%conv = stablehlo.convolution(%quant_arg, %quant_cst) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0039207626791561354:-128>>, tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32, 0.015>>) -> tensor<1x3x4x2x!quant.uniform<i32:f32, 5.8949912267181218E-5>>...

			expectedScores: []int64{0, 99, 98, 97},
		},
		{
			scores:         []int64{1, 1, 1, 1},
			expectedScores: []int64{100, 100, 100, 100},
		},
		{
			scores:         []int64{1000, 1, 1, 1},
			expectedScores: []int64{100, 0, 0, 0},
		},
		{
			reverse:        true,
			scores:         []int64{0, 1, 1, 1},
			expectedScores: []int64{100, 0, 0, 0},
		},
		{
			scores:         []int64{0, 0, 0, 0},

    %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }
}

// CHECK-LABEL: quantize_conv_per_tensor

    %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }
}

// CHECK-LABEL: quantize_conv_fn

func mul(x, y float64) float64 { return x * y }
func div(x, y float64) float64 { return x / y }

func TestFloat64(t *testing.T) {
	base := []float64{
		0,
		math.Copysign(0, -1),
		-1,
		1,
		math.NaN(),
		math.Inf(+1),
		math.Inf(-1),
		0.1,
		1.5,
		1.9999999999999998,     // all 1s mantissa
		1.3333333333333333,     // 1.010101010101...
		1.1428571428571428,     // 1.001001001001...

00000040  a6 d3 b1 c1 5d 86 51 aa  63 67 6b 6e cb 3b 5e 59  |....].Q.cgkn.;^Y|
00000050  02 c2 57 fd 37 39 1b 73  9a 61 b0 78 de e8 cc f8  |..W.79.s.a.x....|
00000060  b3 01 11 e5 e9 31 85 4d  fe 60 d4 12 70 71 64 45  |.....1.M.`..pqdE|
00000070  e8 7d fb be 5b 82 c0 c4  e1 57 09 2c f2 d7 a3 79  |.}..[....W.,...y|
00000080  1c 40 08 e1 e6 cd e2 3e  e7 55 da 14 03 03 00 01  |.@.....>.U......|

var tests = []struct {
	name string
	n    int
	f    func(int) bool
	i    int
}{
	{"empty", 0, nil, 0},
	{"1 1", 1, func(i int) bool { return i >= 1 }, 1},
	{"1 true", 1, func(i int) bool { return true }, 0},
	{"1 false", 1, func(i int) bool { return false }, 1},
	{"1e9 991", 1e9, func(i int) bool { return i >= 991 }, 991},

// CHECK-SAME:          %[[PAD_VAL:.*]]: tensor<f32>) -> tensor<1x64x114x114xf32> {
// CHECK:           %[[PAD:.*]] = stablehlo.pad %[[INPUT]], %[[PAD_VAL]],
// CHECK:               low = [0, 1, 1, 0], high = [0, 1, 1, 0], interior = [0, 0, 0, 0]
// CHECK:               : (tensor<1x112x112x64xf32>, tensor<f32>) -> tensor<1x114x114x64xf32>
// CHECK:           %[[TPOS:.*]] = stablehlo.transpose %[[PAD]], dims = [0, 3, 1, 2]

        messages.each { assert message.contains(it) }

        where:
        numBusy | numIncompatible | numStopped | messages
        0       | 1               | 0          | ["1 incompatible"]
        1       | 0               | 0          | ["1 busy"]
        0       | 0               | 1          | ["1 stopped"]
        1       | 2               | 4          | ["1 busy", "2 incompatible", "4 stopped"]
    }

  <modelVersion>4.0.0</modelVersion>
  <groupId>org.codehaus.plexus</groupId>
  <artifactId>plexus</artifactId>
  <packaging>pom</packaging>
  <name>Plexus</name>
  <version>1.0.11</version>
  <ciManagement>
    <notifiers>
      <notifier>
        <type>mail</type>
        <configuration>
          <address>******@****.***</address>
        </configuration>
      </notifier>