/// <reference types="node" />
/// <reference types="node" />
import CacheManager, { type DownloadOptions } from './cache-manager';
import { ModelManager, Model } from './model-manager';
export interface WllamaLogger {
    debug: typeof console.debug;
    log: typeof console.log;
    warn: typeof console.warn;
    error: typeof console.error;
}
export interface WllamaConfig {
    /**
     * If true, suppress all log messages from native CPP code
     */
    suppressNativeLog?: boolean;
    /**
     * Custom logger functions
     */
    logger?: WllamaLogger;
    /**
     * Maximum number of parallel files to be downloaded
     *
     * Default: parallelDownloads = 3
     */
    parallelDownloads?: number;
    /**
     * Allow offline mode. If true, the model will be loaded from cache if it's available.
     *
     * Default: allowOffline = false
     */
    allowOffline?: boolean;
    /**
     * Custom cache manager (only for advanced usage)
     */
    cacheManager?: CacheManager;
    /**
     * Custom model manager (only for advanced usage)
     */
    modelManager?: ModelManager;
}
export interface WllamaChatMessage {
    role: 'system' | 'user' | 'assistant';
    content: string;
}
export interface AssetsPathConfig {
    'single-thread/wllama.wasm': string;
    'multi-thread/wllama.wasm'?: string;
}
export interface LoadModelConfig {
    seed?: number;
    n_ctx?: number;
    n_batch?: number;
    n_threads?: number;
    embeddings?: boolean;
    offload_kqv?: boolean;
    pooling_type?: 'LLAMA_POOLING_TYPE_UNSPECIFIED' | 'LLAMA_POOLING_TYPE_NONE' | 'LLAMA_POOLING_TYPE_MEAN' | 'LLAMA_POOLING_TYPE_CLS';
    rope_scaling_type?: 'LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED' | 'LLAMA_ROPE_SCALING_TYPE_NONE' | 'LLAMA_ROPE_SCALING_TYPE_LINEAR' | 'LLAMA_ROPE_SCALING_TYPE_YARN';
    rope_freq_base?: number;
    rope_freq_scale?: number;
    yarn_ext_factor?: number;
    yarn_attn_factor?: number;
    yarn_beta_fast?: number;
    yarn_beta_slow?: number;
    yarn_orig_ctx?: number;
    cache_type_k?: 'f32' | 'f16' | 'q8_0' | 'q5_1' | 'q5_0' | 'q4_1' | 'q4_0';
    cache_type_v?: 'f32' | 'f16' | 'q8_0' | 'q5_1' | 'q5_0' | 'q4_1' | 'q4_0';
    flash_attn?: boolean;
}
export interface SamplingConfig {
    mirostat?: number | undefined;
    mirostat_eta?: number | undefined;
    mirostat_tau?: number | undefined;
    samplers_sequence?: string[] | undefined;
    temp?: number | undefined;
    top_p?: number | undefined;
    top_k?: number | undefined;
    penalty_last_n?: number | undefined;
    penalty_repeat?: number | undefined;
    penalty_freq?: number | undefined;
    penalty_present?: number | undefined;
    dynatemp_range?: number | undefined;
    dynatemp_exponent?: number | undefined;
    grammar?: string;
    n_prev?: number | undefined;
    n_probs?: number | undefined;
    min_p?: number | undefined;
    typ_p?: number | undefined;
    typical_p?: number | undefined;
    logit_bias?: {
        token: number;
        bias: number;
    }[] | undefined;
}
export interface CompletionChunk {
    token: number;
    piece: Uint8Array;
    currentText: string;
}
export interface CompletionOptions {
    /**
     * When processing input prompt, we don't need to get output tokens. Only used by llama_decode()
     * Default: false
     */
    skipLogits?: boolean;
    /**
     * Optional abort signal to stop the generation.
     * This can also be used to stop during prompt processing. In this case, it will throw WllamaAbortError.
     */
    abortSignal?: AbortSignal;
    /**
     * If true, return an AsyncIterable instead of a string
     */
    stream?: boolean;
}
export interface ChatCompletionOptions {
    nPredict?: number;
    onNewToken?(token: number, piece: Uint8Array, currentText: string, optionals: {
        /**
         * DEPRECATED, use ChatCompletionOptions["abortSignal"] instead
         */
        abortSignal: () => any;
    }): any;
    sampling?: SamplingConfig;
    /**
     * List of custom token IDs for stopping the generation.
     * Note: To convert from text to token ID, use lookupToken()
     */
    stopTokens?: number[];
    /**
     * Equivalent to `cache_prompt` option in llama.cpp server.
     * Useful for chat, because it skip evaluating the history part of the conversation.
     */
    useCache?: boolean;
    /**
     * Optional abort signal to stop the generation.
     * This can also be used to stop during prompt processing (with a bit of delay.)
     */
    abortSignal?: AbortSignal;
    /**
     * If true, return an AsyncIterable instead of a string
     */
    stream?: boolean;
}
export interface ModelMetadata {
    hparams: {
        nVocab: number;
        nCtxTrain: number;
        nEmbd: number;
        nLayer: number;
    };
    meta: Record<string, string>;
}
export interface ContextOptions {
    /**
     * Allow switching between embeddings / generation mode. Useful for models like GritLM.
     */
    embeddings: boolean;
}
export interface LoadedContextInfo {
    n_vocab: number;
    n_ctx: number;
    n_batch: number;
    n_ubatch: number;
    n_ctx_train: number;
    n_embd: number;
    n_layer: number;
    metadata: Record<string, string>;
    token_bos: number;
    token_eos: number;
    token_eot: number;
    list_tokens_eog: number[];
    has_encoder: boolean;
    token_decoder_start: number;
    add_bos_token: boolean;
    add_eos_token: boolean;
}
/**
 * Logger preset with debug messages suppressed
 */
export declare const LoggerWithoutDebug: {
    debug: () => void;
    assert(condition?: boolean | undefined, ...data: any[]): void;
    assert(value: any, message?: string | undefined, ...optionalParams: any[]): void;
    clear(): void;
    clear(): void;
    count(label?: string | undefined): void;
    count(label?: string | undefined): void;
    countReset(label?: string | undefined): void;
    countReset(label?: string | undefined): void;
    dir(item?: any, options?: any): void;
    dir(obj: any, options?: import("util").InspectOptions | undefined): void;
    dirxml(...data: any[]): void;
    dirxml(...data: any[]): void;
    error(...data: any[]): void;
    error(message?: any, ...optionalParams: any[]): void;
    group(...data: any[]): void;
    group(...label: any[]): void;
    groupCollapsed(...data: any[]): void;
    groupCollapsed(...label: any[]): void;
    groupEnd(): void;
    groupEnd(): void;
    info(...data: any[]): void;
    info(message?: any, ...optionalParams: any[]): void;
    log(...data: any[]): void;
    log(message?: any, ...optionalParams: any[]): void;
    table(tabularData?: any, properties?: string[] | undefined): void;
    table(tabularData: any, properties?: readonly string[] | undefined): void;
    time(label?: string | undefined): void;
    time(label?: string | undefined): void;
    timeEnd(label?: string | undefined): void;
    timeEnd(label?: string | undefined): void;
    timeLog(label?: string | undefined, ...data: any[]): void;
    timeLog(label?: string | undefined, ...data: any[]): void;
    timeStamp(label?: string | undefined): void;
    timeStamp(label?: string | undefined): void;
    trace(...data: any[]): void;
    trace(message?: any, ...optionalParams: any[]): void;
    warn(...data: any[]): void;
    warn(message?: any, ...optionalParams: any[]): void;
    Console: console.ConsoleConstructor;
    profile(label?: string | undefined): void;
    profileEnd(label?: string | undefined): void;
};
export type WllamaErrorType = 'model_not_loaded' | 'download_error' | 'load_error' | 'kv_cache_full' | 'unknown_error' | 'inference_error';
export declare class WllamaError extends Error {
    type: WllamaErrorType;
    constructor(message: string, type?: WllamaErrorType);
}
/**
 * AbortError is thrown when the user wants to abort the current operation.
 * This is equivalent to AbortError in Fetch API.
 */
export declare class WllamaAbortError extends Error {
    name: string;
    constructor();
}
export declare class Wllama {
    cacheManager: CacheManager;
    modelManager: ModelManager;
    private proxy;
    private config;
    private pathConfig;
    private useMultiThread;
    private nbThreads;
    private useEmbeddings;
    private loadedContextInfo;
    private bosToken;
    private eosToken;
    private eotToken;
    private eogTokens;
    private addBosToken;
    private addEosToken;
    private chatTemplate?;
    private metadata?;
    private samplingConfig;
    private hasEncoder;
    private decoderStartToken;
    private nCachedTokens;
    constructor(pathConfig: AssetsPathConfig, wllamaConfig?: WllamaConfig);
    private logger;
    private checkModelLoaded;
    /**
     * Get the libllama version string, e.g. "b6327-4d74393".
     *
     * @returns version string embedded at build time.
     */
    static getLibllamaVersion(): string;
    /**
     * Check if the model is loaded via `loadModel()`
     */
    isModelLoaded(): boolean;
    /**
     * Get token ID associated to BOS (begin of sentence) token.
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns -1 if the model is not loaded.
     */
    getBOS(): number;
    /**
     * Get token ID associated to EOS (end of sentence) token.
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns -1 if the model is not loaded.
     */
    getEOS(): number;
    /**
     * Get token ID associated to EOT (end of turn) token.
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns -1 if the model is not loaded.
     */
    getEOT(): number;
    /**
     * Check if a given token is end-of-generation token (e.g. EOS, EOT, etc.)
     *
     * @param token the token ID to be checked
     * @returns true if the token is EOS, EOT, or any other end-of-generation tokens
     */
    isTokenEOG(token: number): boolean;
    /**
     * Get token ID associated to token used by decoder, to start generating output sequence(only usable for encoder-decoder architecture). In other words, encoder uses normal BOS and decoder uses this token.
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns -1 if the model is not loaded.
     */
    getDecoderStartToken(): number;
    /**
     * Get model hyper-parameters and metadata
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns ModelMetadata
     */
    getModelMetadata(): ModelMetadata;
    /**
     * Check if we're currently using multi-thread build.
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns true if multi-thread is used.
     */
    isMultithread(): boolean;
    /**
     * Get number of threads used in the current context.
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns number of threads
     */
    getNumThreads(): number;
    /**
     * Check if the current model uses encoder-decoder architecture
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns true if multi-thread is used.
     */
    isEncoderDecoderArchitecture(): boolean;
    /**
     * Must we add BOS token to the tokenized sequence?
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns true if BOS token must be added to the sequence
     */
    mustAddBosToken(): boolean;
    /**
     * Must we add EOS token to the tokenized sequence?
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns true if EOS token must be added to the sequence
     */
    mustAddEosToken(): boolean;
    /**
     * Get the jinja chat template comes with the model. It only available if the original model (before converting to gguf) has the template in `tokenizer_config.json`
     *
     * NOTE: This can only being used after `loadModel` is called.
     *
     * @returns the jinja template. null if there is no template in gguf
     */
    getChatTemplate(): string | null;
    /**
     * Load model from a given URL (or a list of URLs, in case the model is splitted into smaller files)
     * - If the model already been downloaded (via `downloadModel()`), then we will use the cached model
     * - Else, we download the model from internet
     * @param modelUrl URL to the GGUF file. If the model is splitted, pass the URL to the first shard.
     * @param config
     */
    loadModelFromUrl(modelUrl: string | string[], config?: LoadModelConfig & DownloadOptions & {
        useCache?: boolean;
    }): Promise<void>;
    /**
     * Load model from a given Hugging Face model ID and file path.
     *
     * @param modelId The HF model ID, for example: 'ggml-org/models'
     * @param filePath The GGUF file path, for example: 'tinyllamas/stories15M-q4_0.gguf'
     * @param config
     */
    loadModelFromHF(modelId: string, filePath: string, config?: LoadModelConfig & DownloadOptions & {
        useCache?: boolean;
    }): Promise<void>;
    /**
     * Load model from a given list of Blob.
     *
     * You can pass multiple buffers into the function (in case the model contains multiple shards).
     *
     * @param ggufBlobsOrModel Can be either list of Blobs (in case you use local file), or a Model object (in case you use ModelManager)
     * @param config LoadModelConfig
     */
    loadModel(ggufBlobsOrModel: Blob[] | Model, config?: LoadModelConfig): Promise<void>;
    getLoadedContextInfo(): LoadedContextInfo;
    /**
     * Calculate embedding vector for a given text.
     * By default, BOS and EOS tokens will be added automatically. You can use the "skipBOS" and "skipEOS" option to disable it.
     * @param text Input text
     * @returns An embedding vector
     */
    createEmbedding(text: string, options?: {
        skipBOS?: boolean;
        skipEOS?: boolean;
    }): Promise<number[]>;
    /**
     * Make completion for a given chat messages.
     *
     * NOTE: this function uses the chat template (if available) to format the chat messages. If the template is not available, it will use the default format (chatml). It can throw an error if the chat template is not compatible.
     *
     * @param messages Chat messages
     * @param options
     * @returns Output completion text (only the completion part)
     */
    createChatCompletion(messages: WllamaChatMessage[], options: ChatCompletionOptions & {
        stream?: false;
    }): Promise<string>;
    createChatCompletion(messages: WllamaChatMessage[], options: ChatCompletionOptions & {
        stream: true;
    }): Promise<AsyncIterable<CompletionChunk>>;
    /**
     * Make completion for a given text.
     * @param prompt Input text
     * @param options
     * @returns Output completion text (only the completion part)
     */
    createCompletion(prompt: string, options: ChatCompletionOptions & {
        stream?: false;
    }): Promise<string>;
    createCompletion(prompt: string, options: ChatCompletionOptions & {
        stream: true;
    }): Promise<AsyncIterable<CompletionChunk>>;
    /**
     * Private implementation of createCompletion
     */
    private createCompletionImpl;
    /**
     * Same with `createCompletion`, but returns an async iterator instead.
     */
    private createCompletionGenerator;
    /**
     * Create or reset the ctx_sampling
     * @param config
     * @param pastTokens In case re-initializing the ctx_sampling, you can re-import past tokens into the new context
     */
    samplingInit(config: SamplingConfig, pastTokens?: number[]): Promise<void>;
    /**
     * Get a list of pieces in vocab.
     * NOTE: This function is slow, should only be used once.
     * @returns A list of Uint8Array. The nth element in the list associated to nth token in vocab
     */
    getVocab(): Promise<Uint8Array[]>;
    /**
     * Lookup to see if a token exist in vocab or not. Useful for searching special tokens like "<|im_start|>"
     * NOTE: It will match the whole token, so do not use it as a replacement for tokenize()
     * @param piece
     * @returns Token ID associated to the given piece. Returns -1 if cannot find the token.
     */
    lookupToken(piece: string): Promise<number>;
    /**
     * Convert a given text to list of tokens
     * @param text
     * @param special Should split special tokens?
     * @returns List of token ID
     */
    tokenize(text: string, special?: boolean): Promise<number[]>;
    /**
     * Convert a list of tokens to text
     * @param tokens
     * @param returnString Return a string instead of Uint8Array
     * @returns Uint8Array, which maybe an unfinished unicode
     */
    detokenize(tokens: number[], returnString?: false): Promise<Uint8Array>;
    detokenize(tokens: number[], returnString: true): Promise<string>;
    /**
     * Run llama_decode()
     * @param tokens A list of tokens to be decoded
     * @param options Additional options
     * @returns n_past (number of tokens so far in the sequence)
     */
    decode(tokens: number[], options: CompletionOptions): Promise<{
        nPast: number;
    }>;
    /**
     * Run llama_encode()
     * @param tokens A list of tokens to be encoded
     * @param options Additional options
     * @returns n_past (number of tokens so far in the sequence)
     */
    encode(tokens: number[], options?: CompletionOptions): Promise<{
        nPast: number;
    }>;
    private breakTokensIntoBatches;
    /**
     * Sample a new token (remember to samplingInit() at least once before calling this function)
     * @returns the token ID and its detokenized value (which maybe an unfinished unicode)
     */
    samplingSample(): Promise<{
        piece: Uint8Array;
        token: number;
    }>;
    /**
     * Accept and save a new token to ctx_sampling
     * @param tokens
     */
    samplingAccept(tokens: number[]): Promise<void>;
    /**
     * Get softmax-ed probability of logits, can be used for custom sampling
     * @param topK Get top K tokens having highest logits value. If topK == -1, we return all n_vocab logits, but this is not recommended because it's slow.
     */
    getLogits(topK?: number): Promise<{
        token: number;
        p: number;
    }[]>;
    /**
     * Calculate embeddings for a given list of tokens. Output vector is always normalized
     * @param tokens
     * @returns A list of number represents an embedding vector of N dimensions
     */
    embeddings(tokens: number[]): Promise<number[]>;
    /**
     * Remove and shift some tokens from KV cache.
     * Keep n_keep, remove n_discard then shift the rest
     * @param nKeep
     * @param nDiscard
     */
    kvRemove(nKeep: number, nDiscard: number): Promise<void>;
    /**
     * Clear all tokens in KV cache
     */
    kvClear(): Promise<void>;
    /**
     * Save session to file (virtual file system)
     * TODO: add ability to download the file
     * @param filePath
     * @returns List of tokens saved to the file
     */
    /**
     * Load session from file (virtual file system)
     * TODO: add ability to download the file
     * @param filePath
     */
    /**
     * Apply chat template to a list of messages
     *
     * @param messages list of messages
     * @param addAssistant whether to add assistant prompt at the end
     * @param template (optional) custom template, see llama-server --chat-template argument for more details
     * @returns formatted chat
     */
    formatChat(messages: WllamaChatMessage[], addAssistant: boolean, template?: string): Promise<string>;
    /**
     * Set options for underlaying llama_context
     */
    setOptions(opt: ContextOptions): Promise<void>;
    /**
     * Unload the model and free all memory.
     *
     * Note: This function will NOT crash if model is not yet loaded
     */
    exit(): Promise<void>;
    /**
     * get debug info
     */
    _getDebugInfo(): Promise<any>;
    /**
     * benchmark function, only used internally
     */
    _testBenchmark(type: 'tg' | 'pp', nSamples: number): Promise<{
        t_ms: number;
    }>;
    /**
     * perplexity function, only used internally
     */
    _testPerplexity(tokens: number[]): Promise<{
        ppl: number;
    }>;
    private getCachedTokens;
    /**
     * Compare the input sequence and cachedToken, then return the part that is not in cache.
     * This function also remove mismatch part in cache (via kvRemove)
     */
    private computeNonCachedTokens;
}