Add testing for Fundamental spaces with full coverage (#3048)

* initial commit

* Fix the multi-binary sample and upgrade multi-discrete sample

* Fix MultiBinary sample tests and pre-commit

* Adds coverage tests and updates test_utils.py to use the utils.py spaces. Fix bug in text.py

* pre-commit

gym/spaces/box.py

@@ -164,7 +164,9 @@ class Box(Space[np.ndarray]):
         elif manner == "above":
             return above
         else:
-            raise ValueError("manner is not in {'below', 'above', 'both'}")
+            raise ValueError(
+                f"manner is not in {{'below', 'above', 'both'}}, actual value: {manner}"
+            )
 
     def sample(self, mask: None = None) -> np.ndarray:
         r"""Generates a single random sample inside the Box.
@@ -223,7 +225,10 @@ class Box(Space[np.ndarray]):
         """Return boolean specifying if x is a valid member of this space."""
         if not isinstance(x, np.ndarray):
             logger.warn("Casting input x to numpy array.")
-            x = np.asarray(x, dtype=self.dtype)
+            try:
+                x = np.asarray(x, dtype=self.dtype)
+            except (ValueError, TypeError):
+                return False
 
         return bool(
             np.can_cast(x.dtype, self.dtype)
@@ -236,7 +241,7 @@ class Box(Space[np.ndarray]):
         """Convert a batch of samples from this space to a JSONable data type."""
         return np.array(sample_n).tolist()
 
-    def from_jsonable(self, sample_n: Sequence[SupportsFloat]) -> List[np.ndarray]:
+    def from_jsonable(self, sample_n: Sequence[Union[float, int]]) -> List[np.ndarray]:
         """Convert a JSONable data type to a batch of samples from this space."""
         return [np.asarray(sample) for sample in sample_n]
 
@@ -252,10 +257,11 @@ class Box(Space[np.ndarray]):
         return f"Box({self.low_repr}, {self.high_repr}, {self.shape}, {self.dtype})"
 
     def __eq__(self, other) -> bool:
-        """Check whether `other` is equivalent to this instance."""
+        """Check whether `other` is equivalent to this instance. Doesn't check dtype equivalence."""
         return (
             isinstance(other, Box)
             and (self.shape == other.shape)
+            # and (self.dtype == other.dtype)
             and np.allclose(self.low, other.low)
             and np.allclose(self.high, other.high)
         )

gym/spaces/discrete.py

@@ -85,18 +85,19 @@ class Discrete(Space[int]):
         if isinstance(x, int):
             as_int = x
         elif isinstance(x, (np.generic, np.ndarray)) and (
-            x.dtype.char in np.typecodes["AllInteger"] and x.shape == ()
+            np.issubdtype(x.dtype, np.integer) and x.shape == ()
         ):
             as_int = int(x)  # type: ignore
         else:
             return False
+
         return self.start <= as_int < self.start + self.n
 
     def __repr__(self) -> str:
         """Gives a string representation of this space."""
         if self.start != 0:
-            return "Discrete(%d, start=%d)" % (self.n, self.start)
+            return f"Discrete({self.n}, start={self.start})"
-        return "Discrete(%d)" % self.n
+        return f"Discrete({self.n})"
 
     def __eq__(self, other) -> bool:
         """Check whether ``other`` is equivalent to this instance."""
@@ -114,8 +115,6 @@ class Discrete(Space[int]):
         Args:
             state: The new state
         """
-        super().__setstate__(state)
-
         # Don't mutate the original state
         state = dict(state)
 
@@ -124,5 +123,4 @@ class Discrete(Space[int]):
         if "start" not in state:
             state["start"] = 0
 
-        # Update our state
+        super().__setstate__(state)
-        self.__dict__.update(state)

gym/spaces/graph.py

@@ -6,7 +6,7 @@ import numpy as np
 
 from gym.spaces.box import Box
 from gym.spaces.discrete import Discrete
-from gym.spaces.multi_discrete import SAMPLE_MASK_TYPE, MultiDiscrete
+from gym.spaces.multi_discrete import MultiDiscrete
 from gym.spaces.space import Space
 
 
@@ -97,8 +97,8 @@ class Graph(Space):
         self,
         mask: Optional[
             Tuple[
-                Optional[Union[np.ndarray, SAMPLE_MASK_TYPE]],
+                Optional[Union[np.ndarray, tuple]],
-                Optional[Union[np.ndarray, SAMPLE_MASK_TYPE]],
+                Optional[Union[np.ndarray, tuple]],
             ]
         ] = None,
         num_nodes: int = 10,

gym/spaces/multi_binary.py

@@ -62,7 +62,8 @@ class MultiBinary(Space[np.ndarray]):
 
         Args:
             mask: An optional np.ndarray to mask samples with expected shape of ``space.shape``.
-                Where mask == 0 then the samples will be 0.
+                For mask == 0 then the samples will be 0 and mask == 1 then random samples will be generated.
+                The expected mask shape is the space shape and mask dtype is `np.int8`.
 
         Returns:
             Sampled values from space
@@ -78,11 +79,13 @@ class MultiBinary(Space[np.ndarray]):
                 mask.shape == self.shape
             ), f"The expected shape of the mask is {self.shape}, actual shape: {mask.shape}"
             assert np.all(
-                np.logical_or(mask == 0, mask == 1)
+                (mask == 0) | (mask == 1) | (mask == 2)
-            ), f"All values of a mask should be 0 or 1, actual values: {mask}"
+            ), f"All values of a mask should be 0, 1 or 2, actual values: {mask}"
 
-            return mask * self.np_random.integers(
+            return np.where(
-                low=0, high=2, size=self.n, dtype=self.dtype
+                mask == 2,
+                self.np_random.integers(low=0, high=2, size=self.n, dtype=self.dtype),
+                mask.astype(self.dtype),
             )
 
         return self.np_random.integers(low=0, high=2, size=self.n, dtype=self.dtype)
@@ -91,9 +94,12 @@ class MultiBinary(Space[np.ndarray]):
         """Return boolean specifying if x is a valid member of this space."""
         if isinstance(x, Sequence):
             x = np.array(x)  # Promote list to array for contains check
-        if self.shape != x.shape:
+
-            return False
+        return bool(
-        return ((x == 0) | (x == 1)).all()
+            isinstance(x, np.ndarray)
+            and self.shape == x.shape
+            and np.all((x == 0) | (x == 1))
+        )
 
     def to_jsonable(self, sample_n) -> list:
         """Convert a batch of samples from this space to a JSONable data type."""
@@ -101,7 +107,7 @@ class MultiBinary(Space[np.ndarray]):
 
     def from_jsonable(self, sample_n) -> list:
         """Convert a JSONable data type to a batch of samples from this space."""
-        return [np.asarray(sample) for sample in sample_n]
+        return [np.asarray(sample, self.dtype) for sample in sample_n]
 
     def __repr__(self) -> str:
         """Gives a string representation of this space."""

gym/spaces/multi_discrete.py

@@ -7,8 +7,6 @@ from gym import logger
 from gym.spaces.discrete import Discrete
 from gym.spaces.space import Space
 
-SAMPLE_MASK_TYPE = Tuple[Union["SAMPLE_MASK_TYPE", np.ndarray], ...]
-
 
 class MultiDiscrete(Space[np.ndarray]):
     """This represents the cartesian product of arbitrary :class:`Discrete` spaces.
@@ -39,7 +37,7 @@ class MultiDiscrete(Space[np.ndarray]):
 
     def __init__(
         self,
-        nvec: Union[np.ndarray, List[int]],
+        nvec: Union[np.ndarray, list],
         dtype=np.int64,
         seed: Optional[Union[int, np.random.Generator]] = None,
     ):
@@ -68,7 +66,7 @@ class MultiDiscrete(Space[np.ndarray]):
         """Checks whether this space can be flattened to a :class:`spaces.Box`."""
         return True
 
-    def sample(self, mask: Optional[SAMPLE_MASK_TYPE] = None) -> np.ndarray:
+    def sample(self, mask: Optional[tuple] = None) -> np.ndarray:
         """Generates a single random sample this space.
 
         Args:
@@ -82,15 +80,30 @@ class MultiDiscrete(Space[np.ndarray]):
         if mask is not None:
 
             def _apply_mask(
-                sub_mask: SAMPLE_MASK_TYPE, sub_nvec: np.ndarray
+                sub_mask: Union[np.ndarray, tuple],
+                sub_nvec: Union[np.ndarray, np.integer],
             ) -> Union[int, List[int]]:
-                if isinstance(sub_mask, np.ndarray):
+                if isinstance(sub_nvec, np.ndarray):
+                    assert isinstance(
+                        sub_mask, tuple
+                    ), f"Expects the mask to be a tuple for sub_nvec ({sub_nvec}), actual type: {type(sub_mask)}"
+                    assert len(sub_mask) == len(
+                        sub_nvec
+                    ), f"Expects the mask length to be equal to the number of actions, mask length: {len(sub_mask)}, nvec length: {len(sub_nvec)}"
+                    return [
+                        _apply_mask(new_mask, new_nvec)
+                        for new_mask, new_nvec in zip(sub_mask, sub_nvec)
+                    ]
+                else:
                     assert np.issubdtype(
                         type(sub_nvec), np.integer
-                    ), f"Expects the mask to be for an action, actual for {sub_nvec}"
+                    ), f"Expects the sub_nvec to be an action, actually: {sub_nvec}, {type(sub_nvec)}"
+                    assert isinstance(
+                        sub_mask, np.ndarray
+                    ), f"Expects the sub mask to be np.ndarray, actual type: {type(sub_mask)}"
                     assert (
                         len(sub_mask) == sub_nvec
-                    ), f"Expects the mask length to be equal to the number of actions, mask length: {len(sub_mask)}, nvec length: {sub_nvec}"
+                    ), f"Expects the mask length to be equal to the number of actions, mask length: {len(sub_mask)}, action: {sub_nvec}"
                     assert (
                         sub_mask.dtype == np.int8
                     ), f"Expects the mask dtype to be np.int8, actual dtype: {sub_mask.dtype}"
@@ -104,17 +117,6 @@ class MultiDiscrete(Space[np.ndarray]):
                         return self.np_random.choice(np.where(valid_action_mask)[0])
                     else:
                         return 0
-                else:
-                    assert isinstance(
-                        sub_mask, tuple
-                    ), f"Expects the mask to be a tuple or np.ndarray, actual type: {type(sub_mask)}"
-                    assert len(sub_mask) == len(
-                        sub_nvec
-                    ), f"Expects the mask length to be equal to the number of actions, mask length: {len(sub_mask)}, nvec length: {len(sub_nvec)}"
-                    return [
-                        _apply_mask(new_mask, new_nvec)
-                        for new_mask, new_nvec in zip(sub_mask, sub_nvec)
-                    ]
 
             return np.array(_apply_mask(mask, self.nvec), dtype=self.dtype)
 
@@ -124,9 +126,16 @@ class MultiDiscrete(Space[np.ndarray]):
         """Return boolean specifying if x is a valid member of this space."""
         if isinstance(x, Sequence):
             x = np.array(x)  # Promote list to array for contains check
+
         # if nvec is uint32 and space dtype is uint32, then 0 <= x < self.nvec guarantees that x
         # is within correct bounds for space dtype (even though x does not have to be unsigned)
-        return bool(x.shape == self.shape and (0 <= x).all() and (x < self.nvec).all())
+        return bool(
+            isinstance(x, np.ndarray)
+            and x.shape == self.shape
+            and x.dtype != object
+            and np.all(0 <= x)
+            and np.all(x < self.nvec)
+        )
 
     def to_jsonable(self, sample_n: Iterable[np.ndarray]):
         """Convert a batch of samples from this space to a JSONable data type."""
@@ -147,13 +156,18 @@ class MultiDiscrete(Space[np.ndarray]):
             subspace = Discrete(nvec)
         else:
             subspace = MultiDiscrete(nvec, self.dtype)  # type: ignore
+
+        # you don't need to deepcopy as np random generator call replaces the state not the data
         subspace.np_random.bit_generator.state = self.np_random.bit_generator.state
+
         return subspace
 
     def __len__(self):
         """Gives the ``len`` of samples from this space."""
         if self.nvec.ndim >= 2:
-            logger.warn("Get length of a multi-dimensional MultiDiscrete space.")
+            logger.warn(
+                "Getting the length of a multi-dimensional MultiDiscrete space."
+            )
         return len(self.nvec)
 
     def __eq__(self, other):

gym/spaces/text.py

@@ -1,5 +1,5 @@
 """Implementation of a space that represents textual strings."""
-from typing import Any, FrozenSet, List, Optional, Set, Tuple, Union
+from typing import Any, Dict, FrozenSet, Optional, Set, Tuple, Union
 
 import numpy as np
 
@@ -27,7 +27,7 @@ class Text(Space[str]):
         self,
         max_length: int,
         *,
-        min_length: int = 0,
+        min_length: int = 1,
         charset: Union[Set[str], str] = alphanumeric,
         seed: Optional[Union[int, np.random.Generator]] = None,
     ):
@@ -36,9 +36,9 @@ class Text(Space[str]):
         Both bounds for text length are inclusive.
 
         Args:
-            min_length (int): Minimum text length (in characters).
+            min_length (int): Minimum text length (in characters). Defaults to 1 to prevent empty strings.
             max_length (int): Maximum text length (in characters).
-            charset (Union[set, SupportsIndex]): Character set, defaults to the lower and upper english alphabet plus latin digits.
+            charset (Union[set], str): Character set, defaults to the lower and upper english alphabet plus latin digits.
             seed: The seed for sampling from the space.
         """
         assert np.issubdtype(
@@ -56,9 +56,13 @@ class Text(Space[str]):
 
         self.min_length: int = int(min_length)
         self.max_length: int = int(max_length)
-        self.charset: FrozenSet[str] = frozenset(charset)
+
-        self._charlist: List[str] = list(charset)
+        self._char_set: FrozenSet[str] = frozenset(charset)
-        self._charset_str: str = "".join(sorted(self._charlist))
+        self._char_list: Tuple[str, ...] = tuple(charset)
+        self._char_index: Dict[str, np.int32] = {
+            val: np.int32(i) for i, val in enumerate(tuple(charset))
+        }
+        self._char_str: str = "".join(sorted(tuple(charset)))
 
         # As the shape is dynamic (between min_length and max_length) then None
         super().__init__(dtype=str, seed=seed)
@@ -71,20 +75,42 @@ class Text(Space[str]):
         Args:
             mask: An optional tuples of length and mask for the text.
                 The length is expected to be between the `min_length` and `max_length` otherwise a random integer between `min_length` and `max_length` is selected.
-                For the mask, we expect a numpy array of length of the charset passed with dtype == np.int8
+                For the mask, we expect a numpy array of length of the charset passed with `dtype == np.int8`.
+                If the charlist mask is all zero then an empty string is returned no matter the `min_length`
 
         Returns:
             A sampled string from the space
         """
         if mask is not None:
+            assert isinstance(
+                mask, tuple
+            ), f"Expects the mask type to be a tuple, actual type: {type(mask)}"
+            assert (
+                len(mask) == 2
+            ), f"Expects the mask length to be two, actual length: {len(mask)}"
             length, charlist_mask = mask
+
             if length is not None:
-                assert self.min_length <= length <= self.max_length
+                assert np.issubdtype(
+                    type(length), np.integer
+                ), f"Expects the Text sample length to be an integer, actual type: {type(length)}"
+                assert (
+                    self.min_length <= length <= self.max_length
+                ), f"Expects the Text sample length be between {self.min_length} and {self.max_length}, actual length: {length}"
 
             if charlist_mask is not None:
-                assert isinstance(charlist_mask, np.ndarray)
+                assert isinstance(
-                assert charlist_mask.dtype is np.int8
+                    charlist_mask, np.ndarray
-                assert charlist_mask.shape == (len(self._charlist),)
+                ), f"Expects the Text sample mask to be an np.ndarray, actual type: {type(charlist_mask)}"
+                assert (
+                    charlist_mask.dtype == np.int8
+                ), f"Expects the Text sample mask to be an np.ndarray, actual dtype: {charlist_mask.dtype}"
+                assert charlist_mask.shape == (
+                    len(self.character_set),
+                ), f"expects the Text sample mask to be {(len(self.character_set),)}, actual shape: {charlist_mask.shape}"
+                assert np.all(
+                    np.logical_or(charlist_mask == 0, charlist_mask == 1)
+                ), f"Expects all masks values to 0 or 1, actual values: {charlist_mask}"
         else:
             length, charlist_mask = None, None
 
@@ -92,10 +118,23 @@ class Text(Space[str]):
             length = self.np_random.integers(self.min_length, self.max_length + 1)
 
         if charlist_mask is None:
-            string = self.np_random.choice(self._charlist, size=length)
+            string = self.np_random.choice(self.character_list, size=length)
         else:
-            masked_charlist = self._charlist[np.where(mask)[0]]
+            valid_mask = charlist_mask == 1
-            string = self.np_random.choice(masked_charlist, size=length)
+            valid_indexes = np.where(valid_mask)[0]
+            if len(valid_indexes) == 0:
+                if self.min_length == 0:
+                    string = ""
+                else:
+                    # Otherwise the string will not be contained in the space
+                    raise ValueError(
+                        f"Trying to sample with a minimum length > 0 ({self.min_length}) but the character mask is all zero meaning that no character could be sampled."
+                    )
+            else:
+                string = "".join(
+                    self.character_list[index]
+                    for index in self.np_random.choice(valid_indexes, size=length)
+                )
 
         return "".join(string)
 
@@ -103,13 +142,13 @@ class Text(Space[str]):
         """Return boolean specifying if x is a valid member of this space."""
         if isinstance(x, str):
             if self.min_length <= len(x) <= self.max_length:
-                return all(c in self.charset for c in x)
+                return all(c in self.character_set for c in x)
         return False
 
     def __repr__(self) -> str:
         """Gives a string representation of this space."""
         return (
-            f"Text({self.min_length}, {self.max_length}, charset={self._charset_str})"
+            f"Text({self.min_length}, {self.max_length}, characters={self.characters})"
         )
 
     def __eq__(self, other) -> bool:
@@ -118,5 +157,29 @@ class Text(Space[str]):
             isinstance(other, Text)
             and self.min_length == other.min_length
             and self.max_length == other.max_length
-            and self.charset == other.charset
+            and self.character_set == other.character_set
         )
+
+    @property
+    def character_set(self) -> FrozenSet[str]:
+        """Returns the character set for the space."""
+        return self._char_set
+
+    @property
+    def character_list(self) -> Tuple[str, ...]:
+        """Returns a tuple of characters in the space."""
+        return self._char_list
+
+    def character_index(self, char: str) -> np.int32:
+        """Returns a unique index for each character in the space's character set."""
+        return self._char_index[char]
+
+    @property
+    def characters(self) -> str:
+        """Returns a string with all Text characters."""
+        return self._char_str
+
+    @property
+    def is_np_flattenable(self) -> bool:
+        """The flattened version is an integer array for each character, padded to the max character length."""
+        return True

gym/spaces/utils.py

@@ -21,6 +21,7 @@ from gym.spaces import (
     MultiDiscrete,
     Sequence,
     Space,
+    Text,
     Tuple,
 )
 
@@ -88,6 +89,11 @@ def _flatdim_dict(space: Dict) -> int:
     )
 
 
+@flatdim.register(Text)
+def _flatdim_text(space: Text) -> int:
+    return space.max_length
+
+
 T = TypeVar("T")
 FlatType = Union[np.ndarray, TypingDict, tuple, GraphInstance]
 
@@ -104,7 +110,9 @@ def flatten(space: Space[T], x: T) -> FlatType:
         x: The value to flatten
 
     Returns:
-        - The flattened ``x``, always returns a 1D array for non-graph spaces.
+        - For ``Box`` and ``MultiBinary``, this is a flattened array
+        - For ``Discrete`` and ``MultiDiscrete``, this is a flattened one-hot array of the sample
+        - For ``Tuple`` and ``Dict``, this is a concatenated array the subspaces (does not support graph subspaces)
         - For graph spaces, returns `GraphInstance` where:
             - `nodes` are n x k arrays
             - `edges` are either:
@@ -179,6 +187,16 @@ def _flatten_graph(space, x) -> GraphInstance:
     return GraphInstance(nodes, edges, x.edge_links)
 
 
+@flatten.register(Text)
+def _flatten_text(space: Text, x: str) -> np.ndarray:
+    arr = np.full(
+        shape=(space.max_length,), fill_value=len(space.character_set), dtype=np.int32
+    )
+    for i, val in enumerate(x):
+        arr[i] = space.character_index(val)
+    return arr
+
+
 @flatten.register(Sequence)
 def _flatten_sequence(space, x) -> tuple:
     return tuple(flatten(space.feature_space, item) for item in x)
@@ -284,6 +302,13 @@ def _unflatten_graph(space: Graph, x: GraphInstance) -> GraphInstance:
     return GraphInstance(nodes, edges, x.edge_links)
 
 
+@unflatten.register(Text)
+def _unflatten_text(space: Text, x: np.ndarray) -> str:
+    return "".join(
+        [space.character_list[val] for val in x if val < len(space.character_set)]
+    )
+
+
 @unflatten.register(Sequence)
 def _unflatten_sequence(space: Sequence, x: tuple) -> tuple:
     return tuple(unflatten(space.feature_space, item) for item in x)
@@ -401,6 +426,13 @@ def _flatten_space_graph(space: Graph) -> Graph:
     )
 
 
+@flatten_space.register(Text)
+def _flatten_space_text(space: Text) -> Box:
+    return Box(
+        low=0, high=len(space.character_set), shape=(space.max_length,), dtype=np.int32
+    )
+
+
 @flatten_space.register(Sequence)
 def _flatten_space_sequence(space: Sequence) -> Sequence:
     return Sequence(flatten_space(space.feature_space))

gym/utils/env_checker.py

@@ -50,7 +50,9 @@ def data_equivalence(data_1, data_2) -> bool:
                 data_equivalence(o_1, o_2) for o_1, o_2 in zip(data_1, data_2)
             )
         elif isinstance(data_1, np.ndarray):
-            return np.all(data_1 == data_2)
+            return data_1.shape == data_2.shape and np.allclose(
+                data_1, data_2, atol=0.00001
+            )
         else:
             return data_1 == data_2
     else:

tests/spaces/test_box.py

@@ -4,26 +4,26 @@ import warnings
 import numpy as np
 import pytest
 
+import gym.error
 from gym.spaces import Box
-
+from gym.spaces.box import get_inf
-# Todo, move Box unique tests from test_spaces.py to test_box.py
 
 
 @pytest.mark.parametrize(
     "box,expected_shape",
     [
-        (
+        (  # Test with same 1-dim low and high shape
-            Box(low=np.zeros(2), high=np.ones(2)),
+            Box(low=np.zeros(2), high=np.ones(2), dtype=np.int32),
             (2,),
-        ),  # Test with same 1-dim low and high shape
+        ),
-        (
+        (  # Test with same multi-dim low and high shape
-            Box(low=np.zeros((2, 1)), high=np.ones((2, 1))),
+            Box(low=np.zeros((2, 1)), high=np.ones((2, 1)), dtype=np.int32),
             (2, 1),
-        ),  # Test with same multi-dim low and high shape
+        ),
-        (
+        (  # Test with scalar low high and different shape
             Box(low=0, high=1, shape=(5, 2)),
             (5, 2),
-        ),  # Test with scalar low high and different shape
+        ),
         (Box(low=0, high=1), (1,)),  # Test with int and int
         (Box(low=0.0, high=1.0), (1,)),  # Test with float and float
         (Box(low=np.zeros(1)[0], high=np.ones(1)[0]), (1,)),
@@ -33,7 +33,8 @@ from gym.spaces import Box
         (Box(low=np.zeros(3), high=1.0), (3,)),  # Test with array and scalar
     ],
 )
-def test_box_shape_inference(box, expected_shape):
+def test_shape_inference(box, expected_shape):
+    """Test that the shape inference is as expected."""
     assert box.shape == expected_shape
     assert box.sample().shape == expected_shape
 
@@ -48,7 +49,7 @@ def test_box_shape_inference(box, expected_shape):
         (np.zeros(2, dtype=np.float32), True),
         (np.zeros((2, 2), dtype=np.float32), True),
         (np.inf, True),
-        (np.nan, True),  # This is a weird side
+        (np.nan, True),  # This is a weird case that we allow
         (True, False),
         (np.bool8(True), False),
         (1 + 1j, False),
@@ -56,7 +57,8 @@ def test_box_shape_inference(box, expected_shape):
         ("string", False),
     ],
 )
-def test_box_values(value, valid):
+def test_low_high_values(value, valid: bool):
+    """Test what `low` and `high` values are valid for `Box` space."""
     if valid:
         with warnings.catch_warnings(record=True) as caught_warnings:
             Box(low=value, high=value)
@@ -66,7 +68,9 @@ def test_box_values(value, valid):
     else:
         with pytest.raises(
             ValueError,
-            match=r"expect their types to be np\.ndarray, an integer or a float",
+            match=re.escape(
+                "expect their types to be np.ndarray, an integer or a float"
+            ),
         ):
             Box(low=value, high=value)
 
@@ -135,6 +139,178 @@ def test_box_values(value, valid):
         ),
     ],
 )
-def test_box_errors(low, high, kwargs, error, message):
+def test_init_errors(low, high, kwargs, error, message):
+    """Test all constructor errors."""
     with pytest.raises(error, match=f"^{re.escape(message)}$"):
         Box(low=low, high=high, **kwargs)
+
+
+def test_dtype_check():
+    """Tests the Box contains function with different dtypes."""
+    # Related Issues:
+    # https://github.com/openai/gym/issues/2357
+    # https://github.com/openai/gym/issues/2298
+
+    space = Box(0, 1, (), dtype=np.float32)
+
+    # casting will match the correct type
+    assert np.array(0.5, dtype=np.float32) in space
+
+    # float16 is in float32 space
+    assert np.array(0.5, dtype=np.float16) in space
+
+    # float64 is not in float32 space
+    assert np.array(0.5, dtype=np.float64) not in space
+
+
+@pytest.mark.parametrize(
+    "space",
+    [
+        Box(low=0, high=np.inf, shape=(2,), dtype=np.int32),
+        Box(low=0, high=np.inf, shape=(2,), dtype=np.float32),
+        Box(low=0, high=np.inf, shape=(2,), dtype=np.int64),
+        Box(low=0, high=np.inf, shape=(2,), dtype=np.float64),
+        Box(low=-np.inf, high=0, shape=(2,), dtype=np.int32),
+        Box(low=-np.inf, high=0, shape=(2,), dtype=np.float32),
+        Box(low=-np.inf, high=0, shape=(2,), dtype=np.int64),
+        Box(low=-np.inf, high=0, shape=(2,), dtype=np.float64),
+        Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.int32),
+        Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.float32),
+        Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.int64),
+        Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.float64),
+        Box(low=0, high=np.inf, shape=(2, 3), dtype=np.int32),
+        Box(low=0, high=np.inf, shape=(2, 3), dtype=np.float32),
+        Box(low=0, high=np.inf, shape=(2, 3), dtype=np.int64),
+        Box(low=0, high=np.inf, shape=(2, 3), dtype=np.float64),
+        Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.int32),
+        Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.float32),
+        Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.int64),
+        Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.float64),
+        Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.int32),
+        Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.float32),
+        Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.int64),
+        Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.float64),
+        Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.int32),
+        Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.float32),
+        Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.int64),
+        Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.float64),
+    ],
+)
+def test_infinite_space(space):
+    """
+    To test spaces that are passed in have only 0 or infinite bounds because `space.high` and `space.low`
+     are both modified within the init, we check for infinite when we know it's not 0
+    """
+
+    assert np.all(
+        space.low < space.high
+    ), f"Box low bound ({space.low}) is not lower than the high bound ({space.high})"
+
+    space.seed(0)
+    sample = space.sample()
+
+    # check if space contains sample
+    assert (
+        sample in space
+    ), f"Sample ({sample}) not inside space according to `space.contains()`"
+
+    # manually check that the sign of the sample is within the bounds
+    assert np.all(
+        np.sign(sample) <= np.sign(space.high)
+    ), f"Sign of sample ({sample}) is less than space upper bound ({space.high})"
+    assert np.all(
+        np.sign(space.low) <= np.sign(sample)
+    ), f"Sign of sample ({sample}) is more than space lower bound ({space.low})"
+
+    # check that int bounds are bounded for everything
+    # but floats are unbounded for infinite
+    if np.any(space.high != 0):
+        assert (
+            space.is_bounded("above") is False
+        ), "inf upper bound supposed to be unbounded"
+    else:
+        assert (
+            space.is_bounded("above") is True
+        ), "non-inf upper bound supposed to be bounded"
+
+    if np.any(space.low != 0):
+        assert (
+            space.is_bounded("below") is False
+        ), "inf lower bound supposed to be unbounded"
+    else:
+        assert (
+            space.is_bounded("below") is True
+        ), "non-inf lower bound supposed to be bounded"
+
+    if np.any(space.low != 0) or np.any(space.high != 0):
+        assert space.is_bounded("both") is False
+    else:
+        assert space.is_bounded("both") is True
+
+    # check for dtype
+    assert (
+        space.high.dtype == space.dtype
+    ), f"High's dtype {space.high.dtype} doesn't match `space.dtype`'"
+    assert (
+        space.low.dtype == space.dtype
+    ), f"Low's dtype {space.high.dtype} doesn't match `space.dtype`'"
+
+    with pytest.raises(
+        ValueError, match="manner is not in {'below', 'above', 'both'}, actual value:"
+    ):
+        space.is_bounded("test")
+
+
+def test_legacy_state_pickling():
+    legacy_state = {
+        "dtype": np.dtype("float32"),
+        "_shape": (5,),
+        "low": np.array([0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float32),
+        "high": np.array([1.0, 1.0, 1.0, 1.0, 1.0], dtype=np.float32),
+        "bounded_below": np.array([True, True, True, True, True]),
+        "bounded_above": np.array([True, True, True, True, True]),
+        "_np_random": None,
+    }
+
+    b = Box(-1, 1, ())
+    assert "low_repr" in b.__dict__ and "high_repr" in b.__dict__
+    del b.__dict__["low_repr"]
+    del b.__dict__["high_repr"]
+    assert "low_repr" not in b.__dict__ and "high_repr" not in b.__dict__
+
+    b.__setstate__(legacy_state)
+    assert b.low_repr == "0.0"
+    assert b.high_repr == "1.0"
+
+
+def test_get_inf():
+    """Tests that get inf function works as expected, primarily for coverage."""
+    assert get_inf(np.float32, "+") == np.inf
+    assert get_inf(np.float16, "-") == -np.inf
+    with pytest.raises(
+        TypeError, match=re.escape("Unknown sign *, use either '+' or '-'")
+    ):
+        get_inf(np.float32, "*")
+
+    assert get_inf(np.int16, "+") == 32765
+    assert get_inf(np.int8, "-") == -126
+    with pytest.raises(
+        TypeError, match=re.escape("Unknown sign *, use either '+' or '-'")
+    ):
+        get_inf(np.int32, "*")
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape("Unknown dtype <class 'numpy.complex128'> for infinite bounds"),
+    ):
+        get_inf(np.complex_, "+")
+
+
+def test_sample_mask():
+    """Box cannot have a mask applied."""
+    space = Box(0, 1)
+    with pytest.raises(
+        gym.error.Error,
+        match=re.escape("Box.sample cannot be provided a mask, actual value: "),
+    ):
+        space.sample(mask=np.array([0, 1, 0], dtype=np.int8))

tests/spaces/test_discrete.py

@@ -0,0 +1,40 @@
+import numpy as np
+
+from gym.spaces import Discrete
+
+
+def test_space_legacy_pickling():
+    """Test the legacy pickle of Discrete that is missing the `start` parameter."""
+    legacy_state = {
+        "shape": (
+            1,
+            2,
+            3,
+        ),
+        "dtype": np.int64,
+        "np_random": np.random.default_rng(),
+        "n": 3,
+    }
+    space = Discrete(1)
+    space.__setstate__(legacy_state)
+
+    assert space.shape == legacy_state["shape"]
+    assert space.np_random == legacy_state["np_random"]
+    assert space.n == 3
+    assert space.dtype == legacy_state["dtype"]
+
+    # Test that start is missing
+    assert "start" in space.__dict__
+    del space.__dict__["start"]  # legacy did not include start param
+    assert "start" not in space.__dict__
+
+    space.__setstate__(legacy_state)
+    assert space.start == 0
+
+
+def test_sample_mask():
+    space = Discrete(4, start=2)
+    assert 2 <= space.sample() < 6
+    assert space.sample(mask=np.array([0, 1, 0, 0], dtype=np.int8)) == 3
+    assert space.sample(mask=np.array([0, 0, 0, 0], dtype=np.int8)) == 2
+    assert space.sample(mask=np.array([0, 1, 0, 1], dtype=np.int8)) in [3, 5]

tests/spaces/test_multibinary.py

@@ -0,0 +1,3 @@
+def test_sample():
+    # todo
+    pass

tests/spaces/test_multidiscrete.py

@@ -0,0 +1,66 @@
+import pytest
+
+from gym.spaces import Discrete, MultiDiscrete
+from gym.utils.env_checker import data_equivalence
+
+
+def test_multidiscrete_as_tuple():
+    # 1D multi-discrete
+    space = MultiDiscrete([3, 4, 5])
+
+    assert space.shape == (3,)
+    assert space[0] == Discrete(3)
+    assert space[0:1] == MultiDiscrete([3])
+    assert space[0:2] == MultiDiscrete([3, 4])
+    assert space[:] == space and space[:] is not space
+
+    # 2D multi-discrete
+    space = MultiDiscrete([[3, 4, 5], [6, 7, 8]])
+
+    assert space.shape == (2, 3)
+    assert space[0, 1] == Discrete(4)
+    assert space[0] == MultiDiscrete([3, 4, 5])
+    assert space[0:1] == MultiDiscrete([[3, 4, 5]])
+    assert space[0:2, :] == MultiDiscrete([[3, 4, 5], [6, 7, 8]])
+    assert space[:, 0:1] == MultiDiscrete([[3], [6]])
+    assert space[0:2, 0:2] == MultiDiscrete([[3, 4], [6, 7]])
+    assert space[:] == space and space[:] is not space
+    assert space[:, :] == space and space[:, :] is not space
+
+
+def test_multidiscrete_subspace_reproducibility():
+    # 1D multi-discrete
+    space = MultiDiscrete([100, 200, 300])
+    space.seed()
+
+    assert data_equivalence(space[0].sample(), space[0].sample())
+    assert data_equivalence(space[0:1].sample(), space[0:1].sample())
+    assert data_equivalence(space[0:2].sample(), space[0:2].sample())
+    assert data_equivalence(space[:].sample(), space[:].sample())
+    assert data_equivalence(space[:].sample(), space.sample())
+
+    # 2D multi-discrete
+    space = MultiDiscrete([[300, 400, 500], [600, 700, 800]])
+    space.seed()
+
+    assert data_equivalence(space[0, 1].sample(), space[0, 1].sample())
+    assert data_equivalence(space[0].sample(), space[0].sample())
+    assert data_equivalence(space[0:1].sample(), space[0:1].sample())
+    assert data_equivalence(space[0:2, :].sample(), space[0:2, :].sample())
+    assert data_equivalence(space[:, 0:1].sample(), space[:, 0:1].sample())
+    assert data_equivalence(space[0:2, 0:2].sample(), space[0:2, 0:2].sample())
+    assert data_equivalence(space[:].sample(), space[:].sample())
+    assert data_equivalence(space[:, :].sample(), space[:, :].sample())
+    assert data_equivalence(space[:, :].sample(), space.sample())
+
+
+def test_multidiscrete_length():
+    space = MultiDiscrete(nvec=[3, 2, 4])
+    assert len(space) == 3
+
+    space = MultiDiscrete(nvec=[[2, 3], [3, 2]])
+    with pytest.warns(
+        UserWarning,
+        match="Getting the length of a multi-dimensional MultiDiscrete space.",
+    ):
+        assert len(space) == 2

tests/spaces/test_space.py

@@ -0,0 +1,24 @@
+from functools import partial
+
+import pytest
+
+from gym import Space
+from gym.spaces import utils
+
+TESTING_SPACE = Space()
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        TESTING_SPACE.sample,
+        partial(TESTING_SPACE.contains, None),
+        partial(utils.flatdim, TESTING_SPACE),
+        partial(utils.flatten, TESTING_SPACE, None),
+        partial(utils.flatten_space, TESTING_SPACE),
+        partial(utils.unflatten, TESTING_SPACE, None),
+    ],
+)
+def test_not_implemented_errors(func):
+    with pytest.raises(NotImplementedError):
+        func()

tests/spaces/test_text.py

@@ -0,0 +1,41 @@
+import re
+
+import numpy as np
+import pytest
+
+from gym.spaces import Text
+
+
+def test_sample_mask():
+    space = Text(min_length=1, max_length=5)
+
+    # Test the sample length
+    sample = space.sample(mask=(3, None))
+    assert sample in space
+    assert len(sample) == 3
+
+    sample = space.sample(mask=None)
+    assert sample in space
+    assert 1 <= len(sample) <= 5
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "Trying to sample with a minimum length > 0 (1) but the character mask is all zero meaning that no character could be sampled."
+        ),
+    ):
+        space.sample(mask=(3, np.zeros(len(space.character_set), dtype=np.int8)))
+
+    space = Text(min_length=0, max_length=5)
+    sample = space.sample(
+        mask=(None, np.zeros(len(space.character_set), dtype=np.int8))
+    )
+    assert sample in space
+    assert sample == ""
+
+    # Test the sample characters
+    space = Text(max_length=5, charset="abcd")
+
+    sample = space.sample(mask=(3, np.array([0, 1, 0, 0], dtype=np.int8)))
+    assert sample in space
+    assert sample == "bbb"

tests/spaces/test_utils.py

@@ -1,601 +1,135 @@
-import re
+from itertools import zip_longest
-from collections import OrderedDict
+from typing import Optional
 
 import numpy as np
 import pytest
 
-from gym.spaces import (
+import gym
-    Box,
+from gym.spaces import Box, Graph, utils
-    Dict,
+from gym.utils.env_checker import data_equivalence
-    Discrete,
+from tests.spaces.utils import TESTING_SPACES, TESTING_SPACES_IDS
-    Graph,
-    GraphInstance,
-    MultiBinary,
-    MultiDiscrete,
-    Sequence,
-    Tuple,
-    utils,
-)
 
-homogeneous_spaces = [
+TESTING_SPACES_EXPECTED_FLATDIMS = [
-    Discrete(3),
+    # Discrete
-    Box(low=0.0, high=np.inf, shape=(2, 2)),
-    Box(low=0.0, high=np.inf, shape=(2, 2), dtype=np.float16),
-    Tuple([Discrete(5), Discrete(10)]),
-    Tuple(
-        [
-            Discrete(5),
-            Box(low=np.array([0.0, 0.0]), high=np.array([1.0, 5.0]), dtype=np.float64),
-        ]
-    ),
-    Tuple((Discrete(5), Discrete(2), Discrete(2))),
-    MultiDiscrete([2, 2, 10]),
-    MultiBinary(10),
-    Dict(
-        {
-            "position": Discrete(5),
-            "velocity": Box(
-                low=np.array([0.0, 0.0]), high=np.array([1.0, 5.0]), dtype=np.float64
-            ),
-        }
-    ),
-    Discrete(3, start=2),
-    Discrete(8, start=-5),
-]
-
-flatdims = [3, 4, 4, 15, 7, 9, 14, 10, 7, 3, 8]
-
-non_homogenous_spaces = [
-    Graph(node_space=Box(low=-100, high=100, shape=(2, 2)), edge_space=Discrete(5)),  #
-    Graph(node_space=Discrete(5), edge_space=Box(low=-100, high=100, shape=(2, 2))),  #
-    Graph(node_space=Discrete(5), edge_space=None),  #
-    Sequence(Discrete(4)),  #
-    Sequence(Box(-10, 10, shape=(2, 2))),  #
-    Sequence(Tuple([Box(-10, 10, shape=(2,)), Box(-10, 10, shape=(2,))])),  #
-    Dict(a=Sequence(Discrete(4)), b=Box(-10, 10, shape=(2, 2))),  #
-    Dict(
-        a=Graph(node_space=Discrete(4), edge_space=Discrete(4)),
-        b=Box(-10, 10, shape=(2, 2)),
-    ),  #
-    Tuple([Sequence(Discrete(4)), Box(-10, 10, shape=(2, 2))]),  #
-    Tuple(
-        [
-            Graph(node_space=Discrete(4), edge_space=Discrete(4)),
-            Box(-10, 10, shape=(2, 2)),
-        ]
-    ),  #
-    Sequence(Graph(node_space=Box(-100, 100, shape=(2, 2)), edge_space=Discrete(4))),  #
-    Dict(
-        a=Dict(
-            a=Sequence(Box(-100, 100, shape=(2, 2))), b=Box(-100, 100, shape=(2, 2))
-        ),
-        b=Tuple([Box(-100, 100, shape=(2,)), Box(-100, 100, shape=(2,))]),
-    ),  #
-    Dict(
-        a=Dict(
-            a=Graph(node_space=Box(-100, 100, shape=(2, 2)), edge_space=None),
-            b=Box(-100, 100, shape=(2, 2)),
-        ),
-        b=Tuple([Box(-100, 100, shape=(2,)), Box(-100, 100, shape=(2,))]),
-    ),
-]
-
-
-@pytest.mark.parametrize("space", non_homogenous_spaces)
-def test_non_flattenable(space):
-    assert space.is_np_flattenable is False
-    with pytest.raises(
-        ValueError,
-        match=re.escape(
-            "cannot be flattened to a numpy array, probably because it contains a `Graph` or `Sequence` subspace"
-        ),
-    ):
-        utils.flatdim(space)
-
-
-@pytest.mark.parametrize(["space", "flatdim"], zip(homogeneous_spaces, flatdims))
-def test_flatdim(space, flatdim):
-    assert space.is_np_flattenable
-    dim = utils.flatdim(space)
-    assert dim == flatdim, f"Expected {dim} to equal {flatdim}"
-
-
-@pytest.mark.parametrize("space", homogeneous_spaces)
-def test_flatten_space_boxes(space):
-    flat_space = utils.flatten_space(space)
-    assert isinstance(flat_space, Box), f"Expected {type(flat_space)} to equal {Box}"
-    flatdim = utils.flatdim(space)
-    (single_dim,) = flat_space.shape
-    assert single_dim == flatdim, f"Expected {single_dim} to equal {flatdim}"
-
-
-@pytest.mark.parametrize("space", homogeneous_spaces + non_homogenous_spaces)
-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_space.contains(
-            flat_sample
-        ), f"Expected sample #{i} {flat_sample} to be in {flat_space}"
-
-
-@pytest.mark.parametrize("space", homogeneous_spaces)
-def test_flatten_dim(space):
-    sample = utils.flatten(space, space.sample())
-    (single_dim,) = sample.shape
-    flatdim = utils.flatdim(space)
-    assert single_dim == flatdim, f"Expected {single_dim} to equal {flatdim}"
-
-
-@pytest.mark.parametrize("space", homogeneous_spaces + non_homogenous_spaces)
-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
-        ), f"Expected sample #{i} {original} to equal {roundtripped}"
-        assert space.contains(roundtripped)
-
-
-def compare_nested(left, right):
-    if type(left) != type(right):
-        return False
-    elif isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
-        return left.shape == right.shape and 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.
-"""
-
-expected_flattened_dtypes = [
-    np.int64,
-    np.float32,
-    np.float16,
-    np.int64,
-    np.float64,
-    np.int64,
-    np.int64,
-    np.int8,
-    np.float64,
-    np.int64,
-    np.int64,
-]
-
-
-@pytest.mark.parametrize(
-    ["original_space", "expected_flattened_dtype"],
-    zip(homogeneous_spaces, expected_flattened_dtypes),
-)
-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
-    ), f"Expected flattened_space's dtype to equal {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)
-        )
-
-
-homogeneous_samples = [
-    2,
-    np.array([[1.0, 3.0], [5.0, 8.0]], dtype=np.float32),
-    np.array([[1.0, 3.0], [5.0, 8.0]], dtype=np.float16),
-    (3, 7),
-    (2, np.array([0.5, 3.5], dtype=np.float32)),
-    (3, 0, 1),
-    np.array([0, 1, 7], dtype=np.int64),
-    np.array([0, 1, 1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int8),
-    OrderedDict(
-        [("position", 3), ("velocity", np.array([0.5, 3.5], dtype=np.float32))]
-    ),
     3,
-    -2,
+    3,
-]
+    # Box
-
+    1,
-
+    4,
-expected_flattened_hom_samples = [
+    2,
-    np.array([0, 0, 1], dtype=np.int64),
+    2,
-    np.array([1.0, 3.0, 5.0, 8.0], dtype=np.float32),
+    2,
-    np.array([1.0, 3.0, 5.0, 8.0], dtype=np.float16),
+    # Multi-discrete
-    np.array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], dtype=np.int64),
+    4,
-    np.array([0, 0, 1, 0, 0, 0.5, 3.5], dtype=np.float64),
+    10,
-    np.array([0, 0, 0, 1, 0, 1, 0, 0, 1], dtype=np.int64),
+    # Multi-binary
-    np.array([1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], dtype=np.int64),
+    8,
-    np.array([0, 1, 1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int8),
+    6,
-    np.array([0, 0, 0, 1, 0, 0.5, 3.5], dtype=np.float64),
+    # Text
-    np.array([0, 1, 0], dtype=np.int64),
+    6,
-    np.array([0, 0, 0, 1, 0, 0, 0, 0], dtype=np.int64),
+    6,
-]
+    6,
-
+    # # Tuple
-non_homogenous_samples = [
+    # 9,
-    GraphInstance(
+    # 7,
-        np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float32),
+    # 10,
-        np.array(
+    # 6,
-            [
+    # None,
-                0,
+    # # Dict
-            ],
+    # 7,
-            dtype=int,
+    # 8,
-        ),
+    # 17,
-        np.array([[0, 1]], dtype=int),
+    # None,
-    ),
+    # # Graph
-    GraphInstance(
+    # None,
-        np.array([0, 1], dtype=int),
+    # None,
-        np.array([[[1, 2], [3, 4]]], dtype=np.float32),
+    # None,
-        np.array([[0, 1]], dtype=int),
+    # # Sequence
-    ),
+    # None,
-    GraphInstance(np.array([0, 1], dtype=int), None, np.array([[0, 1]], dtype=int)),
+    # None,
-    (0, 1, 2),
+    # None,
-    (
-        np.array([[0, 1], [2, 3]], dtype=np.float32),
-        np.array([[4, 5], [6, 7]], dtype=np.float32),
-    ),
-    (
-        (np.array([0, 1], dtype=np.float32), np.array([2, 3], dtype=np.float32)),
-        (np.array([4, 5], dtype=np.float32), np.array([6, 7], dtype=np.float32)),
-    ),
-    OrderedDict(
-        [("a", (0, 1, 2)), ("b", np.array([[0, 1], [2, 3]], dtype=np.float32))]
-    ),
-    OrderedDict(
-        [
-            (
-                "a",
-                GraphInstance(
-                    np.array([1, 2], dtype=np.int),
-                    np.array(
-                        [
-                            0,
-                        ],
-                        dtype=int,
-                    ),
-                    np.array([[0, 1]], dtype=int),
-                ),
-            ),
-            ("b", np.array([[0, 1], [2, 3]], dtype=np.float32)),
-        ]
-    ),
-    ((0, 1, 2), np.array([[0, 1], [2, 3]], dtype=np.float32)),
-    (
-        GraphInstance(
-            np.array([1, 2], dtype=np.int),
-            np.array(
-                [
-                    0,
-                ],
-                dtype=int,
-            ),
-            np.array([[0, 1]], dtype=int),
-        ),
-        np.array([[0, 1], [2, 3]], dtype=np.float32),
-    ),
-    (
-        GraphInstance(
-            nodes=np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32),
-            edges=np.array([0], dtype=int),
-            edge_links=np.array([[0, 1]]),
-        ),
-        GraphInstance(
-            nodes=np.array(
-                [[[8, 9], [10, 11]], [[12, 13], [14, 15]]], dtype=np.float32
-            ),
-            edges=np.array([1], dtype=int),
-            edge_links=np.array([[0, 1]]),
-        ),
-    ),
-    OrderedDict(
-        [
-            (
-                "a",
-                OrderedDict(
-                    [
-                        (
-                            "a",
-                            (
-                                np.array([[0, 1], [2, 3]], dtype=np.float32),
-                                np.array([[4, 5], [6, 7]], dtype=np.float32),
-                            ),
-                        ),
-                        ("b", np.array([[8, 9], [10, 11]], dtype=np.float32)),
-                    ]
-                ),
-            ),
-            (
-                "b",
-                (
-                    np.array([12, 13], dtype=np.float32),
-                    np.array([14, 15], dtype=np.float32),
-                ),
-            ),
-        ]
-    ),
-    OrderedDict(
-        [
-            (
-                "a",
-                OrderedDict(
-                    [
-                        (
-                            "a",
-                            GraphInstance(
-                                np.array(
-                                    [[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
-                                    dtype=np.float32,
-                                ),
-                                None,
-                                np.array([[0, 1]], dtype=int),
-                            ),
-                        ),
-                        ("b", np.array([[8, 9], [10, 11]], dtype=np.float32)),
-                    ]
-                ),
-            ),
-            (
-                "b",
-                (
-                    np.array([12, 13], dtype=np.float32),
-                    np.array([14, 15], dtype=np.float32),
-                ),
-            ),
-        ]
-    ),
-]
-
-
-expected_flattened_non_hom_samples = [
-    GraphInstance(
-        np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.float32),
-        np.array([[1, 0, 0, 0, 0]], dtype=int),
-        np.array([[0, 1]], dtype=int),
-    ),
-    GraphInstance(
-        np.array([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], dtype=int),
-        np.array([[1, 2, 3, 4]], dtype=np.float32),
-        np.array([[0, 1]], dtype=int),
-    ),
-    GraphInstance(
-        np.array([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], dtype=int),
-        None,
-        np.array([[0, 1]], dtype=int),
-    ),
-    (
-        np.array([1, 0, 0, 0], dtype=int),
-        np.array([0, 1, 0, 0], dtype=int),
-        np.array([0, 0, 1, 0], dtype=int),
-    ),
-    (
-        np.array([0, 1, 2, 3], dtype=np.float32),
-        np.array([4, 5, 6, 7], dtype=np.float32),
-    ),
-    (
-        np.array([0, 1, 2, 3], dtype=np.float32),
-        np.array([4, 5, 6, 7], dtype=np.float32),
-    ),
-    OrderedDict(
-        [
-            (
-                "a",
-                (
-                    np.array([1, 0, 0, 0], dtype=int),
-                    np.array([0, 1, 0, 0], dtype=int),
-                    np.array([0, 0, 1, 0], dtype=int),
-                ),
-            ),
-            ("b", np.array([0, 1, 2, 3], dtype=np.float32)),
-        ]
-    ),
-    OrderedDict(
-        [
-            (
-                "a",
-                GraphInstance(
-                    np.array([[0, 1, 0, 0], [0, 0, 1, 0]], dtype=int),
-                    np.array([[1, 0, 0, 0]], dtype=int),
-                    np.array([[0, 1]], dtype=int),
-                ),
-            ),
-            ("b", np.array([0, 1, 2, 3], dtype=np.float32)),
-        ]
-    ),
-    (
-        (
-            np.array([1, 0, 0, 0], dtype=int),
-            np.array([0, 1, 0, 0], dtype=int),
-            np.array([0, 0, 1, 0], dtype=int),
-        ),
-        np.array([0, 1, 2, 3], dtype=np.float32),
-    ),
-    (
-        GraphInstance(
-            np.array([[0, 1, 0, 0], [0, 0, 1, 0]], dtype=np.float32),
-            np.array([[1, 0, 0, 0]], dtype=int),
-            np.array([[0, 1]], dtype=int),
-        ),
-        np.array([0, 1, 2, 3], dtype=np.float32),
-    ),
-    (
-        GraphInstance(
-            np.array([[0, 1, 2, 3], [4, 5, 6, 7]], dtype=np.float32),
-            np.array([[1, 0, 0, 0]]),
-            np.array([[0, 1]]),
-        ),
-        GraphInstance(
-            np.array([[8, 9, 10, 11], [12, 13, 14, 15]], dtype=np.float32),
-            np.array([[0, 1, 0, 0]]),
-            np.array([[0, 1]]),
-        ),
-    ),
-    OrderedDict(
-        [
-            (
-                "a",
-                OrderedDict(
-                    [
-                        (
-                            "a",
-                            (
-                                np.array([0, 1, 2, 3], dtype=np.float32),
-                                np.array([4, 5, 6, 7], dtype=np.float32),
-                            ),
-                        ),
-                        ("b", np.array([8, 9, 10, 11], dtype=np.float32)),
-                    ]
-                ),
-            ),
-            ("b", (np.array([12, 13, 14, 15], dtype=np.float32))),
-        ]
-    ),
-    OrderedDict(
-        [
-            (
-                "a",
-                OrderedDict(
-                    [
-                        (
-                            "a",
-                            GraphInstance(
-                                np.array(
-                                    [[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.float32
-                                ),
-                                None,
-                                np.array([[0, 1]], dtype=int),
-                            ),
-                        ),
-                        ("b", np.array([8, 9, 10, 11], dtype=np.float32)),
-                    ]
-                ),
-            ),
-            ("b", (np.array([12, 13, 14, 15], dtype=np.float32))),
-        ]
-    ),
 ]
 
 
 @pytest.mark.parametrize(
-    ["space", "sample", "expected_flattened_sample"],
+    ["space", "flatdim"],
-    zip(
+    zip_longest(TESTING_SPACES, TESTING_SPACES_EXPECTED_FLATDIMS),
-        homogeneous_spaces + non_homogenous_spaces,
+    ids=TESTING_SPACES_IDS,
-        homogeneous_samples + non_homogenous_samples,
-        expected_flattened_hom_samples + expected_flattened_non_hom_samples,
-    ),
 )
-def test_flatten(space, sample, expected_flattened_sample):
+def test_flatdim(space: gym.spaces.Space, flatdim: Optional[int]):
-    flattened_sample = utils.flatten(space, sample)
+    """Checks that the flattened dims of the space is equal to an expected value."""
+    if space.is_np_flattenable:
+        dim = utils.flatdim(space)
+        assert dim == flatdim, f"Expected {dim} to equal {flatdim}"
+    else:
+        with pytest.raises(
+            ValueError,
+        ):
+            utils.flatdim(space)
+
+
+@pytest.mark.parametrize("space", TESTING_SPACES, ids=TESTING_SPACES_IDS)
+def test_flatten_space(space):
+    """Test that the flattened spaces are a box and have the `flatdim` shape."""
     flat_space = utils.flatten_space(space)
 
-    assert sample in space
+    if space.is_np_flattenable:
-    assert flattened_sample in flat_space
+        assert isinstance(flat_space, Box)
+        (single_dim,) = flat_space.shape
+        flatdim = utils.flatdim(space)
+
+        assert single_dim == flatdim
+    elif isinstance(flat_space, Graph):
+        assert isinstance(space, Graph)
+
+        (node_single_dim,) = flat_space.node_space.shape
+        node_flatdim = utils.flatdim(space.node_space)
+        assert node_single_dim == node_flatdim
+
+        if flat_space.edge_space is not None:
+            (edge_single_dim,) = flat_space.edge_space.shape
+            edge_flatdim = utils.flatdim(space.edge_space)
+            assert edge_single_dim == edge_flatdim
+    else:
+        assert isinstance(
+            space, (gym.spaces.Tuple, gym.spaces.Dict, gym.spaces.Sequence)
+        )
+
+
+@pytest.mark.parametrize("space", TESTING_SPACES, ids=TESTING_SPACES_IDS)
+def test_flatten(space):
+    """Test that a flattened sample have the `flatdim` shape."""
+    flattened_sample = utils.flatten(space, space.sample())
 
     if space.is_np_flattenable:
         assert isinstance(flattened_sample, np.ndarray)
-        assert flattened_sample.shape == expected_flattened_sample.shape
+        (single_dim,) = flattened_sample.shape
-        assert flattened_sample.dtype == expected_flattened_sample.dtype
+        flatdim = utils.flatdim(space)
-        assert np.all(flattened_sample == expected_flattened_sample)
+
+        assert single_dim == flatdim
     else:
-        assert not isinstance(flattened_sample, np.ndarray)
+        assert isinstance(flattened_sample, (tuple, dict, Graph))
-        assert compare_nested(flattened_sample, expected_flattened_sample)
 
 
-@pytest.mark.parametrize(
+@pytest.mark.parametrize("space", TESTING_SPACES, ids=TESTING_SPACES_IDS)
-    ["space", "flattened_sample", "expected_sample"],
+def test_flat_space_contains_flat_points(space):
-    zip(homogeneous_spaces, expected_flattened_hom_samples, homogeneous_samples),
+    """Test that the flattened samples are contained within the flattened space."""
-)
+    flattened_samples = [utils.flatten(space, space.sample()) for _ in range(10)]
-def test_unflatten(space, flattened_sample, expected_sample):
+    flat_space = utils.flatten_space(space)
-    sample = utils.unflatten(space, flattened_sample)
+
-    assert compare_nested(sample, expected_sample)
+    for flat_sample in flattened_samples:
+        assert flat_sample in flat_space
 
 
-expected_flattened_spaces = [
+@pytest.mark.parametrize("space", TESTING_SPACES, ids=TESTING_SPACES_IDS)
-    Box(low=0, high=1, shape=(3,), dtype=np.int64),
+def test_flatten_roundtripping(space):
-    Box(low=0.0, high=np.inf, shape=(4,), dtype=np.float32),
+    """Tests roundtripping with flattening and unflattening are equal to the original sample."""
-    Box(low=0.0, high=np.inf, shape=(4,), dtype=np.float16),
+    samples = [space.sample() for _ in range(10)]
-    Box(low=0, high=1, shape=(15,), dtype=np.int64),
-    Box(
-        low=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float64),
-        high=np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0], dtype=np.float64),
-        dtype=np.float64,
-    ),
-    Box(low=0, high=1, shape=(9,), dtype=np.int64),
-    Box(low=0, high=1, shape=(14,), dtype=np.int64),
-    Box(low=0, high=1, shape=(10,), dtype=np.int8),
-    Box(
-        low=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float64),
-        high=np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0], dtype=np.float64),
-        dtype=np.float64,
-    ),
-    Box(low=0, high=1, shape=(3,), dtype=np.int64),
-    Box(low=0, high=1, shape=(8,), dtype=np.int64),
-]
 
+    flattened_samples = [utils.flatten(space, sample) for sample in samples]
+    unflattened_samples = [
+        utils.unflatten(space, sample) for sample in flattened_samples
+    ]
 
-@pytest.mark.parametrize(
+    for original, roundtripped in zip(samples, unflattened_samples):
-    ["space", "expected_flattened_space"],
+        assert data_equivalence(original, roundtripped)
-    zip(homogeneous_spaces, expected_flattened_spaces),
-)
-def test_flatten_space(space, expected_flattened_space):
-    flattened_space = utils.flatten_space(space)
-    assert flattened_space == expected_flattened_space

tests/spaces/utils.py

@@ -0,0 +1,27 @@
+from typing import List
+
+import numpy as np
+
+from gym.spaces import Box, Discrete, MultiBinary, MultiDiscrete, Space, Text
+
+TESTING_FUNDAMENTAL_SPACES = [
+    Discrete(3),
+    Discrete(3, start=-1),
+    Box(low=0.0, high=1.0),
+    Box(low=0.0, high=np.inf, shape=(2, 2)),
+    Box(low=np.array([-10.0, 0.0]), high=np.array([10.0, 10.0]), dtype=np.float64),
+    Box(low=-np.inf, high=0.0, shape=(2, 1)),
+    Box(low=0.0, high=np.inf, shape=(2, 1)),
+    MultiDiscrete([2, 2]),
+    MultiDiscrete([[2, 3], [3, 2]]),
+    MultiBinary(8),
+    MultiBinary([2, 3]),
+    Text(6),
+    Text(min_length=3, max_length=6),
+    Text(6, charset="abcdef"),
+]
+TESTING_FUNDAMENTAL_SPACES_IDS = [f"{space}" for space in TESTING_FUNDAMENTAL_SPACES]
+
+
+TESTING_SPACES: List[Space] = TESTING_FUNDAMENTAL_SPACES  # + TESTING_COMPOSITE_SPACES
+TESTING_SPACES_IDS = TESTING_FUNDAMENTAL_SPACES_IDS  # + TESTING_COMPOSITE_SPACES_IDS