// RUN: tf-opt -split-input-file -verify-diagnostics -xla-legalize-tf-collective -xla-legalize-tf %s | FileCheck %s


// CHECK: module attributes
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_key = 0
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_size = 2
// CHECK-LABEL: func @all_reduce_cross_replica
func.func @all_reduce_cross_replica(%input: tensor<f32>) -> tensor<f32> {

  }

// CHECK-LABEL: func @conv_with_single_layer

// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%arg0
// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]]
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

  // CHECK: %[[CONST:.*]] = mhlo.constant()
  // CHECK-SAME{LITERAL} value = dense<127> : tensor<2x3x3x2xi8>
  // CHECK-SAME: tensor<2x3x3x2x!quant.uniform<i8:f32, 1.000000e+00:3>>
  // CHECK: mhlo.convolution(%arg0, %[[CONST]])
  // CHECK-SAME{LITERAL}: dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]
  // CHECK-SAME{LITERAL}: window = {stride = [1, 2], pad = [[0, 0], [0, 0]], lhs_dilate = [1, 1], rhs_dilate = [2, 2]}

  %0 = "tf_device.cluster"() ({
    // CHECK: "tf.Const"() <{value = dense<1> : tensor<i32>}>
    // CHECK-NOT: _xla_outside_compilation
    // CHECK: "tf.Const"
    // CHECK-SAME: _xla_outside_compilation
    // CHECK-SAME: tf_type.string
    // CHECK: "tf.Identity"
    // CHECK-NOT: _xla_outside_compilation
    %1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

// PerTensor-SAME: f = @quantized_conv2d_fn_1}> : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xi8>, tensor<f32>, tensor<i32>) -> tensor<*xf32>
// PerTensor: %[[out_2:.*]] = "tf.PartitionedCall"(%arg0, %[[q_w]], %[[scale]], %[[zp]]) <{config = "", config_proto = "", executor_type = "",

    }

    def "repositories have the same id when metadata sources and other configuration is the same"() {
        def repo = newRepo()
        def same = newRepo()
        def different = newRepo()

        given:
        repo.url = new URI("http://localhost")
        source(repo)
        same.url = new URI("http://localhost")
        source(same)
        different.url = new URI("http://localhost")

//CHECK: %0 = "vhlo.convolution_v1"(%arg0, %arg1) <{
//CHECK-SAME: batch_group_count = #vhlo.integer_v1<1 : i64>,
//CHECK-SAME: feature_group_count = #vhlo.integer_v1<32 : i64>,
//CHECK-SAME: input_batch_dimension = #vhlo.integer_v1<0 : i64>,
//CHECK-SAME: input_feature_dimension = #vhlo.integer_v1<3 : i64>,
//CHECK-SAME: input_spatial_dimensions = #vhlo.tensor_v1<dense<[1, 2]> : tensor<2xi64>>,
//CHECK-SAME: kernel_input_feature_dimension = #vhlo.integer_v1<2 : i64>,

// CHECK-LABEL: func private @main_stream_{{[0-9]*}}
// CHECK-SAME: ([[ARG:%.*]]: tensor<i32>, [[FUTURE:%.*]]: !mlrt.future, [[CONTROL_PROMISE:%.*]]: !mlrt.promise)
// CHECK: [[HANDLE:%.*]] = "tf_mlrt.tf_await"([[FUTURE]])
// CHECK: "tf.AssignVariableOp"([[HANDLE]], [[ARG]])
// CHECK-NEXT: mlrt.promise_control [[CONTROL_PROMISE]]

// CHECK-LABEL: func private @main_stream_{{[0-9]*}}

[[concepts_repeatable_task_outputs]]
== Repeatable task outputs

Assume that you have a code generator task as part of your build.
When you have a fully up to date build and you clean and re-run the code generator task on the same code base it should generate _exactly the same output_, so anything that depends on that output will stay up-to-date.

On top of that, an async framework could run some sync code in a threadpool (using `asyncio.run_in_executor`), but belonging to the same request.