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

# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocessing script before training the distilled model.
"""
import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
logger = logging.getLogger(__name__)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
    )
    parser.add_argument(
        "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
    )
    parser.add_argument(
        "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
    )
    parser.add_argument("--vocab_size", default=30522, type=int)
    args = parser.parse_args()

    logger.info(f"Loading data from {args.data_file}")
    with open(args.data_file, "rb") as fp:
        data = pickle.load(fp)

    logger.info("Counting occurrences for MLM.")
    counter = Counter()
    for tk_ids in data:
        counter.update(tk_ids)
    counts = [0] * args.vocab_size
    for k, v in counter.items():
        counts[k] = v

    logger.info(f"Dump to {args.token_counts_dump}")
    with open(args.token_counts_dump, "wb") as handle:
        pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)

# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocessing script before training the distilled model.
"""
import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
logger = logging.getLogger(__name__)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
    )
    parser.add_argument(
        "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
    )
    parser.add_argument(
        "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
    )
    parser.add_argument("--vocab_size", default=30522, type=int)
    args = parser.parse_args()

    logger.info(f"Loading data from {args.data_file}")
    with open(args.data_file, "rb") as fp:
        data = pickle.load(fp)

    logger.info("Counting occurrences for MLM.")
    counter = Counter()
    for tk_ids in data:
        counter.update(tk_ids)
    counts = [0] * args.vocab_size
    for k, v in counter.items():
        counts[k] = v

    logger.info(f"Dump to {args.token_counts_dump}")
    with open(args.token_counts_dump, "wb") as handle:
        pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
