Enable doc in Spanish (#16518)

* Reorganize doc for multilingual support

* Fix style

* Style

* Toc trees

* Adapt templates

docs/source/en/model_doc/beit.mdx

@@ -0,0 +1,114 @@
+<!--Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# BEiT
+
+## Overview
+
+The BEiT model was proposed in [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by
+Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
+Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class
+of an image (as done in the [original ViT paper](https://arxiv.org/abs/2010.11929)), BEiT models are pre-trained to
+predict visual tokens from the codebook of OpenAI's [DALL-E model](https://arxiv.org/abs/2102.12092) given masked
+patches.
+
+The abstract from the paper is the following:
+
+*We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation
+from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image
+modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image
+patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first "tokenize" the original image into
+visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training
+objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we
+directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder.
+Experimental results on image classification and semantic segmentation show that our model achieves competitive results
+with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K,
+significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains
+86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%).*
+
+Tips:
+
+- BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
+  outperform both the [original model (ViT)](vit) as well as [Data-efficient Image Transformers (DeiT)](deit) when fine-tuned on ImageNet-1K and CIFAR-100. You can check out demo notebooks regarding inference as well as
+  fine-tuning on custom data [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer) (you can just replace
+  [`ViTFeatureExtractor`] by [`BeitFeatureExtractor`] and
+  [`ViTForImageClassification`] by [`BeitForImageClassification`]).
+- There's also a demo notebook available which showcases how to combine DALL-E's image tokenizer with BEiT for
+  performing masked image modeling. You can find it [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/BEiT).
+- As the BEiT models expect each image to be of the same size (resolution), one can use
+  [`BeitFeatureExtractor`] to resize (or rescale) and normalize images for the model.
+- Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
+  each checkpoint. For example, `microsoft/beit-base-patch16-224` refers to a base-sized architecture with patch
+  resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the [hub](https://huggingface.co/models?search=microsoft/beit).
+- The available checkpoints are either (1) pre-trained on [ImageNet-22k](http://www.image-net.org/) (a collection of
+  14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/) (also referred to as ILSVRC 2012, a collection of 1.3 million
+  images and 1,000 classes).
+- BEiT uses relative position embeddings, inspired by the T5 model. During pre-training, the authors shared the
+  relative position bias among the several self-attention layers. During fine-tuning, each layer's relative position
+  bias is initialized with the shared relative position bias obtained after pre-training. Note that, if one wants to
+  pre-train a model from scratch, one needs to either set the `use_relative_position_bias` or the
+  `use_relative_position_bias` attribute of [`BeitConfig`] to `True` in order to add
+  position embeddings.
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The JAX/FLAX version of this model was
+contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/beit).
+
+
+## BEiT specific outputs
+
+[[autodoc]] models.beit.modeling_beit.BeitModelOutputWithPooling
+
+[[autodoc]] models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling
+
+## BeitConfig
+
+[[autodoc]] BeitConfig
+
+## BeitFeatureExtractor
+
+[[autodoc]] BeitFeatureExtractor
+    - __call__
+
+## BeitModel
+
+[[autodoc]] BeitModel
+    - forward
+
+## BeitForMaskedImageModeling
+
+[[autodoc]] BeitForMaskedImageModeling
+    - forward
+
+## BeitForImageClassification
+
+[[autodoc]] BeitForImageClassification
+    - forward
+
+## BeitForSemanticSegmentation
+
+[[autodoc]] BeitForSemanticSegmentation
+    - forward
+
+## FlaxBeitModel
+
+[[autodoc]] FlaxBeitModel
+    - __call__
+
+## FlaxBeitForMaskedImageModeling
+
+[[autodoc]] FlaxBeitForMaskedImageModeling
+    - __call__
+
+## FlaxBeitForImageClassification
+
+[[autodoc]] FlaxBeitForImageClassification
+    - __call__