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

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace


@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
    reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]
)
class MultiNodeTest(unittest.TestCase):
    def setUp(self):
        if self.framework == "pytorch":
            subprocess.run(
                f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
                encoding="utf-8",
                check=True,
            )
        assert hasattr(self, "env")

    def create_estimator(self, instance_count):
        # configuration for running training on smdistributed Model Parallel
        mpi_options = {
            "enabled": True,
            "processes_per_host": 8,
        }
        smp_options = {
            "enabled": True,
            "parameters": {
                "microbatches": 4,
                "placement_strategy": "spread",
                "pipeline": "interleaved",
                "optimize": "speed",
                "partitions": 4,
                "ddp": True,
            },
        }

        distribution = {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

        name_extension = "trainer" if self.script == "run_glue.py" else "smtrainer"
        # creates estimator
        return HuggingFace(
            entry_point=self.script,
            source_dir=self.env.test_path,
            role=self.env.role,
            image_uri=self.env.image_uri,
            base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}",
            instance_count=instance_count,
            instance_type=self.instance_type,
            debugger_hook_config=False,
            hyperparameters={
                **self.env.hyperparameters,
                "model_name_or_path": self.model_name_or_path,
                "max_steps": 500,
            },
            metric_definitions=self.env.metric_definitions,
            distribution=distribution,
            py_version="py36",
        )

    def save_results_as_csv(self, job_name):
        TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")

    # @parameterized.expand([(2,), (4,),])
    @parameterized.expand([(1,)])
    def test_scripz(self, instance_count):
        # create estimator
        estimator = self.create_estimator(instance_count)

        # run training
        estimator.fit()

        # result dataframe
        result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

        # extract kpis
        eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
        eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        train_runtime = (
            Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
        )

        # assert kpis
        assert train_runtime <= self.results["train_runtime"]
        assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
        assert all(t <= self.results["eval_loss"] for t in eval_loss)

        # dump tests result into json file to share in PR
        with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
            json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace


@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
    reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]
)
class MultiNodeTest(unittest.TestCase):
    def setUp(self):
        if self.framework == "pytorch":
            subprocess.run(
                f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
                encoding="utf-8",
                check=True,
            )
        assert hasattr(self, "env")

    def create_estimator(self, instance_count):
        # configuration for running training on smdistributed Model Parallel
        mpi_options = {
            "enabled": True,
            "processes_per_host": 8,
        }
        smp_options = {
            "enabled": True,
            "parameters": {
                "microbatches": 4,
                "placement_strategy": "spread",
                "pipeline": "interleaved",
                "optimize": "speed",
                "partitions": 4,
                "ddp": True,
            },
        }

        distribution = {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

        name_extension = "trainer" if self.script == "run_glue.py" else "smtrainer"
        # creates estimator
        return HuggingFace(
            entry_point=self.script,
            source_dir=self.env.test_path,
            role=self.env.role,
            image_uri=self.env.image_uri,
            base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}",
            instance_count=instance_count,
            instance_type=self.instance_type,
            debugger_hook_config=False,
            hyperparameters={
                **self.env.hyperparameters,
                "model_name_or_path": self.model_name_or_path,
                "max_steps": 500,
            },
            metric_definitions=self.env.metric_definitions,
            distribution=distribution,
            py_version="py36",
        )

    def save_results_as_csv(self, job_name):
        TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")

    # @parameterized.expand([(2,), (4,),])
    @parameterized.expand([(1,)])
    def test_scripz(self, instance_count):
        # create estimator
        estimator = self.create_estimator(instance_count)

        # run training
        estimator.fit()

        # result dataframe
        result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

        # extract kpis
        eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
        eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        train_runtime = (
            Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
        )

        # assert kpis
        assert train_runtime <= self.results["train_runtime"]
        assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
        assert all(t <= self.results["eval_loss"] for t in eval_loss)

        # dump tests result into json file to share in PR
        with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
            json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)
