# Sourcing this enables local disk cache
if [[ $(uname -s) == "Darwin" ]]; then
  echo "Please note that using disk cache on macOS is not recommended because the"
  echo "cache can end up being pretty big and make the build process inefficient."
fi

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

#   export TF_ANY_TARGETS="quoted list of targets, like on the command line"
#   export TF_ANY_MODE=test
set -euxo pipefail
cd "$(dirname "$0")/../../"  # tensorflow/
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
git bisect start "$TF_BISECT_BAD" "$TF_BISECT_GOOD"

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

  TFE_ContextAddFunctionDef(ctx, function_def.data(), function_def.size(),
                            status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  for (bool clear_cache : {true, false, true}) {
    if (clear_cache) {
      TFE_ContextClearCaches(ctx);
    }
    TFE_TensorHandle* m = TestMatrixTensorHandle(ctx);
    TFE_TensorHandle* retval[1] = {nullptr};
    int num_retvals = 1;

# Any request that includes "nightly_upload" should just use the
# local multi-cache (public read-only cache + disk cache) instead.
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
if [[ -n "${TF_ANY_EXTRA_ENV:-}" ]]; then
  export TFCI="$TFCI,$TF_ANY_EXTRA_ENV"
fi
if [[ -n "${TF_ANY_SCRIPT:-}" ]]; then
  "$TF_ANY_SCRIPT"
elif [[ -n "${TF_ANY_TARGETS:-}" ]]; then

TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=1

# 3. Push results to public build cache (unique cache for MacOS)
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push"
else
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_cache_push"

            gcr.io/tensorflow-sigs/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-from: type=registry,ref=tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-to: type=inline
      -
        name: Image digest

  else
    TAG="pr-${KOKORO_GITHUB_PULL_REQUEST_NUMBER}"
  fi
fi

# Build for both JAX and TF usage.  We do these in one place because they share
# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG"
  docker pull "$IMAGE" || true
  # Due to some flakiness of resources pulled in the build, allow the docker

# Sourcing this enables Bazel remote cache (public, read-only)
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache"
else
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_cache"