// to the macros in tensorflow/core/framework/op_requires.h. This is provided
// for plugin OpKernel developer's convenience.

#define C_OPKERNELCONTEXT_REQUIRES_OK(CTX, C_STATUS, __VA_ARGS__) \
  do {                                                            \
    ::tensorflow::Status _s(__VA_ARGS__);                         \
    if (!TF_PREDICT_TRUE(_s.ok())) {                              \

name: ARM CI

on:
  push:
    branches:
      - master
      - r2.**
permissions:
  contents: read

jobs:
  build:
    # Don't do this in forks, and if labeled, only for 'kokoro:force-run'
    if: github.repository == 'tensorflow/tensorflow' && (github.event.action != 'labeled' || (github.event.action == 'labeled' && github.event.label.name == 'kokoro:force-run'))

  }
  return status.status;
}

TEST(RamFileBlockCacheTest, IsCacheEnabled) {
  auto fetcher = [](const string& filename, size_t offset, size_t n,
                    char* buffer, TF_Status* status) -> int64_t {
    // Do nothing.
    TF_SetStatus(status, TF_OK, "");
    return 0;
  };
  tf_gcs_filesystem::RamFileBlockCache cache1(0, 0, 0, fetcher);
  tf_gcs_filesystem::RamFileBlockCache cache2(16, 0, 0, fetcher);

  new_entry->lra_iterator = lra_list_.begin();
  new_entry->timestamp = timer_seconds_();
  block_map_.emplace(std::make_pair(key, new_entry));
  return new_entry;
}

// Remove blocks from the cache until we do not exceed our maximum size.
void RamFileBlockCache::Trim() {
  while (!lru_list_.empty() && cache_size_ > max_bytes_) {
    RemoveBlock(block_map_.find(lru_list_.back()));
  }
}

# "pip tests" run a similar suite of tests the "nonpip" tests, but do something
# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all

  TF_FilesystemPluginInfo info;
  memset(&info, 0, sizeof(info));
  auto TF_InitPlugin =
      reinterpret_cast<int (*)(TF_FilesystemPluginInfo*)>(dso_symbol);
  TF_InitPlugin(&info);

  // Step 4: Do the actual registration
  return filesystem_registration::RegisterFilesystemPluginImpl(&info);
}

}

@test "Pip package generated license includes all dependencies' licenses" {
  do_external_licenses_check \
    "//tensorflow/tools/pip_package:build_pip_package" \
    "//tensorflow/tools/pip_package:licenses"
}

@test "Libtensorflow generated license includes all dependencies' licenses" {
  do_external_licenses_check \
    "//tensorflow:libtensorflow.so" \

// https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md
#define TF_VALIDATE_STRUCT_SIZE(STRUCT_NAME, STRUCT_OBJ, SIZE_VALUE_NAME) \
  do {                                                                    \
    if (STRUCT_OBJ.struct_size == 0) {                                    \
      return tensorflow::Status(absl::StatusCode::kFailedPrecondition,    \

  schedule:
    - cron: 0 4 * * *  # 4am UTC is 9pm PDT and 8pm PST
name: Set nightly branch to master HEAD

permissions: {}

jobs:
  master-to-nightly:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: ubuntu-latest
    permissions:
      contents: write
    steps:
    - uses: zofrex/mirror-branch@a8809f0b42f9dfe9b2c5c2162a46327c23d15266 # v1.0.3
      name: Set nightly branch to master HEAD

  # Install the necessary Python, unless it's already present
  pyenv install -s "$TFCI_PYTHON_VERSION"
  pyenv local "$TFCI_PYTHON_VERSION"
  # Do a sanity check to make sure that we using the correct Python version
  python --version
fi

# TFCI Mac VM images do not have twine installed by default so we need to
# install it manually. We use Twine in nightly builds to upload Python packages
# to PyPI.