Add Flax image captioning example

examples/flax/image-captioning/README.md

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+# Image Captioning (vision-encoder-text-decoder model) training examples
+
+The following example showcases how to finetune a vision-encoder-text-decoder model for image captioning
+using the JAX/Flax backend, leveraging 🤗 Transformers library's `FlaxVisionEncoderDecoderModel`.
+
+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.
+
+`run_image_captioning_flax.py` is a lightweight example of how to download and preprocess a dataset from the 🤗 Datasets
+library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.
+
+For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files and you also will find examples of these below.
+
+This example uses COCO dataset (2017) through a custom dataset script, which requires users to manually download the
+COCO dataset before training.
+
+### Download COCO dataset (2017)
+```bash
+mkdir data
+cd data
+wget http://images.cocodataset.org/zips/train2017.zip
+wget http://images.cocodataset.org/zips/val2017.zip
+wget http://images.cocodataset.org/zips/test2017.zip
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+wget http://images.cocodataset.org/annotations/image_info_test2017.zip
+cd ..
+```
+
+### Train the model
+Next we can run the example script to train the model:
+
+```bash
+python3 run_image_captioning_flax.py \
+	--output_dir ./image-captioning-training-results \
+	--encoder_model_name_or_path "google/vit-base-patch16-224-in21k" \
+	--decoder_model_name_or_path "gpt2" \
+	--dataset_name ydshieh/dummy_coco_dataset \
+	--dataset_config_name=2017 \
+	--data_dir $PWD/data \
+	--image_column image_path \
+	--caption_column caption \
+	--do_train --do_eval --predict_with_generate \
+	--num_train_epochs 1 \
+	--eval_steps 500 \
+	--learning_rate 3e-5 --warmup_steps 0 \
+	--per_device_train_batch_size 32 \
+	--per_device_eval_batch_size 32 \
+	--overwrite_output_dir \
+	--max_target_length 32 \
+	--num_beams 8 \
+	--preprocessing_num_workers 16 \
+	--logging_steps 10 \
+	--block_size 16384 \
+	--push_to_hub
+```
+
+This should finish in about 1h30, with validation loss and ROUGE2 score of 2.0153 and 14.64 respectively after 1 epoch.
+Training statistics can be accessed on [Models](https://huggingface.co/ydshieh/image-captioning-training-results/tensorboard).