import { ChatHistoryItem, ChatModelFunctions, ChatWrapperGenerateContextStateOptions, ChatWrapperGeneratedContextState, ChatWrapperSettings, Tokenizer } from "../../types.js";
import { LlamaText } from "../../utils/LlamaText.js";
import { ChatWrapper } from "../../ChatWrapper.js";
import { ChatHistoryFunctionCallMessageTemplate } from "./utils/chatHistoryFunctionCallMessageTemplate.js";
import { TemplateChatWrapperSegmentsOptions } from "./utils/templateSegmentOptionsToChatWrapperSettings.js";
export type JinjaTemplateChatWrapperOptions = {
    template: string;
    /**
     * Defaults to `"assistant"`.
     */
    modelRoleName?: string;
    /**
     * Defaults to `"user"`.
     */
    userRoleName?: string;
    /**
     * Defaults to `"system"`.
     */
    systemRoleName?: string;
    /**
     * Some Jinja templates may not support system messages, and in such cases,
     * it'll be detected and system messages can be converted to user messages.
     *
     * You can specify the format of the converted user message.
     * - **"auto"**: Convert system messages to user messages only if the template does not support system messages.
     * - **`true`**: Always convert system messages to user messages.
     * - **`false`**: Never convert system messages to user messages.
     * May throw an error if some system messages don't appear in the template.
     * - **`{use: "ifNeeded", format: "..."}`**: Convert system messages to user messages only if the template does not support system
     * messages with the specified format.
     * - **`{use: "always", format: "..."}`**: Always convert system messages to user messages with the specified format.
     *
     * Defaults to `"auto"`.
     */
    convertUnsupportedSystemMessagesToUserMessages?: "auto" | boolean | JinjaTemplateChatWrapperOptionsConvertMessageFormat;
    /**
     * Template format for how functions can be called by the model and how their results are fed to the model after function calls.
     *
     * - **`"auto"`**: Extract the function call message template from the Jinja template.
     *   Fallback to the default template if not found.
     * - **`"noJinja"`**: Use the default template.
     * - **Custom template**: Use the specified {@link ChatHistoryFunctionCallMessageTemplate template}.
     *   See {@link ChatHistoryFunctionCallMessageTemplate `ChatHistoryFunctionCallMessageTemplate`} for more details.
     *
     * Defaults to `"auto"`.
     */
    functionCallMessageTemplate?: "auto" | "noJinja" | ChatHistoryFunctionCallMessageTemplate;
    /**
     * Whether to join adjacent messages of the same type.
     * Some Jinja templates may throw an error if this is not set to `true`.
     *
     * Defaults to `true`.
     */
    joinAdjacentMessagesOfTheSameType?: boolean;
    /**
     * Whether to trim leading whitespace in responses.
     *
     * Defaults to `true`.
     */
    trimLeadingWhitespaceInResponses?: boolean;
    /**
     * Additional parameters to use for rendering the Jinja template.
     */
    additionalRenderParameters?: Record<string, any>;
    /**
     * Format of the segments generated by the model (like thought segments)
     */
    segments?: TemplateChatWrapperSegmentsOptions;
    /**
     * Pass a model's tokenizer to attempt to detect common tokens used for chat formatting from it.
     *
     * Currently only used for detecting support for `<think>` tags for thought segments.
     */
    tokenizer?: Tokenizer;
};
export type JinjaTemplateChatWrapperOptionsConvertMessageFormat = {
    use?: "always" | "ifNeeded";
    format: `${string}{{message}}${string}`;
};
/**
 * A chat wrapper based on a Jinja template.
 * Useful for using the original model's Jinja template as-is without any additional conversion work to chat with a model.
 *
 * If you want to create a new chat wrapper from scratch, using this chat wrapper is not recommended, and instead you better inherit
 * from the `ChatWrapper` class and implement a custom chat wrapper of your own in TypeScript.
 *
 * For a simpler way to create a chat wrapper, see the `TemplateChatWrapper` class.
 * @example
 * <span v-pre>
 *
 * ```ts
 * import {JinjaTemplateChatWrapper} from "node-llama-cpp";
 *
 * const chatWrapper = new JinjaTemplateChatWrapper({
 *     template: "<Jinja template here>",
 *     // functionCallMessageTemplate: { // optional
 *     //     call: "[[call: {{functionName}}({{functionParams}})]]",
 *     //     result: " [[result: {{functionCallResult}}]]"
 *     // },
 *     // segments: {
 *     //     thoughtTemplate: "<think>{{content}}</think>",
 *     //     reopenThoughtAfterFunctionCalls: true
 *     // }
 * });
 * ```
 *
 * </span>
 */
export declare class JinjaTemplateChatWrapper extends ChatWrapper {
    readonly wrapperName = "JinjaTemplate";
    readonly settings: ChatWrapperSettings;
    readonly template: string;
    readonly modelRoleName: string;
    readonly userRoleName: string;
    readonly systemRoleName: string;
    readonly convertUnsupportedSystemMessagesToUserMessages?: JinjaTemplateChatWrapperOptionsConvertMessageFormat;
    readonly joinAdjacentMessagesOfTheSameType: boolean;
    readonly trimLeadingWhitespaceInResponses: boolean;
    readonly additionalRenderParameters?: Record<string, any>;
    /**
     * @param options
     */
    constructor(options: JinjaTemplateChatWrapperOptions);
    /**
     * Whether the function call syntax settings were extracted from the given Jinja template.
     *
     * The function call syntax settings can be accessed using the `.settings.functions` property.
     */
    get usingJinjaFunctionCallTemplate(): boolean;
    generateContextState({ chatHistory, availableFunctions, documentFunctionParams }: ChatWrapperGenerateContextStateOptions): ChatWrapperGeneratedContextState & {
        transformedSystemMessagesToUserMessages: boolean;
    };
    addAvailableFunctionsSystemMessageToHistory(history: readonly ChatHistoryItem[], availableFunctions?: ChatModelFunctions, options?: {
        documentParams?: boolean;
    }): readonly ChatHistoryItem[];
    generateFunctionCall(name: string, params: any): LlamaText;
    generateFunctionCallResult(functionName: string, functionParams: any, result: any): LlamaText;
}