from dirty_equals import IsDict
from fastapi.testclient import TestClient

from docs_src.extra_data_types.tutorial001 import app

client = TestClient(app)


def test_extra_types():
    item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }

      - name: Decompress source distribution
        run: |
          cd dist
          tar xvf fastapi*.tar.gz
      - name: Install test dependencies
        run: |
          cd dist/fastapi*/
          pip install -r requirements-tests.txt
      - name: Run source distribution tests
        run: |
          cd dist/fastapi*/
          bash scripts/test.sh
      - name: Build wheel distribution
        run: |
          cd dist

@pytest.fixture(name="client")
def get_client():
    from docs_src.extra_data_types.tutorial001_an_py310 import app

    client = TestClient(app)
    return client


@needs_py310
def test_extra_types(client: TestClient):
    item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",

By considering these concepts, you will be able to **evaluate and design** the best way to deploy **your own APIs**.

In the next chapters, I'll give you more **concrete recipes** to deploy FastAPI applications.

* **WebSocket** support.
* In-process background tasks.
* Startup and shutdown events.
* Test client built on HTTPX.
* **CORS**, GZip, Static Files, Streaming responses.
* **Session and Cookie** support.
* 100% test coverage.
* 100% type annotated codebase.

## Pydantic features

@pytest.fixture(name="client")
def get_client():
    from docs_src.extra_data_types.tutorial001_py310 import app

    client = TestClient(app)
    return client


@needs_py310
def test_extra_types(client: TestClient):
    item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",

from dirty_equals import IsDict
from fastapi.testclient import TestClient

from docs_src.extra_data_types.tutorial001_an import app

client = TestClient(app)


def test_extra_types():
    item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }

@pytest.fixture(name="client")
def get_client():
    from docs_src.extra_data_types.tutorial001_an_py39 import app

    client = TestClient(app)
    return client


@needs_py39
def test_extra_types(client: TestClient):
    item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",

    This is where we'll put the `requirements.txt` file and the `app` directory.

3. Copy the file with the requirements to the `/code` directory.

    Copy **only** the file with the requirements first, not the rest of the code.

    As this file **doesn't change often**, Docker will detect it and use the **cache** for this step, enabling the cache for the next step too.

4. Install the package dependencies in the requirements file.