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",
)

python_repository(name = "python_version_repo")

load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION")

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,

   While this isn't necessary for running the updater, it is required for
   actually using the new version with Tensorflow.

2) In the `WORKSPACE` file, add the new version to the `python_versions`
   parameter of the `python_register_multi_toolchains` function.

3) In the `BUILD.bazel` file, add a load statement for the new version, e.g.

   ```
      load("@python//3.11:defs.bzl",

)

updater_config_repository(name = "updater_config_repository")

python_register_multi_toolchains(
    name = "python",
    default_version = default_python_version,
    ignore_root_user_error = True,
    python_versions = [
        "3.9",
        "3.10",
        "3.11",
        "3.12",
    ],
    register_coverage_tool = True,
)

#     visibility = ["//visibility:public"],
# )
# copybara:uncomment_end

# 'enable_registration_v2' opts-in to a different implementation of op and
# kernel registration - REGISTER_OP, REGISTER_KERNEL_BUILDER, etc.
#
# This setting is currently experimental. The 'v2' implementation does _not_
# correspond to a particular, finalized design; rather, it relates to
# developing one.
#

    *   Added `dataset_id` to `tf.data.experimental.service.register_dataset`.
        If provided, `tf.data` service will use the provided ID for the dataset.
        If the dataset ID already exists, no new dataset will be registered.
        This is useful if multiple training jobs need to use the same dataset
        for training. In this case, users should call `register_dataset` with
        the same `dataset_id`.

)

python_repository(name = "python_version_repo")

load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION")

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

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = TF_PYTHON_VERSION,
)

build:cross_compile_macos_x86 --cpu=darwin
build:cross_compile_macos_x86 --crosstool_top=//tensorflow/tools/toolchains/cross_compile/cc:cross_compile_toolchain_suite
# When RBE cross-compiling for macOS, we need to explicitly register the
# toolchain. Otherwise, oddly, RBE complains that a "docker container must be
# specified".