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…#18499) * Update methods to optionally rescale This is necessary to allow for casting our images / videos to numpy arrays within the feature extractors' call. We want to do this to make sure the behaviour is as expected when flags like are False. If some transformations aren't applied, then the output type can't be unexpected e.g. a list of PIL images instead of numpy arrays. * Cast images to numpy arrays in call to enable consistent behaviour with different configs * Remove accidental clip changes * Update tests to reflect the scaling logic We write a generic function to handle rescaling of our arrays. In order for the API to be intuitive, we take some factor c and rescale the image values by that. This means, the rescaling done in normalize and to_numpy_array are now done with array * (1/255) instead of array / 255. This leads to small differences in the resulting image. When testing, this was in the order of 1e-8, and so deemed OK
* Allow users to force TF availability * Correctly name the envvar!
* add first generation tutorial * [LongT5 Docs] Correct docs * correct expected string * remove incorrect file
Co-authored-by: ydshieh <[email protected]>
* Fix quantization * Save model * Remove unused comments * Fix formatting
* fix bnb documentation - move bnb documentation to `infer_gpu_many` * small refactoring - added text on infer_gpu_one - added a small note on infer_gpu_many - added customized multi gpu example on infer_gpu_many * Update docs/source/en/perf_infer_gpu_many.mdx Co-authored-by: Stas Bekman <[email protected]> * apply suggestions Co-authored-by: Stas Bekman <[email protected]> * Apply suggestions from code review Co-authored-by: Stas Bekman <[email protected]> Co-authored-by: Stas Bekman <[email protected]>
* add examples subfolder * mention examples in codeparrot readme * use Trainer optimizer and scheduler type and add output_dir as argument * add example of text-to-python and python-to-text models * mention the downstream examples in the readme * fix typo
…#18676) * `model.tie_weights()` should be applied after `accelerator.prepare` Weight tying should be done after the model has been moved to XLA device as mentioned on PyTorch/XLA Troubleshooting guide [here](https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md#xla-tensor-quirks) * format code
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What does this PR do?
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