tfrun bash ./ci/official/utilities/rename_and_verify_wheels.sh

if [[ -n "$TFCI_BUILD_PIP_PACKAGE_ADDITIONAL_WHEEL_NAMES" ]]; then
  # Re-build the wheel with the same config, but with different name(s), if any.
  # This is done after the rename_and_verify_wheel.sh run above, not to have
  # to contend with extra wheels there.
  for wheel_name in ${TFCI_BUILD_PIP_PACKAGE_ADDITIONAL_WHEEL_NAMES}; do
    echo "Building for additional WHEEL_NAME: ${wheel_name}"

# renaming the wheels as uploading to PyPI would fail with "File already exists"
# error. In order to upload to PyPI, we unpack the wheel and change the tag
# inside a metadata file to the one we want (e.g cp38-cp38-win_amd) and then
# re-pack it to generate it as a platform specific wheel with this new wheel
#tag.
python3 -m wheel unpack "${pure_python_whl}"

# Remove the pure python wheel.
rm -rf "${pure_python_whl}"

# This script is aware of TFCI_ variables, so it doesn't need any arguments.
# Puts new wheel through auditwheel to rename and verify it, deletes the old
# one, checks the filesize, and then ensures the new wheel is installable.
set -exo pipefail

cd "$TFCI_OUTPUT_DIR"

# Move extra wheel files somewhere out of the way. This script
# expects just one wheel file to exist.
if [[ "$(ls *.whl | wc -l | tr -d ' ')" != "1" ]]; then

# Requirements for NumPy 1.x
numpy ~= 1.26.0
wheel ~= 0.41.2
h5py >= 3.11.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

# For wheel verification, and uploading
auditwheel ~= 6.1.0
twine ~= 6.1.0
id
urllib3
requests

# For JAX
build ~= 1.2.2
# uv is faster than pip for installing Python packages.
uv ~= 0.5.30

# For running wheel verification script

# Note that numpy 2.1.0 does not support python 3.9
numpy >= 2.0.0, < 2.2.0
wheel ~= 0.41.2
h5py >= 3.11.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras

      - name: Run source distribution tests
        run: |
          cd dist/fastapi*/
          bash scripts/test.sh
      - name: Build wheel distribution
        run: |
          cd dist
          pip wheel --no-deps fastapi*.tar.gz
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"

# ==============================================================================
TFCI_INSTALLER_WHL_ENABLE=1
TFCI_INSTALLER_WHL_PROJECT_NAME="tensorflow_cpu"
TFCI_INSTALLER_WHL_NIGHTLY_PROJECT_NAME="tf_nightly_cpu"
# Supported wheel tags are stored as strings in TFCI_INSTALLER_WHL_TAGS
# separated by spaces.
TFCI_INSTALLER_WHL_TAGS="cp39-cp39-win_amd64 cp310-cp310-win_amd64 cp311-cp311-win_amd64 cp312-cp312-win_amd64"

system, Kokoro, schedules our CI jobs by combining a build script with a file
from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

portpicker==1.6.0
# For wheel verification, and uploading
auditwheel ~= 6.1.0
twine ~= 6.1.0

# uv is faster than pip for installing Python packages.