shell: bash

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.
try:
  del python
except NameError:
  pass
try:
  del core
except NameError:
  pass
try:
  del compiler

  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).
  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).

#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

load("@//tensorflow:workspace2.bzl", "tf_workspace2")

tf_workspace2()

# 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 -euxo 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
  echo "More than one wheel file is present: moving the oldest to"

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

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
  # replace tensorflow to tf_nightly in the wheel name

  };
  return tensorflow::unwrap(cancellation_manager)
      ->RegisterCallbackWithErrorLogging(token, callback, callback_name);
}

bool TFE_CancellationManagerDeregisterCallback(
    TFE_CancellationManager* cancellation_manager,
    TFE_CancellationToken token) {
  return tensorflow::unwrap(cancellation_manager)->DeregisterCallback(token);
}

bool TFE_CancellationManagerTryDeregisterCallback(

    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f

      d="$${d#bazel-out/*/bin/}"

      if [[ $${d} == *local_config_* ]]; then
        continue
      fi

      if [[ $${d} == external* ]]; then
        extname="$${d#*external/}"
        extname="$${extname%%/*}"
        if [[ $${TF_SYSTEM_LIBS:-} == *$${extname}* ]]; then
          continue
        fi

        d="$${d#*external/farmhash_archive/src}"

    *   Updates to Preprocessing layers API for consistency and clarity:
        *   `StringLookup` and `IntegerLookup` default for `mask_token` changed
            to `None`. This matches the default masking behavior of `Hashing`
            and `Embedding` layers. To keep existing behavior, pass
            `mask_token=""` during layer creation.
        *   Renamed `"binary"` output mode to `"multi_hot"` for