Add tf_keras imports to prepare for Keras 3 (#28588)
* Port core files + ESM (because ESM code is odd) * Search-replace in modelling code * Fix up transfo_xl as well * Fix other core files + tests (still need to add correct import to tests) * Fix cookiecutter * make fixup, fix imports in some more core files * Auto-add imports to tests * Cleanup, add imports to sagemaker tests * Use correct exception for importing tf_keras * Fixes in modeling_tf_utils * make fixup * Correct version parsing code * Ensure the pipeline tests correctly revert to float32 after each test * Ensure the pipeline tests correctly revert to float32 after each test * More tf.keras -> keras * Add dtype cast * Better imports of tf_keras * Add a cast for tf.assign, just in case * Fix callback imports
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@@ -27,6 +27,7 @@ from typing import Optional
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import numpy as np
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from datasets import load_dataset
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from packaging.version import parse
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from transformers import (
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AutoConfig,
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@@ -46,11 +47,24 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1" # Reduce the amount of console output
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import tensorflow as tf # noqa: E402
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try:
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import tf_keras as keras
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except (ModuleNotFoundError, ImportError):
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import keras
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if parse(keras.__version__).major > 2:
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raise ValueError(
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"Your currently installed version of Keras is Keras 3, but this is not yet supported in "
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"Transformers. Please install the backwards-compatible tf-keras package with "
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"`pip install tf-keras`."
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)
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logger = logging.getLogger(__name__)
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# region Helper classes
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class SavePretrainedCallback(tf.keras.callbacks.Callback):
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class SavePretrainedCallback(keras.callbacks.Callback):
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# Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
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# metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
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# that saves the model with this method after each epoch.
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