[Flax] ViT training example (#12300)

* begin script

* clean example, add readme

* update readme

* remove decay mask

* remove masking

* update readme & make flake happy

examples/flax/vision/README.md

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+<!---
+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.
+-->
+
+# Image Classification training examples
+
+The following example showcases how to train/fine-tune `ViT` for image-classification using the JAX/Flax backend.
+
+JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
+Models written in JAX/Flax are **immutable** and updated in a purely functional
+way which enables simple and efficient model parallelism.
+
+
+In this example we will train/fine-tune the model on the [imagenette](https://github.com/fastai/imagenette) dataset.
+
+Let's start by creating a model repository to save the trained model and logs.
+Here we call the model `"vit-base-patch16-imagenette"`, but you can change the model name as you like.
+
+You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
+you are logged in) or via the command line:
+
+```
+huggingface-cli repo create vit-base-patch16-imagenette
+```
+Next we clone the model repository to add the tokenizer and model files.
+```
+git clone https://huggingface.co/<your-username>/vit-base-patch16-imagenette
+```
+To ensure that all tensorboard traces will be uploaded correctly, we need to 
+track them. You can run the following command inside your model repo to do so.
+
+```
+cd vit-base-patch16-imagenette
+git lfs track "*tfevents*"
+```
+
+Great, we have set up our model repository. During training, we will automatically
+push the training logs and model weights to the repo.
+
+Next, let's add a symbolic link to the `run_image_classification_flax.py`.
+
+```bash
+export MODEL_DIR="./vit-base-patch16-imagenette
+ln -s ~/transformers/examples/flax/summarization/run_image_classification_flax.py run_image_classification_flax.py
+```
+
+## Prepare the dataset
+
+We will use the [imagenette](https://github.com/fastai/imagenette) dataset to train/fine-tune our model. Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
+
+
+### Download and extract the data.
+
+```bash
+wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz
+tar -xvzf imagenette2.tgz
+```
+
+This will create a `imagenette2` dir with two subdirectories `train` and `val` each with multiple subdirectories per class. The training script expects the following directory structure
+
+```bash
+root/dog/xxx.png
+root/dog/xxy.png
+root/dog/[...]/xxz.png
+
+root/cat/123.png
+root/cat/nsdf3.png
+root/cat/[...]/asd932_.png
+```
+
+## Train the model
+
+Next we can run the example script to fine-tune the model:
+
+```bash
+python run_image_classification.py \
+    --output_dir ${MODEL_DIR} \
+    --model_name_or_path google/vit-base-patch16-224-in21k \
+    --train_dir="imagenette2/train" \
+    --validation_dir="imagenette2/val" \
+    --num_train_epochs 5 \
+    --learning_rate 1e-3 \
+    --per_device_train_batch_size 128 --per_device_eval_batch_size 128 \
+    --overwrite_output_dir \
+    --preprocessing_num_workers 32 \
+    --push_to_hub
+```
+
+This should finish in ~7mins with 99% validation accuracy.