#include <unistd.h>

#include "tensorflow/c/experimental/filesystem/plugins/posix/copy_file.h"

namespace tf_posix_filesystem {

int TransferFileContents(const char* src, const char* dst, mode_t mode,
                         off_t size) {
  int src_fd = open(src, O_RDONLY);
  if (src_fd < 0) return -1;

  // When creating file, use the same permissions as original
  mode_t open_mode = mode & (S_IRWXU | S_IRWXG | S_IRWXO);

  return absl::OkStatus();
}

#define CONFIG_TOGGLE(optimizer)                             \
  if (tp_configs.optimizer == TF_TriState_Off)               \
    configs.toggle_config[#optimizer] = RewriterConfig::OFF; \
  else                                                       \
    configs.toggle_config[#optimizer] = RewriterConfig::ON;

void CGraphOptimizerRegister(

                        TF_Status* const status) -> void {
    TF_SetStatus(status, TF_OK, "");
    PopulateDefaultParam(params);
    params->device_type = "Success";
    params->optimizer_configs->remapping = TF_TriState_Off;
  };

  TF_ASSERT_OK(InitGraphPlugin(plugin_init));
  ASSERT_EQ(PluginGraphOptimizerRegistry::CreateOptimizers(
                std::set<string>{"Success"})
                .size(),
            1);

        // copies/broadcasts. Other implicit copies may be supported eventually,
        // but need special handling for gradients (gradient of copy-on is not
        // just copy-off but includes a sum) and consideration of performance.
        //
        // TODO(allenl): There may be smarter ways to do this copy in some
        // cases, i.e. with a collective broadcast. We'll need to be careful

      context.get(), components, device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  // Copies off of parallel devices must be explicit.
  TensorHandlePtr copy_off(TFE_TensorHandleCopyToDevice(
      device_value.get(), context.get(), first_device_name, status.get()));
  EXPECT_EQ(TF_GetCode(status.get()), TF_UNIMPLEMENTED);
}

  }

 private:
  bool first_call_ = true;
};

// After the graph pass is registered, it takes effect globally and can affect
// other test cases. Define a static variable to switch it on and off.
bool GraphErrorInjectionPass::enabled_ = false;

// Test to ensure that a registered graph optimization pass is only executed
// once (i.e., on the main function side) in running distributed functions.

        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
  }
}

unsigned char TF_SetXlaEnableLazyCompilation(unsigned char enable) {
  tensorflow::BuildXlaOpsPassFlags* flags =
      tensorflow::GetBuildXlaOpsPassFlags();

    // cross-platform, return type of `Read` is `int64_t`.
    int64_t r = int64_t{pread(posix_file->fd, dst, requested_read_length,
                              static_cast<off_t>(offset))};
    if (r > 0) {
      dst += r;
      offset += static_cast<uint64_t>(r);
      n -= r;  // safe as 0 < r <= n so n will never underflow
      read += r;
    } else if (r == 0) {

//
// This method uses a temporary buffer to hold contents.
int CopyFileContents(int dst_fd, int src_fd, off_t size) {
  // Use a copy buffer of 128KB but don't store it on the stack.
  constexpr static size_t kPosixCopyFileBufferSize = 128 * 1024;
  std::unique_ptr<char[]> buffer(new char[kPosixCopyFileBufferSize]);

  off_t offset = 0;
  int bytes_transferred = 0;
  int rc = 1;

    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  tf_gcs_filesystem::RamFileBlockCache cache(block_size, block_size, 0,
                                             fetcher);
  // Fork off thread for parallel read.
  std::unique_ptr<Thread> concurrent(
      Env::Default()->StartThread({}, "concurrent", [block_size, &cache] {
        std::vector<char> out;