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LED

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# What does this PR do?

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Adds LongformerEncoderDecoder (LED) from @ibeltagy - see: https://github.com/allenai/longformer#longformer

Todo:

- [x] **Important**: position embeddings have to be cut to correctly convert original Bart-ilke checkpoints to LED. The reason is that Bart uses a position embedding hack because of which the embedding idx 0 and 1 are never used resulting in an embedding matrix that has a length of 1026 instead of 1024, see: https://github.com/huggingface/transformers/blob/88ef8893cd649cc2b4adb9885aba88c750118cff/src/transformers/models/bart/modeling_bart.py#L131. All LED checkpoints are hence cut to remove this hack in LED:
```python
model = LEDForConditionalGeneration.from_pretrained("./led-base-16384")
model.model.encoder.embed_positions.weight = torch.nn.Parameter(model.model.encoder.embed_positions.weight[2:, :])
model.model.decoder.embed_positions.weight = torch.nn.Parameter(model.model.decoder.embed_positions.weight[2:, :])
model.save_pretrained("./led-base-16384")
```

- [x] Make Pytorch integration tests pass. See `LEDIntegrationTests` in `tests/test_modeling_led.py`.
- [x] Add gradient_checkpointing
- [x] Make common tests work
- [x] Add convenient padding function so that input can be of whatever size and add global_attn logic to mask
- [x] Automatically create attention_mask in encoder if not provided
- [x] Finish PT version
- [x] Make TF version work
- [x] Add tips in docs for LED
- [x] Eval notebook: https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing
- [x] Nice to have: Fine-tune notebook: https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing
- [ ] Make nice model cards

## TODO after PR is merged:
- [ ] Correctly add `# Copied from ....` statements from Bart and Longformer (this probably requires the Bart refactor to be merged before)
- [ ] Open issue regarding problems with TF save_model test
- [ ] Correct templates: delete unnecessary test for tf bart; add gradient checkpointing by default in PT

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors which may be interested in your PR.

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 albert, bert, XLM: @LysandreJik
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