Merge branch 'master' into improve-coverage-manipulaiton

Author: vtavana

.github/workflows/array-api-skips.txt

@@ -1,19 +1,5 @@
 # array API tests to be skipped
 
-# missing unique-like functions
-array_api_tests/test_has_names.py::test_has_names[set-unique_all]
-array_api_tests/test_has_names.py::test_has_names[set-unique_counts]
-array_api_tests/test_has_names.py::test_has_names[set-unique_inverse]
-array_api_tests/test_has_names.py::test_has_names[set-unique_values]
-array_api_tests/test_set_functions.py::test_unique_all
-array_api_tests/test_set_functions.py::test_unique_counts
-array_api_tests/test_set_functions.py::test_unique_inverse
-array_api_tests/test_set_functions.py::test_unique_values
-array_api_tests/test_signatures.py::test_func_signature[unique_all]
-array_api_tests/test_signatures.py::test_func_signature[unique_counts]
-array_api_tests/test_signatures.py::test_func_signature[unique_inverse]
-array_api_tests/test_signatures.py::test_func_signature[unique_values]
-
 # hypothesis found failures
 array_api_tests/test_operators_and_elementwise_functions.py::test_clip
 

doc/reference/creation.rst renamed to doc/reference/array-creation.rst

@@ -1,4 +1,4 @@
-.. _routines.creation:
+.. _routines.array-creation:
 
 Array creation routines
 =======================

doc/reference/manipulation.rst renamed to doc/reference/array-manipulation.rst

@@ -135,7 +135,6 @@ Adding and removing elements
    dpnp.append
    dpnp.resize
    dpnp.trim_zeros
-   dpnp.unique
    dpnp.pad
 
 

doc/reference/binary.rst renamed to doc/reference/bitwise.rst

@@ -10,8 +10,8 @@ Utility
    :toctree: generated/
    :nosignatures:
 
-   dpnp.broadcast_shapes
    dpnp.byte_bounds
    dpnp.get_include
    dpnp.show_config
    dpnp.show_runtime
+   dpnp.broadcast_shapes

doc/reference/misc.rst renamed to doc/reference/other.rst

@@ -11,17 +11,19 @@ These functions cover a subset of
 .. toctree::
    :maxdepth: 2
 
-   creation
-   manipulation
-   binary
+   array-creation
+   array-manipulation
+   bitwise
    dtype
    fft
    functional
    indexing
    linalg
    logic
    math
+   other
    .. polynomials
    random
-   sorting
+   set
+   sort
    statistics

doc/reference/routines.rst

@@ -0,0 +1,29 @@
+Set routines
+============
+
+.. https://numpy.org/doc/stable/reference/routines.set.html
+
+Making proper sets
+------------------
+.. autosummary::
+   :toctree: generated/
+   :nosignatures:
+
+   dpnp.unique
+   dpnp.unique_all
+   dpnp.unique_counts
+   dpnp.unique_inverse
+   dpnp.unique_values
+
+Boolean operations
+------------------
+.. autosummary::
+   :toctree: generated/
+   :nosignatures:
+
+   dpnp.in1d
+   dpnp.intersect1d
+   dpnp.isin
+   dpnp.setdiff1d
+   dpnp.setxor1d
+   dpnp.union1d

doc/reference/set.rst

@@ -71,6 +71,24 @@ class InsertDeleteParams(NamedTuple):
     usm_type: str
 
 
+# pylint:disable=missing-class-docstring
+class UniqueAllResult(NamedTuple):
+    values: dpnp.ndarray
+    indices: dpnp.ndarray
+    inverse_indices: dpnp.ndarray
+    counts: dpnp.ndarray
+
+
+class UniqueCountsResult(NamedTuple):
+    values: dpnp.ndarray
+    counts: dpnp.ndarray
+
+
+class UniqueInverseResult(NamedTuple):
+    values: dpnp.ndarray
+    inverse_indices: dpnp.ndarray
+
+
 __all__ = [
     "append",
     "array_split",
@@ -122,6 +140,10 @@ class InsertDeleteParams(NamedTuple):
     "transpose",
     "trim_zeros",
     "unique",
+    "unique_all",
+    "unique_counts",
+    "unique_inverse",
+    "unique_values",
     "unstack",
     "vsplit",
     "vstack",
@@ -4275,6 +4297,189 @@ def unique(
     return _unpack_tuple(result)
 
 
+def unique_all(x, /):
+    """
+    Find the unique elements of an array, and counts, inverse, and indices.
+
+    For full documentation refer to :obj:`numpy.unique_all`.
+
+    Parameters
+    ----------
+    x : {dpnp.ndarray, usm_ndarray}
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    A namedtuple with the following attributes:
+
+    values : dpnp.ndarray
+        The unique elements of an input array.
+    indices : dpnp.ndarray
+        The first occurring indices for each unique element.
+    inverse_indices : dpnp.ndarray
+        The indices from the set of unique elements that reconstruct `x`.
+    counts : dpnp.ndarray
+        The corresponding counts for each unique element.
+
+    See Also
+    --------
+    :obj:`dpnp.unique` : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> x = np.array([1, 1, 2])
+    >>> uniq = np.unique_all(x)
+    >>> uniq.values
+    array([1, 2])
+    >>> uniq.indices
+    array([0, 2])
+    >>> uniq.inverse_indices
+    array([0, 0, 1])
+    >>> uniq.counts
+    array([2, 1])
+
+    """
+
+    result = dpnp.unique(
+        x,
+        return_index=True,
+        return_inverse=True,
+        return_counts=True,
+        equal_nan=False,
+    )
+    return UniqueAllResult(*result)
+
+
+def unique_counts(x, /):
+    """
+    Find the unique elements and counts of an input array `x`.
+
+    For full documentation refer to :obj:`numpy.unique_counts`.
+
+    Parameters
+    ----------
+    x : {dpnp.ndarray, usm_ndarray}
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    A namedtuple with the following attributes:
+
+    values : dpnp.ndarray
+        The unique elements of an input array.
+    counts : dpnp.ndarray
+        The corresponding counts for each unique element.
+
+    See Also
+    --------
+    :obj:`dpnp.unique` : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> x = np.array([1, 1, 2])
+    >>> uniq = np.unique_counts(x)
+    >>> uniq.values
+    array([1, 2])
+    >>> uniq.counts
+    array([2, 1])
+
+    """
+
+    result = dpnp.unique(
+        x,
+        return_index=False,
+        return_inverse=False,
+        return_counts=True,
+        equal_nan=False,
+    )
+    return UniqueCountsResult(*result)
+
+
+def unique_inverse(x, /):
+    """
+    Find the unique elements of `x` and indices to reconstruct `x`.
+
+    For full documentation refer to :obj:`numpy.unique_inverse`.
+
+    Parameters
+    ----------
+    x : {dpnp.ndarray, usm_ndarray}
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    A namedtuple with the following attributes:
+
+    values : dpnp.ndarray
+        The unique elements of an input array.
+    inverse_indices : dpnp.ndarray
+        The indices from the set of unique elements that reconstruct `x`.
+
+    See Also
+    --------
+    :obj:`dpnp.unique` : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> x = np.array([1, 1, 2])
+    >>> uniq = np.unique_inverse(x)
+    >>> uniq.values
+    array([1, 2])
+    >>> uniq.inverse_indices
+    array([0, 0, 1])
+
+    """
+
+    result = dpnp.unique(
+        x,
+        return_index=False,
+        return_inverse=True,
+        return_counts=False,
+        equal_nan=False,
+    )
+    return UniqueInverseResult(*result)
+
+
+def unique_values(x, /):
+    """
+    Returns the unique elements of an input array `x`.
+
+    For full documentation refer to :obj:`numpy.unique_values`.
+
+    Parameters
+    ----------
+    x : {dpnp.ndarray, usm_ndarray}
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    out : dpnp.ndarray
+        The unique elements of an input array.
+
+    See Also
+    --------
+    :obj:`dpnp.unique` : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> np.unique_values(np.array([1, 1, 2]))
+    array([1, 2])
+
+    """
+
+    return dpnp.unique(
+        x,
+        return_index=False,
+        return_inverse=False,
+        return_counts=False,
+        equal_nan=False,
+    )
+
+
 def unstack(x, /, *, axis=0):
     """
     Split an array into a sequence of arrays along the given axis.

doc/reference/sorting.rst renamed to doc/reference/sort.rst

@@ -21,6 +21,7 @@
     get_float_dtypes,
     get_integer_dtypes,
     has_support_aspect64,
+    numpy_version,
 )
 from .third_party.cupy import testing
 
@@ -1152,7 +1153,7 @@ def test_ndarray(self):
         result = ia.reshape((2, 5))
         assert_array_equal(result, expected)
 
-    @testing.with_requires("numpy>=2.0")
+    @testing.with_requires("numpy>=2.1")
     def test_copy(self):
         a = numpy.arange(10).reshape(2, 5)
         ia = dpnp.array(a)
@@ -1870,10 +1871,7 @@ def test_2d_axis_nans(self, dt, axis_kwd, return_kwds, row):
         if len(return_kwds) == 0:
             assert_array_equal(result, expected)
         else:
-            if (
-                len(axis_kwd) == 0
-                and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.1"
-            ):
+            if len(axis_kwd) == 0 and numpy_version() < "2.0.1":
                 # gh-26961: numpy.unique(..., return_inverse=True, axis=None)
                 # returned flatten unique_inverse till 2.0.1 version
                 expected = (
@@ -1884,6 +1882,20 @@ def test_2d_axis_nans(self, dt, axis_kwd, return_kwds, row):
             for iv, v in zip(result, expected):
                 assert_array_equal(iv, v)
 
+    @testing.with_requires("numpy>=2.0")
+    @pytest.mark.parametrize(
+        "func",
+        ["unique_all", "unique_counts", "unique_inverse", "unique_values"],
+    )
+    def test_array_api_functions(self, func):
+        a = numpy.array([numpy.nan, 1, 4, 1, 3, 4, 5, 5, 1])
+        ia = dpnp.array(a)
+
+        result = getattr(dpnp, func)(ia)
+        expected = getattr(numpy, func)(a)
+        for iv, v in zip(result, expected):
+            assert_array_equal(iv, v)
+
 
 class TestVsplit:
     @pytest.mark.parametrize("xp", [numpy, dpnp])

dpnp/dpnp_iface_manipulation.py

@@ -2445,10 +2445,10 @@ def test_out(self, func_params, dtype):
 
         assert result is dp_out
         # numpy.ceil, numpy.floor, numpy.trunc always return float dtype for
-        # NumPy < 2.0.0 while output has the dtype of input for NumPy >= 2.0.0
+        # NumPy < 2.1.0 while output has the dtype of input for NumPy >= 2.1.0
         # (dpnp follows the latter behavior except for boolean dtype where it
         # returns int8)
-        if numpy_version() < "2.0.0" or dtype == numpy.bool:
+        if numpy_version() < "2.1.0" or dtype == numpy.bool:
             check_type = False
         else:
             check_type = True