Update keras to 3.7.0

[pyup-bot]

This PR updates keras from 3.4.1 to 3.7.0.

Changelog

3.7.0

API changes

- Add `flash_attention` argument to `keras.ops.dot_product_attention` and to `keras.layers.MultiHeadAttention`.
- Add `keras.layers.STFTSpectrogram` layer (to extract STFT spectrograms from inputs as a preprocessing step) as well as its initializer `keras.initializers.STFTInitializer`.
- Add `celu`, `glu`, `log_sigmoid`, `hard_tanh`, `hard_shrink`, `squareplus` activations.
- Add `keras.losses.Circle` loss.
- Add image visualization utilities `keras.visualization.draw_bounding_boxes`, `keras.visualization.draw_segmentation_masks`, `keras.visualization.plot_image_gallery`, `keras.visualization.plot_segmentation_mask_gallery`.
- Add `double_checkpoint` argument to `BackupAndRestore` to save a fallback checkpoint in case the first checkpoint gets corrupted.
- Add bounding box preprocessing support to image augmentation layers `CenterCrop`, `RandomFlip`, `RandomZoom`, `RandomTranslation`, `RandomCrop`.
- Add `keras.ops.exp2`, `keras.ops.inner` operations.

Performance improvements

- JAX backend: add native Flash Attention support for GPU (via cuDNN) and TPU (via a Pallas kernel). Flash Attention is now used automatically when the hardware supports it.
- PyTorch backend: add native Flash Attention support for GPU (via cuDNN). It is currently opt-in.
- TensorFlow backend: enable more kernel fusion via `bias_add`.
- PyTorch backend: add support for Intel XPU devices.

New Contributors

* mostafa-mahmoud made their first contribution in https://github.com/keras-team/keras/pull/20313
* TrAyZeN made their first contribution in https://github.com/keras-team/keras/pull/20321
* dryglicki made their first contribution in https://github.com/keras-team/keras/pull/20353
* jm-willy made their first contribution in https://github.com/keras-team/keras/pull/20352
* Gopi-Uppari made their first contribution in https://github.com/keras-team/keras/pull/20377
* nicolaspi made their first contribution in https://github.com/keras-team/keras/pull/20383
* sineeli made their first contribution in https://github.com/keras-team/keras/pull/20368
* LakshmiKalaKadali made their first contribution in https://github.com/keras-team/keras/pull/20403
* mwtoews made their first contribution in https://github.com/keras-team/keras/pull/20427
* mrry made their first contribution in https://github.com/keras-team/keras/pull/20438
* rohithpudari made their first contribution in https://github.com/keras-team/keras/pull/20447
* ma7555 made their first contribution in https://github.com/keras-team/keras/pull/20452
* jakevdp made their first contribution in https://github.com/keras-team/keras/pull/20469
* lcs-crr made their first contribution in https://github.com/keras-team/keras/pull/20503
* rameshdange5191 made their first contribution in https://github.com/keras-team/keras/pull/20525

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.6.0...v3.7.0

3.6.0

Highlights

* New file editor utility: `keras.saving.KerasFileEditor`. Use it to inspect, diff, modify and resave Keras weights files. [See basic workflow here](https://colab.research.google.com/drive/1b1Rxf8xbOkMyvjpdJDrGzSnisyXatJsW?usp=sharing).
* New `keras.utils.Config` class for managing experiment config parameters.

BREAKING changes

* When using `keras.utils.get_file`, with `extract=True` or `untar=True`, the return value will be the path of the extracted directory, rather than the path of the archive.

Other changes and additions

* Logging is now asynchronous in `fit()`, `evaluate()`, `predict()`. This enables 100% compact stacking of `train_step` calls on accelerators (e.g. when running small models on TPU).
 - If you are using custom callbacks that rely on `on_batch_end`, this will disable async logging. You can force it back by adding `self.async_safe = True` to your callbacks. Note that the `TensorBoard` callback isn't considered async safe by default. Default callbacks like the progress bar are async safe.
* Added `keras.saving.KerasFileEditor` utility to inspect, diff, modify and resave Keras weights file.
* Added `keras.utils.Config` class. It behaves like a dictionary, with a few nice features:
 - All entries are accessible and settable as attributes, in addition to dict-style (e.g. `config.foo = 2` or `config["foo"]` are both valid)
 - You can easily serialize it to JSON via `config.to_json()`.
 - You can easily freeze it, preventing future changes, via `config.freeze()`. 
* Added bitwise numpy ops:
 * `bitwise_and`
 * `bitwise_invert`
 * `bitwise_left_shift`
 * `bitwise_not`
 * `bitwise_or`
 * `bitwise_right_shift`
 * `bitwise_xor`
* Added math op `keras.ops.logdet`.
* Added numpy op `keras.ops.trunc`.
* Added `keras.ops.dot_product_attention`.
* Added `keras.ops.histogram`.
* Allow infinite `PyDataset` instances to use multithreading.
* Added argument `verbose` in `keras.saving.ExportArchive.write_out()` method for exporting TF SavedModel.
* Added `epsilon` argument in `keras.ops.normalize`.
* Added `Model.get_state_tree()` method for retrieving a nested dict mapping variable paths to variable values (either as numpy arrays or backend tensors (default)). This is useful for rolling out custom JAX training loops.
* Added image augmentation/preprocessing layers `keras.layers.AutoContrast`, `keras.layers.Solarization`.
* Added `keras.layers.Pipeline` class, to apply a sequence of layers to an input. This class is useful to build a preprocessing pipeline. Compared to a `Sequential` model, `Pipeline` features a few important differences:
 - It's not a `Model`, just a plain layer.
 - When the layers in the pipeline are compatible with `tf.data`, the pipeline will also remain `tf.data` compatible, independently of the backend you use.


New Contributors
* alexhartl made their first contribution in https://github.com/keras-team/keras/pull/20125
* Doch88 made their first contribution in https://github.com/keras-team/keras/pull/20156
* edbosne made their first contribution in https://github.com/keras-team/keras/pull/20151
* ghsanti made their first contribution in https://github.com/keras-team/keras/pull/20185
* joehiggi1758 made their first contribution in https://github.com/keras-team/keras/pull/20223
* AryazE made their first contribution in https://github.com/keras-team/keras/pull/20228
* sanskarmodi8 made their first contribution in https://github.com/keras-team/keras/pull/20237
* himalayo made their first contribution in https://github.com/keras-team/keras/pull/20262
* nate2s made their first contribution in https://github.com/keras-team/keras/pull/20305
* DavidLandup0 made their first contribution in https://github.com/keras-team/keras/pull/20316

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.5.0...v3.6.0

3.5.0

What's Changed

* Add integration with the Hugging Face Hub. You can now save models to Hugging Face Hub directly from `keras.Model.save()` and load `.keras` models directly from Hugging Face Hub with `keras.saving.load_model()`.
* Ensure compatibility with NumPy 2.0.
* Add `keras.optimizers.Lamb` optimizer.
* Improve `keras.distribution` API support for very large models.
* Add `keras.ops.associative_scan` op.
* Add `keras.ops.searchsorted` op.
* Add `keras.utils.PyDataset.on_epoch_begin()` method.
* Add `data_format` argument to `keras.layers.ZeroPadding1D` layer. 
* Bug fixes and performance improvements.


**Full Changelog**: https://github.com/keras-team/keras/compare/v3.4.1...v3.5.0

Links

• PyPI: https://pypi.org/project/keras
• Changelog: https://data.safetycli.com/changelogs/keras/