Gymnasium/gymnasium/spaces/oneof.py

"""Implementation of a space that represents the cartesian product of other spaces."""

from __future__ import annotations

import typing
from collections.abc import Iterable
from typing import Any

import numpy as np

from gymnasium.spaces.space import Space


class OneOf(Space[Any]):
    """An exclusive tuple (more precisely: the direct sum) of :class:`Space` instances.

    Elements of this space are elements of one of the constituent spaces.

    Example:
        >>> from gymnasium.spaces import OneOf, Box, Discrete
        >>> observation_space = OneOf((Discrete(2), Box(-1, 1, shape=(2,))), seed=123)
        >>> observation_space.sample()  # the first element is the space index (Discrete in this case) and the second element is the sample from Discrete
        (np.int64(0), np.int64(0))
        >>> observation_space.sample()  # this time the Box space was sampled as index=1
        (np.int64(1), array([-0.00711833, -0.7257502 ], dtype=float32))
        >>> observation_space[0]
        Discrete(2)
        >>> observation_space[1]
        Box(-1.0, 1.0, (2,), float32)
        >>> len(observation_space)
        2
    """

    def __init__(
        self,
        spaces: Iterable[Space[Any]],
        seed: int | typing.Sequence[int] | np.random.Generator | None = None,
    ):
        r"""Constructor of :class:`OneOf` space.

        The generated instance will represent the cartesian product :math:`\text{spaces}[0] \times ... \times \text{spaces}[-1]`.

        Args:
            spaces (Iterable[Space]): The spaces that are involved in the cartesian product.
            seed: Optionally, you can use this argument to seed the RNGs of the ``spaces`` to ensure reproducible sampling.
        """
        assert isinstance(spaces, Iterable), f"{spaces} is not an iterable"
        self.spaces = tuple(spaces)
        assert len(self.spaces) > 0, "Empty `OneOf` spaces are not supported."
        for space in self.spaces:
            assert isinstance(
                space, Space
            ), f"{space} does not inherit from `gymnasium.Space`. Actual Type: {type(space)}"
        super().__init__(None, None, seed)

    @property
    def is_np_flattenable(self):
        """Checks whether this space can be flattened to a :class:`spaces.Box`."""
        return all(space.is_np_flattenable for space in self.spaces)

    def seed(self, seed: int | tuple[int, ...] | None = None) -> tuple[int, ...]:
        """Seed the PRNG of this space and all subspaces.

        Depending on the type of seed, the subspaces will be seeded differently

        * ``None`` - All the subspaces will use a random initial seed
        * ``Int`` - The integer is used to seed the :class:`Tuple` space that is used to generate seed values for each of the subspaces. Warning, this does not guarantee unique seeds for all the subspaces.
        * ``Tuple[int, ...]`` - Values used to seed the subspaces, first value seeds the OneOf and subsequent seed the subspaces. This allows the seeding of multiple composite subspaces ``[42, 54, ...]``.

        Args:
            seed: An optional int or tuple of ints to seed the OneOf space and subspaces. See above for more details.

        Returns:
            A tuple of ints used to seed the OneOf space and subspaces
        """
        if seed is None:
            super_seed = super().seed(None)
            return (super_seed,) + tuple(space.seed(None) for space in self.spaces)
        elif isinstance(seed, int):
            super_seed = super().seed(seed)
            subseeds = self.np_random.integers(
                np.iinfo(np.int32).max, size=len(self.spaces)
            )
            # this is necessary such that after int or list/tuple seeding, the OneOf PRNG are equivalent
            super().seed(seed)
            return (super_seed,) + tuple(
                space.seed(int(subseed))
                for space, subseed in zip(self.spaces, subseeds)
            )
        elif isinstance(seed, (tuple, list)):
            if len(seed) != len(self.spaces) + 1:
                raise ValueError(
                    f"Expects that the subspaces of seeds equals the number of subspaces + 1. Actual length of seeds: {len(seed)}, length of subspaces: {len(self.spaces)}"
                )

            return (super().seed(seed[0]),) + tuple(
                space.seed(subseed) for space, subseed in zip(self.spaces, seed[1:])
            )
        else:
            raise TypeError(
                f"Expected None, int, or tuple of ints, actual type: {type(seed)}"
            )

    def sample(
        self,
        mask: tuple[Any | None, ...] | None = None,
        probability: tuple[Any | None, ...] | None = None,
    ) -> tuple[int, Any]:
        """Generates a single random sample inside this space.

        This method draws independent samples from the subspaces.

        Args:
            mask: An optional tuple of optional masks for each of the subspace's samples,
                expects the same number of masks as spaces
            probability: An optional tuple of optional probability masks for each of the subspace's samples,
                expects the same number of probability masks as spaces

        Returns:
            Tuple of the subspace's samples
        """
        subspace_idx = self.np_random.integers(0, len(self.spaces), dtype=np.int64)
        subspace = self.spaces[subspace_idx]

        if mask is not None and probability is not None:
            raise ValueError(
                f"Only one of `mask` or `probability` can be provided, actual values: mask={mask}, probability={probability}"
            )
        elif mask is not None:
            assert isinstance(
                mask, tuple
            ), f"Expected type of `mask` is tuple, actual type: {type(mask)}"
            assert len(mask) == len(
                self.spaces
            ), f"Expected length of `mask` is {len(self.spaces)}, actual length: {len(mask)}"

            subspace_sample = subspace.sample(mask=mask[subspace_idx])

        elif probability is not None:
            assert isinstance(
                probability, tuple
            ), f"Expected type of `probability` is tuple, actual type: {type(probability)}"
            assert len(probability) == len(
                self.spaces
            ), f"Expected length of `probability` is {len(self.spaces)}, actual length: {len(probability)}"

            subspace_sample = subspace.sample(probability=probability[subspace_idx])
        else:
            subspace_sample = subspace.sample()

        return subspace_idx, subspace_sample

    def contains(self, x: tuple[int, Any]) -> bool:
        """Return boolean specifying if x is a valid member of this space."""
        # subspace_idx, subspace_value = x
        return (
            isinstance(x, tuple)
            and len(x) == 2
            and isinstance(x[0], (np.int64, int))
            and 0 <= x[0] < len(self.spaces)
            and self.spaces[x[0]].contains(x[1])
        )

    def __repr__(self) -> str:
        """Gives a string representation of this space."""
        return "OneOf(" + ", ".join([str(s) for s in self.spaces]) + ")"

    def to_jsonable(
        self, sample_n: typing.Sequence[tuple[int, Any]]
    ) -> list[list[Any]]:
        """Convert a batch of samples from this space to a JSONable data type."""
        return [
            [int(i), self.spaces[i].to_jsonable([subsample])[0]]
            for (i, subsample) in sample_n
        ]

    def from_jsonable(self, sample_n: list[list[Any]]) -> list[tuple[Any, ...]]:
        """Convert a JSONable data type to a batch of samples from this space."""
        return [
            (
                np.int64(space_idx),
                self.spaces[space_idx].from_jsonable([jsonable_sample])[0],
            )
            for space_idx, jsonable_sample in sample_n
        ]

    def __getitem__(self, index: int) -> Space[Any]:
        """Get the subspace at specific `index`."""
        return self.spaces[index]

    def __len__(self) -> int:
        """Get the number of subspaces that are involved in the cartesian product."""
        return len(self.spaces)

    def __eq__(self, other: Any) -> bool:
        """Check whether ``other`` is equivalent to this instance."""
        return isinstance(other, OneOf) and self.spaces == other.spaces