# ==============================================================================

set -euxo pipefail

function resultstore_extract_fallback {
  # In case the main script fails somehow.
  cat <<EOF
IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you
can view more detailed results that are probably easier to read than this log.
Try the links below:
EOF
  # Find any "Streaming build results to" lines,

if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  cp ./ci/official/requirements_updater/numpy1_requirements/*.txt .
fi

tfrun bazel build $TFCI_BAZEL_COMMON_ARGS --config=cuda_wheel //tensorflow/tools/pip_package:wheel $TFCI_BUILD_PIP_PACKAGE_ARGS
tfrun find ./bazel-bin/tensorflow/tools/pip_package -iname "*.whl" -exec cp {} $TFCI_OUTPUT_DIR \;
tfrun mkdir ./dist
tfrun cp $TFCI_OUTPUT_DIR/*.whl ./dist

# limitations under the License.
# ==============================================================================
# any.sh has two run modes.
#
# 1. RUN, TEST, OR BUILD BAZEL TARGET(S) WITHIN A TFCI ENVIRONMENT
#    To use:
#       export TFCI=ci/official/envs/env_goes_here
#       export TF_ANY_TARGETS="quoted list of targets, like on the command line"

#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#
#   Note: new files created from the container are owned by "root".

# git history files
.history_rewritten_*
# Eclipse artifacts
.project
.pydevproject
#Vagrant
tools/vagrant/.vagrant/
# Intellij
*.iml
.idea/
.run/
# Visual Studio Code
.vscode/
# Bazel
/bazel-*
# vi swap files
.*.swp
# vi backups
*.bak
# common backups
*~
# python artifacts
*.pyc
# pilot
pilot/pkg/kube/config
pilot/pkg/proxy/envoy/envoy
# lint
lintconfig.gen.json
.istiorc
.istiorc.mk

-   type: input id: Python attributes: label: Python version description:
    placeholder: e.g., 3.9 validations: required: false

-   type: input id: Bazel attributes: label: Bazel version description: if
    compiling from source placeholder: validations: required: false

-   type: input id: Compiler attributes: label: GCC/Compiler version

#                                           ^^^^
# This env is only valid when RBE is available on the selected platform, since
# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early
if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then
  cat <<EOF

if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS test $TFCI_BAZEL_COMMON_ARGS --config=linux_libtensorflow_test
tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS build $TFCI_BAZEL_COMMON_ARGS --config=linux_libtensorflow_build

tfrun ./ci/official/utilities/repack_libtensorflow.sh "$TFCI_OUTPUT_DIR" "$TFCI_LIB_SUFFIX"

      placeholder: e.g., Linux Ubuntu 16.04
  - type: input
    id: Python
    attributes:
      label: Python version
      placeholder: e.g., 3.9
  - type: input
    id: Bazel
    attributes:
      label: Bazel version
      description: If compiling from source
  - type: input
    id: Compiler
    attributes:
      label: GCC/compiler version
      description: If compiling from source
  - type: input

numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"
scipy==1.9.2;python_version=="3.11"
scipy==1.7.3;python_version<"3.11"
ml_dtypes>=0.2.0

# For using Python 3.11 with Bazel 6 (b/286090018)