# What does this PR do?

This PR adds the option to set `Accelerator`'s `dispatch_batches` argument through the `TrainingArguments`. This is needed in cases such as https://github.com/huggingface/transformers/issues/24999, when `dispatch_batches=False` is needed on the streaming dataset

Fixes # (issue)

Solves https://github.com/huggingface/transformers/issues/24999


## 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?
- [ ] 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.
- [ ] 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?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sgugger 
Add dispatch_batches to training arguments. # What does this PR do?

This PR adds the option to set `Accelerator`'s `dispatch_batches` argument through the `TrainingArguments`. This is needed in cases such as https://github.com/huggingface/transformers/issues/24999, when `dispatch_batches=False` is needed on the streaming dataset

Fixes # (issue)

Solves https://github.com/huggingface/transformers/issues/24999


## 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?
- [ ] 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.
- [ ] 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?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sgugger 
# Copyright 2021 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 warnings

from ..trainer import Trainer
from ..utils import logging


logger = logging.get_logger(__name__)


class SageMakerTrainer(Trainer):
    def __init__(self, args=None, **kwargs):
        warnings.warn(
            "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
            "instead.",
            FutureWarning,
        )
        super().__init__(args=args, **kwargs)

# Copyright 2021 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 warnings

from ..trainer import Trainer
from ..utils import logging


logger = logging.get_logger(__name__)


class SageMakerTrainer(Trainer):
    def __init__(self, args=None, **kwargs):
        warnings.warn(
            "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
            "instead.",
            FutureWarning,
        )
        super().__init__(args=args, **kwargs)
