Convert formatting to use ruff instead of yapf + isort (#26247)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/test_routing_simulator.py

@@ -13,7 +13,9 @@ import pytest
 import torch
 
 from vllm.model_executor.layers.fused_moe.routing_simulator import (
-    DistributionBasedRouting, RoutingSimulator)
+    DistributionBasedRouting,
+    RoutingSimulator,
+)
 
 
 @pytest.fixture
@@ -60,10 +62,10 @@ def test_basic_functionality(
         ), f"Wrong ids shape for {strategy}"
 
         # Check that expert IDs are valid
-        assert (topk_ids.min()
-                >= 0), f"Invalid expert ID (negative) for {strategy}"
-        assert (topk_ids.max()
-                < num_experts), f"Invalid expert ID (too large) for {strategy}"
+        assert topk_ids.min() >= 0, f"Invalid expert ID (negative) for {strategy}"
+        assert topk_ids.max() < num_experts, (
+            f"Invalid expert ID (too large) for {strategy}"
+        )
 
 
 def test_routing_strategy_integration(monkeypatch, device):
@@ -102,19 +104,20 @@ def test_routing_strategy_integration(monkeypatch, device):
             top_k=top_k,
             use_grouped_topk=False,
             renormalize=True,
-            indices_type=torch.long)
+            indices_type=torch.long,
+        )
 
         # Verify output shapes
-        assert topk_weights.shape == (
-            num_tokens, top_k), f"Wrong weights shape for {strategy}"
-        assert topk_ids.shape == (num_tokens,
-                                  top_k), f"Wrong ids shape for {strategy}"
+        assert topk_weights.shape == (num_tokens, top_k), (
+            f"Wrong weights shape for {strategy}"
+        )
+        assert topk_ids.shape == (num_tokens, top_k), f"Wrong ids shape for {strategy}"
 
         # Verify expert IDs are valid
-        assert topk_ids.min(
-        ) >= 0, f"Invalid expert ID (negative) for {strategy}"
-        assert topk_ids.max(
-        ) < num_experts, f"Invalid expert ID (too large) for {strategy}"
+        assert topk_ids.min() >= 0, f"Invalid expert ID (negative) for {strategy}"
+        assert topk_ids.max() < num_experts, (
+            f"Invalid expert ID (too large) for {strategy}"
+        )
 
 
 def test_distribution_based_routing_with_custom_strategy():
@@ -123,9 +126,7 @@ def test_distribution_based_routing_with_custom_strategy():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     # Register custom distribution-based strategy
-    custom_strategy = DistributionBasedRouting(distribution="normal",
-                                               mean=2.0,
-                                               std=0.5)
+    custom_strategy = DistributionBasedRouting(distribution="normal", mean=2.0, std=0.5)
     RoutingSimulator.register_strategy("custom_normal", custom_strategy)
 
     # Test data
@@ -142,7 +143,8 @@ def test_distribution_based_routing_with_custom_strategy():
         hidden_states=hidden_states,
         router_logits=router_logits,
         strategy_name="custom_normal",
-        top_k=top_k)
+        top_k=top_k,
+    )
 
     # Check output shapes
     assert topk_weights.shape == (num_tokens, top_k)
@@ -165,7 +167,8 @@ def test_instance_compatibility():
         hidden_states=hidden_states,
         router_logits=router_logits,
         strategy_name="uniform_random",
-        top_k=2)
+        top_k=2,
+    )
 
     assert topk_weights.shape == (10, 2)
     assert topk_ids.shape == (10, 2)