style: 使用ruff 工具优化代码风格

tests/data/test_dataset.py

@@ -5,30 +5,32 @@ from khaosz.data.serialization import save_h5
 from khaosz.data.dataset import *
 
 
-
 def test_dataset_loader_random_paths(base_test_env):
     """Test dataset loader with multiple random paths"""
     test_dir = base_test_env["test_dir"]
-    
+
     # Create multiple mmap dataset directories with random data
     num_files = np.random.randint(2, 5)
-    
+
     for i in range(num_files):
         seq_length = np.random.randint(200, 400)
         dummy_data = {
-            "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64) for _ in range(10)],
+            "sequence": [
+                torch.randint(0, 1000, (seq_length,), dtype=torch.int64)
+                for _ in range(10)
+            ],
         }
         save_h5(test_dir, f"data_{i}", dummy_data)
-    
+
         # Test loading with multiple paths
         loaded_dataset = DatasetLoader.load(
-            train_type="seq", 
-            load_path=test_dir, 
-            window_size=64, 
+            train_type="seq",
+            load_path=test_dir,
+            window_size=64,
         )
         assert loaded_dataset is not None
         assert len(loaded_dataset) > 0
-    
+
     # Test that we can get items without errors
     for i in range(len(loaded_dataset)):
         item = loaded_dataset[i]
@@ -41,30 +43,30 @@ def test_dataset_loader_random_paths(base_test_env):
 def test_dpo_strategy_with_random_data(base_test_env):
     """Test DPO strategy with randomized preference data"""
     test_dir = base_test_env["test_dir"]
-    
+
     # Create DPO-style data with memory mapping format
     seq_length = np.random.randint(100, 200)
-    
+
     dummy_data = {
         "chosen": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
         "rejected": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
         "chosen_mask": [torch.ones(seq_length, dtype=torch.bool)],
-        "rejected_mask": [torch.ones(seq_length, dtype=torch.bool)]
+        "rejected_mask": [torch.ones(seq_length, dtype=torch.bool)],
     }
-    
+
     save_h5(test_dir, "dpo_data", dummy_data)
-    
+
     # Load DPO dataset
     dpo_dataset = DatasetLoader.load(
-        train_type="dpo", 
-        load_path=test_dir, 
-        window_size=64, 
+        train_type="dpo",
+        load_path=test_dir,
+        window_size=64,
     )
-    
+
     assert dpo_dataset is not None
-    assert hasattr(dpo_dataset, 'fetcher')
+    assert hasattr(dpo_dataset, "fetcher")
     assert len(dpo_dataset) > 0
-    
+
     # Test that we can get DPO items without errors
     for i in range(min(3, len(dpo_dataset))):
         item = dpo_dataset[i]
@@ -79,28 +81,28 @@ def test_dpo_strategy_with_random_data(base_test_env):
 def test_sft_dataset_with_random_data(base_test_env):
     """Test SFT dataset with random data"""
     test_dir = base_test_env["test_dir"]
-    
+
     # Create SFT-style data with memory mapping format
     seq_length = np.random.randint(100, 200)
-    
+
     dummy_data = {
         "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
-        "loss_mask": [torch.ones(seq_length, dtype=torch.bool)]
+        "loss_mask": [torch.ones(seq_length, dtype=torch.bool)],
     }
-    
+
     save_h5(test_dir, "sft_data", dummy_data)
-    
+
     # Load SFT dataset
     sft_dataset = DatasetLoader.load(
-        train_type="sft", 
-        load_path=test_dir, 
-        window_size=64, 
+        train_type="sft",
+        load_path=test_dir,
+        window_size=64,
     )
-    
+
     assert sft_dataset is not None
-    assert hasattr(sft_dataset, 'fetcher')
+    assert hasattr(sft_dataset, "fetcher")
     assert len(sft_dataset) > 0
-    
+
     # Test that we can get SFT items without errors
     for i in range(min(3, len(sft_dataset))):
         item = sft_dataset[i]
@@ -114,33 +116,30 @@ def test_sft_dataset_with_random_data(base_test_env):
 def test_dataset_with_custom_stride(base_test_env):
     """Test dataset with custom stride parameter"""
     test_dir = base_test_env["test_dir"]
-    
+
     # Create test data
     seq_length = 200
     dummy_data = {
         "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
     }
-    
-    save_h5(test_dir,"stride_test_data", dummy_data)
-    
+
+    save_h5(test_dir, "stride_test_data", dummy_data)
+
     # Test with custom stride
     custom_stride = 32
     dataset = DatasetLoader.load(
-        train_type="seq", 
-        load_path=test_dir, 
-        window_size=64, 
-        stride=custom_stride
+        train_type="seq", load_path=test_dir, window_size=64, stride=custom_stride
     )
-    
+
     assert dataset is not None
     assert len(dataset) > 0
-    
+
     # With stride 32 and window 64 on 200 length data, we should get more samples
     # than with default stride (which equals window size)
     default_stride_dataset = DatasetLoader.load(
-        train_type="seq", 
-        load_path=test_dir, 
-        window_size=64, 
+        train_type="seq",
+        load_path=test_dir,
+        window_size=64,
     )
-    
+
     assert len(dataset) > len(default_stride_dataset)