fix: avoids local ruff mismatch

.pre-commit-config.yaml

@@ -1,4 +1,12 @@
 repos:
+  - repo: local
+    hooks:
+      - id: ruff-format
+        name: ruff format (pipx)
+        entry: pipx run ruff format
+        language: system
+        types: [python]
+
   - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
     rev: v9.16.0
     hooks:
@@ -6,13 +14,6 @@ repos:
         stages: [commit-msg]
         additional_dependencies: ["@commitlint/config-conventional"]
 
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.9.9
-    hooks:
-      - id: ruff
-        args: [ --fix ]
-      - id: ruff-format
-
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0
     hooks:

ruff.toml

@@ -1,3 +1,5 @@
+line-length = 100
+
 [lint]
 extend-select = [
   # "PLC0103",  # invalid-name
@@ -347,6 +349,7 @@ extend-ignore = [
   # "PLR1705",  # no-else-return
   # "PLR1706",  # consider-using-ternary
   # "PLR1707",  # trailing-comma-tuple
+  "PLR1708",  # stop-iteration-return
   # "PLR1709",  # simplify-boolean-expression
   # "PLR1710",  # inconsistent-return-statements
   "PLR1711",  # useless-return
@@ -391,4 +394,3 @@ extend-ignore = [
   # "PLW1404",  # implicit-str-concat
 ]
 
-

src/ppo.py

@@ -8,9 +8,7 @@ import jax.numpy as jnp
 # Chose to use a class as it seemed the easiest way to integrate the CleanRL code style
 # with our need to seperate concerns
 class PPO:
-    def __init__(
-        self, args, input_network, action_network, critic, message_passer=None
-    ):
+    def __init__(self, args, input_network, action_network, critic, message_passer=None):
         self.args = args
 
         if not message_passer:
@@ -50,15 +48,13 @@ class PPO:
             shuffled_storage = jax.tree.map(convert_data, flatten_storage)
 
             def update_minibatch(agent_state, minibatch):
-                (loss, (pg_loss, v_loss, entropy_loss, approx_kl)), grads = (
-                    ppo_loss_grad_fn(
-                        agent_state.params,
-                        minibatch.obs,
-                        minibatch.actions,
-                        minibatch.logprobs,
-                        minibatch.advantages,
-                        minibatch.returns,
-                    )
+                (loss, (pg_loss, v_loss, entropy_loss, approx_kl)), grads = ppo_loss_grad_fn(
+                    agent_state.params,
+                    minibatch.obs,
+                    minibatch.actions,
+                    minibatch.logprobs,
+                    minibatch.advantages,
+                    minibatch.returns,
                 )
                 agent_state = agent_state.apply_gradients(grads=grads)
                 return agent_state, (
@@ -70,15 +66,11 @@ class PPO:
                     grads,
                 )
 
-            agent_state, metrics = jax.lax.scan(
-                update_minibatch, agent_state, shuffled_storage
-            )
+            agent_state, metrics = jax.lax.scan(update_minibatch, agent_state, shuffled_storage)
             return (agent_state, key), metrics
 
-        (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads) = (
-            jax.lax.scan(
-                update_epoch, (agent_state, key), (), length=args.update_epochs
-            )
+        (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads) = jax.lax.scan(
+            update_epoch, (agent_state, key), (), length=args.update_epochs
         )
         return agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key
 
@@ -106,9 +98,7 @@ def get_action_and_value2(
     mean, log_std = action_apply(params["actor_params"], hidden)
     std = jnp.exp(log_std)
 
-    logprob = -0.5 * (
-        ((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)
-    ).sum(-1)
+    logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1)
     entropy = (0.5 + 0.5 * jnp.log(2 * jnp.pi) + log_std).sum(-1)
     value = critic_apply(params["critic_params"], hidden).squeeze(-1)
 
@@ -142,9 +132,7 @@ def ppo_loss(
     approx_kl = ((ratio - 1) - logratio).mean()
 
     if args.norm_adv:
-        mb_advantages = (mb_advantages - mb_advantages.mean()) / (
-            mb_advantages.std() + 1e-8
-        )
+        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
 
     pg_loss1 = -mb_advantages * ratio
     pg_loss2 = -mb_advantages * jnp.clip(ratio, 1 - args.clip_coef, 1 + args.clip_coef)