declare const PreTrainedModel_base: new () => {
    (...args: any[]): any;
    _call(...args: any[]): any;
};
/**
 * A base class for pre-trained models that provides the model configuration and an ONNX session.
 * @extends Callable
 */
export class PreTrainedModel extends PreTrainedModel_base {
    /**
     * Instantiate one of the model classes of the library from a pretrained model.
     *
     * The model class to instantiate is selected based on the `model_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 model 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 model weights, e.g., `./my_model_directory/`.
     * @param {PretrainedOptions} options Additional options for loading the model.
     *
     * @returns {Promise<PreTrainedModel>} A new instance of the `PreTrainedModel` class.
     */
    static from_pretrained(pretrained_model_name_or_path: string, { quantized, progress_callback, config, cache_dir, local_files_only, revision, }?: PretrainedOptions): Promise<PreTrainedModel>;
    /**
     * Creates a new instance of the `PreTrainedModel` class.
     * @param {Object} config The model configuration.
     * @param {any} session session for the model.
     */
    constructor(config: any, session: any);
    config: any;
    session: any;
    /**
    * Disposes of all the ONNX sessions that were created during inference.
    * @returns {Promise<unknown[]>} An array of promises, one for each ONNX session that is being disposed.
    * @todo Use https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/FinalizationRegistry
    */
    dispose(): Promise<unknown[]>;
    /**
     * Runs the model with the provided inputs
     * @param {Object} model_inputs Object containing input tensors
     * @returns {Promise<Object>} Object containing output tensors
     */
    _call(model_inputs: any): Promise<any>;
    /**
     * Forward method for a pretrained model. If not overridden by a subclass, the correct forward method
     * will be chosen based on the model type.
     * @param {Object} model_inputs The input data to the model in the format specified in the ONNX model.
     * @returns {Promise<Object>} The output data from the model in the format specified in the ONNX model.
     * @throws {Error} This method must be implemented in subclasses.
     */
    forward(model_inputs: any): Promise<any>;
    /**
     * @param {GenerationConfig} generation_config
     * @param {number} input_ids_seq_length The starting sequence length for the input ids.
     * @returns {LogitsProcessorList}
     */
    _get_logits_processor(generation_config: GenerationConfig, input_ids_seq_length: number, logits_processor?: any): LogitsProcessorList;
    /**
   * This function merges multiple generation configs together to form a final generation config to be used by the model for text generation.
   * It first creates an empty `GenerationConfig` object, then it applies the model's own `generation_config` property to it. Finally, if a `generation_config` object was passed in the arguments, it overwrites the corresponding properties in the final config with those of the passed config object.
   *
   * @param {GenerationConfig} generation_config A `GenerationConfig` object containing generation parameters.
   * @returns {GenerationConfig} The final generation config object to be used by the model for text generation.
   */
    _get_generation_config(generation_config: GenerationConfig): GenerationConfig;
    /**
     * @typedef {import('./utils/maths.js').TypedArray} TypedArray
     */
    /**
     * @typedef {{ sequences: Tensor, decoder_attentions: Tensor, cross_attentions: Tensor }} EncoderDecoderOutput
     * @typedef {Object} DecoderOutput
     *
     * Generates text based on the given inputs and generation configuration using the model.
     * @param {Tensor|Array|TypedArray} inputs An array of input token IDs.
     * @param {Object|GenerationConfig|null} generation_config The generation configuration to use. If null, default configuration will be used.
     * @param {Object|null} logits_processor An optional logits processor to use. If null, a new LogitsProcessorList instance will be created.
     * @param {Object} options options
     * @param {Object} [options.inputs_attention_mask=null] An optional attention mask for the inputs.
     * @returns {Promise<number[][]|EncoderDecoderOutput|DecoderOutput>} An array of generated output sequences, where each sequence is an array of token IDs.
     * @throws {Error} Throws an error if the inputs array is empty.
     */
    generate(inputs: any[] | import("./transformers.js").TypedArray | Tensor, generation_config?: any | GenerationConfig | null, logits_processor?: any | null, { inputs_attention_mask }?: {
        inputs_attention_mask?: any;
    }): Promise<any>;
    /**
     * Helper function to add attentions to beam
     * @param {Object} beam
     * @param {Object} output
     * @private
     */
    private addAttentionsToBeam;
    /**
     * Groups an array of beam objects by their ids.
     *
     * @param {Array} beams The array of beam objects to group.
     * @returns {Array} An array of arrays, where each inner array contains beam objects with the same id.
     */
    groupBeams(beams: any[]): any[];
    /**
     * Returns an object containing past key values from the given decoder results object.
     *
     * @param {Object} decoderResults The decoder results object.
     * @param {Object} pastKeyValues The previous past key values.
     * @returns {Object} An object containing past key values.
     * @private
     */
    private getPastKeyValues;
    /**
     * Returns an object containing attentions from the given decoder results object.
     *
     * @param {Object} decoderResults The decoder results object.
     * @returns {Object} An object containing attentions.
     * @private
     */
    private getAttentions;
    /**
     * Adds past key values to the decoder feeds object. If pastKeyValues is null, creates new tensors for past key values.
     *
     * @param {Object} decoderFeeds The decoder feeds object to add past key values to.
     * @param {Object} pastKeyValues An object containing past key values.
     * @param {boolean} [hasDecoder=false] Whether the model has a decoder.
     */
    addPastKeyValues(decoderFeeds: any, pastKeyValues: any, hasDecoder?: boolean): void;
}
export class ModelOutput {
}
/**
 * Base class for model's outputs, with potential hidden states and attentions.
 */
export class BaseModelOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.last_hidden_state Sequence of hidden-states at the output of the last layer of the model.
     * @param {Tensor} [output.hidden_states] Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
     * @param {Tensor} [output.attentions] Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
     */
    constructor({ last_hidden_state, hidden_states, attentions }: {
        last_hidden_state: Tensor;
        hidden_states?: Tensor;
        attentions?: Tensor;
    });
    last_hidden_state: Tensor;
    hidden_states: Tensor;
    attentions: Tensor;
}
export class BertPreTrainedModel extends PreTrainedModel {
}
export class BertModel extends BertPreTrainedModel {
}
/**
 * BertForMaskedLM is a class representing a BERT model for masked language modeling.
 * @extends BertPreTrainedModel
 */
export class BertForMaskedLM extends BertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
/**
 * BertForSequenceClassification is a class representing a BERT model for sequence classification.
 * @extends BertPreTrainedModel
 */
export class BertForSequenceClassification extends BertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
/**
 * BertForTokenClassification is a class representing a BERT model for token classification.
 * @extends BertPreTrainedModel
 */
export class BertForTokenClassification extends BertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
     */
    _call(model_inputs: any): Promise<TokenClassifierOutput>;
}
/**
 * BertForQuestionAnswering is a class representing a BERT model for question answering.
 * @extends BertPreTrainedModel
 */
export class BertForQuestionAnswering extends BertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
export class DistilBertPreTrainedModel extends PreTrainedModel {
}
export class DistilBertModel extends DistilBertPreTrainedModel {
}
/**
 * DistilBertForSequenceClassification is a class representing a DistilBERT model for sequence classification.
 * @extends DistilBertPreTrainedModel
 */
export class DistilBertForSequenceClassification extends DistilBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
/**
 * DistilBertForTokenClassification is a class representing a DistilBERT model for token classification.
 * @extends DistilBertPreTrainedModel
 */
export class DistilBertForTokenClassification extends DistilBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
     */
    _call(model_inputs: any): Promise<TokenClassifierOutput>;
}
/**
 * DistilBertForQuestionAnswering is a class representing a DistilBERT model for question answering.
 * @extends DistilBertPreTrainedModel
 */
export class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
/**
 * DistilBertForMaskedLM is a class representing a DistilBERT model for masking task.
 * @extends DistilBertPreTrainedModel
 */
export class DistilBertForMaskedLM extends DistilBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} returned object
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
export class MobileBertPreTrainedModel extends PreTrainedModel {
}
export class MobileBertModel extends MobileBertPreTrainedModel {
}
/**
 * MobileBertForMaskedLM is a class representing a MobileBERT model for masking task.
 * @extends MobileBertPreTrainedModel
 */
export class MobileBertForMaskedLM extends MobileBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} returned object
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
/**
 * @extends MobileBertPreTrainedModel
 */
export class MobileBertForSequenceClassification extends MobileBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} returned object
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
/**
 * @extends MobileBertPreTrainedModel
 */
export class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} returned object
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
export class MPNetPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare MPNet Model transformer outputting raw hidden-states without any specific head on top.
 * @extends MPNetPreTrainedModel
 */
export class MPNetModel extends MPNetPreTrainedModel {
}
/**
 * MPNetForMaskedLM is a class representing a MPNet model for masked language modeling.
 * @extends MPNetPreTrainedModel
 */
export class MPNetForMaskedLM extends MPNetPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
/**
 * MPNetForSequenceClassification is a class representing a MPNet model for sequence classification.
 * @extends MPNetPreTrainedModel
 */
export class MPNetForSequenceClassification extends MPNetPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
/**
 * MPNetForTokenClassification is a class representing a MPNet model for token classification.
 * @extends MPNetPreTrainedModel
 */
export class MPNetForTokenClassification extends MPNetPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
     */
    _call(model_inputs: any): Promise<TokenClassifierOutput>;
}
/**
 * MPNetForQuestionAnswering is a class representing a MPNet model for question answering.
 * @extends MPNetPreTrainedModel
 */
export class MPNetForQuestionAnswering extends MPNetPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
export class SqueezeBertPreTrainedModel extends PreTrainedModel {
}
export class SqueezeBertModel extends SqueezeBertPreTrainedModel {
}
export class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} returned object
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
export class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} returned object
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
export class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} returned object
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
export class AlbertPreTrainedModel extends PreTrainedModel {
}
export class AlbertModel extends AlbertPreTrainedModel {
}
export class AlbertForSequenceClassification extends AlbertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} returned object
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
export class AlbertForQuestionAnswering extends AlbertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} returned object
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
export class AlbertForMaskedLM extends AlbertPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} returned object
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
export class T5PreTrainedModel extends PreTrainedModel {
}
export class T5Model extends T5PreTrainedModel {
    /**
     * Generates text based on the provided arguments.
     * @throws {Error} Throws an error as the current model class (T5Model) is not compatible with `.generate()`.
     * @returns {Promise<any>}
     * @param {any[]} args
     */
    generate(...args: any[]): Promise<any>;
}
/**
 * T5Model is a class representing a T5 model for conditional generation.
 * @extends T5PreTrainedModel
 */
export class T5ForConditionalGeneration extends T5PreTrainedModel {
    /**
     * Creates a new instance of the `T5ForConditionalGeneration` class.
     * @param {Object} config The model configuration.
     * @param {any} session session for the model.
     * @param {any} decoder_merged_session session for the decoder.
     * @param {GenerationConfig} generation_config The generation configuration.
     */
    constructor(config: any, session: any, decoder_merged_session: any, generation_config: GenerationConfig);
    decoder_merged_session: any;
    generation_config: GenerationConfig;
    num_decoder_layers: any;
    num_decoder_heads: any;
    decoder_dim_kv: any;
    num_encoder_layers: any;
    num_encoder_heads: any;
    encoder_dim_kv: any;
    /**
     * Generates the start beams for a given set of inputs and output length.
     * @param {number[][]} inputs The input token IDs.
     * @param {number} numOutputTokens The desired output length.
     * @returns {Array} The start beams.
     */
    getStartBeams(inputs: number[][], numOutputTokens: number, ...args: any[]): any[];
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the given beam with a new token ID.
     * @param {any} beam The current beam.
     * @param {number} newTokenId The new token ID to add to the output sequence.
     */
    updateBeam(beam: any, newTokenId: number): void;
}
export class MT5PreTrainedModel extends PreTrainedModel {
}
export class MT5Model extends MT5PreTrainedModel {
    /**
     *
     * @param  {...any} args
     * @returns {Promise<any>}
     * @throws {Error}
     */
    generate(...args: any[]): Promise<any>;
}
/**
 * A class representing a conditional sequence-to-sequence model based on the MT5 architecture.
 *
 * @extends MT5PreTrainedModel
 */
export class MT5ForConditionalGeneration extends MT5PreTrainedModel {
    /**
     * Creates a new instance of the `MT5ForConditionalGeneration` class.
     * @param {any} config The model configuration.
     * @param {any} session The ONNX session containing the encoder weights.
     * @param {any} decoder_merged_session The ONNX session containing the merged decoder weights.
     * @param {GenerationConfig} generation_config The generation configuration.
     */
    constructor(config: any, session: any, decoder_merged_session: any, generation_config: GenerationConfig);
    decoder_merged_session: any;
    generation_config: GenerationConfig;
    num_decoder_layers: any;
    num_decoder_heads: any;
    decoder_dim_kv: any;
    num_encoder_layers: any;
    num_encoder_heads: any;
    encoder_dim_kv: any;
    /**
   * Generates the start beams for the given input tokens and output sequence length.
   *
   * @param {any[]} inputs The input sequence.
   * @param {number} numOutputTokens The desired length of the output sequence.
   * @param {...*} args Additional arguments to pass to the `seq2seqStartBeams` function.
   * @returns {any[]} An array of `Beam` objects representing the start beams.
   */
    getStartBeams(inputs: any[], numOutputTokens: number, ...args: any[]): any[];
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the given beam with the new predicted token.
     * @param {any} beam The beam to update.
     * @param {number} newTokenId The index of the predicted token.
    */
    updateBeam(beam: any, newTokenId: number): void;
}
export class BartPretrainedModel extends PreTrainedModel {
}
/**
 * BART encoder and decoder model.
 *
 * @hideconstructor
 * @extends BartPretrainedModel
 */
export class BartModel extends BartPretrainedModel {
    /**
     * Throws an error because the current model class (BartModel) is not compatible with `.generate()`.
     *
     * @throws {Error} The current model class (BartModel) is not compatible with `.generate()`.
     * @returns {Promise<any>}
     */
    generate(...args: any[]): Promise<any>;
}
/**
 * BART model with a language model head for conditional generation.
 * @extends BartPretrainedModel
 */
export class BartForConditionalGeneration extends BartPretrainedModel {
    /**
     * Creates a new instance of the `BartForConditionalGeneration` class.
     * @param {Object} config The configuration object for the Bart model.
     * @param {Object} session The ONNX session used to execute the model.
     * @param {Object} decoder_merged_session The ONNX session used to execute the decoder.
     * @param {Object} generation_config The generation configuration object.
     */
    constructor(config: any, session: any, decoder_merged_session: any, generation_config: any);
    decoder_merged_session: any;
    generation_config: any;
    num_decoder_layers: any;
    num_decoder_heads: any;
    decoder_dim_kv: number;
    num_encoder_layers: any;
    num_encoder_heads: any;
    encoder_dim_kv: number;
    /**
     * Returns the initial beam for generating output text.
     * @param {Object} inputs The input object containing the encoded input text.
     * @param {number} numOutputTokens The maximum number of output tokens to generate.
     * @param  {...any} args Additional arguments to pass to the sequence-to-sequence generation function.
     * @returns {any} The initial beam for generating output text.
     */
    getStartBeams(inputs: any, numOutputTokens: number, ...args: any[]): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the beam by appending the newly generated token ID to the list of output token IDs.
     * @param {any} beam The current beam being generated.
     * @param {number} newTokenId The ID of the newly generated token to append to the list of output token IDs.
     */
    updateBeam(beam: any, newTokenId: number): void;
}
export class BartForSequenceClassification extends BartPretrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
export class RobertaPreTrainedModel extends PreTrainedModel {
}
export class RobertaModel extends RobertaPreTrainedModel {
}
/**
 * RobertaForMaskedLM class for performing masked language modeling on Roberta models.
 * @extends RobertaPreTrainedModel
 */
export class RobertaForMaskedLM extends RobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} returned object
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
/**
 * RobertaForSequenceClassification class for performing sequence classification on Roberta models.
 * @extends RobertaPreTrainedModel
 */
export class RobertaForSequenceClassification extends RobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} returned object
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
/**
 * RobertaForTokenClassification class for performing token classification on Roberta models.
 * @extends RobertaPreTrainedModel
 */
export class RobertaForTokenClassification extends RobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
     */
    _call(model_inputs: any): Promise<TokenClassifierOutput>;
}
/**
 * RobertaForQuestionAnswering class for performing question answering on Roberta models.
 * @extends RobertaPreTrainedModel
 */
export class RobertaForQuestionAnswering extends RobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} returned object
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
export class XLMRobertaPreTrainedModel extends PreTrainedModel {
}
export class XLMRobertaModel extends XLMRobertaPreTrainedModel {
}
/**
 * XLMRobertaForMaskedLM class for performing masked language modeling on XLMRoberta models.
 * @extends XLMRobertaPreTrainedModel
 */
export class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<MaskedLMOutput>} returned object
     */
    _call(model_inputs: any): Promise<MaskedLMOutput>;
}
/**
 * XLMRobertaForSequenceClassification class for performing sequence classification on XLMRoberta models.
 * @extends XLMRobertaPreTrainedModel
 */
export class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<SequenceClassifierOutput>} returned object
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
/**
 * XLMRobertaForTokenClassification class for performing token classification on XLMRoberta models.
 * @extends XLMRobertaPreTrainedModel
 */
export class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
     */
    _call(model_inputs: any): Promise<TokenClassifierOutput>;
}
/**
 * XLMRobertaForQuestionAnswering class for performing question answering on XLMRoberta models.
 * @extends XLMRobertaPreTrainedModel
 */
export class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel {
    /**
     * Calls the model on new inputs.
     *
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<QuestionAnsweringModelOutput>} returned object
     */
    _call(model_inputs: any): Promise<QuestionAnsweringModelOutput>;
}
export class WhisperPreTrainedModel extends PreTrainedModel {
}
/**
 * WhisperModel class for training Whisper models without a language model head.
 * @extends WhisperPreTrainedModel
 */
export class WhisperModel extends WhisperPreTrainedModel {
    /**
     * Throws an error when attempting to generate output since this model doesn't have a language model head.
     * @throws Error
     * @returns {Promise<any>}
     * @param {any[]} args
     */
    generate(...args: any[]): Promise<any>;
}
/**
 * WhisperForConditionalGeneration class for generating conditional outputs from Whisper models.
 * @extends WhisperPreTrainedModel
 */
export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
    /**
     * Creates a new instance of the `WhisperForConditionalGeneration` class.
     * @param {Object} config Configuration object for the model.
     * @param {Object} session ONNX Session object for the model.
     * @param {Object} decoder_merged_session ONNX Session object for the decoder.
     * @param {Object} generation_config Configuration object for the generation process.
     */
    constructor(config: any, session: any, decoder_merged_session: any, generation_config: any);
    decoder_merged_session: any;
    generation_config: any;
    num_decoder_layers: any;
    num_decoder_heads: any;
    decoder_dim_kv: number;
    num_encoder_layers: any;
    num_encoder_heads: any;
    encoder_dim_kv: number;
    /**
     * Generates outputs based on input and generation configuration.
     * @param {Object} inputs Input data for the model.
     * @param {Object} generation_config Configuration object for the generation process.
     * @param {Object} logits_processor Optional logits processor object.
     * @param {Object} options options
     * @param {Object} [options.return_timestamps=null] Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`.
     * @param {Object} [options.return_token_timestamps=null] Whether to return token-level timestamps
     * with the text. This can be used with or without the `return_timestamps` option. To get word-level
     * timestamps, use the tokenizer to group the tokens into words.
     * @returns {Promise<Object>} Promise object represents the generated outputs.
     */
    generate(inputs: any, generation_config?: any, logits_processor?: any): Promise<any>;
    /**
     * Gets the start beams for generating outputs.
     * @param {Array} inputTokenIds Array of input token IDs.
     * @param {number} numOutputTokens Number of output tokens to generate.
     * @returns {Array} Array of start beams.
     */
    getStartBeams(inputTokenIds: any[], numOutputTokens: number, ...args: any[]): any[];
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the beam by appending the newly generated token ID to the list of output token IDs.
     * @param {any} beam The current beam being generated.
     * @param {number} newTokenId The ID of the newly generated token to append to the list of output token IDs.
     */
    updateBeam(beam: any, newTokenId: number): void;
    /**
     * Calculates token-level timestamps using the encoder-decoder cross-attentions and
     * dynamic time-warping (DTW) to map each output token to a position in the input audio.
     * @param {Object} generate_outputs Outputs generated by the model
     * @param {Tensor[][][]} generate_outputs.cross_attentions The cross attentions output by the model
     * @param {Tensor[][][]} generate_outputs.decoder_attentions The decoder attentions output by the model
     * @param {number[][]} generate_outputs.sequences The sequences output by the model
     * @param {number[][]} alignment_heads Alignment heads of the model
     * @param {number} time_precision Precision of the timestamps in seconds
     * @returns {Tensor} tensor containing the timestamps in seconds for each predicted token
     */
    _extract_token_timestamps(generate_outputs: {
        cross_attentions: Tensor[][][];
        decoder_attentions: Tensor[][][];
        sequences: number[][];
    }, alignment_heads: number[][], time_precision?: number): Tensor;
}
/**
 * Vision Encoder-Decoder model based on OpenAI's GPT architecture for image captioning and other vision tasks
 * @extends PreTrainedModel
 */
export class VisionEncoderDecoderModel extends PreTrainedModel {
    /**
     * Creates a new instance of the `VisionEncoderDecoderModel` class.
     * @param {Object} config The configuration object specifying the hyperparameters and other model settings.
     * @param {Object} session The ONNX session containing the encoder model.
     * @param {any} decoder_merged_session The ONNX session containing the merged decoder model.
     */
    constructor(config: any, session: any, decoder_merged_session: any);
    decoder_merged_session: any;
    num_layers: any;
    num_heads: any;
    dim_kv: number;
    /**
     * Generate beam search outputs for the given input pixels and number of output tokens.
     *
     * @param {array} inputs The input pixels as a Tensor.
     * @param {number} numOutputTokens The number of output tokens to generate.
     * @param {...*} args Optional additional arguments to pass to seq2seqStartBeams.
     * @returns {any} An array of Beam objects representing the top-K output sequences.
     */
    getStartBeams(inputs: any[], numOutputTokens: number, ...args: any[]): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Update the given beam with the additional predicted token ID.
     *
     * @param {any} beam The current beam.
     * @param {number} newTokenId The new predicted token ID to add to the beam's output sequence.
     */
    updateBeam(beam: any, newTokenId: number): void;
}
export class CLIPPreTrainedModel extends PreTrainedModel {
}
export class CLIPModel extends CLIPPreTrainedModel {
}
export class GPT2PreTrainedModel extends PreTrainedModel {
    num_heads: any;
    num_layers: any;
    dim_kv: number;
}
export class GPT2Model extends GPT2PreTrainedModel {
    /**
     * GPT2Model is not compatible with `.generate()`, as it doesn't have a language model head.
     * @param  {...any} args
     * @throws {Error}
     * @returns {Promise<any>}
     */
    generate(...args: any[]): Promise<any>;
}
/**
 * GPT-2 language model head on top of the GPT-2 base model. This model is suitable for text generation tasks.
 * @extends GPT2PreTrainedModel
 */
export class GPT2LMHeadModel extends GPT2PreTrainedModel {
    /**
     * Initializes and returns the beam for text generation task
     * @param {Tensor} inputTokenIds The input token ids.
     * @param {number} numOutputTokens The number of tokens to be generated.
     * @param {Tensor} inputs_attention_mask Optional input attention mask.
     * @returns {any} A Beam object representing the initialized beam.
     */
    getStartBeams(inputTokenIds: Tensor, numOutputTokens: number, inputs_attention_mask: Tensor): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the given beam with the new generated token id.
     * @param {any} beam The Beam object representing the beam.
     * @param {number} newTokenId The new generated token id to be added to the beam.
     */
    updateBeam(beam: any, newTokenId: number): void;
}
export class GPTNeoPreTrainedModel extends PreTrainedModel {
    num_heads: any;
    num_layers: any;
    dim_kv: number;
}
export class GPTNeoModel extends GPTNeoPreTrainedModel {
    /**
     *
     * @param  {...any} args
     * @throws {Error}
     * @returns {Promise<any>}
     */
    generate(...args: any[]): Promise<any>;
}
export class GPTNeoForCausalLM extends GPTNeoPreTrainedModel {
    /**
     * Initializes and returns the beam for text generation task
     * @param {Tensor} inputTokenIds The input token ids.
     * @param {number} numOutputTokens The number of tokens to be generated.
     * @param {Tensor} inputs_attention_mask Optional input attention mask.
     * @returns {any} A Beam object representing the initialized beam.
     */
    getStartBeams(inputTokenIds: Tensor, numOutputTokens: number, inputs_attention_mask: Tensor): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the given beam with the new generated token id.
     * @param {any} beam The Beam object representing the beam.
     * @param {number} newTokenId The new generated token id to be added to the beam.
     */
    updateBeam(beam: any, newTokenId: number): void;
}
export class GPTBigCodePreTrainedModel extends PreTrainedModel {
    num_heads: any;
    num_layers: any;
    dim_kv: number;
}
export class GPTBigCodeModel extends GPTBigCodePreTrainedModel {
    /**
     *
     * @param  {...any} args
     * @throws {Error}
     * @returns {Promise<any>}
     */
    generate(...args: any[]): Promise<any>;
}
export class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel {
    /**
     * Initializes and returns the beam for text generation task
     * @param {Tensor} inputTokenIds The input token ids.
     * @param {number} numOutputTokens The number of tokens to be generated.
     * @param {Tensor} inputs_attention_mask Optional input attention mask.
     * @returns {any} A Beam object representing the initialized beam.
     */
    getStartBeams(inputTokenIds: Tensor, numOutputTokens: number, inputs_attention_mask: Tensor): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the given beam with the new generated token id.
     * @param {any} beam The Beam object representing the beam.
     * @param {number} newTokenId The new generated token id to be added to the beam.
     */
    updateBeam(beam: any, newTokenId: number): void;
}
export class CodeGenPreTrainedModel extends PreTrainedModel {
    num_heads: any;
    num_layers: any;
    dim_kv: number;
}
/**
 * CodeGenModel is a class representing a code generation model without a language model head.
 *
 * @extends CodeGenPreTrainedModel
 */
export class CodeGenModel extends CodeGenPreTrainedModel {
    /**
     * Throws an error indicating that the current model class is not compatible with `.generate()`,
     * as it doesn't have a language model head.
     *
     * @throws {Error} The current model class is not compatible with `.generate()`
     *
     * @param  {...any} args Arguments passed to the generate function
     * @returns {Promise<any>}
     */
    generate(...args: any[]): Promise<any>;
}
/**
 * CodeGenForCausalLM is a class that represents a code generation model based on the GPT-2 architecture. It extends the `CodeGenPreTrainedModel` class.
 * @extends CodeGenPreTrainedModel
 */
export class CodeGenForCausalLM extends CodeGenPreTrainedModel {
    /**
     * Initializes and returns the beam for text generation task
     * @param {Tensor} inputTokenIds The input token ids.
     * @param {number} numOutputTokens The number of tokens to be generated.
     * @param {Tensor} inputs_attention_mask Optional input attention mask.
     * @returns {any} A Beam object representing the initialized beam.
     */
    getStartBeams(inputTokenIds: Tensor, numOutputTokens: number, inputs_attention_mask: Tensor): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * Updates the given beam with the new generated token id.
     * @param {any} beam The Beam object representing the beam.
     * @param {number} newTokenId The new generated token id to be added to the beam.
     */
    updateBeam(beam: any, newTokenId: number): void;
}
export class ViTPreTrainedModel extends PreTrainedModel {
}
export class ViTForImageClassification extends ViTPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
export class MobileViTPreTrainedModel extends PreTrainedModel {
}
export class MobileViTForImageClassification extends MobileViTPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    _call(model_inputs: any): Promise<SequenceClassifierOutput>;
}
export class DetrPreTrainedModel extends PreTrainedModel {
}
export class DetrForObjectDetection extends DetrPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    _call(model_inputs: any): Promise<DetrObjectDetectionOutput>;
}
export class DetrForSegmentation extends DetrPreTrainedModel {
    /**
     * Runs the model with the provided inputs
     * @param {Object} model_inputs Model inputs
     * @returns {Promise<DetrSegmentationOutput>} Object containing segmentation outputs
     */
    _call(model_inputs: any): Promise<DetrSegmentationOutput>;
}
export class DetrObjectDetectionOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits Classification logits (including no-object) for all queries.
     * @param {Tensor} output.pred_boxes Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height).
     * These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding).
     */
    constructor({ logits, pred_boxes }: {
        logits: Tensor;
        pred_boxes: Tensor;
    });
    logits: Tensor;
    pred_boxes: Tensor;
}
export class DetrSegmentationOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits The output logits of the model.
     * @param {Tensor} output.pred_boxes Predicted boxes.
     * @param {Tensor} output.pred_masks Predicted masks.
     */
    constructor({ logits, pred_boxes, pred_masks }: {
        logits: Tensor;
        pred_boxes: Tensor;
        pred_masks: Tensor;
    });
    logits: Tensor;
    pred_boxes: Tensor;
    pred_masks: Tensor;
}
export class SamPreTrainedModel extends PreTrainedModel {
}
export class SamModel extends SamPreTrainedModel {
    /**
     * @param {Object} model_inputs
     * @param {Tensor} model_inputs.pixel_values Pixel values as a Tensor with shape `(batch_size, num_channels, height, width)`.
     * @param {Tensor} model_inputs.input_points Input 2D spatial points with shape `(batch_size, num_points, 2)`. This is used by the prompt encoder to encode the prompt.
     * @todo Add support for `input_labels`, `input_boxes`, `input_masks`, and `image_embeddings`.
     */
    _call(model_inputs: {
        pixel_values: Tensor;
        input_points: Tensor;
    }): Promise<SamImageSegmentationOutput>;
}
/**
 * Base class for Segment-Anything model's output.
 */
export class SamImageSegmentationOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.iou_scores The output logits of the model.
     * @param {Tensor} output.pred_masks Predicted boxes.
     */
    constructor({ iou_scores, pred_masks }: {
        iou_scores: Tensor;
        pred_masks: Tensor;
    });
    iou_scores: Tensor;
    pred_masks: Tensor;
}
export class MarianPreTrainedModel extends PreTrainedModel {
}
export class MarianModel extends MarianPreTrainedModel {
    /**
     *
     * @param  {...any} args
     * @throws {Error}
     * @returns {Promise<any>}
     */
    generate(...args: any[]): Promise<any>;
}
export class MarianMTModel extends MarianPreTrainedModel {
    /**
     * Creates a new instance of the `MarianMTModel` class.
    * @param {Object} config The model configuration object.
    * @param {Object} session The ONNX session object.
    * @param {any} decoder_merged_session
    * @param {any} generation_config
    */
    constructor(config: any, session: any, decoder_merged_session: any, generation_config: any);
    decoder_merged_session: any;
    generation_config: any;
    num_decoder_layers: any;
    num_decoder_heads: any;
    decoder_dim_kv: number;
    num_encoder_layers: any;
    num_encoder_heads: any;
    encoder_dim_kv: number;
    /**
     * Initializes and returns the beam for text generation task
     * @param {any[]} inputs The input token ids.
     * @param {number} numOutputTokens The number of tokens to be generated.
     * @returns {any} A Beam object representing the initialized beam.
     * @param {any[]} args
     */
    getStartBeams(inputs: any[], numOutputTokens: number, ...args: any[]): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * @param {any} beam
     * @param {any} newTokenId
     */
    updateBeam(beam: any, newTokenId: any): void;
    /**
     * @param {any} model_inputs
     * @returns {Promise<Seq2SeqLMOutput>}
     */
    forward(model_inputs: any): Promise<Seq2SeqLMOutput>;
}
export class M2M100PreTrainedModel extends PreTrainedModel {
}
export class M2M100Model extends M2M100PreTrainedModel {
    /**
     *
     * @param  {...any} args
     * @throws {Error}
     * @returns {Promise<any>}
     */
    generate(...args: any[]): Promise<any>;
}
export class M2M100ForConditionalGeneration extends M2M100PreTrainedModel {
    /**
     * Creates a new instance of the `M2M100ForConditionalGeneration` class.
    * @param {Object} config The model configuration object.
    * @param {Object} session The ONNX session object.
    * @param {any} decoder_merged_session
    * @param {any} generation_config
    */
    constructor(config: any, session: any, decoder_merged_session: any, generation_config: any);
    decoder_merged_session: any;
    generation_config: any;
    num_decoder_layers: any;
    num_decoder_heads: any;
    decoder_dim_kv: number;
    num_encoder_layers: any;
    num_encoder_heads: any;
    encoder_dim_kv: number;
    /**
     * Initializes and returns the beam for text generation task
     * @param {any[]} inputs The input token ids.
     * @param {number} numOutputTokens The number of tokens to be generated.
     * @returns {any} A Beam object representing the initialized beam.
     * @param {any[]} args
     */
    getStartBeams(inputs: any[], numOutputTokens: number, ...args: any[]): any;
    /**
     * Runs a single step of the beam search generation algorithm.
     * @param {any} beam The current beam being generated.
     * @returns {Promise<any>} The updated beam after a single generation step.
     */
    runBeam(beam: any): Promise<any>;
    /**
     * @param {any} beam
     * @param {any} newTokenId
     */
    updateBeam(beam: any, newTokenId: any): void;
    /**
     * @param {any} model_inputs
     * @returns {Promise<Seq2SeqLMOutput>}
     */
    forward(model_inputs: any): Promise<Seq2SeqLMOutput>;
}
/**
 * Base class of all AutoModels. Contains the `from_pretrained` function
 * which is used to instantiate pretrained models.
 */
export class PretrainedMixin {
    /**
     * Mapping from model type to model class.
     * @type {Map<string, Object>[]}
     */
    static MODEL_CLASS_MAPPINGS: Map<string, any>[];
    /**
     * Whether to attempt to instantiate the base class (`PretrainedModel`) if
     * the model type is not found in the mapping.
     */
    static BASE_IF_FAIL: boolean;
    /**
     * Instantiate one of the model classes of the library from a pretrained model.
     *
     * The model class to instantiate is selected based on the `model_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 model 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 model weights, e.g., `./my_model_directory/`.
     * @param {PretrainedOptions} options Additional options for loading the model.
     *
     * @returns {Promise<PreTrainedModel>} A new instance of the `PreTrainedModel` class.
     */
    static from_pretrained(pretrained_model_name_or_path: string, { quantized, progress_callback, config, cache_dir, local_files_only, revision, }?: import("./utils/hub.js").PretrainedOptions): Promise<PreTrainedModel>;
}
/**
 * Helper class which is used to instantiate pretrained models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModel.from_pretrained('bert-base-uncased');
 */
export class AutoModel extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof T5Model>[];
}
/**
 * Helper class which is used to instantiate pretrained sequence classification models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased-finetuned-sst-2-english');
 */
export class AutoModelForSequenceClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof BertForSequenceClassification>[];
}
/**
 * Helper class which is used to instantiate pretrained token classification models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl');
 */
export class AutoModelForTokenClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof BertForTokenClassification>[];
}
/**
 * Helper class which is used to instantiate pretrained sequence-to-sequence models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForSeq2SeqLM.from_pretrained('t5-small');
 */
export class AutoModelForSeq2SeqLM extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof T5ForConditionalGeneration>[];
}
/**
 * Helper class which is used to instantiate pretrained causal language models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForCausalLM.from_pretrained('gpt2');
 */
export class AutoModelForCausalLM extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof GPT2LMHeadModel>[];
}
/**
 * Helper class which is used to instantiate pretrained masked language models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForMaskedLM.from_pretrained('bert-base-uncased');
 */
export class AutoModelForMaskedLM extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof BertForMaskedLM>[];
}
/**
 * Helper class which is used to instantiate pretrained question answering models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForQuestionAnswering.from_pretrained('distilbert-base-cased-distilled-squad');
 */
export class AutoModelForQuestionAnswering extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof BertForQuestionAnswering>[];
}
/**
 * Helper class which is used to instantiate pretrained vision-to-sequence models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForVision2Seq.from_pretrained('nlpconnect/vit-gpt2-image-captioning');
 */
export class AutoModelForVision2Seq extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof VisionEncoderDecoderModel>[];
}
/**
 * Helper class which is used to instantiate pretrained image classification models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForImageClassification.from_pretrained('google/vit-base-patch16-224');
 */
export class AutoModelForImageClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof ViTForImageClassification>[];
}
/**
 * Helper class which is used to instantiate pretrained image segmentation models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForImageSegmentation.from_pretrained('facebook/detr-resnet-50-panoptic');
 */
export class AutoModelForImageSegmentation extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof DetrForSegmentation>[];
}
/**
 * Helper class which is used to instantiate pretrained object detection models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForObjectDetection.from_pretrained('facebook/detr-resnet-50');
 */
export class AutoModelForObjectDetection extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof DetrForObjectDetection>[];
}
/**
 * Helper class which is used to instantiate pretrained object detection models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 *
 * @example
 * let model = await AutoModelForMaskGeneration.from_pretrained('Xenova/sam-vit-base');
 */
export class AutoModelForMaskGeneration extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS: Map<string, typeof SamModel>[];
}
export class Seq2SeqLMOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits The output logits of the model.
     * @param {Tensor} output.past_key_values An tensor of key/value pairs that represent the previous state of the model.
     * @param {Tensor} output.encoder_outputs The output of the encoder in a sequence-to-sequence model.
     * @param {Tensor} [output.decoder_attentions] Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
     * @param {Tensor} [output.cross_attentions] Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
     */
    constructor({ logits, past_key_values, encoder_outputs, decoder_attentions, cross_attentions }: {
        logits: Tensor;
        past_key_values: Tensor;
        encoder_outputs: Tensor;
        decoder_attentions?: Tensor;
        cross_attentions?: Tensor;
    });
    logits: Tensor;
    past_key_values: Tensor;
    encoder_outputs: Tensor;
    decoder_attentions: Tensor;
    cross_attentions: Tensor;
}
/**
 * Base class for outputs of sentence classification models.
 */
export class SequenceClassifierOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits classification (or regression if config.num_labels==1) scores (before SoftMax).
     */
    constructor({ logits }: {
        logits: Tensor;
    });
    logits: Tensor;
}
/**
 * Base class for outputs of token classification models.
 */
export class TokenClassifierOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits Classification scores (before SoftMax).
     */
    constructor({ logits }: {
        logits: Tensor;
    });
    logits: Tensor;
}
/**
 * Base class for masked language models outputs.
 */
export class MaskedLMOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
     */
    constructor({ logits }: {
        logits: Tensor;
    });
    logits: Tensor;
}
/**
 * Base class for outputs of question answering models.
 */
export class QuestionAnsweringModelOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.start_logits Span-start scores (before SoftMax).
     * @param {Tensor} output.end_logits Span-end scores (before SoftMax).
     */
    constructor({ start_logits, end_logits }: {
        start_logits: Tensor;
        end_logits: Tensor;
    });
    start_logits: Tensor;
    end_logits: Tensor;
}
/**
 * Base class for causal language model (or autoregressive) outputs.
 */
export class CausalLMOutputWithPast extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits Prediction scores of the language modeling head (scores for each vocabulary token before softmax).
     * @param {Tensor} output.past_key_values Contains pre-computed hidden-states (key and values in the self-attention blocks)
     * that can be used (see `past_key_values` input) to speed up sequential decoding.
     */
    constructor({ logits, past_key_values }: {
        logits: Tensor;
        past_key_values: Tensor;
    });
    logits: Tensor;
    past_key_values: Tensor;
}
export type PretrainedOptions = import('./utils/hub.js').PretrainedOptions;
import { GenerationConfig } from './utils/generation.js';
import { LogitsProcessorList } from './utils/generation.js';
import { Tensor } from './utils/tensor.js';
export {};
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