)

bazel_dep(name = "xla", repo_name = "xla")
local_path_override(
    module_name = "xla",
    path = "third_party/xla",
)

tsl_extension = use_extension("@xla//third_party/extensions:tsl.bzl", "tsl_extension")
use_repo(tsl_extension, tsl = "tsl")

llvm = use_extension("@xla//third_party/extensions:llvm.bzl", "llvm_extension")
use_repo(llvm, "llvm-project")

rules_shell_dependencies()

rules_shell_toolchains()

# Initialize hermetic Python
load("@xla//third_party/py:python_init_rules.bzl", "python_init_rules")

python_init_rules()

load("@xla//third_party/py:python_init_repositories.bzl", "python_init_repositories")

python_init_repositories(
    default_python_version = "system",

    disallowed_deps = if_static(
        [],
        otherwise = [
            "@xla//xla/stream_executor/cuda:all_runtime",
            "@xla//xla/stream_executor/cuda:cuda_platform",
            "@xla//xla/stream_executor/cuda:cudnn_plugin",
            "@xla//xla/stream_executor/cuda:cufft_plugin",
            "@xla//xla/stream_executor:cuda_platform",
        ],
    ),
    deps = if_cuda([

        "//tensorflow/core/transforms:__subpackages__",
    ],
    deps = [
        ":c_api_macros_hdrs",
        "@xla//xla/tsl/c:tsl_status",
        "@xla//xla/tsl/c:tsl_status_internal",
        "@xla//xla/tsl/platform:status",
    ] + select({
        "//tensorflow:android": [
            "//tensorflow/core:portable_tensorflow_lib_lite",  # TODO(annarev): exclude runtime srcs

  cp tensorflow/core/platform/ctstring.h \
    tensorflow/core/platform/ctstring_internal.h \
    ${LIB_PKG}/include/tensorflow/core/platform
  cp third_party/xla/xla/tsl/c/tsl_status.h ${LIB_PKG}/include/xla/tsl/c
  cp third_party/xla/third_party/tsl/tsl/platform/ctstring.h \
     third_party/xla/third_party/tsl/tsl/platform/ctstring_internal.h \
     ${LIB_PKG}/include/tsl/platform
  cd ${LIB_PKG}
  libtensorflow_zip="libtensorflow${TARBALL_SUFFIX}.zip"

#     --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g
# Since this .bazelrc file is synced between the tensorflow/tensorflow repo and
# the openxla/xla repo, also include debug info for files under xla/.
common:dbg --per_file_copt=+.*,-tensorflow.*,-xla.*@-g0
common:dbg --per_file_copt=+tensorflow/core/kernels.*@-g0
# for now, disable arm_neon. see: https://github.com/tensorflow/tensorflow/issues/33360

        "@com_google_absl//absl/container:flat_hash_map",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/time",
        "@xla//xla/tsl/c:tsl_status_internal",
        "@xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(
    name = "c_api_experimental_reader",
    testonly = True,

**Linux XLA**                 | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.html)         | TBA

        Eager mode.

*   `tf.lite`:

    *   Enable TFLite experimental new converter by default.

*   XLA

    *   XLA now builds and works on windows. All prebuilt packages come with XLA
        available.
    *   XLA can be
        [enabled for a `tf.function`](https://www.tensorflow.org/xla#explicit_compilation_with_tffunction)
        with “compile or throw exception” semantics on CPU and GPU.