Import dpnp to be working with numpy 2.0 in the environment (#1985)

* Remove alias on numpy.complex_

* Remove alias on numpy.cfloat

* Remove alias on numpy.float and numpy.float_

* Remove alias on numpy.float and numpy.singlecomplex

* Remove aliases on numpy.Inf, numpy.Infinity and numpy.infty constants

* Get rid of dpnp.float in the code

* Remove aliases on numpy.NAN and numpy.NaN constants

* Remove aliases on numpy.NINF and numpy.PINF constants

* Remove aliases on numpy.NZERO and numpy.PZERO constants

doc/reference/dtypes_table.rst

@@ -15,35 +15,23 @@ Table below shows a list of all supported data types (dtypes) and constants of t
         - :obj:`bool <numpy.bool_>`
         - :obj:`int32 <numpy.int32>`
         - :obj:`int64 <numpy.int64>`
+        - :obj:`float32 <numpy.float32>`
+        - :obj:`float64 <numpy.float64>`
         - :obj:`complex64 <numpy.complex64>`
         - :obj:`complex128 <numpy.complex128>`
       -
         - :obj:`bool_ <numpy.bool_>`
         - :obj:`cdouble <numpy.cdouble>`
-        - :obj:`complex <numpy.complex_>`
-        - :obj:`cfloat <numpy.cfloat>`
         - :obj:`csingle <numpy.csingle>`
         - :obj:`double <numpy.double>`
-        - :obj:`float <numpy.float>`
-        - :obj:`float_ <numpy.float_>`
         - :obj:`float16 <numpy.float16>`
         - :obj:`int <numpy.int>`
         - :obj:`int_ <numpy.int_>`
         - :obj:`intc <numpy.intc>`
         - :obj:`single <numpy.single>`
-        - :obj:`singlecomplex <numpy.singlecomplex>`
       -
         - :obj:`e <numpy.e>`
         - :obj:`euler_gamma <numpy.euler_gamma>`
-        - :obj:`Inf <numpy.Inf>`
         - :obj:`inf <numpy.inf>`
-        - :obj:`Infinity <numpy.Infinity>`
-        - :obj:`infty <numpy.infty>`
-        - :obj:`NAN <numpy.NAN>`
-        - :obj:`NaN <numpy.NaN>`
         - :obj:`nan <numpy.nan>`
-        - :obj:`NINF <numpy.NINF>`
-        - :obj:`NZERO <numpy.NZERO>`
         - :obj:`pi <numpy.pi>`
-        - :obj:`PINF <numpy.PINF>`
-        - :obj:`PZERO <numpy.PZERO>`

dpnp/dpnp_iface_arraycreation.py

@@ -3266,7 +3266,7 @@ def tri(
     /,
     M=None,
     k=0,
-    dtype=dpnp.float,
+    dtype=float,
     *,
     device=None,
     usm_type="device",
@@ -3376,7 +3376,10 @@ def tri(
     if _k is None:
         raise TypeError(f"`k` must be a integer data type, but got {type(k)}")
 
-    _dtype = dpnp.default_float_type() if dtype in (dpnp.float, None) else dtype
+    sycl_dev = dpnp.get_normalized_queue_device(
+        sycl_queue=sycl_queue, device=device
+    ).sycl_device
+    _dtype = map_dtype_to_device(dtype, sycl_dev)
 
     if usm_type is None:
         usm_type = "device"

dpnp/dpnp_iface_logic.py

@@ -839,7 +839,7 @@ def isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
     _b += atol
     result = less_equal(dpnp.abs(a - b), _b)
 
-    # Handle "Inf" values: they are treated as equal if they are in the same
+    # Handle "inf" values: they are treated as equal if they are in the same
     # place and of the same sign in both arrays
     result &= isfinite(b)
     result |= a == b

dpnp/dpnp_iface_mathematical.py

@@ -2295,7 +2295,7 @@ def gradient(f, *varargs, axis=None, edge_order=1):
 >>> np.maximum(x1, x2)
 array([nan, nan, nan])
 
->>> np.maximum(np.array(np.Inf), 1)
+>>> np.maximum(np.array(np.inf), 1)
 array(inf)
 """
 
@@ -2375,7 +2375,7 @@ def gradient(f, *varargs, axis=None, edge_order=1):
 >>> np.minimum(x1, x2)
 array([nan, nan, nan])
 
->>> np.minimum(np.array(-np.Inf), 1)
+>>> np.minimum(np.array(-np.inf), 1)
 array(-inf)
 """
 

dpnp/dpnp_iface_nanfunctions.py

@@ -437,7 +437,7 @@ def nanmax(a, axis=None, out=None, keepdims=False, initial=None, where=True):
 
     When positive infinity and negative infinity are present:
 
-    >>> np.nanmax(np.array([1, 2, np.nan, np.NINF]))
+    >>> np.nanmax(np.array([1, 2, np.nan, -np.inf]))
     array(2.)
     >>> np.nanmax(np.array([1, 2, np.nan, np.inf]))
     array(inf)
@@ -639,7 +639,7 @@ def nanmin(a, axis=None, out=None, keepdims=False, initial=None, where=True):
 
     >>> np.nanmin(np.array([1, 2, np.nan, np.inf]))
     array(1.)
-    >>> np.nanmin(np.array([1, 2, np.nan, np.NINF]))
+    >>> np.nanmin(np.array([1, 2, np.nan, -np.inf]))
     array(-inf)
 
     """
@@ -831,7 +831,7 @@ def nansum(
     array([2.,  1.])
     >>> np.nansum(np.array([1, np.nan, np.inf]))
     array(inf)
-    >>> np.nansum(np.array([1, np.nan, np.NINF]))
+    >>> np.nansum(np.array([1, np.nan, -np.inf]))
     array(-inf)
     >>> # both +/- infinity present
     >>> np.nansum(np.array([1, np.nan, np.inf, -np.inf]))

dpnp/dpnp_iface_statistics.py

@@ -522,7 +522,7 @@ def max(a, axis=None, out=None, keepdims=False, initial=None, where=True):
     array([1, 3])
 
     >>> b = np.arange(5, dtype=float)
-    >>> b[2] = np.NaN
+    >>> b[2] = np.nan
     >>> np.max(b)
     array(nan)
 
@@ -736,7 +736,7 @@ def min(a, axis=None, out=None, keepdims=False, initial=None, where=True):
     array([0, 2])
 
     >>> b = np.arange(5, dtype=float)
-    >>> b[2] = np.NaN
+    >>> b[2] = np.nan
     >>> np.min(b)
     array(nan)
 

dpnp/dpnp_iface_types.py

@@ -41,29 +41,22 @@
     "bool",
     "bool_",
     "cdouble",
-    "complex_",
     "complex128",
     "complex64",
     "complexfloating",
-    "cfloat",
     "csingle",
     "double",
     "dtype",
     "e",
     "euler_gamma",
     "finfo",
-    "float",
-    "float_",
     "float16",
     "float32",
     "float64",
     "floating",
     "iinfo",
     "inexact",
-    "Inf",
     "inf",
-    "Infinity",
-    "infty",
     "int",
     "int_",
     "int32",
@@ -74,19 +67,12 @@
     "issubdtype",
     "issubsctype",
     "is_type_supported",
-    "NAN",
-    "NaN",
     "nan",
     "newaxis",
-    "NINF",
-    "NZERO",
     "number",
     "pi",
-    "PINF",
-    "PZERO",
     "signedinteger",
     "single",
-    "singlecomplex",
 ]
 
 
@@ -97,16 +83,12 @@
 bool = numpy.bool_
 bool_ = numpy.bool_
 cdouble = numpy.cdouble
-complex_ = numpy.complex_
 complex128 = numpy.complex128
 complex64 = numpy.complex64
 complexfloating = numpy.complexfloating
-cfloat = numpy.cfloat
 csingle = numpy.csingle
 double = numpy.double
 dtype = numpy.dtype
-float = numpy.float_
-float_ = numpy.float_
 float16 = numpy.float16
 float32 = numpy.float32
 float64 = numpy.float64
@@ -122,27 +104,17 @@
 number = numpy.number
 signedinteger = numpy.signedinteger
 single = numpy.single
-singlecomplex = numpy.singlecomplex
 
 
 # =============================================================================
 # Constants (borrowed from NumPy)
 # =============================================================================
 e = numpy.e
 euler_gamma = numpy.euler_gamma
-Inf = numpy.Inf
 inf = numpy.inf
-Infinity = numpy.Infinity
-infty = numpy.infty
-NAN = numpy.NAN
-NaN = numpy.NaN
 nan = numpy.nan
 newaxis = None
-NINF = numpy.NINF
-NZERO = numpy.NZERO
 pi = numpy.pi
-PINF = numpy.PINF
-PZERO = numpy.PZERO
 
 
 # pylint: disable=redefined-outer-name

dpnp/linalg/dpnp_utils_linalg.py

@@ -997,7 +997,7 @@ def _multi_dot_matrix_chain_order(n, arrays, return_costs=False):
     for ll in range(1, n):
         for i in range(n - ll):
             j = i + ll
-            m[i, j] = dpnp.Inf
+            m[i, j] = dpnp.inf
             for k in range(i, j):
                 q = m[i, k] + m[k + 1, j] + p[i] * p[k + 1] * p[j + 1]
                 if q < m[i, j]: