Hello!

# What does this PR do?
1. Deprecate `OpenLlama` following #24787.
2. Add a disclaimer pointing users to `LLaMA` for the [OpenLLaMA models](https://huggingface.co/models?search=openllama).
3. Resolve a typo in a warning in `check_repo.py`
4. Read modeling files with `encoding="utf8"` in `check_config_attributes_being_used`.

If preferred, I can revert 3 and 4 and cherry-pick them into a separate PR.
Follow-up of #24913. This is a considerably better solution - I feel a bit embarrassed I even considered the other one.

## Details
I've followed the steps that @sgugger seems to have taken from #24787 to move OpenLlama into deprecation. This involves moving the main code, adapting the `__init__` files, removing the tests, and updating the documentation with a disclaimer.

Feel free to let me know if you'd rather keep the model non-deprecated for the time being, and then I'll revert to only the addition of the disclaimer.

## 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?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Discussed in #24913.
- [ ] 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).
- [ ] Did you write any new necessary tests?


## Who can review?

@sgugger 

- Tom Aarsen

Deprecate unused OpenLlama architecture. Hello!

# What does this PR do?
1. Deprecate `OpenLlama` following #24787.
2. Add a disclaimer pointing users to `LLaMA` for the [OpenLLaMA models](https://huggingface.co/models?search=openllama).
3. Resolve a typo in a warning in `check_repo.py`
4. Read modeling files with `encoding="utf8"` in `check_config_attributes_being_used`.

If preferred, I can revert 3 and 4 and cherry-pick them into a separate PR.
Follow-up of #24913. This is a considerably better solution - I feel a bit embarrassed I even considered the other one.

## Details
I've followed the steps that @sgugger seems to have taken from #24787 to move OpenLlama into deprecation. This involves moving the main code, adapting the `__init__` files, removing the tests, and updating the documentation with a disclaimer.

Feel free to let me know if you'd rather keep the model non-deprecated for the time being, and then I'll revert to only the addition of the disclaimer.

## 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?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Discussed in #24913.
- [ ] 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).
- [ ] Did you write any new necessary tests?


## Who can review?

@sgugger 

- Tom Aarsen

from datetime import datetime

import matplotlib.pyplot as plt
import torch


def freeze_module(module):
    for param in module.parameters():
        param.requires_grad = False


def get_device():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        device = "mps"
    if device == "mps":
        print(
            "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
            " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
            " with generations."
        )
    return device


def show_pil(img):
    fig = plt.imshow(img)
    fig.axes.get_xaxis().set_visible(False)
    fig.axes.get_yaxis().set_visible(False)
    plt.show()


def get_timestamp():
    current_time = datetime.now()
    timestamp = current_time.strftime("%H:%M:%S")
    return timestamp

from datetime import datetime

import matplotlib.pyplot as plt
import torch


def freeze_module(module):
    for param in module.parameters():
        param.requires_grad = False


def get_device():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        device = "mps"
    if device == "mps":
        print(
            "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
            " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
            " with generations."
        )
    return device


def show_pil(img):
    fig = plt.imshow(img)
    fig.axes.get_xaxis().set_visible(False)
    fig.axes.get_yaxis().set_visible(False)
    plt.show()


def get_timestamp():
    current_time = datetime.now()
    timestamp = current_time.strftime("%H:%M:%S")
    return timestamp
