)

# buildifier: disable=same-origin-load
load("@rules_python//python:repositories.bzl", "py_repositories")

py_repositories()

load("@rules_python//python:repositories.bzl", "python_register_multi_toolchains")  # buildifier: disable=same-origin-load
load("@rules_python//python/pip_install:repositories.bzl", "pip_install_dependencies")

            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}

# See https://github.com/protocolbuffers/upb/blob/9effcbcb27f0a665f9f345030188c0b291e32482/upb/upb.c#L183.
build:rbe_linux_cpu --copt=-Wno-gnu-offsetof-extensions
# Python config is the same across all containers because the binary is the same
build:rbe_linux_cpu --repo_env=TF_PYTHON_CONFIG_REPO="@sigbuild-r2.17-clang_config_python"
build:rbe_linux_cpu --python_path="/usr/bin/python3"
# These you may need to change for your own GCP project.

)

# buildifier: disable=same-origin-load
load("@rules_python//python:repositories.bzl", "py_repositories")

py_repositories()

load("@rules_python//python:repositories.bzl", "python_register_toolchains")  # buildifier: disable=same-origin-load
load(
    "//tensorflow/tools/toolchains/python:python_repo.bzl",
    "python_repository",
)

            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}

}

// Set server_def on the context, possibly updating it.
// TODO(b/291142876) Simplify TFE_ContextSetServerDefWithTimeoutAndRetries and
// TFE_ContextUpdateServerDefWithTimeout to be simple wrappers around the same
// C++ function.
// Retries are used for CreateContext calls, which is used in
// ParameterServerStrategy initialization to be robust to worker preemption.

        map sigbuild-r2.12-python3.9 2.12-python3.9
        map sigbuild-r2.12-python3.10 2.12-python3.10
        map sigbuild-r2.12-python3.11 2.12-python3.11
        # TF 2.12 + Clang (containers are the same, but env vars in configs.bzl are different)
        map sigbuild-r2.12-clang 2.12-python3.9
        map sigbuild-r2.12-clang-python3.8 2.12-python3.8
        map sigbuild-r2.12-clang-python3.9 2.12-python3.9

    strip_prefix = "rules_python-0.26.0",
    url = "https://github.com/bazelbuild/rules_python/releases/download/0.26.0/rules_python-0.26.0.tar.gz",
)

# buildifier: disable=same-origin-load
load("@rules_python//python:repositories.bzl", "py_repositories")

py_repositories()

## Load HERMETIC_PYTHON_VERSION variable
local_repository(
    name = "local_tensorflow",
    path = "../../..",

  # Find all one-step dependencies of those tests which are from //tensorflow
  # (since external deps will come from Python-level pip dependencies),
  # excluding dependencies and files that are known to be unneccessary.
  # This creates a list of targets under //tensorflow that are required for
  # TensorFlow python tests.

  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a