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

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class MT5IntegrationTest(unittest.TestCase):
    @slow
    def test_small_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.7.1
        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary

        >>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
        >>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path)
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small", return_dict=True).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")

        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
        labels = tokenizer("Hi I am", return_tensors="pt").input_ids

        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class MT5IntegrationTest(unittest.TestCase):
    @slow
    def test_small_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.7.1
        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary

        >>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
        >>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path)
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small", return_dict=True).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")

        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
        labels = tokenizer("Hi I am", return_tensors="pt").input_ids

        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
