models.js

/**
 * @file Definitions of all models available in Transformers.js.
 * 
 * **Example:** Load and run an `AutoModel`.
 * 
 * ```javascript
 * import { AutoModel, AutoTokenizer } from '@huggingface/transformers';
 * 
 * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
 * let model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');
 * 
 * let inputs = await tokenizer('I love transformers!');
 * let { logits } = await model(inputs);
 * // Tensor {
 * //     data: Float32Array(183132) [-7.117443084716797, -7.107812881469727, -7.092104911804199, ...]
 * //     dims: (3) [1, 6, 30522],
 * //     type: "float32",
 * //     size: 183132,
 * // }
 * ```
 * 
 * We also provide other `AutoModel`s (listed below), which you can use in the same way as the Python library. For example:
 * 
 * **Example:** Load and run an `AutoModelForSeq2SeqLM`.
 * ```javascript
 * import { AutoModelForSeq2SeqLM, AutoTokenizer } from '@huggingface/transformers';
 * 
 * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/t5-small');
 * let model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small');
 * 
 * let { input_ids } = await tokenizer('translate English to German: I love transformers!');
 * let outputs = await model.generate(input_ids);
 * let decoded = tokenizer.decode(outputs[0], { skip_special_tokens: true });
 * // 'Ich liebe Transformatoren!'
 * ```
 * 
 * @module models
 */

import {
    AutoConfig,
    getKeyValueShapes,
} from './configs.js';

import {
    deviceToExecutionProviders,
    createInferenceSession,
    isONNXTensor,
    isONNXProxy,
} from './backends/onnx.js';
import {
    DATA_TYPES,
    DEFAULT_DEVICE_DTYPE_MAPPING,
    DEFAULT_DTYPE_SUFFIX_MAPPING,
    isWebGpuFp16Supported,
} from './utils/dtypes.js';

import {
    Callable,
} from './utils/generic.js';

import {
    mergeArrays,
    pick,
} from './utils/core.js';

import {
    getModelFile,
    getModelJSON,
} from './utils/hub.js';

import {
    GITHUB_ISSUE_URL,
} from './utils/constants.js';

import {
    LogitsProcessorList,
    ForcedBOSTokenLogitsProcessor,
    ForcedEOSTokenLogitsProcessor,
    SuppressTokensAtBeginLogitsProcessor,
    WhisperTimeStampLogitsProcessor,
    NoRepeatNGramLogitsProcessor,
    RepetitionPenaltyLogitsProcessor,
    NoBadWordsLogitsProcessor,
    MinLengthLogitsProcessor,
    MinNewTokensLengthLogitsProcessor,

    TemperatureLogitsWarper,
    TopKLogitsWarper,
    TopPLogitsWarper,
    ClassifierFreeGuidanceLogitsProcessor,
} from './generation/logits_process.js';

import {
    GenerationConfig,
} from './generation/configuration_utils.js';

import {
    cat,
    mean,
    zeros,
    zeros_like,
    ones,
    ones_like,
    full,
    full_like,
    stack,
    std_mean,
    Tensor,
} from './utils/tensor.js';
import { RawImage } from './utils/image.js';

import { dynamic_time_warping, max, medianFilter } from './utils/maths.js';
import { EosTokenCriteria, MaxLengthCriteria, StoppingCriteriaList } from './generation/stopping_criteria.js';
import { LogitsSampler } from './generation/logits_sampler.js';
import { apis } from './env.js';

import { WhisperGenerationConfig } from './models/whisper/generation_whisper.js';
import { whisper_language_to_code } from './models/whisper/common_whisper.js';

//////////////////////////////////////////////////
// Model types: used internally
const MODEL_TYPES = {
    EncoderOnly: 0,
    EncoderDecoder: 1,
    Seq2Seq: 2,
    Vision2Seq: 3,
    DecoderOnly: 4,
    MaskGeneration: 5,
    ImageTextToText: 6,
    Musicgen: 7,
    MultiModality: 8,
    Phi3V: 9,
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Helper functions

// NOTE: These will be populated fully later
const MODEL_TYPE_MAPPING = new Map();
const MODEL_NAME_TO_CLASS_MAPPING = new Map();
const MODEL_CLASS_TO_NAME_MAPPING = new Map();


/**
 * Constructs an InferenceSession using a model file located at the specified path.
 * @param {string} pretrained_model_name_or_path The path to the directory containing the model file.
 * @param {string} fileName The name of the model file.
 * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model.
 * @returns {Promise<{buffer: Uint8Array, session_options: Object, session_config: Object}>} A Promise that resolves to the data needed to create an InferenceSession object.
 * @private
 */
async function getSession(pretrained_model_name_or_path, fileName, options) {
    const custom_config = options.config?.['transformers.js_config'] ?? {};
    let device = options.device ?? custom_config.device;
    if (device && typeof device !== 'string') {
        if (device.hasOwnProperty(fileName)) {
            device = device[fileName];
        } else {
            console.warn(`device not specified for "${fileName}". Using the default device.`);
            device = null;
        }
    }

    // If the device is not specified, we use the default (supported) execution providers.
    const selectedDevice = /** @type {import("./utils/devices.js").DeviceType} */(
        device ?? (apis.IS_NODE_ENV ? 'cpu' : 'wasm')
    );
    const executionProviders = deviceToExecutionProviders(selectedDevice);

    // If options.dtype is specified, we use it to choose the suffix for the model file.
    // Otherwise, we use the default dtype for the device.
    let dtype = options.dtype ?? custom_config.dtype;
    if (typeof dtype !== 'string') {
        if (dtype && dtype.hasOwnProperty(fileName)) {
            dtype = dtype[fileName];
        } else {
            dtype = DEFAULT_DEVICE_DTYPE_MAPPING[selectedDevice] ?? DATA_TYPES.fp32;
            console.warn(`dtype not specified for "${fileName}". Using the default dtype (${dtype}) for this device (${selectedDevice}).`);
        }
    }

    if (dtype === DATA_TYPES.auto) {
        // Try to choose the auto dtype based on the custom config
        let config_dtype = custom_config.dtype;
        if (typeof config_dtype !== 'string') {
            config_dtype = config_dtype[fileName];
        }

        if (config_dtype && config_dtype !== DATA_TYPES.auto && DATA_TYPES.hasOwnProperty(config_dtype)) {
            // Defined by the custom config, and is not "auto"
            dtype = config_dtype;
        } else {
            // Choose default dtype based on device, falling back to fp32
            dtype = DEFAULT_DEVICE_DTYPE_MAPPING[selectedDevice] ?? DATA_TYPES.fp32;
        }
    }

    const selectedDtype = /** @type {import("./utils/dtypes.js").DataType} */(dtype);

    if (!DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(selectedDtype)) {
        throw new Error(`Invalid dtype: ${selectedDtype}. Should be one of: ${Object.keys(DATA_TYPES).join(', ')}`);
    } else if (selectedDtype === DATA_TYPES.fp16 && selectedDevice === 'webgpu' && !(await isWebGpuFp16Supported())) {
        throw new Error(`The device (${selectedDevice}) does not support fp16.`);
    }

    // Only valid for models with a decoder
    const kv_cache_dtype = custom_config.kv_cache_dtype
        ? (typeof custom_config.kv_cache_dtype === 'string'
            ? custom_config.kv_cache_dtype
            : custom_config.kv_cache_dtype[selectedDtype] ?? 'float32')
        : undefined;

    if (kv_cache_dtype && !['float32', 'float16'].includes(kv_cache_dtype)) {
        throw new Error(`Invalid kv_cache_dtype: ${kv_cache_dtype}. Should be one of: float32, float16`);
    }

    const session_config = {
        dtype: selectedDtype,
        kv_cache_dtype,
    }

    // Construct the model file name
    const suffix = DEFAULT_DTYPE_SUFFIX_MAPPING[selectedDtype];
    const modelFileName = `${options.subfolder ?? ''}/${fileName}${suffix}.onnx`;

    const session_options = { ...options.session_options };

    // Overwrite `executionProviders` if not specified
    session_options.executionProviders ??= executionProviders;

    // Overwrite `freeDimensionOverrides` if specified in config and not set in session options
    const free_dimension_overrides = custom_config.free_dimension_overrides;
    if (free_dimension_overrides) {
        session_options.freeDimensionOverrides ??= free_dimension_overrides;
    } else if (selectedDevice.startsWith('webnn') && !session_options.freeDimensionOverrides) {
        console.warn(
            'WebNN does not currently support dynamic shapes and requires `free_dimension_overrides` to be set in config.json as a field within "transformers.js_config". ' +
            'When `free_dimension_overrides` is not set, you may experience significant performance degradation.'
        );
    }

    const bufferPromise = getModelFile(pretrained_model_name_or_path, modelFileName, true, options);

    // handle onnx external data files
    const use_external_data_format = options.use_external_data_format ?? custom_config.use_external_data_format;
    /** @type {Promise<{path: string, data: Uint8Array}>[]} */
    let externalDataPromises = [];
    if (use_external_data_format && (
        use_external_data_format === true ||
        (
            typeof use_external_data_format === 'object' &&
            use_external_data_format.hasOwnProperty(fileName) &&
            use_external_data_format[fileName] === true
        )
    )) {
        if (apis.IS_NODE_ENV) {
            throw new Error('External data format is not yet supported in Node.js');
        }
        const path = `${fileName}${suffix}.onnx_data`;
        const fullPath = `${options.subfolder ?? ''}/${path}`;
        externalDataPromises.push(new Promise(async (resolve, reject) => {
            const data = await getModelFile(pretrained_model_name_or_path, fullPath, true, options);
            resolve({ path, data })
        }));

    } else if (session_options.externalData !== undefined) {
        externalDataPromises = session_options.externalData.map(async (ext) => {
            // if the external data is a string, fetch the file and replace the string with its content
            if (typeof ext.data === "string") {
                const ext_buffer = await getModelFile(pretrained_model_name_or_path, ext.data, true, options);
                return { ...ext, data: ext_buffer };
            }
            return ext;
        });
    }

    if (externalDataPromises.length > 0) {
        session_options.externalData = await Promise.all(externalDataPromises);
    }

    if (selectedDevice === 'webgpu') {
        const shapes = getKeyValueShapes(options.config, {
            prefix: 'present',
        });
        if (Object.keys(shapes).length > 0 && !isONNXProxy()) {
            // Only set preferredOutputLocation if shapes are present and we aren't proxying ONNX
            /** @type {Record<string, import('onnxruntime-common').Tensor.DataLocation>} */
            const preferredOutputLocation = {};
            for (const key in shapes) {
                preferredOutputLocation[key] = 'gpu-buffer';
            }
            session_options.preferredOutputLocation = preferredOutputLocation;
        }
    }

    const buffer = await bufferPromise;

    return { buffer, session_options, session_config };
}

/**
 * Helper function to create multiple InferenceSession objects.
 * 
 * @param {string} pretrained_model_name_or_path The path to the directory containing the model file.
 * @param {Record<string, string>} names The names of the model files to load.
 * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model.
 * @returns {Promise<Record<string, any>>} A Promise that resolves to a dictionary of InferenceSession objects.
 * @private
 */
async function constructSessions(pretrained_model_name_or_path, names, options) {
    return Object.fromEntries(await Promise.all(
        Object.keys(names).map(async (name) => {
            const { buffer, session_options, session_config } = await getSession(pretrained_model_name_or_path, names[name], options);
            const session = await createInferenceSession(buffer, session_options, session_config);
            return [name, session];
        })
    ));
}

/**
 * Helper function to load multiple optional configuration files
 * @param {string} pretrained_model_name_or_path The path to the directory containing the config file.
 * @param {Record<string, string>} names The names of the config files to load.
 * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the configs.
 * @returns {Promise<Record<string, any>>} A Promise that resolves to a dictionary of configuration objects.
 * @private
 */
async function getOptionalConfigs(pretrained_model_name_or_path, names, options) {
    return Object.fromEntries(await Promise.all(
        Object.keys(names).map(async (name) => {
            const config = await getModelJSON(pretrained_model_name_or_path, names[name], false, options);
            return [name, config];
        })
    ));
}

/**
 * Validate model inputs
 * @param {Object} session The InferenceSession object that will be run.
 * @param {Object} inputs The inputs to check.
 * @returns {Record<string, Tensor>} The checked inputs.
 * @throws {Error} If any inputs are missing.
 * @private
 */
function validateInputs(session, inputs) {
    /**
     * NOTE: Create either a shallow or deep copy based on `onnx.wasm.proxy`
     * @type {Record<string, Tensor>}
     */
    const checkedInputs = Object.create(null);
    const missingInputs = [];
    for (const inputName of session.inputNames) {
        const tensor = inputs[inputName];
        // Rare case where one of the model's input names corresponds to a built-in
        // object name (e.g., toString), which would cause a simple (!tensor) check to fail,
        // because it's not undefined but a function.
        if (!(tensor instanceof Tensor)) {
            missingInputs.push(inputName);
            continue;
        }
        // NOTE: When `env.wasm.proxy is true` the tensor is moved across the Worker
        // boundary, transferring ownership to the worker and invalidating the tensor.
        // So, in this case, we simply sacrifice a clone for it.
        checkedInputs[inputName] = isONNXProxy() ? tensor.clone() : tensor;
    }
    if (missingInputs.length > 0) {
        throw new Error(
            `An error occurred during model execution: "Missing the following inputs: ${missingInputs.join(', ')}.`);
    }

    const numInputsProvided = Object.keys(inputs).length;
    const numInputsNeeded = session.inputNames.length;
    if (numInputsProvided > numInputsNeeded) {
        // No missing inputs, but too many inputs were provided.
        // Warn the user and ignore the extra inputs.
        let ignored = Object.keys(inputs).filter(inputName => !session.inputNames.includes(inputName));
        console.warn(`WARNING: Too many inputs were provided (${numInputsProvided} > ${numInputsNeeded}). The following inputs will be ignored: "${ignored.join(', ')}".`);
    }

    return checkedInputs;
}

/**
 * Executes an InferenceSession using the specified inputs.
 * NOTE: `inputs` must contain at least the input names of the model.
 *  - If additional inputs are passed, they will be ignored.
 *  - If inputs are missing, an error will be thrown.
 * 
 * @param {Object} session The InferenceSession object to run.
 * @param {Object} inputs An object that maps input names to input tensors.
 * @returns {Promise<Object>} A Promise that resolves to an object that maps output names to output tensors.
 * @private
 */
async function sessionRun(session, inputs) {
    const checkedInputs = validateInputs(session, inputs);
    try {
        // pass the original ort tensor
        const ortFeed = Object.fromEntries(Object.entries(checkedInputs).map(([k, v]) => [k, v.ort_tensor]));
        let output = await session.run(ortFeed);
        output = replaceTensors(output);
        return output;
    } catch (e) {
        // Error messages can be long (nested) and uninformative. For this reason,
        // we apply minor formatting to show the most important information
        const formatted = Object.fromEntries(Object.entries(checkedInputs)
            .map(([k, { type, dims, data }]) => [k, {
                // Extract these properties from the underlying ORT tensor
                type, dims, data,
            }]));

        // This usually occurs when the inputs are of the wrong type.
        console.error(`An error occurred during model execution: "${e}".`);
        console.error('Inputs given to model:', formatted);
        throw e;
    }
}

/**
 * Replaces ONNX Tensor objects with custom Tensor objects to support additional functions.
 * @param {Object} obj The object to replace tensor objects in.
 * @returns {Object} The object with tensor objects replaced by custom Tensor objects.
 * @private
 */
function replaceTensors(obj) {
    for (let prop in obj) {
        if (isONNXTensor(obj[prop])) {
            obj[prop] = new Tensor(obj[prop]);
        } else if (typeof obj[prop] === 'object') {
            replaceTensors(obj[prop]);
        }
    }
    return obj;
}


/**
 * Converts an array or Tensor of integers to an int64 Tensor.
 * @param {any[]|Tensor} items The input integers to be converted.
 * @returns {Tensor} The int64 Tensor with the converted values.
 * @throws {Error} If the input array is empty or the input is a batched Tensor and not all sequences have the same length.
 * @private
 */
function toI64Tensor(items) {
    if (items instanceof Tensor) {
        return items;
    }
    // items is an array
    if (items.length === 0) {
        throw Error("items must be non-empty");
    }

    if (Array.isArray(items[0])) {
        // batched
        if (items.some(x => x.length !== items[0].length)) {
            throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.")
        }

        return new Tensor('int64',
            BigInt64Array.from(items.flat().map(x => BigInt(x))),
            [items.length, items[0].length]
        );
    } else {
        //flat
        return new Tensor('int64',
            BigInt64Array.from(items.map(x => BigInt(x))),
            [1, items.length]
        );
    }
}

/**
 * Creates a boolean tensor with a single value.
 * @param {boolean} value The value of the tensor.
 * @returns {Tensor} The boolean tensor.
 * @private
 */
function boolTensor(value) {
    return new Tensor('bool', [value], [1]);
}

// JS doesn't support mixins, so we define some reused functions here, and allow "this" to be passed in
/**
 * Perform forward pass on the seq2seq model (both encoder and decoder).
 * @param {Object} self The seq2seq model object.
 * @param {Object} model_inputs The input object for the model containing encoder and decoder inputs.
 * @returns {Promise<Seq2SeqLMOutput>} Promise that resolves with the output of the seq2seq model.
 * @private
 */
async function seq2seqForward(self, model_inputs) {
    let { encoder_outputs, input_ids, decoder_input_ids, ...other_decoder_inputs } = model_inputs;
    // Encode if needed
    if (!encoder_outputs) {
        const encoder_inputs = pick(model_inputs, self.sessions['model'].inputNames);
        // Encoder outputs are not given, so we must compute them.
        encoder_outputs = (await encoderForward(self, encoder_inputs)).last_hidden_state;
    }

    other_decoder_inputs.input_ids = decoder_input_ids;
    other_decoder_inputs.encoder_hidden_states = encoder_outputs;

    if (self.sessions['decoder_model_merged'].inputNames.includes('encoder_attention_mask')) {
        other_decoder_inputs.encoder_attention_mask = model_inputs.attention_mask
    }

    const decoderResults = await decoderForward(self, other_decoder_inputs, true);

    return decoderResults;
}

/**
 * Forward pass of an encoder model.
 * @param {Object} self The encoder model.
 * @param {Object} model_inputs The input data to be used for the forward pass.
 * @returns {Promise<Object>} The model's outputs.
 * @private
 */
async function encoderForward(self, model_inputs) {
    const session = self.sessions['model'];
    const encoderFeeds = pick(model_inputs, session.inputNames);

    if (session.inputNames.includes('inputs_embeds') && !encoderFeeds.inputs_embeds) {
        if (!model_inputs.input_ids) {
            throw new Error('Both `input_ids` and `inputs_embeds` are missing in the model inputs.');
        }
        encoderFeeds.inputs_embeds = await self.encode_text({ input_ids: model_inputs.input_ids });
    }
    if (session.inputNames.includes('token_type_ids') && !encoderFeeds.token_type_ids) {
        // Assign default `token_type_ids` (all zeroes) to the `encoderFeeds` if the model expects it,
        // but they weren't created by the tokenizer.
        encoderFeeds.token_type_ids = new Tensor(
            'int64',
            new BigInt64Array(encoderFeeds.input_ids.data.length),
            encoderFeeds.input_ids.dims
        )
    }
    return await sessionRun(session, encoderFeeds);
}

/**
 * Forward pass of a decoder model.
 * @param {Object} self The decoder model.
 * @param {Object} model_inputs The input data to be used for the forward pass.
 * @returns {Promise<Object>} The logits and past key values.
 * @private
 */
async function decoderForward(self, model_inputs, is_encoder_decoder = false) {

    const session = self.sessions[
        is_encoder_decoder ? 'decoder_model_merged' : 'model'
    ]

    const { past_key_values, ...new_model_inputs } = model_inputs;

    if (session.inputNames.includes('use_cache_branch')) {
        new_model_inputs.use_cache_branch = boolTensor(!!past_key_values);
    }
    if (session.inputNames.includes('position_ids') && new_model_inputs.attention_mask && !new_model_inputs.position_ids) {
        // NOTE: Handle a special case for paligemma models, where positions are 1-indexed
        const start_index = self.config.model_type === 'paligemma' ? 1 : 0;
        new_model_inputs.position_ids = createPositionIds(new_model_inputs, past_key_values, start_index);
    }

    // Unpack the `past_key_values` object into model inputs
    self.addPastKeyValues(new_model_inputs, past_key_values);

    // Select only the inputs that are needed for the current session
    const fixed = pick(new_model_inputs, session.inputNames);
    return await sessionRun(session, fixed);
}



function default_merge_input_ids_with_image_features({
    image_token_id,
    inputs_embeds,
    image_features,
    input_ids,
    attention_mask,
}) {
    const image_tokens = input_ids.tolist().map(ids =>
        ids.reduce((acc, x, idx) => {
            if (x == image_token_id) acc.push(idx);
            return acc;
        }, [])
    );
    const n_image_tokens = image_tokens.reduce((acc, x) => acc + x.length, 0);
    const n_image_features = image_features.dims[0];
    if (n_image_tokens !== n_image_features) {
        throw new Error(`Image features and image tokens do not match: tokens: ${n_image_tokens}, features ${n_image_features}`);
    }

    // Equivalent to performing a masked_scatter
    let img = 0;
    for (let i = 0; i < image_tokens.length; ++i) {
        const tokens = image_tokens[i];
        const embeds = inputs_embeds[i];
        for (let j = 0; j < tokens.length; ++j) {
            embeds[tokens[j]].data.set(image_features[img++].data)
        }
    }
    return { inputs_embeds, attention_mask }
}


/**
 * Forward pass of an image-text-to-text model.
 * @param {Object} self The image-text-to-text model model.
 * @param {Object} model_inputs The input data to be used for the forward pass.
 * @param {Tensor} [model_inputs.input_ids=null]
 * @param {Tensor} [model_inputs.attention_mask=null]
 * @param {Tensor} [model_inputs.pixel_values=null]
 * @param {Tensor} [model_inputs.position_ids=null]
 * @param {Tensor} [model_inputs.inputs_embeds=null]
 * @param {Tensor} [model_inputs.past_key_values=null]
 * @param {Object} [model_inputs.generation_config=null]
 * @param {Object} [model_inputs.logits_processor=null]
 * @returns {Promise<Tensor>} The model's output tensor
 * @private
 */
async function imageTextToTextForward(self, {
    // Produced by the tokenizer/processor:
    input_ids = null,
    attention_mask = null,
    pixel_values = null,

    // Used during generation:
    position_ids = null,
    inputs_embeds = null,
    past_key_values = null,

    // Generic generation parameters
    generation_config = null,
    logits_processor = null,

    // TODO: needed?
    ...kwargs
}) {

    if (!inputs_embeds) {
        // 1. Extract the input embeddings
        inputs_embeds = await self.encode_text({ input_ids, ...kwargs });

        // 2. Possibly, merge text and images
        if (pixel_values && input_ids.dims[1] !== 1) {
            const image_features = await self.encode_image({ pixel_values, ...kwargs });

            ({ inputs_embeds, attention_mask } = self._merge_input_ids_with_image_features({
                image_features,
                inputs_embeds,
                input_ids,
                attention_mask,
            }));

        } else if (past_key_values && pixel_values && input_ids.dims[1] === 1) {
            // This is the case when we are generating with cache
            const target_length = input_ids.dims[1]; // always 1
            const past_length = Object.values(past_key_values)[0].dims.at(-2);

            attention_mask = cat([
                ones([input_ids.dims[0], past_length]),
                attention_mask.slice(null, [attention_mask.dims[1] - target_length, attention_mask.dims[1]]),
            ], 1);
        }
    }

    if (!position_ids) {

        if (self.config.model_type === 'qwen2_vl') {
            // Special case for qwen2_vl models
            // @ts-ignore
            const { image_grid_thw, video_grid_thw } = kwargs;
            [position_ids] = self.get_rope_index(input_ids, image_grid_thw, video_grid_thw, attention_mask)
        }
    }

    const outputs = await decoderForward(self, {
        inputs_embeds,
        past_key_values,
        attention_mask,
        position_ids,
        generation_config,
        logits_processor,
    }, true);
    return outputs;
}

/**
 * Helper function to perform the following:
 * ```python
 * x = attention_mask.long().cumsum(-1) - 1
 * x.masked_fill_(attention_mask == 0, 1)
 * ```
 * @param {Tensor} attention_mask
 * @returns {{data: BigInt64Array, dims: number[]}}
 */
function cumsum_masked_fill(attention_mask, start_index = 0) {
    const [bz, seq_len] = attention_mask.dims;
    const attn_mask_data = attention_mask.data;

    const data = new BigInt64Array(attn_mask_data.length);
    for (let i = 0; i < bz; ++i) {
        const start = i * seq_len;
        let sum = BigInt(start_index);
        for (let j = 0; j < seq_len; ++j) {
            const index = start + j;
            if (attn_mask_data[index] === 0n) {
                data[index] = BigInt(1);
            } else { // === 1n
                data[index] = sum;
                sum += attn_mask_data[index];
            }
        }
    }
    return { data, dims: attention_mask.dims };

}

/**
 * If the model supports providing position_ids, we create position_ids on the fly for batch generation,
 * by computing the cumulative sum of the attention mask along the sequence length dimension.
 * 
 * Equivalent to:
 * ```python
 * position_ids = attention_mask.long().cumsum(-1) - 1
 * position_ids.masked_fill_(attention_mask == 0, 1)
 * if past_key_values:
 *     position_ids = position_ids[:, -input_ids.shape[1] :]
 * ```
 */
function createPositionIds(model_inputs, past_key_values = null, start_index = 0) {
    const { input_ids, inputs_embeds, attention_mask } = model_inputs;

    const { data, dims } = cumsum_masked_fill(attention_mask, start_index);
    let position_ids = new Tensor('int64', data, dims);
    if (past_key_values) {
        const offset = -(input_ids ?? inputs_embeds).dims.at(1);
        position_ids = position_ids.slice(null, [offset, null]);
    }
    return position_ids;
}

function decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) {
    if (model_inputs.past_key_values) {
        const past_length = Object.values(model_inputs.past_key_values)[0].dims.at(-2);
        const { input_ids, attention_mask } = model_inputs;

        // Keep only the unprocessed tokens:
        // 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
        // some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
        // input)
        if (attention_mask && attention_mask.dims[1] > input_ids.dims[1]) {
            // NOTE: not needed since we only pass the generated tokens to the next forward pass
            // const offset = -(attention_mask.dims[1] - past_length);
            // model_inputs.input_ids = input_ids.slice(null, [offset, null]);
        }
        // 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens.
        // We can discard input_ids based on the past_length.
        else if (past_length < input_ids.dims[1]) {
            // NOTE: Required for phi models.
            // See https://github.com/huggingface/transformers/issues/30809#issuecomment-2111918479 for more information.
            model_inputs.input_ids = input_ids.slice(null, [past_length, null]);
        }
        // 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
        else {
            if (
                // NOTE: Only used by VLMs (!= so that null matches undefined)
                self.config.image_token_index != null &&
                // Equivalent to `self.config.image_token_index in input_ids` (== so that int matches bigint)
                input_ids.data.some(x => x == self.config.image_token_index)
            ) {
                // TODO: Support multiple image tokens
                const num_image_tokens = self.config.num_image_tokens;
                if (!num_image_tokens) {
                    throw new Error('`num_image_tokens` is missing in the model configuration.');
                }

                const num_new_tokens = input_ids.dims[1] - (past_length - num_image_tokens);
                model_inputs.input_ids = input_ids.slice(null, [-num_new_tokens, null]);

                // TODO: The attention mask should be formed from the attention mask passed in model_inputs
                model_inputs.attention_mask = ones([1, past_length + num_new_tokens]);
            }
        }
    }

    return model_inputs;
}

function encoder_decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) {
    if (model_inputs.past_key_values) {
        input_ids = input_ids.map(x => [x.at(-1)]);
    }

    return {
        ...model_inputs,
        decoder_input_ids: toI64Tensor(input_ids),
    };
}

function image_text_to_text_prepare_inputs_for_generation(self, ...args) {
    if (self.config.is_encoder_decoder) {
        return encoder_decoder_prepare_inputs_for_generation(self, ...args);
    } else {
        return decoder_prepare_inputs_for_generation(self, ...args);
    }
}

function multimodality_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) {
    const has_past_key_values = !!model_inputs.past_key_values;

    if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) {
        if (has_past_key_values) {
            model_inputs.input_ids = cat([
                model_inputs.input_ids,
                model_inputs.input_ids,
            ], 0)
            // NOTE: attention_mask handled in generation
        } else {
            model_inputs.input_ids = cat([
                model_inputs.input_ids,
                full_like(model_inputs.input_ids, BigInt(generation_config.pad_token_id)),
            ], 0);
            model_inputs.attention_mask = cat([
                model_inputs.attention_mask,
                full_like(model_inputs.attention_mask, 0n),
            ], 0);
        }
    }

    if (has_past_key_values || !model_inputs.pixel_values) {
        model_inputs.pixel_values = full([0, 0, 3, 384, 384], 1.0);
    }

    if (has_past_key_values) {
        const num_img_tokens = 0;
        const num_text_tokens = 1;
        const has_image = num_img_tokens > 0 ? 1 : 0;

        const batch_size = 1;
        model_inputs.images_seq_mask = new Tensor(
            'bool',
            new Array(num_img_tokens + num_text_tokens).fill(true).fill(false, 0, num_text_tokens),
            [batch_size, num_img_tokens + num_text_tokens],
        );
        model_inputs.images_emb_mask = new Tensor(
            'bool',
            new Array(num_img_tokens).fill(!!has_image),
            [batch_size, 1, num_img_tokens],
        );
    }
    return model_inputs;
}

//////////////////////////////////////////////////

//////////////////////////////////////////////////
/**
 * A base class for pre-trained models that provides the model configuration and an ONNX session.
 */
export class PreTrainedModel extends Callable {
    main_input_name = 'input_ids';
    forward_params = ['input_ids', 'attention_mask'];
    /**
     * 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, sessions, configs) {
        super();

        this.config = config;
        this.sessions = sessions;
        this.configs = configs;

        const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor);
        const modelType = MODEL_TYPE_MAPPING.get(modelName);

        this.can_generate = false;
        this._forward = null;

        this._prepare_inputs_for_generation = null;
        switch (modelType) {
            case MODEL_TYPES.DecoderOnly:
                this.can_generate = true;
                this._forward = decoderForward;
                this._prepare_inputs_for_generation = decoder_prepare_inputs_for_generation;
                break;
            case MODEL_TYPES.Seq2Seq:
            case MODEL_TYPES.Vision2Seq:
            case MODEL_TYPES.Musicgen:
                this.can_generate = true;

                this._forward = seq2seqForward;
                this._prepare_inputs_for_generation = encoder_decoder_prepare_inputs_for_generation;
                break;

            case MODEL_TYPES.EncoderDecoder:
                this._forward = seq2seqForward;
                break;
            case MODEL_TYPES.ImageTextToText:
                this.can_generate = true;
                this._forward = imageTextToTextForward;
                this._prepare_inputs_for_generation = image_text_to_text_prepare_inputs_for_generation;
                break;
            case MODEL_TYPES.Phi3V:
                this.can_generate = true;
                this._prepare_inputs_for_generation = image_text_to_text_prepare_inputs_for_generation;
                break;

            case MODEL_TYPES.MultiModality:
                this.can_generate = true;
                this._prepare_inputs_for_generation = multimodality_prepare_inputs_for_generation;
                break;

            default:
                // should be MODEL_TYPES.EncoderOnly
                this._forward = encoderForward;
                break;
        }

        if (this.can_generate) {
            this.forward_params.push('past_key_values');
        }

        /** @type {import('./configs.js').TransformersJSConfig} */
        this.custom_config = this.config['transformers.js_config'] ?? {};
    }

    /**
    * 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
    */
    async dispose() {
        const promises = [];
        for (const session of Object.values(this.sessions)) {
            if (session?.handler?.dispose) {
                promises.push(session.handler.dispose())
            }
        }
        return await Promise.all(promises);
    }

    /**
     * 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 async from_pretrained(pretrained_model_name_or_path, {
        progress_callback = null,
        config = null,
        cache_dir = null,
        local_files_only = false,
        revision = 'main',
        model_file_name = null,
        subfolder = 'onnx',
        device = null,
        dtype = null,
        use_external_data_format = null,
        session_options = {},
    } = {}) {

        let options = {
            progress_callback,
            config,
            cache_dir,
            local_files_only,
            revision,
            model_file_name,
            subfolder,
            device,
            dtype,
            use_external_data_format,
            session_options,
        }

        const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this);
        const modelType = MODEL_TYPE_MAPPING.get(modelName);

        config = options.config = await AutoConfig.from_pretrained(pretrained_model_name_or_path, options);

        let info;
        if (modelType === MODEL_TYPES.DecoderOnly) {
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    model: options.model_file_name ?? 'model',
                }, options),
                getOptionalConfigs(pretrained_model_name_or_path, {
                    generation_config: 'generation_config.json',
                }, options),
            ]);

        } else if (modelType === MODEL_TYPES.Seq2Seq || modelType === MODEL_TYPES.Vision2Seq) {
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    model: 'encoder_model',
                    decoder_model_merged: 'decoder_model_merged',
                }, options),
                getOptionalConfigs(pretrained_model_name_or_path, {
                    generation_config: 'generation_config.json',
                }, options),
            ]);

        } else if (modelType === MODEL_TYPES.MaskGeneration) {
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    model: 'vision_encoder',
                    prompt_encoder_mask_decoder: 'prompt_encoder_mask_decoder',
                }, options),
            ]);

        } else if (modelType === MODEL_TYPES.EncoderDecoder) {
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    model: 'encoder_model',
                    decoder_model_merged: 'decoder_model_merged',
                }, options),
            ]);

        } else if (modelType === MODEL_TYPES.ImageTextToText) {
            const sessions = {
                embed_tokens: 'embed_tokens',
                vision_encoder: 'vision_encoder',
                decoder_model_merged: 'decoder_model_merged',
            }
            if (config.is_encoder_decoder) {
                sessions['model'] = 'encoder_model';
            }
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, sessions, options),
                getOptionalConfigs(pretrained_model_name_or_path, {
                    generation_config: 'generation_config.json',
                }, options),
            ]);

        } else if (modelType === MODEL_TYPES.Musicgen) {
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    model: 'text_encoder',
                    decoder_model_merged: 'decoder_model_merged',
                    encodec_decode: 'encodec_decode',
                }, options),
                getOptionalConfigs(pretrained_model_name_or_path, {
                    generation_config: 'generation_config.json',
                }, options),
            ]);

        } else if (modelType === MODEL_TYPES.MultiModality) {
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    prepare_inputs_embeds: 'prepare_inputs_embeds',
                    model: 'language_model',
                    lm_head: 'lm_head',
                    gen_head: 'gen_head',
                    gen_img_embeds: 'gen_img_embeds',
                    image_decode: 'image_decode',
                }, options),
                getOptionalConfigs(pretrained_model_name_or_path, {
                    generation_config: 'generation_config.json',
                }, options),
            ]);

        } else if (modelType === MODEL_TYPES.Phi3V) {
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    prepare_inputs_embeds: 'prepare_inputs_embeds',
                    model: 'model',
                    vision_encoder: 'vision_encoder',
                }, options),
                getOptionalConfigs(pretrained_model_name_or_path, {
                    generation_config: 'generation_config.json',
                }, options),
            ]);

        } else { // should be MODEL_TYPES.EncoderOnly
            if (modelType !== MODEL_TYPES.EncoderOnly) {
                const type = modelName ?? config?.model_type;
                if (type !== 'custom') {
                    console.warn(`Model type for '${type}' not found, assuming encoder-only architecture. Please report this at ${GITHUB_ISSUE_URL}.`)
                }
            }
            info = await Promise.all([
                constructSessions(pretrained_model_name_or_path, {
                    model: options.model_file_name ?? 'model',
                }, options),
            ]);
        }

        // @ts-ignore
        return new this(config, ...info);
    }

    /**
     * Runs the model with the provided inputs
     * @param {Object} model_inputs Object containing input tensors
     * @returns {Promise<Object>} Object containing output tensors
     */
    async _call(model_inputs) {
        return await this.forward(model_inputs);
    }

    /**
     * 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.
     */
    async forward(model_inputs) {
        return await this._forward(this, model_inputs);
    }

    /**
     * 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() {
        return this.configs?.generation_config ?? 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) {

        // instantiate warpers list
        const warpers = new LogitsProcessorList();

        if (generation_config.temperature !== null && generation_config.temperature !== 1.0) {
            warpers.push(new TemperatureLogitsWarper(generation_config.temperature));
        }
        if (generation_config.top_k !== null && generation_config.top_k !== 0) {
            // TODO: add min_tokens_to_keep
            warpers.push(new TopKLogitsWarper(generation_config.top_k));
        }
        if (generation_config.top_p !== null && generation_config.top_p < 1.0) {
            // TODO: add min_tokens_to_keep
            warpers.push(new TopPLogitsWarper(generation_config.top_p));
        }

        return warpers;
    }

    /**
     * @param {GenerationConfig} generation_config 
     * @param {number} input_ids_seq_length The starting sequence length for the input ids.
     * @returns {LogitsProcessorList}
     * @private
     */
    _get_logits_processor(
        generation_config,
        input_ids_seq_length,
        // encoder_input_ids, TODO
        // prefix_allowed_tokens_fn, TODO
        logits_processor = null
    ) {
        const processors = new LogitsProcessorList();

        // if (generation_config.diversity_penalty !== null && generation_config.diversity_penalty > 0.0) {
        //     processors.push(new HammingDiversityLogitsProcessor(
        //         generation_config.diversity_penalty,
        //         generation_config.num_beams,
        //         generation_config.num_beam_groups
        //     ));
        // }

        // if (generation_config.encoder_repetition_penalty !== null && generation_config.encoder_repetition_penalty !== 1.0) {
        //     processors.push(new EncoderRepetitionPenaltyLogitsProcessor(
        //         generation_config.encoder_repetition_penalty,
        //         encoder_input_ids
        //     ));
        // }

        if (generation_config.repetition_penalty !== null && generation_config.repetition_penalty !== 1.0) {
            processors.push(new RepetitionPenaltyLogitsProcessor(generation_config.repetition_penalty));
        }

        if (generation_config.no_repeat_ngram_size !== null && generation_config.no_repeat_ngram_size > 0) {
            processors.push(new NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size));
        }

        // if (generation_config.encoder_no_repeat_ngram_size !== null && generation_config.encoder_no_repeat_ngram_size > 0) {
        //     if (this.config.is_encoder_decoder) {
        //         processors.push(new EncoderNoRepeatNGramLogitsProcessor(
        //             generation_config.encoder_no_repeat_ngram_size,
        //             encoder_input_ids
        //         ));
        //     } else {
        //         throw new Error("It's impossible to use `encoder_no_repeat_ngram_size` with decoder-only architecture");
        //     }
        // }

        if (generation_config.bad_words_ids !== null) {
            processors.push(new NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id));
        }

        if (generation_config.min_length !== null && generation_config.eos_token_id !== null && generation_config.min_length > 0) {
            processors.push(new MinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id));
        }

        if (generation_config.min_new_tokens !== null && generation_config.eos_token_id !== null && generation_config.min_new_tokens > 0) {
            processors.push(new MinNewTokensLengthLogitsProcessor(
                input_ids_seq_length,
                generation_config.min_new_tokens,
                generation_config.eos_token_id
            ));
        }

        // if (prefix_allowed_tokens_fn !== null) {
        //     processors.push(new PrefixConstrainedLogitsProcessor(
        //         prefix_allowed_tokens_fn,
        //         generation_config.num_beams / generation_config.num_beam_groups
        //     ));
        // }


        if (generation_config.forced_bos_token_id !== null) {
            processors.push(new ForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id));
        }

        if (generation_config.forced_eos_token_id !== null) {
            processors.push(new ForcedEOSTokenLogitsProcessor(
                generation_config.max_length,
                generation_config.forced_eos_token_id
            ));
        }

        // if (generation_config.remove_invalid_values === true) {
        //     processors.push(new InfNanRemoveLogitsProcessor());
        // }

        // if (generation_config.exponential_decay_length_penalty !== null) {
        //     processors.push(new ExponentialDecayLengthPenalty(
        //         generation_config.exponential_decay_length_penalty,
        //         generation_config.eos_token_id,
        //         input_ids_seq_length
        //     ));
        // }

        // if (generation_config.suppress_tokens !== null) {
        //     processors.push(new SuppressTokensLogitsProcessor(generation_config.suppress_tokens));
        // }

        if (generation_config.begin_suppress_tokens !== null) {
            const begin_index = (input_ids_seq_length > 1 || generation_config.forced_bos_token_id === null)
                ? input_ids_seq_length
                : input_ids_seq_length + 1;

            processors.push(new SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index));
        }

        // DEPRECATED: https://github.com/huggingface/transformers/pull/29485
        // if (generation_config.forced_decoder_ids !== null) {
        //     processors.push(new ForceTokensLogitsProcessor(generation_config.forced_decoder_ids));
        // }


        // 8. prepare batched CFG externally
        if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) {
            processors.push(new ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale));
        }

        if (logits_processor !== null) {
            processors.extend(logits_processor)
        }

        // `LogitNormalization` should always be the last logit processor, when present
        // if (generation_config.renormalize_logits === true) {
        //     processors.push(new LogitNormalization());
        // }

        return processors;
    }

    /**
     * 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, kwargs, cls = GenerationConfig) {
        // Create empty generation config (contains defaults)
        // We pass `this.config` so that if `eos_token_id` or `bos_token_id` exist in the model's config, we will use them
        const config = { ...this.config };
        for (const key of ["decoder", "generator", "text_config"]) {
            // Special case: some models have generation attributes set in the decoder.
            // Use them if still unset in the generation config.
            if (key in config) {
                Object.assign(config, config[key]);
            }
        }

        const gen_config = new cls(config);

        // Apply model's generation config, if it exists
        Object.assign(gen_config, this.generation_config ?? {});

        // Next, use any generation config specified by the user
        // when calling `generate`
        if (generation_config) {
            Object.assign(gen_config, generation_config);
        }

        // Finally, if any kwargs were passed, use them to overwrite
        if (kwargs) {
            Object.assign(gen_config, pick(kwargs, Object.getOwnPropertyNames(gen_config)));
        }

        return gen_config;
    }

    /**
     * 
     * @param {GenerationConfig} generation_config 
     * @param {StoppingCriteriaList} [stopping_criteria=null] 
     */
    _get_stopping_criteria(generation_config, stopping_criteria = null) {
        const criteria = new StoppingCriteriaList();

        if (generation_config.max_length !== null) {
            criteria.push(new MaxLengthCriteria(
                generation_config.max_length,
                this.config.max_position_embeddings ?? null,
            ));
        }
        // if (generation_config.max_time !== null) {
        //     criteria.push(new MaxTimeCriteria(generation_config.max_time));
        // }
        if (generation_config.eos_token_id !== null) {
            criteria.push(new EosTokenCriteria(generation_config.eos_token_id));
        }

        if (stopping_criteria) {
            criteria.extend(stopping_criteria);
        }
        return criteria;

    }

    /**
     * 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() {
        if (!this.can_generate) {
            const generate_compatible_mappings = [
                MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
                // MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, // TODO
                MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,
                MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
                MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
            ];

            const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor);

            const generate_compatible_classes = new Set();
            const modelType = this.config.model_type;
            for (const model_mapping of generate_compatible_mappings) {
                const supported_models = model_mapping.get(modelType);
                if (supported_models) {
                    generate_compatible_classes.add(supported_models[0]);
                }
            }

            let errorMessage = `The current model class (${modelName}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`
            if (generate_compatible_classes.size > 0) {
                errorMessage += ` Please use the following class instead: ${[...generate_compatible_classes].join(', ')}`;
            }
            throw Error(errorMessage);
        }
    }

    prepare_inputs_for_generation(...args) {
        return this._prepare_inputs_for_generation(this, ...args);
    }

    /**
     * 
     * @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 }) {
        // update past_key_values
        model_inputs['past_key_values'] = this.getPastKeyValues(outputs, model_inputs.past_key_values);

        // update inputs for next run
        model_inputs['input_ids'] = new Tensor('int64', generated_input_ids.flat(), [generated_input_ids.length, 1]);

        if (!is_encoder_decoder) {
            // update attention mask
            model_inputs.attention_mask = cat(
                [
                    model_inputs.attention_mask,
                    ones([model_inputs.attention_mask.dims[0], 1]),
                ], 1
            );
        } else if ('decoder_attention_mask' in model_inputs) {
            // TODO: update decoder attention mask if the model requires it
        }

        // force recreate position_ids in next iteration
        model_inputs['position_ids'] = null;

        return model_inputs;
    }

    /**
     * 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 }) {
        const model_inputs = pick(model_kwargs, this.forward_params);
        const input_name = this.main_input_name;
        if (input_name in model_inputs) {
            if (inputs) {
                throw new Error(
                    "`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. " +
                    "Make sure to either pass {inputs} or {input_name}=..."
                );
            }
        } else {
            model_inputs[input_name] = inputs;
        }

        const inputs_tensor = model_inputs[input_name];

        return { inputs_tensor, model_inputs, model_input_name: input_name };
    }

    async _prepare_encoder_decoder_kwargs_for_generation({ inputs_tensor, model_inputs, model_input_name, generation_config }) {
        if (
            this.sessions['model'].inputNames.includes('inputs_embeds')
            && !model_inputs.inputs_embeds
            && '_prepare_inputs_embeds' in this
        ) {
            // Encoder expects `inputs_embeds` instead of `input_ids`
            const { input_ids, pixel_values, attention_mask, ...kwargs } = model_inputs;
            // @ts-ignore
            const prepared_inputs = await this._prepare_inputs_embeds(model_inputs);
            model_inputs = {
                ...kwargs,
                ...pick(prepared_inputs, ['inputs_embeds', 'attention_mask']),
            };
        }
        let { last_hidden_state } = await encoderForward(this, model_inputs);

        // for classifier free guidance we need to add a 'null' input to our encoder hidden states
        if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) {

            last_hidden_state = cat([
                last_hidden_state,
                full_like(last_hidden_state, 0.0),
            ], 0);

            if ('attention_mask' in model_inputs) {
                model_inputs['attention_mask'] = cat([
                    model_inputs['attention_mask'],
                    zeros_like(model_inputs['attention_mask']),
                ], 0);
            }

        } else if (model_inputs.decoder_input_ids) {
            // Ensure that the encoder outputs have the same batch size as the decoder inputs,
            // allowing for more efficient batched generation for single inputs
            const decoder_input_ids_batch_size = toI64Tensor(model_inputs.decoder_input_ids).dims[0];
            if (decoder_input_ids_batch_size !== last_hidden_state.dims[0]) {
                if (last_hidden_state.dims[0] !== 1) {
                    throw new Error(
                        `The encoder outputs have a different batch size (${last_hidden_state.dims[0]}) than the decoder inputs (${decoder_input_ids_batch_size}).`
                    )
                }
                last_hidden_state = cat(Array.from({ length: decoder_input_ids_batch_size }, () => last_hidden_state), 0);
            }
        }
        model_inputs['encoder_outputs'] = last_hidden_state;

        return model_inputs;
    }

    /**
     * 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 }) {
        let { decoder_input_ids, ...model_inputs } = model_kwargs;

        // Prepare input ids if the user has not defined `decoder_input_ids` manually.
        if (!(decoder_input_ids instanceof Tensor)) {
            if (!decoder_input_ids) {
                decoder_start_token_id ??= bos_token_id;

                if (this.config.model_type === 'musicgen') {
                    // Custom logic (TODO: move to Musicgen class)
                    decoder_input_ids = Array.from({
                        length: batch_size * this.config.decoder.num_codebooks
                    }, () => [decoder_start_token_id]);

                } else if (Array.isArray(decoder_start_token_id)) {
                    if (decoder_start_token_id.length !== batch_size) {
                        throw new Error(
                            `\`decoder_start_token_id\` expcted to have length ${batch_size} but got ${decoder_start_token_id.length}`
                        )
                    }
                    decoder_input_ids = decoder_start_token_id;
                } else {
                    decoder_input_ids = Array.from({
                        length: batch_size,
                    }, () => [decoder_start_token_id]);
                }
            } else if (!Array.isArray(decoder_input_ids[0])) {
                // Correct batch size
                decoder_input_ids = Array.from({
                    length: batch_size,
                }, () => decoder_input_ids);
            }
            decoder_input_ids = toI64Tensor(decoder_input_ids);
        }

        model_kwargs['decoder_attention_mask'] = ones_like(decoder_input_ids);

        return { input_ids: decoder_input_ids, model_inputs };
    }

    /**
     * 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.
     */
    async generate({
        inputs = null,
        generation_config = null,
        logits_processor = null,
        stopping_criteria = null,
        streamer = null,

        // inputs_attention_mask = null,
        ...kwargs
    }) {
        this._validate_model_class();

        // Update generation config with defaults and kwargs
        generation_config = this._prepare_generation_config(generation_config, kwargs);

        // 3. Define model inputs
        let { inputs_tensor, model_inputs, model_input_name } = this._prepare_model_inputs({
            inputs,
            model_kwargs: kwargs,
        });

        const is_encoder_decoder = this.config.is_encoder_decoder;

        // 4. Define other model kwargs
        if (!is_encoder_decoder) {
            // decoder-only models should use left-padding for generation
        } else if (!('encoder_outputs' in model_inputs)) {
            // if model is encoder decoder encoder_outputs are created
            // and added to `model_kwargs`
            model_inputs = await this._prepare_encoder_decoder_kwargs_for_generation(
                { inputs_tensor, model_inputs, model_input_name, generation_config }
            )
        }

        // 5. Prepare `input_ids` which will be used for auto-regressive generation
        // TODO: Update to align with HF transformers' implementation
        let input_ids;
        if (is_encoder_decoder) {
            // Generating from the encoder outputs
            ({ input_ids, model_inputs } = this._prepare_decoder_input_ids_for_generation({
                batch_size: model_inputs[model_input_name].dims.at(0),
                model_input_name,
                model_kwargs: model_inputs,
                decoder_start_token_id: generation_config.decoder_start_token_id,
                bos_token_id: generation_config.bos_token_id,
                generation_config,
            }));
        } else {
            input_ids = model_inputs[model_input_name]
        }

        // 6. Prepare `max_length` depending on other stopping criteria.
        let input_ids_length = input_ids.dims.at(-1);

        if (generation_config.max_new_tokens !== null) {
            generation_config.max_length = input_ids_length + generation_config.max_new_tokens;
        }

        // input_ids_length = model_inputs[model_input_name].dims.at(1);
        // // inputs instanceof Tensor ?  : inputs.length;

        // // decoder-only
        // if (input_ids_length === 0) {
        //     throw Error("Must supply a non-empty array of input token ids.")
        // }

        // let decoder_input_ids =
        // generation_config.decoder_input_ids
        // ?? generation_config.decoder_start_token_id
        // ?? generation_config.bos_token_id
        // ?? generation_config.eos_token_id;

        // Update logits processor
        // 8. prepare distribution pre_processing samplers
        const prepared_logits_processor = this._get_logits_processor(
            generation_config,
            input_ids_length,
            logits_processor,
        )

        // 9. prepare stopping criteria
        const prepared_stopping_criteria = this._get_stopping_criteria(
            generation_config, stopping_criteria
        )

        // /** @type {number[]} */
        // let eos_token_ids = generation_config.eos_token_id;
        // if (eos_token_ids !== null && !Array.isArray(eos_token_ids)) {
        //     eos_token_ids = [eos_token_ids];
        // }

        const numInputs = model_inputs[model_input_name].dims.at(0);

        // TODO:
        // done is a list of booleans to keep track of which inputs are done
        // const done = new Array(numInputs).fill(false);
        // For efficiency purposes, we remove completed rows from model_inputs
        // when the beam is complete, and we keep track of the row index
        // const rowIndexToBatchIndex = new Map();

        const sampler = LogitsSampler.getSampler(generation_config);

        // TODO make > numInputs
        const scores = new Array(numInputs).fill(0);
        /** @type {bigint[][]} */
        const all_input_ids = input_ids.tolist();
        if (streamer) {
            streamer.put(all_input_ids);
        }
        // const all_generated_input_ids = Array.from({ length: numInputs }, () => []);

        // NOTE: For now, we don't support spawning new beams
        // TODO: when we do, we simply copy past key values and accumulate into single large tensor

        ////////////////////////////////////////////////////
        // Generic search which handles 4 generation modes:
        // - GenerationMode.GREEDY_SEARCH
        // - GenerationMode.SAMPLE
        // - GenerationMode.BEAM_SEARCH
        // - GenerationMode.BEAM_SAMPLE
        ////////////////////////////////////////////////////
        let outputs;
        let attentions = {};
        while (true) {
            // prepare model inputs
            model_inputs = this.prepare_inputs_for_generation(all_input_ids, model_inputs, generation_config);
            outputs = await this.forward(model_inputs);

            if (generation_config.output_attentions && generation_config.return_dict_in_generate) {
                // Get attentions if they are present
                const token_attentions = this.getAttentions(outputs);
                for (const key in token_attentions) {
                    if (!(key in attentions)) {
                        attentions[key] = [];
                    }
                    attentions[key].push(token_attentions[key]);
                }
            }

            // Logits are of the form [batch_size, out_seq_length, vocab_size]
            // In most cases, this will be [batch_size, 1, vocab_size]
            // So, we select the last token's logits:
            // (equivalent to `logits = outputs.logits[:, -1, :]`)
            const logits = outputs.logits.slice(null, -1, null);

            const next_tokens_scores = prepared_logits_processor(all_input_ids, logits);

            /** @type {[bigint][]} */
            const generated_input_ids = [];
            // const new_kv_cache = [];// NOTE: Only used for beam search when concatenating new kv
            // Loop over each batch
            for (let batch_idx = 0; batch_idx < next_tokens_scores.dims.at(0); ++batch_idx) {
                const logs = next_tokens_scores[batch_idx];

                const sampledTokens = await sampler(logs);
                for (const [newTokenId, logProb] of sampledTokens) {
                    const bigint = BigInt(newTokenId);
                    // TODO: If branching, use previous beam as a starting point
                    // update generated ids, model inputs, and length for next step
                    scores[batch_idx] += logProb;
                    all_input_ids[batch_idx].push(bigint);
                    generated_input_ids.push([bigint]);

                    // TODO: Support beam search
                    break;
                }
            }
            if (streamer) {
                streamer.put(generated_input_ids);
            }

            const stop = prepared_stopping_criteria(all_input_ids);
            if (stop.every(x => x)) {
                break;
            }

            model_inputs = this._update_model_kwargs_for_generation({
                generated_input_ids, outputs, model_inputs, is_encoder_decoder,
            });
        }

        if (streamer) {
            streamer.end();
        }

        // Retrieve and dispose all final past key values (including encoder attentions)
        const past_key_values = this.getPastKeyValues(outputs, model_inputs.past_key_values, true);

        // TODO: ensure all_input_ids is padded correctly...
        const sequences = new Tensor('int64', all_input_ids.flat(), [all_input_ids.length, all_input_ids[0].length]);

        if (generation_config.return_dict_in_generate) {
            return {
                sequences,
                past_key_values,
                ...attentions,
                // TODO:
                // scores,
                // logits,
            }
        } else {
            // Dispose all remaining tensors
            for (const tensor of Object.values(outputs)) {
                if (tensor.location === 'gpu-buffer') {
                    tensor.dispose();
                }
            }
            return sequences;
        }
    }

    /**
     * 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, pastKeyValues, disposeEncoderPKVs = false) {
        const pkvs = Object.create(null);

        for (const name in decoderResults) {
            if (name.startsWith('present')) {
                const newName = name.replace('present', 'past_key_values');
                const is_encoder_pkv = name.includes('encoder');
                if (is_encoder_pkv && pastKeyValues) {
                    // Optimization introduced by optimum to reuse past key values.
                    // So, we just replace the constant outputs (`decoderResults[name]`) with the previous past key values.
                    // https://github.com/huggingface/optimum/blob/0bf2c05fb7e1182b52d21b703cfc95fd9e4ea3dc/optimum/onnxruntime/base.py#L677-L704
                    pkvs[newName] = pastKeyValues[newName];
                } else { // decoder or using first encoder PKVs
                    pkvs[newName] = decoderResults[name];
                }

                if (pastKeyValues && (!is_encoder_pkv || disposeEncoderPKVs)) {
                    // - Always dispose decoder PKVs
                    // - Only dispose encoder past key values when requested (after generation)
                    const t = pastKeyValues[newName];
                    if (t.location === 'gpu-buffer') {
                        t.dispose();
                    }
                }
            }
        }
        return pkvs;
    }

    /**
     * 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) {
        const attentions = {};

        for (const attnName of ['cross_attentions', 'encoder_attentions', 'decoder_attentions']) {
            for (const name in model_output) {
                if (name.startsWith(attnName)) {
                    if (!(attnName in attentions)) {
                        attentions[attnName] = [];
                    }
                    attentions[attnName].push(model_output[name]);
                }
            }
        }
        return attentions;
    }

    /**
     * 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, pastKeyValues) {
        if (pastKeyValues) {
            Object.assign(decoderFeeds, pastKeyValues)
        } else {
            const session = this.sessions['decoder_model_merged'] ?? this.sessions['model'];
            const dtype = session?.config?.kv_cache_dtype ?? 'float32';
            const empty = (dtype === 'float16') ? new Uint16Array() : [];

            const batch_size = (decoderFeeds[this.main_input_name] ?? decoderFeeds.attention_mask)?.dims?.[0] ?? 1;
            const shapes = getKeyValueShapes(this.config, { batch_size });

            for (const name in shapes) {
                decoderFeeds[name] = new Tensor(dtype, empty, shapes[name]);
            }
        }
    }

    async encode_image({ pixel_values }) {
        // image_inputs === { pixel_values }
        const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values })).image_features;
        if (!this.config.num_image_tokens) {
            console.warn(
                'The number of image tokens was not set in the model configuration. ' +
                `Setting it to the number of features detected by the vision encoder (${features.dims[1]}).`
            )
            this.config.num_image_tokens = features.dims[1];
        }
        return features;
    }

    async encode_text({ input_ids }) {
        // text_inputs === { input_ids, attention_mask }
        return (await sessionRun(this.sessions['embed_tokens'], { input_ids })).inputs_embeds;
    }
}

//////////////////////////////////////////////////
// Base model output class
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 = null, attentions = null }) {
        super();
        this.last_hidden_state = last_hidden_state;
        this.hidden_states = hidden_states;
        this.attentions = attentions;
    }
}
//////////////////////////////////////////////////
// Bert models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// ModernBert models
export class ModernBertPreTrainedModel extends PreTrainedModel { }
export class ModernBertModel extends ModernBertPreTrainedModel { }

export class ModernBertForMaskedLM extends ModernBertPreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

export class ModernBertForSequenceClassification extends ModernBertPreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

export class ModernBertForTokenClassification extends ModernBertPreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// NomicBert models
export class NomicBertPreTrainedModel extends PreTrainedModel { }
export class NomicBertModel extends NomicBertPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// RoFormer models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
// TODO: Add RoFormerForCausalLM and RoFormerForMultipleChoice
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// ConvBert models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Electra models
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 { }
// TODO add ElectraForPreTraining
/**
 * 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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// CamemBERT models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// DeBERTa models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// DeBERTa-v2 models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// DistilBert models
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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}


/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// ESM models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// MobileBert models
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
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// MPNet models
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.
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// SqueezeBert models
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
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}
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
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
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
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Albert models
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
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
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
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
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
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// T5 models
export class T5PreTrainedModel extends PreTrainedModel {
    forward_params = [
        'input_ids',
        'attention_mask',
        'encoder_outputs',
        'decoder_input_ids',
        'decoder_attention_mask',
        'past_key_values',
    ];
};

export class T5Model extends T5PreTrainedModel { }

/**
 * T5Model is a class representing a T5 model for conditional generation.
 */
export class T5ForConditionalGeneration extends T5PreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// LONGT5 models
/**
 * 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 { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// MT5 models
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 { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Bart models
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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

//////////////////////////////////////////////////

//////////////////////////////////////////////////
// MBart models
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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}


export class MBartForCausalLM extends MBartPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Blenderbot models
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 { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Blenderbot models
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 { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Roberta models
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
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// XLM models
/**
 * 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
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// XLMRoberta models
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
     */
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * 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
     */
    async _call(model_inputs) {
        return new QuestionAnsweringModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Audio Spectrogram Transformer (AST) models
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 { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Whisper models
export class WhisperPreTrainedModel extends PreTrainedModel {

    requires_attention_mask = false;
    main_input_name = 'input_features';
    forward_params = [
        'input_features',
        'attention_mask',
        'decoder_input_ids',
        'decoder_attention_mask',
        'past_key_values',
    ];
};

/**
 * 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, kwargs) {
        return /** @type {WhisperGenerationConfig} */ (super._prepare_generation_config(generation_config, kwargs, WhisperGenerationConfig));
    }

    /**
     * 
     * @param {WhisperGenerationConfig} generation_config 
     */
    _retrieve_init_tokens(generation_config) {
        // prefix tokens are of the form: 
        //  - Multilingual: <|startoftranscript|> <|lang_id|> <|task|> [<|notimestamps|>]
        //  - English-only: <|startoftranscript|> [<|notimestamps|>]

        // 1. Handle <|startoftranscript|> token
        const init_tokens = [generation_config.decoder_start_token_id];

        // 2. Handle <|lang_id|> and <|task> tokens
        let language = generation_config.language;
        const task = generation_config.task;
        if (generation_config.is_multilingual) {
            if (!language) {
                // TODO: Implement language detection
                console.warn('No language specified - defaulting to English (en).');
                language = 'en';
            }

            // Add language token
            const language_code = whisper_language_to_code(language);
            const language_token = `<|${language_code}|>`;
            init_tokens.push(generation_config.lang_to_id[language_token])

            // Add task token
            // NOTE: Defaults to 'transcribe' if no task is specified
            init_tokens.push(generation_config.task_to_id[task ?? 'transcribe']);

        } else if (language || task) {
            throw new Error(
                "Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config."
            )
        }

        // 3. Handle <|notimestamps|> token
        if (
            !generation_config.return_timestamps
            && generation_config.no_timestamps_token_id
            && init_tokens.at(-1) !== generation_config.no_timestamps_token_id
        ) {
            init_tokens.push(generation_config.no_timestamps_token_id);
        } else if (
            generation_config.return_timestamps
            &&
            init_tokens.at(-1) === generation_config.no_timestamps_token_id
        ) {
            console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`.");
            init_tokens.pop();
        }

        // let's make sure we don't pass `null` tokens as prompt tokens
        return init_tokens.filter(token => token != null);
    }

    /**
     * Transcribes or translates log-mel input features to a sequence of auto-regressively generated token ids.
     * @param {import('./models/whisper/generation_whisper.js').WhisperGenerationFunctionParameters} options
     * @returns {Promise<ModelOutput|Tensor>} The output of the model, which can contain the generated token ids, attentions, and scores.
     */
    async generate({
        inputs = null,
        generation_config = null,
        logits_processor = null,
        stopping_criteria = null,

        // Whisper-specific options (passed to kwargs)
        // prompt_ids = null,
        // language = null,
        // task = null,

        ...kwargs
    }) {
        generation_config = this._prepare_generation_config(generation_config, kwargs);

        const init_tokens = kwargs.decoder_input_ids ?? this._retrieve_init_tokens(generation_config);

        if (generation_config.return_timestamps) {
            logits_processor ??= new LogitsProcessorList();
            logits_processor.push(
                new WhisperTimeStampLogitsProcessor(generation_config, init_tokens)
            );
        }

        if (generation_config.begin_suppress_tokens) {
            logits_processor ??= new LogitsProcessorList();
            logits_processor.push(
                new SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, init_tokens.length)
            );
        }

        if (generation_config.return_token_timestamps) {
            if (!generation_config.alignment_heads) {
                throw new Error(
                    "Model generation config has no `alignment_heads`, token-level timestamps not available. " +
                    "See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config."
                )
            }

            if (generation_config.task === 'translate') {
                console.warn("Token-level timestamps may not be reliable for task 'translate'.")
            }

            generation_config.output_attentions = true;
            generation_config.return_dict_in_generate = true;
        }

        const outputs = await super.generate({
            inputs,
            generation_config,
            logits_processor,
            decoder_input_ids: init_tokens,
            ...kwargs
        });

        if (generation_config.return_token_timestamps) {
            outputs["token_timestamps"] = this._extract_token_timestamps(
                outputs,
                generation_config.alignment_heads,
                generation_config.num_frames,
            );
        }

        return outputs;
    }

    /**
     * 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, alignment_heads, num_frames = null, time_precision = 0.02) {
        if (!generate_outputs.cross_attentions) {
            throw new Error(
                "Model outputs must contain cross attentions to extract timestamps. " +
                "This is most likely because the model was not exported with `output_attentions=True`."
            )
        }
        if (num_frames == null) {
            console.warn(
                "`num_frames` has not been set, meaning the entire audio will be analyzed. " +
                "This may lead to inaccurate token-level timestamps for short audios (< 30 seconds)."
            );
        }

        let median_filter_width = this.config.median_filter_width;
        if (median_filter_width === undefined) {
            console.warn("Model config has no `median_filter_width`, using default value of 7.")
            median_filter_width = 7;
        }

        // TODO: Improve batch processing
        const batch = generate_outputs.cross_attentions;
        // Create a list with `decoder_layers` elements, each a tensor of shape
        // (batch size, attention_heads, output length, input length).
        const cross_attentions = Array.from({ length: this.config.decoder_layers },
            // Concatenate the cross attentions for each layer across sequence length dimension.
            (_, i) => cat(batch.map(x => x[i]), 2)
        );

        const weights = stack(alignment_heads.map(([l, h]) => {
            if (l >= cross_attentions.length) {
                throw new Error(`Layer index ${l} is out of bounds for cross attentions (length ${cross_attentions.length}).`)
            }
            return num_frames
                ? cross_attentions[l].slice(null, h, null, [0, num_frames])
                : cross_attentions[l].slice(null, h);
        })).transpose(1, 0, 2, 3);

        const [std, calculatedMean] = std_mean(weights, -2, 0, true);

        // Normalize and smoothen the weights.
        const smoothedWeights = weights.clone(); // [1, 8, seqLength, 1500]

        for (let a = 0; a < smoothedWeights.dims[0]; ++a) {
            const aTensor = smoothedWeights[a]; // [8, seqLength, 1500]

            for (let b = 0; b < aTensor.dims[0]; ++b) {
                const bTensor = aTensor[b]; // [seqLength, 1500]

                const stdTensorData = std[a][b][0].data; // [1500]
                const meanTensorData = calculatedMean[a][b][0].data; // [1500]

                for (let c = 0; c < bTensor.dims[0]; ++c) {

                    let cTensorData = bTensor[c].data; // [1500]
                    for (let d = 0; d < cTensorData.length; ++d) {
                        cTensorData[d] = (cTensorData[d] - meanTensorData[d]) / stdTensorData[d]
                    }

                    // Apply median filter.
                    cTensorData.set(medianFilter(cTensorData, median_filter_width))
                }
            }
        }

        // Average the different cross-attention heads.
        const batchedMatrices = [mean(smoothedWeights, 1)];

        const timestampsShape = generate_outputs.sequences.dims;

        const timestamps = new Tensor(
            'float32',
            new Float32Array(timestampsShape[0] * timestampsShape[1]),
            timestampsShape
        );

        // Perform dynamic time warping on each element of the batch.
        for (let batch_idx = 0; batch_idx < timestampsShape[0]; ++batch_idx) {
            // NOTE: Since we run only one batch at a time, we can squeeze to get the same dimensions
            // as the python implementation
            const matrix = batchedMatrices[batch_idx].neg().squeeze_(0);
            const [text_indices, time_indices] = dynamic_time_warping(matrix.tolist());

            const diffs = Array.from({ length: text_indices.length - 1 }, (v, i) => text_indices[i + 1] - text_indices[i]);
            const jumps = mergeArrays([1], diffs).map(x => !!x); // convert to boolean

            const jump_times = [];
            for (let i = 0; i < jumps.length; ++i) {
                if (jumps[i]) {
                    // NOTE: No point in rounding here, since we set to Float32Array later
                    jump_times.push(time_indices[i] * time_precision);
                }
            }
            timestamps[batch_idx].data.set(jump_times, 1)
        }

        return timestamps;
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Moonshine models
export class MoonshinePreTrainedModel extends PreTrainedModel {

    requires_attention_mask = false;
    main_input_name = 'input_values';
    forward_params = [
        'input_values',
        'decoder_input_ids',
        'past_key_values',
    ];
};

/**
 * MoonshineModel class for training Moonshine models without a language model head.
 */
export class MoonshineModel extends MoonshinePreTrainedModel { }

export class MoonshineForConditionalGeneration extends MoonshinePreTrainedModel { } 
//////////////////////////////////////////////////


//////////////////////////////////////////////////
/**
 * Vision Encoder-Decoder model based on OpenAI's GPT architecture for image captioning and other vision tasks
 */
export class VisionEncoderDecoderModel extends PreTrainedModel {
    main_input_name = 'pixel_values';
    forward_params = [
        // Encoder inputs
        'pixel_values',

        // Decoder inpputs
        'decoder_input_ids',
        'encoder_hidden_states',
        'past_key_values',
    ];
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// LLaVa Models
export class LlavaPreTrainedModel extends PreTrainedModel {
    forward_params = [
        'input_ids',
        'attention_mask',
        'pixel_values',
        'position_ids',
        'past_key_values',
    ];
}

/**
 * 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,
    }) {

        const image_token_index = this.config.image_token_index;

        const idsList = input_ids.tolist();

        // NOTE: we use .findIndex instead of .indexOf to perform weak comparison (==) between BigInt and Number
        const indexOfImage = idsList.map(x => x.findIndex(x => x == image_token_index));

        const noImages = indexOfImage.every(x => x === -1);
        const allImages = indexOfImage.every(x => x !== -1);
        if (!noImages && !allImages) {
            // Check for padding reasons
            throw new Error('Every input should contain either 0 or 1 image token.');
        }

        if (noImages) {
            return {
                inputs_embeds,
                attention_mask,
            }
        }

        const stacked = [];
        const stacked_attention_mask = [];
        for (let i = 0; i < indexOfImage.length; ++i) {
            const index = indexOfImage[i];

            const e = inputs_embeds[i];
            const im = image_features[i];
            const am = attention_mask[i];
            stacked.push(
                cat([
                    e.slice([0, index]),
                    im,
                    e.slice([index + 1, e.dims[0]]),
                ], 0)
            );

            stacked_attention_mask.push(
                cat([
                    am.slice([0, index]),
                    ones([im.dims[0]]),
                    am.slice([index + 1, am.dims[0]])
                ], 0)
            )
        }

        return {
            inputs_embeds: stack(stacked, 0),
            attention_mask: stack(stacked_attention_mask, 0),
        }
    }
}
//////////////////////////////////////////////////

export class LlavaOnevisionForConditionalGeneration extends LlavaForConditionalGeneration { } // NOTE: extends LlavaForConditionalGeneration
export class Moondream1ForConditionalGeneration extends LlavaForConditionalGeneration { } // NOTE: extends LlavaForConditionalGeneration

export class Florence2PreTrainedModel extends PreTrainedModel {
    forward_params = [
        // Encoder inputs
        'input_ids',
        'inputs_embeds',
        'attention_mask',
        'pixel_values',

        // Decoder inputs
        'encoder_outputs',
        'decoder_input_ids',
        'decoder_inputs_embeds',
        'decoder_attention_mask',
        'past_key_values',
    ];
    main_input_name = 'inputs_embeds';
}

export class Florence2ForConditionalGeneration extends Florence2PreTrainedModel {

    _merge_input_ids_with_image_features({
        inputs_embeds,
        image_features,
        input_ids,
        attention_mask,
    }) {
        return {
            inputs_embeds: cat([
                image_features, // image embeds
                inputs_embeds, // task prefix embeds
            ], 1),
            attention_mask: cat([
                ones(image_features.dims.slice(0, 2)), // image attention mask
                attention_mask, // task prefix attention mask
            ], 1),
        }
    }

    async _prepare_inputs_embeds({ input_ids, pixel_values, inputs_embeds, attention_mask }) {
        if (!input_ids && !pixel_values) {
            throw new Error('Either `input_ids` or `pixel_values` should be provided.');
        }

        // 1. Possibly, extract the input embeddings
        let text_features, image_features;
        if (input_ids) {
            text_features = await this.encode_text({ input_ids });
        }
        if (pixel_values) {
            image_features = await this.encode_image({ pixel_values });
        }

        // 2. Possibly, merge text and images
        if (text_features && image_features) {
            ({ inputs_embeds, attention_mask } = this._merge_input_ids_with_image_features({
                inputs_embeds: text_features,
                image_features,
                input_ids,
                attention_mask,
            }));
        } else {
            inputs_embeds = text_features || image_features;
        }

        return { inputs_embeds, attention_mask };
    }

    async forward({
        input_ids,
        pixel_values,
        attention_mask,
        decoder_input_ids,
        decoder_attention_mask,
        encoder_outputs,
        past_key_values,

        inputs_embeds,
        decoder_inputs_embeds,
    }) {
        if (!inputs_embeds) {
            ({ inputs_embeds, attention_mask } = await this._prepare_inputs_embeds({ input_ids, pixel_values, inputs_embeds, attention_mask }));
        }

        if (!encoder_outputs) {
            // Must compute encoder outputs
            let { last_hidden_state } = await encoderForward(this, { inputs_embeds, attention_mask });
            encoder_outputs = last_hidden_state;
        }

        if (!decoder_inputs_embeds) {
            if (!decoder_input_ids) {
                throw new Error('Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.');
            }
            decoder_inputs_embeds = await this.encode_text({ input_ids: decoder_input_ids });
        }

        const decoderFeeds = {
            inputs_embeds: decoder_inputs_embeds,
            attention_mask: decoder_attention_mask,
            encoder_attention_mask: attention_mask,
            encoder_hidden_states: encoder_outputs,
            past_key_values,
        };
        const decoder_outputs = await decoderForward(this, decoderFeeds, true);
        return decoder_outputs;
    }
}

export class PaliGemmaPreTrainedModel extends PreTrainedModel {
    forward_params = [
        'input_ids',
        // 'inputs_embeds',
        'attention_mask',
        'pixel_values',
        'position_ids',
        'past_key_values',
    ];
}

export class PaliGemmaForConditionalGeneration extends PaliGemmaPreTrainedModel {
    _merge_input_ids_with_image_features(kwargs) {
        const vision_hidden_size = kwargs.image_features.dims.at(-1);
        const reshaped_image_hidden_states = kwargs.image_features.view(-1, vision_hidden_size);

        return default_merge_input_ids_with_image_features({
            // @ts-ignore
            image_token_id: this.config.image_token_index,
            ...kwargs,
            image_features: reshaped_image_hidden_states,
        })
    }
}

//////////////////////////////////////////////////
// Idefics3 Models
export class Idefics3PreTrainedModel extends PreTrainedModel {
    forward_params = [
        'input_ids',
        'attention_mask',
        'pixel_values',
        'pixel_attention_mask',
        'position_ids',
        'past_key_values',
    ];
}

/**
 * The LLAVA model which consists of a vision backbone and a language model.
 */
export class Idefics3ForConditionalGeneration extends Idefics3PreTrainedModel {

    async encode_image({ pixel_values, pixel_attention_mask }) {
        const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, pixel_attention_mask })).image_features;
        return features;
    }

    _merge_input_ids_with_image_features(kwargs) {
        const vision_hidden_size = kwargs.image_features.dims.at(-1);
        const reshaped_image_hidden_states = kwargs.image_features.view(-1, vision_hidden_size);

        return default_merge_input_ids_with_image_features({
            // @ts-ignore
            image_token_id: this.config.image_token_id,
            ...kwargs,
            image_features: reshaped_image_hidden_states,
        })
    }
}
//////////////////////////////////////////////////

export class Phi3VPreTrainedModel extends PreTrainedModel {
    forward_params = [
        'input_ids',
        'inputs_embeds',
        'attention_mask',
        'position_ids',
        'pixel_values',
        'image_sizes',
        'past_key_values',
    ];
}
export class Phi3VForCausalLM extends Phi3VPreTrainedModel {

    async forward({
        // Produced by the tokenizer/processor:
        input_ids = null,
        attention_mask = null,
        pixel_values = null,
        image_sizes = null,

        // Used during generation:
        position_ids = null,
        inputs_embeds = null,
        past_key_values = null,

        // Generic generation parameters
        generation_config = null,
        logits_processor = null,

        // TODO: needed?
        ...kwargs
    }) {
        if (!inputs_embeds) {
            let image_features;
            if (pixel_values && input_ids.dims[1] !== 1) {
                if (!image_sizes) {
                    throw new Error('`image_sizes` must be provided when `pixel_values` is provided.');
                }

                // Encode the image
                ({ image_features } = await sessionRun(this.sessions['vision_encoder'], {
                    pixel_values,
                    image_sizes,
                }));
            } else {
                const hidden_size = this.config.normalized_config.hidden_size;
                image_features = new Tensor(
                    'float32',
                    [],
                    [0, hidden_size],
                );
            }

            ({ inputs_embeds } = await sessionRun(this.sessions['prepare_inputs_embeds'], {
                input_ids,
                image_features,
            }));
        }

        const outputs = await decoderForward(this, {
            inputs_embeds,
            past_key_values,
            attention_mask,
            position_ids,
            generation_config,
            logits_processor,
        }, false);
        return outputs;
    }
}

//////////////////////////////////////////////////
export class CLIPPreTrainedModel extends PreTrainedModel { }

/**
 * CLIP Text and Vision Model with a projection layers on top
 * 
 * **Example:** Perform zero-shot image classification with a `CLIPModel`.
 * 
 * ```javascript
 * import { AutoTokenizer, AutoProcessor, CLIPModel, RawImage } from '@huggingface/transformers';
 * 
 * // Load tokenizer, processor, and model
 * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/clip-vit-base-patch16');
 * let processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16');
 * let model = await CLIPModel.from_pretrained('Xenova/clip-vit-base-patch16');
 * 
 * // Run tokenization
 * let texts = ['a photo of a car', 'a photo of a football match']
 * let text_inputs = tokenizer(texts, { padding: true, truncation: true });
 * 
 * // Read image and run processor
 * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
 * let image_inputs = await processor(image);
 * 
 * // Run model with both text and pixel inputs
 * let output = await model({ ...text_inputs, ...image_inputs });
 * // {
 * //   logits_per_image: Tensor {
 * //     dims: [ 1, 2 ],
 * //     data: Float32Array(2) [ 18.579734802246094, 24.31830596923828 ],
 * //   },
 * //   logits_per_text: Tensor {
 * //     dims: [ 2, 1 ],
 * //     data: Float32Array(2) [ 18.579734802246094, 24.31830596923828 ],
 * //   },
 * //   text_embeds: Tensor {
 * //     dims: [ 2, 512 ],
 * //     data: Float32Array(1024) [ ... ],
 * //   },
 * //   image_embeds: Tensor {
 * //     dims: [ 1, 512 ],
 * //     data: Float32Array(512) [ ... ],
 * //   }
 * // }
 * ```
 */
export class CLIPModel extends CLIPPreTrainedModel { }

/**
 * The text model from CLIP without any head or projection on top.
 */
export class CLIPTextModel extends CLIPPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'text_model',
            ...options,
        });
    }
}

/**
 * CLIP Text Model with a projection layer on top (a linear layer on top of the pooled output)
 * 
 * **Example:** Compute text embeddings with `CLIPTextModelWithProjection`.
 * 
 * ```javascript
 * import { AutoTokenizer, CLIPTextModelWithProjection } from '@huggingface/transformers';
 * 
 * // Load tokenizer and text model
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clip-vit-base-patch16');
 * const text_model = await CLIPTextModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16');
 * 
 * // Run tokenization
 * let texts = ['a photo of a car', 'a photo of a football match'];
 * let text_inputs = tokenizer(texts, { padding: true, truncation: true });
 * 
 * // Compute embeddings
 * const { text_embeds } = await text_model(text_inputs);
 * // Tensor {
 * //   dims: [ 2, 512 ],
 * //   type: 'float32',
 * //   data: Float32Array(1024) [ ... ],
 * //   size: 1024
 * // }
 * ```
 */
export class CLIPTextModelWithProjection extends CLIPPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'text_model',
            ...options,
        });
    }
}

/**
 * The vision model from CLIP without any head or projection on top.
 */
export class CLIPVisionModel extends CLIPPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'vision_model',
            ...options,
        });
    }
}

/**
 * CLIP Vision Model with a projection layer on top (a linear layer on top of the pooled output)
 * 
 * **Example:** Compute vision embeddings with `CLIPVisionModelWithProjection`.
 * 
 * ```javascript
 * import { AutoProcessor, CLIPVisionModelWithProjection, RawImage} from '@huggingface/transformers';
 * 
 * // Load processor and vision model
 * const processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16');
 * const vision_model = await CLIPVisionModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16');
 * 
 * // Read image and run processor
 * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
 * let image_inputs = await processor(image);
 * 
 * // Compute embeddings
 * const { image_embeds } = await vision_model(image_inputs);
 * // Tensor {
 * //   dims: [ 1, 512 ],
 * //   type: 'float32',
 * //   data: Float32Array(512) [ ... ],
 * //   size: 512
 * // }
 * ```
 */
export class CLIPVisionModelWithProjection extends CLIPPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'vision_model',
            ...options,
        });
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// SigLIP models
export class SiglipPreTrainedModel extends PreTrainedModel { }

/**
 * SigLIP Text and Vision Model with a projection layers on top
 * 
 * **Example:** Perform zero-shot image classification with a `SiglipModel`.
 * 
 * ```javascript
 * import { AutoTokenizer, AutoProcessor, SiglipModel, RawImage } from '@huggingface/transformers';
 * 
 * // Load tokenizer, processor, and model
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-224');
 * const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-224');
 * const model = await SiglipModel.from_pretrained('Xenova/siglip-base-patch16-224');
 * 
 * // Run tokenization
 * const texts = ['a photo of 2 cats', 'a photo of 2 dogs'];
 * const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });
 * 
 * // Read image and run processor
 * const image = await RawImage.read('http://images.cocodataset.org/val2017/000000039769.jpg');
 * const image_inputs = await processor(image);
 * 
 * // Run model with both text and pixel inputs
 * const output = await model({ ...text_inputs, ...image_inputs });
 * // {
 * //   logits_per_image: Tensor {
 * //     dims: [ 1, 2 ],
 * //     data: Float32Array(2) [ -1.6019744873046875, -10.720091819763184 ],
 * //   },
 * //   logits_per_text: Tensor {
 * //     dims: [ 2, 1 ],
 * //     data: Float32Array(2) [ -1.6019744873046875, -10.720091819763184 ],
 * //   },
 * //   text_embeds: Tensor {
 * //     dims: [ 2, 768 ],
 * //     data: Float32Array(1536) [ ... ],
 * //   },
 * //   image_embeds: Tensor {
 * //     dims: [ 1, 768 ],
 * //     data: Float32Array(768) [ ... ],
 * //   }
 * // }
 * ```
 */
export class SiglipModel extends SiglipPreTrainedModel { }

/**
 * The text model from SigLIP without any head or projection on top.
 * 
 * **Example:** Compute text embeddings with `SiglipTextModel`.
 * 
 * ```javascript
 * import { AutoTokenizer, SiglipTextModel } from '@huggingface/transformers';
 * 
 * // Load tokenizer and text model
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-224');
 * const text_model = await SiglipTextModel.from_pretrained('Xenova/siglip-base-patch16-224');
 * 
 * // Run tokenization
 * const texts = ['a photo of 2 cats', 'a photo of 2 dogs'];
 * const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });
 * 
 * // Compute embeddings
 * const { pooler_output } = await text_model(text_inputs);
 * // Tensor {
 * //   dims: [ 2, 768 ],
 * //   type: 'float32',
 * //   data: Float32Array(1536) [ ... ],
 * //   size: 1536
 * // }
 * ```
 */
export class SiglipTextModel extends SiglipPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'text_model',
            ...options,
        });
    }
}

/**
 * The vision model from SigLIP without any head or projection on top.
 * 
 * **Example:** Compute vision embeddings with `SiglipVisionModel`.
 * 
 * ```javascript
 * import { AutoProcessor, SiglipVisionModel, RawImage} from '@huggingface/transformers';
 * 
 * // Load processor and vision model
 * const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-224');
 * const vision_model = await SiglipVisionModel.from_pretrained('Xenova/siglip-base-patch16-224');
 * 
 * // Read image and run processor
 * const image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
 * const image_inputs = await processor(image);
 * 
 * // Compute embeddings
 * const { pooler_output } = await vision_model(image_inputs);
 * // Tensor {
 * //   dims: [ 1, 768 ],
 * //   type: 'float32',
 * //   data: Float32Array(768) [ ... ],
 * //   size: 768
 * // }
 * ```
 */
export class SiglipVisionModel extends CLIPPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'vision_model',
            ...options,
        });
    }
}
//////////////////////////////////////////////////
// ChineseCLIP models
export class ChineseCLIPPreTrainedModel extends PreTrainedModel { }

export class ChineseCLIPModel extends ChineseCLIPPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// JinaCLIP models
export class JinaCLIPPreTrainedModel extends PreTrainedModel { }

export class JinaCLIPModel extends JinaCLIPPreTrainedModel {
    async forward(model_inputs) {
        const missing_text_inputs = !model_inputs.input_ids;
        const missing_image_inputs = !model_inputs.pixel_values;

        if (missing_text_inputs && missing_image_inputs) {
            throw new Error('Either `input_ids` or `pixel_values` should be provided.');
        }

        // If either `input_ids` or `pixel_values` aren't passed, we need to create dummy input since the model requires a value to be specified.
        if (missing_text_inputs) {
            // NOTE: We cannot pass zero-dimension tensor as input for input_ids.
            // Fortunately, the majority of time is spent in the vision encoder, so this shouldn't significantly impact performance.
            model_inputs.input_ids = ones([model_inputs.pixel_values.dims[0], 1]);
        }

        if (missing_image_inputs) {
            // NOTE: Since we create a zero-sized tensor, this does not increase computation time.
            // @ts-ignore
            const { image_size } = this.config.vision_config;
            model_inputs.pixel_values = full([0, 3, image_size, image_size], 0.0); // (pass zero-dimension tensor)
        }

        const { text_embeddings, image_embeddings, l2norm_text_embeddings, l2norm_image_embeddings } = await super.forward(model_inputs);

        const result = {};
        if (!missing_text_inputs) {
            result.text_embeddings = text_embeddings;
            result.l2norm_text_embeddings = l2norm_text_embeddings;
        }
        if (!missing_image_inputs) {
            result.image_embeddings = image_embeddings;
            result.l2norm_image_embeddings = l2norm_image_embeddings;
        }
        return result
    }
}

export class JinaCLIPTextModel extends JinaCLIPPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'text_model',
            ...options,
        });
    }
}

export class JinaCLIPVisionModel extends JinaCLIPPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'vision_model',
            ...options,
        });
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// CLIPSeg models
export class CLIPSegPreTrainedModel extends PreTrainedModel { }

export class CLIPSegModel extends CLIPSegPreTrainedModel { }

/**
 * CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.
 * 
 * **Example:** Perform zero-shot image segmentation with a `CLIPSegForImageSegmentation` model.
 * 
 * ```javascript
 * import { AutoTokenizer, AutoProcessor, CLIPSegForImageSegmentation, RawImage } from '@huggingface/transformers';
 * 
 * // Load tokenizer, processor, and model
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clipseg-rd64-refined');
 * const processor = await AutoProcessor.from_pretrained('Xenova/clipseg-rd64-refined');
 * const model = await CLIPSegForImageSegmentation.from_pretrained('Xenova/clipseg-rd64-refined');
 * 
 * // Run tokenization
 * const texts = ['a glass', 'something to fill', 'wood', 'a jar'];
 * const text_inputs = tokenizer(texts, { padding: true, truncation: true });
 * 
 * // Read image and run processor
 * const image = await RawImage.read('https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true');
 * const image_inputs = await processor(image);
 * 
 * // Run model with both text and pixel inputs
 * const { logits } = await model({ ...text_inputs, ...image_inputs });
 * // logits: Tensor {
 * //   dims: [4, 352, 352],
 * //   type: 'float32',
 * //   data: Float32Array(495616) [ ... ],
 * //   size: 495616
 * // }
 * ```
 * 
 * You can visualize the predictions as follows:
 * ```javascript
 * const preds = logits
 *   .unsqueeze_(1)
 *   .sigmoid_()
 *   .mul_(255)
 *   .round_()
 *   .to('uint8');
 * 
 * for (let i = 0; i < preds.dims[0]; ++i) {
 *   const img = RawImage.fromTensor(preds[i]);
 *   img.save(`prediction_${i}.png`);
 * }
 * ```
 */
export class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// GPT2 models
export class GPT2PreTrainedModel extends PreTrainedModel { }

export class GPT2Model extends GPT2PreTrainedModel { }

/**
 * GPT-2 language model head on top of the GPT-2 base model. This model is suitable for text generation tasks.
 */
export class GPT2LMHeadModel extends GPT2PreTrainedModel { }
// export class GPT2ForSequenceClassification extends GPT2PreTrainedModel {
// TODO
// }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// JAIS models
export class JAISPreTrainedModel extends PreTrainedModel { }

/**
 * The bare JAIS Model transformer outputting raw hidden-states without any specific head on top.
 */
export class JAISModel extends JAISPreTrainedModel { }

/**
 * The JAIS Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
 */
export class JAISLMHeadModel extends JAISPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// GPTNeo models
export class GPTNeoPreTrainedModel extends PreTrainedModel { }
export class GPTNeoModel extends GPTNeoPreTrainedModel { }

export class GPTNeoForCausalLM extends GPTNeoPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// GPTNeoX models
export class GPTNeoXPreTrainedModel extends PreTrainedModel { }
export class GPTNeoXModel extends GPTNeoXPreTrainedModel { }

export class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// GPT-J models
export class GPTJPreTrainedModel extends PreTrainedModel { }

export class GPTJModel extends GPTJPreTrainedModel { }

export class GPTJForCausalLM extends GPTJPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// GPTBigCode models
export class GPTBigCodePreTrainedModel extends PreTrainedModel { }

export class GPTBigCodeModel extends GPTBigCodePreTrainedModel { }

export class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// CodeGen models
export class CodeGenPreTrainedModel extends PreTrainedModel { }
/**
 * CodeGenModel is a class representing a code generation model without a language model head.
 */
export class CodeGenModel extends CodeGenPreTrainedModel { }

/**
 * CodeGenForCausalLM is a class that represents a code generation model based on the GPT-2 architecture. It extends the `CodeGenPreTrainedModel` class.
 */
export class CodeGenForCausalLM extends CodeGenPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// LLama models

/**
 * The bare LLama Model outputting raw hidden-states without any specific head on top.
 */
export class LlamaPreTrainedModel extends PreTrainedModel { }
/**
 * The bare LLaMA Model outputting raw hidden-states without any specific head on top.
 */
export class LlamaModel extends LlamaPreTrainedModel { }

export class LlamaForCausalLM extends LlamaPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// EXAONE models
export class ExaonePreTrainedModel extends PreTrainedModel { }
export class ExaoneModel extends ExaonePreTrainedModel { }
export class ExaoneForCausalLM extends ExaonePreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// MobileLLM models
export class MobileLLMPreTrainedModel extends PreTrainedModel { }
export class MobileLLMModel extends MobileLLMPreTrainedModel { }
export class MobileLLMForCausalLM extends MobileLLMPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// OLMo models
export class OlmoPreTrainedModel extends PreTrainedModel { }
export class OlmoModel extends OlmoPreTrainedModel { }
export class OlmoForCausalLM extends OlmoPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// OLMo2 models
export class Olmo2PreTrainedModel extends PreTrainedModel { }
export class Olmo2Model extends Olmo2PreTrainedModel { }
export class Olmo2ForCausalLM extends Olmo2PreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Granite models
export class GranitePreTrainedModel extends PreTrainedModel { }
export class GraniteModel extends GranitePreTrainedModel { }
export class GraniteForCausalLM extends GranitePreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Cohere models

/**
 * The bare Cohere Model outputting raw hidden-states without any specific head on top.
 */
export class CoherePreTrainedModel extends PreTrainedModel { }
export class CohereModel extends CoherePreTrainedModel { }

export class CohereForCausalLM extends CoherePreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Gemma models

/**
 * The bare Gemma Model outputting raw hidden-states without any specific head on top.
 */
export class GemmaPreTrainedModel extends PreTrainedModel { }
/**
 * The bare Gemma Model outputting raw hidden-states without any specific head on top.
 */
export class GemmaModel extends GemmaPreTrainedModel { }

export class GemmaForCausalLM extends GemmaPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Gemma2 models

/**
 * The bare Gemma2 Model outputting raw hidden-states without any specific head on top.
 */
export class Gemma2PreTrainedModel extends PreTrainedModel { }
/**
 * The bare Gemma2 Model outputting raw hidden-states without any specific head on top.
 */
export class Gemma2Model extends Gemma2PreTrainedModel { }

export class Gemma2ForCausalLM extends Gemma2PreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class OpenELMPreTrainedModel extends PreTrainedModel { }
export class OpenELMModel extends OpenELMPreTrainedModel { }

export class OpenELMForCausalLM extends OpenELMPreTrainedModel { }


//////////////////////////////////////////////////
// Qwen2 models

/**
 * The bare Qwen2 Model outputting raw hidden-states without any specific head on top.
 */
export class Qwen2PreTrainedModel extends PreTrainedModel { }
/**
 * The bare Qwen2 Model outputting raw hidden-states without any specific head on top.
 */
export class Qwen2Model extends Qwen2PreTrainedModel { }

export class Qwen2ForCausalLM extends Qwen2PreTrainedModel { }
//////////////////////////////////////////////////

export class Qwen2VLPreTrainedModel extends PreTrainedModel {
    forward_params = [
        // Text inputs
        'input_ids',
        'attention_mask',
        'position_ids',
        'past_key_values',

        // Vision inputs
        'pixel_values',
        'image_grid_thw',
    ];
}
export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {

    /**
     * Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
     *
     * Explanation:
     *     Each embedding sequence contains vision embedding and text embedding or just contains text embedding.
     *
     *     For pure text embedding sequence, the rotary position embedding has no difference with mordern LLMs.
     *     Examples:
     *         input_ids: [T T T T T], here T is for text.
     *         temporal position_ids: [0, 1, 2, 3, 4]
     *         height position_ids: [0, 1, 2, 3, 4]
     *         width position_ids: [0, 1, 2, 3, 4]
     *
     *     For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part
     *     and 1D rotary position embeddin for text part.
     *     Examples:
     *         Assume we have a video input with 3 temporal patches, 2 height patches and 2 width patches.
     *         input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision.
     *         vision temporal position_ids: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]
     *         vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
     *         vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
     *         text temporal position_ids: [3, 4, 5, 6, 7]
     *         text height position_ids: [3, 4, 5, 6, 7]
     *         text width position_ids: [3, 4, 5, 6, 7]
     *         Here we calculate the text start position_ids as the max vision position_ids plus 1.
     * 
     * @param {Tensor} input_ids Indices of input sequence tokens in the vocabulary. Tensor of shape `(batch_size, sequence_length)`.
     * @param {Tensor} image_grid_thw (Optional) The temporal, height and width of feature shape of each image in LLM. Tensor of shape `(num_images, 3)`.
     * @param {Tensor} video_grid_thw (Optional) The temporal, height and width of feature shape of each video in LLM. Tensor of shape `(num_videos, 3)`.
     * @param {Tensor} attention_mask (Optional) Mask to avoid performing attention on padding token indices. Tensor of shape `(batch_size, sequence_length)`. Mask values selected in `[0, 1]`:
     * - 1 for tokens that are **not masked**,
     * - 0 for tokens that are **masked**.
     * @returns {[Tensor, Tensor]} [position_ids, mrope_position_deltas] with:
     * - position_ids: Tensor of shape `(3, batch_size, sequence_length)`.
     * - mrope_position_deltas: Tensor of shape `(batch_size)`.
     */
    get_rope_index(input_ids, image_grid_thw, video_grid_thw, attention_mask) {
        // @ts-ignore
        const { vision_config, image_token_id, video_token_id, vision_start_token_id } = this.config;
        const spatial_merge_size = vision_config.spatial_merge_size ?? 2;

        const mrope_position_deltas = [];
        if (image_grid_thw || video_grid_thw) {
            let total_input_ids = input_ids.tolist();
            if (!attention_mask) {
                attention_mask = ones_like(input_ids);
            }

            const attention_mask_list = attention_mask.tolist();
            const position_ids_list = Array.from({ length: 3 }, _ => Array.from({ length: input_ids.dims[0] }, _ => Array.from({ length: input_ids.dims[1] }, _ => 1)));

            const image_grid_thw_list = image_grid_thw ? image_grid_thw.tolist() : [];
            const video_grid_thw_list = video_grid_thw ? video_grid_thw.tolist() : [];

            let image_index = 0;
            let video_index = 0;
            for (let i = 0; i < total_input_ids.length; ++i) {
                const ids = total_input_ids[i].filter((_, j) => attention_mask_list[i][j] == 1);

                const vision_start_indices = ids.reduce((acc, x, idx) => {
                    if (x == vision_start_token_id) acc.push(idx);
                    return acc;
                }, []);

                const vision_tokens = vision_start_indices.map(x => ids[x + 1]);
                const image_nums = vision_tokens.filter(x => x == image_token_id).length;
                const video_nums = vision_tokens.filter(x => x == video_token_id).length;

                let llm_pos_ids_list = [];
                let st = 0;
                let remain_images = image_nums;
                let remain_videos = video_nums;
                for (let j = 0; j < vision_tokens.length; ++j) {
                    const next_image_token = ids.findIndex((x, i) => i > st && x == image_token_id);
                    const next_video_token = ids.findIndex((x, i) => i > st && x == video_token_id);

                    const ed_image = (remain_images > 0 && next_image_token !== -1)
                        ? next_image_token
                        : ids.length + 1;

                    const ed_video = (remain_videos > 0 && next_video_token !== -1)
                        ? next_video_token
                        : ids.length + 1;

                    let ed;
                    let t, h, w;
                    if (ed_image < ed_video) {
                        ([t, h, w] = image_grid_thw_list[image_index]);
                        ++image_index;
                        --remain_images;
                        ed = ed_image;
                    } else {
                        ([t, h, w] = video_grid_thw_list[video_index]);
                        ++video_index;
                        --remain_videos;
                        ed = ed_video;
                    }

                    const [llm_grid_t, llm_grid_h, llm_grid_w] = [
                        Number(t),
                        Math.floor(Number(h) / spatial_merge_size),
                        Math.floor(Number(w) / spatial_merge_size)
                    ]
                    const text_len = ed - st;
                    const st_idx = llm_pos_ids_list.length > 0
                        ? max(llm_pos_ids_list.at(-1))[0] + 1
                        : 0;

                    llm_pos_ids_list.push(
                        Array.from({ length: 3 * text_len }, (_, i) => st_idx + (i % text_len))
                    )

                    const offset = text_len + st_idx;
                    const grid_size = llm_grid_t * llm_grid_h * llm_grid_w;
                    const t_index = Array.from({ length: grid_size }, (_, i) => offset + Math.floor(i / (llm_grid_h * llm_grid_w)))
                    const h_index = Array.from({ length: grid_size }, (_, i) => offset + Math.floor(i / llm_grid_w) % llm_grid_h)
                    const w_index = Array.from({ length: grid_size }, (_, i) => offset + i % llm_grid_w)

                    llm_pos_ids_list.push([t_index, h_index, w_index].flat())

                    st = ed + grid_size;
                }

                if (st < ids.length) {
                    const st_idx = llm_pos_ids_list.length > 0
                        ? max(llm_pos_ids_list.at(-1))[0] + 1
                        : 0;
                    const text_len = ids.length - st;

                    llm_pos_ids_list.push(
                        Array.from({ length: 3 * text_len }, (_, i) => (st_idx + (i % text_len)))
                    )
                }

                // NOTE: Each item in llm_pos_ids_list is an array of shape (3, text_len),
                // meaning to perform concatenation along dim=1, we can do the following:
                const num_items = llm_pos_ids_list.reduce((acc, x) => acc + x.length, 0);
                const llm_positions = new Array(num_items);
                let index = 0;
                for (let x = 0; x < 3; ++x) {
                    for (let y = 0; y < llm_pos_ids_list.length; ++y) {
                        const val = llm_pos_ids_list[y];
                        const text_len = val.length / 3;
                        for (let z = x * text_len; z < (x + 1) * text_len; ++z) {
                            llm_positions[index++] = val[z];
                        }
                    }
                }

                let count = 0;
                const attn_mask = attention_mask_list[i];
                for (let y = 0; y < attn_mask.length; ++y) {
                    if (attn_mask[y] == 1) {
                        for (let x = 0; x < 3; ++x) {
                            position_ids_list[x][i][y] = llm_positions[x * num_items / 3 + count];
                        }
                        ++count;
                    }
                }

                const max_llm_positions = max(llm_positions)[0];
                mrope_position_deltas.push(max_llm_positions + 1 - total_input_ids[i].length);
            }

            return [
                new Tensor('int64', position_ids_list.flat(Infinity), [3, input_ids.dims[0], input_ids.dims[1]]),
                new Tensor('int64', mrope_position_deltas, [mrope_position_deltas.length, 1]),
            ];

        } else { // Text-only
            if (attention_mask) {
                const { data, dims } = cumsum_masked_fill(attention_mask);

                const position_ids = BigInt64Array.from(
                    { length: 3 * data.length },
                    (_, i) => data[i % data.length]
                );
                const mrope_position_deltas = Array.from(
                    { length: dims[0] },
                    (_, i) => max(data.subarray(dims[1] * i, dims[1] * (i + 1)))[0] + 1 + dims[1]
                );

                return [
                    new Tensor('int64', position_ids, [3, ...dims]),
                    new Tensor('int64', mrope_position_deltas, [mrope_position_deltas.length, 1]),
                ]
            } else {
                const [batch_size, seq_length] = input_ids.dims;
                const position_ids = BigInt64Array.from(
                    { length: 3 * batch_size * seq_length },
                    (_, i) => BigInt(Math.floor(i % seq_length / batch_size)),
                );

                return [
                    new Tensor('int64', position_ids, [3, ...input_ids.dims]),
                    zeros([batch_size, 1]),
                ]
            }
        }
    }

    async encode_image({ pixel_values, image_grid_thw }) {
        const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, grid_thw: image_grid_thw })).image_features;
        return features;
    }

    _merge_input_ids_with_image_features(kwargs) {
        return default_merge_input_ids_with_image_features({
            // @ts-ignore
            image_token_id: this.config.image_token_id,
            ...kwargs
        })
    }

    prepare_inputs_for_generation(input_ids, model_inputs, generation_config) {
        // Overwritten -- in specific circumstances we don't want to forward image inputs to the model
        if (model_inputs.attention_mask && !model_inputs.position_ids) {
            // Calculate position_ids and rope_deltas
            if (!model_inputs.past_key_values) {
                ([model_inputs.position_ids, model_inputs.rope_deltas] = this.get_rope_index(
                    model_inputs.input_ids,
                    model_inputs.image_grid_thw,
                    model_inputs.video_grid_thw,
                    model_inputs.attention_mask,
                ));

            } else {
                model_inputs.pixel_values = null;
                // model_inputs.pixel_values_videos = null;

                const delta = BigInt(Object.values(model_inputs.past_key_values)[0].dims.at(-2));
                const rope_deltas_list = model_inputs.rope_deltas.map(x => delta + x);
                model_inputs.position_ids = stack([rope_deltas_list, rope_deltas_list, rope_deltas_list], 0)
            }
        }

        return model_inputs;
    }
}


//////////////////////////////////////////////////
// Phi models
export class PhiPreTrainedModel extends PreTrainedModel { }
/**
 * The bare Phi Model outputting raw hidden-states without any specific head on top.
 */
export class PhiModel extends PhiPreTrainedModel { }

export class PhiForCausalLM extends PhiPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Phi3 models
export class Phi3PreTrainedModel extends PreTrainedModel { }

/**
 * The bare Phi3 Model outputting raw hidden-states without any specific head on top.
 */
export class Phi3Model extends Phi3PreTrainedModel { }

export class Phi3ForCausalLM extends Phi3PreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Bloom models
/**
 * The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
 */
export class BloomPreTrainedModel extends PreTrainedModel { }

/**
 * The bare Bloom Model transformer outputting raw hidden-states without any specific head on top.
 */
export class BloomModel extends BloomPreTrainedModel { }

/**
 * The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
 */
export class BloomForCausalLM extends BloomPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// MPT models
export class MptPreTrainedModel extends PreTrainedModel { }

/**
 * The bare Mpt Model transformer outputting raw hidden-states without any specific head on top.
 */
export class MptModel extends MptPreTrainedModel { }

/**
 * The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
 */
export class MptForCausalLM extends MptPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// OPT models
export class OPTPreTrainedModel extends PreTrainedModel { }

/**
 * The bare OPT Model outputting raw hidden-states without any specific head on top.
 */
export class OPTModel extends OPTPreTrainedModel { }

/**
 * The OPT Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
 */
export class OPTForCausalLM extends OPTPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class ViTPreTrainedModel extends PreTrainedModel { }
export class ViTModel extends ViTPreTrainedModel { }
export class ViTForImageClassification extends ViTPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class IJepaPreTrainedModel extends PreTrainedModel { }
export class IJepaModel extends IJepaPreTrainedModel { }
export class IJepaForImageClassification extends IJepaPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class VitPosePreTrainedModel extends PreTrainedModel { }

/**
 * The VitPose model with a pose estimation head on top.
 */
export class VitPoseForPoseEstimation extends VitPosePreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class PvtPreTrainedModel extends PreTrainedModel { }
export class PvtModel extends PvtPreTrainedModel { }
export class PvtForImageClassification extends PvtPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class ViTMAEPreTrainedModel extends PreTrainedModel { }
export class ViTMAEModel extends ViTMAEPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class ViTMSNPreTrainedModel extends PreTrainedModel { }
export class ViTMSNModel extends ViTMSNPreTrainedModel { }
export class ViTMSNForImageClassification extends ViTMSNPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class GroupViTPreTrainedModel extends PreTrainedModel { }
export class GroupViTModel extends GroupViTPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class FastViTPreTrainedModel extends PreTrainedModel { }
export class FastViTModel extends FastViTPreTrainedModel { }
export class FastViTForImageClassification extends FastViTPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class VitMattePreTrainedModel extends PreTrainedModel { }

/**
 * ViTMatte framework leveraging any vision backbone e.g. for ADE20k, CityScapes.
 * 
 * **Example:** Perform image matting with a `VitMatteForImageMatting` model.
 * ```javascript
 * import { AutoProcessor, VitMatteForImageMatting, RawImage } from '@huggingface/transformers';
 * 
 * // Load processor and model
 * const processor = await AutoProcessor.from_pretrained('Xenova/vitmatte-small-distinctions-646');
 * const model = await VitMatteForImageMatting.from_pretrained('Xenova/vitmatte-small-distinctions-646');
 * 
 * // Load image and trimap
 * const image = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_image.png');
 * const trimap = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_trimap.png');
 * 
 * // Prepare image + trimap for the model
 * const inputs = await processor(image, trimap);
 * 
 * // Predict alpha matte
 * const { alphas } = await model(inputs);
 * // Tensor {
 * //   dims: [ 1, 1, 640, 960 ],
 * //   type: 'float32',
 * //   size: 614400,
 * //   data: Float32Array(614400) [ 0.9894027709960938, 0.9970508813858032, ... ]
 * // }
 * ```
 * 
 * You can visualize the alpha matte as follows:
 * ```javascript
 * import { Tensor, cat } from '@huggingface/transformers';
 * 
 * // Visualize predicted alpha matte
 * const imageTensor = image.toTensor();
 * 
 * // Convert float (0-1) alpha matte to uint8 (0-255)
 * const alphaChannel = alphas
 *   .squeeze(0)
 *   .mul_(255)
 *   .clamp_(0, 255)
 *   .round_()
 *   .to('uint8');
 * 
 * // Concatenate original image with predicted alpha
 * const imageData = cat([imageTensor, alphaChannel], 0);
 * 
 * // Save output image
 * const outputImage = RawImage.fromTensor(imageData);
 * outputImage.save('output.png');
 * ```
 */
export class VitMatteForImageMatting extends VitMattePreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new ImageMattingOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class MobileViTPreTrainedModel extends PreTrainedModel { }
export class MobileViTModel extends MobileViTPreTrainedModel { }
export class MobileViTForImageClassification extends MobileViTPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
// TODO: MobileViTForSemanticSegmentation

//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class MobileViTV2PreTrainedModel extends PreTrainedModel { }
export class MobileViTV2Model extends MobileViTV2PreTrainedModel { }
export class MobileViTV2ForImageClassification extends MobileViTV2PreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
// TODO: MobileViTV2ForSemanticSegmentation

//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class OwlViTPreTrainedModel extends PreTrainedModel { }
export class OwlViTModel extends OwlViTPreTrainedModel { }
export class OwlViTForObjectDetection extends OwlViTPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class Owlv2PreTrainedModel extends PreTrainedModel { }
export class Owlv2Model extends Owlv2PreTrainedModel { }
export class Owlv2ForObjectDetection extends Owlv2PreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Beit Models
export class BeitPreTrainedModel extends PreTrainedModel { }
export class BeitModel extends BeitPreTrainedModel { }
export class BeitForImageClassification extends BeitPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class DetrPreTrainedModel extends PreTrainedModel { }
export class DetrModel extends DetrPreTrainedModel { }
export class DetrForObjectDetection extends DetrPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new DetrObjectDetectionOutput(await super._call(model_inputs));
    }
}

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
     */
    async _call(model_inputs) {
        return new DetrSegmentationOutput(await super._call(model_inputs));
    }
}

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 }) {
        super();
        this.logits = logits;
        this.pred_boxes = pred_boxes;
    }
}

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 }) {
        super();
        this.logits = logits;
        this.pred_boxes = pred_boxes;
        this.pred_masks = pred_masks;
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class RTDetrPreTrainedModel extends PreTrainedModel { }
export class RTDetrModel extends RTDetrPreTrainedModel { }
export class RTDetrForObjectDetection extends RTDetrPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new RTDetrObjectDetectionOutput(await super._call(model_inputs));
    }
}

export class RTDetrObjectDetectionOutput 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 }) {
        super();
        this.logits = logits;
        this.pred_boxes = pred_boxes;
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class TableTransformerPreTrainedModel extends PreTrainedModel { }

/**
 * The bare Table Transformer Model (consisting of a backbone and encoder-decoder Transformer)
 * outputting raw hidden-states without any specific head on top.
 */
export class TableTransformerModel extends TableTransformerPreTrainedModel { }

/**
 * Table Transformer Model (consisting of a backbone and encoder-decoder Transformer)
 * with object detection heads on top, for tasks such as COCO detection.
 */
export class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new TableTransformerObjectDetectionOutput(await super._call(model_inputs));
    }
}
export class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class DeiTPreTrainedModel extends PreTrainedModel { }
export class DeiTModel extends DeiTPreTrainedModel { }
export class DeiTForImageClassification extends DeiTPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class HieraPreTrainedModel extends PreTrainedModel { }
export class HieraModel extends HieraPreTrainedModel { }
export class HieraForImageClassification extends HieraPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
/**
 * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
 */
export class ResNetPreTrainedModel extends PreTrainedModel { }

/**
 * The bare ResNet model outputting raw features without any specific head on top.
 */
export class ResNetModel extends ResNetPreTrainedModel { }

/**
 * ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.
 */
export class ResNetForImageClassification extends ResNetPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class SwinPreTrainedModel extends PreTrainedModel { }
export class SwinModel extends SwinPreTrainedModel { }
export class SwinForImageClassification extends SwinPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class Swin2SRPreTrainedModel extends PreTrainedModel { }

/**
 * The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top.
 */
export class Swin2SRModel extends Swin2SRPreTrainedModel { }

/**
 * Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration.
 * 
 * **Example:** Super-resolution w/ `Xenova/swin2SR-classical-sr-x2-64`.
 * 
 * ```javascript
 * import { AutoProcessor, Swin2SRForImageSuperResolution, RawImage } from '@huggingface/transformers';
 * 
 * // Load processor and model
 * const model_id = 'Xenova/swin2SR-classical-sr-x2-64';
 * const processor = await AutoProcessor.from_pretrained(model_id);
 * const model = await Swin2SRForImageSuperResolution.from_pretrained(model_id);
 * 
 * // Prepare model inputs
 * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg';
 * const image = await RawImage.fromURL(url);
 * const inputs = await processor(image);
 * 
 * // Run model
 * const outputs = await model(inputs);
 * 
 * // Convert Tensor to RawImage
 * const output = outputs.reconstruction.squeeze().clamp_(0, 1).mul_(255).round_().to('uint8');
 * const outputImage = RawImage.fromTensor(output);
 * // RawImage {
 * //   data: Uint8Array(786432) [ 41, 31, 24, ... ],
 * //   width: 512,
 * //   height: 512,
 * //   channels: 3
 * // }
 * ```
 */
export class Swin2SRForImageSuperResolution extends Swin2SRPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class DPTPreTrainedModel extends PreTrainedModel { }

/**
 * The bare DPT Model transformer outputting raw hidden-states without any specific head on top.
 */
export class DPTModel extends DPTPreTrainedModel { }

/**
 * DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2.
 * 
 * **Example:** Depth estimation w/ `Xenova/dpt-hybrid-midas`.
 * ```javascript
 * import { DPTForDepthEstimation, AutoProcessor, RawImage, interpolate, max } from '@huggingface/transformers';
 * 
 * // Load model and processor
 * const model_id = 'Xenova/dpt-hybrid-midas';
 * const model = await DPTForDepthEstimation.from_pretrained(model_id);
 * const processor = await AutoProcessor.from_pretrained(model_id);
 * 
 * // Load image from URL
 * const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
 * const image = await RawImage.fromURL(url);
 * 
 * // Prepare image for the model
 * const inputs = await processor(image);
 * 
 * // Run model
 * const { predicted_depth } = await model(inputs);
 * 
 * // Interpolate to original size
 * const prediction = interpolate(predicted_depth, image.size.reverse(), 'bilinear', false);
 * 
 * // Visualize the prediction
 * const formatted = prediction.mul_(255 / max(prediction.data)[0]).to('uint8');
 * const depth = RawImage.fromTensor(formatted);
 * // RawImage {
 * //   data: Uint8Array(307200) [ 85, 85, 84, ... ],
 * //   width: 640,
 * //   height: 480,
 * //   channels: 1
 * // }
 * ```
 */
export class DPTForDepthEstimation extends DPTPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class DepthAnythingPreTrainedModel extends PreTrainedModel { }

/**
 * Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2.
 */
export class DepthAnythingForDepthEstimation extends DepthAnythingPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class SapiensPreTrainedModel extends PreTrainedModel { }
export class SapiensForSemanticSegmentation extends SapiensPreTrainedModel { }
export class SapiensForDepthEstimation extends SapiensPreTrainedModel { }
export class SapiensForNormalEstimation extends SapiensPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class DepthProPreTrainedModel extends PreTrainedModel { }
export class DepthProForDepthEstimation extends DepthProPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class MaskFormerPreTrainedModel extends PreTrainedModel { }
export class MaskFormerModel extends MaskFormerPreTrainedModel { }
export class MaskFormerForInstanceSegmentation extends MaskFormerPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class GLPNPreTrainedModel extends PreTrainedModel { }

/**
 * The bare GLPN encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.
 */
export class GLPNModel extends GLPNPreTrainedModel { }

/**
 * GLPN Model transformer with a lightweight depth estimation head on top e.g. for KITTI, NYUv2.
 * 
 * **Example:** Depth estimation w/ `Xenova/glpn-kitti`.
 * ```javascript
 * import { GLPNForDepthEstimation, AutoProcessor, RawImage, interpolate, max } from '@huggingface/transformers';
 * 
 * // Load model and processor
 * const model_id = 'Xenova/glpn-kitti';
 * const model = await GLPNForDepthEstimation.from_pretrained(model_id);
 * const processor = await AutoProcessor.from_pretrained(model_id);
 * 
 * // Load image from URL
 * const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
 * const image = await RawImage.fromURL(url);
 * 
 * // Prepare image for the model
 * const inputs = await processor(image);
 * 
 * // Run model
 * const { predicted_depth } = await model(inputs);
 * 
 * // Interpolate to original size
 * const prediction = interpolate(predicted_depth, image.size.reverse(), 'bilinear', false);
 * 
 * // Visualize the prediction
 * const formatted = prediction.mul_(255 / max(prediction.data)[0]).to('uint8');
 * const depth = RawImage.fromTensor(formatted);
 * // RawImage {
 * //   data: Uint8Array(307200) [ 207, 169, 154, ... ],
 * //   width: 640,
 * //   height: 480,
 * //   channels: 1
 * // }
 * ```
 */
export class GLPNForDepthEstimation extends GLPNPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class DonutSwinPreTrainedModel extends PreTrainedModel { }

/**
 * The bare Donut Swin Model transformer outputting raw hidden-states without any specific head on top.
 * 
 * **Example:** Step-by-step Document Parsing.
 * 
 * ```javascript
 * import { AutoProcessor, AutoTokenizer, AutoModelForVision2Seq, RawImage } from '@huggingface/transformers';
 * 
 * // Choose model to use
 * const model_id = 'Xenova/donut-base-finetuned-cord-v2';
 * 
 * // Prepare image inputs
 * const processor = await AutoProcessor.from_pretrained(model_id);
 * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/receipt.png';
 * const image = await RawImage.read(url);
 * const image_inputs = await processor(image);
 * 
 * // Prepare decoder inputs
 * const tokenizer = await AutoTokenizer.from_pretrained(model_id);
 * const task_prompt = '<s_cord-v2>';
 * const decoder_input_ids = tokenizer(task_prompt, {
 *   add_special_tokens: false,
 * }).input_ids;
 * 
 * // Create the model
 * const model = await AutoModelForVision2Seq.from_pretrained(model_id);
 * 
 * // Run inference
 * const output = await model.generate(image_inputs.pixel_values, {
 *   decoder_input_ids,
 *   max_length: model.config.decoder.max_position_embeddings,
 * });
 * 
 * // Decode output
 * const decoded = tokenizer.batch_decode(output)[0];
 * // <s_cord-v2><s_menu><s_nm> CINNAMON SUGAR</s_nm><s_unitprice> 17,000</s_unitprice><s_cnt> 1 x</s_cnt><s_price> 17,000</s_price></s_menu><s_sub_total><s_subtotal_price> 17,000</s_subtotal_price></s_sub_total><s_total><s_total_price> 17,000</s_total_price><s_cashprice> 20,000</s_cashprice><s_changeprice> 3,000</s_changeprice></s_total></s>
 * ```
 * 
 * **Example:** Step-by-step Document Visual Question Answering (DocVQA)
 * 
 * ```javascript
 * import { AutoProcessor, AutoTokenizer, AutoModelForVision2Seq, RawImage } from '@huggingface/transformers';
 * 
 * // Choose model to use
 * const model_id = 'Xenova/donut-base-finetuned-docvqa';
 * 
 * // Prepare image inputs
 * const processor = await AutoProcessor.from_pretrained(model_id);
 * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png';
 * const image = await RawImage.read(url);
 * const image_inputs = await processor(image);
 * 
 * // Prepare decoder inputs
 * const tokenizer = await AutoTokenizer.from_pretrained(model_id);
 * const question = 'What is the invoice number?';
 * const task_prompt = `<s_docvqa><s_question>${question}</s_question><s_answer>`;
 * const decoder_input_ids = tokenizer(task_prompt, {
 *   add_special_tokens: false,
 * }).input_ids;
 * 
 * // Create the model
 * const model = await AutoModelForVision2Seq.from_pretrained(model_id);
 * 
 * // Run inference
 * const output = await model.generate(image_inputs.pixel_values, {
 *   decoder_input_ids,
 *   max_length: model.config.decoder.max_position_embeddings,
 * });
 * 
 * // Decode output
 * const decoded = tokenizer.batch_decode(output)[0];
 * // <s_docvqa><s_question> What is the invoice number?</s_question><s_answer> us-001</s_answer></s>
 * ```
 */
export class DonutSwinModel extends DonutSwinPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class ConvNextPreTrainedModel extends PreTrainedModel { }

/**
 * The bare ConvNext model outputting raw features without any specific head on top.
 */
export class ConvNextModel extends ConvNextPreTrainedModel { }

/**
 * ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.
 */
export class ConvNextForImageClassification extends ConvNextPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class ConvNextV2PreTrainedModel extends PreTrainedModel { }

/**
 * The bare ConvNextV2 model outputting raw features without any specific head on top.
 */
export class ConvNextV2Model extends ConvNextV2PreTrainedModel { }

/**
 * ConvNextV2 Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.
 */
export class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
export class Dinov2PreTrainedModel extends PreTrainedModel { }

/**
 * The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.
 */
export class Dinov2Model extends Dinov2PreTrainedModel { }

/**
 * Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.
 */
export class Dinov2ForImageClassification extends Dinov2PreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class YolosPreTrainedModel extends PreTrainedModel { }
export class YolosModel extends YolosPreTrainedModel { }
export class YolosForObjectDetection extends YolosPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new YolosObjectDetectionOutput(await super._call(model_inputs));
    }
}

export class YolosObjectDetectionOutput 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 }) {
        super();
        this.logits = logits;
        this.pred_boxes = pred_boxes;
    }
}
//////////////////////////////////////////////////




//////////////////////////////////////////////////
export class SamPreTrainedModel extends PreTrainedModel { }

/**
 * Segment Anything Model (SAM) for generating segmentation masks, given an input image
 * and optional 2D location and bounding boxes.
 * 
 * **Example:** Perform mask generation w/ `Xenova/sam-vit-base`.
 * ```javascript
 * import { SamModel, AutoProcessor, RawImage } from '@huggingface/transformers';
 * 
 * const model = await SamModel.from_pretrained('Xenova/sam-vit-base');
 * const processor = await AutoProcessor.from_pretrained('Xenova/sam-vit-base');
 * 
 * const img_url = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png';
 * const raw_image = await RawImage.read(img_url);
 * const input_points = [[[450, 600]]] // 2D localization of a window
 * 
 * const inputs = await processor(raw_image, { input_points });
 * const outputs = await model(inputs);
 * 
 * const masks = await processor.post_process_masks(outputs.pred_masks, inputs.original_sizes, inputs.reshaped_input_sizes);
 * // [
 * //   Tensor {
 * //     dims: [ 1, 3, 1764, 2646 ],
 * //     type: 'bool',
 * //     data: Uint8Array(14002632) [ ... ],
 * //     size: 14002632
 * //   }
 * // ]
 * const scores = outputs.iou_scores;
 * // Tensor {
 * //   dims: [ 1, 1, 3 ],
 * //   type: 'float32',
 * //   data: Float32Array(3) [
 * //     0.8892380595207214,
 * //     0.9311248064041138,
 * //     0.983696699142456
 * //   ],
 * //   size: 3
 * // }
 * ```
 */
export class SamModel extends SamPreTrainedModel {

    /**
     * Compute image embeddings and positional image embeddings, given the pixel values of an image.
     * @param {Object} model_inputs Object containing the model inputs.
     * @param {Tensor} model_inputs.pixel_values Pixel values obtained using a `SamProcessor`.
     * @returns {Promise<{ image_embeddings: Tensor, image_positional_embeddings: Tensor }>} The image embeddings and positional image embeddings.
     */
    async get_image_embeddings({ pixel_values }) {
        // in:
        //  - pixel_values: tensor.float32[batch_size,3,1024,1024]
        // 
        // out:
        //  - image_embeddings: tensor.float32[batch_size,256,64,64]
        //  - image_positional_embeddings: tensor.float32[batch_size,256,64,64]
        return await encoderForward(this, { pixel_values })
    }

    /**
     * @typedef {Object} SamModelInputs Object containing the model inputs.
     * @property {Tensor} pixel_values Pixel values as a Tensor with shape `(batch_size, num_channels, height, width)`.
     * These can be obtained using a `SamProcessor`.
     * @property {Tensor} [input_points] Input 2D spatial points with shape `(batch_size, num_points, 2)`.
     * This is used by the prompt encoder to encode the prompt.
     * @property {Tensor} [input_labels] Input labels for the points, as a Tensor of shape `(batch_size, point_batch_size, num_points)`.
     * This is used by the prompt encoder to encode the prompt. There are 4 types of labels:
     *  - `1`: the point is a point that contains the object of interest
     *  - `0`: the point is a point that does not contain the object of interest
     *  - `-1`: the point corresponds to the background
     *  - `-10`: the point is a padding point, thus should be ignored by the prompt encoder
     * @property {Tensor} [input_boxes] Input bounding boxes with shape `(batch_size, num_boxes, 4)`.
     * @property {Tensor} [image_embeddings] Image embeddings used by the mask decoder.
     * @property {Tensor} [image_positional_embeddings] Image positional embeddings used by the mask decoder.
     */

    /**
     * @param {SamModelInputs} model_inputs Object containing the model inputs.
     * @returns {Promise<Object>} The output of the model.
     */
    async forward(model_inputs) {
        if (!model_inputs.image_embeddings || !model_inputs.image_positional_embeddings) {
            // Compute the image embeddings if they are missing
            model_inputs = {
                ...model_inputs,
                ...(await this.get_image_embeddings(model_inputs))
            }
        }

        if (!model_inputs.input_labels && model_inputs.input_points) {
            // Set default input labels if they are missing
            const shape = model_inputs.input_points.dims.slice(0, -1);
            const numElements = shape.reduce((a, b) => a * b, 1);
            model_inputs.input_labels = new Tensor(
                'int64',
                new BigInt64Array(numElements).fill(1n),
                shape
            );
        }

        const decoder_inputs = {
            image_embeddings: model_inputs.image_embeddings,
            image_positional_embeddings: model_inputs.image_positional_embeddings,
        };
        if (model_inputs.input_points) {
            decoder_inputs.input_points = model_inputs.input_points;
        }
        if (model_inputs.input_labels) {
            decoder_inputs.input_labels = model_inputs.input_labels;
        }
        if (model_inputs.input_boxes) {
            decoder_inputs.input_boxes = model_inputs.input_boxes;
        }

        // Returns:
        //  - iou_scores: tensor.float32[batch_size,point_batch_size,3]
        //  - pred_masks: tensor.float32[batch_size,point_batch_size,3,256,256]
        return await sessionRun(this.sessions['prompt_encoder_mask_decoder'], decoder_inputs);
    }

    /**
     * Runs the model with the provided inputs
     * @param {Object} model_inputs Model inputs
     * @returns {Promise<SamImageSegmentationOutput>} Object containing segmentation outputs
     */
    async _call(model_inputs) {
        return new SamImageSegmentationOutput(await super._call(model_inputs));
    }
}


/**
 * 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 }) {
        super();
        this.iou_scores = iou_scores;
        this.pred_masks = pred_masks;
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// MarianMT models
export class MarianPreTrainedModel extends PreTrainedModel { };

export class MarianModel extends MarianPreTrainedModel { }

export class MarianMTModel extends MarianPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// M2M100 models
export class M2M100PreTrainedModel extends PreTrainedModel { };

export class M2M100Model extends M2M100PreTrainedModel { }

export class M2M100ForConditionalGeneration extends M2M100PreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Wav2Vec2 models
export class Wav2Vec2PreTrainedModel extends PreTrainedModel { };

/**
 * The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.
 * 
 * **Example:** Load and run a `Wav2Vec2Model` for feature extraction.
 * 
 * ```javascript
 * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
 * 
 * // Read and preprocess audio
 * const processor = await AutoProcessor.from_pretrained('Xenova/mms-300m');
 * const audio = await read_audio('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac', 16000);
 * const inputs = await processor(audio);
 * 
 * // Run model with inputs
 * const model = await AutoModel.from_pretrained('Xenova/mms-300m');
 * const output = await model(inputs);
 * // {
 * //   last_hidden_state: Tensor {
 * //     dims: [ 1, 1144, 1024 ],
 * //     type: 'float32',
 * //     data: Float32Array(1171456) [ ... ],
 * //     size: 1171456
 * //   }
 * // }
 * ```
 */
export class Wav2Vec2Model extends Wav2Vec2PreTrainedModel { }

export class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel {
    /**
     * @param {Object} model_inputs
     * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
     * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
     */
    async _call(model_inputs) {
        return new CausalLMOutput(await super._call(model_inputs));
    }
}

export class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * Wav2Vec2 Model with a frame classification head on top for tasks like Speaker Diarization.
 */
export class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel {
    /**
     * 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 sequence classification.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// PyAnnote models
export class PyAnnotePreTrainedModel extends PreTrainedModel { };

/**
 * The bare PyAnnote Model transformer outputting raw hidden-states without any specific head on top.
 */
export class PyAnnoteModel extends PyAnnotePreTrainedModel { }

/**
 * PyAnnote Model with a frame classification head on top for tasks like Speaker Diarization.
 * 
 * **Example:** Load and run a `PyAnnoteForAudioFrameClassification` for speaker diarization.
 * 
 * ```javascript
 * import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@huggingface/transformers';
 * 
 * // Load model and processor
 * const model_id = 'onnx-community/pyannote-segmentation-3.0';
 * const model = await AutoModelForAudioFrameClassification.from_pretrained(model_id);
 * const processor = await AutoProcessor.from_pretrained(model_id);
 * 
 * // Read and preprocess audio
 * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/mlk.wav';
 * const audio = await read_audio(url, processor.feature_extractor.config.sampling_rate);
 * const inputs = await processor(audio);
 * 
 * // Run model with inputs
 * const { logits } = await model(inputs);
 * // {
 * //   logits: Tensor {
 * //     dims: [ 1, 767, 7 ],  // [batch_size, num_frames, num_classes]
 * //     type: 'float32',
 * //     data: Float32Array(5369) [ ... ],
 * //     size: 5369
 * //   }
 * // }
 * 
 * const result = processor.post_process_speaker_diarization(logits, audio.length);
 * // [
 * //   [
 * //     { id: 0, start: 0, end: 1.0512535626298245, confidence: 0.8220156481664611 },
 * //     { id: 2, start: 1.0512535626298245, end: 2.3398869619825127, confidence: 0.9008811707860472 },
 * //     ...
 * //   ]
 * // ]
 * 
 * // Display result
 * console.table(result[0], ['start', 'end', 'id', 'confidence']);
 * // ┌─────────┬────────────────────┬────────────────────┬────┬─────────────────────┐
 * // │ (index) │ start              │ end                │ id │ confidence          │
 * // ├─────────┼────────────────────┼────────────────────┼────┼─────────────────────┤
 * // │ 0       │ 0                  │ 1.0512535626298245 │ 0  │ 0.8220156481664611  │
 * // │ 1       │ 1.0512535626298245 │ 2.3398869619825127 │ 2  │ 0.9008811707860472  │
 * // │ 2       │ 2.3398869619825127 │ 3.5946089560890773 │ 0  │ 0.7521651315796233  │
 * // │ 3       │ 3.5946089560890773 │ 4.578039708226655  │ 2  │ 0.8491978128022479  │
 * // │ 4       │ 4.578039708226655  │ 4.594995410849717  │ 0  │ 0.2935352600416393  │
 * // │ 5       │ 4.594995410849717  │ 6.121008646925269  │ 3  │ 0.6788051309866024  │
 * // │ 6       │ 6.121008646925269  │ 6.256654267909762  │ 0  │ 0.37125512393851134 │
 * // │ 7       │ 6.256654267909762  │ 8.630452635138397  │ 2  │ 0.7467035186353542  │
 * // │ 8       │ 8.630452635138397  │ 10.088643060721703 │ 0  │ 0.7689364814666032  │
 * // │ 9       │ 10.088643060721703 │ 12.58113134631177  │ 2  │ 0.9123324509131324  │
 * // │ 10      │ 12.58113134631177  │ 13.005023911888312 │ 0  │ 0.4828358177572041  │
 * // └─────────┴────────────────────┴────────────────────┴────┴─────────────────────┘
 * ```
 */
export class PyAnnoteForAudioFrameClassification extends PyAnnotePreTrainedModel {
    /**
     * 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 sequence classification.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// WeSpeakerResNet models
export class WeSpeakerResNetPreTrainedModel extends PreTrainedModel { };
export class WeSpeakerResNetModel extends WeSpeakerResNetPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// UniSpeech models
export class UniSpeechPreTrainedModel extends PreTrainedModel { };

/**
 * The bare UniSpeech Model transformer outputting raw hidden-states without any specific head on top.
 */
export class UniSpeechModel extends UniSpeechPreTrainedModel { }

/**
 * UniSpeech Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
 */
export class UniSpeechForCTC extends UniSpeechPreTrainedModel {
    /**
     * @param {Object} model_inputs
     * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
     * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
     */
    async _call(model_inputs) {
        return new CausalLMOutput(await super._call(model_inputs));
    }
}

/**
 * UniSpeech Model with a sequence classification head on top (a linear layer over the pooled output).
 */
export class UniSpeechForSequenceClassification extends UniSpeechPreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// UniSpeechSat models
export class UniSpeechSatPreTrainedModel extends PreTrainedModel { };

/**
 * The bare UniSpeechSat Model transformer outputting raw hidden-states without any specific head on top.
 */
export class UniSpeechSatModel extends UniSpeechSatPreTrainedModel { }

/**
 * UniSpeechSat Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
 */
export class UniSpeechSatForCTC extends UniSpeechSatPreTrainedModel {
    /**
     * @param {Object} model_inputs
     * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
     * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
     */
    async _call(model_inputs) {
        return new CausalLMOutput(await super._call(model_inputs));
    }
}

/**
 * UniSpeechSat Model with a sequence classification head on top (a linear layer over the pooled output).
 */
export class UniSpeechSatForSequenceClassification extends UniSpeechSatPreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * UniSpeechSat Model with a frame classification head on top for tasks like Speaker Diarization.
 */
export class UniSpeechSatForAudioFrameClassification extends UniSpeechSatPreTrainedModel {
    /**
     * 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 sequence classification.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Wav2Vec2Bert models
export class Wav2Vec2BertPreTrainedModel extends PreTrainedModel { };

/**
 * The bare Wav2Vec2Bert Model transformer outputting raw hidden-states without any specific head on top.
 */
export class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel { }

/**
 * Wav2Vec2Bert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
 */
export class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel {
    /**
     * @param {Object} model_inputs
     * @param {Tensor} model_inputs.input_features Float values of input mel-spectrogram.
     * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
     */
    async _call(model_inputs) {
        return new CausalLMOutput(await super._call(model_inputs));
    }
}

/**
 * Wav2Vec2Bert Model with a sequence classification head on top (a linear layer over the pooled output).
 */
export class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Hubert models
export class HubertPreTrainedModel extends PreTrainedModel { }

/**
 * The bare Hubert Model transformer outputting raw hidden-states without any specific head on top.
 * 
 * **Example:** Load and run a `HubertModel` for feature extraction.
 * 
 * ```javascript
 * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
 * 
 * // Read and preprocess audio
 * const processor = await AutoProcessor.from_pretrained('Xenova/hubert-base-ls960');
 * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000);
 * const inputs = await processor(audio);
 * 
 * // Load and run model with inputs
 * const model = await AutoModel.from_pretrained('Xenova/hubert-base-ls960');
 * const output = await model(inputs);
 * // {
 * //   last_hidden_state: Tensor {
 * //     dims: [ 1, 549, 768 ],
 * //     type: 'float32',
 * //     data: Float32Array(421632) [0.0682469978928566, 0.08104046434164047, -0.4975186586380005, ...],
 * //     size: 421632
 * //   }
 * // }
 * ```
 */
export class HubertModel extends Wav2Vec2PreTrainedModel { }

/**
 * Hubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
 */
export class HubertForCTC extends Wav2Vec2PreTrainedModel {
    /**
     * @param {Object} model_inputs
     * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
     * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
     */
    async _call(model_inputs) {
        return new CausalLMOutput(await super._call(model_inputs));
    }
}

/**
 * Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting.
 */
export class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// WavLM models
/**
 * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
 */
export class WavLMPreTrainedModel extends PreTrainedModel { };

/**
 * The bare WavLM Model transformer outputting raw hidden-states without any specific head on top.
 * 
 * **Example:** Load and run a `WavLMModel` for feature extraction.
 * 
 * ```javascript
 * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
 * 
 * // Read and preprocess audio
 * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base');
 * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000);
 * const inputs = await processor(audio);
 * 
 * // Run model with inputs
 * const model = await AutoModel.from_pretrained('Xenova/wavlm-base');
 * const output = await model(inputs);
 * // {
 * //   last_hidden_state: Tensor {
 * //     dims: [ 1, 549, 768 ],
 * //     type: 'float32',
 * //     data: Float32Array(421632) [-0.349443256855011, -0.39341306686401367,  0.022836603224277496, ...],
 * //     size: 421632
 * //   }
 * // }
 * ```
 */
export class WavLMModel extends WavLMPreTrainedModel { }

/**
 * WavLM Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
 */
export class WavLMForCTC extends WavLMPreTrainedModel {
    /**
     * @param {Object} model_inputs
     * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
     * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
     */
    async _call(model_inputs) {
        return new CausalLMOutput(await super._call(model_inputs));
    }
}

/**
 * WavLM Model with a sequence classification head on top (a linear layer over the pooled output).
 */
export class WavLMForSequenceClassification extends WavLMPreTrainedModel {
    /**
     * 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.
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

/**
 * WavLM Model with an XVector feature extraction head on top for tasks like Speaker Verification.
 * 
 * **Example:** Extract speaker embeddings with `WavLMForXVector`.
 * ```javascript
 * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
 * 
 * // Read and preprocess audio
 * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base-plus-sv');
 * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
 * const audio = await read_audio(url, 16000);
 * const inputs = await processor(audio);
 * 
 * // Run model with inputs
 * const model = await AutoModel.from_pretrained('Xenova/wavlm-base-plus-sv');
 * const outputs = await model(inputs);
 * // {
 * //   logits: Tensor {
 * //     dims: [ 1, 512 ],
 * //     type: 'float32',
 * //     data: Float32Array(512) [0.5847219228744507, ...],
 * //     size: 512
 * //   },
 * //   embeddings: Tensor {
 * //     dims: [ 1, 512 ],
 * //     type: 'float32',
 * //     data: Float32Array(512) [-0.09079201519489288, ...],
 * //     size: 512
 * //   }
 * // }
 * ```
 */
export class WavLMForXVector extends WavLMPreTrainedModel {
    /**
     * Calls the model on new inputs.
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<XVectorOutput>} An object containing the model's output logits and speaker embeddings.
     */
    async _call(model_inputs) {
        return new XVectorOutput(await super._call(model_inputs));
    }
}

/**
 * WavLM Model with a frame classification head on top for tasks like Speaker Diarization.
 * 
 * **Example:** Perform speaker diarization with `WavLMForAudioFrameClassification`.
 * ```javascript
 * import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@huggingface/transformers';
 * 
 * // Read and preprocess audio
 * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base-plus-sd');
 * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
 * const audio = await read_audio(url, 16000);
 * const inputs = await processor(audio);
 * 
 * // Run model with inputs
 * const model = await AutoModelForAudioFrameClassification.from_pretrained('Xenova/wavlm-base-plus-sd');
 * const { logits } = await model(inputs);
 * // {
 * //   logits: Tensor {
 * //     dims: [ 1, 549, 2 ],  // [batch_size, num_frames, num_speakers]
 * //     type: 'float32',
 * //     data: Float32Array(1098) [-3.5301010608673096, ...],
 * //     size: 1098
 * //   }
 * // }
 * 
 * const labels = logits[0].sigmoid().tolist().map(
 *     frames => frames.map(speaker => speaker > 0.5 ? 1 : 0)
 * );
 * console.log(labels); // labels is a one-hot array of shape (num_frames, num_speakers)
 * // [
 * //     [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
 * //     [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
 * //     [0, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1],
 * //     ...
 * // ]
 * ```
 */
export class WavLMForAudioFrameClassification extends WavLMPreTrainedModel {
    /**
     * 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 sequence classification.
     */
    async _call(model_inputs) {
        return new TokenClassifierOutput(await super._call(model_inputs));
    }
}

//////////////////////////////////////////////////
// SpeechT5 models
/**
 * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
 */
export class SpeechT5PreTrainedModel extends PreTrainedModel { };

/**
 * The bare SpeechT5 Encoder-Decoder Model outputting raw hidden-states without any specific pre- or post-nets.
 */
export class SpeechT5Model extends SpeechT5PreTrainedModel { };

/**
 * SpeechT5 Model with a speech encoder and a text decoder.
 * 
 * **Example:** Generate speech from text with `SpeechT5ForSpeechToText`.
 * ```javascript
 * import { AutoTokenizer, AutoProcessor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, Tensor } from '@huggingface/transformers';
 * 
 * // Load the tokenizer and processor
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/speecht5_tts');
 * const processor = await AutoProcessor.from_pretrained('Xenova/speecht5_tts');
 * 
 * // Load the models
 * // NOTE: We use the full-precision versions as they are more accurate
 * const model = await SpeechT5ForTextToSpeech.from_pretrained('Xenova/speecht5_tts', { dtype: 'fp32' });
 * const vocoder = await SpeechT5HifiGan.from_pretrained('Xenova/speecht5_hifigan', { dtype: 'fp32' });
 * 
 * // Load speaker embeddings from URL
 * const speaker_embeddings_data = new Float32Array(
 *     await (await fetch('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin')).arrayBuffer()
 * );
 * const speaker_embeddings = new Tensor(
 *     'float32',
 *     speaker_embeddings_data,
 *     [1, speaker_embeddings_data.length]
 * )
 * 
 * // Run tokenization
 * const { input_ids } = tokenizer('Hello, my dog is cute');
 * 
 * // Generate waveform
 * const { waveform } = await model.generate_speech(input_ids, speaker_embeddings, { vocoder });
 * console.log(waveform)
 * // Tensor {
 * //   dims: [ 26112 ],
 * //   type: 'float32',
 * //   size: 26112,
 * //   data: Float32Array(26112) [ -0.00043630177970044315, -0.00018082228780258447, ... ],
 * // }
 * ```
 */
export class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel { }

/**
 * SpeechT5 Model with a text encoder and a speech decoder.
 */
export class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {

    /**
     * @typedef {Object} SpeechOutput
     * @property {Tensor} [spectrogram] The predicted log-mel spectrogram of shape
     * `(output_sequence_length, config.num_mel_bins)`. Returned when no `vocoder` is provided
     * @property {Tensor} [waveform] The predicted waveform of shape `(num_frames,)`. Returned when a `vocoder` is provided.
     * @property {Tensor} [cross_attentions] The outputs of the decoder's cross-attention layers of shape
     * `(config.decoder_layers, config.decoder_attention_heads, output_sequence_length, input_sequence_length)`. returned when `output_cross_attentions` is `true`.
     */

    /**
     * Converts a sequence of input tokens into a sequence of mel spectrograms, which are subsequently turned into a speech waveform using a vocoder.
     * @param {Tensor} input_values Indices of input sequence tokens in the vocabulary.
     * @param {Tensor} speaker_embeddings Tensor containing the speaker embeddings.
     * @param {Object} options Optional parameters for generating speech.
     * @param {number} [options.threshold=0.5] The generated sequence ends when the predicted stop token probability exceeds this value.
     * @param {number} [options.minlenratio=0.0] Used to calculate the minimum required length for the output sequence.
     * @param {number} [options.maxlenratio=20.0] Used to calculate the maximum allowed length for the output sequence.
     * @param {Object} [options.vocoder=null] The vocoder that converts the mel spectrogram into a speech waveform. If `null`, the output is the mel spectrogram.
     * @param {boolean} [options.output_cross_attentions=false] Whether or not to return the attentions tensors of the decoder's cross-attention layers.
     * @returns {Promise<SpeechOutput>} A promise which resolves to an object containing the spectrogram, waveform, and cross-attention tensors.
     */
    async generate_speech(input_values, speaker_embeddings, {
        threshold = 0.5,
        minlenratio = 0.0,
        maxlenratio = 20.0,
        vocoder = null,
        // output_cross_attentions = false, // TODO add
    } = {}) {

        const model_inputs = {
            input_ids: input_values
        }

        const { encoder_outputs, encoder_attention_mask } = await encoderForward(this, model_inputs);

        const r = encoder_outputs.dims[1] / this.config.reduction_factor;
        const maxlen = Math.floor(r * maxlenratio);
        const minlen = Math.floor(r * minlenratio);

        const num_mel_bins = this.config.num_mel_bins;

        let spectrogramParts = [];
        let past_key_values = null;
        let decoder_outputs = null;
        let idx = 0;

        while (true) {
            ++idx;

            const use_cache_branch = boolTensor(!!decoder_outputs);
            let output_sequence;
            if (decoder_outputs) {
                output_sequence = decoder_outputs.output_sequence_out;
            } else {
                output_sequence = new Tensor(
                    'float32',
                    new Float32Array(num_mel_bins),
                    [1, 1, num_mel_bins],
                )
            }
            let decoderFeeds = {
                use_cache_branch,
                output_sequence,
                encoder_attention_mask: encoder_attention_mask,
                speaker_embeddings: speaker_embeddings,
                encoder_hidden_states: encoder_outputs,
            };

            this.addPastKeyValues(decoderFeeds, past_key_values);
            decoder_outputs = await sessionRun(this.sessions['decoder_model_merged'], decoderFeeds);
            past_key_values = this.getPastKeyValues(decoder_outputs, past_key_values);

            const { prob, spectrum } = decoder_outputs;
            spectrogramParts.push(spectrum);

            if (idx >= minlen && (
                // Finished when stop token or maximum length is reached.
                Array.from(prob.data).filter(p => p >= threshold).length > 0 || idx >= maxlen
            )) {
                break;
            }
        }

        const spectrogram = cat(spectrogramParts);
        const { waveform } = await sessionRun(vocoder.sessions['model'], { spectrogram });

        return {
            spectrogram,
            waveform,
            // cross_attentions: null, // TODO add
        }
    }
}

/**
 * HiFi-GAN vocoder.
 * 
 * See [SpeechT5ForSpeechToText](./models#module_models.SpeechT5ForSpeechToText) for example usage.
 */
export class SpeechT5HifiGan extends PreTrainedModel {
    main_input_name = 'spectrogram';
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// TrOCR models
export class TrOCRPreTrainedModel extends PreTrainedModel { }

/**
 * The TrOCR Decoder with a language modeling head.
 */
export class TrOCRForCausalLM extends TrOCRPreTrainedModel { }

//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Mistral models
/**
 * The bare Mistral Model outputting raw hidden-states without any specific head on top.
 */
export class MistralPreTrainedModel extends PreTrainedModel { }

export class MistralModel extends MistralPreTrainedModel { }

export class MistralForCausalLM extends MistralPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Starcoder2 models
/**
 * The bare Starcoder2 Model outputting raw hidden-states without any specific head on top.
 */
export class Starcoder2PreTrainedModel extends PreTrainedModel { }

export class Starcoder2Model extends Starcoder2PreTrainedModel { }

export class Starcoder2ForCausalLM extends Starcoder2PreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// Falcon models
/**
 * The bare Falcon Model outputting raw hidden-states without any specific head on top.
 */
export class FalconPreTrainedModel extends PreTrainedModel { }

export class FalconModel extends FalconPreTrainedModel { }

export class FalconForCausalLM extends FalconPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// CLAP models
export class ClapPreTrainedModel extends PreTrainedModel { }

export class ClapModel extends ClapPreTrainedModel { }

/**
 * CLAP Text Model with a projection layer on top (a linear layer on top of the pooled output).
 * 
 * **Example:** Compute text embeddings with `ClapTextModelWithProjection`.
 * 
 * ```javascript
 * import { AutoTokenizer, ClapTextModelWithProjection } from '@huggingface/transformers';
 * 
 * // Load tokenizer and text model
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clap-htsat-unfused');
 * const text_model = await ClapTextModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused');
 * 
 * // Run tokenization
 * const texts = ['a sound of a cat', 'a sound of a dog'];
 * const text_inputs = tokenizer(texts, { padding: true, truncation: true });
 * 
 * // Compute embeddings
 * const { text_embeds } = await text_model(text_inputs);
 * // Tensor {
 * //   dims: [ 2, 512 ],
 * //   type: 'float32',
 * //   data: Float32Array(1024) [ ... ],
 * //   size: 1024
 * // }
 * ```
 */
export class ClapTextModelWithProjection extends ClapPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'text_model',
            ...options,
        });
    }
}

/**
 * CLAP Audio Model with a projection layer on top (a linear layer on top of the pooled output).
 * 
 * **Example:** Compute audio embeddings with `ClapAudioModelWithProjection`.
 * 
 * ```javascript
 * import { AutoProcessor, ClapAudioModelWithProjection, read_audio } from '@huggingface/transformers';
 * 
 * // Load processor and audio model
 * const processor = await AutoProcessor.from_pretrained('Xenova/clap-htsat-unfused');
 * const audio_model = await ClapAudioModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused');
 * 
 * // Read audio and run processor
 * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav');
 * const audio_inputs = await processor(audio);
 * 
 * // Compute embeddings
 * const { audio_embeds } = await audio_model(audio_inputs);
 * // Tensor {
 * //   dims: [ 1, 512 ],
 * //   type: 'float32',
 * //   data: Float32Array(512) [ ... ],
 * //   size: 512
 * // }
 * ```
 */
export class ClapAudioModelWithProjection extends ClapPreTrainedModel {
    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
        return super.from_pretrained(pretrained_model_name_or_path, {
            // Update default model file name if not provided
            model_file_name: 'audio_model',
            ...options,
        });
    }
}
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// VITS models
export class VitsPreTrainedModel extends PreTrainedModel { }

/**
 * The complete VITS model, for text-to-speech synthesis.
 * 
 * **Example:** Generate speech from text with `VitsModel`.
 * ```javascript
 * import { AutoTokenizer, VitsModel } from '@huggingface/transformers';
 * 
 * // Load the tokenizer and model
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/mms-tts-eng');
 * const model = await VitsModel.from_pretrained('Xenova/mms-tts-eng');
 * 
 * // Run tokenization
 * const inputs = tokenizer('I love transformers');
 * 
 * // Generate waveform
 * const { waveform } = await model(inputs);
 * // Tensor {
 * //   dims: [ 1, 35328 ],
 * //   type: 'float32',
 * //   data: Float32Array(35328) [ ... ],
 * //   size: 35328,
 * // }
 * ```
 */
export class VitsModel extends VitsPreTrainedModel {
    /**
     * Calls the model on new inputs.
     * @param {Object} model_inputs The inputs to the model.
     * @returns {Promise<VitsModelOutput>} The outputs for the VITS model.
     */
    async _call(model_inputs) {
        return new VitsModelOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Segformer models
export class SegformerPreTrainedModel extends PreTrainedModel { }

/**
 * The bare SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.
 */
export class SegformerModel extends SegformerPreTrainedModel { }

/**
 * SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden states) e.g. for ImageNet.
 */
export class SegformerForImageClassification extends SegformerPreTrainedModel { }

/**
 * SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.
 */
export class SegformerForSemanticSegmentation extends SegformerPreTrainedModel { }

//////////////////////////////////////////////////

//////////////////////////////////////////////////
// StableLm models
export class StableLmPreTrainedModel extends PreTrainedModel { }

/**
 * The bare StableLm Model transformer outputting raw hidden-states without any specific head on top.
 */
export class StableLmModel extends StableLmPreTrainedModel { }

/**
 * StableLm Model with a `language modeling` head on top for Causal Language Modeling (with past).
 */
export class StableLmForCausalLM extends StableLmPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
export class EfficientNetPreTrainedModel extends PreTrainedModel { }

/**
 * The bare EfficientNet model outputting raw features without any specific head on top.
 */
export class EfficientNetModel extends EfficientNetPreTrainedModel { }

/**
 * EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features).
 */
export class EfficientNetForImageClassification extends EfficientNetPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Musicgen models
export class MusicgenPreTrainedModel extends PreTrainedModel { }

/**
 * The bare Musicgen decoder model outputting raw hidden-states without any specific head on top.
 */
export class MusicgenModel extends MusicgenPreTrainedModel { }

/**
 * The MusicGen decoder model with a language modelling head on top.
 */
export class MusicgenForCausalLM extends MusicgenPreTrainedModel { }

/**
 * The composite MusicGen model with a text encoder, audio encoder and Musicgen decoder,
 * for music generation tasks with one or both of text and audio prompts.
 * 
 * **Example:** Generate music from text with `Xenova/musicgen-small`.
 * ```javascript
 * import { AutoTokenizer, MusicgenForConditionalGeneration } from '@huggingface/transformers';
 * 
 * // Load tokenizer and model
 * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/musicgen-small');
 * const model = await MusicgenForConditionalGeneration.from_pretrained(
 *   'Xenova/musicgen-small', { dtype: 'fp32' }
 * );
 * 
 * // Prepare text input
 * const prompt = '80s pop track with bassy drums and synth';
 * const inputs = tokenizer(prompt);
 * 
 * // Generate audio
 * const audio_values = await model.generate({
 *   ...inputs,
 *   max_new_tokens: 512,
 *   do_sample: true,
 *   guidance_scale: 3,
 * });
 * 
 * // (Optional) Write the output to a WAV file
 * import wavefile from 'wavefile';
 * import fs from 'fs';
 * 
 * const wav = new wavefile.WaveFile();
 * wav.fromScratch(1, model.config.audio_encoder.sampling_rate, '32f', audio_values.data);
 * fs.writeFileSync('musicgen_out.wav', wav.toBuffer());
 * ```
 */
export class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE: not MusicgenPreTrainedModel
    forward_params = [
        'input_ids',
        'attention_mask',
        'encoder_outputs',
        'decoder_input_ids',
        'decoder_attention_mask',
        'past_key_values',
    ];

    /**
     * Apply the pattern mask to the final ids,
     * then revert the pattern delay mask by filtering the pad token id in a single step.
     * @param {Tensor} outputs The output tensor from the model.
     * @returns {Tensor} The filtered output tensor.
     */
    _apply_and_filter_by_delay_pattern_mask(outputs) {
        const [bs_x_codebooks, seqLength] = outputs.dims;
        const num_codebooks = this.config.decoder.num_codebooks;
        const upperBound = (seqLength - num_codebooks);

        let newDataSize = 0;
        for (let i = 0; i < outputs.size; ++i) {
            if (outputs.data[i] === this.config.decoder.pad_token_id) {
                continue;
            }

            const row = (i % seqLength);
            const col = Math.floor(i / seqLength) % num_codebooks;

            const diff = row - col;
            if (diff > 0 && diff <= upperBound) {
                outputs.data[newDataSize++] = outputs.data[i];
            }
        }

        const batch_size = Math.floor(bs_x_codebooks / num_codebooks);
        const inferred = newDataSize / (batch_size * num_codebooks);
        // TODO: assert `inferred` is an integer
        return new Tensor(
            outputs.type,
            outputs.data.slice(0, newDataSize),
            [batch_size, num_codebooks, inferred]
        );
    }


    prepare_inputs_for_generation(input_ids, model_inputs, generation_config) {
        // apply the delay pattern mask
        let clonedInputIds = structuredClone(input_ids);
        for (let i = 0; i < clonedInputIds.length; ++i) {
            for (let j = 0; j < clonedInputIds[i].length; ++j) {
                if ((i % this.config.decoder.num_codebooks) >= j) {
                    clonedInputIds[i][j] = BigInt(this.config.decoder.pad_token_id);
                }
            }
        }
        // for classifier free guidance we need to replicate the decoder args across the batch dim
        // (we'll split these before sampling)
        if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) {
            // [batch, seqLength] -> [2 * batch, seqLength]
            clonedInputIds = clonedInputIds.concat(clonedInputIds);
        }

        const prepped = super.prepare_inputs_for_generation(clonedInputIds, model_inputs, generation_config);
        return prepped;
    }

    /**
     * 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.
     */
    async generate(options) {

        const output_ids = await super.generate(options);

        // apply the pattern mask to the final ids
        // tensor: int64[1,batch_size,4,chunk_length]
        const audio_codes = this._apply_and_filter_by_delay_pattern_mask(
            /** @type {Tensor} */(output_ids)
        ).unsqueeze_(0); // append the frame dimension back to the audio codes

        const { audio_values } = await sessionRun(this.sessions['encodec_decode'], { audio_codes })

        return audio_values;
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// MobileNetV1 models
export class MobileNetV1PreTrainedModel extends PreTrainedModel { }

/**
 * The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.
 */
export class MobileNetV1Model extends MobileNetV1PreTrainedModel { }

/**
 * MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features),
 * e.g. for ImageNet.
 */
export class MobileNetV1ForImageClassification extends MobileNetV1PreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// MobileNetV2 models
export class MobileNetV2PreTrainedModel extends PreTrainedModel { }

/**
 * The bare MobileNetV2 model outputting raw hidden-states without any specific head on top.
 */
export class MobileNetV2Model extends MobileNetV2PreTrainedModel { }

/**
 * MobileNetV2 model with an image classification head on top (a linear layer on top of the pooled features),
 * e.g. for ImageNet.
 */
export class MobileNetV2ForImageClassification extends MobileNetV2PreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// MobileNetV3 models
export class MobileNetV3PreTrainedModel extends PreTrainedModel { }

/**
 * The bare MobileNetV3 model outputting raw hidden-states without any specific head on top.
 */
export class MobileNetV3Model extends MobileNetV3PreTrainedModel { }

/**
 * MobileNetV3 model with an image classification head on top (a linear layer on top of the pooled features),
 * e.g. for ImageNet.
 */
export class MobileNetV3ForImageClassification extends MobileNetV3PreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// MobileNetV4 models
export class MobileNetV4PreTrainedModel extends PreTrainedModel { }

/**
 * The bare MobileNetV4 model outputting raw hidden-states without any specific head on top.
 */
export class MobileNetV4Model extends MobileNetV4PreTrainedModel { }

/**
 * MobileNetV4 model with an image classification head on top (a linear layer on top of the pooled features),
 * e.g. for ImageNet.
 */
export class MobileNetV4ForImageClassification extends MobileNetV4PreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// Decision Transformer models
export class DecisionTransformerPreTrainedModel extends PreTrainedModel { }

/**
 * The model builds upon the GPT2 architecture to perform autoregressive prediction of actions in an offline RL setting.
 * Refer to the paper for more details: https://arxiv.org/abs/2106.01345
 */
export class DecisionTransformerModel extends DecisionTransformerPreTrainedModel { }

//////////////////////////////////////////////////

export class MultiModalityPreTrainedModel extends PreTrainedModel { }
export class MultiModalityCausalLM extends MultiModalityPreTrainedModel {
    forward_params = [
        // prepare_inputs_embeds
        'input_ids',
        'pixel_values',
        'images_seq_mask',
        'images_emb_mask',

        // language_model
        'attention_mask',
        'position_ids',
        'past_key_values',
    ];

    constructor(...args) {
        super(...args);

        // State-based approach to switch out which heads to use during generation
        this._generation_mode = 'text';
    }

    async forward(model_inputs) {
        const mode = this._generation_mode ?? 'text';

        // TODO support re-using PKVs for input_ids.dims[1] !== 1
        // if (model_inputs.past_key_values) {
        //     //  && model_inputs.input_ids.dims[1] === 1
        // }

        let output_1;
        if (mode === 'text' || !model_inputs.past_key_values) {
            const session = this.sessions['prepare_inputs_embeds'];
            const prep_inputs = pick(model_inputs, session.inputNames);
            output_1 = await sessionRun(session, prep_inputs);
        } else {
            const session = this.sessions['gen_img_embeds'];
            const prep_inputs = pick({
                image_ids: model_inputs.input_ids,
            }, session.inputNames);
            output_1 = await sessionRun(session, prep_inputs);
        }

        const input_2 = { ...model_inputs, ...output_1 }
        const output_2 = await decoderForward(this, input_2);

        const head = this.sessions[
            mode === 'text'
                ? 'lm_head'
                : 'gen_head'
        ];
        if (!head) {
            throw new Error(`Unable to find "${head}" generation head`);
        }

        const output_3 = await sessionRun(head, pick(output_2, head.inputNames))

        return {
            ...output_1,
            ...output_2,
            ...output_3,
        };
    }

    /**
     * @param {import('./generation/parameters.js').GenerationFunctionParameters} options
     */
    async generate(options) {
        this._generation_mode = 'text';
        return super.generate(options);
    }

    /**
     * @param {import('./generation/parameters.js').GenerationFunctionParameters} options
     */
    async generate_images(options) {
        this._generation_mode = 'image';

        const start_num_tokens = (options.inputs ?? options[this.main_input_name]).dims[1];
        const all_tokens = await super.generate(options);

        const generated_tokens = (/** @type {Tensor} */(all_tokens)).slice(null, [start_num_tokens, null])

        const image_decode = this.sessions['image_decode'];
        const { decoded_image } = await sessionRun(image_decode, {
            generated_tokens,
        });

        // Equivalent to `np.clip((dec + 1) / 2 * 255, 0, 255)`
        const clamped = decoded_image
            .add_(1)
            .mul_(255 / 2)
            .clamp_(0, 255)
            .to('uint8');

        // Return as a list of images
        const images = [];
        for (const tensor of clamped) {
            const img = RawImage.fromTensor(tensor);
            images.push(img);
        }
        return images;
    }
}

export class MgpstrModelOutput extends ModelOutput {
    constructor({ char_logits, bpe_logits, wp_logits }) {
        super();
        this.char_logits = char_logits;
        this.bpe_logits = bpe_logits;
        this.wp_logits = wp_logits;
    }

    get logits() {
        return [this.char_logits, this.bpe_logits, this.wp_logits];
    }
}

export class MgpstrPreTrainedModel extends PreTrainedModel { }

/**
 * MGP-STR Model transformer with three classification heads on top
 * (three A^3 modules and three linear layer on top of the transformer encoder output) for scene text recognition (STR).
 */
export class MgpstrForSceneTextRecognition extends MgpstrPreTrainedModel {
    /**
     * @param {any} model_inputs
     */
    async _call(model_inputs) {
        return new MgpstrModelOutput(await super._call(model_inputs));
    }
}

//////////////////////////////////////////////////
// PatchTST Transformer models
export class PatchTSTPreTrainedModel extends PreTrainedModel { }

/**
 * The bare PatchTST Model outputting raw hidden-states without any specific head.
 */
export class PatchTSTModel extends PatchTSTPreTrainedModel { }

/**
 * The PatchTST for prediction model.
 */
export class PatchTSTForPrediction extends PatchTSTPreTrainedModel { }
//////////////////////////////////////////////////

//////////////////////////////////////////////////
// PatchTSMixer Transformer models
export class PatchTSMixerPreTrainedModel extends PreTrainedModel { }

/**
 * The bare PatchTSMixer Model outputting raw hidden-states without any specific head.
 */
export class PatchTSMixerModel extends PatchTSMixerPreTrainedModel { }

/**
 * The PatchTSMixer for prediction model.
 */
export class PatchTSMixerForPrediction extends PatchTSMixerPreTrainedModel { }
//////////////////////////////////////////////////


//////////////////////////////////////////////////
// AutoModels, used to simplify construction of PreTrainedModels
// (uses config to instantiate correct class)

/**
 * 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 = null;

    /**
     * Whether to attempt to instantiate the base class (`PretrainedModel`) if 
     * the model type is not found in the mapping.
     */
    static BASE_IF_FAIL = false;


    /** @type {typeof PreTrainedModel.from_pretrained} */
    static async from_pretrained(pretrained_model_name_or_path, {
        progress_callback = null,
        config = null,
        cache_dir = null,
        local_files_only = false,
        revision = 'main',
        model_file_name = null,
        subfolder = 'onnx',
        device = null,
        dtype = null,
        use_external_data_format = null,
        session_options = {},
    } = {}) {

        const options = {
            progress_callback,
            config,
            cache_dir,
            local_files_only,
            revision,
            model_file_name,
            subfolder,
            device,
            dtype,
            use_external_data_format,
            session_options,
        }
        options.config = await AutoConfig.from_pretrained(pretrained_model_name_or_path, options);

        if (!this.MODEL_CLASS_MAPPINGS) {
            throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: " + this.name);
        }

        for (const MODEL_CLASS_MAPPING of this.MODEL_CLASS_MAPPINGS) {
            const modelInfo = MODEL_CLASS_MAPPING.get(options.config.model_type);
            if (!modelInfo) {
                continue; // Item not found in this mapping
            }
            return await modelInfo[1].from_pretrained(pretrained_model_name_or_path, options);
        }

        if (this.BASE_IF_FAIL) {
            console.warn(`Unknown model class "${options.config.model_type}", attempting to construct from base class.`);
            return await PreTrainedModel.from_pretrained(pretrained_model_name_or_path, options);
        } else {
            throw Error(`Unsupported model type: ${options.config.model_type}`)
        }
    }
}

const MODEL_MAPPING_NAMES_ENCODER_ONLY = new Map([
    ['bert', ['BertModel', BertModel]],
    ['modernbert', ['ModernBertModel', ModernBertModel]],
    ['nomic_bert', ['NomicBertModel', NomicBertModel]],
    ['roformer', ['RoFormerModel', RoFormerModel]],
    ['electra', ['ElectraModel', ElectraModel]],
    ['esm', ['EsmModel', EsmModel]],
    ['convbert', ['ConvBertModel', ConvBertModel]],
    ['camembert', ['CamembertModel', CamembertModel]],
    ['deberta', ['DebertaModel', DebertaModel]],
    ['deberta-v2', ['DebertaV2Model', DebertaV2Model]],
    ['mpnet', ['MPNetModel', MPNetModel]],
    ['albert', ['AlbertModel', AlbertModel]],
    ['distilbert', ['DistilBertModel', DistilBertModel]],
    ['roberta', ['RobertaModel', RobertaModel]],
    ['xlm', ['XLMModel', XLMModel]],
    ['xlm-roberta', ['XLMRobertaModel', XLMRobertaModel]],
    ['clap', ['ClapModel', ClapModel]],
    ['clip', ['CLIPModel', CLIPModel]],
    ['clipseg', ['CLIPSegModel', CLIPSegModel]],
    ['chinese_clip', ['ChineseCLIPModel', ChineseCLIPModel]],
    ['siglip', ['SiglipModel', SiglipModel]],
    ['jina_clip', ['JinaCLIPModel', JinaCLIPModel]],
    ['mobilebert', ['MobileBertModel', MobileBertModel]],
    ['squeezebert', ['SqueezeBertModel', SqueezeBertModel]],
    ['wav2vec2', ['Wav2Vec2Model', Wav2Vec2Model]],
    ['wav2vec2-bert', ['Wav2Vec2BertModel', Wav2Vec2BertModel]],
    ['unispeech', ['UniSpeechModel', UniSpeechModel]],
    ['unispeech-sat', ['UniSpeechSatModel', UniSpeechSatModel]],
    ['hubert', ['HubertModel', HubertModel]],
    ['wavlm', ['WavLMModel', WavLMModel]],
    ['audio-spectrogram-transformer', ['ASTModel', ASTModel]],
    ['vits', ['VitsModel', VitsModel]],
    ['pyannote', ['PyAnnoteModel', PyAnnoteModel]],
    ['wespeaker-resnet', ['WeSpeakerResNetModel', WeSpeakerResNetModel]],

    ['detr', ['DetrModel', DetrModel]],
    ['rt_detr', ['RTDetrModel', RTDetrModel]],
    ['table-transformer', ['TableTransformerModel', TableTransformerModel]],
    ['vit', ['ViTModel', ViTModel]],
    ['ijepa', ['IJepaModel', IJepaModel]],
    ['pvt', ['PvtModel', PvtModel]],
    ['vit_msn', ['ViTMSNModel', ViTMSNModel]],
    ['vit_mae', ['ViTMAEModel', ViTMAEModel]],
    ['groupvit', ['GroupViTModel', GroupViTModel]],
    ['fastvit', ['FastViTModel', FastViTModel]],
    ['mobilevit', ['MobileViTModel', MobileViTModel]],
    ['mobilevitv2', ['MobileViTV2Model', MobileViTV2Model]],
    ['owlvit', ['OwlViTModel', OwlViTModel]],
    ['owlv2', ['Owlv2Model', Owlv2Model]],
    ['beit', ['BeitModel', BeitModel]],
    ['deit', ['DeiTModel', DeiTModel]],
    ['hiera', ['HieraModel', HieraModel]],
    ['convnext', ['ConvNextModel', ConvNextModel]],
    ['convnextv2', ['ConvNextV2Model', ConvNextV2Model]],
    ['dinov2', ['Dinov2Model', Dinov2Model]],
    ['resnet', ['ResNetModel', ResNetModel]],
    ['swin', ['SwinModel', SwinModel]],
    ['swin2sr', ['Swin2SRModel', Swin2SRModel]],
    ['donut-swin', ['DonutSwinModel', DonutSwinModel]],
    ['yolos', ['YolosModel', YolosModel]],
    ['dpt', ['DPTModel', DPTModel]],
    ['glpn', ['GLPNModel', GLPNModel]],

    ['hifigan', ['SpeechT5HifiGan', SpeechT5HifiGan]],
    ['efficientnet', ['EfficientNetModel', EfficientNetModel]],

    ['decision_transformer', ['DecisionTransformerModel', DecisionTransformerModel]],
    ['patchtst', ['PatchTSTForPrediction', PatchTSTModel]],
    ['patchtsmixer', ['PatchTSMixerForPrediction', PatchTSMixerModel]],

    ['mobilenet_v1', ['MobileNetV1Model', MobileNetV1Model]],
    ['mobilenet_v2', ['MobileNetV2Model', MobileNetV2Model]],
    ['mobilenet_v3', ['MobileNetV3Model', MobileNetV3Model]],
    ['mobilenet_v4', ['MobileNetV4Model', MobileNetV4Model]],

    ['maskformer', ['MaskFormerModel', MaskFormerModel]],
    ['mgp-str', ['MgpstrForSceneTextRecognition', MgpstrForSceneTextRecognition]],
]);

const MODEL_MAPPING_NAMES_ENCODER_DECODER = new Map([
    ['t5', ['T5Model', T5Model]],
    ['longt5', ['LongT5Model', LongT5Model]],
    ['mt5', ['MT5Model', MT5Model]],
    ['bart', ['BartModel', BartModel]],
    ['mbart', ['MBartModel', MBartModel]],
    ['marian', ['MarianModel', MarianModel]],
    ['whisper', ['WhisperModel', WhisperModel]],
    ['m2m_100', ['M2M100Model', M2M100Model]],
    ['blenderbot', ['BlenderbotModel', BlenderbotModel]],
    ['blenderbot-small', ['BlenderbotSmallModel', BlenderbotSmallModel]],
]);


const MODEL_MAPPING_NAMES_DECODER_ONLY = new Map([
    ['bloom', ['BloomModel', BloomModel]],
    ['jais', ['JAISModel', JAISModel]],
    ['gpt2', ['GPT2Model', GPT2Model]],
    ['gptj', ['GPTJModel', GPTJModel]],
    ['gpt_bigcode', ['GPTBigCodeModel', GPTBigCodeModel]],
    ['gpt_neo', ['GPTNeoModel', GPTNeoModel]],
    ['gpt_neox', ['GPTNeoXModel', GPTNeoXModel]],
    ['codegen', ['CodeGenModel', CodeGenModel]],
    ['llama', ['LlamaModel', LlamaModel]],
    ['exaone', ['ExaoneModel', ExaoneModel]],
    ['olmo', ['OlmoModel', OlmoModel]],
    ['olmo2', ['Olmo2Model', Olmo2Model]],
    ['mobilellm', ['MobileLLMModel', MobileLLMModel]],
    ['granite', ['GraniteModel', GraniteModel]],
    ['cohere', ['CohereModel', CohereModel]],
    ['gemma', ['GemmaModel', GemmaModel]],
    ['gemma2', ['Gemma2Model', Gemma2Model]],
    ['openelm', ['OpenELMModel', OpenELMModel]],
    ['qwen2', ['Qwen2Model', Qwen2Model]],
    ['phi', ['PhiModel', PhiModel]],
    ['phi3', ['Phi3Model', Phi3Model]],
    ['mpt', ['MptModel', MptModel]],
    ['opt', ['OPTModel', OPTModel]],
    ['mistral', ['MistralModel', MistralModel]],
    ['starcoder2', ['Starcoder2Model', Starcoder2Model]],
    ['falcon', ['FalconModel', FalconModel]],
    ['stablelm', ['StableLmModel', StableLmModel]],
]);

const MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = new Map([
    ['speecht5', ['SpeechT5ForSpeechToText', SpeechT5ForSpeechToText]],
    ['whisper', ['WhisperForConditionalGeneration', WhisperForConditionalGeneration]],
    ['moonshine', ['MoonshineForConditionalGeneration', MoonshineForConditionalGeneration]],
]);

const MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES = new Map([
    ['speecht5', ['SpeechT5ForTextToSpeech', SpeechT5ForTextToSpeech]],
]);

const MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES = new Map([
    ['vits', ['VitsModel', VitsModel]],
    ['musicgen', ['MusicgenForConditionalGeneration', MusicgenForConditionalGeneration]],
]);

const MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = new Map([
    ['bert', ['BertForSequenceClassification', BertForSequenceClassification]],
    ['modernbert', ['ModernBertForSequenceClassification', ModernBertForSequenceClassification]],
    ['roformer', ['RoFormerForSequenceClassification', RoFormerForSequenceClassification]],
    ['electra', ['ElectraForSequenceClassification', ElectraForSequenceClassification]],
    ['esm', ['EsmForSequenceClassification', EsmForSequenceClassification]],
    ['convbert', ['ConvBertForSequenceClassification', ConvBertForSequenceClassification]],
    ['camembert', ['CamembertForSequenceClassification', CamembertForSequenceClassification]],
    ['deberta', ['DebertaForSequenceClassification', DebertaForSequenceClassification]],
    ['deberta-v2', ['DebertaV2ForSequenceClassification', DebertaV2ForSequenceClassification]],
    ['mpnet', ['MPNetForSequenceClassification', MPNetForSequenceClassification]],
    ['albert', ['AlbertForSequenceClassification', AlbertForSequenceClassification]],
    ['distilbert', ['DistilBertForSequenceClassification', DistilBertForSequenceClassification]],
    ['roberta', ['RobertaForSequenceClassification', RobertaForSequenceClassification]],
    ['xlm', ['XLMForSequenceClassification', XLMForSequenceClassification]],
    ['xlm-roberta', ['XLMRobertaForSequenceClassification', XLMRobertaForSequenceClassification]],
    ['bart', ['BartForSequenceClassification', BartForSequenceClassification]],
    ['mbart', ['MBartForSequenceClassification', MBartForSequenceClassification]],
    ['mobilebert', ['MobileBertForSequenceClassification', MobileBertForSequenceClassification]],
    ['squeezebert', ['SqueezeBertForSequenceClassification', SqueezeBertForSequenceClassification]],
]);

const MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = new Map([
    ['bert', ['BertForTokenClassification', BertForTokenClassification]],
    ['modernbert', ['ModernBertForTokenClassification', ModernBertForTokenClassification]],
    ['roformer', ['RoFormerForTokenClassification', RoFormerForTokenClassification]],
    ['electra', ['ElectraForTokenClassification', ElectraForTokenClassification]],
    ['esm', ['EsmForTokenClassification', EsmForTokenClassification]],
    ['convbert', ['ConvBertForTokenClassification', ConvBertForTokenClassification]],
    ['camembert', ['CamembertForTokenClassification', CamembertForTokenClassification]],
    ['deberta', ['DebertaForTokenClassification', DebertaForTokenClassification]],
    ['deberta-v2', ['DebertaV2ForTokenClassification', DebertaV2ForTokenClassification]],
    ['mpnet', ['MPNetForTokenClassification', MPNetForTokenClassification]],
    ['distilbert', ['DistilBertForTokenClassification', DistilBertForTokenClassification]],
    ['roberta', ['RobertaForTokenClassification', RobertaForTokenClassification]],
    ['xlm', ['XLMForTokenClassification', XLMForTokenClassification]],
    ['xlm-roberta', ['XLMRobertaForTokenClassification', XLMRobertaForTokenClassification]],
]);

const MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = new Map([
    ['t5', ['T5ForConditionalGeneration', T5ForConditionalGeneration]],
    ['longt5', ['LongT5ForConditionalGeneration', LongT5ForConditionalGeneration]],
    ['mt5', ['MT5ForConditionalGeneration', MT5ForConditionalGeneration]],
    ['bart', ['BartForConditionalGeneration', BartForConditionalGeneration]],
    ['mbart', ['MBartForConditionalGeneration', MBartForConditionalGeneration]],
    ['marian', ['MarianMTModel', MarianMTModel]],
    ['m2m_100', ['M2M100ForConditionalGeneration', M2M100ForConditionalGeneration]],
    ['blenderbot', ['BlenderbotForConditionalGeneration', BlenderbotForConditionalGeneration]],
    ['blenderbot-small', ['BlenderbotSmallForConditionalGeneration', BlenderbotSmallForConditionalGeneration]],
]);

const MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = new Map([
    ['bloom', ['BloomForCausalLM', BloomForCausalLM]],
    ['gpt2', ['GPT2LMHeadModel', GPT2LMHeadModel]],
    ['jais', ['JAISLMHeadModel', JAISLMHeadModel]],
    ['gptj', ['GPTJForCausalLM', GPTJForCausalLM]],
    ['gpt_bigcode', ['GPTBigCodeForCausalLM', GPTBigCodeForCausalLM]],
    ['gpt_neo', ['GPTNeoForCausalLM', GPTNeoForCausalLM]],
    ['gpt_neox', ['GPTNeoXForCausalLM', GPTNeoXForCausalLM]],
    ['codegen', ['CodeGenForCausalLM', CodeGenForCausalLM]],
    ['llama', ['LlamaForCausalLM', LlamaForCausalLM]],
    ['exaone', ['ExaoneForCausalLM', ExaoneForCausalLM]],
    ['olmo', ['OlmoForCausalLM', OlmoForCausalLM]],
    ['olmo2', ['Olmo2ForCausalLM', Olmo2ForCausalLM]],
    ['mobilellm', ['MobileLLMForCausalLM', MobileLLMForCausalLM]],
    ['granite', ['GraniteForCausalLM', GraniteForCausalLM]],
    ['cohere', ['CohereForCausalLM', CohereForCausalLM]],
    ['gemma', ['GemmaForCausalLM', GemmaForCausalLM]],
    ['gemma2', ['Gemma2ForCausalLM', Gemma2ForCausalLM]],
    ['openelm', ['OpenELMForCausalLM', OpenELMForCausalLM]],
    ['qwen2', ['Qwen2ForCausalLM', Qwen2ForCausalLM]],
    ['phi', ['PhiForCausalLM', PhiForCausalLM]],
    ['phi3', ['Phi3ForCausalLM', Phi3ForCausalLM]],
    ['mpt', ['MptForCausalLM', MptForCausalLM]],
    ['opt', ['OPTForCausalLM', OPTForCausalLM]],
    ['mbart', ['MBartForCausalLM', MBartForCausalLM]],
    ['mistral', ['MistralForCausalLM', MistralForCausalLM]],
    ['starcoder2', ['Starcoder2ForCausalLM', Starcoder2ForCausalLM]],
    ['falcon', ['FalconForCausalLM', FalconForCausalLM]],
    ['trocr', ['TrOCRForCausalLM', TrOCRForCausalLM]],
    ['stablelm', ['StableLmForCausalLM', StableLmForCausalLM]],

    // Also image-text-to-text
    ['phi3_v', ['Phi3VForCausalLM', Phi3VForCausalLM]],
]);

const MODEL_FOR_MULTIMODALITY_MAPPING_NAMES = new Map([
    ['multi_modality', ['MultiModalityCausalLM', MultiModalityCausalLM]],
]);


const MODEL_FOR_MASKED_LM_MAPPING_NAMES = new Map([
    ['bert', ['BertForMaskedLM', BertForMaskedLM]],
    ['modernbert', ['ModernBertForMaskedLM', ModernBertForMaskedLM]],
    ['roformer', ['RoFormerForMaskedLM', RoFormerForMaskedLM]],
    ['electra', ['ElectraForMaskedLM', ElectraForMaskedLM]],
    ['esm', ['EsmForMaskedLM', EsmForMaskedLM]],
    ['convbert', ['ConvBertForMaskedLM', ConvBertForMaskedLM]],
    ['camembert', ['CamembertForMaskedLM', CamembertForMaskedLM]],
    ['deberta', ['DebertaForMaskedLM', DebertaForMaskedLM]],
    ['deberta-v2', ['DebertaV2ForMaskedLM', DebertaV2ForMaskedLM]],
    ['mpnet', ['MPNetForMaskedLM', MPNetForMaskedLM]],
    ['albert', ['AlbertForMaskedLM', AlbertForMaskedLM]],
    ['distilbert', ['DistilBertForMaskedLM', DistilBertForMaskedLM]],
    ['roberta', ['RobertaForMaskedLM', RobertaForMaskedLM]],
    ['xlm', ['XLMWithLMHeadModel', XLMWithLMHeadModel]],
    ['xlm-roberta', ['XLMRobertaForMaskedLM', XLMRobertaForMaskedLM]],
    ['mobilebert', ['MobileBertForMaskedLM', MobileBertForMaskedLM]],
    ['squeezebert', ['SqueezeBertForMaskedLM', SqueezeBertForMaskedLM]],
]);

const MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = new Map([
    ['bert', ['BertForQuestionAnswering', BertForQuestionAnswering]],
    ['roformer', ['RoFormerForQuestionAnswering', RoFormerForQuestionAnswering]],
    ['electra', ['ElectraForQuestionAnswering', ElectraForQuestionAnswering]],
    ['convbert', ['ConvBertForQuestionAnswering', ConvBertForQuestionAnswering]],
    ['camembert', ['CamembertForQuestionAnswering', CamembertForQuestionAnswering]],
    ['deberta', ['DebertaForQuestionAnswering', DebertaForQuestionAnswering]],
    ['deberta-v2', ['DebertaV2ForQuestionAnswering', DebertaV2ForQuestionAnswering]],
    ['mpnet', ['MPNetForQuestionAnswering', MPNetForQuestionAnswering]],
    ['albert', ['AlbertForQuestionAnswering', AlbertForQuestionAnswering]],
    ['distilbert', ['DistilBertForQuestionAnswering', DistilBertForQuestionAnswering]],
    ['roberta', ['RobertaForQuestionAnswering', RobertaForQuestionAnswering]],
    ['xlm', ['XLMForQuestionAnswering', XLMForQuestionAnswering]],
    ['xlm-roberta', ['XLMRobertaForQuestionAnswering', XLMRobertaForQuestionAnswering]],
    ['mobilebert', ['MobileBertForQuestionAnswering', MobileBertForQuestionAnswering]],
    ['squeezebert', ['SqueezeBertForQuestionAnswering', SqueezeBertForQuestionAnswering]],
]);

const MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = new Map([
    ['vision-encoder-decoder', ['VisionEncoderDecoderModel', VisionEncoderDecoderModel]],
    ['idefics3', ['Idefics3ForConditionalGeneration', Idefics3ForConditionalGeneration]],
]);

const MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES = new Map([
    ['llava', ['LlavaForConditionalGeneration', LlavaForConditionalGeneration]],
    ['llava_onevision', ['LlavaOnevisionForConditionalGeneration', LlavaOnevisionForConditionalGeneration]],
    ['moondream1', ['Moondream1ForConditionalGeneration', Moondream1ForConditionalGeneration]],
    ['florence2', ['Florence2ForConditionalGeneration', Florence2ForConditionalGeneration]],
    ['qwen2-vl', ['Qwen2VLForConditionalGeneration', Qwen2VLForConditionalGeneration]],
    ['idefics3', ['Idefics3ForConditionalGeneration', Idefics3ForConditionalGeneration]],
    ['paligemma', ['PaliGemmaForConditionalGeneration', PaliGemmaForConditionalGeneration]],
]);

const MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = new Map([
    ['vision-encoder-decoder', ['VisionEncoderDecoderModel', VisionEncoderDecoderModel]],
]);

const MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = new Map([
    ['vit', ['ViTForImageClassification', ViTForImageClassification]],
    ['ijepa', ['IJepaForImageClassification', IJepaForImageClassification]],
    ['pvt', ['PvtForImageClassification', PvtForImageClassification]],
    ['vit_msn', ['ViTMSNForImageClassification', ViTMSNForImageClassification]],
    ['fastvit', ['FastViTForImageClassification', FastViTForImageClassification]],
    ['mobilevit', ['MobileViTForImageClassification', MobileViTForImageClassification]],
    ['mobilevitv2', ['MobileViTV2ForImageClassification', MobileViTV2ForImageClassification]],
    ['beit', ['BeitForImageClassification', BeitForImageClassification]],
    ['deit', ['DeiTForImageClassification', DeiTForImageClassification]],
    ['hiera', ['HieraForImageClassification', HieraForImageClassification]],
    ['convnext', ['ConvNextForImageClassification', ConvNextForImageClassification]],
    ['convnextv2', ['ConvNextV2ForImageClassification', ConvNextV2ForImageClassification]],
    ['dinov2', ['Dinov2ForImageClassification', Dinov2ForImageClassification]],
    ['resnet', ['ResNetForImageClassification', ResNetForImageClassification]],
    ['swin', ['SwinForImageClassification', SwinForImageClassification]],
    ['segformer', ['SegformerForImageClassification', SegformerForImageClassification]],
    ['efficientnet', ['EfficientNetForImageClassification', EfficientNetForImageClassification]],
    ['mobilenet_v1', ['MobileNetV1ForImageClassification', MobileNetV1ForImageClassification]],
    ['mobilenet_v2', ['MobileNetV2ForImageClassification', MobileNetV2ForImageClassification]],
    ['mobilenet_v3', ['MobileNetV3ForImageClassification', MobileNetV3ForImageClassification]],
    ['mobilenet_v4', ['MobileNetV4ForImageClassification', MobileNetV4ForImageClassification]],
]);

const MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = new Map([
    ['detr', ['DetrForObjectDetection', DetrForObjectDetection]],
    ['rt_detr', ['RTDetrForObjectDetection', RTDetrForObjectDetection]],
    ['table-transformer', ['TableTransformerForObjectDetection', TableTransformerForObjectDetection]],
    ['yolos', ['YolosForObjectDetection', YolosForObjectDetection]],
]);

const MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = new Map([
    ['owlvit', ['OwlViTForObjectDetection', OwlViTForObjectDetection]],
    ['owlv2', ['Owlv2ForObjectDetection', Owlv2ForObjectDetection]],
]);

const MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = new Map([
    // TODO: Do not add new models here
    ['detr', ['DetrForSegmentation', DetrForSegmentation]],
    ['clipseg', ['CLIPSegForImageSegmentation', CLIPSegForImageSegmentation]],
]);

const MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = new Map([
    ['segformer', ['SegformerForSemanticSegmentation', SegformerForSemanticSegmentation]],
    ['sapiens', ['SapiensForSemanticSegmentation', SapiensForSemanticSegmentation]],
]);

const MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES = new Map([
    ['detr', ['DetrForSegmentation', DetrForSegmentation]],
    ['maskformer', ['MaskFormerForInstanceSegmentation', MaskFormerForInstanceSegmentation]],
]);

const MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = new Map([
    ['sam', ['SamModel', SamModel]],
]);

const MODEL_FOR_CTC_MAPPING_NAMES = new Map([
    ['wav2vec2', ['Wav2Vec2ForCTC', Wav2Vec2ForCTC]],
    ['wav2vec2-bert', ['Wav2Vec2BertForCTC', Wav2Vec2BertForCTC]],
    ['unispeech', ['UniSpeechForCTC', UniSpeechForCTC]],
    ['unispeech-sat', ['UniSpeechSatForCTC', UniSpeechSatForCTC]],
    ['wavlm', ['WavLMForCTC', WavLMForCTC]],
    ['hubert', ['HubertForCTC', HubertForCTC]],
]);

const MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = new Map([
    ['wav2vec2', ['Wav2Vec2ForSequenceClassification', Wav2Vec2ForSequenceClassification]],
    ['wav2vec2-bert', ['Wav2Vec2BertForSequenceClassification', Wav2Vec2BertForSequenceClassification]],
    ['unispeech', ['UniSpeechForSequenceClassification', UniSpeechForSequenceClassification]],
    ['unispeech-sat', ['UniSpeechSatForSequenceClassification', UniSpeechSatForSequenceClassification]],
    ['wavlm', ['WavLMForSequenceClassification', WavLMForSequenceClassification]],
    ['hubert', ['HubertForSequenceClassification', HubertForSequenceClassification]],
    ['audio-spectrogram-transformer', ['ASTForAudioClassification', ASTForAudioClassification]],
]);

const MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = new Map([
    ['wavlm', ['WavLMForXVector', WavLMForXVector]],
]);

const MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = new Map([
    ['unispeech-sat', ['UniSpeechSatForAudioFrameClassification', UniSpeechSatForAudioFrameClassification]],
    ['wavlm', ['WavLMForAudioFrameClassification', WavLMForAudioFrameClassification]],
    ['wav2vec2', ['Wav2Vec2ForAudioFrameClassification', Wav2Vec2ForAudioFrameClassification]],
    ['pyannote', ['PyAnnoteForAudioFrameClassification', PyAnnoteForAudioFrameClassification]],
]);

const MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES = new Map([
    ['vitmatte', ['VitMatteForImageMatting', VitMatteForImageMatting]],
]);

const MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING_NAMES = new Map([
    ['patchtst', ['PatchTSTForPrediction', PatchTSTForPrediction]],
    ['patchtsmixer', ['PatchTSMixerForPrediction', PatchTSMixerForPrediction]],
])

const MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES = new Map([
    ['swin2sr', ['Swin2SRForImageSuperResolution', Swin2SRForImageSuperResolution]],
])

const MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = new Map([
    ['dpt', ['DPTForDepthEstimation', DPTForDepthEstimation]],
    ['depth_anything', ['DepthAnythingForDepthEstimation', DepthAnythingForDepthEstimation]],
    ['glpn', ['GLPNForDepthEstimation', GLPNForDepthEstimation]],
    ['sapiens', ['SapiensForDepthEstimation', SapiensForDepthEstimation]],
    ['depth_pro', ['DepthProForDepthEstimation', DepthProForDepthEstimation]],
])

const MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES = new Map([
    ['sapiens', ['SapiensForNormalEstimation', SapiensForNormalEstimation]],
])

const MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES = new Map([
    ['vitpose', ['VitPoseForPoseEstimation', VitPoseForPoseEstimation]],
])

// NOTE: This is custom to Transformers.js, and is necessary because certain models
// (e.g., CLIP) are split into vision and text components
const MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES = new Map([
    ['clip', ['CLIPVisionModelWithProjection', CLIPVisionModelWithProjection]],
    ['siglip', ['SiglipVisionModel', SiglipVisionModel]],
    ['jina_clip', ['JinaCLIPVisionModel', JinaCLIPVisionModel]],
])

const MODEL_CLASS_TYPE_MAPPING = [
    [MODEL_MAPPING_NAMES_ENCODER_ONLY, MODEL_TYPES.EncoderOnly],
    [MODEL_MAPPING_NAMES_ENCODER_DECODER, MODEL_TYPES.EncoderDecoder],
    [MODEL_MAPPING_NAMES_DECODER_ONLY, MODEL_TYPES.DecoderOnly],
    [MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, MODEL_TYPES.Seq2Seq],
    [MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, MODEL_TYPES.Seq2Seq],
    [MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_TYPES.DecoderOnly],
    [MODEL_FOR_MULTIMODALITY_MAPPING_NAMES, MODEL_TYPES.MultiModality],
    [MODEL_FOR_MASKED_LM_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES, MODEL_TYPES.Vision2Seq],
    [MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES, MODEL_TYPES.ImageTextToText],
    [MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_MASK_GENERATION_MAPPING_NAMES, MODEL_TYPES.MaskGeneration],
    [MODEL_FOR_CTC_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES, MODEL_TYPES.Seq2Seq],
    [MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
    [MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],

    // Custom:
    [MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
];

for (const [mappings, type] of MODEL_CLASS_TYPE_MAPPING) {
    // @ts-ignore
    for (const [name, model] of mappings.values()) {
        MODEL_TYPE_MAPPING.set(name, type);
        MODEL_CLASS_TO_NAME_MAPPING.set(model, name);
        MODEL_NAME_TO_CLASS_MAPPING.set(name, model);
    }
}

const CUSTOM_MAPPING = [
    // OVERRIDE:
    // TODO: Refactor to allow class to specify model
    ['MusicgenForConditionalGeneration', MusicgenForConditionalGeneration, MODEL_TYPES.Musicgen],
    ['Phi3VForCausalLM', Phi3VForCausalLM, MODEL_TYPES.Phi3V],

    ['CLIPTextModelWithProjection', CLIPTextModelWithProjection, MODEL_TYPES.EncoderOnly],
    ['SiglipTextModel', SiglipTextModel, MODEL_TYPES.EncoderOnly],
    ['JinaCLIPTextModel', JinaCLIPTextModel, MODEL_TYPES.EncoderOnly],
    ['ClapTextModelWithProjection', ClapTextModelWithProjection, MODEL_TYPES.EncoderOnly],
    ['ClapAudioModelWithProjection', ClapAudioModelWithProjection, MODEL_TYPES.EncoderOnly],
]
for (const [name, model, type] of CUSTOM_MAPPING) {
    MODEL_TYPE_MAPPING.set(name, type);
    MODEL_CLASS_TO_NAME_MAPPING.set(model, name);
    MODEL_NAME_TO_CLASS_MAPPING.set(name, model);
}


/**
 * 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('Xenova/bert-base-uncased');
 */
export class AutoModel extends PretrainedMixin {
    /** @type {Map<string, Object>[]} */
    // @ts-ignore
    static MODEL_CLASS_MAPPINGS = MODEL_CLASS_TYPE_MAPPING.map(x => x[0]);
    static BASE_IF_FAIL = true;
}

/**
 * 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('Xenova/distilbert-base-uncased-finetuned-sst-2-english');
 */
export class AutoModelForSequenceClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES];
}

/**
 * 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('Xenova/distilbert-base-multilingual-cased-ner-hrl');
 */
export class AutoModelForTokenClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES];
}

/**
 * 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('Xenova/t5-small');
 */
export class AutoModelForSeq2SeqLM extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES];
}

/**
 * Helper class which is used to instantiate pretrained sequence-to-sequence speech-to-text models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 * 
 * @example
 * let model = await AutoModelForSpeechSeq2Seq.from_pretrained('openai/whisper-tiny.en');
 */
export class AutoModelForSpeechSeq2Seq extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES];
}

/**
 * Helper class which is used to instantiate pretrained sequence-to-sequence text-to-spectrogram models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 * 
 * @example
 * let model = await AutoModelForTextToSpectrogram.from_pretrained('microsoft/speecht5_tts');
 */
export class AutoModelForTextToSpectrogram extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES];
}

/**
 * Helper class which is used to instantiate pretrained text-to-waveform models with the `from_pretrained` function.
 * The chosen model class is determined by the type specified in the model config.
 * 
 * @example
 * let model = await AutoModelForTextToSpectrogram.from_pretrained('facebook/mms-tts-eng');
 */
export class AutoModelForTextToWaveform extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES];
}

/**
 * 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('Xenova/gpt2');
 */
export class AutoModelForCausalLM extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_CAUSAL_LM_MAPPING_NAMES];
}

/**
 * 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('Xenova/bert-base-uncased');
 */
export class AutoModelForMaskedLM extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_MASKED_LM_MAPPING_NAMES];
}

/**
 * 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('Xenova/distilbert-base-cased-distilled-squad');
 */
export class AutoModelForQuestionAnswering extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES];
}

/**
 * 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('Xenova/vit-gpt2-image-captioning');
 */
export class AutoModelForVision2Seq extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES];
}

/**
 * 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('Xenova/vit-base-patch16-224');
 */
export class AutoModelForImageClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES];
}

/**
 * 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('Xenova/detr-resnet-50-panoptic');
 */
export class AutoModelForImageSegmentation extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES];
}

/**
 * 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 AutoModelForSemanticSegmentation.from_pretrained('nvidia/segformer-b3-finetuned-cityscapes-1024-1024');
 */
export class AutoModelForSemanticSegmentation extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES];
}

/**
 * Helper class which is used to instantiate pretrained universal 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 AutoModelForUniversalSegmentation.from_pretrained('hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation');
 */
export class AutoModelForUniversalSegmentation extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES];
}

/**
 * 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('Xenova/detr-resnet-50');
 */
export class AutoModelForObjectDetection extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES];
}

export class AutoModelForZeroShotObjectDetection extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES];
}


/**
 * Helper class which is used to instantiate pretrained mask generation 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 = [MODEL_FOR_MASK_GENERATION_MAPPING_NAMES];
}

export class AutoModelForCTC extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_CTC_MAPPING_NAMES];
}

export class AutoModelForAudioClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES];
}

export class AutoModelForXVector extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES];
}

export class AutoModelForAudioFrameClassification extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES];
}

export class AutoModelForDocumentQuestionAnswering extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES];
}

export class AutoModelForImageMatting extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES];
}

export class AutoModelForImageToImage extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES];
}

export class AutoModelForDepthEstimation extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES];
}

export class AutoModelForNormalEstimation extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES];
}

export class AutoModelForPoseEstimation extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES];
}

export class AutoModelForImageFeatureExtraction extends PretrainedMixin {
    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES];
}

//////////////////////////////////////////////////

//////////////////////////////////////////////////
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 = null, cross_attentions = null }) {
        super();
        this.logits = logits;
        this.past_key_values = past_key_values;
        this.encoder_outputs = encoder_outputs;
        this.decoder_attentions = decoder_attentions;
        this.cross_attentions = cross_attentions;
    }
}

/**
 * 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 }) {
        super();
        this.logits = logits;
    }
}

/**
 * Base class for outputs of XVector models.
 */
export class XVectorOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.logits Classification hidden states before AMSoftmax, of shape `(batch_size, config.xvector_output_dim)`.
     * @param {Tensor} output.embeddings Utterance embeddings used for vector similarity-based retrieval, of shape `(batch_size, config.xvector_output_dim)`.
     */
    constructor({ logits, embeddings }) {
        super();
        this.logits = logits;
        this.embeddings = embeddings;
    }
}

/**
 * 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 }) {
        super();
        this.logits = logits;
    }
}

/**
 * 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 }) {
        super();
        this.logits = logits;
    }
}

/**
 * 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 }) {
        super();
        this.start_logits = start_logits;
        this.end_logits = end_logits;
    }
}


/**
 * Base class for causal language model (or autoregressive) outputs.
 */
export class CausalLMOutput 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 }) {
        super();
        this.logits = logits;
    }
}

/**
 * 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 }) {
        super();
        this.logits = logits;
        this.past_key_values = past_key_values;
    }
}

export class ImageMattingOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.alphas Estimated alpha values, of shape `(batch_size, num_channels, height, width)`.
     */
    constructor({ alphas }) {
        super();
        this.alphas = alphas;
    }
}

/**
 * Describes the outputs for the VITS model.
 */
export class VitsModelOutput extends ModelOutput {
    /**
     * @param {Object} output The output of the model.
     * @param {Tensor} output.waveform The final audio waveform predicted by the model, of shape `(batch_size, sequence_length)`.
     * @param {Tensor} output.spectrogram The log-mel spectrogram predicted at the output of the flow model.
     * This spectrogram is passed to the Hi-Fi GAN decoder model to obtain the final audio waveform.
     */
    constructor({ waveform, spectrogram }) {
        super();
        this.waveform = waveform;
        this.spectrogram = spectrogram;
    }
}