# BSD 3-Clause License; see https://github.com/scikit-hep/awkward/blob/main/LICENSE

from __future__ import annotations

import awkward as ak
from awkward._connect.numpy import UNSUPPORTED
from awkward._dispatch import high_level_function
from awkward._layout import HighLevelContext
from awkward._namedaxis import (
    _get_named_axis,
    _keep_named_axis,
    _named_axis_to_positional_axis,
    _remove_named_axis,
)
from awkward._nplikes.numpy_like import NumpyMetadata
from awkward._regularize import regularize_axis

__all__ = ("argmax", "nanargmax")

np = NumpyMetadata.instance()


@high_level_function()
def argmax(
    array,
    axis=None,
    *,
    keepdims=False,
    mask_identity=True,
    highlevel=True,
    behavior=None,
    attrs=None,
):
    """
    Args:
        array: Array-like data (anything #ak.to_layout recognizes).
        axis (None or int or str): If None, combine all values from the array into
            a single scalar result; if an int, group by that axis: `0` is the
            outermost, `1` is the first level of nested lists, etc., and
            negative `axis` counts from the innermost: `-1` is the innermost,
            `-2` is the next level up, etc; if a str, it is interpreted as the
            name of the axis which maps to an int if named axes are present.
            Named axes are attached to an array using #ak.with_named_axis and
            removed with #ak.without_named_axis; also see the
            [Named axes user guide](../../user-guide/how-to-array-properties-named-axis.html).
        keepdims (bool): If False, this reducer decreases the number of
            dimensions by 1; if True, the reduced values are wrapped in a new
            length-1 dimension so that the result of this operation may be
            broadcasted with the original array.
        mask_identity (bool): If True, reducing over empty lists results in
            None (an option type); otherwise, reducing over empty lists
            results in the operation's identity.
        highlevel (bool): If True, return an #ak.Array; otherwise, return
            a low-level #ak.contents.Content subclass.
        behavior (None or dict): Custom #ak.behavior for the output array, if
            high-level.
        attrs (None or dict): Custom attributes for the output array, if
            high-level.

    Returns the index position of the maximum value in each group of elements
    from `array` (many types supported, including all Awkward Arrays and
    Records). The identity of maximization would be negative infinity, but
    argmax must return the position of the maximum element, which has no value
    for empty lists. Therefore, the identity should be masked: the argmax of
    an empty list is None. If `mask_identity=False`, the result would be `-1`,
    which is distinct from all valid index positions, but care should be taken
    that it is not misinterpreted as "the last element of the list."

    This operation is the same as NumPy's
    [argmax](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html)
    if all lists at a given dimension have the same length and no None values,
    but it generalizes to cases where they do not.

    See #ak.sum for a more complete description of nested list and missing
    value (None) handling in reducers.

    See also #ak.nanargmax.
    """
    # Dispatch
    yield (array,)

    # Implementation
    return _impl(array, axis, keepdims, mask_identity, highlevel, behavior, attrs)


@high_level_function()
def nanargmax(
    array,
    axis=None,
    *,
    keepdims=False,
    mask_identity=True,
    highlevel=True,
    behavior=None,
    attrs=None,
):
    """
    Args:
        array: Array-like data (anything #ak.to_layout recognizes).
        axis (None or int or str): If None, combine all values from the array into
            a single scalar result; if an int, group by that axis: `0` is the
            outermost, `1` is the first level of nested lists, etc., and
            negative `axis` counts from the innermost: `-1` is the innermost,
            `-2` is the next level up, etc; if a str, it is interpreted as the
            name of the axis which maps to an int if named axes are present.
            Named axes are attached to an array using #ak.with_named_axis and
            removed with #ak.without_named_axis; also see the
            [Named axes user guide](../../user-guide/how-to-array-properties-named-axis.html).
        keepdims (bool): If False, this reducer decreases the number of
            dimensions by 1; if True, the reduced values are wrapped in a new
            length-1 dimension so that the result of this operation may be
            broadcasted with the original array.
        mask_identity (bool): If True, reducing over empty lists results in
            None (an option type); otherwise, reducing over empty lists
            results in the operation's identity.
        highlevel (bool): If True, return an #ak.Array; otherwise, return
            a low-level #ak.contents.Content subclass.
        behavior (None or dict): Custom #ak.behavior for the output array, if
            high-level.
        attrs (None or dict): Custom attributes for the output array, if
            high-level.

    Like #ak.argmax, but treating NaN ("not a number") values as missing.

    Equivalent to::

        ak.argmax(ak.nan_to_none(array))

    with all other arguments unchanged.

    See also #ak.argmax.
    """
    # Dispatch
    yield (array,)

    # Implementation
    return _impl(
        ak.operations.ak_nan_to_none._impl(array, True, behavior, None),
        axis,
        keepdims,
        mask_identity,
        highlevel,
        behavior,
        attrs,
    )


def _impl(array, axis, keepdims, mask_identity, highlevel, behavior, attrs):
    with HighLevelContext(behavior=behavior, attrs=attrs) as ctx:
        layout = ctx.unwrap(array, allow_record=False, primitive_policy="error")

    # Handle named axis
    named_axis = _get_named_axis(ctx)
    # Step 1: Normalize named axis to positional axis
    axis = _named_axis_to_positional_axis(named_axis, axis)
    # Step 2: propagate named axis from input to output,
    #   keepdims=True: use strategy "keep all" (see: awkward._namedaxis)
    #   keepdims=False: use strategy "remove one" (see: awkward._namedaxis)
    out_named_axis = _keep_named_axis(named_axis, None)
    if not keepdims:
        out_named_axis = _remove_named_axis(
            named_axis=out_named_axis,
            axis=axis,
            total=layout.minmax_depth[1],
        )

    axis = regularize_axis(axis, none_allowed=True)

    reducer = ak._reducers.ArgMax()

    out = ak._do.reduce(
        layout,
        reducer,
        axis=axis,
        mask=mask_identity,
        keepdims=keepdims,
        behavior=ctx.behavior,
    )

    wrapped_out = ctx.wrap(
        out,
        highlevel=highlevel,
        allow_other=True,
    )

    # propagate named axis to output
    return ak.operations.ak_with_named_axis._impl(
        wrapped_out,
        named_axis=out_named_axis,
        highlevel=highlevel,
        behavior=ctx.behavior,
        attrs=ctx.attrs,
    )


@ak._connect.numpy.implements("argmax")
def _nep_18_impl_argmax(a, axis=None, out=UNSUPPORTED, *, keepdims=False):
    return argmax(a, axis=axis, keepdims=keepdims)


@ak._connect.numpy.implements("nanargmax")
def _nep_18_impl_nanargmax(a, axis=None, out=UNSUPPORTED, *, keepdims=False):
    return nanargmax(a, axis=axis, keepdims=keepdims)
