chore: bump uv.lock and fix ruff 0.16 default-rule lint findings

uv.lock was stale (ty 0.0.50 -> 0.0.78, ruff 0.15 -> 0.16, polars, numpy,
typer, wandb, pytest, and others), all within existing pyproject.toml
bounds. ruff 0.16 widened its default rule selection, taking this repo
from 0 to 274 lint errors under the same config; --fix handled most of
it (import sorting, Optional[X] -> X | None, ...), and the remainder
(unused unpacked variables, dict()-as-literal, subprocess.run without
explicit check=, a couple of intentional broad excepts/naive datetimes)
were fixed or annotated by hand. Also fixes a real type-narrowing gap
ty 0.0.78 caught in test_config_consumed_keys.py's `or`-combined
isinstance check.

torch stays pinned to 2.3.x (deliberate, see CLAUDE.md); pyarrow's <25
ceiling is left as a separate decision.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TMdZFqXXig7i3XkirSUxef

tests/test_train.py

@@ -12,6 +12,7 @@ import pytest
 import torch
 import torch.nn.functional as F
 
+from giant.checkpoint_io import load_for_inference
 from giant.config import ParticleTypeConfig
 from giant.constants import (
     COND_DIM,
@@ -21,7 +22,6 @@ from giant.constants import (
     SEC_SLOT_DIM,
     X_DIM,
 )
-from giant.checkpoint_io import load_for_inference
 from giant.data.dataset import StepBatch
 from giant.data.transforms import Normalizer
 from giant.model.network import Stage2Autoregressive, build_critics, build_models
@@ -36,7 +36,6 @@ from giant.training import (
     train,
 )
 from giant.training.metrics import _wandb_run_config
-from giant.training.trainers import _type_class_weight_vector
 from giant.training.stage2_inputs import (
     _ar_has_prev,
     _assemble_stage2_ar_inputs,
@@ -51,6 +50,7 @@ from giant.training.stage2_inputs import (
     _stop_target_and_mask,
     _type_repr,
 )
+from giant.training.trainers import _type_class_weight_vector
 
 PDG_VOCAB = 6
 MAT_VOCAB = 3
@@ -543,19 +543,18 @@ def test_train_raises_when_no_active_stage():
     model_config = _model_config(cfg)
     models = build_models(model_config)
     critics = build_critics(model_config)
-    with tempfile.TemporaryDirectory() as tmp:
-        with pytest.raises(ValueError, match="no active stage"):
-            train(
-                cfg=cfg,
-                models=models,
-                critics=critics,
-                train_loader=_fake_batches(1, 8),
-                val_loader=_fake_batches(1, 8),
-                device=torch.device("cpu"),
-                out_dir=Path(tmp) / "run",
-                model_config=model_config,
-                total_train_batches=1,
-            )
+    with tempfile.TemporaryDirectory() as tmp, pytest.raises(ValueError, match="no active stage"):
+        train(
+            cfg=cfg,
+            models=models,
+            critics=critics,
+            train_loader=_fake_batches(1, 8),
+            val_loader=_fake_batches(1, 8),
+            device=torch.device("cpu"),
+            out_dir=Path(tmp) / "run",
+            model_config=model_config,
+            total_train_batches=1,
+        )
 
 
 def test_metrics_csv_columns_are_stage_prefixed():