address comments

Author: Vahid Tavanashad

dpnp/dpnp_algo/CMakeLists.txt

@@ -8,7 +8,6 @@ set(dpnp_algo_pyx_deps
   ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_sorting.pxi
   ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_arraycreation.pxi
   ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_mathematical.pxi
-  ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_searching.pxi
   ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_indexing.pxi
   ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_logic.pxi
   ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_special.pxi

dpnp/dpnp_algo/dpnp_algo.pyx

@@ -66,7 +66,6 @@ include "dpnp_algo_linearalgebra.pxi"
 include "dpnp_algo_logic.pxi"
 include "dpnp_algo_manipulation.pxi"
 include "dpnp_algo_mathematical.pxi"
-include "dpnp_algo_searching.pxi"
 include "dpnp_algo_sorting.pxi"
 include "dpnp_algo_special.pxi"
 include "dpnp_algo_statistics.pxi"

dpnp/dpnp_array.py

@@ -492,17 +492,6 @@ def argmax(self, axis=None, out=None):
 
         Refer to :obj:`dpnp.argmax` for full documentation.
 
-        Examples
-        --------
-        >>> import dpnp as np
-        >>> a = np.arange(6).reshape(2,3)
-        >>> a.argmax()
-        array(5)
-        >>> a.argmax(0)
-        array([1, 1, 1])
-        >>> a.argmax(1)
-        array([2, 2])
-
         """
         return dpnp.argmax(self, axis, out)
 

dpnp/dpnp_iface_searching.py

@@ -49,7 +49,7 @@
 __all__ = ["argmax", "argmin", "searchsorted", "where"]
 
 
-def argmax(a, axis=None, out=None, keepdims=False):
+def argmax(a, axis=None, out=None, *, keepdims=False):
     """
     Returns the indices of the maximum values along an axis.
 
@@ -62,11 +62,13 @@ def argmax(a, axis=None, out=None, keepdims=False):
 
     Limitations
     -----------
-    Input array is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
+    Input and output arrays are only supported as either :class:`dpnp.ndarray`
+    or :class:`dpctl.tensor.usm_ndarray`.
     Input array data types are limited by supported DPNP :ref:`Data types`.
 
     See Also
     --------
+    :obj:`dpnp.ndarray.argmax` : Equivalent function.
     :obj:`dpnp.argmin` : Returns the indices of the minimum values along an axis.
     :obj:`dpnp.amax` : The maximum value along a given axis.
     :obj:`dpnp.unravel_index` : Convert a flat index into an index tuple.
@@ -147,7 +149,7 @@ def argmax(a, axis=None, out=None, keepdims=False):
         return out
 
 
-def argmin(a, axis=None, out=None, keepdims=False):
+def argmin(a, axis=None, out=None, *, keepdims=False):
     """
     Returns the indices of the minimum values along an axis.
 
@@ -160,11 +162,13 @@ def argmin(a, axis=None, out=None, keepdims=False):
 
     Limitations
     -----------
-    Input array is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
+    Input and output arrays are only supported as either :class:`dpnp.ndarray`
+    or :class:`dpctl.tensor.usm_ndarray`.
     Input array data types are limited by supported DPNP :ref:`Data types`.
 
     See Also
     --------
+    :obj:`dpnp.ndarray.argmin` : Equivalent function.
     :obj:`dpnp.argmax` : Returns the indices of the maximum values along an axis.
     :obj:`dpnp.amin` : The minimum value along a given axis.
     :obj:`dpnp.unravel_index` : Convert a flat index into an index tuple.

tests/third_party/cupy/core_tests/test_ndarray_reduction.py

@@ -229,97 +229,101 @@ def test_ptp_nan_imag(self, xp, dtype):
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_all(self, xp, dtype):
-        a = testing.shaped_random((2, 3), xp, dtype)
+        a = testing.shaped_random((2, 3), xp, dtype, order=self.order)
         return a.argmax()
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_axis_large(self, xp, dtype):
-        a = testing.shaped_random((3, 1000), xp, dtype)
+        a = testing.shaped_random((3, 1000), xp, dtype, order=self.order)
         return a.argmax(axis=0)
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_axis0(self, xp, dtype):
-        a = testing.shaped_random((2, 3, 4), xp, dtype)
+        a = testing.shaped_random((2, 3, 4), xp, dtype, order=self.order)
         return a.argmax(axis=0)
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_axis1(self, xp, dtype):
-        a = testing.shaped_random((2, 3, 4), xp, dtype)
+        a = testing.shaped_random((2, 3, 4), xp, dtype, order=self.order)
         return a.argmax(axis=1)
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_axis2(self, xp, dtype):
-        a = testing.shaped_random((2, 3, 4), xp, dtype)
+        a = testing.shaped_random((2, 3, 4), xp, dtype, order=self.order)
         return a.argmax(axis=2)
 
     @testing.for_float_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_nan(self, xp, dtype):
-        a = xp.array([float("nan"), 1, -1], dtype)
+        a = xp.array([float("nan"), 1, -1], dtype, order=self.order)
         return a.argmax()
 
     @testing.for_complex_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_nan_real(self, xp, dtype):
-        a = xp.array([float("nan"), 1, -1], dtype)
+        a = xp.array([float("nan"), 1, -1], dtype, order=self.order)
         return a.argmax()
 
     @testing.for_complex_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmax_nan_imag(self, xp, dtype):
-        a = xp.array([float("nan") * 1.0j, 1.0j, -1.0j], dtype)
+        a = xp.array(
+            [float("nan") * 1.0j, 1.0j, -1.0j], dtype, order=self.order
+        )
         return a.argmax()
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_all(self, xp, dtype):
-        a = testing.shaped_random((2, 3), xp, dtype)
+        a = testing.shaped_random((2, 3), xp, dtype, order=self.order)
         return a.argmin()
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_axis_large(self, xp, dtype):
-        a = testing.shaped_random((3, 1000), xp, dtype)
+        a = testing.shaped_random((3, 1000), xp, dtype, order=self.order)
         return a.argmin(axis=0)
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_axis0(self, xp, dtype):
-        a = testing.shaped_random((2, 3, 4), xp, dtype)
+        a = testing.shaped_random((2, 3, 4), xp, dtype, order=self.order)
         return a.argmin(axis=0)
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_axis1(self, xp, dtype):
-        a = testing.shaped_random((2, 3, 4), xp, dtype)
+        a = testing.shaped_random((2, 3, 4), xp, dtype, order=self.order)
         return a.argmin(axis=1)
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_axis2(self, xp, dtype):
-        a = testing.shaped_random((2, 3, 4), xp, dtype)
+        a = testing.shaped_random((2, 3, 4), xp, dtype, order=self.order)
         return a.argmin(axis=2)
 
     @testing.for_float_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_nan(self, xp, dtype):
-        a = xp.array([float("nan"), 1, -1], dtype)
+        a = xp.array([float("nan"), 1, -1], dtype, order=self.order)
         return a.argmin()
 
     @testing.for_complex_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_nan_real(self, xp, dtype):
-        a = xp.array([float("nan"), 1, -1], dtype)
+        a = xp.array([float("nan"), 1, -1], dtype, order=self.order)
         return a.argmin()
 
     @testing.for_complex_dtypes()
     @testing.numpy_cupy_allclose(contiguous_check=False)
     def test_argmin_nan_imag(self, xp, dtype):
-        a = xp.array([float("nan") * 1.0j, 1.0j, -1.0j], dtype)
+        a = xp.array(
+            [float("nan") * 1.0j, 1.0j, -1.0j], dtype, order=self.order
+        )
         return a.argmin()
 
 

tests/third_party/cupy/sorting_tests/test_search.py

@@ -91,8 +91,8 @@ def test_argmin_all(self, xp, dtype):
         a = testing.shaped_random((2, 3), xp, dtype)
         return a.argmin()
 
-    @testing.for_all_dtypes(no_complex=True)
-    @testing.numpy_cupy_allclose(accept_error=ValueError)
+    @testing.for_float_dtypes()
+    @testing.numpy_cupy_allclose()
     def test_argmin_nan(self, xp, dtype):
         a = xp.array([float("nan"), -1, 1], dtype)
         return a.argmin()