#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"
#include "tsl/platform/status.h"
#include "tsl/platform/status_matchers.h"

namespace tensorflow {
namespace quantization {
namespace {

using ::tensorflow::test::AsTensor;
using ::tensorflow::test::ExpectEqual;
using ::testing::IsEmpty;
using ::testing::Not;

REGISTER_OP("StreamOp").Output("output1: float");

TEST_F(DeviceKernelOpTest, TestStream) {
  auto my_compute_func = [](void* kernel, TF_OpKernelContext* ctx) {
    TF_Status* s = TF_NewStatus();
    SP_Stream stream = TF_GetStream(ctx, s);
    // Stream is always null if device is not a pluggable device. More test
    // cases will be added when pluggable device mechanism is supported.

  mlir::OpBuilder builder_;
};

TEST_F(XlaLegalizeTargetsTest, CreatesConversionTargets) {
  auto const_int = builder_.create<mlir::arith::ConstantIntOp>(
      builder_.getUnknownLoc(), /*value=*/10, builder_.getI32Type());

  ConversionTarget target =
      GetDefaultLegalConversionTargets(context_, /*legalize_chlo=*/false);
  EXPECT_TRUE(target.isLegal(const_int));
}

TEST_F(XlaLegalizeTargetsTest, AllowsCHLODialect) {

  std::unique_ptr<StaticDeviceMgr> device_mgr_;
  EagerContextPtr ctx_;
};

// Sanity check that constructing a tensorflow::Variable from a SavedVariable
// 1. does not cause an error
// 2. preserves dtype and shape.
TEST_P(SavedVariableLoadingTest, LoadSavedVariableSuccessful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;

      /*ret_def=*/{{"out", "out:output:0"}});
  *fdef_lib.add_function() = std::move(func);
  return fdef_lib;
}

// This tests a rewrite that only makes sense and is active in a CUDA-enabled
// build.  Specifically we check that we insert an IdentityN op to avoid extra
// device-to-host copies.
TEST_F(BuildXlaOpsTest, NoDeviceToHostCopiesForClustersWithInt32Inputs) {
  const char* kXlaDeviceName = "/job:worker/replica:0/task:0/device:GPU:0";

//     in its Cancel function
//   - if the test has a deadline, adds a Context timeout and WaitDelay
//     for an arbitrary grace period before the test's deadline expires,
//   - fails the test if the command does not complete before the test's deadline, and
//   - sets a Cleanup function that verifies that the test did not leak a subprocess.

	}
	for _, test := range tl {
		if test.got != test.exp {
			t.Errorf("On %v, expected '%v', but got '%v'",
				test.name, test.exp, test.got)
		}
	}
}

func TestPrintDeployment(t *testing.T) {

	testDeployment := apps.Deployment{
		ObjectMeta: metav1.ObjectMeta{
			Name:              "test1",
			CreationTimestamp: metav1.Time{Time: time.Now().Add(1.9e9)},

    tf_executor.graph {
      %out, %ctl = tf_executor.island wraps "tf.Const"() {device = "", value = dense<["test_1"]> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
      %out_0, %ctl_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<[1]> : tensor<1xi64>} : () -> tensor<1xi64>

  EXPECT_EQ(histogram.num(), 1);
  EXPECT_EQ(compilation_status.Delta("kOldBridgeNoMlirSuccess"), 1);
}

TEST_F(CompileTFGraphTest, SuccessfullyCompilesWithManualSharding) {
  // MLIR module from failing test.
  constexpr char kSupportedManualSharding[] = R"(

# Floating-point conversion test cases.
# Empty lines and lines beginning with # are ignored.
# The rest have four fields per line: type, format, input, and output.
# The input is given either in decimal or binary scientific notation.
# The output is the string that should be produced by formatting the
# input with the given format.
#
# The formats are as in C's printf, except that %b means print
# binary scientific notation: NpE = N x 2^E.

# TODO: