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Infer NumPy Generator.integers result shapes #5652

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157 changes: 156 additions & 1 deletion tensor-shapes/pyrefly-numpy-stubs/numpy-stubs/random/__init__.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -12,16 +12,21 @@ from numpy._shapes import (
from numpy._typing import (
_ArrayLikeFloat_co,
_ArrayLikeInt_co,
_DTypeLike,
_FloatLike_co,
_Int64Codes,
_ShapeLike,
ArrayLike,
DTypeLike,
NDArray,
)
from numpy.random._generator import (
_ArrayF32,
_ArrayF64,
_DTypeLikeF32,
_DTypeLikeF64,
_DTypeLikeFloat,
_DTypeLikeI64,
_MethodExp,
_NDArrayLikeFloat,
Generator as _NumpyGenerator,
Expand Down Expand Up @@ -99,7 +104,7 @@ from numpy.random.mtrand import (
)
from shape_extensions import broadcast, Int, IntTuple, IntVar

from .. import dtype, float32, float64, ndarray
from .. import bool as _bool, dtype, float32, float64, int64, integer, ndarray

@overload
def randn() -> float: ...
Expand Down Expand Up @@ -166,6 +171,156 @@ class Generator(_NumpyGenerator):
@overload
def standard_cauchy(self, size: _ShapeLike) -> _ArrayF64: ...
@overload
def integers[AnyIntT: (bool, int)](
self,
low: int,
high: int | None = None,
size: None = None,
*,
dtype: type[AnyIntT],
endpoint: bool = False,
) -> AnyIntT: ...
@overload
def integers[ScalarT: integer | _bool](
self,
low: int,
high: int | None = None,
size: None = None,
*,
dtype: _DTypeLike[ScalarT],
endpoint: bool = False,
) -> ScalarT: ...
@overload
def integers(
self,
low: int,
high: int | None = None,
size: None = None,
dtype: _DTypeLikeI64 = ...,
endpoint: bool = False,
) -> int64: ...
@overload
def integers(
self,
low: int,
high: int | None = None,
size: None = None,
dtype: DTypeLike | None = ...,
endpoint: bool = False,
) -> Any: ...
# With `size`, the result has exactly that shape, and NumPy raises if `low` or
# `high` does not broadcast to it. Without `dtype`, `ScalarT` defaults to `int64`.
@overload
def integers[N: IntVar, ScalarT: integer | _bool = int64](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None,
size: Int[N],
dtype: _DTypeLike[ScalarT] | _Int64Codes = ...,
endpoint: bool = False,
) -> ndarray[[N], dtype[ScalarT]]: ...
@overload
def integers[Shape: IntTuple, ScalarT: integer | _bool = int64](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None,
size: Shape,
dtype: _DTypeLike[ScalarT] | _Int64Codes = ...,
endpoint: bool = False,
) -> ndarray[Shape, dtype[ScalarT]]: ...
@overload
def integers[N: IntVar, ScalarT: integer | _bool = int64](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: Int[N],
dtype: _DTypeLike[ScalarT] | _Int64Codes = ...,
endpoint: bool = False,
) -> ndarray[[N], dtype[ScalarT]]: ...
@overload
def integers[Shape: IntTuple, ScalarT: integer | _bool = int64](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: Shape,
dtype: _DTypeLike[ScalarT] | _Int64Codes = ...,
endpoint: bool = False,
) -> ndarray[Shape, dtype[ScalarT]]: ...
# Without `size`, the result has the broadcast shape of `low` and `high`.
@overload
def integers[
LowShape: IntTuple = [],
HighShape: IntTuple = [],
ScalarT: integer | _bool = int64,
](
self,
low: ndarray[LowShape] | int,
high: ndarray[HighShape] | int | None = None,
size: None = None,
dtype: _DTypeLike[ScalarT] | _Int64Codes = ...,
endpoint: bool = False,
) -> ndarray[broadcast(LowShape, HighShape), dtype[ScalarT]]: ...
@overload
def integers[ScalarT: integer | _bool](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: _ShapeLike,
dtype: _DTypeLike[ScalarT],
endpoint: bool = False,
) -> NDArray[ScalarT]: ...
@overload
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: _ShapeLike,
dtype: _DTypeLikeI64 = ...,
endpoint: bool = False,
) -> NDArray[int64]: ...
@overload
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: _ShapeLike,
dtype: DTypeLike | None = ...,
endpoint: bool = False,
) -> ndarray: ...
@overload
def integers[ScalarT: integer | _bool](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
size: _ShapeLike | None = None,
*,
dtype: _DTypeLike[ScalarT],
endpoint: bool = False,
) -> NDArray[ScalarT] | Any: ...
@overload
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
size: _ShapeLike | None = None,
dtype: _DTypeLikeI64 = ...,
endpoint: bool = False,
) -> NDArray[int64] | Any: ...
@overload
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
size: _ShapeLike | None = None,
dtype: DTypeLike | None = ...,
endpoint: bool = False,
) -> ndarray | Any: ...
@overload
def multivariate_normal[MeanShape: IntTuple, CovShape: IntTuple](
self,
/,
Expand Down
80 changes: 80 additions & 0 deletions tensor-shapes/pyrefly-numpy-stubs/test/test_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -269,3 +269,83 @@ def check_random_and_standard_cauchy_symbolic_size[N: IntVar, M: IntVar](
rng = np.random.default_rng(0)
assert_type(rng.random(n), np.ndarray[[N], np.dtype[np.float64]])
assert_type(rng.standard_cauchy((n, m)), np.ndarray[[N, M], np.dtype[np.float64]])


def check_integers_return_scalars_without_size() -> None:
rng = np.random.default_rng(0)
assert_type(rng.integers(10), np.int64)
assert_type(rng.integers(0, 10), np.int64)
assert_type(rng.integers(0, 10, dtype=np.uint8), np.uint8)


def test_integers_int_size() -> None:
rng = np.random.default_rng(0)
result = rng.integers(0, 10, size=5)
assert_type(result, np.ndarray[[5], np.dtype[np.int64]])
assert_shape(result.shape, (5,))
assert_shape(rng.integers(0, 10, 5).shape, (5,))
assert_shape(rng.integers(10, size=5).shape, (5,))
assert_shape(rng.integers(0, 10, 5, endpoint=True).shape, (5,))


def test_integers_tuple_size() -> None:
rng = np.random.default_rng(0)
result = rng.integers(0, 10, (2, 3))
assert_type(result, np.ndarray[[2, 3], np.dtype[np.int64]])
assert_shape(result.shape, (2, 3))
assert_shape(rng.integers(10, size=(2, 3)).shape, (2, 3))


def test_integers_dtype() -> None:
rng = np.random.default_rng(0)
result = rng.integers(0, 10, (2, 3), dtype=np.int32)
assert_type(result, np.ndarray[[2, 3], np.dtype[np.int32]])
assert_shape(result.shape, (2, 3))
mask = rng.integers(0, 2, size=4, dtype=np.bool_)
assert_type(mask, np.ndarray[[4], np.dtype[np.bool_]])
assert_shape(mask.shape, (4,))
named = rng.integers(0, 10, size=4, dtype="int64")
assert_type(named, np.ndarray[[4], np.dtype[np.int64]])
assert_shape(named.shape, (4,))


def test_integers_array_bounds_with_size() -> None:
rng = np.random.default_rng(0)
result = rng.integers(np.full(3, 5), size=(2, 3))
assert_type(result, np.ndarray[[2, 3], np.dtype[np.int64]])
assert_shape(result.shape, (2, 3))


def test_integers_array_bounds_without_size() -> None:
rng = np.random.default_rng(0)
result = rng.integers(np.zeros(3, dtype=np.int64), np.full((2, 1), 5))
assert_type(result, np.ndarray[[2, 3], np.dtype[np.int64]])
assert_shape(result.shape, (2, 3))
assert_shape(rng.integers(np.full(3, 5)).shape, (3,))
assert_shape(rng.integers(0, np.full((2, 1), 5)).shape, (2, 1))


def test_integers_unshaped_arguments() -> None:
rng = np.random.default_rng(0)
assert_shape(rng.integers(10, size=[2, 3]).shape, IntTuple, runtime=(2, 3))
assert_shape(
rng.integers(10, size=(2, 3), dtype=int).shape, IntTuple, runtime=(2, 3)
)


def test_integers_mismatched_bounds() -> None:
rng = np.random.default_rng(0)
with assert_raises(ValueError):
rng.integers(np.full(3, 5), np.full(2, 9)) # E: Cannot broadcast


def check_integers_symbolic_shapes[N: IntVar, M: IntVar](
n: Int[N], low: np.ndarray[[N, 1]], high: np.ndarray[[M]]
) -> None:
rng = np.random.default_rng(0)
assert_type(rng.integers(10, size=n), np.ndarray[[N], np.dtype[np.int64]])
assert_type(rng.integers(low, high), np.ndarray[[N, M], np.dtype[np.int64]])
assert_type(
rng.integers(10, size=(n, n), dtype=np.int16),
np.ndarray[[N, N], np.dtype[np.int16]],
)
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