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Copy pathtest_array_object.py
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346 lines (300 loc) · 12.1 KB
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import cmath
import math
from itertools import product
from typing import List, Sequence, Tuple, Union, get_args
import pytest
from hypothesis import assume, given, note
from hypothesis import strategies as st
from . import _array_module as xp
from . import dtype_helpers as dh
from . import hypothesis_helpers as hh
from . import pytest_helpers as ph
from . import shape_helpers as sh
from . import xps
from .typing import DataType, Index, Param, Scalar, ScalarType, Shape
def scalar_objects(
dtype: DataType, shape: Shape
) -> st.SearchStrategy[Union[Scalar, List[Scalar]]]:
"""Generates scalars or nested sequences which are valid for xp.asarray()"""
size = math.prod(shape)
return st.lists(hh.from_dtype(dtype), min_size=size, max_size=size).map(
lambda l: sh.reshape(l, shape)
)
def normalize_key(key: Index, shape: Shape) -> Tuple[Union[int, slice], ...]:
"""
Normalize an indexing key.
* If a non-tuple index, wrap as a tuple.
* Represent ellipsis as equivalent slices.
"""
_key = tuple(key) if isinstance(key, tuple) else (key,)
if Ellipsis in _key:
nonexpanding_key = tuple(i for i in _key if i is not None)
start_a = nonexpanding_key.index(Ellipsis)
stop_a = start_a + (len(shape) - (len(nonexpanding_key) - 1))
slices = tuple(slice(None) for _ in range(start_a, stop_a))
start_pos = _key.index(Ellipsis)
_key = _key[:start_pos] + slices + _key[start_pos + 1 :]
return _key
def get_indexed_axes_and_out_shape(
key: Tuple[Union[int, slice, None], ...], shape: Shape
) -> Tuple[Tuple[Sequence[int], ...], Shape]:
"""
From the (normalized) key and input shape, calculates:
* indexed_axes: For each dimension, the axes which the key indexes.
* out_shape: The resulting shape of indexing an array (of the input shape)
with the key.
"""
axes_indices = []
out_shape = []
a = 0
for i in key:
if i is None:
out_shape.append(1)
else:
side = shape[a]
if isinstance(i, int):
if i < 0:
i += side
axes_indices.append((i,))
else:
indices = range(side)[i]
axes_indices.append(indices)
out_shape.append(len(indices))
a += 1
return tuple(axes_indices), tuple(out_shape)
@given(shape=hh.shapes(), dtype=hh.all_dtypes, data=st.data())
def test_getitem(shape, dtype, data):
zero_sided = any(side == 0 for side in shape)
if zero_sided:
x = xp.zeros(shape, dtype=dtype)
else:
obj = data.draw(scalar_objects(dtype, shape), label="obj")
x = xp.asarray(obj, dtype=dtype)
note(f"{x=}")
key = data.draw(xps.indices(shape=shape, allow_newaxis=True), label="key")
repro_snippet = ph.format_snippet(f"{x!r}[{key!r}]")
try:
out = x[key]
ph.assert_dtype("__getitem__", in_dtype=x.dtype, out_dtype=out.dtype)
_key = normalize_key(key, shape)
axes_indices, expected_shape = get_indexed_axes_and_out_shape(_key, shape)
ph.assert_shape("__getitem__", out_shape=out.shape, expected=expected_shape)
out_zero_sided = any(side == 0 for side in expected_shape)
if not zero_sided and not out_zero_sided:
out_obj = []
for idx in product(*axes_indices):
val = obj
for i in idx:
val = val[i]
out_obj.append(val)
out_obj = sh.reshape(out_obj, expected_shape)
expected = xp.asarray(out_obj, dtype=dtype)
ph.assert_array_elements("__getitem__", out=out, expected=expected)
except Exception as exc:
ph.add_note(exc, repro_snippet)
raise
@pytest.mark.unvectorized
@given(
shape=hh.shapes(),
dtypes=hh.oneway_promotable_dtypes(dh.all_dtypes),
data=st.data(),
)
def test_setitem(shape, dtypes, data):
zero_sided = any(side == 0 for side in shape)
if zero_sided:
x = xp.zeros(shape, dtype=dtypes.result_dtype)
else:
obj = data.draw(scalar_objects(dtypes.result_dtype, shape), label="obj")
x = xp.asarray(obj, dtype=dtypes.result_dtype)
note(f"{x=}")
key = data.draw(xps.indices(shape=shape), label="key")
_key = normalize_key(key, shape)
axes_indices, out_shape = get_indexed_axes_and_out_shape(_key, shape)
value_strat = hh.arrays(dtype=dtypes.result_dtype, shape=out_shape)
if out_shape == ():
# We can pass scalars if we're only indexing one element
value_strat |= hh.from_dtype(dtypes.result_dtype)
value = data.draw(value_strat, label="value")
res = xp.asarray(x, copy=True)
res[key] = value
ph.assert_dtype("__setitem__", in_dtype=x.dtype, out_dtype=res.dtype, repr_name="x.dtype")
ph.assert_shape("__setitem__", out_shape=res.shape, expected=x.shape, repr_name="x.shape")
f_res = sh.fmt_idx("x", key)
if isinstance(value, get_args(Scalar)):
msg = f"{f_res}={res[key]!r}, but should be {value=} [__setitem__()]"
if cmath.isnan(value):
assert xp.isnan(res[key]), msg
else:
assert res[key] == value, msg
else:
ph.assert_array_elements("__setitem__", out=res[key], expected=value, out_repr=f_res)
unaffected_indices = set(sh.ndindex(res.shape)) - set(product(*axes_indices))
for idx in unaffected_indices:
ph.assert_0d_equals(
"__setitem__",
x_repr=f"old {f_res}",
x_val=x[idx],
out_repr=f"modified {f_res}",
out_val=res[idx],
)
@pytest.mark.unvectorized
@pytest.mark.data_dependent_shapes
@given(hh.shapes(), st.data())
def test_getitem_masking(shape, data):
x = data.draw(hh.arrays(hh.all_dtypes, shape=shape), label="x")
mask_shapes = st.one_of(
st.sampled_from([x.shape, ()]),
st.lists(st.booleans(), min_size=x.ndim, max_size=x.ndim).map(
lambda l: tuple(s if b else 0 for s, b in zip(x.shape, l))
),
hh.shapes(),
)
key = data.draw(hh.arrays(dtype=xp.bool, shape=mask_shapes), label="key")
if key.ndim > x.ndim or not all(
ks in (xs, 0) for xs, ks in zip(x.shape, key.shape)
):
with pytest.raises(IndexError):
x[key]
return
out = x[key]
ph.assert_dtype("__getitem__", in_dtype=x.dtype, out_dtype=out.dtype)
if key.ndim == 0:
expected_shape = (1,) if key else (0,)
expected_shape += x.shape
else:
size = int(xp.sum(xp.astype(key, xp.uint8)))
expected_shape = (size,) + x.shape[key.ndim :]
ph.assert_shape("__getitem__", out_shape=out.shape, expected=expected_shape)
if not any(s == 0 for s in key.shape):
assume(key.ndim == x.ndim) # TODO: test key.ndim < x.ndim scenarios
out_indices = sh.ndindex(out.shape)
for x_idx in sh.ndindex(x.shape):
if key[x_idx]:
out_idx = next(out_indices)
ph.assert_0d_equals(
"__getitem__",
x_repr=f"x[{x_idx}]",
x_val=x[x_idx],
out_repr=f"out[{out_idx}]",
out_val=out[out_idx],
)
@pytest.mark.unvectorized
@given(hh.shapes(), st.data())
def test_setitem_masking(shape, data):
x = data.draw(hh.arrays(hh.all_dtypes, shape=shape), label="x")
key = data.draw(hh.arrays(dtype=xp.bool, shape=shape), label="key")
value = data.draw(
hh.from_dtype(x.dtype) | hh.arrays(dtype=x.dtype, shape=()), label="value"
)
res = xp.asarray(x, copy=True)
res[key] = value
ph.assert_dtype("__setitem__", in_dtype=x.dtype, out_dtype=res.dtype, repr_name="x.dtype")
ph.assert_shape("__setitem__", out_shape=res.shape, expected=x.shape, repr_name="x.dtype")
scalar_type = dh.get_scalar_type(x.dtype)
for idx in sh.ndindex(x.shape):
if key[idx]:
if isinstance(value, get_args(Scalar)):
ph.assert_scalar_equals(
"__setitem__",
type_=scalar_type,
idx=idx,
out=scalar_type(res[idx]),
expected=value,
repr_name="modified x",
)
else:
ph.assert_0d_equals(
"__setitem__",
x_repr="value",
x_val=value,
out_repr=f"modified x[{idx}]",
out_val=res[idx]
)
else:
ph.assert_0d_equals(
"__setitem__",
x_repr=f"old x[{idx}]",
x_val=x[idx],
out_repr=f"modified x[{idx}]",
out_val=res[idx]
)
# ### Fancy indexing ###
@pytest.mark.min_version("2024.12")
@pytest.mark.unvectorized
@pytest.mark.parametrize("idx_max_dims", [1, None])
@given(shape=hh.shapes(min_dims=2), data=st.data())
def test_getitem_arrays_and_ints_1(shape, data, idx_max_dims):
# min_dims=2 : test multidim `x` arrays
# index arrays are 1D for idx_max_dims=1 and multidim for idx_max_dims=None
_test_getitem_arrays_and_ints(shape, data, idx_max_dims)
@pytest.mark.min_version("2024.12")
@pytest.mark.unvectorized
@pytest.mark.parametrize("idx_max_dims", [1, None])
@given(shape=hh.shapes(min_dims=1), data=st.data())
def test_getitem_arrays_and_ints_2(shape, data, idx_max_dims):
# min_dims=1 : favor 1D `x` arrays
# index arrays are 1D for idx_max_dims=1 and multidim for idx_max_dims=None
_test_getitem_arrays_and_ints(shape, data, idx_max_dims)
def _test_getitem_arrays_and_ints(shape, data, idx_max_dims):
assume((len(shape) > 0) and all(sh > 0 for sh in shape))
dtype = xp.int32
obj = data.draw(scalar_objects(dtype, shape), label="obj")
x = xp.asarray(obj, dtype=dtype)
# draw a mix of ints and index arrays
arr_index = [data.draw(st.booleans()) for _ in range(len(shape))]
assume(sum(arr_index) > 0)
# draw shapes for index arrays: max_dims=1 ==> 1D indexing arrays ONLY
# max_dims=None ==> multidim indexing arrays
if sum(arr_index) > 0:
index_shapes = data.draw(
hh.mutually_broadcastable_shapes(
sum(arr_index), min_dims=1, max_dims=idx_max_dims, min_side=1
)
)
index_shapes = list(index_shapes)
# prepare the indexing tuple, a mix of integer indices and index arrays
key = []
for i,typ in enumerate(arr_index):
if typ:
# draw an array index
this_idx = data.draw(
xps.arrays(
dtype,
shape=index_shapes.pop(),
elements=st.integers(0, shape[i]-1)
)
)
key.append(this_idx)
else:
# draw an integer
key.append(data.draw(st.integers(-shape[i], shape[i]-1)))
key = tuple(key)
out = x[key]
arrays = [xp.asarray(k) for k in key]
bcast_shape = sh.broadcast_shapes(*[arr.shape for arr in arrays])
bcast_key = [xp.broadcast_to(arr, bcast_shape) for arr in arrays]
for idx in sh.ndindex(bcast_shape):
tpl = tuple(k[idx] for k in bcast_key)
assert out[idx] == x[tpl], f"failing at {idx = } w/ {key = }"
def make_scalar_casting_param(
method_name: str, dtype: DataType, stype: ScalarType
) -> Param:
dtype_name = dh.dtype_to_name[dtype]
return pytest.param(
method_name, dtype, stype, id=f"{method_name}({dtype_name})"
)
@pytest.mark.parametrize(
"method_name, dtype, stype",
[make_scalar_casting_param("__bool__", xp.bool, bool)]
+ [make_scalar_casting_param("__int__", n, int) for n in dh.all_int_dtypes]
+ [make_scalar_casting_param("__index__", n, int) for n in dh.all_int_dtypes]
+ [make_scalar_casting_param("__float__", n, float) for n in dh.real_float_dtypes],
)
@given(data=st.data())
def test_scalar_casting(method_name, dtype, stype, data):
x = data.draw(hh.arrays(dtype, shape=()), label="x")
method = getattr(x, method_name)
out = method()
assert isinstance(
out, stype
), f"{method_name}({x})={out}, which is not a {stype.__name__} scalar"
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