WithJsonSchema,
    )

    class FakeNumpyArray:
        def __init__(self):
            self.data = [1.0, 2.0, 3.0]

    FakeNumpyArrayPydantic = Annotated[
        FakeNumpyArray,
        WithJsonSchema(TypeAdapter(list[float]).json_schema()),
        PlainSerializer(lambda v: v.data),
    ]

    class MyModel(BaseModel):
        model_config = ConfigDict(arbitrary_types_allowed=True)

def test_login(client: TestClient):
    response = client.post("/token", data={"username": "johndoe", "password": "secret"})
    assert response.status_code == 200, response.text
    assert response.json() == {"access_token": "johndoe", "token_type": "bearer"}


def test_login_incorrect_password(client: TestClient):
    response = client.post(
        "/token", data={"username": "johndoe", "password": "incorrect"}
    )

    extends AbstractTableReadTest<C> {

  protected void populate(Table<String, Integer, C> table, @Nullable Object... data) {
    checkArgument(data.length % 3 == 0);
    for (int i = 0; i < data.length; i += 3) {
      table.put(
          (String) data[i], (Integer) data[i + 1], nullableCellValue((Character) data[i + 2]));
    }
  }

  protected boolean supportsRemove() {
    return true;
  }

	Online  map[string]struct{}
	Offline map[string]Error
}

// DEK is a data encryption key. It consists of a
// plaintext-ciphertext pair and the ID of the key
// used to generate the ciphertext.
//
// The plaintext can be used for cryptographic
// operations - like encrypting some data. The
// ciphertext is the encrypted version of the
// plaintext data and can be stored on untrusted
// storage.
type DEK struct {

TEXT foo(SB), 0, $0
	RET
TEXT foo(SB), 0, $0 // ERROR "symbol foo redeclared"
	RET

GLOBL bar(SB), 0, $8
GLOBL bar(SB), 0, $8 // ERROR "symbol bar redeclared"

DATA bar+0(SB)/8, $0

	}

	for _, testCase := range testCases {
		data, _ := testCase.flag.MarshalJSON()
		if testCase.expectedResult != string(data) {
			t.Fatalf("expected: %v, got: %v", testCase.expectedResult, string(data))
		}
	}
}

// Test BoolFlag.UnmarshalJSON()
func TestBoolFlagUnmarshalJSON(t *testing.T) {
	testCases := []struct {
		data           []byte
		expectedResult BoolFlag
		expectedErr    bool
	}{

from fastapi import FastAPI, Form
from pydantic import BaseModel

app = FastAPI()


class FormData(BaseModel):
    username: str
    password: str


@app.post("/login/")
async def login(data: FormData = Form()):

    }


@pytest.mark.parametrize(
    "path",
    ["/required-list-str", "/model-required-list-str"],
)
def test_required_list_str(path: str):
    client = TestClient(app)
    response = client.post(path, data={"p": ["hello", "world"]})
    assert response.status_code == 200
    assert response.json() == {"p": ["hello", "world"]}


# =====================================================================================
# Alias

Traditional retrieval of objects is always as whole entities, i.e GetObject for a 5 GiB object, will always return 5 GiB of data. S3 Select API allows us to retrieve a subset of data by using simple SQL expressions. By using Select API to retrieve only the data needed by the application, drastic performance improvements can be achieved.

You can use the Select API to query objects with following features:

        // Parse FILE_NOTIFY_INFORMATION structures from response
        byte[] data = response.getOutputBuffer();
        int offset = 0;
        
        while (offset < data.length) {
            int nextEntryOffset = readInt4(data, offset);
            int action = readInt4(data, offset + 4);
            int fileNameLength = readInt4(data, offset + 8);
            
            // Extract filename