transformers-fork/types/base/processing_utils.d.ts

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declare const Processor_base: new () => {
    (...args: any[]): any;
    _call(...args: any[]): any;
};
/**
 * @typedef {Object} ProcessorProperties Additional processor-specific properties.
 * @typedef {import('../utils/hub.js').PretrainedOptions & ProcessorProperties} PretrainedProcessorOptions
 */
/**
 * Represents a Processor that extracts features from an input.
 */
export class Processor extends Processor_base {
    static classes: string[];
    static uses_processor_config: boolean;
    /**
     * Instantiate one of the processor classes of the library from a pretrained model.
     *
     * The processor class to instantiate is selected based on the `feature_extractor_type` property of the config object
     * (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
     *
     * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
     * - A string, the *model id* of a pretrained processor hosted inside a model repo on huggingface.co.
     *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
     *   user or organization name, like `dbmdz/bert-base-german-cased`.
     * - A path to a *directory* containing processor files, e.g., `./my_model_directory/`.
     * @param {PretrainedProcessorOptions} options Additional options for loading the processor.
     *
     * @returns {Promise<Processor>} A new instance of the Processor class.
     */
    static from_pretrained(pretrained_model_name_or_path: string, options: PretrainedProcessorOptions): Promise<Processor>;
    /**
     * Creates a new Processor with the given components
     * @param {Object} config
     * @param {Record<string, Object>} components
     */
    constructor(config: any, components: Record<string, any>);
    config: any;
    components: Record<string, any>;
    /**
     * @returns {import('./image_processors_utils.js').ImageProcessor|undefined} The image processor of the processor, if it exists.
     */
    get image_processor(): import("./image_processors_utils.js").ImageProcessor | undefined;
    /**
     * @returns {import('../tokenizers.js').PreTrainedTokenizer|undefined} The tokenizer of the processor, if it exists.
     */
    get tokenizer(): import("../tokenizers.js").PreTrainedTokenizer | undefined;
    /**
     * @returns {import('./feature_extraction_utils.js').FeatureExtractor|undefined} The feature extractor of the processor, if it exists.
     */
    get feature_extractor(): import("./feature_extraction_utils.js").FeatureExtractor | undefined;
    apply_chat_template(messages: any, options?: {}): string | number[] | number[][] | import("../transformers.js").Tensor | {
        /**
         * List of token ids to be fed to a model.
         */
        input_ids: number[] | number[][] | import("../transformers.js").Tensor;
        /**
         * List of indices specifying which tokens should be attended to by the model.
         */
        attention_mask: number[] | number[][] | import("../transformers.js").Tensor;
        /**
         * List of token type ids to be fed to a model.
         */
        token_type_ids?: number[] | number[][] | import("../transformers.js").Tensor;
    };
    batch_decode(...args: any[]): string[];
    /**
     * Calls the feature_extractor function with the given input.
     * @param {any} input The input to extract features from.
     * @param {...any} args Additional arguments.
     * @returns {Promise<any>} A Promise that resolves with the extracted features.
     */
    _call(input: any, ...args: any[]): Promise<any>;
}
/**
 * Additional processor-specific properties.
 */
export type ProcessorProperties = any;
export type PretrainedProcessorOptions = import("../utils/hub.js").PretrainedOptions & ProcessorProperties;
export {};
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