from ...utils import needs_pydanticv1


@pytest.fixture(name="app", scope="module")
def get_app():
    with pytest.warns(DeprecationWarning):
        from docs_src.async_sql_databases.tutorial001 import app
    yield app


# TODO: pv2 add version with Pydantic v2
@needs_pydanticv1
def test_create_read(app: FastAPI):
    with TestClient(app) as client:
        note = {"text": "Foo bar", "completed": False}

        if (!executableFile.isAbsolute()) {
            DeprecationLogger.deprecateBehaviour("Configuring a Java executable via a relative path.")
                    .withContext("Resolving relative file paths might yield unexpected results, there is no single clear location it would make sense to resolve against.")
                    .withAdvice("Configure an absolute path to a Java executable instead.")
                    .willBecomeAnErrorInGradle9()

     * The transformer may be called multiple times with the same input.
     * Implementations should ensure uniqueness of the return value for a distinct input.
     * That is, no two inputs should yield the same return value.
     *
     * @param antBuildFile The build file. This is resolved as per {@link org.gradle.api.Project#file(Object)}.

* ⚫️ 💪 ⤴️ 🕳 👈 **📨** ⚖️ 🏃 🙆 💪 📟.
* ⤴️ ⚫️ 🚶‍♀️ **📨** 🛠️ 🎂 🈸 ( *➡ 🛠️*).
* ⚫️ ⤴️ ✊ **📨** 🏗 🈸 ( *➡ 🛠️*).
* ⚫️ 💪 🕳 👈 **📨** ⚖️ 🏃 🙆 💪 📟.
* ⤴️ ⚫️ 📨 **📨**.

!!! note "📡 ℹ"
    🚥 👆 ✔️ 🔗 ⏮️ `yield`, 🚪 📟 🔜 🏃 *⏮️* 🛠️.

    🚥 📤 🙆 🖥 📋 (📄 ⏪), 👫 🔜 🏃 *⏮️* 🌐 🛠️.

## ✍ 🛠️

✍ 🛠️ 👆 ⚙️ 👨‍🎨 `@app.middleware("http")` 🔛 🔝 🔢.

🛠️ 🔢 📨:

*  `request`.

def test_app_lifespan_state(state: State) -> None:
    @asynccontextmanager
    async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
        state.app_startup = True
        yield
        state.app_shutdown = True

    app = FastAPI(lifespan=lifespan)

    @app.get("/")
    def main() -> Dict[str, str]:
        return {"message": "Hello World"}

    assert state.app_startup is False

				}
				m[v.Int()] = true
				i++
			}
			if i != 3 {
				t.Fatalf("should loop three times")
			}
		}},
		{"func", ValueOf(func(yield func(int) bool) {
			for i := range 4 {
				if !yield(i) {
					return
				}
			}
		}), func(t *testing.T, s iter.Seq[Value]) {
			i := int64(0)
			for v := range s {
				if v.Int() != i {

fun zipTo(zipFile: File, baseDir: File) {
    zipTo(zipFile, baseDir, baseDir.walkReproducibly())
}


internal
fun File.walkReproducibly(): Sequence<File> = sequence {

    require(isDirectory)

    yield(this@walkReproducibly)

    var directories: List<File> = listOf(this@walkReproducibly)
    while (directories.isNotEmpty()) {
        val subDirectories = mutableListOf<File>()
        directories.forEach { dir ->

                                         island_body.begin(),
                                         std::prev(island_body.end()));
    // Forward island fetches (tf_executor.yield operands) to island op result
    // uses.
    for (auto result :
         llvm::zip(island_op.getOutputs(), island_op.GetYield().getFetches()))
      std::get<0>(result).replaceAllUsesWith(std::get<1>(result));
  }

              ),
          ]
      )
      def add(self, x, y):
        res = math_ops.add(x, y)
        return {'output': res}

    def data_gen():
      for _ in range(255):
        yield {
            'x': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')
            ),
            'y': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')

    %4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>