[lint] Update lint (#16)

* update lint

* update readme

* update ruff lint

scripts/fake_inference.py

@@ -10,10 +10,14 @@ batch_size = 1
 seq_length = 50
 
 torch.manual_seed(0)
-fake_input_ids = torch.randint(0, len(model.processor.tokenizer), (batch_size, seq_length), dtype=torch.long)
+fake_input_ids = torch.randint(
+    0, len(model.processor.tokenizer), (batch_size, seq_length), dtype=torch.long
+)
 fake_attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long)
 fake_moe_token_types = torch.zeros((batch_size, seq_length), dtype=torch.long)
-fake_position_ids = torch.arange(seq_length, dtype=torch.long).unsqueeze(0).expand(batch_size, -1)
+fake_position_ids = (
+    torch.arange(seq_length, dtype=torch.long).unsqueeze(0).expand(batch_size, -1)
+)
 fake_proprioception = torch.randn((batch_size, 1, 20), dtype=torch.float32)
 fake_agent_pos_mask = torch.ones((batch_size, 1, 20), dtype=torch.float32)
 fake_dof_mask = torch.ones((batch_size, 32, 20), dtype=torch.float32)
@@ -44,37 +48,38 @@ try:
             agent_pos_mask=fake_agent_pos_mask,
             dof_mask=fake_dof_mask,
             dataset_names=fake_dataset_names,
-            mode="validate"
+            mode="validate",
         )
-    
+
     print("✅ Fake inference test successful!")
     print(f"Output logits shape: {outputs.logits.shape}")
     print(f"Output logits dtype: {outputs.logits.dtype}")
     print(f"Output logits device: {outputs.logits.device}")
-    
+
     # Check if output is reasonable
     if outputs.logits.shape == (batch_size, seq_length, model.config.vocab_size):
         print("✅ Output shape correct")
     else:
         print("❌ Output shape incorrect")
-        
+
     if not torch.isnan(outputs.logits).any():
         print("✅ Output contains no NaN values")
     else:
         print("❌ Output contains NaN values")
-        
+
     if not torch.isinf(outputs.logits).any():
         print("✅ Output contains no infinity values")
     else:
         print("❌ Output contains infinity values")
-        
-    print(f"Output logits statistics:")
+
+    print("Output logits statistics:")
     print(f"  Min value: {outputs.logits.min().item():.4f}")
     print(f"  Max value: {outputs.logits.max().item():.4f}")
     print(f"  Mean: {outputs.logits.mean().item():.4f}")
     print(f"  Standard deviation: {outputs.logits.std().item():.4f}")
-    
+
 except Exception as e:
     print(f"❌ Fake inference test failed: {e}")
     import traceback
-    traceback.print_exc()
+
+    traceback.print_exc()