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

#ifndef TENSORFLOW_C_TF_STATUS_INTERNAL_H_
#define TENSORFLOW_C_TF_STATUS_INTERNAL_H_

#include "xla/tsl/c/tsl_status_internal.h"

typedef struct TSL_Status TF_Status;

node {
  name: "arg0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {

#define TENSORFLOW_COMPILER_JIT_INCREASE_DYNAMISM_FOR_AUTO_JIT_PASS_H_

#include "tensorflow/core/common_runtime/optimization_registry.h"

namespace tensorflow {

// Increases the amount of "dynamism" representable by XLA clusters by rewriting
// the TensorFlow graph.  This pass does the following rewrites:
//
// Slice
// -----
//
//   Slice(op, begin, size <must be constant>) =>

// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=10,19:19,10 -tf-xla-emit-use-tuple-args -tf-xla-emit-return-tuple | FileCheck %s

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<10x19xf32>, %arg1: tensor<19x10xf32> {mhlo.is_same_data_across_replicas = true}) -> tensor<10x19xf32> {
    %0 = "tf.Shape"(%arg0) : (tensor<10x19xf32>) -> tensor<2xi64>

enum OpSet {
  OP_SET_UNSPECIFIED = 0;  // go/do-include-enum-unspecified
  // Uses TF ops that mimic quantization behavior. Used when the corresponding
  // integer op is not yet present.
  TF = 1;
  // Uses TF XLA ops
  XLA = 2;
  // Uses TF Uniform Quantized ops
  UNIFORM_QUANTIZED = 3;
  // Uses the StableHLO Quantizer. StableHLO Quantizer will be available as
  // an option in the TF Quantizer in StableHLO Quantizer v1.

#include "tensorflow/compiler/mlir/tosa/tf_tfl_passes.h"
#include "tensorflow/compiler/mlir/tosa/tfl_passes.h"
#include "tensorflow/compiler/mlir/tosa/transforms/passes.h"
#include "xla/mlir/framework/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

int main(int argc, char **argv) {
  tensorflow::InitMlir y(&argc, &argv);

  mlir::registerAllPasses();
  mlir::registerTransformsPasses();

#include "tensorflow/core/common_runtime/optimization_registry.h"

namespace tensorflow {

// PRE_PLACEMENT passes:

// EncapsulateXlaComputationsPass rewrites computations generated by the
// xla.compile() Python code into XlaLaunch nodes.
REGISTER_OPTIMIZATION(OptimizationPassRegistry::PRE_PLACEMENT, 36,
                      EncapsulateXlaComputationsPass);

// from

#include "tensorflow/core/common_runtime/optimization_registry.h"

namespace tensorflow {

// An optimization pass which marks the constants which have to be resolved for
// XLA compilation with `_input_hostmem`.
class ForceXlaConstantsOnHostPass : public GraphOptimizationPass {
 public:
  ForceXlaConstantsOnHostPass() = default;

  Status Run(const GraphOptimizationPassOptions& options) override;

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-xla-validate-inputs

// expected-error @+1 {{expects no nested calls of entry functions as they prevent graph traversal in some passes from working correctly}}
func.func @nested_entry_functions() attributes {tf.entry_function = {}} {
  tf_executor.graph {

// RUN: tf-quant-opt %s -split-input-file -quant-convert-fake-quant-to-qdq -quant-lift-quantizable-spots-as-functions='target-opset=XLA' -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA' -symbol-dce -inline -tf-shape-inference -canonicalize -quant-replace-cast-hacks-with-tf-xla-ops -cse -quant-optimize | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1219 : i32}, tf_saved_model.semantics} {