Merge pull request #1 from arrayfire/master

Up to Date Master Branch

Author: sakchal

arrayfire_wrapper/lib/create_and_modify_array/create_array/pad.py

@@ -22,4 +22,4 @@ def pad(arr: AFArray, begin_shape: tuple[int, ...], end_shape: tuple[int, ...],
         end_c_shape.c_array,
         border_type.value,
     )
-    return NotImplemented
+    return out

arrayfire_wrapper/lib/create_and_modify_array/manage_array.py

@@ -166,7 +166,7 @@ def get_scalar(arr: AFArray, dtype: Dtype, /) -> int | float | complex | bool |
     out = dtype.c_type()
     call_from_clib(get_scalar.__name__, ctypes.pointer(out), arr)
     if dtype == c32 or dtype == c64:
-        return complex(out[0], out[1]) # type: ignore
+        return complex(out[0], out[1])  # type: ignore
     else:
         return cast(int | float | complex | bool | None, out.value)
 

tests/test_diag.py

@@ -0,0 +1,43 @@
+import pytest
+
+import arrayfire_wrapper.dtypes as dtypes
+import arrayfire_wrapper.lib as wrapper
+
+
+@pytest.mark.parametrize("diagonal_shape", [(2,), (10,), (100,), (1000,)])
+def test_diagonal_shape(diagonal_shape: tuple) -> None:
+    """Test if diagonal array is keeping the shape of the passed into the input array"""
+    in_arr = wrapper.constant(1, diagonal_shape, dtypes.s16)
+    diag_array = wrapper.diag_create(in_arr, 0)
+
+    extracted_diagonal = wrapper.diag_extract(diag_array, 0)
+
+    assert wrapper.get_dims(extracted_diagonal)[0 : len(diagonal_shape)] == diagonal_shape  # noqa: E203
+
+
+@pytest.mark.parametrize("diagonal_shape", [(2,), (10,), (100,), (1000,)])
+def test_diagonal_val(diagonal_shape: tuple) -> None:
+    """Test if diagonal array is keeping the same value as that of the values passed into the input array"""
+    dtype = dtypes.s16
+    in_arr = wrapper.constant(1, diagonal_shape, dtype)
+    diag_array = wrapper.diag_create(in_arr, 0)
+
+    extracted_diagonal = wrapper.diag_extract(diag_array, 0)
+
+    assert wrapper.get_scalar(extracted_diagonal, dtype) == wrapper.get_scalar(in_arr, dtype)
+
+
+@pytest.mark.parametrize(
+    "diagonal_shape",
+    [
+        (10, 10, 10),
+        (100, 100, 100, 100),
+    ],
+)
+def test_invalid_diagonal(diagonal_shape: tuple) -> None:
+    """Test if an invalid diagonal shape is being properly handled"""
+    with pytest.raises(RuntimeError):
+        in_arr = wrapper.constant(1, diagonal_shape, dtypes.s16)
+        diag_array = wrapper.diag_create(in_arr, 0)
+
+        wrapper.diag_extract(diag_array, 0)

tests/test_identity.py

@@ -0,0 +1,70 @@
+import random
+
+import pytest
+
+import arrayfire_wrapper.dtypes as dtypes
+import arrayfire_wrapper.lib as wrapper
+
+
+@pytest.mark.parametrize(
+    "shape",
+    [
+        (),
+        (random.randint(1, 10), 1),
+        (random.randint(1, 10), random.randint(1, 10)),
+        (random.randint(1, 10), random.randint(1, 10), random.randint(1, 10)),
+        (random.randint(1, 10), random.randint(1, 10), random.randint(1, 10), random.randint(1, 10)),
+    ],
+)
+def test_identity_shape(shape: tuple) -> None:
+    """Test if identity creates an array with the correct shape"""
+    dtype = dtypes.s16
+
+    result = wrapper.identity(shape, dtype)
+
+    assert wrapper.get_dims(result)[0 : len(shape)] == shape  # noqa: E203
+
+
+def test_identity_invalid_shape() -> None:
+    """Test if identity handles a shape with greater than 4 dimensions"""
+    with pytest.raises(TypeError) as excinfo:
+        invalid_shape = (
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+        )
+        dtype = dtypes.s16
+
+        wrapper.identity(invalid_shape, dtype)
+
+    assert f"CShape.__init__() takes from 1 to 5 positional arguments but {len(invalid_shape) + 1} were given" in str(
+        excinfo.value
+    )
+
+
+def test_identity_nonsquare_shape() -> None:
+    dtype = dtypes.s16
+    shape = (5, 6)
+
+    result = wrapper.identity(shape, dtype)
+
+    assert wrapper.get_dims(result)[0 : len(shape)] == shape  # noqa: E203
+
+
+@pytest.mark.parametrize(
+    "dtype_index",
+    [i for i in range(13)],
+)
+def test_identity_dtype(dtype_index: int) -> None:
+    """Test if identity creates an array with the correct dtype"""
+    if dtype_index in [2, 3] and not wrapper.get_dbl_support():
+        pytest.skip()
+
+    shape = (5, 5)
+    dtype = dtypes.c_api_value_to_dtype(dtype_index)
+
+    result = wrapper.identity(shape, dtype)
+
+    assert dtypes.c_api_value_to_dtype(wrapper.get_type(result)) == dtype

tests/test_iota.py

@@ -0,0 +1,125 @@
+import random
+
+import numpy as np
+import pytest
+
+import arrayfire_wrapper.dtypes as dtypes
+import arrayfire_wrapper.lib as wrapper
+
+
+@pytest.mark.parametrize(
+    "shape",
+    [
+        (),
+        (random.randint(1, 10), 1),
+        (random.randint(1, 10), random.randint(1, 10)),
+        (random.randint(1, 10), random.randint(1, 10), random.randint(1, 10)),
+        (random.randint(1, 10), random.randint(1, 10), random.randint(1, 10), random.randint(1, 10)),
+    ],
+)
+def test_iota_shape(shape: tuple) -> None:
+    """Test if identity creates an array with the correct shape"""
+    dtype = dtypes.s16
+    t_shape = (1, 1)
+
+    result = wrapper.iota(shape, t_shape, dtype)
+
+    assert wrapper.get_dims(result)[0 : len(shape)] == shape  # noqa: E203
+
+
+def test_iota_invalid_shape() -> None:
+    """Test if iota handles a shape with greater than 4 dimensions"""
+    with pytest.raises(TypeError) as excinfo:
+        invalid_shape = (
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+        )
+        dtype = dtypes.s16
+        t_shape = ()
+
+        wrapper.iota(invalid_shape, t_shape, dtype)
+
+    assert f"CShape.__init__() takes from 1 to 5 positional arguments but {len(invalid_shape) + 1} were given" in str(
+        excinfo.value
+    )
+
+
+@pytest.mark.parametrize(
+    "t_shape",
+    [
+        (1,),
+        (random.randint(1, 10), 1),
+        (random.randint(1, 10), random.randint(1, 10)),
+        (random.randint(1, 10), random.randint(1, 10), random.randint(1, 10)),
+        (random.randint(1, 10), random.randint(1, 10), random.randint(1, 10), random.randint(1, 10)),
+    ],
+)
+def test_iota_tshape(t_shape: tuple) -> None:
+    """Test if iota properly uses t_shape to change the size of the array and result in correct dimensions"""
+    shape = np.array([2, 2])
+    dtype = dtypes.s64
+
+    if len(shape.shape) < len(t_shape):
+        shape = np.append(shape, np.ones(len(t_shape) - len(shape), dtype=int))
+
+    result_shape = shape * t_shape
+
+    result = wrapper.iota(tuple(shape), t_shape, dtype)
+
+    result_dims = tuple(int(value) for value in wrapper.get_dims(result))
+
+    assert (result_dims[0 : len(result_shape)] == result_shape).all()  # noqa: E203
+
+
+@pytest.mark.parametrize(
+    "t_shape",
+    [
+        (0,),
+        (-1, -1),
+    ],
+)
+def test_iota_tshape_zero(t_shape: tuple) -> None:
+    """Test it iota properly handles negative or zero t_shapes"""
+    with pytest.raises(RuntimeError):
+        shape = (2, 2)
+
+        dtype = dtypes.s16
+
+        wrapper.iota(shape, t_shape, dtype)
+
+
+def test_iota_tshape_invalid() -> None:
+    """Test it iota properly handles a tshape with greater than 4 dimensions"""
+    with pytest.raises(TypeError):
+        shape = (2, 2)
+        invalid_tshape = (
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+            random.randint(1, 10),
+        )
+        dtype = dtypes.s16
+
+        wrapper.iota(shape, invalid_tshape, dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype_index",
+    [i for i in range(13)],
+)
+def test_iota_dtype(dtype_index: int) -> None:
+    """Test if iota creates an array with the correct dtype"""
+    if (dtype_index in [1, 4]) or (dtype_index in [2, 3] and not wrapper.get_dbl_support()):
+        pytest.skip()
+
+    shape = (5, 5)
+    t_shape = (2, 2)
+    dtype = dtypes.c_api_value_to_dtype(dtype_index)
+
+    result = wrapper.iota(shape, t_shape, dtype)
+
+    assert dtypes.c_api_value_to_dtype(wrapper.get_type(result)) == dtype

tests/test_lower.py

@@ -0,0 +1,45 @@
+import pytest
+
+import arrayfire_wrapper.dtypes as dtypes
+import arrayfire_wrapper.lib as wrapper
+
+
+@pytest.mark.parametrize(
+    "shape",
+    [
+        (3, 3),
+        (3, 3, 3),
+        (3, 3, 3, 3),
+    ],
+)
+def test_diag_is_unit(shape: tuple) -> None:
+    """Test if when is_unit_diag in lower returns an array with a unit diagonal"""
+    dtype = dtypes.s64
+    constant_array = wrapper.constant(3, shape, dtype)
+
+    lower_array = wrapper.lower(constant_array, True)
+    diagonal = wrapper.diag_extract(lower_array, 0)
+    diagonal_value = wrapper.get_scalar(diagonal, dtype)
+
+    assert diagonal_value == 1
+
+
+@pytest.mark.parametrize(
+    "shape",
+    [
+        (3, 3),
+        (3, 3, 3),
+        (3, 3, 3, 3),
+    ],
+)
+def test_is_original(shape: tuple) -> None:
+    """Test if is_original keeps the diagonal the same as the original array"""
+    dtype = dtypes.s64
+    constant_array = wrapper.constant(3, shape, dtype)
+    original_value = wrapper.get_scalar(constant_array, dtype)
+
+    lower_array = wrapper.lower(constant_array, False)
+    diagonal = wrapper.diag_extract(lower_array, 0)
+    diagonal_value = wrapper.get_scalar(diagonal, dtype)
+
+    assert original_value == diagonal_value

tests/test_pad.py

@@ -0,0 +1,71 @@
+import random
+
+import numpy as np
+import pytest
+
+import arrayfire_wrapper.dtypes as dtypes
+import arrayfire_wrapper.lib as wrapper
+
+
+@pytest.mark.parametrize(
+    "original_shape",
+    [
+        (random.randint(1, 100),),
+        (random.randint(1, 100), random.randint(1, 100)),
+        (random.randint(1, 100), random.randint(1, 100), random.randint(1, 100)),
+        (random.randint(1, 100), random.randint(1, 100), random.randint(1, 100), random.randint(1, 100)),
+    ],
+)
+def test_zero_padding(original_shape: tuple) -> None:
+    """Test if pad creates an array with no padding if no padding is given"""
+    original_array = wrapper.constant(2, original_shape, dtypes.s64)
+    padding = wrapper.Pad(0)
+
+    zero_shape = tuple(0 for _ in range(len(original_shape)))
+    result = wrapper.pad(original_array, zero_shape, zero_shape, padding)
+
+    assert wrapper.get_dims(result)[0 : len(original_shape)] == original_shape  # noqa: E203
+
+
+@pytest.mark.parametrize(
+    "original_shape",
+    [
+        (random.randint(1, 100),),
+        (random.randint(1, 100), random.randint(1, 100)),
+        (random.randint(1, 100), random.randint(1, 100), random.randint(1, 100)),
+        (random.randint(1, 100), random.randint(1, 100), random.randint(1, 100), random.randint(1, 100)),
+    ],
+)
+def test_negative_padding(original_shape: tuple) -> None:
+    """Test if pad can properly handle if negative padding is given"""
+    with pytest.raises(RuntimeError):
+        original_array = wrapper.constant(2, original_shape, dtypes.s64)
+        padding = wrapper.Pad(0)
+
+        neg_shape = tuple(-1 for _ in range(len(original_shape)))
+        result = wrapper.pad(original_array, neg_shape, neg_shape, padding)
+
+        assert wrapper.get_dims(result)[0 : len(original_shape)] == original_shape  # noqa: E203
+
+
+@pytest.mark.parametrize(
+    "original_shape",
+    [
+        (random.randint(1, 100),),
+        (random.randint(1, 100), random.randint(1, 100)),
+        (random.randint(1, 100), random.randint(1, 100), random.randint(1, 100)),
+        (random.randint(1, 100), random.randint(1, 100), random.randint(1, 100), random.randint(1, 100)),
+    ],
+)
+def test_padding_shape(original_shape: tuple) -> None:
+    """Test if pad outputs the correct shape when a padding is adding to the original array"""
+    original_array = wrapper.constant(2, original_shape, dtypes.s64)
+    padding = wrapper.Pad(0)
+
+    beg_shape = tuple(random.randint(1, 10) for _ in range(len(original_shape)))
+    end_shape = tuple(random.randint(1, 10) for _ in range(len(original_shape)))
+
+    result = wrapper.pad(original_array, beg_shape, end_shape, padding)
+    new_shape = np.array(beg_shape) + np.array(end_shape) + np.array(original_shape)
+
+    assert wrapper.get_dims(result)[0 : len(original_shape)] == tuple(new_shape)  # noqa: E203