"""Implementation of a space that represents the cartesian product of other spaces."""
from typing import Iterable, List, Optional, Sequence, Tuple, Union

import numpy as np

from gym.spaces.space import Space


class Tuple(Space[tuple], Sequence):
    """A tuple (more precisely: the cartesian product) of :class:`Space` instances.

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

    Example usage::

        >>> from gym.spaces import Box, Discrete
        >>> observation_space = Tuple((Discrete(2), Box(-1, 1, shape=(2,))))
        >>> observation_space.sample()
        (0, array([0.03633198, 0.42370757], dtype=float32))
    """

    def __init__(
        self,
        spaces: Iterable[Space],
        seed: Optional[Union[int, List[int], np.random.Generator]] = None,
    ):
        r"""Constructor of :class:`Tuple` 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.
        """
        self.spaces = tuple(spaces)
        for space in self.spaces:
            assert isinstance(
                space, Space
            ), "Elements of the tuple must be instances of gym.Space"
        super().__init__(None, None, seed)  # type: ignore

    @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: Optional[Union[int, List[int]]] = None) -> list:
        """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 `Tuple` space that is used to generate seed values for each of the subspaces. Warning, this does not guarantee unique seeds for all of the subspaces.
        * List - Values used to seed the subspaces. This allows the seeding of multiple composite subspaces (`List(42, 54, ...)`).

        Args:
            seed: An optional list of ints or int to seed the (sub-)spaces.
        """
        seeds = []

        if isinstance(seed, list):
            for i, space in enumerate(self.spaces):
                seeds += space.seed(seed[i])
        elif isinstance(seed, int):
            seeds = super().seed(seed)
            subseeds = self.np_random.integers(
                np.iinfo(np.int32).max, size=len(self.spaces)
            )
            for subspace, subseed in zip(self.spaces, subseeds):
                seeds.append(subspace.seed(int(subseed))[0])
        elif seed is None:
            for space in self.spaces:
                seeds += space.seed(seed)
        else:
            raise TypeError("Passed seed not of an expected type: list or int or None")

        return seeds

    def sample(self, mask: Optional[Tuple[Optional[np.ndarray]]] = None) -> tuple:
        """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

        Returns:
            Tuple of the subspace's samples
        """
        if 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)}"

            return tuple(
                space.sample(mask=sub_mask)
                for space, sub_mask in zip(self.spaces, mask)
            )

        return tuple(space.sample() for space in self.spaces)

    def contains(self, x) -> bool:
        """Return boolean specifying if x is a valid member of this space."""
        if isinstance(x, (list, np.ndarray)):
            x = tuple(x)  # Promote list and ndarray to tuple for contains check
        return (
            isinstance(x, tuple)
            and len(x) == len(self.spaces)
            and all(space.contains(part) for (space, part) in zip(self.spaces, x))
        )

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

    def to_jsonable(self, sample_n: Sequence) -> list:
        """Convert a batch of samples from this space to a JSONable data type."""
        # serialize as list-repr of tuple of vectors
        return [
            space.to_jsonable([sample[i] for sample in sample_n])
            for i, space in enumerate(self.spaces)
        ]

    def from_jsonable(self, sample_n) -> list:
        """Convert a JSONable data type to a batch of samples from this space."""
        return [
            sample
            for sample in zip(
                *[
                    space.from_jsonable(sample_n[i])
                    for i, space in enumerate(self.spaces)
                ]
            )
        ]

    def __getitem__(self, index: int) -> Space:
        """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) -> bool:
        """Check whether ``other`` is equivalent to this instance."""
        return isinstance(other, Tuple) and self.spaces == other.spaces