Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/array_api/tests/test_array_object.py
import operator
from numpy.testing import assert_raises, suppress_warnings
import numpy as np
import pytest
from .. import ones, asarray, reshape, result_type, all, equal
from .._array_object import Array
from .._dtypes import (
_all_dtypes,
_boolean_dtypes,
_real_floating_dtypes,
_floating_dtypes,
_complex_floating_dtypes,
_integer_dtypes,
_integer_or_boolean_dtypes,
_real_numeric_dtypes,
_numeric_dtypes,
int8,
int16,
int32,
int64,
uint64,
bool as bool_,
)
def test_validate_index():
# The indexing tests in the official array API test suite test that the
# array object correctly handles the subset of indices that are required
# by the spec. But the NumPy array API implementation specifically
# disallows any index not required by the spec, via Array._validate_index.
# This test focuses on testing that non-valid indices are correctly
# rejected. See
# https://data-apis.org/array-api/latest/API_specification/indexing.html
# and the docstring of Array._validate_index for the exact indexing
# behavior that should be allowed. This does not test indices that are
# already invalid in NumPy itself because Array will generally just pass
# such indices directly to the underlying np.ndarray.
a = ones((3, 4))
# Out of bounds slices are not allowed
assert_raises(IndexError, lambda: a[:4])
assert_raises(IndexError, lambda: a[:-4])
assert_raises(IndexError, lambda: a[:3:-1])
assert_raises(IndexError, lambda: a[:-5:-1])
assert_raises(IndexError, lambda: a[4:])
assert_raises(IndexError, lambda: a[-4:])
assert_raises(IndexError, lambda: a[4::-1])
assert_raises(IndexError, lambda: a[-4::-1])
assert_raises(IndexError, lambda: a[...,:5])
assert_raises(IndexError, lambda: a[...,:-5])
assert_raises(IndexError, lambda: a[...,:5:-1])
assert_raises(IndexError, lambda: a[...,:-6:-1])
assert_raises(IndexError, lambda: a[...,5:])
assert_raises(IndexError, lambda: a[...,-5:])
assert_raises(IndexError, lambda: a[...,5::-1])
assert_raises(IndexError, lambda: a[...,-5::-1])
# Boolean indices cannot be part of a larger tuple index
assert_raises(IndexError, lambda: a[a[:,0]==1,0])
assert_raises(IndexError, lambda: a[a[:,0]==1,...])
assert_raises(IndexError, lambda: a[..., a[0]==1])
assert_raises(IndexError, lambda: a[[True, True, True]])
assert_raises(IndexError, lambda: a[(True, True, True),])
# Integer array indices are not allowed (except for 0-D)
idx = asarray([[0, 1]])
assert_raises(IndexError, lambda: a[idx])
assert_raises(IndexError, lambda: a[idx,])
assert_raises(IndexError, lambda: a[[0, 1]])
assert_raises(IndexError, lambda: a[(0, 1), (0, 1)])
assert_raises(IndexError, lambda: a[[0, 1]])
assert_raises(IndexError, lambda: a[np.array([[0, 1]])])
# Multiaxis indices must contain exactly as many indices as dimensions
assert_raises(IndexError, lambda: a[()])
assert_raises(IndexError, lambda: a[0,])
assert_raises(IndexError, lambda: a[0])
assert_raises(IndexError, lambda: a[:])
def test_operators():
# For every operator, we test that it works for the required type
# combinations and raises TypeError otherwise
binary_op_dtypes = {
"__add__": "numeric",
"__and__": "integer_or_boolean",
"__eq__": "all",
"__floordiv__": "real numeric",
"__ge__": "real numeric",
"__gt__": "real numeric",
"__le__": "real numeric",
"__lshift__": "integer",
"__lt__": "real numeric",
"__mod__": "real numeric",
"__mul__": "numeric",
"__ne__": "all",
"__or__": "integer_or_boolean",
"__pow__": "numeric",
"__rshift__": "integer",
"__sub__": "numeric",
"__truediv__": "floating",
"__xor__": "integer_or_boolean",
}
# Recompute each time because of in-place ops
def _array_vals():
for d in _integer_dtypes:
yield asarray(1, dtype=d)
for d in _boolean_dtypes:
yield asarray(False, dtype=d)
for d in _floating_dtypes:
yield asarray(1.0, dtype=d)
BIG_INT = int(1e30)
for op, dtypes in binary_op_dtypes.items():
ops = [op]
if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]:
rop = "__r" + op[2:]
iop = "__i" + op[2:]
ops += [rop, iop]
for s in [1, 1.0, 1j, BIG_INT, False]:
for _op in ops:
for a in _array_vals():
# Test array op scalar. From the spec, the following combinations
# are supported:
# - Python bool for a bool array dtype,
# - a Python int within the bounds of the given dtype for integer array dtypes,
# - a Python int or float for real floating-point array dtypes
# - a Python int, float, or complex for complex floating-point array dtypes
if ((dtypes == "all"
or dtypes == "numeric" and a.dtype in _numeric_dtypes
or dtypes == "real numeric" and a.dtype in _real_numeric_dtypes
or dtypes == "integer" and a.dtype in _integer_dtypes
or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
or dtypes == "boolean" and a.dtype in _boolean_dtypes
or dtypes == "floating" and a.dtype in _floating_dtypes
)
# bool is a subtype of int, which is why we avoid
# isinstance here.
and (a.dtype in _boolean_dtypes and type(s) == bool
or a.dtype in _integer_dtypes and type(s) == int
or a.dtype in _real_floating_dtypes and type(s) in [float, int]
or a.dtype in _complex_floating_dtypes and type(s) in [complex, float, int]
)):
if a.dtype in _integer_dtypes and s == BIG_INT:
assert_raises(OverflowError, lambda: getattr(a, _op)(s))
else:
# Only test for no error
with suppress_warnings() as sup:
# ignore warnings from pow(BIG_INT)
sup.filter(RuntimeWarning,
"invalid value encountered in power")
getattr(a, _op)(s)
else:
assert_raises(TypeError, lambda: getattr(a, _op)(s))
# Test array op array.
for _op in ops:
for x in _array_vals():
for y in _array_vals():
# See the promotion table in NEP 47 or the array
# API spec page on type promotion. Mixed kind
# promotion is not defined.
if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes
or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes
or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
):
assert_raises(TypeError, lambda: getattr(x, _op)(y))
# Ensure in-place operators only promote to the same dtype as the left operand.
elif (
_op.startswith("__i")
and result_type(x.dtype, y.dtype) != x.dtype
):
assert_raises(TypeError, lambda: getattr(x, _op)(y))
# Ensure only those dtypes that are required for every operator are allowed.
elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
or (dtypes == "real numeric" and x.dtype in _real_numeric_dtypes and y.dtype in _real_numeric_dtypes)
or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes)
or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes
):
getattr(x, _op)(y)
else:
assert_raises(TypeError, lambda: getattr(x, _op)(y))
unary_op_dtypes = {
"__abs__": "numeric",
"__invert__": "integer_or_boolean",
"__neg__": "numeric",
"__pos__": "numeric",
}
for op, dtypes in unary_op_dtypes.items():
for a in _array_vals():
if (
dtypes == "numeric"
and a.dtype in _numeric_dtypes
or dtypes == "integer_or_boolean"
and a.dtype in _integer_or_boolean_dtypes
):
# Only test for no error
getattr(a, op)()
else:
assert_raises(TypeError, lambda: getattr(a, op)())
# Finally, matmul() must be tested separately, because it works a bit
# different from the other operations.
def _matmul_array_vals():
for a in _array_vals():
yield a
for d in _all_dtypes:
yield ones((3, 4), dtype=d)
yield ones((4, 2), dtype=d)
yield ones((4, 4), dtype=d)
# Scalars always error
for _op in ["__matmul__", "__rmatmul__", "__imatmul__"]:
for s in [1, 1.0, False]:
for a in _matmul_array_vals():
if (type(s) in [float, int] and a.dtype in _floating_dtypes
or type(s) == int and a.dtype in _integer_dtypes):
# Type promotion is valid, but @ is not allowed on 0-D
# inputs, so the error is a ValueError
assert_raises(ValueError, lambda: getattr(a, _op)(s))
else:
assert_raises(TypeError, lambda: getattr(a, _op)(s))
for x in _matmul_array_vals():
for y in _matmul_array_vals():
if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
or x.dtype in _boolean_dtypes
or y.dtype in _boolean_dtypes
):
assert_raises(TypeError, lambda: x.__matmul__(y))
assert_raises(TypeError, lambda: y.__rmatmul__(x))
assert_raises(TypeError, lambda: x.__imatmul__(y))
elif x.shape == () or y.shape == () or x.shape[1] != y.shape[0]:
assert_raises(ValueError, lambda: x.__matmul__(y))
assert_raises(ValueError, lambda: y.__rmatmul__(x))
if result_type(x.dtype, y.dtype) != x.dtype:
assert_raises(TypeError, lambda: x.__imatmul__(y))
else:
assert_raises(ValueError, lambda: x.__imatmul__(y))
else:
x.__matmul__(y)
y.__rmatmul__(x)
if result_type(x.dtype, y.dtype) != x.dtype:
assert_raises(TypeError, lambda: x.__imatmul__(y))
elif y.shape[0] != y.shape[1]:
# This one fails because x @ y has a different shape from x
assert_raises(ValueError, lambda: x.__imatmul__(y))
else:
x.__imatmul__(y)
def test_python_scalar_construtors():
b = asarray(False)
i = asarray(0)
f = asarray(0.0)
c = asarray(0j)
assert bool(b) == False
assert int(i) == 0
assert float(f) == 0.0
assert operator.index(i) == 0
# bool/int/float/complex should only be allowed on 0-D arrays.
assert_raises(TypeError, lambda: bool(asarray([False])))
assert_raises(TypeError, lambda: int(asarray([0])))
assert_raises(TypeError, lambda: float(asarray([0.0])))
assert_raises(TypeError, lambda: complex(asarray([0j])))
assert_raises(TypeError, lambda: operator.index(asarray([0])))
# bool should work on all types of arrays
assert bool(b) is bool(i) is bool(f) is bool(c) is False
# int should fail on complex arrays
assert int(b) == int(i) == int(f) == 0
assert_raises(TypeError, lambda: int(c))
# float should fail on complex arrays
assert float(b) == float(i) == float(f) == 0.0
assert_raises(TypeError, lambda: float(c))
# complex should work on all types of arrays
assert complex(b) == complex(i) == complex(f) == complex(c) == 0j
# index should only work on integer arrays
assert operator.index(i) == 0
assert_raises(TypeError, lambda: operator.index(b))
assert_raises(TypeError, lambda: operator.index(f))
assert_raises(TypeError, lambda: operator.index(c))
def test_device_property():
a = ones((3, 4))
assert a.device == 'cpu'
assert all(equal(a.to_device('cpu'), a))
assert_raises(ValueError, lambda: a.to_device('gpu'))
assert all(equal(asarray(a, device='cpu'), a))
assert_raises(ValueError, lambda: asarray(a, device='gpu'))
def test_array_properties():
a = ones((1, 2, 3))
b = ones((2, 3))
assert_raises(ValueError, lambda: a.T)
assert isinstance(b.T, Array)
assert b.T.shape == (3, 2)
assert isinstance(a.mT, Array)
assert a.mT.shape == (1, 3, 2)
assert isinstance(b.mT, Array)
assert b.mT.shape == (3, 2)
def test___array__():
a = ones((2, 3), dtype=int16)
assert np.asarray(a) is a._array
b = np.asarray(a, dtype=np.float64)
assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64)))
assert b.dtype == np.float64
def test_allow_newaxis():
a = ones(5)
indexed_a = a[None, :]
assert indexed_a.shape == (1, 5)
def test_disallow_flat_indexing_with_newaxis():
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[None, 0, 0]
def test_disallow_mask_with_newaxis():
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[None, asarray(True)]
@pytest.mark.parametrize("shape", [(), (5,), (3, 3, 3)])
@pytest.mark.parametrize("index", ["string", False, True])
def test_error_on_invalid_index(shape, index):
a = ones(shape)
with pytest.raises(IndexError):
a[index]
def test_mask_0d_array_without_errors():
a = ones(())
a[asarray(True)]
@pytest.mark.parametrize(
"i", [slice(5), slice(5, 0), asarray(True), asarray([0, 1])]
)
def test_error_on_invalid_index_with_ellipsis(i):
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[..., i]
with pytest.raises(IndexError):
a[i, ...]
def test_array_keys_use_private_array():
"""
Indexing operations convert array keys before indexing the internal array
Fails when array_api array keys are not converted into NumPy-proper arrays
in __getitem__(). This is achieved by passing array_api arrays with 0-sized
dimensions, which NumPy-proper treats erroneously - not sure why!
TODO: Find and use appropriate __setitem__() case.
"""
a = ones((0, 0), dtype=bool_)
assert a[a].shape == (0,)
a = ones((0,), dtype=bool_)
key = ones((0, 0), dtype=bool_)
with pytest.raises(IndexError):
a[key]
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