Gymnasium/gym/spaces/tests/test_utils.py

from collections import OrderedDict

import numpy as np
import pytest

from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Tuple, utils


@pytest.mark.parametrize(
    ["space", "flatdim"],
    [
        (Discrete(3), 3),
        (Box(low=0.0, high=np.inf, shape=(2, 2)), 4),
        (Tuple([Discrete(5), Discrete(10)]), 15),
        (
            Tuple(
                [
                    Discrete(5),
                    Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
                ]
            ),
            7,
        ),
        (Tuple((Discrete(5), Discrete(2), Discrete(2))), 9),
        (MultiDiscrete([2, 2, 100]), 3),
        (MultiBinary(10), 10),
        (
            Dict(
                {
                    "position": Discrete(5),
                    "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
                }
            ),
            7,
        ),
    ],
)
def test_flatdim(space, flatdim):
    dim = utils.flatdim(space)
    assert dim == flatdim, "Expected {} to equal {}".format(dim, flatdim)


@pytest.mark.parametrize(
    "space",
    [
        Discrete(3),
        Box(low=0.0, high=np.inf, shape=(2, 2)),
        Tuple([Discrete(5), Discrete(10)]),
        Tuple(
            [
                Discrete(5),
                Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            ]
        ),
        Tuple((Discrete(5), Discrete(2), Discrete(2))),
        MultiDiscrete([2, 2, 100]),
        MultiBinary(10),
        Dict(
            {
                "position": Discrete(5),
                "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            }
        ),
    ],
)
def test_flatten_space_boxes(space):
    flat_space = utils.flatten_space(space)
    assert isinstance(flat_space, Box), "Expected {} to equal {}".format(type(flat_space), Box)
    flatdim = utils.flatdim(space)
    (single_dim,) = flat_space.shape
    assert single_dim == flatdim, "Expected {} to equal {}".format(single_dim, flatdim)


@pytest.mark.parametrize(
    "space",
    [
        Discrete(3),
        Box(low=0.0, high=np.inf, shape=(2, 2)),
        Tuple([Discrete(5), Discrete(10)]),
        Tuple(
            [
                Discrete(5),
                Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            ]
        ),
        Tuple((Discrete(5), Discrete(2), Discrete(2))),
        MultiDiscrete([2, 2, 100]),
        MultiBinary(10),
        Dict(
            {
                "position": Discrete(5),
                "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            }
        ),
    ],
)
def test_flat_space_contains_flat_points(space):
    some_samples = [space.sample() for _ in range(10)]
    flattened_samples = [utils.flatten(space, sample) for sample in some_samples]
    flat_space = utils.flatten_space(space)
    for i, flat_sample in enumerate(flattened_samples):
        assert flat_sample in flat_space, "Expected sample #{} {} to be in {}".format(i, flat_sample, flat_space)


@pytest.mark.parametrize(
    "space",
    [
        Discrete(3),
        Box(low=0.0, high=np.inf, shape=(2, 2)),
        Tuple([Discrete(5), Discrete(10)]),
        Tuple(
            [
                Discrete(5),
                Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            ]
        ),
        Tuple((Discrete(5), Discrete(2), Discrete(2))),
        MultiDiscrete([2, 2, 100]),
        MultiBinary(10),
        Dict(
            {
                "position": Discrete(5),
                "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            }
        ),
    ],
)
def test_flatten_dim(space):
    sample = utils.flatten(space, space.sample())
    (single_dim,) = sample.shape
    flatdim = utils.flatdim(space)
    assert single_dim == flatdim, "Expected {} to equal {}".format(single_dim, flatdim)


@pytest.mark.parametrize(
    "space",
    [
        Discrete(3),
        Box(low=0.0, high=np.inf, shape=(2, 2)),
        Tuple([Discrete(5), Discrete(10)]),
        Tuple(
            [
                Discrete(5),
                Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            ]
        ),
        Tuple((Discrete(5), Discrete(2), Discrete(2))),
        MultiDiscrete([2, 2, 100]),
        MultiBinary(10),
        Dict(
            {
                "position": Discrete(5),
                "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
            }
        ),
    ],
)
def test_flatten_roundtripping(space):
    some_samples = [space.sample() for _ in range(10)]
    flattened_samples = [utils.flatten(space, sample) for sample in some_samples]
    roundtripped_samples = [utils.unflatten(space, sample) for sample in flattened_samples]
    for i, (original, roundtripped) in enumerate(zip(some_samples, roundtripped_samples)):
        assert compare_nested(original, roundtripped), "Expected sample #{} {} to equal {}".format(i, original, roundtripped)


def compare_nested(left, right):
    if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
        return np.allclose(left, right)
    elif isinstance(left, OrderedDict) and isinstance(right, OrderedDict):
        res = len(left) == len(right)
        for ((left_key, left_value), (right_key, right_value)) in zip(left.items(), right.items()):
            if not res:
                return False
            res = left_key == right_key and compare_nested(left_value, right_value)
        return res
    elif isinstance(left, (tuple, list)) and isinstance(right, (tuple, list)):
        res = len(left) == len(right)
        for (x, y) in zip(left, right):
            if not res:
                return False
            res = compare_nested(x, y)
        return res
    else:
        return left == right


"""
Expecteded flattened types are based off:
1. The type that the space is hardcoded as(ie. multi_discrete=np.int64, discrete=np.int64, multi_binary=np.int8)
2. The type that the space is instantiated with(ie. box=np.float32 by default unless instantiated with a different type)
3. The smallest type that the composite space(tuple, dict) can be represented as. In flatten, this is determined
   internally by numpy when np.concatenate is called.
"""


@pytest.mark.parametrize(
    ["original_space", "expected_flattened_dtype"],
    [
        (Discrete(3), np.int64),
        (Box(low=0.0, high=np.inf, shape=(2, 2)), np.float32),
        (Box(low=0.0, high=np.inf, shape=(2, 2), dtype=np.float16), np.float16),
        (Tuple([Discrete(5), Discrete(10)]), np.int64),
        (
            Tuple(
                [
                    Discrete(5),
                    Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
                ]
            ),
            np.float64,
        ),
        (Tuple((Discrete(5), Discrete(2), Discrete(2))), np.int64),
        (MultiDiscrete([2, 2, 100]), np.int64),
        (MultiBinary(10), np.int8),
        (
            Dict(
                {
                    "position": Discrete(5),
                    "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float16),
                }
            ),
            np.float64,
        ),
    ],
)
def test_dtypes(original_space, expected_flattened_dtype):
    flattened_space = utils.flatten_space(original_space)

    original_sample = original_space.sample()
    flattened_sample = utils.flatten(original_space, original_sample)
    unflattened_sample = utils.unflatten(original_space, flattened_sample)

    assert flattened_space.contains(flattened_sample), "Expected flattened_space to contain flattened_sample"
    assert flattened_space.dtype == expected_flattened_dtype, "Expected flattened_space's dtype to equal " "{}".format(
        expected_flattened_dtype
    )

    assert flattened_sample.dtype == flattened_space.dtype, (
        "Expected flattened_space's dtype to equal " "flattened_sample's dtype "
    )

    compare_sample_types(original_space, original_sample, unflattened_sample)


def compare_sample_types(original_space, original_sample, unflattened_sample):
    if isinstance(original_space, Discrete):
        assert isinstance(
            unflattened_sample, int
        ), "Expected unflattened_sample to be an int. unflattened_sample: " "{} original_sample: {}".format(
            unflattened_sample, original_sample
        )
    elif isinstance(original_space, Tuple):
        for index in range(len(original_space)):
            compare_sample_types(
                original_space.spaces[index],
                original_sample[index],
                unflattened_sample[index],
            )
    elif isinstance(original_space, Dict):
        for key, space in original_space.spaces.items():
            compare_sample_types(space, original_sample[key], unflattened_sample[key])
    else:
        assert unflattened_sample.dtype == original_sample.dtype, (
            "Expected unflattened_sample's dtype to equal "
            "original_sample's dtype. unflattened_sample: "
            "{} original_sample: {}".format(unflattened_sample, original_sample)
        )