Flamingo Implementation
Author: king159
Pull request: yes

# What does this PR do?

Implementation of Flamingo models (https://arxiv.org/abs/2204.14198). Model weights trained by Open Flamingo team can be downloaded [here](https://huggingface.co/openflamingo/OpenFlamingo-9B). Weight conversion script is included.

Weights conversion can be run via:
``` python
python src/transformers/models/flamingo/converting_flamingo_to_hf.py \
    --old_ckpt_path /path/to/open/flamingo/weights \
    --new_hf_path /output/path
```
Models can then be loaded via:
``` python
model = transformers.FlamingoForConditionalGeneration.from_pretrained("/output/path")
```
Example:
``` python
import requests
import torch
import transformers
from PIL import Image

tokenizer = model.text_tokenizer
image_processor = transformers.CLIPImageProcessor()
demo_image_one = Image.open(
    requests.get(
        "http://images.cocodataset.org/val2017/000000039769.jpg", stream=True
    ).raw
)
demo_image_two = Image.open(
    requests.get(
        "http://images.cocodataset.org/test-stuff2017/000000028137.jpg", stream=True
    ).raw
)
query_image = Image.open(
    requests.get(
        "http://images.cocodataset.org/test-stuff2017/000000028352.jpg", stream=True
    ).raw
)
vision_x = (
    image_processor.preprocess(
        [demo_image_one, demo_image_two, query_image], return_tensors="pt"
    )["pixel_values"]
    .unsqueeze(1)
    .unsqueeze(0)
)
model.text_tokenizer.padding_side = "left"
lang_x = tokenizer(
    ["<image>An image of two cats.
<image>An image of a bathroom sink.
<image>An image of"],
    return_tensors="pt",
)

generated_text = model.generate(
    vision_x=vision_x,
    lang_x=lang_x["input_ids"],
    attention_mask=lang_x["attention_mask"],
    max_new_tokens=20,
    num_beams=3,
)

print("Generated text: ", model.text_tokenizer.decode(generated_text[0]))
```

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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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      to it if that's the case.
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

Comments:
Respect! Openflamingo needs be built with huggingface transformers for more efficient training and inference.

We have already adapted it in our [Otter model](https://github.com/Luodian/Otter) (an instruction tuned model based on flamingo). We uploaded a converted openflamingo-9b weights at [luodian/openflamingo-9b-hf](https://huggingface.co/luodian/openflamingo-9b-hf).

The model could be loaded via 
```python
model = transformers.FlamingoForConditionalGeneration.from_pretrained("luodian/openflamingo-9b-hf")
```

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_23063). All of your documentation changes will be reflected on that endpoint.

cc @amyeroberts and @younesbelkada 

Awesome work! Let us know when the PR is ready for review!