[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

sheeprl/algos/dreamer_v3/agent.py

@@ -83,9 +83,7 @@ def __init__(
                 layer_args={"kernel_size": 4, "stride": 2, "padding": 1, "bias": layer_norm_cls == nn.Identity},
                 activation=activation,
                 norm_layer=[layer_norm_cls] * stages,
-                norm_args=[
-                    {**layer_norm_kw, "normalized_shape": (2**i) * channels_multiplier} for i in range(stages)
-                ],
+                norm_args=[{**layer_norm_kw, "normalized_shape": (2**i) * channels_multiplier} for i in range(stages)],
             ),
             nn.Flatten(-3, -1),
         )

sheeprl/data/buffers.py

@@ -135,12 +135,10 @@ def to_tensor(
         return buf
 
     @typing.overload
-    def add(self, data: "ReplayBuffer", validate_args: bool = False) -> None:
-        ...
+    def add(self, data: "ReplayBuffer", validate_args: bool = False) -> None: ...
 
     @typing.overload
-    def add(self, data: Dict[str, np.ndarray], validate_args: bool = False) -> None:
-        ...
+    def add(self, data: Dict[str, np.ndarray], validate_args: bool = False) -> None: ...
 
     def add(self, data: "ReplayBuffer" | Dict[str, np.ndarray], validate_args: bool = False) -> None:
         """Add data to the replay buffer. If the replay buffer is full, then the oldest data is overwritten.
@@ -617,12 +615,10 @@ def __len__(self) -> int:
         return self.buffer_size
 
     @typing.overload
-    def add(self, data: "ReplayBuffer", validate_args: bool = False) -> None:
-        ...
+    def add(self, data: "ReplayBuffer", validate_args: bool = False) -> None: ...
 
     @typing.overload
-    def add(self, data: Dict[str, np.ndarray], validate_args: bool = False) -> None:
-        ...
+    def add(self, data: Dict[str, np.ndarray], validate_args: bool = False) -> None: ...
 
     def add(
         self,
@@ -860,17 +856,17 @@ def __len__(self) -> int:
         return self._cum_lengths[-1] if len(self._buf) > 0 else 0
 
     @typing.overload
-    def add(self, data: "ReplayBuffer", env_idxes: Sequence[int] | None = None, validate_args: bool = False) -> None:
-        ...
+    def add(
+        self, data: "ReplayBuffer", env_idxes: Sequence[int] | None = None, validate_args: bool = False
+    ) -> None: ...
 
     @typing.overload
     def add(
         self,
         data: Dict[str, np.ndarray],
         env_idxes: Sequence[int] | None = None,
         validate_args: bool = False,
-    ) -> None:
-        ...
+    ) -> None: ...
 
     def add(
         self,

sheeprl/utils/distribution.py

@@ -307,6 +307,7 @@ class OneHotCategoricalValidateArgs(Distribution):
         probs (Tensor): event probabilities
         logits (Tensor): event log probabilities (unnormalized)
     """
+
     arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
     support = constraints.one_hot
     has_enumerate_support = True
@@ -391,6 +392,7 @@ class OneHotCategoricalStraightThroughValidateArgs(OneHotCategoricalValidateArgs
     [1] Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
     (Bengio et al, 2013)
     """
+
     has_rsample = True
 
     def rsample(self, sample_shape=torch.Size()):