# CHECK-LABEL:       func @main() {

# Verify that the NameAttrList is properly turned into reference to functions on import
# CHECK:    tf.Case
# CHECK-SAME: branches = [@[[FOO:[a-z0-9]+]], @[[BAR:[a-z0-9]+]]]
# CHECK-DAG:  func private @[[FOO]]()
# CHECK-DAG:  func private @[[BAR]]()

node {
  name: "predicate"
  op: "Const"
  attr {
    key: "dtype"

# CHECK-DAG: func private @custom_embedding_matmul{{[0-9]*}}(){{.+}}tf._implements = #tf_type.func<@tensorflow.embedding_matmul, {key1 = 2 : i64, key2 = false}>, tf._original_func_name = "custom_embedding_matmul"}...

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "foo,bar", outputs = "Add"}} {
  %graph = tf_executor.graph {
    // This node would be renamed to bar1 [note: if imported from TF graphdef this would not be possible]

  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    "func.return"(%arg0) : (tensor<i32>) -> ()
  }) {sym_name = "main_while_body_4225150", sym_visibility = "private", tf._input_shapes = [#tf_type.shape<>], tf.signature.is_stateful, function_type = (tensor<i32>) -> (tensor<i32>)} : () -> ()
  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):

# This check that we don't error out when importing GraphDef containing
# functions with arg name that are the same as the graph input name

# CHECK: func @main(%arg0: tensor<{{.*}}i32>) -> tensor<{{.*}}i32>
# CHECK: func private @while_body
# CHECK: func private @while_cond

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "_user_specified_name"
    value {
      s: "input"
    }
  }

		f      func(int32) int32
		golden func(int32) int32
	}{
		{"x|x", func(x int32) int32 { return x | x }, id},
		{"x|0", func(x int32) int32 { return x | 0 }, id},
		{"x|-1", func(x int32) int32 { return x | -1 }, func(x int32) int32 { return -1 }},
		{"x&x", func(x int32) int32 { return x & x }, id},
		{"x&0", func(x int32) int32 { return x & 0 }, zero},
		{"x&-1", func(x int32) int32 { return x & -1 }, id},

func sqrshrun_asisdshf_n_cond(instr uint32) bool {
	return instr&0x780000 != 0x0
}
func sqrshrun_asimdshf_n_cond(instr uint32) bool {
	return instr&0x780000 != 0x0
}
func sqshl_asisdshf_r_cond(instr uint32) bool {
	return instr&0x780000 != 0x0
}
func sqshl_asimdshf_r_cond(instr uint32) bool {
	return instr&0x780000 != 0x0
}
func sqshlu_asisdshf_r_cond(instr uint32) bool {
	return instr&0x780000 != 0x0
}

  func.return %0 : tensor<i1>
}

// CHECK-LABEL: func.func @while_body_func
func.func @while_body_func(%arg0: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @outer_stateful_pcall_func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @outer_stateful_pcall_func(%arg0: tensor<i32>) -> (tensor<i32>) {

}

func _() (_, _ func(int)) { return f1, f1 }
func _() (_, _ func(int)) { return f1, f2 /* ERROR "cannot infer P" */ }

// Argument passing
func g1(func(int))                           {}
func g2(func(int, int))                      {}
func g3(func(int) string)                    {}
func g4[P any](func(P) string)               {}
func g5[P, Q any](func(P) string, func(P) Q) {}

func Test4339(t *testing.T)                  { test4339(t) }
func Test5227(t *testing.T)                  { test5227(t) }
func Test5242(t *testing.T)                  { test5242(t) }
func Test5337(t *testing.T)                  { test5337(t) }
func Test5548(t *testing.T)                  { test5548(t) }
func Test5603(t *testing.T)                  { test5603(t) }
func Test5986(t *testing.T)                  { test5986(t) }