Test refactoring (#2427)

* Move tests to root with automatic PyCharm import refactoring. This will likely fail some tests

* Changed entry point for a registration test env.

* Move a stray lunar_lander test to tests/envs/...

* black

* Change the version from which importlib_metadata is replaced with importlib.metadata. Also requiring installing importlib_metadata for python 3.8 now.

???????????

* Undo last commit

tests/vector/utils.py

@@ -0,0 +1,122 @@
+import numpy as np
+import gym
+import time
+
+from gym.spaces import Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict
+
+spaces = [
+    Box(low=np.array(-1.0), high=np.array(1.0), dtype=np.float64),
+    Box(low=np.array([0.0]), high=np.array([10.0]), dtype=np.float32),
+    Box(
+        low=np.array([-1.0, 0.0, 0.0]), high=np.array([1.0, 1.0, 1.0]), dtype=np.float32
+    ),
+    Box(
+        low=np.array([[-1.0, 0.0], [0.0, -1.0]]), high=np.ones((2, 2)), dtype=np.float32
+    ),
+    Box(low=0, high=255, shape=(), dtype=np.uint8),
+    Box(low=0, high=255, shape=(32, 32, 3), dtype=np.uint8),
+    Discrete(2),
+    Tuple((Discrete(3), Discrete(5))),
+    Tuple(
+        (
+            Discrete(7),
+            Box(low=np.array([0.0, -1.0]), high=np.array([1.0, 1.0]), dtype=np.float32),
+        )
+    ),
+    MultiDiscrete([11, 13, 17]),
+    MultiBinary(19),
+    Dict(
+        {
+            "position": Discrete(23),
+            "velocity": Box(
+                low=np.array([0.0]), high=np.array([1.0]), dtype=np.float32
+            ),
+        }
+    ),
+    Dict(
+        {
+            "position": Dict({"x": Discrete(29), "y": Discrete(31)}),
+            "velocity": Tuple(
+                (Discrete(37), Box(low=0, high=255, shape=(), dtype=np.uint8))
+            ),
+        }
+    ),
+]
+
+HEIGHT, WIDTH = 64, 64
+
+
+class UnittestSlowEnv(gym.Env):
+    def __init__(self, slow_reset=0.3):
+        super(UnittestSlowEnv, self).__init__()
+        self.slow_reset = slow_reset
+        self.observation_space = Box(
+            low=0, high=255, shape=(HEIGHT, WIDTH, 3), dtype=np.uint8
+        )
+        self.action_space = Box(low=0.0, high=1.0, shape=(), dtype=np.float32)
+
+    def reset(self):
+        if self.slow_reset > 0:
+            time.sleep(self.slow_reset)
+        return self.observation_space.sample()
+
+    def step(self, action):
+        time.sleep(action)
+        observation = self.observation_space.sample()
+        reward, done = 0.0, False
+        return observation, reward, done, {}
+
+
+class CustomSpace(gym.Space):
+    """Minimal custom observation space."""
+
+    def __eq__(self, other):
+        return isinstance(other, CustomSpace)
+
+
+custom_spaces = [
+    CustomSpace(),
+    Tuple((CustomSpace(), Box(low=0, high=255, shape=(), dtype=np.uint8))),
+]
+
+
+class CustomSpaceEnv(gym.Env):
+    def __init__(self):
+        super(CustomSpaceEnv, self).__init__()
+        self.observation_space = CustomSpace()
+        self.action_space = CustomSpace()
+
+    def reset(self):
+        return "reset"
+
+    def step(self, action):
+        observation = "step({0:s})".format(action)
+        reward, done = 0.0, False
+        return observation, reward, done, {}
+
+
+def make_env(env_name, seed):
+    def _make():
+        env = gym.make(env_name)
+        env.seed(seed)
+        return env
+
+    return _make
+
+
+def make_slow_env(slow_reset, seed):
+    def _make():
+        env = UnittestSlowEnv(slow_reset=slow_reset)
+        env.seed(seed)
+        return env
+
+    return _make
+
+
+def make_custom_space_env(seed):
+    def _make():
+        env = CustomSpaceEnv()
+        env.seed(seed)
+        return env
+
+    return _make