Convert .pyre_configuration.local to fast by default architecture] [b…

…atch:32/764] [shard:13/N]

Reviewed By: MaggieMoss

Differential Revision: D63206193

fbshipit-source-id: 16839dc455defeb6748c3983eb32a79bf3328aff

pearl/user_envs/envs/bandit.py

@@ -17,6 +17,7 @@
     print("gymnasium module is not found")
 
 
+# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 class MeanVarBanditEnv(gym.Env[np.ndarray, Union[int, np.ndarray]]):
     """environment to test if safe RL algorithms
     prefer a policy that achieves lower variance return"""
@@ -33,20 +34,29 @@ def __init__(
         self.observation_space = gym.spaces.Box(-high, high, dtype=np.float32)
         self.idx: Optional[int] = None
 
+    # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
     def get_observation(self) -> np.ndarray:
         obs = np.zeros(self._size, dtype=np.float32)
         obs[self.idx] = 1.0
         return obs
 
     def reset(
-        self, *, seed: Optional[int] = None, options: Optional[Dict[str, float]] = None
+        self,
+        *,
+        seed: Optional[int] = None,
+        options: Optional[Dict[str, float]] = None
+        # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
     ) -> Tuple[np.ndarray, Dict[str, float]]:
         super().reset(seed=seed)
         self.idx = 0
         return self.get_observation(), {}
 
+    # pyre-fixme[3]: Return annotation cannot contain `Any`.
     def step(
-        self, action: Union[int, np.ndarray]
+        self,
+        # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
+        action: Union[int, np.ndarray],
+        # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
     ) -> Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]:
         reward = 0.0
         if action == 0:

pearl/utils/instantiations/environments/gym_environment.py

@@ -37,6 +37,7 @@ def single_element_tensor_to_int(x: Tensor) -> int:
     return int(x)
 
 
+# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 def tensor_to_numpy(x: Tensor) -> np.ndarray:
     return x.numpy(force=True)
 
@@ -51,6 +52,7 @@ def tensor_to_numpy(x: Tensor) -> np.ndarray:
     "Box": BoxSpace,
     # Add more here as needed
 }
+# pyre-fixme[5]: Global expression must be annotated.
 PEARL_TO_GYM_ACTION = {
     "Discrete": single_element_tensor_to_int,
     "Box": tensor_to_numpy,
@@ -179,7 +181,9 @@ def __str__(self) -> str:
 
 
 def _get_gym_action(
-    pearl_action: Action, gym_space: gym.Space
+    pearl_action: Action,
+    gym_space: gym.Space,
+    # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 ) -> Union[int, np.ndarray]:
     """A helper function to convert a Pearl `Action` to an action compatible with
     the Gym action space `gym_space`."""

pearl/utils/instantiations/spaces/discrete.py

@@ -49,6 +49,7 @@ def __init__(self, elements: List[Tensor], seed: Optional[int] = None) -> None:
         if len(elements) == 0:
             raise ValueError("`DiscreteSpace` requires at least one element.")
         self._set_validated_elements(elements=elements)  # sets self.elements
+        # pyre-fixme[28]: Unexpected keyword argument `start`.
         self._gym_space = Discrete(n=len(elements), seed=seed, start=0)
 
     def _set_validated_elements(self, elements: List[Tensor]) -> None:
@@ -94,6 +95,7 @@ def sample(self, mask: Optional[Tensor] = None) -> Tensor:
             A randomly sampled (available) element.
         """
         mask_np = mask.numpy().astype(int) if mask is not None else None
+        # pyre-fixme[28]: Unexpected keyword argument `mask`.
         idx = self._gym_space.sample(mask=mask_np)
         return self.elements[idx]
 

test/unit/with_pytorch/test_cnn_based_q_value_networks.py

@@ -31,6 +31,8 @@ def setUp(self) -> None:
         self.num_data_points = 5000
         self.mnist_train_dataset = Subset(
             mnist_dataset,
+            # pyre-fixme[6]: For 2nd argument expected `Sequence[int]` but got
+            #  `ndarray[typing.Any, dtype[typing.Any]]`.
             np.arange(1, self.num_data_points),
         )
         self.learning_rate = 0.001

test/unit/with_pytorch/test_vanilla_cnns.py

@@ -30,6 +30,8 @@ def setUp(self) -> None:
         self.num_data_points = 5000
         self.mnist_train_dataset = Subset(
             mnist_dataset,
+            # pyre-fixme[6]: For 2nd argument expected `Sequence[int]` but got
+            #  `ndarray[typing.Any, dtype[typing.Any]]`.
             np.arange(1, self.num_data_points),
         )
         self.learning_rate = 0.001