],
    licenses = ["notice"],
)

package_group(
    name = "friends",
    packages = [
        "//tensorflow/compiler/mlir/tfr/...",
    ],
)

gen_op_libraries(
    name = "mnist_ops",
    src = "ops_defs.py",
    deps = [
        "//tensorflow:tensorflow_py",
        "//tensorflow/python/framework:ops",
        "//tensorflow/python/ops:math_ops",
        "//tensorflow/python/ops:math_ops_gen",

      "tf_saved_model.session_initializer"() {initializers = [@init_func]} : () -> ()
      func.func @init_func() attributes {tf_saved_model.exported_names = ["init_func"], tf_saved_model.initializer_type = "init_op"} {
        func.return
      }
    }
  )mlir";

  Block block;
  ModuleOp module_op = ParseModuleOp(kModuleOpStr, block, ctx_);

  func::FuncOp init_func_op = GetInitializerFunction(

                  .Input(inputs)
                  .Attr("ids", ids)
                  .Attr("calibration_methods", calibration_methods)
                  .Finalize(node_def()));
  ASSERT_THAT(InitOp(),
              StatusIs(tsl::error::INVALID_ARGUMENT,
                       HasSubstr("NodeDef missing attr 'output_file_path'")));
}

TEST_F(CalibrationStatisticsSaverTest, WrongNumInputs) {

  // Test case: Variable removed for the initializer function whose
  // initializer_type is "init_op".
  "tf_saved_model.session_initializer"() { initializers = [@init] } : () -> ()

  func.func @init() attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"], tf_saved_model.initializer_type = "init_op"} {

"tf_saved_model.session_initializer"() {initializers = [@init1]} : () -> ()
func.func @init1() attributes {tf_saved_model.exported_names = ["init1"], tf_saved_model.initializer_type = "init_op"} {
  %0 = "tf.Const"() {value = dense<42> : tensor<1xi64>} : () -> tensor<1xi64>
  return
}
}

// -----

// CHECK-LABEL: multiple_initializers
// CHECK-NOT: tf_saved_model.session_initializer

  func.func @init() -> (tensor<1xf32> {tf_saved_model.index_path = ["output"]})
    attributes { tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"], tf_saved_model.initializer_type = "init_op" } {
    %0 = "tf.Const"() {value = dense<[1.0]> : tensor<1xf32> } : () -> tensor<1xf32>
    func.return %0 : tensor<1xf32>
  }
}

// -----

module attributes {tf_saved_model.semantics} {

		fmt.Printf("words = %3d deltaT = %10s (%4d%%) is %s better: %v", i, delta, percent, upper, pos)
		if i == from {
			initPos = pos
		}
		if threshold == 0 && pos != initPos {
			threshold = i
			fmt.Printf("  threshold  found")
		}
		fmt.Println()

	}
	if threshold != 0 {
		fmt.Printf("Found threshold = %d between %d - %d\n", threshold, from, to)
	} else {

// in the initializer function of "init_op" type.

module attributes {tf_saved_model.semantics} {
  "tf_saved_model.session_initializer"() {initializers = [@init_func_init_op]} : () -> ()

  func.func @init_func_init_op() -> () attributes {
      tf_saved_model.initializer_type = "init_op",
      tf_saved_model.exported_names = ["tf_saved_model.session_initializer_init_op"]} {

  func.func @init_all_tables() attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer_init_all_tables"], tf_saved_model.initializer_type = "init_op"} {
    %cst = "tf.Const"() {value = dense<["hello", "model", "quantization"]> : tensor<3x!tf_type.string>} : () -> tensor<3x!tf_type.string>
    %cst_0 = "tf.Const"() {value = dense<[0, 1, 2]> : tensor<3xi64>} : () -> tensor<3xi64>

  func.func @init() attributes {tf_saved_model.exported_names = ["tf_saved_model.session_initializer_init"], tf_saved_model.initializer_type = "init_op"} {
    return
  }
// Check that @init is not removed.
// CHECK: @init()
// CHECK-SAME: tf_saved_model.initializer_type = "init_op"

  func.func @serving_default() -> (tensor<8xf32> {tf_saved_model.index_path = ["output"]})