/**
 * @copyright Sister Software
 * @license AGPL-3.0
 * @author Teffen Ellis, et al.
 *
 *   Pre-compute corpus-wide token + bigram label distributions for the corpus linter.
 *
 *   Reads one or more parquet files, builds per-(token, label) and per-(bigram, label-bigram)
 *   histograms. The tool serializes them as JSON. `lint/recipe-output/index.ts` consumes the output file
 *   as the baseline against which a new recipe output is compared.
 *
 *   Stats are cheap to compute (~5–30s per 100K rows) but expensive enough that we cache them between
 *   linter invocations. Re-run this whenever the corpus changes substantially (a new mainline
 *   recipe output added, a source-pool re-weighted, etc.).
 *
 *   Output schema (`slice_paths` is the stats file's own key. a stats file already on disk includes it,
 *   so the linter reads it under that spelling):
 *
 *   ```ts
 *   interface CorpusStats {
 *     row_count: number
 *     slice_paths: string[]
 *     tokens: { [token: string]: { [label: string]: number } }
 *     bigrams: { [token_bigram: string]: { [label_bigram: string]: number } }
 *     // token_bigram = "tok1tok2" (US sep), label_bigram = "lab1lab2"
 *     // For memory: only keep bigrams with count >= MIN_BIGRAM_COUNT (2).
 *   }
 * ```
 *
 *   Usage: mailwoman corpus stats\
 *   --parquet <parquet-file-or-dir>\
 *   --out <stats.json>
 *
 *   For a quick local-corpus baseline (limited but useful for linter testing): mailwoman corpus stats\
 *   --parquet $MAILWOMAN_DATA_ROOT/corpus/versioned/v0.4.0/corpus-v0.4.0/train/\
 *   --out /tmp/corpus-stats-local.json
 */
/**
 * Options for {@linkcode buildCorpusStats}.
 */
export interface CorpusStatsOptions {
    /**
     * A directory of parquet files, one parquet file, or a literal path.
     */
    parquetPath: string;
    outputPath: string;
    /**
     * Read at most this many rows per parquet file.
     */
    limitPerFile?: number;
}
export declare function buildCorpusStats(args: CorpusStatsOptions): Promise<void>;
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