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

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lerobot/common/policies/dot/modeling_dot.py

@@ -68,6 +68,7 @@ class DOT(nn.Module):
           │                         Outputs                      │
           └──────────────────────────────────────────────────────┘
     """
+
     def __init__(self, config: DOTConfig):
         super().__init__()
         self.config = config
@@ -196,18 +197,22 @@ class DOT(nn.Module):
             Tensor: A tensor of shape (B, horizon, action_dim) containing predicted future actions.
         """
         # Project image/state/env_state inputs to the model dimension and concatenate along the time axis.
-        inputs_projections = self._process_inputs(batch) # (B, T, D)
+        inputs_projections = self._process_inputs(batch)  # (B, T, D)
         batch_size = inputs_projections.shape[0]
 
         # Add learnable positional embeddings to each projected input token.
         inputs_projections += self.inputs_pos_emb.expand(batch_size, -1, -1)
 
         # Prepend a trainable prefix token to the input sequence
-        inputs_projections = torch.cat([self.prefix_input.expand(batch_size, -1, -1), inputs_projections], dim=1) # (B, T+1, D)
+        inputs_projections = torch.cat(
+            [self.prefix_input.expand(batch_size, -1, -1), inputs_projections], dim=1
+        )  # (B, T+1, D)
 
         # Use different positional encodings and masks for training vs. inference.
         if self.training:
-            decoder_out = self.decoder(self.decoder_pos.expand(batch_size, -1, -1), inputs_projections, self.mask)
+            decoder_out = self.decoder(
+                self.decoder_pos.expand(batch_size, -1, -1), inputs_projections, self.mask
+            )
         else:
             decoder_out = self.decoder(self.decoder_pos_inf.expand(batch_size, -1, -1), inputs_projections)
         return self.action_head(decoder_out)
@@ -307,7 +312,7 @@ class DOTPolicy(PreTrainedPolicy):
         return self.model.parameters()
 
     def _update_observation_buffers(self, buffer_name: str, observation: Tensor) -> Tensor:
-        # Maintain a rolling buffer of lookback_obs_steps + 1; 
+        # Maintain a rolling buffer of lookback_obs_steps + 1;
         # shift left and append new observation each step
         if buffer_name not in self._input_buffers:
             self._input_buffers[buffer_name] = observation.unsqueeze(1).repeat(
@@ -472,6 +477,7 @@ class LoRAConv2d(nn.Module):
         base_conv (nn.Conv2d): The original convolutional layer to be adapted.
         rank (int): The rank of the low-rank approximation (default: 4).
     """
+
     def __init__(self, base_conv: nn.Conv2d, rank: int = 4):
         super().__init__()
         self.base_conv = base_conv