# What does this PR do?

Moves center_crop to BaseImageProcessor as the logic is the same for almost all models' image processors (except [bridgetower](https://github.com/huggingface/transformers/blob/659829b6ae558dd2e178462a797bf8b1a749f070/src/transformers/models/bridgetower/image_processing_bridgetower.py#L229) and [owlvit](https://github.com/huggingface/transformers/blob/659829b6ae558dd2e178462a797bf8b1a749f070/src/transformers/models/owlvit/image_processing_owlvit.py#L184)), and is a standard transformation all processors might want to apply.

## 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).
- [ ] Did you write any new necessary tests?

Move center_crop to BaseImageProcessor. # What does this PR do?

Moves center_crop to BaseImageProcessor as the logic is the same for almost all models' image processors (except [bridgetower](https://github.com/huggingface/transformers/blob/659829b6ae558dd2e178462a797bf8b1a749f070/src/transformers/models/bridgetower/image_processing_bridgetower.py#L229) and [owlvit](https://github.com/huggingface/transformers/blob/659829b6ae558dd2e178462a797bf8b1a749f070/src/transformers/models/owlvit/image_processing_owlvit.py#L184)), and is a standard transformation all processors might want to apply.

## 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).
- [ ] Did you write any new necessary tests?

<!---
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.
-->

# Question answering example

This folder contains the `run_qa.py` script, demonstrating *question answering* with the 🤗 Transformers library.
For straightforward use-cases you may be able to use this script without modification, although we have also
included comments in the code to indicate areas that you may need to adapt to your own projects. 

### Usage notes
Note that when contexts are long they may be split into multiple training cases, not all of which may contain
the answer span. 

As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user
inputs as well.

### Multi-GPU and TPU usage

By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument. There are some issues surrounding
these strategies and our models right now, which are most likely to appear in the evaluation/prediction steps. We're
actively working on better support for multi-GPU and TPU training in TF, but if you encounter problems a quick 
workaround is to train in the multi-GPU or TPU context and then perform predictions outside of it.

### Memory usage and data loading

One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and 
README, but for more information you can see the 'Input Datasets' section of 
[this document](https://www.tensorflow.org/guide/tpu).

### Example command
```
python run_qa.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name squad \
--do_train \
--do_eval \
```

<!---
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.
-->

# Question answering example

This folder contains the `run_qa.py` script, demonstrating *question answering* with the 🤗 Transformers library.
For straightforward use-cases you may be able to use this script without modification, although we have also
included comments in the code to indicate areas that you may need to adapt to your own projects. 

### Usage notes
Note that when contexts are long they may be split into multiple training cases, not all of which may contain
the answer span. 

As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user
inputs as well.

### Multi-GPU and TPU usage

By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument. There are some issues surrounding
these strategies and our models right now, which are most likely to appear in the evaluation/prediction steps. We're
actively working on better support for multi-GPU and TPU training in TF, but if you encounter problems a quick 
workaround is to train in the multi-GPU or TPU context and then perform predictions outside of it.

### Memory usage and data loading

One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and 
README, but for more information you can see the 'Input Datasets' section of 
[this document](https://www.tensorflow.org/guide/tpu).

### Example command
```
python run_qa.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name squad \
--do_train \
--do_eval \
```
