
 
     # should not happen
-    return None, None
+    return None, None  # pragma: no cover
 
 
 def _ravel_check_a_and_weights(a, weights):
@@ -392,7 +392,7 @@ def bincount(x, weights=None, minlength=None):
         x.dtype, x.dtype, ntype, supported_types, device
     )
 
-    if x_casted_dtype is None or ntype_casted is None:
+    if x_casted_dtype is None or ntype_casted is None:  # pragma: no cover
         raise ValueError(
             f"function '{bincount}' does not support input types "
             f"({x.dtype}, {ntype}), "
@@ -607,7 +607,7 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
         a.dtype, bin_edges.dtype, ntype, supported_types, device
     )
 
-    if a_bin_dtype is None or hist_dtype is None:
+    if a_bin_dtype is None or hist_dtype is None:  # pragma: no cover
         raise ValueError(
             f"function '{histogram}' does not support input types "
             f"({a.dtype}, {bin_edges.dtype}, {ntype}), "

dpnp/dpnp_iface_indexing.py

@@ -138,7 +138,7 @@ def choose(x1, choices, out=None, mode="raise"):
         )
 
     if x1_desc:
-        if dpnp.is_cuda_backend(x1_desc.get_array()):
+        if dpnp.is_cuda_backend(x1_desc.get_array()):  # pragma: no cover
             raise NotImplementedError(
                 "Running on CUDA is currently not supported"
             )

dpnp/dpnp_iface_libmath.py

@@ -82,7 +82,7 @@ def erf(in_array1):
         in_array1, copy_when_strides=False, copy_when_nondefault_queue=False
     )
     if x1_desc:
-        if dpnp.is_cuda_backend(x1_desc.get_array()):
+        if dpnp.is_cuda_backend(x1_desc.get_array()):  # pragma: no cover
             raise NotImplementedError(
                 "Running on CUDA is currently not supported"
             )

dpnp/dpnp_iface_mathematical.py

@@ -2946,7 +2946,7 @@ def modf(x1, **kwargs):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
     if x1_desc:
-        if dpnp.is_cuda_backend(x1_desc.get_array()):
+        if dpnp.is_cuda_backend(x1_desc.get_array()):  # pragma: no cover
             raise NotImplementedError(
                 "Running on CUDA is currently not supported"
             )

dpnp/dpnp_iface_sorting.py

@@ -215,7 +215,7 @@ def partition(x1, kth, axis=-1, kind="introselect", order=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
     if x1_desc:
-        if dpnp.is_cuda_backend(x1_desc.get_array()):
+        if dpnp.is_cuda_backend(x1_desc.get_array()):  # pragma: no cover
             raise NotImplementedError(
                 "Running on CUDA is currently not supported"
             )

dpnp/dpnp_iface_statistics.py

@@ -108,7 +108,7 @@ def _count_reduce_items(arr, axis, where=True):
         for ax in axis:
             items *= arr.shape[normalize_axis_index(ax, arr.ndim)]
         items = dpnp.intp(items)
-    else:
+    else:  # pragma: no cover
         raise NotImplementedError(
             "where keyword argument is only supported with its default value."
         )
@@ -576,7 +576,7 @@ def correlate(a, v, mode="valid"):
     rdtype = result_type_for_device([a.dtype, v.dtype], device)
     supported_dtype = to_supported_dtypes(rdtype, supported_types, device)
 
-    if supported_dtype is None:
+    if supported_dtype is None:  # pragma: no cover
         raise ValueError(
             f"function does not support input types "
             f"({a.dtype.name}, {v.dtype.name}), "

dpnp/dpnp_utils/dpnp_utils_common.py

@@ -78,4 +78,4 @@ def is_castable(dtype, stype):
             ):
                 return stypes
 
-    return None
+    return None  # pragma: no cover

dpnp/linalg/dpnp_utils_linalg.py

@@ -401,7 +401,7 @@ def _batched_qr(a, mode="reduced"):
     # w/a to avoid raice conditional on CUDA during multiple runs
     # TODO: Remove it ones the OneMath issue is resolved
     # https://github.com/uxlfoundation/oneMath/issues/626
-    if dpnp.is_cuda_backend(a_sycl_queue):
+    if dpnp.is_cuda_backend(a_sycl_queue):  # pragma: no cover
         ht_ev.wait()
     else:
         _manager.add_event_pair(ht_ev, geqrf_ev)
@@ -2479,7 +2479,7 @@ def dpnp_qr(a, mode="reduced"):
     # w/a to avoid raice conditional on CUDA during multiple runs
     # TODO: Remove it ones the OneMath issue is resolved
     # https://github.com/uxlfoundation/oneMath/issues/626
-    if dpnp.is_cuda_backend(a_sycl_queue):
+    if dpnp.is_cuda_backend(a_sycl_queue):  # pragma: no cover
         ht_ev.wait()
     else:
         _manager.add_event_pair(ht_ev, geqrf_ev)