Fix CLI/tooling robustness gaps and dedupe the Conditioning enum

- run_create_manifest gains --force; it previously overwrote an
  existing manifest (including holdout.manifest, which
  check_holdout_overlap exists specifically to protect) with no
  warning or backup on a second run.
- _git_user_name only caught OSError, not subprocess.TimeoutExpired (a
  SubprocessError, not an OSError) — a slow/loaded shared portal
  machine could crash `dwarf bump-gen`/`bump-schema` instead of
  degrading to by=None as intended.
- `dwarf convert --jobs`/`make-root --jobs` now warn (never block) when
  the requested count exceeds ~1/4 of the machine's CPUs, matching the
  same shared-machine etiquette check added to giant train in the
  previous commit.
- The Conditioning enum was independently redefined in both
  giant/cli.py and scripts/dwarf.py; moved to a single
  giant.config.Conditioning both now import, removing the drift risk
  of a third conditioning mode being added to one but not the other.

Each fix has a regression test.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

giant/cli.py

@@ -211,9 +211,10 @@ class Mode(str, Enum):
     wgan = "wgan"
 
 
-class Conditioning(str, Enum):
-    physical = "physical"
-    embedding = "embedding"
+# Conditioning itself lives in giant.config (imported below as gconfig) —
+# shared with scripts/dwarf.py's Typer commands so the two CLIs can't
+# silently drift apart on the option's valid values.
+Conditioning = gconfig.Conditioning
 
 
 class Coord(str, Enum):

giant/config.py

@@ -4,11 +4,23 @@ import subprocess
 import sys
 import tomllib
 from datetime import datetime, timezone
+from enum import Enum
 from pathlib import Path
 
 import numpy as np
 import torch
 
+
+class Conditioning(str, Enum):
+    """`model.conditioning` choices — shared by `giant.cli` and `scripts.dwarf`'s
+    Typer commands so the two CLIs can't silently drift apart on the option's
+    valid values (see DEFAULT_CONFIG["model"]["conditioning"] for what each
+    value means)."""
+
+    physical = "physical"
+    embedding = "embedding"
+
+
 DEFAULT_CONFIG: dict = {
     "train": {
         "mode": "flow",