update test_random.py and test_random_state.py

dpnp/tests/helper.py

@@ -87,61 +87,102 @@ def assert_dtype_allclose(
                 assert dpnp_arr.dtype == numpy_arr.dtype
 
 
-def get_integer_dtypes(all_int_types=False, no_unsigned=False):
-    """
-    Build a list of integer types supported by DPNP.
+def generate_random_numpy_array(
+    shape,
+    dtype=None,
+    order="C",
+    hermitian=False,
+    seed_value=None,
+    low=-10,
+    high=10,
+    probability=0.5,
+):
     """
+    Generate a random numpy array with the specified shape and dtype.
 
-    dtypes = [dpnp.int32, dpnp.int64]
+    If required, the array can be made Hermitian (for complex data types) or
+    symmetric (for real data types).
 
-    if config.all_int_types or all_int_types:
-        dtypes += [dpnp.int8, dpnp.int16]
-        if not no_unsigned:
-            dtypes += [dpnp.uint8, dpnp.uint16, dpnp.uint32, dpnp.uint64]
+    Parameters
+    ----------
+    shape : tuple
+        Shape of the generated array.
+    dtype : str or dtype, optional
+        Desired data-type for the output array.
+        If not specified, data type will be determined by numpy.
 
-    return dtypes
+        Default : ``None``
+    order : {"C", "F"}, optional
+        Specify the memory layout of the output array.
 
+        Default: ``"C"``.
+    hermitian : bool, optional
+        If True, generates a Hermitian (symmetric if `dtype` is real) matrix.
 
-def get_complex_dtypes(device=None):
-    """
-    Build a list of complex types supported by DPNP based on device capabilities.
-    """
+        Default : ``False``
+    seed_value : int, optional
+        The seed value to initialize the random number generator.
 
-    dev = dpctl.select_default_device() if device is None else device
+        Default : ``None``
+    low : {int, float}, optional
+        Lower boundary of the generated samples from a uniform distribution.
 
-    # add complex types
-    dtypes = [dpnp.complex64]
-    if dev.has_aspect_fp64:
-        dtypes.append(dpnp.complex128)
-    return dtypes
+        Default : ``-10``.
+    high : {int, float}, optional
+        Upper boundary of the generated samples from a uniform distribution.
 
+        Default : ``10``.
+    probability : float, optional
+        If dtype is bool, the probability of True. Ignored for other dtypes.
+
+        Default : ``0.5``.
+    Returns
+    -------
+    out : numpy.ndarray
+        A random numpy array of the specified shape, dtype and memory layout.
+        The array is Hermitian or symmetric if `hermitian` is True.
+
+    Note:
+    For arrays with more than 2 dimensions, the Hermitian or
+    symmetric property is ensured for each 2D sub-array.
 
-def get_float_dtypes(no_float16=True, device=None):
-    """
-    Build a list of floating types supported by DPNP based on device capabilities.
     """
 
-    dev = dpctl.select_default_device() if device is None else device
+    if seed_value is None:
+        seed_value = 42
+    numpy.random.seed(seed_value)
 
-    # add floating types
-    dtypes = []
-    if not no_float16 and dev.has_aspect_fp16:
-        dtypes.append(dpnp.float16)
+    if numpy.issubdtype(dtype, numpy.unsignedinteger):
+        low = 0
 
-    dtypes.append(dpnp.float32)
-    if dev.has_aspect_fp64:
-        dtypes.append(dpnp.float64)
-    return dtypes
+    # dtype=int is needed for 0d arrays
+    size = numpy.prod(shape, dtype=int)
+    if dtype == dpnp.bool:
+        a = numpy.random.choice(
+            [False, True], size, p=[1 - probability, probability]
+        )
+    else:
+        a = numpy.random.uniform(low, high, size).astype(dtype)
 
+        if numpy.issubdtype(a.dtype, numpy.complexfloating):
+            a += 1j * numpy.random.uniform(low, high, size)
 
-def get_float_complex_dtypes(no_float16=True, device=None):
-    """
-    Build a list of floating and complex types supported by DPNP based on device capabilities.
-    """
+    a = a.reshape(shape)
+    if hermitian and a.size > 0:
+        if a.ndim > 2:
+            orig_shape = a.shape
+            # get 3D array
+            a = a.reshape(-1, orig_shape[-2], orig_shape[-1])
+            for i in range(a.shape[0]):
+                a[i] = numpy.conj(a[i].T) @ a[i]
+            a = a.reshape(orig_shape)
+        else:
+            a = numpy.conj(a.T) @ a
 
-    dtypes = get_float_dtypes(no_float16, device)
-    dtypes.extend(get_complex_dtypes(device))
-    return dtypes
+    # a.reshape(shape) returns an array in C order by default
+    if order != "C" and a.ndim > 1:
+        a = numpy.array(a, order=order)
+    return a
 
 
 def get_abs_array(data, dtype=None):
@@ -214,102 +255,79 @@ def get_array(xp, a):
     return a
 
 
-def generate_random_numpy_array(
-    shape,
-    dtype=None,
-    order="C",
-    hermitian=False,
-    seed_value=None,
-    low=-10,
-    high=10,
-    probability=0.5,
-):
+def get_complex_dtypes(device=None):
+    """
+    Build a list of complex types supported by DPNP based on device capabilities.
     """
-    Generate a random numpy array with the specified shape and dtype.
 
-    If required, the array can be made Hermitian (for complex data types) or
-    symmetric (for real data types).
+    dev = dpctl.select_default_device() if device is None else device
 
-    Parameters
-    ----------
-    shape : tuple
-        Shape of the generated array.
-    dtype : str or dtype, optional
-        Desired data-type for the output array.
-        If not specified, data type will be determined by numpy.
+    # add complex types
+    dtypes = [dpnp.complex64]
+    if dev.has_aspect_fp64:
+        dtypes.append(dpnp.complex128)
+    return dtypes
 
-        Default : ``None``
-    order : {"C", "F"}, optional
-        Specify the memory layout of the output array.
 
-        Default: ``"C"``.
-    hermitian : bool, optional
-        If True, generates a Hermitian (symmetric if `dtype` is real) matrix.
+def get_float_dtypes(no_float16=True, device=None):
+    """
+    Build a list of floating types supported by DPNP based on device capabilities.
+    """
 
-        Default : ``False``
-    seed_value : int, optional
-        The seed value to initialize the random number generator.
+    dev = dpctl.select_default_device() if device is None else device
 
-        Default : ``None``
-    low : {int, float}, optional
-        Lower boundary of the generated samples from a uniform distribution.
+    # add floating types
+    dtypes = []
+    if not no_float16 and dev.has_aspect_fp16:
+        dtypes.append(dpnp.float16)
 
-        Default : ``-10``.
-    high : {int, float}, optional
-        Upper boundary of the generated samples from a uniform distribution.
+    dtypes.append(dpnp.float32)
+    if dev.has_aspect_fp64:
+        dtypes.append(dpnp.float64)
+    return dtypes
 
-        Default : ``10``.
-    probability : float, optional
-        If dtype is bool, the probability of True. Ignored for other dtypes.
 
-        Default : ``0.5``.
-    Returns
-    -------
-    out : numpy.ndarray
-        A random numpy array of the specified shape, dtype and memory layout.
-        The array is Hermitian or symmetric if `hermitian` is True.
+def get_float_complex_dtypes(no_float16=True, device=None):
+    """
+    Build a list of floating and complex types supported by DPNP based on device capabilities.
+    """
+
+    dtypes = get_float_dtypes(no_float16, device)
+    dtypes.extend(get_complex_dtypes(device))
+    return dtypes
 
-    Note:
-    For arrays with more than 2 dimensions, the Hermitian or
-    symmetric property is ensured for each 2D sub-array.
 
+def get_integer_dtypes(all_int_types=False, no_unsigned=False):
+    """
+    Build a list of integer types supported by DPNP.
     """
 
-    if seed_value is None:
-        seed_value = 42
-    numpy.random.seed(seed_value)
+    dtypes = [dpnp.int32, dpnp.int64]
 
-    if numpy.issubdtype(dtype, numpy.unsignedinteger):
-        low = 0
+    if config.all_int_types or all_int_types:
+        dtypes += [dpnp.int8, dpnp.int16]
+        if not no_unsigned:
+            dtypes += [dpnp.uint8, dpnp.uint16, dpnp.uint32, dpnp.uint64]
 
-    # dtype=int is needed for 0d arrays
-    size = numpy.prod(shape, dtype=int)
-    if dtype == dpnp.bool:
-        a = numpy.random.choice(
-            [False, True], size, p=[1 - probability, probability]
-        )
-    else:
-        a = numpy.random.uniform(low, high, size).astype(dtype)
+    return dtypes
 
-        if numpy.issubdtype(a.dtype, numpy.complexfloating):
-            a += 1j * numpy.random.uniform(low, high, size)
 
-    a = a.reshape(shape)
-    if hermitian and a.size > 0:
-        if a.ndim > 2:
-            orig_shape = a.shape
-            # get 3D array
-            a = a.reshape(-1, orig_shape[-2], orig_shape[-1])
-            for i in range(a.shape[0]):
-                a[i] = numpy.conj(a[i].T) @ a[i]
-            a = a.reshape(orig_shape)
-        else:
-            a = numpy.conj(a.T) @ a
+def has_support_aspect16(device=None):
+    """
+    Return True if the device supports 16-bit precision floating point operations,
+    False otherwise.
+    """
+    dev = dpctl.select_default_device() if device is None else device
+    return dev.has_aspect_fp16
 
-    # a.reshape(shape) returns an array in C order by default
-    if order != "C" and a.ndim > 1:
-        a = numpy.array(a, order=order)
-    return a
+
+def has_support_aspect64(device=None):
+    """
+    Return True if the device supports 64-bit precision floating point operations,
+    False otherwise.
+    """
+    dev = dpctl.select_default_device() if device is None else device
+    return dev.has_aspect_fp64
 
 
 def is_cpu_device(device=None):
@@ -335,23 +353,5 @@ def is_win_platform():
     return platform.startswith("win")
 
 
-def has_support_aspect16(device=None):
-    """
-    Return True if the device supports 16-bit precision floating point operations,
-    False otherwise.
-    """
-    dev = dpctl.select_default_device() if device is None else device
-    return dev.has_aspect_fp16
-
-
-def has_support_aspect64(device=None):
-    """
-    Return True if the device supports 64-bit precision floating point operations,
-    False otherwise.
-    """
-    dev = dpctl.select_default_device() if device is None else device
-    return dev.has_aspect_fp64
-
-
 def numpy_version():
     return numpy.lib.NumpyVersion(numpy.__version__)