namespace mlir {
namespace TF {

llvm::SmallVector<ResourceHandleValueAndId, 4>
_TfrtGetResourceOp::GetResourceHandleValueAndIdList(
    llvm::SmallDenseMap<ResourceHandle, int64_t> &resource_handle_id_map,
    int64_t &next_id) {
  llvm::SmallVector<ResourceHandleValueAndId, 4> resource_vec;
  llvm::StringRef device = GetDeviceOrEmpty(getOperation());

  for (const auto &iter : llvm::enumerate(getResults())) {

void Cleanup(TF_RandomAccessFile* file);
int64_t Read(const TF_RandomAccessFile* file, uint64_t offset, size_t n,
             char* buffer, TF_Status* status);
}  // namespace tf_random_access_file

namespace tf_writable_file {
void Cleanup(TF_WritableFile* file);
void Append(const TF_WritableFile* file, const char* buffer, size_t n,
            TF_Status* status);
int64_t Tell(const TF_WritableFile* file, TF_Status* status);

  const absl::StatusOr<std::string> checkpoint_prefix = MakeTempDir();
  EXPECT_TRUE(checkpoint_prefix.ok());

  const absl::Cleanup checkpoint_prefix_cleanup = [this, &checkpoint_prefix]() {
    int64_t undeleted_files, undeleted_dirs;
    TF_CHECK_OK(env_->DeleteRecursively(*checkpoint_prefix, &undeleted_files,
                                        &undeleted_dirs));
  };

  // Get a list of the names of functions that have been registered.
  virtual std::vector<string> ListFunctionNames() = 0;

  struct CacheStats {
    int64_t kernel_cache_size;
    int64_t device_cache_size;
    std::map<std::string, int64_t> func_kernel_cache_entries;
    int64_t local_rendezvous_cache_active_size;
  };
  virtual CacheStats GetCacheStats() = 0;

    flatbuffers::FlatBufferBuilder* builder, const tflite::Model* input_model,
    const tflite::TensorType& inference_type,
    const absl::flat_hash_set<std::string>& denylisted_ops,
    const CustomOpMap& custom_op_map,
    int64_t minimum_elements_for_weights = 1024,
    bool disable_per_channel = false, bool weight_only_quantization = false,
    bool legacy_float_scale = false);

// Overloading methods to support old quantizer versions API

#include "mlir/IR/Types.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project

namespace mlir {
namespace TF {

LogicalResult VerifyShapeOfReshapeOp(ArrayRef<int64_t> shape) {
  bool has_dynamic_dim = false;
  for (int64_t dim : shape) {
    if (dim != ShapedType::kDynamic) {
      if (dim < 0) return failure();
      continue;
    }
    if (has_dynamic_dim) return failure();
    has_dynamic_dim = true;

TEST(TestUtil, MatchesFiltered) { ASSERT_THAT(filtered(), IsOkOrFiltered()); }

TEST(TestUtil, IncrementsOk) {
  CellReader<int64_t> reader(kMetric);
  counter->GetCell(kOkStatus)->IncrementBy(1);

  ASSERT_THAT(success(), IncrementedOrFiltered(reader.Delta(kOkStatus), 1));
}

TEST(TestUtil, FilteredDoesntIncrementsOk) {
  CellReader<int64_t> reader(kMetric);

  ASSERT_THAT(filtered(), IncrementedOrFiltered(reader.Delta(kOkStatus), 1));
}

static void AddEmbeddedProtocolBufferToLlvmModule(
    llvm::Module* module, const ::tensorflow::protobuf::MessageLite& proto,
    absl::string_view unique_identifier, string* protobuf_array_symbol_name,
    int64_t* protobuf_array_size) {
  string protobuf_array_contents = proto.SerializeAsString();
  *protobuf_array_symbol_name =
      absl::StrCat(unique_identifier, "_protobuf_array_contents");

  StringRef getDescription() const override {
    return "Unfreeze large constants.";
  }

  void runOnOperation() override;

 private:
  Option<int64_t> CreateSizeThresholdInBytesOption(const int64_t init_value) {
    return Option<int64_t>(
        *this, "size_threshold_in_bytes", llvm::cl::init(init_value),
        llvm::cl::desc(
            "Lower threshold of the constant size for unfreezing. Constants "

#include <cstdint>

namespace mlir {
namespace TFL {

// Converts a TF size (64-bit) to TFLite (32-bit) and properly converts TF's
// value for dynamic size (`std::numeric_limits<int64_t>::min()`) to the
// TFLite-specific value.
int32_t ConvertToTfliteSize(int64_t size);

}  // namespace TFL
}  // namespace mlir