#define TENSORFLOW_C_C_API_INTERNAL_H_

#include <list>
#include <set>
#include <string>
#include <unordered_map>
#include <vector>

#include "tensorflow/c/c_api.h"

// clang-format off
// Required for IS_MOBILE_PLATFORM
#include "tensorflow/core/platform/platform.h"
// clang-format on

#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/c/tf_tensor_internal.h"

==============================================================================*/

#include "tensorflow/c/eager/c_api.h"

#include <string.h>

#include <memory>
#include <string>

// clang-format off
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/platform.h"
// clang-format on

#include "absl/strings/match.h"

typedef enum TF_TriState {
  TF_TriState_Default = 0,
  TF_TriState_Off,
  TF_TriState_On,
} TF_TriState;

// TF_GrapplerItem represents a combination of a graph, one of more fetch nodes,
// and potentially a set of nodes to feed.
typedef struct TF_GrapplerItem TF_GrapplerItem;

// Flags indicating whether existing optimizers should be turned off.
// It's optional for plugin to set functions to return true/false. If not

  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.device("CUSTOM"):` from Python if `device_name` for this call is
// "/job:localhost/replica:0/task:0/device:CUSTOM:0".
//
// The custom device defines copy operations for moving TensorHandles on and
// off, and an execution operation for named operations. Often execution will
// simply wrap op execution on one or more physical devices.
//
// device_info is an opaque caller-defined type stored with the custom device

            from when they are off due to floating-point round-off errors from
            different computation approaches and orders.
        *   To verify that the optimizations are on, look for a message with
            "*oneDNN custom operations are on*" in the log. If the exact phrase
            is not there, it means they are off.

## Bug Fixes and Other Changes

extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.
// TODO: Migrate to TF_CreateConfig() below.

        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) {

build:android_x86_64 --cpu=x86_64
build:android_x86_64 --fat_apk_cpu=x86_64

# Build everything statically for Android since all static libs are later
# bundled together into a single .so for deployment.
build:android --dynamic_mode=off

# Sets the default Apple platform to macOS.
build:macos --apple_platform_type=macos

# gRPC on MacOS requires this #define
build:macos --copt=-DGRPC_BAZEL_BUILD

# Avoid hitting command line argument limit