#define TENSORFLOW_COMPILER_MLIR_QUANTIZATION_TENSORFLOW_PASSES_MANIPULATE_MODEL_ATTR_H_

#include "llvm/ADT/StringRef.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

namespace mlir {
namespace quant {

// Adds a new input name to the `inputs` field of the `tf.entry_function`
// attribute if the attribute exist in the given function. Otherwise, no
// attribute is modified.

limitations under the License.
==============================================================================*/
#include <memory>
#include <string>

#include "absl/strings/str_cat.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/ADT/StringSet.h"
#include "llvm/Support/Debug.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project

// ops.
std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>> CreateTPURewritePass(
    llvm::StringRef module_name = llvm::StringRef());

// Creates a pass that adds ops which perform formatting on variables at
// run-time according to compilation result.
std::unique_ptr<OperationPass<ModuleOp>>
CreateTPUVariableRuntimeReformattingPass();

// Creates a pass that merges device variable reads/updates into the surrounded

class Pass;

namespace mhlo {

/// Converter to be used along with the fallback Tf2Xla patterns below.
class Tf2XlaTypeConverter : public TypeConverter {
 public:
  Tf2XlaTypeConverter();
};

/// Adds the TF to XLA via TF2XLA rewrite patterns to the pattern list.
/// `prefer_tf2xla` means an op will be included iff it is not in
/// `MlirLegalizedUnderPreferTf2XlaSet`. `!prefer_tf2xla` mean an op will be

# job chain to GCS and PyPI.
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Update the version numbers for Nightly only, then fetch the version numbers
# for choosing the final directory name. This adds "-devYYYYMMDD" to the end of
# the version string (.devYYYYMMDD for Python; see pypi.org/project/tf-nightly)
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then

// The Tensorflow Executor Dialect is required to export an MLIR module to a
// Tensorflow GraphDef. This API will add control dependencies and verify that
// the conversion was successful. This version adds extra control dependencies
// for replication and parallel execution ops, which may slow performance.
// Prefer to use the v2 of this API.
//
// This also converts the Tensorflow Dialect MLIR into the Tensorflow Executor

#include <algorithm>
#include <string>

#include "absl/container/inlined_vector.h"
#include "absl/strings/string_view.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringRef.h"
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/Types.h"  // from @llvm-project

limitations under the License.
==============================================================================*/

#include <memory>

#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project

#include "tensorflow/core/common_runtime/device_set.h"
#include "tensorflow/core/util/device_name_utils.h"

namespace tensorflow {

// Collects all devices known to the system by name and adds them as a
// `tf.devices` dictionary attribute with a full device name as a key, and
// device metadata as a value.
//
// Device names added in full parsed device form:
//   /job:<name>/replica:<replica>/task:<task>/device:<type>:<device_num>

                          const GraphExportConfig& configs,
                          std::unique_ptr<Graph>* graph,
                          FunctionLibraryDefinition* flib_def);

// Converts an MLIR function and adds it to a FunctionLibraryDefinition.
Status ConvertMlirFunctionToFunctionLibraryDef(mlir::func::FuncOp func,
                                               const GraphExportConfig& configs,