# Lazy-load Keras v2/3.
_tf_uses_legacy_keras = (
    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
setattr(_current_module, "keras", _KerasLazyLoader(globals()))
_module_dir = _module_util.get_parent_dir_for_name("keras._tf_keras.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__
if _tf_uses_legacy_keras:

    extra_args = [
        "--allow-unsafe",
        "-P keras-nightly",
        "-P tb-nightly",
    ],
    requirements_in = "requirements.in",
    requirements_txt = "requirements_lock_3_9.txt",
)

compile_pip_requirements_3_10(
    name = "requirements_3_10_release",
    extra_args = [
        "--allow-unsafe",
        "-P keras-nightly",
        "-P tb-nightly",
    ],

# Lazy-load Keras v1.
_tf_uses_legacy_keras = (
    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
setattr(_current_module, "keras", _KerasLazyLoader(globals(), mode="v1"))
_module_dir = _module_util.get_parent_dir_for_name("keras._tf_keras.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__
if _tf_uses_legacy_keras:

}

# Is this still useful?
@test "TensorFlow has Keras" {
    source /tf/venv/bin/activate
    python3 -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'

    # via
    #   -r requirements.in
    #   keras-nightly
idna==3.7 \
    --hash=sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc \
    --hash=sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0
    # via requests
jax==0.4.7 \
    --hash=sha256:5e7002d74db25f97c99b979d4ba1233b1ef26e1597e5fc468ad11d1c8a9dc4f8
    # via -r requirements.in
keras-nightly==3.0.4.dev2024021403 \

    --hash=sha256:f47619d978ab1ae7dfdc4052ea97c636c6263e1f19bd1be0e42c346b98d15ff4
    # via
    #   jax
    #   keras-nightly
namex==0.0.7 \
    --hash=sha256:84ba65bc4d22bd909e3d26bf2ffb4b9529b608cb3f9a4336f776b04204ced69b \
    --hash=sha256:8a4f062945f405d77cb66b907f16aa2fd83681945e998be840eb6c4154d40108
    # via keras-nightly
numpy==1.23.5 ; python_version <= "3.11" \

    # via
    #   -r requirements.in
    #   keras-nightly
idna==3.7 \
    --hash=sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc \
    --hash=sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0
    # via requests
jax==0.4.7 \
    --hash=sha256:5e7002d74db25f97c99b979d4ba1233b1ef26e1597e5fc468ad11d1c8a9dc4f8
    # via -r requirements.in
keras-nightly==3.0.4.dev2024021403 \

*   Keras 3.0 will be the default Keras version. You may need to update your
    script to use Keras 3.0.

*   Please refer to the new Keras documentation for Keras 3.0
    (https://keras.io/keras_3).

*   To continue using Keras 2.0, do the following.

*   1.  Install tf-keras via pip install tf-keras~=2.16

    1.  To switch tf.keras to use Keras 2 (tf-keras), set the environment

typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.17.0.a

# Test dependencies
grpcio >= 1.24.3, < 2.0
portpicker == 1.6.0

    # via
    #   -r requirements.in
    #   keras-nightly
idna==3.7 \
    --hash=sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc \
    --hash=sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0
    # via requests
jax==0.4.7 \
    --hash=sha256:5e7002d74db25f97c99b979d4ba1233b1ef26e1597e5fc468ad11d1c8a9dc4f8
    # via -r requirements.in
keras-nightly==3.0.4.dev2024021403 \