# 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?

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# Addestramento su TPU

<Tip>

 Nota: Molte delle strategie introdotte nella [sezione sulla GPU singola](perf_train_gpu_one) (come mixed precision training o gradient accumulation) e [sezione multi-GPU](perf_train_gpu_many) sono generiche e applicabili all'addestramento di modelli in generale quindi assicurati di dargli un'occhiata prima di immergerti in questa sezione.

</Tip>

Questo documento sarà presto completato con informazioni su come effettuare la formazione su TPU.

<!--Copyright 2022 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

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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

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

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# Addestramento su TPU

<Tip>

 Nota: Molte delle strategie introdotte nella [sezione sulla GPU singola](perf_train_gpu_one) (come mixed precision training o gradient accumulation) e [sezione multi-GPU](perf_train_gpu_many) sono generiche e applicabili all'addestramento di modelli in generale quindi assicurati di dargli un'occhiata prima di immergerti in questa sezione.

</Tip>

Questo documento sarà presto completato con informazioni su come effettuare la formazione su TPU.
