fix(review): lock OCR backend init, warn on rollback fallthrough, zero-page metric

Address PR #858 review round 2:

- OcrProcessor backend resolution is now guarded by an anyio.Lock (lazy-init,
  double-checked) so a burst of concurrent first-OCR calls resolves the backend
  once instead of each fetching its own gateway M2M token.
- The document_tier1_engine=pymupdf rollback now logs a warning when it falls
  back to the fast processor (no 'structured' registered) instead of silently
  using the very engine the operator opted out of.
- classify_from_text defaults ocr_frac to 0.0 (not 1.0) for a zero-page PDF, so
  the recorded classification metric is "fast" (no OCR evidence) rather than a
  misleading "ocr"; the no_text_layer/bad_text_layer flags are gated on having
  sampled at least one page.

New tests: zero-page classify routes fast, rollback-fallback warning.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

nextcloud_mcp_server/document_processors/classifier.py

@@ -208,13 +208,17 @@ def classify_from_text(
     mean_quality = (
         round(sum(p.text_quality for p in pages) / sampled, 3) if sampled else 0.0
     )
-    ocr_frac = (sum(p.needs_ocr for p in pages) / sampled) if sampled else 1.0
+    # No pages (empty/corrupt PDF) => no OCR evidence => "fast" (the registry's
+    # page_count guard also skips escalation; defaulting to 0.0 keeps the
+    # recorded classification metric accurate rather than a misleading "ocr").
+    ocr_frac = (sum(p.needs_ocr for p in pages) / sampled) if sampled else 0.0
 
     flags: set[str] = set()
-    if total_chars == 0:
-        flags.add("no_text_layer")
-    elif mean_quality < MIN_TEXT_QUALITY:
-        flags.add("bad_text_layer")
+    if sampled:
+        if total_chars == 0:
+            flags.add("no_text_layer")
+        elif mean_quality < MIN_TEXT_QUALITY:
+            flags.add("bad_text_layer")
 
     recommended = "ocr" if ocr_frac >= OCR_PAGE_FRACTION else "fast"