Merge branch 'main' into betatim-multiple-devices

Author: asmeurer

array_api_strict/_elementwise_functions.py

@@ -342,7 +342,7 @@ def conj(x: Array, /) -> Array:
     """
     if x.dtype not in _complex_floating_dtypes:
         raise TypeError("Only complex floating-point dtypes are allowed in conj")
-    return Array._new(np.conj(x), device=x.device)
+    return Array._new(np.conj(x._array), device=x.device)
 
 @requires_api_version('2023.12')
 def copysign(x1: Array, x2: Array, /) -> Array:
@@ -520,7 +520,7 @@ def imag(x: Array, /) -> Array:
     """
     if x.dtype not in _complex_floating_dtypes:
         raise TypeError("Only complex floating-point dtypes are allowed in imag")
-    return Array._new(np.imag(x), device=x.device)
+    return Array._new(np.imag(x._array), device=x.device)
 
 
 def isfinite(x: Array, /) -> Array:
@@ -817,7 +817,7 @@ def real(x: Array, /) -> Array:
     """
     if x.dtype not in _complex_floating_dtypes:
         raise TypeError("Only complex floating-point dtypes are allowed in real")
-    return Array._new(np.real(x), device=x.device)
+    return Array._new(np.real(x._array), device=x.device)
 
 
 def remainder(x1: Array, x2: Array, /) -> Array:

array_api_strict/_manipulation_functions.py

@@ -2,8 +2,8 @@
 
 from ._array_object import Array
 from ._creation_functions import asarray
-from ._data_type_functions import result_type
-from ._dtypes import _integer_dtypes
+from ._data_type_functions import astype, result_type
+from ._dtypes import _integer_dtypes, int64, uint64
 from ._flags import requires_api_version, get_array_api_strict_flags
 
 from typing import TYPE_CHECKING
@@ -98,7 +98,13 @@ def repeat(
     else:
         raise TypeError("repeats must be an int or array")
 
-    return Array._new(np.repeat(x._array, repeats, axis=axis), device=x.device)
+    if repeats.dtype == uint64:
+        # NumPy does not allow uint64 because can't be cast down to x.dtype
+        # with 'safe' casting. However, repeats values larger than 2**63 are
+        # infeasable, and even if they are present by mistake, this will
+        # lead to underflow and an error.
+        repeats = astype(repeats, int64)
+    return Array._new(np.repeat(x._array, repeats._array, axis=axis), device=x.device)
 
 # Note: the optional argument is called 'shape', not 'newshape'
 def reshape(x: Array,