# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.

import re
from types import EllipsisType
from typing import NamedTuple

from hypothesis import assume, strategies as st
from hypothesis.errors import InvalidArgument
from hypothesis.internal.conjecture.utils import _calc_p_continue
from hypothesis.internal.coverage import check_function
from hypothesis.internal.validation import check_type, check_valid_interval
from hypothesis.strategies._internal.utils import defines_strategy
from hypothesis.utils.conventions import UniqueIdentifier, not_set

__all__ = [
    "NDIM_MAX",
    "_BIE",
    "BasicIndex",
    "BasicIndexStrategy",
    "BroadcastableShapes",
    "MutuallyBroadcastableShapesStrategy",
    "Shape",
    "_BIENoEllipsis",
    "_BIENoEllipsisNoNewaxis",
    "_BIENoNewaxis",
    "array_shapes",
    "broadcastable_shapes",
    "check_argument",
    "check_valid_dims",
    "mutually_broadcastable_shapes",
    "order_check",
    "valid_tuple_axes",
]


Shape = tuple[int, ...]

# Type aliases for basic array index elements. Variants exist to accurately
# type the return value of basic_indices() based on allow_ellipsis/allow_newaxis.
_BIE = int | slice | None | EllipsisType
_BIENoEllipsis = int | slice | None
_BIENoNewaxis = int | slice | EllipsisType
_BIENoEllipsisNoNewaxis = int | slice

BasicIndex = _BIE | tuple[_BIE, ...]


class BroadcastableShapes(NamedTuple):
    input_shapes: tuple[Shape, ...]
    result_shape: Shape


@check_function
def check_argument(condition, fail_message, *f_args, **f_kwargs):
    if not condition:
        raise InvalidArgument(fail_message.format(*f_args, **f_kwargs))


@check_function
def order_check(name, floor, min_, max_):
    if floor > min_:
        raise InvalidArgument(f"min_{name} must be at least {floor} but was {min_}")
    if min_ > max_:
        raise InvalidArgument(f"min_{name}={min_} is larger than max_{name}={max_}")


# 32 is a dimension limit specific to NumPy, and does not necessarily apply to
# other array/tensor libraries. Historically these strategies were built for the
# NumPy extra, so it's nice to keep these limits, and it's seemingly unlikely
# someone would want to generate >32 dim arrays anyway.
# See https://github.com/HypothesisWorks/hypothesis/pull/3067.
NDIM_MAX = 32


@check_function
def check_valid_dims(dims, name):
    if dims > NDIM_MAX:
        raise InvalidArgument(
            f"{name}={dims}, but Hypothesis does not support arrays with "
            f"more than {NDIM_MAX} dimensions"
        )


@defines_strategy()
def array_shapes(
    *,
    min_dims: int = 1,
    max_dims: int | None = None,
    min_side: int = 1,
    max_side: int | None = None,
) -> st.SearchStrategy[Shape]:
    """Return a strategy for array shapes (tuples of int >= 1).

    * ``min_dims`` is the smallest length that the generated shape can possess.
    * ``max_dims`` is the largest length that the generated shape can possess,
      defaulting to ``min_dims + 2``.
    * ``min_side`` is the smallest size that a dimension can possess.
    * ``max_side`` is the largest size that a dimension can possess,
      defaulting to ``min_side + 5``.
    """
    check_type(int, min_dims, "min_dims")
    check_type(int, min_side, "min_side")
    check_valid_dims(min_dims, "min_dims")

    if max_dims is None:
        max_dims = min(min_dims + 2, NDIM_MAX)
    check_type(int, max_dims, "max_dims")
    check_valid_dims(max_dims, "max_dims")

    if max_side is None:
        max_side = min_side + 5
    check_type(int, max_side, "max_side")

    order_check("dims", 0, min_dims, max_dims)
    order_check("side", 0, min_side, max_side)

    return st.lists(
        st.integers(min_side, max_side), min_size=min_dims, max_size=max_dims
    ).map(tuple)


@defines_strategy()
def valid_tuple_axes(
    ndim: int,
    *,
    min_size: int = 0,
    max_size: int | None = None,
) -> st.SearchStrategy[tuple[int, ...]]:
    """All tuples will have a length >= ``min_size`` and <= ``max_size``. The default
    value for ``max_size`` is ``ndim``.

    Examples from this strategy shrink towards an empty tuple, which render most
    sequential functions as no-ops.

    The following are some examples drawn from this strategy.

    .. code-block:: pycon

      >>> [valid_tuple_axes(3).example() for i in range(4)]
      [(-3, 1), (0, 1, -1), (0, 2), (0, -2, 2)]

    ``valid_tuple_axes`` can be joined with other strategies to generate
    any type of valid axis object, i.e. integers, tuples, and ``None``:

    .. code-block:: python

      any_axis_strategy = none() | integers(-ndim, ndim - 1) | valid_tuple_axes(ndim)

    """
    check_type(int, ndim, "ndim")
    check_type(int, min_size, "min_size")
    if max_size is None:
        max_size = ndim
    check_type(int, max_size, "max_size")
    order_check("size", 0, min_size, max_size)
    check_valid_interval(max_size, ndim, "max_size", "ndim")

    axes = st.integers(0, max(0, 2 * ndim - 1)).map(
        lambda x: x if x < ndim else x - 2 * ndim
    )

    return st.lists(
        axes, min_size=min_size, max_size=max_size, unique_by=lambda x: x % ndim
    ).map(tuple)


@defines_strategy()
def broadcastable_shapes(
    shape: Shape,
    *,
    min_dims: int = 0,
    max_dims: int | None = None,
    min_side: int = 1,
    max_side: int | None = None,
) -> st.SearchStrategy[Shape]:
    """Return a strategy for shapes that are broadcast-compatible with the
    provided shape.

    Examples from this strategy shrink towards a shape with length ``min_dims``.
    The size of an aligned dimension shrinks towards size ``1``. The size of an
    unaligned dimension shrink towards ``min_side``.

    * ``shape`` is a tuple of integers.
    * ``min_dims`` is the smallest length that the generated shape can possess.
    * ``max_dims`` is the largest length that the generated shape can possess,
      defaulting to ``max(len(shape), min_dims) + 2``.
    * ``min_side`` is the smallest size that an unaligned dimension can possess.
    * ``max_side`` is the largest size that an unaligned dimension can possess,
      defaulting to 2 plus the size of the largest aligned dimension.

    The following are some examples drawn from this strategy.

    .. code-block:: pycon

        >>> [broadcastable_shapes(shape=(2, 3)).example() for i in range(5)]
        [(1, 3), (), (2, 3), (2, 1), (4, 1, 3), (3, )]

    """
    check_type(tuple, shape, "shape")
    check_type(int, min_side, "min_side")
    check_type(int, min_dims, "min_dims")
    check_valid_dims(min_dims, "min_dims")

    strict_check = max_side is None or max_dims is None

    if max_dims is None:
        max_dims = min(max(len(shape), min_dims) + 2, NDIM_MAX)
    check_type(int, max_dims, "max_dims")
    check_valid_dims(max_dims, "max_dims")

    if max_side is None:
        max_side = max(shape[-max_dims:] + (min_side,)) + 2
    check_type(int, max_side, "max_side")

    order_check("dims", 0, min_dims, max_dims)
    order_check("side", 0, min_side, max_side)

    if strict_check:
        dims = max_dims
        bound_name = "max_dims"
    else:
        dims = min_dims
        bound_name = "min_dims"

    # check for unsatisfiable min_side
    if not all(min_side <= s for s in shape[::-1][:dims] if s != 1):
        raise InvalidArgument(
            f"Given shape={shape}, there are no broadcast-compatible "
            f"shapes that satisfy: {bound_name}={dims} and min_side={min_side}"
        )

    # check for unsatisfiable [min_side, max_side]
    if not (
        min_side <= 1 <= max_side or all(s <= max_side for s in shape[::-1][:dims])
    ):
        raise InvalidArgument(
            f"Given base_shape={shape}, there are no broadcast-compatible "
            f"shapes that satisfy all of {bound_name}={dims}, "
            f"min_side={min_side}, and max_side={max_side}"
        )

    if not strict_check:
        # reduce max_dims to exclude unsatisfiable dimensions
        for n, s in zip(range(max_dims), shape[::-1], strict=False):
            if s < min_side and s != 1:
                max_dims = n
                break
            if not (min_side <= 1 <= max_side or s <= max_side):
                max_dims = n
                break

    return MutuallyBroadcastableShapesStrategy(
        num_shapes=1,
        base_shape=shape,
        min_dims=min_dims,
        max_dims=max_dims,
        min_side=min_side,
        max_side=max_side,
    ).map(lambda x: x.input_shapes[0])


# See https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html
# Implementation based on numpy.lib.function_base._parse_gufunc_signature
# with minor upgrades to handle numeric and optional dimensions.  Examples:
#
#     add       (),()->()                   binary ufunc
#     sum1d     (i)->()                     reduction
#     inner1d   (i),(i)->()                 vector-vector multiplication
#     matmat    (m,n),(n,p)->(m,p)          matrix multiplication
#     vecmat    (n),(n,p)->(p)              vector-matrix multiplication
#     matvec    (m,n),(n)->(m)              matrix-vector multiplication
#     matmul    (m?,n),(n,p?)->(m?,p?)      combination of the four above
#     cross1d   (3),(3)->(3)                cross product with frozen dimensions
#
# Note that while no examples of such usage are given, Numpy does allow
# generalised ufuncs that have *multiple output arrays*.  This is not
# currently supported by Hypothesis - please contact us if you would use it!
#
# We are unsure if gufuncs allow frozen dimensions to be optional, but it's
# easy enough to support here - and so we will unless we learn otherwise.
_DIMENSION = r"\w+\??"  # Note that \w permits digits too!
_SHAPE = rf"\((?:{_DIMENSION}(?:,{_DIMENSION}){{0,31}})?\)"
_ARGUMENT_LIST = f"{_SHAPE}(?:,{_SHAPE})*"
_SIGNATURE = rf"^{_ARGUMENT_LIST}->{_SHAPE}$"
_SIGNATURE_MULTIPLE_OUTPUT = rf"^{_ARGUMENT_LIST}->{_ARGUMENT_LIST}$"


class _GUfuncSig(NamedTuple):
    input_shapes: tuple[Shape, ...]
    result_shape: Shape


def _hypothesis_parse_gufunc_signature(signature):
    # Disable all_checks to better match the Numpy version, for testing
    if not re.match(_SIGNATURE, signature):
        if re.match(_SIGNATURE_MULTIPLE_OUTPUT, signature):
            raise InvalidArgument(
                "Hypothesis does not yet support generalised ufunc signatures "
                "with multiple output arrays - mostly because we don't know of "
                "anyone who uses them!  Please get in touch with us to fix that."
                f"\n ({signature=})"
            )
        if re.match(
            (
                # Taken from np.lib.function_base._SIGNATURE
                r"^\((?:\w+(?:,\w+)*)?\)(?:,\((?:\w+(?:,\w+)*)?\))*->"
                r"\((?:\w+(?:,\w+)*)?\)(?:,\((?:\w+(?:,\w+)*)?\))*$"
            ),
            signature,
        ):
            raise InvalidArgument(
                f"{signature=} matches Numpy's regex for gufunc signatures, "
                f"but contains shapes with more than {NDIM_MAX} dimensions and is thus invalid."
            )
        raise InvalidArgument(f"{signature!r} is not a valid gufunc signature")
    input_shapes, output_shapes = (
        tuple(tuple(re.findall(_DIMENSION, a)) for a in re.findall(_SHAPE, arg_list))
        for arg_list in signature.split("->")
    )
    assert len(output_shapes) == 1
    result_shape = output_shapes[0]
    # Check that there are no names in output shape that do not appear in inputs.
    # (kept out of parser function for easier generation of test values)
    # We also disallow frozen optional dimensions - this is ambiguous as there is
    # no way to share an un-named dimension between shapes.  Maybe just padding?
    # Anyway, we disallow it pending clarification from upstream.
    for shape in (*input_shapes, result_shape):
        for name in shape:
            try:
                int(name.strip("?"))
                if "?" in name:
                    raise InvalidArgument(
                        f"Got dimension {name!r}, but handling of frozen optional dimensions "
                        "is ambiguous.  If you known how this should work, please "
                        f"contact us to get this fixed and documented ({signature=})."
                    )
            except ValueError:
                names_in = {n.strip("?") for shp in input_shapes for n in shp}
                names_out = {n.strip("?") for n in result_shape}
                if name.strip("?") in (names_out - names_in):
                    raise InvalidArgument(
                        f"The {name!r} dimension only appears in the output shape, and is "
                        f"not frozen, so the size is not determined ({signature=})."
                    ) from None
    return _GUfuncSig(input_shapes=input_shapes, result_shape=result_shape)


@defines_strategy()
def mutually_broadcastable_shapes(
    *,
    num_shapes: UniqueIdentifier | int = not_set,
    signature: UniqueIdentifier | str = not_set,
    base_shape: Shape = (),
    min_dims: int = 0,
    max_dims: int | None = None,
    min_side: int = 1,
    max_side: int | None = None,
) -> st.SearchStrategy[BroadcastableShapes]:
    """Return a strategy for a specified number of shapes N that are
    mutually-broadcastable with one another and with the provided base shape.

    * ``num_shapes`` is the number of mutually broadcast-compatible shapes to generate.
    * ``base_shape`` is the shape against which all generated shapes can broadcast.
      The default shape is empty, which corresponds to a scalar and thus does
      not constrain broadcasting at all.
    * ``min_dims`` is the smallest length that the generated shape can possess.
    * ``max_dims`` is the largest length that the generated shape can possess,
      defaulting to ``max(len(shape), min_dims) + 2``.
    * ``min_side`` is the smallest size that an unaligned dimension can possess.
    * ``max_side`` is the largest size that an unaligned dimension can possess,
      defaulting to 2 plus the size of the largest aligned dimension.

    The strategy will generate a :obj:`python:typing.NamedTuple` containing:

    * ``input_shapes`` as a tuple of the N generated shapes.
    * ``result_shape`` as the resulting shape produced by broadcasting the N shapes
      with the base shape.

    The following are some examples drawn from this strategy.

    .. code-block:: pycon

        >>> # Draw three shapes where each shape is broadcast-compatible with (2, 3)
        ... strat = mutually_broadcastable_shapes(num_shapes=3, base_shape=(2, 3))
        >>> for _ in range(5):
        ...     print(strat.example())
        BroadcastableShapes(input_shapes=((4, 1, 3), (4, 2, 3), ()), result_shape=(4, 2, 3))
        BroadcastableShapes(input_shapes=((3,), (1, 3), (2, 3)), result_shape=(2, 3))
        BroadcastableShapes(input_shapes=((), (), ()), result_shape=())
        BroadcastableShapes(input_shapes=((3,), (), (3,)), result_shape=(3,))
        BroadcastableShapes(input_shapes=((1, 2, 3), (3,), ()), result_shape=(1, 2, 3))

    """
    arg_msg = "Pass either the `num_shapes` or the `signature` argument, but not both."
    if num_shapes is not not_set:
        check_argument(signature is not_set, arg_msg)
        check_type(int, num_shapes, "num_shapes")
        assert isinstance(num_shapes, int)  # for mypy
        parsed_signature = None
        sig_dims = 0
    else:
        check_argument(signature is not not_set, arg_msg)
        if signature is None:
            raise InvalidArgument(
                "Expected a string, but got invalid signature=None.  "
                "(maybe .signature attribute of an element-wise ufunc?)"
            )
        check_type(str, signature, "signature")
        parsed_signature = _hypothesis_parse_gufunc_signature(signature)
        all_shapes = (*parsed_signature.input_shapes, parsed_signature.result_shape)
        sig_dims = min(len(s) for s in all_shapes)
        num_shapes = len(parsed_signature.input_shapes)

    if num_shapes < 1:
        raise InvalidArgument(f"num_shapes={num_shapes} must be at least 1")

    check_type(tuple, base_shape, "base_shape")
    check_type(int, min_side, "min_side")
    check_type(int, min_dims, "min_dims")
    check_valid_dims(min_dims, "min_dims")

    strict_check = max_dims is not None

    if max_dims is None:
        max_dims = min(max(len(base_shape), min_dims) + 2, NDIM_MAX - sig_dims)
    check_type(int, max_dims, "max_dims")
    check_valid_dims(max_dims, "max_dims")

    if max_side is None:
        max_side = max(base_shape[-max_dims:] + (min_side,)) + 2
    check_type(int, max_side, "max_side")

    order_check("dims", 0, min_dims, max_dims)
    order_check("side", 0, min_side, max_side)

    if signature is not None and max_dims > NDIM_MAX - sig_dims:
        raise InvalidArgument(
            f"max_dims={signature!r} would exceed the {NDIM_MAX}-dimension"
            "limit Hypothesis imposes on array shapes, "
            f"given signature={parsed_signature!r}"
        )

    if strict_check:
        dims = max_dims
        bound_name = "max_dims"
    else:
        dims = min_dims
        bound_name = "min_dims"

    # check for unsatisfiable min_side
    if not all(min_side <= s for s in base_shape[::-1][:dims] if s != 1):
        raise InvalidArgument(
            f"Given base_shape={base_shape}, there are no broadcast-compatible "
            f"shapes that satisfy: {bound_name}={dims} and min_side={min_side}"
        )

    # check for unsatisfiable [min_side, max_side]
    if not (
        min_side <= 1 <= max_side or all(s <= max_side for s in base_shape[::-1][:dims])
    ):
        raise InvalidArgument(
            f"Given base_shape={base_shape}, there are no broadcast-compatible "
            f"shapes that satisfy all of {bound_name}={dims}, "
            f"min_side={min_side}, and max_side={max_side}"
        )

    if not strict_check:
        # reduce max_dims to exclude unsatisfiable dimensions
        for n, s in zip(range(max_dims), base_shape[::-1], strict=False):
            if s < min_side and s != 1:
                max_dims = n
                break
            if not (min_side <= 1 <= max_side or s <= max_side):
                max_dims = n
                break

    return MutuallyBroadcastableShapesStrategy(
        num_shapes=num_shapes,
        signature=parsed_signature,
        base_shape=base_shape,
        min_dims=min_dims,
        max_dims=max_dims,
        min_side=min_side,
        max_side=max_side,
    )


class MutuallyBroadcastableShapesStrategy(st.SearchStrategy):
    def __init__(
        self,
        num_shapes,
        signature=None,
        base_shape=(),
        min_dims=0,
        max_dims=None,
        min_side=1,
        max_side=None,
    ):
        super().__init__()
        self.base_shape = base_shape
        self.side_strat = st.integers(min_side, max_side)
        self.num_shapes = num_shapes
        self.signature = signature
        self.min_dims = min_dims
        self.max_dims = max_dims
        self.min_side = min_side
        self.max_side = max_side

        self.size_one_allowed = self.min_side <= 1 <= self.max_side

    def do_draw(self, data):
        # We don't usually have a gufunc signature; do the common case first & fast.
        if self.signature is None:
            return self._draw_loop_dimensions(data)

        # When we *do*, draw the core dims, then draw loop dims, and finally combine.
        core_in, core_res = self._draw_core_dimensions(data)

        # If some core shape has omitted optional dimensions, it's an error to add
        # loop dimensions to it.  We never omit core dims if min_dims >= 1.
        # This ensures that we respect Numpy's gufunc broadcasting semantics and user
        # constraints without needing to check whether the loop dims will be
        # interpreted as an invalid substitute for the omitted core dims.
        # We may implement this check later!
        use = [None not in shp for shp in core_in]
        loop_in, loop_res = self._draw_loop_dimensions(data, use=use)

        def add_shape(loop, core):
            return tuple(x for x in (loop + core)[-NDIM_MAX:] if x is not None)

        return BroadcastableShapes(
            input_shapes=tuple(
                add_shape(l_in, c) for l_in, c in zip(loop_in, core_in, strict=True)
            ),
            result_shape=add_shape(loop_res, core_res),
        )

    def _draw_core_dimensions(self, data):
        # Draw gufunc core dimensions, with None standing for optional dimensions
        # that will not be present in the final shape.  We track omitted dims so
        # that we can do an accurate per-shape length cap.
        dims = {}
        shapes = []
        for shape in (*self.signature.input_shapes, self.signature.result_shape):
            shapes.append([])
            for name in shape:
                if name.isdigit():
                    shapes[-1].append(int(name))
                    continue
                if name not in dims:
                    dim = name.strip("?")
                    dims[dim] = data.draw(self.side_strat)
                    if self.min_dims == 0 and not data.draw_boolean(7 / 8):
                        dims[dim + "?"] = None
                    else:
                        dims[dim + "?"] = dims[dim]
                shapes[-1].append(dims[name])
        return tuple(tuple(s) for s in shapes[:-1]), tuple(shapes[-1])

    def _draw_loop_dimensions(self, data, use=None):
        # All shapes are handled in column-major order; i.e. they are reversed
        base_shape = self.base_shape[::-1]
        result_shape = list(base_shape)
        shapes = [[] for _ in range(self.num_shapes)]
        if use is None:
            use = [True for _ in range(self.num_shapes)]
        else:
            assert len(use) == self.num_shapes
            assert all(isinstance(x, bool) for x in use)

        _gap = self.max_dims - self.min_dims
        p_keep_extending_shape = _calc_p_continue(desired_avg=_gap / 2, max_size=_gap)

        for dim_count in range(1, self.max_dims + 1):
            dim = dim_count - 1

            # We begin by drawing a valid dimension-size for the given
            # dimension. This restricts the variability across the shapes
            # at this dimension such that they can only choose between
            # this size and a singleton dimension.
            if len(base_shape) < dim_count or base_shape[dim] == 1:
                # dim is unrestricted by the base-shape: shrink to min_side
                dim_side = data.draw(self.side_strat)
            elif base_shape[dim] <= self.max_side:
                # dim is aligned with non-singleton base-dim
                dim_side = base_shape[dim]
            else:
                # only a singleton is valid in alignment with the base-dim
                dim_side = 1

            allowed_sides = sorted([1, dim_side])  # shrink to 0 when available
            for shape_id, shape in enumerate(shapes):
                # Populating this dimension-size for each shape, either
                # the drawn size is used or, if permitted, a singleton
                # dimension.
                if dim <= len(result_shape) and self.size_one_allowed:
                    # aligned: shrink towards size 1
                    side = data.draw(st.sampled_from(allowed_sides))
                else:
                    side = dim_side

                # Use a trick where where a biased coin is queried to see
                # if the given shape-tuple will continue to be grown. All
                # of the relevant draws will still be made for the given
                # shape-tuple even if it is no longer being added to.
                # This helps to ensure more stable shrinking behavior.
                if self.min_dims < dim_count:
                    use[shape_id] &= data.draw_boolean(p_keep_extending_shape)

                if use[shape_id]:
                    shape.append(side)
                    if len(result_shape) < len(shape):
                        result_shape.append(shape[-1])
                    elif shape[-1] != 1 and result_shape[dim] == 1:
                        result_shape[dim] = shape[-1]
            if not any(use):
                break

        result_shape = result_shape[: max(map(len, [self.base_shape, *shapes]))]

        assert len(shapes) == self.num_shapes
        assert all(self.min_dims <= len(s) <= self.max_dims for s in shapes)
        assert all(self.min_side <= s <= self.max_side for side in shapes for s in side)

        return BroadcastableShapes(
            input_shapes=tuple(tuple(reversed(shape)) for shape in shapes),
            result_shape=tuple(reversed(result_shape)),
        )


class BasicIndexStrategy(st.SearchStrategy):
    def __init__(
        self,
        shape,
        min_dims,
        max_dims,
        allow_ellipsis,
        allow_newaxis,
        allow_fewer_indices_than_dims,
    ):
        super().__init__()
        self.shape = shape
        self.min_dims = min_dims
        self.max_dims = max_dims
        self.allow_ellipsis = allow_ellipsis
        self.allow_newaxis = allow_newaxis
        # allow_fewer_indices_than_dims=False will disable generating indices
        # that don't cover all axes, i.e. indices that will flat index arrays.
        # This is necessary for the Array API as such indices are not supported.
        self.allow_fewer_indices_than_dims = allow_fewer_indices_than_dims

    def do_draw(self, data):
        # General plan: determine the actual selection up front with a straightforward
        # approach that shrinks well, then complicate it by inserting other things.
        result = []
        for dim_size in self.shape:
            if dim_size == 0:
                result.append(slice(None))
                continue
            strategy = st.integers(-dim_size, dim_size - 1) | st.slices(dim_size)
            result.append(data.draw(strategy))
        # Insert some number of new size-one dimensions if allowed
        result_dims = sum(isinstance(idx, slice) for idx in result)
        while (
            self.allow_newaxis
            and result_dims < self.max_dims
            and (result_dims < self.min_dims or data.draw(st.booleans()))
        ):
            i = data.draw(st.integers(0, len(result)))
            result.insert(i, None)  # Note that `np.newaxis is None`
            result_dims += 1
        # Check that we'll have the right number of dimensions; reject if not.
        # It's easy to do this by construction if you don't care about shrinking,
        # which is really important for array shapes.  So we filter instead.
        assume(self.min_dims <= result_dims <= self.max_dims)
        # This is a quick-and-dirty way to insert ..., xor shorten the indexer,
        # but it means we don't have to do any structural analysis.
        if self.allow_ellipsis and data.draw(st.booleans()):
            # Choose an index; then replace all adjacent whole-dimension slices.
            i = j = data.draw(st.integers(0, len(result)))
            while i > 0 and result[i - 1] == slice(None):
                i -= 1
            while j < len(result) and result[j] == slice(None):
                j += 1
            result[i:j] = [Ellipsis]
        elif self.allow_fewer_indices_than_dims:  # pragma: no cover
            while result[-1:] == [slice(None, None)] and data.draw(st.integers(0, 7)):
                result.pop()
        if len(result) == 1 and data.draw(st.booleans()):
            # Sometimes generate bare element equivalent to a length-one tuple
            return result[0]
        return tuple(result)
