Add Doge model
Author: LoserCheems
Pull request: yes

# What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

As per title:
Paper: https://arxiv.org/abs/2407.16958 (The latest revision will be updated on 2024-11-5)

This paper proposes inner function attention with dynamic masks to greatly improve accuracy on multi-query associative recall tasks. I finished the pytorch implementation of the attention and feedforward network of this paper, but removed the SSM algorithm to fit most environments.

![output](https://github.com/user-attachments/assets/4b89ea3e-c9eb-4a57-a56d-4332896de724)

## Before submitting
- This pull request introduces the new Doge model to the codebase, adding documentation, configuration, and model files. The changes include updates to the documentation, configuration, and initialization files to integrate the Doge model into the existing framework.

### Documentation Updates:
* Added Doge model documentation to the table of contents (`docs/source/en/_toctree.yml`).
* Included Doge model in the model availability table (`docs/source/en/index.md`).
* Added a new documentation file for the Doge model (`docs/source/en/model_doc/doge.md`).

### Codebase Integration:
* Updated `__init__.py` to include Doge model imports and configurations (`src/transformers/__init__.py`). [[1]](diffhunk://#diff-7723156f6b075b1bf1525f769d90a7cc0b5f233becdcbe28707aaa753960d897R399) [[2]](diffhunk://#diff-7723156f6b075b1bf1525f769d90a7cc0b5f233becdcbe28707aaa753960d897R2115-R2122) [[3]](diffhunk://#diff-7723156f6b075b1bf1525f769d90a7cc0b5f233becdcbe28707aaa753960d897R5272) [[4]](diffhunk://#diff-7723156f6b075b1bf1525f769d90a7cc0b5f233becdcbe28707aaa753960d897R6901-R6906)
* Added Doge model to the models list (`src/transformers/models/__init__.py`).
* Included Doge model configurations and tokenizers in `configuration_auto.py` and `tokenization_auto.py` (`src/transformers/models/auto/configuration_auto.py`, `src/transformers/models/auto/tokenization_auto.py`). [[1]](diffhunk://#diff-311e25086b9c91ebbaa8fdeff4fdad9c69e8c18645c2e21f8aa0a5e76f5bf64dR93) [[2]](diffhunk://#diff-311e25086b9c91ebbaa8fdeff4fdad9c69e8c18645c2e21f8aa0a5e76f5bf64dR395) [[3]](diffhunk://#diff-d804e851851cdebeb8048938f1f8beec1cfa78bf7b1f06af86faa450f9d18defR171)
* Added Doge model classes to `modeling_auto.py` (`src/transformers/models/auto/modeling_auto.py`). [[1]](diffhunk://#diff-4819b26eea3e1076eaede516032f9baa81a0f5b7e3139d6ef5f7a05c86443489R92) [[2]](diffhunk://#diff-4819b26eea3e1076eaede516032f9baa81a0f5b7e3139d6ef5f7a05c86443489R308) [[3]](diffhunk://#diff-4819b26eea3e1076eaede516032f9baa81a0f5b7e3139d6ef5f7a05c86443489R397) [[4]](diffhunk://#diff-4819b26eea3e1076eaede516032f9baa81a0f5b7e3139d6ef5f7a05c86443489R485) [[5]](diffhunk://#diff-4819b26eea3e1076eaede516032f9baa81a0f5b7e3139d6ef5f7a05c86443489R940)

These changes collectively add support for the new "Doge" model, including its configuration and various model classes for different tasks.

- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [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).
- [x] Did you write any new necessary tests?

![image](https://github.com/user-attachments/assets/fb1e11ee-eadc-49e0-8210-2182774db5e9)

Comments:
Hi @LoserCheems, we generally recommend adding models as [custom code models](https://huggingface.co/docs/transformers/en/custom_models) first. Custom code models can be loaded by AutoClasses just like core models (with `trust_remote_code=True`). We usually only add models to the library with a PR once they have a lot of usage, because the `transformers` team takes responsibility for maintaining them once they're merged into the main library.

Thank you very much for your suggestion, we will now add the model as custom code and share it with huggingface to continue our research!