// Copyright 2009 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

//go:build test_run

// Compute Fibonacci numbers with two goroutines
// that pass integers back and forth.  No actual
// concurrency, just threads and synchronization
// and foreign code on multiple pthreads.

package main

import (
	"runtime"
	"strconv"

	"cgostdio/stdio"

CU_pTest CU_add_test(CU_pSuite pSuite, const char* strName, CU_TestFunc pTestFunc);
/**<
 *  This function creates a new test having the specified name 
 *  and function, and adds it to the specified suite.  The new test 
 *  is active and able to be executed during a test run.  At present, 
 *  there is no mechanism for creating a test case independent of a 
 *  suite.  Neither pSuite, strName, nor pTestFunc may be NULL.
 *

 %1965:4 = "tf._XlaHostComputeMlir"(%0, %cst_34, %cst_63, %cst_33) <{host_mlir_module = "#loc1 = loc(\22Reshape:\22)\0A#loc2 = loc(\22Reshape_4\22)\0A#loc3 = loc(\22Reshape\22)\0A#loc9 = loc(fused[#loc1, #loc2, #loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, ...

      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,
        init_func = @__init_func_530, next_func = @__next_func_680,
        next_func.experimental_ints_on_device = true,
        operandSegmentSizes = array<i32: 2, 2, 1>,
        output_shapes = [#tf_type.shape<?>],
        output_types = [f32],
        metadata = ""
      } :

          %outputs_5, %control_6 = tf_executor.island(%control_4) wraps "tf._XlaHostComputeMlir"() {host_mlir_module = "module {\0A func.func @host_func() -> tensor<1x2xf32> {\0A %0 = \22tf.Const\22() {value = dense<0.1> : tensor<1x2xf32>} : () -> tensor<1x2xf32> \0A return %0 : tensor<1x2xf32>}}", manual_sharding = true, recv_key = "host_compute_channel_1_retvals", send_key = "host_compute_channel_1_args"}...

    GTEST_SKIP() << "Tracing only test.";
  }
  if (UseMlir()) {
    // TODO(b/173074167): Remove this.
    GTEST_SKIP() << "MlirTensor::Shape is not implemented yet.";
  }
  AbstractContextPtr ctx(BuildFunction("test_fn"));
  AbstractTensorHandlePtr x;
  {
    tracing::TracingTensorHandle* x_raw = nullptr;
    PartialTensorShape shape;
    Status s = dyn_cast<tracing::TracingContext>(ctx.get())->AddParameter(

  %0 = "tf.GeneratorDataset"(%arg0, %arg1, %arg2, %arg3) {
      device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0",
      finalize_func = @finalize,
      init_func = @init,
      next_func = @next,
      operandSegmentSizes = array<i32: 1, 2, 1>,
      output_shapes = [#tf_type.shape<>],
      output_types = [!tf_type.string],
      metadata = ""} : (
              tensor<4xf32>,

	{"variable declared by template", `{{template "nested" $x:=.SI}},{{index $x 1}}`, "[3 4 5],4", tVal, true},

	// User-defined function: test argument evaluator.
	{"testFunc literal", `{{oneArg "joe"}}`, "oneArg=joe", tVal, true},
	{"testFunc .", `{{oneArg .}}`, "oneArg=joe", "joe", true},
}

// These strings are also in testdata/*.
const multiText1 = `
	{{define "x"}}TEXT{{end}}
	{{define "dotV"}}{{.V}}{{end}}

				featuregatetesting.SetFeatureGateDuringTest(t, utilfeature.DefaultFeatureGate, fg, f)
				t.Run(fmt.Sprintf("%v (%t)", fg, f), tf)
			}()
		}
	}
}

func TestGetTargetNodeName(t *testing.T) {
	testFun := func(t *testing.T) {
		tests := []struct {
			pod         *v1.Pod
			nodeName    string
			expectedErr bool
		}{
			{
				pod: &v1.Pod{
					ObjectMeta: metav1.ObjectMeta{
						Name:      "pod1",

      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,
        init_func = @__init_func_530, next_func = @__next_func_680,
        next_func.experimental_ints_on_device = true,
        operandSegmentSizes = array<i32: 2, 2, 1>,
        output_shapes = [#tf_type.shape<?>],
        output_types = [f32],
        metadata = ""
      } :