// RUN: odml_to_stablehlo %s --allow-tf=true -o /tmp/temp2.mlir; [ -f /tmp/temp2.mlir ]

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 975 : i32}, tf_saved_model.semantics}  {

  func.func @main(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
    %0 = "tf.Acos"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
   func.return %0 : tensor<1xf32>
  }
})";

static constexpr char kBadMlirModuleStr[] = R"(
  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {

// RUN: odml-to-stablehlo-opt %s --smuggle-disallowed-ops-pass | FileCheck %s --check-prefix=CHECK-OPT

// CHECK-LABEL: @main
module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 975 : i32}, tf_saved_model.semantics}  {

  %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)
  %size = "tf.Const"() {device = "/device:CPU:0", value = dense<1> : tensor<i32>} : () -> (tensor<i32>)
  %indices = "tf.Const"() {device = "/device:CPU:0", value = dense<[0]> : tensor<1xi32>} : () -> (tensor<1xi32>)

  // CHECK-LABEL: hashtable_string_to_int64
  // CHECK:       [[CST:%.*]] = arith.constant dense<"f"> : tensor<!tf_type.string>
  // CHECK-NEXT:  [[HASH_TABLE:%.*]] = "tfl.hashtable"() <{key_dtype = !tf_type.string, table_id = 1530976467 : i32, value_dtype = i64}> : () -> tensor<1x!tf_type.resource>
  // CHECK-NEXT:  [[FIND:%.*]] = "tfl.hashtable_find"([[HASH_TABLE]], [[CST]], %arg0) : (tensor<1x!tf_type.resource>, tensor<!tf_type.string>, tensor<i64>) -> tensor<*xi64>

// RUN: tf-tfrt-opt -tf-executor-to-tfrt-pipeline=decompose-resource-ops=true %s | FileCheck %s --dump-input=fail

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 293 : i32}} {

// CHECK-LABEL: func @gather
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg0:%.*]]: !tfrt_fallback.tf_tensor, [[arg1:%.*]]: !tfrt_fallback.tf_tensor)
// CHECK: [[const:%.*]] = tfrt_fallback_async.const_dense_tensor

  func.return %1 : tensor<*xf32>
}

// -----

// The num_batch_threads remains 3 (the same as the original attribute)

// CHECK-NEXT:    "tf.MatMul"(%arg0, [[TENSOR]]) : (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32>
// CHECK-NEXT:    "tf.IfrtCall"(%arg0, [[ARRAYKEY]]) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [1 : i32]}> {__tpu_compile_metadata_text = "retvals { sharding { } }"} : (tensor<1x3xf32>, tensor<!tf_type.string>) -> tensor<1x1xf32>
// CHECK-NEXT:    return
//
 func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {

// CHECK-NEXT: tfrt.return [[out_ch]], [[o1]] : !tfrt.chain, !tfrt_fallback.tf_tensor
module attributes {tf.devices = ["/job:localhost/replica:0/task:0/device:CPU:0"], tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 679 : i32}}  {
  func.func @__inference_pruned_131() -> tensor<*xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "variable", outputs = "identity_retval_RetVal"}} {
    %0 = tf_executor.graph {

  %q_bias = "quantfork.qcast"(%bias) : (tensor<2xf32>) -> tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>
  %dq_bias = "quantfork.dcast"(%q_bias) : (tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>) -> tensor<2xf32>