_delegation.py

"""Delegation to existing implementations for Public API Functions."""

from collections.abc import Sequence
from types import ModuleType
from typing import Literal

from ._lib import Backend, _funcs
from ._lib._utils._compat import array_namespace
from ._lib._utils._typing import Array

__all__ = ["isclose", "pad"]


def _delegate(xp: ModuleType, *backends: Backend) -> bool:
    """
    Check whether `xp` is one of the `backends` to delegate to.

    Parameters
    ----------
    xp : array_namespace
        Array namespace to check.
    *backends : IsNamespace
        Arbitrarily many backends (from the ``IsNamespace`` enum) to check.

    Returns
    -------
    bool
        ``True`` if `xp` matches one of the `backends`, ``False`` otherwise.
    """
    return any(backend.is_namespace(xp) for backend in backends)


def isclose(
    a: Array | complex,
    b: Array | complex,
    *,
    rtol: float = 1e-05,
    atol: float = 1e-08,
    equal_nan: bool = False,
    xp: ModuleType | None = None,
) -> Array:
    """
    Return a boolean array where two arrays are element-wise equal within a tolerance.

    The tolerance values are positive, typically very small numbers. The relative
    difference ``(rtol * abs(b))`` and the absolute difference `atol` are added together
    to compare against the absolute difference between `a` and `b`.

    NaNs are treated as equal if they are in the same place and if ``equal_nan=True``.
    Infs are treated as equal if they are in the same place and of the same sign in both
    arrays.

    Parameters
    ----------
    a, b : Array | int | float | complex | bool
        Input objects to compare. At least one must be an array.
    rtol : array_like, optional
        The relative tolerance parameter (see Notes).
    atol : array_like, optional
        The absolute tolerance parameter (see Notes).
    equal_nan : bool, optional
        Whether to compare NaN's as equal. If True, NaN's in `a` will be considered
        equal to NaN's in `b` in the output array.
    xp : array_namespace, optional
        The standard-compatible namespace for `a` and `b`. Default: infer.

    Returns
    -------
    Array
        A boolean array of shape broadcasted from `a` and `b`, containing ``True`` where
        `a` is close to `b`, and ``False`` otherwise.

    Warnings
    --------
    The default `atol` is not appropriate for comparing numbers with magnitudes much
    smaller than one (see notes).

    See Also
    --------
    math.isclose : Similar function in stdlib for Python scalars.

    Notes
    -----
    For finite values, `isclose` uses the following equation to test whether two
    floating point values are equivalent::

        absolute(a - b) <= (atol + rtol * absolute(b))

    Unlike the built-in `math.isclose`,
    the above equation is not symmetric in `a` and `b`,
    so that ``isclose(a, b)`` might be different from ``isclose(b, a)`` in some rare
    cases.

    The default value of `atol` is not appropriate when the reference value `b` has
    magnitude smaller than one. For example, it is unlikely that ``a = 1e-9`` and
    ``b = 2e-9`` should be considered "close", yet ``isclose(1e-9, 2e-9)`` is ``True``
    with default settings. Be sure to select `atol` for the use case at hand, especially
    for defining the threshold below which a non-zero value in `a` will be considered
    "close" to a very small or zero value in `b`.

    The comparison of `a` and `b` uses standard broadcasting, which means that `a` and
    `b` need not have the same shape in order for ``isclose(a, b)`` to evaluate to
    ``True``.

    `isclose` is not defined for non-numeric data types.
    ``bool`` is considered a numeric data-type for this purpose.
    """
    xp = array_namespace(a, b) if xp is None else xp

    if _delegate(
        xp,
        Backend.NUMPY,
        Backend.CUPY,
        Backend.DASK,
        Backend.JAX,
        Backend.TORCH,
    ):
        return xp.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan)

    return _funcs.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan, xp=xp)


def pad(
    x: Array,
    pad_width: int | tuple[int, int] | Sequence[tuple[int, int]],
    mode: Literal["constant"] = "constant",
    *,
    constant_values: complex = 0,
    xp: ModuleType | None = None,
) -> Array:
    """
    Pad the input array.

    Parameters
    ----------
    x : array
        Input array.
    pad_width : int or tuple of ints or sequence of pairs of ints
        Pad the input array with this many elements from each side.
        If a sequence of tuples, ``[(before_0, after_0), ... (before_N, after_N)]``,
        each pair applies to the corresponding axis of ``x``.
        A single tuple, ``(before, after)``, is equivalent to a list of ``x.ndim``
        copies of this tuple.
    mode : str, optional
        Only "constant" mode is currently supported, which pads with
        the value passed to `constant_values`.
    constant_values : python scalar, optional
        Use this value to pad the input. Default is zero.
    xp : array_namespace, optional
        The standard-compatible namespace for `x`. Default: infer.

    Returns
    -------
    array
        The input array,
        padded with ``pad_width`` elements equal to ``constant_values``.
    """
    xp = array_namespace(x) if xp is None else xp

    if mode != "constant":
        msg = "Only `'constant'` mode is currently supported"
        raise NotImplementedError(msg)

    # https://github.com/pytorch/pytorch/blob/cf76c05b4dc629ac989d1fb8e789d4fac04a095a/torch/_numpy/_funcs_impl.py#L2045-L2056
    if _delegate(xp, Backend.TORCH):
        pad_width = xp.asarray(pad_width)
        pad_width = xp.broadcast_to(pad_width, (x.ndim, 2))
        pad_width = xp.flip(pad_width, axis=(0,)).flatten()
        return xp.nn.functional.pad(x, tuple(pad_width), value=constant_values)  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]

    if _delegate(xp, Backend.NUMPY, Backend.JAX, Backend.CUPY, Backend.SPARSE):
        return xp.pad(x, pad_width, mode, constant_values=constant_values)

    return _funcs.pad(x, pad_width, constant_values=constant_values, xp=xp)