testUniformity(t, 3, 0.10)
	testUniformity(t, 10, 0.20)
	testWeighting(t, 0.05)
}

func testWeighting(t *testing.T, margin float64) {
	data := []*SRV{
		{Target: "a", Weight: 60},
		{Target: "b", Weight: 30},
		{Target: "c", Weight: 10},
	}
	checkDistribution(t, data, margin)
}

func TestWeighting(t *testing.T) {
	testWeighting(t, 0.05)

	assert(equal(0x1.Fp+0, 1.9375))
	assert(equal(0x.8p-0, 0.5))
	assert(equal(0x1FFFp-16, 0.1249847412109375))
	assert(equal(0x1.fffffffffffffp1023, 1.7976931348623157e308))
	assert(equal(0x1.fffffffffffffp1023i, complex(0, 1.7976931348623157e308)))

	assert(equal(0x_1p-2, 0.25))
	assert(equal(0x2.p1_0, 2048.0))
	assert(equal(0x1_0.Fp+0, 16.9375))
	assert(equal(0x_0.8p-0, 0.5))
	assert(equal(0x_1FF_Fp-16, 0.1249847412109375))

		{
			kubeReserved:   getResourceList("100m", "100Mi"),
			systemReserved: getResourceList("50m", "50Mi"),
			capacity:       getResourceList("10", "10Gi"),
			hardThreshold: evictionapi.ThresholdValue{
				Percentage: 0.05,
			},
			expected: getResourceList("150m", "694157320"),
		},

		{
			kubeReserved:   v1.ResourceList{},
			systemReserved: v1.ResourceList{},
			capacity:       getResourceList("10", "10Gi"),

  func.return %1 : tensor<256x8x7x16xf16>

  // CHECK-DAG: %cst = arith.constant dense<[2.500000e+00, 3.500000e+00, 4.500000e+00, 5.500000e+00, 6.500000e+00, 7.500000e+00, 8.500000e+00, 9.500000e+00, 1.050000e+01, 1.150000e+01, 1.250000e+01, 1.350000e+01, 1.450000e+01, 1.550000e+01, 1.650000e+01, 1.750000e+01]> : tensor<16xf16>

}

func.func @sparse_f32() -> tensor<3x2xf32> {
  // CHECK-LABEL: @sparse_f32

}

func.func @sparse_f32() -> tensor<3x2xf32> {
  // CHECK-LABEL: @sparse_f32

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package main

import "fmt"

var a = []int64{
	0.0005 * 1e9,
	0.001 * 1e9,
	0.005 * 1e9,
	0.01 * 1e9,
	0.05 * 1e9,
	0.1 * 1e9,
	0.5 * 1e9,
	1 * 1e9,
	5 * 1e9,
}

func main() {
	s := ""
	for _, v := range a {
		s += fmt.Sprint(v) + " "
	}

func.func @fuseMulAndConv2D(%input: tensor<1x256x256x3xf32>) -> (tensor<1x256x256x2xf32>) {
  // CHECK-DAG: %[[FILTER:.+]] = mhlo.constant dense<{{\[\[\[\[}}1.000000e+00, 2.000000e+00], [3.000000e+00, 4.000000e+00], [5.000000e+00, 6.000000e+00]]]]> : tensor<1x1x3x2xf32>
  // CHECK-DAG: %[[CST:.+]] = mhlo.constant dense<[1.000000e-01, 2.000000e-01]> : tensor<2xf32>

                "{\"bool\":{\"should\":[{\"match_phrase\":{\"title\":{\"query\":\"aaa\",\"slop\":0,\"zero_terms_query\":\"NONE\",\"boost\":0.5}}},{\"match_phrase\":{\"content\":{\"query\":\"aaa\",\"slop\":0,\"zero_terms_query\":\"NONE\",\"boost\":0.05}}}],\"adjust_pure_negative\":true,\"boost\":1.0}}",
                "aaa");
        assertQueryBuilder(MatchPhraseQueryBuilder.class,

		&metrics.HistogramOpts{
			Subsystem: "apiserver",
			Name:      "request_body_size_bytes",
			Help:      "Apiserver request body size in bytes broken out by resource and verb.",
			// we use 0.05 KB as the smallest bucket with 0.1 KB increments up to the
			// apiserver limit.
			Buckets:        metrics.LinearBuckets(50000, 100000, 31),
			StabilityLevel: metrics.ALPHA,
		},
		[]string{"resource", "verb"},
	)
)