A. Unique TensorFlower <******@****.***> 1711560469 +0000

  rm -rf "${tmp_dir}"
}
DIR=$1
TARBALL_SUFFIX=$2
mkdir -p "$DIR"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz "${DIR}/libtensorflow_jni${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"
cp_normalized_srcjar bazel-bin/tensorflow/java/libtensorflow-src.jar "${DIR}/libtensorflow-src.jar"

Building dialects and utilities here follow the standard approach using
`bazel` as the rest of TensorFlow.

### Using local LLVM repo

To develop across MLIR core and TensorFlow, it is useful to override the repo to
use a local version instead of fetching from head. This can be achieved by
setting up your local repository for Bazel build. For this you will need to
create bazel workspace and build files:

```sh

source "${BASH_SOURCE%/*}/utilities/setup.sh"

if [[ $TFCI_PYCPP_SWAP_TO_BUILD_ENABLE == 1 ]]; then
   tfrun bazel build $TFCI_BAZEL_COMMON_ARGS --profile "$TFCI_OUTPUT_DIR/profile.json.gz" --config="${TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX}_pycpp_test"
else
   tfrun bazel test $TFCI_BAZEL_COMMON_ARGS --profile "$TFCI_OUTPUT_DIR/profile.json.gz" --config="${TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX}_pycpp_test"
fi

  rm -rf "${tmp_dir}"
}
DIR=$1
TARBALL_SUFFIX=$2
mkdir -p "$DIR"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz "${DIR}/libtensorflow_jni${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"
cp_normalized_srcjar bazel-bin/tensorflow/java/libtensorflow-src.jar "${DIR}/libtensorflow-src.jar"

    urls = [
        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
    ],
)

http_archive(
    name = "rules_python",

    urls = [
        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
    ],
)

http_archive(
    name = "rules_python",

# if there is a change in requirements.in then all lock files will be updated
# accordingly.

mv BUILD.bazel BUILD

SUPPORTED_VERSIONS=("3_9" "3_10" "3_11" "3_12")

for VERSION in "${SUPPORTED_VERSIONS[@]}"
do
  cp ../../../requirements_lock_"$VERSION".txt "requirements_lock_"$VERSION".txt"
  bazel run \
    --experimental_convenience_symlinks=ignore \
    --enable_bzlmod=false \

IMAGE_PREFIX ?= /wasm
IMG := $(HUB)$(IMAGE_PREFIX)/header-injector

all: docker-push

build: plugin.cc plugin.h BUILD WORKSPACE
	rm -f *.wasm
	$(foreach VERSION, $(VERSION_LIST), bazel build :plugin-0.0.$(VERSION).wasm && cp bazel-bin/plugin-0.0.$(VERSION).wasm .;)

docker-build: build
	$(foreach VERSION, $(VERSION_LIST), docker buildx build . -t $(IMG):0.0.$(VERSION) --build-arg WASM_BINARY=plugin-0.0.$(VERSION).wasm;)

    urls = [
        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
    ],
)

http_archive(
    name = "rules_java",