-v raises the CLI's log level per repeat; -q prints only fatal errors (closes #212)

Every line quak prints on stderr now goes through one log, in src/cli-log.ts, at one of six levels. The default, info, prints what quak printed before. -v adds each retry and the start and end of each refresh and ML data fetch; -vv adds each request with its result. -q prints only fatal lines, and is refused with -v; both are program options. Every non-zero exit logs one fatal line, so a run that exits 1 because files failed says how many and where to look. Every line reporting a failure is at error. --json changes stdout only, so backup and --json runs log their progress on stderr. withRetry and ApiClient report retries and requests through new callbacks.

Model: opus-5-5

src/metadata-backup.ts

@@ -17,7 +17,10 @@ import {
 } from "./mldata-fetch.js";
 import type { EnteFile } from "./model/types.js";
 
-export type ProgressCallback = (message: string) => void;
+// Reports one line of the dump's progress. `level` is "error" when the line
+// reports ML data that could not be read, and is left out for every other
+// line.
+export type ProgressCallback = (message: string, level?: "error") => void;
 
 export interface MetadataBackupOptions {
     exif?: boolean;
@@ -195,13 +198,17 @@ export const runMetadataBackup = async (
             for (const [id, payload] of payloads) mlDataMap.set(id, payload);
             for (const [id, err] of errors) {
                 const reason = describeError(err);
-                log(`ML data of file ${id} could not be read: ${reason}`);
+                log(
+                    `ML data of file ${id} could not be read: ${reason}`,
+                    "error",
+                );
                 mlDataErrors.set(id, reason);
             }
         } catch (err) {
             const reason = err instanceof Error ? err.message : String(err);
             log(
                 `ML data request for ${batch.length} file(s) failed: ${reason}`,
+                "error",
             );
             for (const id of batch) mlDataErrors.set(id, reason);
         }