# Sourcing this enables local disk cache
if [[ $(uname -s) == "Darwin" ]]; then
  echo "Please note that using disk cache on macOS is not recommended because the"
  echo "cache can end up being pretty big and make the build process inefficient."
fi

  tf_gcs_filesystem::ExpiringLRUCache<int> cache(1, 4);
  cache.Insert("a", 1);
  cache.Insert("b", 2);
  cache.Insert("c", 3);
  cache.Insert("d", 4);
  int value = 0;
  EXPECT_TRUE(cache.Lookup("a", &value));
  EXPECT_EQ(value, 1);
  EXPECT_TRUE(cache.Lookup("b", &value));
  EXPECT_EQ(value, 2);
  EXPECT_TRUE(cache.Lookup("c", &value));
  EXPECT_EQ(value, 3);
  EXPECT_TRUE(cache.Lookup("d", &value));

  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()));
  }
}

/// Move the block to the front of the LRU list if it isn't already there.

  calls.push_back(block_size);
  // Cache miss followed by cache hit.
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, 1, &out));
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, 1, &out));
  calls.push_back(2 * block_size);
  // Cache miss followed by cache hit.  Causes eviction of LRU element.
  TF_EXPECT_OK(ReadCache(&cache, "", 2 * block_size, 1, &out));

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

#   export TF_ANY_TARGETS="quoted list of targets, like on the command line"
#   export TF_ANY_MODE=test
set -euxo pipefail
cd "$(dirname "$0")/../../"  # tensorflow/
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
git bisect start "$TF_BISECT_BAD" "$TF_BISECT_GOOD"

  TFE_ContextAddFunctionDef(ctx, function_def.data(), function_def.size(),
                            status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  for (bool clear_cache : {true, false, true}) {
    if (clear_cache) {
      TFE_ContextClearCaches(ctx);
    }
    TFE_TensorHandle* m = TestMatrixTensorHandle(ctx);
    TFE_TensorHandle* retval[1] = {nullptr};
    int num_retvals = 1;

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

# Any request that includes "nightly_upload" should just use the
# local multi-cache (public read-only cache + disk cache) instead.
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
if [[ -n "${TF_ANY_EXTRA_ENV:-}" ]]; then
  export TFCI="$TFCI,$TF_ANY_EXTRA_ENV"
fi
if [[ -n "${TF_ANY_SCRIPT:-}" ]]; then
  "$TF_ANY_SCRIPT"
elif [[ -n "${TF_ANY_TARGETS:-}" ]]; then

  bool compose;
  absl::Mutex block_cache_lock;
  std::shared_ptr<RamFileBlockCache> file_block_cache
      ABSL_GUARDED_BY(block_cache_lock);
  uint64_t block_size;  // Reads smaller than block_size will trigger a read
                        // of block_size.
  std::unique_ptr<ExpiringLRUCache<GcsFileStat>> stat_cache;
  GCSFile(google::cloud::storage::Client&& gcs_client);