@huggingface/transformers/src/models/donut_swin/modeling_donut_swin.js

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import { PreTrainedModel } from '../modeling_utils.js';

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 {}