# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/linux_arm64"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-arm64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1
TFCI_WHL_AUDIT_PLAT=manylinux2014_aarch64
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=240M

*   [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html)
*   [Google JavaScript Style Guide](https://google.github.io/styleguide/jsguide.html)
*   [Google Shell Style Guide](https://google.github.io/styleguide/shellguide.html)
*   [Google Objective-C Style Guide](https://google.github.io/styleguide/objcguide.html)

#### Running sanity check

fi

# Note: the profile can be viewed by visiting chrome://tracing in a Chrome browser.
# See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling

        affected.
    *   The methods `Model.to_yaml()` and `keras.models.model_from_yaml` have
        been replaced to raise a `RuntimeError` as they can be abused to cause
        arbitrary code execution. It is recommended to use JSON serialization
        instead of YAML, or, a better alternative, serialize to H5.
    *   `LinearModel` and `WideDeepModel` are moved to the

TFCI_LIB_SUFFIX="-tpu-linux-x86_64"
TFCI_WHL_BAZEL_TEST_ENABLE=0
TFCI_WHL_IMPORT_TEST_ENABLE=0
TFCI_WHL_SIZE_LIMIT=580M

TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_macos_arm64"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=macos_arm64
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-darwin-arm64"
TFCI_MACOS_BAZEL_TEST_DIR_ENABLE=1
TFCI_MACOS_BAZEL_TEST_DIR_PATH="/Volumes/BuildData/bazel_output"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_BAZEL_TEST_ENABLE=1

            echo "The wheel's name is in an unknown format."
            exit 1
            ;;
    esac
    # >&3 forces output in bats even if the test passes. See
    # https://bats-core.readthedocs.io/en/stable/writing-tests.html#printing-to-the-terminal
    echo "# Size of $TF_WHEEL is $WHEEL_MEGABYTES / $LARGEST_OK_SIZE megabytes." >&3
    test "$WHEEL_MEGABYTES" -le "$LARGEST_OK_SIZE"
}

# Android configs. Bazel needs to have --cpu and --fat_apk_cpu both set to the
# target CPU to build transient dependencies correctly. See
# https://docs.bazel.build/versions/master/user-manual.html#flag--fat_apk_cpu
build:android --crosstool_top=//external:android/crosstool
build:android --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
build:android_arm --config=android
build:android_arm --cpu=armeabi-v7a

if [[ "$TFCI_DOCKER_ENABLE" == 1 ]]; then
  source ./ci/official/utilities/setup_docker.sh
fi

# Generate an overview page describing the build
if [[ "$TFCI_INDEX_HTML_ENABLE" == 1 ]]; then
  ./ci/official/utilities/generate_index_html.sh "$TFCI_OUTPUT_DIR/index.html"
fi

# Single handler for all cleanup actions, triggered on an EXIT trap.
# TODO(angerson) Making this use different scripts may be overkill.
cleanup() {