Downgrade sphinx-autodoc-typehints (#1291)

* Update setup.py

* black

* hotfix pytype

setup.py

@@ -117,7 +117,7 @@ setup(
             # For spelling
             "sphinxcontrib.spelling",
             # Type hints support
-            "sphinx-autodoc-typehints",
+            "sphinx-autodoc-typehints==1.21.1",  # TODO: remove version constraint, see #1290
             # Copy button for code snippets
             "sphinx_copybutton",
         ],

stable_baselines3/common/buffers.py

@@ -474,7 +474,7 @@ class RolloutBuffer(BaseBuffer):
             yield self._get_samples(indices[start_idx : start_idx + batch_size])
             start_idx += batch_size
 
-    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> RolloutBufferSamples:
+    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> RolloutBufferSamples:  # type: ignore[signature-mismatch] #FIXME
         data = (
             self.observations[batch_inds],
             self.actions[batch_inds],
@@ -603,7 +603,7 @@ class DictReplayBuffer(ReplayBuffer):
             self.full = True
             self.pos = 0
 
-    def sample(self, batch_size: int, env: Optional[VecNormalize] = None) -> DictReplayBufferSamples:
+    def sample(self, batch_size: int, env: Optional[VecNormalize] = None) -> DictReplayBufferSamples:  # type: ignore[signature-mismatch] #FIXME:
         """
         Sample elements from the replay buffer.
 
@@ -614,7 +614,7 @@ class DictReplayBuffer(ReplayBuffer):
         """
         return super(ReplayBuffer, self).sample(batch_size=batch_size, env=env)
 
-    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> DictReplayBufferSamples:
+    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> DictReplayBufferSamples:  # type: ignore[signature-mismatch] #FIXME:
         # Sample randomly the env idx
         env_indices = np.random.randint(0, high=self.n_envs, size=(len(batch_inds),))
 
@@ -743,7 +743,7 @@ class DictRolloutBuffer(RolloutBuffer):
         if self.pos == self.buffer_size:
             self.full = True
 
-    def get(self, batch_size: Optional[int] = None) -> Generator[DictRolloutBufferSamples, None, None]:
+    def get(self, batch_size: Optional[int] = None) -> Generator[DictRolloutBufferSamples, None, None]:  # type: ignore[signature-mismatch] #FIXME
         assert self.full, ""
         indices = np.random.permutation(self.buffer_size * self.n_envs)
         # Prepare the data
@@ -767,7 +767,7 @@ class DictRolloutBuffer(RolloutBuffer):
             yield self._get_samples(indices[start_idx : start_idx + batch_size])
             start_idx += batch_size
 
-    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> DictRolloutBufferSamples:
+    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> DictRolloutBufferSamples:  # type: ignore[signature-mismatch] #FIXME
 
         return DictRolloutBufferSamples(
             observations={key: self.to_torch(obs[batch_inds]) for (key, obs) in self.observations.items()},

stable_baselines3/common/envs/identity_env.py

@@ -71,7 +71,7 @@ class IdentityEnvBox(IdentityEnv[np.ndarray]):
         super().__init__(ep_length=ep_length, space=space)
         self.eps = eps
 
-    def step(self, action: np.ndarray) -> GymStepReturn:
+    def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, Dict[str, Any]]:
         reward = self._get_reward(action)
         self._choose_next_state()
         self.current_step += 1
@@ -83,7 +83,7 @@ class IdentityEnvBox(IdentityEnv[np.ndarray]):
 
 
 class IdentityEnvMultiDiscrete(IdentityEnv[np.ndarray]):
-    def __init__(self, dim: int = 1, ep_length: int = 100):
+    def __init__(self, dim: int = 1, ep_length: int = 100) -> None:
         """
         Identity environment for testing purposes
 
@@ -95,7 +95,7 @@ class IdentityEnvMultiDiscrete(IdentityEnv[np.ndarray]):
 
 
 class IdentityEnvMultiBinary(IdentityEnv[np.ndarray]):
-    def __init__(self, dim: int = 1, ep_length: int = 100):
+    def __init__(self, dim: int = 1, ep_length: int = 100) -> None:
         """
         Identity environment for testing purposes
 
@@ -126,7 +126,7 @@ class FakeImageEnv(gym.Env):
         n_channels: int = 1,
         discrete: bool = True,
         channel_first: bool = False,
-    ):
+    ) -> None:
         self.observation_shape = (screen_height, screen_width, n_channels)
         if channel_first:
             self.observation_shape = (n_channels, screen_height, screen_width)