// amd64:`CMPW\tcommand-line-arguments[.+_a-z0-9]+\(SP\), [A-Z]`
	return a == b
}

func CompareArray4(a, b [12]int8) bool {
	// amd64:`CMPQ\tcommand-line-arguments[.+_a-z0-9]+\(SP\), [A-Z]`
	// amd64:`CMPL\tcommand-line-arguments[.+_a-z0-9]+\(SP\), [A-Z]`
	return a == b
}

func CompareArray5(a, b [15]byte) bool {
	// amd64:`CMPQ\tcommand-line-arguments[.+_a-z0-9]+\(SP\), [A-Z]`

        ' A((,))'              | 'A'       | [tokens('(,)')]
        ' A((,,,), c)'         | 'A'       | [tokens('(,,,)'), token('c')]
        '_a ( p1 p2 p3 )'      | '_a'      | [tokens('p1 p2 p3')]
        '_a ( 1, (2+3) )'      | '_a'      | [token('1'), tokens('(2+3)')]
        '_a((, ) a b c)'       | '_a'      | [tokens('(,) a b c')]
        'func( (a) ( b ))'     | 'func'    | [tokens('(a)(b)')]
    }

OkCurl
======

_A curl for the next-generation web._

OkCurl is an OkHttp-backed curl clone which allows you to test OkHttp's HTTP engine (including

module attributes {tf.versions = {producer = 888 : i32}} {
  // CHECK-LABEL: func @annotate_broadcast_values
  func.func @annotate_broadcast_values(%arg0: tensor<?xi32>) -> tensor<?xi32> {
    %0 = "tf._A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>
    %1 = "tf._B"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>
    %5:2 = tf_device.replicate([%0, %arg0] as %ri_0: tensor<?xi32>) {n = 2 : i32} {

            <property name='staticFinalRegex' value='^logger$|^[A-Z][A-Z_0-9]*$|^serialVersionUID$'/>
        </rule-config>
        <rule-config name='MethodName'>
            <property name='regex' value='^[a-z][\$_a-zA-Z0-9]*$|^.*\s.*$'/>
        </rule-config>
        <rule-config name='VariableName'>
            <property name='finalRegex' value='^[a-z][a-zA-Z0-9]*$'/>
        </rule-config>
        <exclude name="ConfusingMethodName"/>

            <property name='staticFinalRegex' value='^logger$|^[A-Z][A-Z_0-9]*$|^serialVersionUID$'/>
        </rule-config>
        <rule-config name='MethodName'>
            <property name='regex' value='^[a-z][\$_a-zA-Z0-9]*$|^.*\s.*$'/>
        </rule-config>
        <rule-config name='VariableName'>
            <property name='finalRegex' value='^[a-z][a-zA-Z0-9]*$'/>
        </rule-config>
        <exclude name="ConfusingMethodName"/>

        -> (tensor<?x256x56x56xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<*xf32>)

  // CHECK: %[[BATCH_NORM1:[_a-z0-9]*]], {{.*}} = "tf.FusedBatchNormV3"
  // CHECK-SAME: %[[CONV1]]
  // CHECK-SAME: data_format = "NHWC"

  // ------------------------------------------------------------------------ //
  // Convolution layer #2.

			t.Errorf("expected false for '%s'", val)
		}
	}
}

func TestIsCIdentifier(t *testing.T) {
	goodValues := []string{
		"a", "ab", "abc", "a1", "_a", "a_", "a_b", "a_1", "a__1__2__b", "__abc_123",
		"A", "AB", "AbC", "A1", "_A", "A_", "A_B", "A_1", "A__1__2__B", "__123_ABC",
	}
	for _, val := range goodValues {
		if msgs := IsCIdentifier(val); len(msgs) != 0 {
			t.Errorf("expected true for '%s': %v", val, msgs)

		"abc*",
		"a.abc*",
		"a.b.*",
		"a/b/c/d",
		"a/*",
		"a/b/*",
		"a.b/c*",
		"a.b/c.d",
		"a/b.c/d",
	}
	invalid := []string{
		"",
		"ä",
		"a_",
		"_",
		"_a",
		"_a._b",
		"__",
		"-",
		".",
		"a.",
		".a",
		"a.b.",
		"a*.b",
		"a*b",
		"*a",
		"Abc",
		"/",
		"a/",
		"/a",
		"a*/b",
		func(n int) string {

        ModelPath.validateName("Z9z")
        ModelPath.validateName("abc")
        ModelPath.validateName("aBC")
        ModelPath.validateName("a10")
        ModelPath.validateName("_")
        ModelPath.validateName("_a")
        ModelPath.validateName("__")
        ModelPath.validateName("a_Z")

        then:
        noExceptionThrown()
    }

    def "validate model path"() {
        when: