[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()):