/**
 * evermind_video.ts — thin bridge that makes the EXISTING EvermindLM generate
 * video. No new model, no new generator: the codec turns frames into tokens, the
 * unqualified `EvermindLM.generate` autoregresses over them, and the codec turns
 * the emitted tokens back into frames. Deliberately DRY — everything the text
 * path already has (sampling, checkpoints, delta export, training) is reused.
 */
import type { EvermindLM, LMGenerateOptions } from "../lm/evermind_lm.js";
import type { VideoRVQCodec, Video, Frame } from "./video_rvq.js";
import type { ImageRVQCodec } from "./image_rvq.js";
/**
 * Build a unified training sequence `text… <vid> frames… </vid>` for
 * `EvermindLMTrainer.fit`. `textTokens` are ids in the codec's text region (from
 * a BPE tokenizer whose vocab size equals `codec.vocab.textVocabSize`). Training
 * the LM on many such sequences is what teaches text→video.
 */
export declare function buildVideoSequence(codec: VideoRVQCodec, textTokens: number[], video: Video): number[];
/**
 * Generate video from a prompt of already-tokenised context (text ids and/or a
 * partial video stream). The prompt should normally end just before or at the
 * `<vid>` marker; generation stops at `</vid>` or after `maxNewTokens`.
 *
 * Returns the decoded clip plus the raw produced token ids (useful for chaining
 * or continued generation).
 */
export declare function generateVideo(lm: EvermindLM, codec: VideoRVQCodec, promptTokens: number[], opts: LMGenerateOptions): {
    video: Video;
    tokens: number[];
};
/** Generate a single image — the still-image case of {@link generateVideo}. */
export declare function generateImage(lm: EvermindLM, codec: ImageRVQCodec, promptTokens: number[], opts: LMGenerateOptions): {
    image: Frame;
    tokens: number[];
};
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