// This pattern optimizes:
//   (x + cst1) + cst2 -> x + cst
//   (x - cst1) - cst2 -> x - cst
// Where: cst = cst1 + cst2
foreach BinaryOp = [TF_AddV2Op, TF_SubOp] in {
  def OptimizeConsecutive#BinaryOp : Pat<
    (BinaryOp
      (BinaryOp $x, (TF_ConstOp:$cst1 $cst1_value)),
      (TF_ConstOp:$cst2 $cst2_value)),
    (BinaryOp
      $x, (TF_AddV2Op $cst1, $cst2))>;
}

// This pattern optimizes:

  %cst0 = mhlo.constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32>
  %cst1 = mhlo.constant dense<[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]]> : tensor<1x1x2x4xf32>
  %0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xf32>) -> tensor<1x1x2x4xf32>
  %1 = mhlo.multiply %0, %cst1 : tensor<1x1x2x4xf32>
  // CHECK:      return %[[RES]] : tensor<1x1x2x4xf32>

  // CHECK-DAG: %[[CST1:.*]] =  "tf.Const"() <{value = dense<[8, 6, 6, 18]> : tensor<4xi64>}> : () -> tensor<4xi64>
  // CHECK: %[[RESHAPE:.*]] = "tf.Reshape"(%arg0, %[[CST]]) : (tensor<1x8x1x1x1x1x1x18xbf16>, tensor<4xi64>) -> tensor<8x1x1x18xbf16>
  // CHECK: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[RESHAPE]], %[[CST1]]) : (tensor<8x1x1x18xbf16>, tensor<4xi64>) -> tensor<8x6x6x18xbf16>

func.func @splat_scalar_no_change() -> (tensor<f32>, tensor<i32>) {
  // CHECK-NOT: mhlo.broadcast_in_dim
  %cst0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %cst1 = mhlo.constant dense<0> : tensor<i32>
  func.return %cst0, %cst1 : tensor<f32>, tensor<i32>

import org.gradle.internal.time.Time
import org.slf4j.Logger
import org.slf4j.LoggerFactory
import spock.lang.Specification

class Slf4jLoggingConfigurerTest extends Specification {
    Logger logger = LoggerFactory.getLogger("cat1")
    OutputEventListener listener = Mock()
    Slf4jLoggingConfigurer configurer = new Slf4jLoggingConfigurer(listener)

    def cleanup() {

func.func @mixed_no_constant_folding() -> (tensor<f32>) {
  // CHECK:      %[[CST0:.+]] = arith.constant dense<0.000000e+00>
  // CHECK-NEXT: %[[CST1:.+]] = arith.constant dense<0.000000e+00>
  // CHECK-NEXT: "vhlo.add_v1"(%[[CST0]], %[[CST1]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %cst_0 = arith.constant dense<0.000000e+00> : tensor<f32>
  %cst_1 = arith.constant dense<0.000000e+00> : tensor<f32>

  // CHECK-DAG: %[[cst1:.*]] = stablehlo.constant dense<0x7FC00000> : tensor<f32>
  // CHECK-DAG: %[[cst2:.*]] = stablehlo.constant dense<6.000000e+00> : tensor<f32>
  // CHECK-DAG: %[[cst3:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<f32>
  // CHECK-NOT: %[[q0:.*]] = "quantfork.qcast"(%[[cst0]])
  // CHECK-NOT: %[[q1:.*]] = "quantfork.qcast"(%[[cst1]])
  // CHECK: %[[q2:.*]] = "quantfork.qcast"(%[[cst2]])

                }

            repositories {
                ivy {
                    // Url of Android Studio archive
                    url = uri("https://redirector.gvt1.com/edgedl/android/studio/ide-zips")
                    patternLayout {
                        artifact("[revision]/[artifact]-[revision]-[ext]")
                    }
                    metadataSources { artifact() }

            if (line.length > 7 && line[3] == "FILE") {
                def name = line[7]
                // These are objects added by GCC or Swift toolchains
                if (!(name in ["crt1.o", "crtstuff.c", "SwiftRT-ELF.cpp"])) {
                    def type = 'F' as char
                    symbols.add(new Symbol(name, type, false))
                }
            }
        }
        return symbols
    }

        then:
        createdTasksFor(":a:t1") == [":a:t1"]
        createdTasksFor(":a:t1", "b:t1") == [":a:t1", ":b:t1"]
        createdTasksFor(":t1") == [":t1"]
        createdTasksFor("t1") == [":a:t1", ":b:t1", ":c:t1", ":t1"]
    }

    def "rule based tasks that are not requested on the command line are not created"() {
        when:
        buildFile << """
            ${ruleBasedTasks()}
            model {