// Add more supported headers here.
}

// isDirectiveValid - check if tagging-directive is valid.
func isDirectiveValid(v string) bool {
	// Check if set metadata-directive is valid.
	return isDirectiveCopy(v) || isDirectiveReplace(v)
}

// Check if the directive COPY is requested.
func isDirectiveCopy(value string) bool {

## Verify the `username` and data shape { #verify-the-username-and-data-shape }

We verify that we get a `username`, and extract the scopes.

And then we validate that data with the Pydantic model (catching the `ValidationError` exception), and if we get an error reading the JWT token or validating the data with Pydantic, we raise the `HTTPException` we created before.

    * 使用 Pydantic 模型時，你可以把資料結構分層設計，並且用 Python 的 `List` 和 `Dict` 等型別來定義。
    * 驗證器讓我們可以輕鬆地定義和檢查複雜的資料結構，並把它們轉換成 JSON Schema 進行記錄。
    * 你可以擁有深層**巢狀的 JSON** 物件，並對它們進行驗證和註釋。
* **可擴展**：
    * Pydantic 讓我們可以定義客製化的資料型別，或者你可以使用帶有 validator 裝飾器的方法來擴展模型中的驗證功能。

    @DisplayName("write(boolean/byte): writes single byte")
    void write_oneByteVariants() throws Exception {
        SmbRandomAccessFile raf = spy(newInstance("rw", false, true, false));
        // Intercept the 3-arg write to validate len
        doNothing().when(raf).write(any(byte[].class), anyInt(), anyInt());
        raf.writeBoolean(true);
        raf.writeByte(0x7F);
        ArgumentCaptor<Integer> lenCap = ArgumentCaptor.forClass(Integer.class);

   * Attack the CA intermediates check by presenting unrelated chains to the handshake vs.
   * certificate pinner.
   *
   * This chain is valid but not pinned:
   *
   * ```
   *   attackerCa
   *    -> phonyVictim
   * ```
   *
   *
   * This chain is pinned but not valid:
   *
   * ```
   *   attackerCa
   *     -> pinnedRoot (trusted by CertificatePinner)

## **4. Run Sample Applications**

After installing Hive, Hadoop and Spark successfully, we can now proceed to run some sample applications to see if they are configured appropriately.  We can use Spark Pi and Spark WordCount programs to validate our Spark installation. We can also explore how to run Spark jobs from the command line and Spark shell.

### **4.1 Spark Pi**

func TestScanRune(t *testing.T) {
	for n, test := range scanTests {
		buf := strings.NewReader(test)
		s := NewScanner(buf)
		s.Split(ScanRunes)
		var i, runeCount int
		var expect rune
		// Use a string range loop to validate the sequence of runes.
		for i, expect = range test {
			if !s.Scan() {
				break
			}
			runeCount++
			got, _ := utf8.DecodeRune(s.Bytes())
			if got != expect {

	fields := strings.Split(releaseTag, ".")
	if len(fields) < 2 || len(fields) > 4 {
		return releaseTime, fmt.Errorf("%s is not a valid release tag", releaseTag)
	}
	if fields[0] != "RELEASE" {
		return releaseTime, fmt.Errorf("%s is not a valid release tag", releaseTag)
	}
	return time.Parse(MinioReleaseTagTimeLayout, fields[1])
}

// getModTime - get the file modification time of `path`

github.com/envoyproxy/go-control-plane/ratelimit v0.1.0/go.mod h1:Wk+tMFAFbCXaJPzVVHnPgRKdUdwW/KdbRt94AzgRee4=
github.com/envoyproxy/protoc-gen-validate v1.2.1 h1:DEo3O99U8j4hBFwbJfrz9VtgcDfUKS7KJ7spH3d86P8=
github.com/envoyproxy/protoc-gen-validate v1.2.1/go.mod h1:d/C80l/jxXLdfEIhX1W2TmLfsJ31lvEjwamM4DxlWXU=
github.com/erikgeiser/coninput v0.0.0-20211004153227-1c3628e74d0f h1:Y/CXytFA4m6baUTXGLOoWe4PQhGxaX0KpnayAqC48p4=

	if group == "" {
		return updatedAt, errInvalidArgument
	}

	cache := store.lock()
	defer store.unlock()

	// Validate that all members exist.
	for _, member := range members {
		u, ok := cache.iamUsersMap[member]
		if !ok {
			return updatedAt, errNoSuchUser
		}
		cr := u.Credentials