transformers-fork/types/models.d.ts

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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.
 */
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 {import('./utils/hub.js').PretrainedModelOptions} 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, { progress_callback, config, cache_dir, local_files_only, revision, model_file_name, subfolder, device, dtype, use_external_data_format, session_options, }?: import("./utils/hub.js").PretrainedModelOptions): Promise<PreTrainedModel>;
    /**
     * Creates a new instance of the `PreTrainedModel` class.
     * @param {import('./configs.js').PretrainedConfig} config The model configuration.
     * @param {Record<string, any>} sessions The inference sessions for the model.
     * @param {Record<string, Object>} configs Additional configuration files (e.g., generation_config.json).
     */
    constructor(config: import("./configs.js").PretrainedConfig, sessions: Record<string, any>, configs: Record<string, any>);
    main_input_name: string;
    forward_params: string[];
    config: import("./configs.js").PretrainedConfig;
    sessions: Record<string, any>;
    configs: Record<string, any>;
    can_generate: boolean;
    _forward: typeof decoderForward;
    _prepare_inputs_for_generation: typeof image_text_to_text_prepare_inputs_for_generation;
    /** @type {import('./configs.js').TransformersJSConfig} */
    custom_config: import("./configs.js").TransformersJSConfig;
    /**
    * 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>;
    /**
     * Get the model's generation config, if it exists.
     * @returns {GenerationConfig|null} The model's generation config if it exists, otherwise `null`.
     */
    get generation_config(): GenerationConfig | null;
    /**
     * This function returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsWarper`]
     * instances used for multinomial sampling.
     * @param {GenerationConfig} generation_config The generation config.
     * @returns {LogitsProcessorList} generation_config
     */
    _get_logits_warper(generation_config: GenerationConfig): LogitsProcessorList;
    /**
     * @param {GenerationConfig} generation_config
     * @param {number} input_ids_seq_length The starting sequence length for the input ids.
     * @returns {LogitsProcessorList}
     * @private
     */
    private _get_logits_processor;
    /**
     * 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|null} generation_config A `GenerationConfig` object containing generation parameters.
     * @param {Object} kwargs Additional generation parameters to be used in place of those in the `generation_config` object.
     * @returns {GenerationConfig} The final generation config object to be used by the model for text generation.
     */
    _prepare_generation_config(generation_config: GenerationConfig | null, kwargs: any, cls?: typeof GenerationConfig): GenerationConfig;
    /**
     *
     * @param {GenerationConfig} generation_config
     * @param {StoppingCriteriaList} [stopping_criteria=null]
     */
    _get_stopping_criteria(generation_config: GenerationConfig, stopping_criteria?: StoppingCriteriaList): StoppingCriteriaList;
    /**
     * Confirms that the model class is compatible with generation.
     * If not, raises an exception that points to the right class to use.
     */
    _validate_model_class(): void;
    prepare_inputs_for_generation(...args: any[]): any;
    /**
     *
     * @param {Object} inputs
     * @param {bigint[][]} inputs.generated_input_ids
     * @param {Object} inputs.outputs
     * @param {Object} inputs.model_inputs
     * @param {boolean} inputs.is_encoder_decoder
     * @returns {Object} The updated model inputs for the next generation iteration.
     */
    _update_model_kwargs_for_generation({ generated_input_ids, outputs, model_inputs, is_encoder_decoder }: {
        generated_input_ids: bigint[][];
        outputs: any;
        model_inputs: any;
        is_encoder_decoder: boolean;
    }): any;
    /**
     * This function extracts the model-specific `inputs` for generation.
     * @param {Object} params
     * @param {Tensor} [params.inputs=null]
     * @param {number} [params.bos_token_id=null]
     * @param {Record<string, Tensor|number[]>} [params.model_kwargs]
     * @returns {{inputs_tensor: Tensor, model_inputs: Record<string, Tensor>, model_input_name: string}} The model-specific inputs for generation.
     */
    _prepare_model_inputs({ inputs, bos_token_id, model_kwargs }: {
        inputs?: Tensor;
        bos_token_id?: number;
        model_kwargs?: Record<string, Tensor | number[]>;
    }): {
        inputs_tensor: Tensor;
        model_inputs: Record<string, Tensor>;
        model_input_name: string;
    };
    _prepare_encoder_decoder_kwargs_for_generation({ inputs_tensor, model_inputs, model_input_name, generation_config }: {
        inputs_tensor: any;
        model_inputs: any;
        model_input_name: any;
        generation_config: any;
    }): Promise<any>;
    /**
     * Prepares `decoder_input_ids` for generation with encoder-decoder models
     * @param {*} param0
     */
    _prepare_decoder_input_ids_for_generation({ batch_size, model_input_name, model_kwargs, decoder_start_token_id, bos_token_id, generation_config }: any): {
        input_ids: any;
        model_inputs: any;
    };
    /**
     * Generates sequences of token ids for models with a language modeling head.
     * @param {import('./generation/parameters.js').GenerationFunctionParameters} options
     * @returns {Promise<ModelOutput|Tensor>} The output of the model, which can contain the generated token ids, attentions, and scores.
     */
    generate({ inputs, generation_config, logits_processor, stopping_criteria, streamer, ...kwargs }: any): Promise<ModelOutput | Tensor>;
    /**
     * 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.
     */
    getPastKeyValues(decoderResults: any, pastKeyValues: any, disposeEncoderPKVs?: boolean): any;
    /**
     * Returns an object containing attentions from the given model output object.
     *
     * @param {Object} model_output The output of the model.
     * @returns {{cross_attentions?: Tensor[]}} An object containing attentions.
     */
    getAttentions(model_output: any): {
        cross_attentions?: Tensor[];
    };
    /**
     * 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.
     */
    addPastKeyValues(decoderFeeds: any, pastKeyValues: any): void;
    encode_image({ pixel_values }: {
        pixel_values: any;
    }): Promise<any>;
    encode_text({ input_ids }: {
        input_ids: any;
    }): Promise<any>;
}
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.
 */
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.
 */
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.
 */
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.
 */
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 NomicBertPreTrainedModel extends PreTrainedModel {
}
export class NomicBertModel extends NomicBertPreTrainedModel {
}
export class RoFormerPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top.
 */
export class RoFormerModel extends RoFormerPreTrainedModel {
}
/**
 * RoFormer Model with a `language modeling` head on top.
 */
export class RoFormerForMaskedLM extends RoFormerPreTrainedModel {
    /**
     * 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>;
}
/**
 * RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
export class RoFormerForSequenceClassification extends RoFormerPreTrainedModel {
    /**
     * 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>;
}
/**
 * RoFormer Model with a token classification head on top (a linear layer on top of the hidden-states output)
 * e.g. for Named-Entity-Recognition (NER) tasks.
 */
export class RoFormerForTokenClassification extends RoFormerPreTrainedModel {
    /**
     * 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>;
}
/**
 * RoFormer Model with a span classification head on top for extractive question-answering tasks like SQuAD
 * (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
 */
export class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel {
    /**
     * 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 ConvBertPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare ConvBERT Model transformer outputting raw hidden-states without any specific head on top.
 */
export class ConvBertModel extends ConvBertPreTrainedModel {
}
/**
 * ConvBERT Model with a language modeling head on top.
 */
export class ConvBertForMaskedLM extends ConvBertPreTrainedModel {
    /**
     * 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>;
}
/**
 * ConvBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
export class ConvBertForSequenceClassification extends ConvBertPreTrainedModel {
    /**
     * 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>;
}
/**
 * ConvBERT Model with a token classification head on top (a linear layer on top of the hidden-states output)
 * e.g. for Named-Entity-Recognition (NER) tasks.
 */
export class ConvBertForTokenClassification extends ConvBertPreTrainedModel {
    /**
     * 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>;
}
/**
 * ConvBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD
 * (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`)
 */
export class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel {
    /**
     * 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 ElectraPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare Electra Model transformer outputting raw hidden-states without any specific head on top.
 * Identical to the BERT model except that it uses an additional linear layer between the embedding
 * layer and the encoder if the hidden size and embedding size are different.
 */
export class ElectraModel extends ElectraPreTrainedModel {
}
/**
 * Electra model with a language modeling head on top.
 */
export class ElectraForMaskedLM extends ElectraPreTrainedModel {
    /**
     * 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>;
}
/**
 * ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
export class ElectraForSequenceClassification extends ElectraPreTrainedModel {
    /**
     * 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>;
}
/**
 * Electra model with a token classification head on top.
 */
export class ElectraForTokenClassification extends ElectraPreTrainedModel {
    /**
     * 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>;
}
/**
 * LECTRA Model with a span classification head on top for extractive question-answering tasks like SQuAD
 * (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
 */
export class ElectraForQuestionAnswering extends ElectraPreTrainedModel {
    /**
     * 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 CamembertPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.
 */
export class CamembertModel extends CamembertPreTrainedModel {
}
/**
 * CamemBERT Model with a `language modeling` head on top.
 */
export class CamembertForMaskedLM extends CamembertPreTrainedModel {
    /**
     * 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>;
}
/**
 * CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
 */
export class CamembertForSequenceClassification extends CamembertPreTrainedModel {
    /**
     * 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>;
}
/**
 * CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
 */
export class CamembertForTokenClassification extends CamembertPreTrainedModel {
    /**
     * 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>;
}
/**
 * CamemBERT Model with a span classification head on top for extractive question-answering tasks
 */
export class CamembertForQuestionAnswering extends CamembertPreTrainedModel {
    /**
     * 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 DebertaPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.
 */
export class DebertaModel extends DebertaPreTrainedModel {
}
/**
 * DeBERTa Model with a `language modeling` head on top.
 */
export class DebertaForMaskedLM extends DebertaPreTrainedModel {
    /**
     * 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>;
}
/**
 * DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
export class DebertaForSequenceClassification extends DebertaPreTrainedModel {
    /**
     * 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>;
}
/**
 * DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
 */
export class DebertaForTokenClassification extends DebertaPreTrainedModel {
    /**
     * 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>;
}
/**
 * DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
 * layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
 */
export class DebertaForQuestionAnswering extends DebertaPreTrainedModel {
    /**
     * 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 DebertaV2PreTrainedModel extends PreTrainedModel {
}
/**
 * The bare DeBERTa-V2 Model transformer outputting raw hidden-states without any specific head on top.
 */
export class DebertaV2Model extends DebertaV2PreTrainedModel {
}
/**
 * DeBERTa-V2 Model with a `language modeling` head on top.
 */
export class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel {
    /**
     * 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>;
}
/**
 * DeBERTa-V2 Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
export class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel {
    /**
     * 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>;
}
/**
 * DeBERTa-V2 Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
 */
export class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel {
    /**
     * 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>;
}
/**
 * DeBERTa-V2 Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
 * layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
 */
export class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel {
    /**
     * 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.
 */
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.
 */
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.
 */
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.
 */
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 EsmPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare ESM Model transformer outputting raw hidden-states without any specific head on top.
 */
export class EsmModel extends EsmPreTrainedModel {
}
/**
 * ESM Model with a `language modeling` head on top.
 */
export class EsmForMaskedLM extends EsmPreTrainedModel {
    /**
     * 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>;
}
/**
 * ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
export class EsmForSequenceClassification extends EsmPreTrainedModel {
    /**
     * 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>;
}
/**
 * ESM Model with a token classification head on top (a linear layer on top of the hidden-states output)
 * e.g. for Named-Entity-Recognition (NER) tasks.
 */
export class EsmForTokenClassification extends EsmPreTrainedModel {
    /**
     * 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>;
}
export class MobileBertPreTrainedModel extends PreTrainedModel {
}
export class MobileBertModel extends MobileBertPreTrainedModel {
}
/**
 * MobileBertForMaskedLM is a class representing a MobileBERT model for masking task.
 */
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>;
}
/**
 * MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
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>;
}
/**
 * MobileBert Model with a span classification head on top for extractive question-answering tasks
 */
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.
 */
export class MPNetModel extends MPNetPreTrainedModel {
}
/**
 * MPNetForMaskedLM is a class representing a MPNet model for masked language modeling.
 */
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.
 */
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.
 */
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.
 */
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 {
}
/**
 * T5Model is a class representing a T5 model for conditional generation.
 */
export class T5ForConditionalGeneration extends T5PreTrainedModel {
}
/**
 * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
 */
export class LongT5PreTrainedModel extends PreTrainedModel {
}
/**
 * The bare LONGT5 Model transformer outputting raw hidden-states without any specific head on top.
 */
export class LongT5Model extends LongT5PreTrainedModel {
}
/**
 * LONGT5 Model with a `language modeling` head on top.
 */
export class LongT5ForConditionalGeneration extends LongT5PreTrainedModel {
}
export class MT5PreTrainedModel extends PreTrainedModel {
}
export class MT5Model extends MT5PreTrainedModel {
}
/**
 * A class representing a conditional sequence-to-sequence model based on the MT5 architecture.
 */
export class MT5ForConditionalGeneration extends MT5PreTrainedModel {
}
export class BartPretrainedModel extends PreTrainedModel {
}
/**
 * The bare BART Model outputting raw hidden-states without any specific head on top.
 */
export class BartModel extends BartPretrainedModel {
}
/**
 * The BART Model with a language modeling head. Can be used for summarization.
 */
export class BartForConditionalGeneration extends BartPretrainedModel {
}
/**
 * Bart model with a sequence classification/head on top (a linear layer on top of the pooled output)
 */
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 MBartPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare MBART Model outputting raw hidden-states without any specific head on top.
 */
export class MBartModel extends MBartPreTrainedModel {
}
/**
 * The MBART Model with a language modeling head. Can be used for summarization, after fine-tuning the pretrained models.
 */
export class MBartForConditionalGeneration extends MBartPreTrainedModel {
}
/**
 * MBart model with a sequence classification/head on top (a linear layer on top of the pooled output).
 */
export class MBartForSequenceClassification extends MBartPreTrainedModel {
    /**
     * 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 MBartForCausalLM extends MBartPreTrainedModel {
}
export class BlenderbotPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare Blenderbot Model outputting raw hidden-states without any specific head on top.
 */
export class BlenderbotModel extends BlenderbotPreTrainedModel {
}
/**
 * The Blenderbot Model with a language modeling head. Can be used for summarization.
 */
export class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel {
}
export class BlenderbotSmallPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare BlenderbotSmall Model outputting raw hidden-states without any specific head on top.
 */
export class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel {
}
/**
 * The BlenderbotSmall Model with a language modeling head. Can be used for summarization.
 */
export class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel {
}
export class RobertaPreTrainedModel extends PreTrainedModel {
}
export class RobertaModel extends RobertaPreTrainedModel {
}
/**
 * RobertaForMaskedLM class for performing masked language modeling on Roberta models.
 */
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.
 */
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.
 */
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.
 */
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>;
}
/**
 * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
 */
export class XLMPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare XLM Model transformer outputting raw hidden-states without any specific head on top.
 */
export class XLMModel extends XLMPreTrainedModel {
}
/**
 * The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
 */
export class XLMWithLMHeadModel extends XLMPreTrainedModel {
    /**
     * 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>;
}
/**
 * XLM Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
 */
export class XLMForSequenceClassification extends XLMPreTrainedModel {
    /**
     * 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>;
}
/**
 * XLM Model with a token classification head on top (a linear layer on top of the hidden-states output)
 */
export class XLMForTokenClassification extends XLMPreTrainedModel {
    /**
     * 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>;
}
/**
 * XLM Model with a span classification head on top for extractive question-answering tasks
 */
export class XLMForQuestionAnswering extends XLMPreTrainedModel {
    /**
     * 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.
 */
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.
 */
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.
 */
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.
 */
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 ASTPreTrainedModel extends PreTrainedModel {
}
/**
 * The bare AST Model transformer outputting raw hidden-states without any specific head on top.
 */
export class ASTModel extends ASTPreTrainedModel {
}
/**
 * Audio Spectrogram Transformer model with an audio classification head on top
 * (a linear layer on top of the pooled output) e.g. for datasets like AudioSet, Speech Commands v2.
 */
export class ASTForAudioClassification extends ASTPreTrainedModel {
}
export class WhisperPreTrainedModel extends PreTrainedModel {
    requires_attention_mask: boolean;
}
/**
 * WhisperModel class for training Whisper models without a language model head.
 */
export class WhisperModel extends WhisperPreTrainedModel {
}
/**
 * WhisperForConditionalGeneration class for generating conditional outputs from Whisper models.
 */
export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
    _prepare_generation_config(generation_config: any, kwargs: any): WhisperGenerationConfig;
    /**
     *
     * @param {WhisperGenerationConfig} generation_config
     */
    _retrieve_init_tokens(generation_config: WhisperGenerationConfig): number[];
    /**
     * 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.
     * If `num_frames` is specified, the encoder-decoder cross-attentions will be cropped before applying DTW.
     * @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.sequences The sequences output by the model
     * @param {number[][]} alignment_heads Alignment heads of the model
     * @param {number} [num_frames=null] Number of frames in the input audio.
     * @param {number} [time_precision=0.02] 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[][];
        sequences: Tensor;
    }, alignment_heads: number[][], num_frames?: number, time_precision?: number): Tensor;
}
/**
 * Vision Encoder-Decoder model based on OpenAI's GPT architecture for image captioning and other vision tasks
 */
export class VisionEncoderDecoderModel extends PreTrainedModel {
}
export class LlavaPreTrainedModel extends PreTrainedModel {
}
/**
 * The LLAVA model which consists of a vision backbone and a language model.
 */
export class LlavaForConditionalGeneration extends LlavaPreTrainedModel {
    _merge_input_ids_with_image_features({ inputs_embeds, image_features, input_ids, attention_mask, }: {
        inputs_embeds: any;
        image_features: any;
        input_ids: any;
        attention_mask: any;
    }): {
        inputs_embeds: any;
        attention_mask: any;
    };
}
export class LlavaOnevisionForConditionalGeneration extends LlavaForConditionalGeneration {
}
export class Moondream1ForConditionalGeneration extends LlavaForConditionalGeneration {
}
export class Florence2PreTrainedModel extends PreTrainedModel {
}
export class Florence2ForConditionalGeneration extends Florence2PreTrainedModel {
    _merge_input_ids_with_image_features({ inputs_embeds, image_features, input_ids, attention_mask, }: {
        inputs_embeds: any;
        image_features: any;
        input_ids: any;
        attention_mask: any;
    }): {
        inputs_embeds: Tensor;
        attention_mask: Tensor;
    };
    _prepare_inputs_embeds({ input_ids, pixel_values, inputs_embeds, atte