Add support for DonutSwin models (#320)

* Add `add_special_tokens` option to tokenizers

* Improve error messages for loading processors

* Add `DonutFeatureExtractor`

* Add `DonutSwinModel` and `MBartForCausalLM` models

* Fix `addPastKeyValues` for `VisionEncoderDecoder` models

* Add `Donut` to list of supported models

* Make encode parameters optional

* Support batched decoder input ids

* Remove unused import

* Add `do_thumbnail` for donut image processing

* Fix `TypeError: decoder_input_ids[i].map is not a function`

* Only pad if width and height specified in size

* Only pad if `pad_size` is defined

* Only cut `decoder_input_ids` if past model output

* Add donut model

* Add example usage to JSDoc for `DonutSwinModel`

* Add support for `DocumentQuestionAnsweringPipeline`

* Add simple document question answering unit test

* Add listed support for document QA pipeline

Author: xenova

README.md

@@ -237,7 +237,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 
 | Task                     | ID | Description | Supported? |
 |--------------------------|----|-------------|------------|
-| [Document Question Answering](https://huggingface.co/tasks/document-question-answering)         | `document-question-answering`  | Answering questions on document images. | ❌ |
+| [Document Question Answering](https://huggingface.co/tasks/document-question-answering)         | `document-question-answering`  | Answering questions on document images. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.DocumentQuestionAnsweringPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=document-question-answering&library=transformers.js) |
 | [Feature Extraction](https://huggingface.co/tasks/feature-extraction)         |  `feature-extraction`  | Transforming raw data into numerical features that can be processed while preserving the information in the original dataset. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js) |
 | [Image-to-Text](https://huggingface.co/tasks/image-to-text)         |  `image-to-text`  | Output text from a given image. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageToTextPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=image-to-text&library=transformers.js) |
 | [Text-to-Image](https://huggingface.co/tasks/text-to-image)         |  `text-to-image`  | Generates images from input text.  | ❌ |
@@ -271,6 +271,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
+1. **[Donut](https://huggingface.co/docs/transformers/model_doc/donut)** (from NAVER), released together with the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
 1. **[FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
 1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach

docs/snippets/5_supported-tasks.snippet

@@ -53,7 +53,7 @@
 
 | Task                     | ID | Description | Supported? |
 |--------------------------|----|-------------|------------|
-| [Document Question Answering](https://huggingface.co/tasks/document-question-answering)         | `document-question-answering`  | Answering questions on document images. | ❌ |
+| [Document Question Answering](https://huggingface.co/tasks/document-question-answering)         | `document-question-answering`  | Answering questions on document images. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.DocumentQuestionAnsweringPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=document-question-answering&library=transformers.js) |
 | [Feature Extraction](https://huggingface.co/tasks/feature-extraction)         |  `feature-extraction`  | Transforming raw data into numerical features that can be processed while preserving the information in the original dataset. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js) |
 | [Image-to-Text](https://huggingface.co/tasks/image-to-text)         |  `image-to-text`  | Output text from a given image. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageToTextPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=image-to-text&library=transformers.js) |
 | [Text-to-Image](https://huggingface.co/tasks/text-to-image)         |  `text-to-image`  | Generates images from input text.  | ❌ |

docs/snippets/6_supported-models.snippet

@@ -17,6 +17,7 @@
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
+1. **[Donut](https://huggingface.co/docs/transformers/model_doc/donut)** (from NAVER), released together with the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
 1. **[FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
 1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach

scripts/supported_models.py

@@ -203,6 +203,7 @@
     'donut': [
         # Image-to-text
         'naver-clova-ix/donut-base-finetuned-cord-v2',
+        'naver-clova-ix/donut-base-finetuned-zhtrainticket',
 
         # Document Question Answering
         'naver-clova-ix/donut-base-finetuned-docvqa',