# See https://pre-commit.com/hooks.html for more hooks
default_language_version:
    python: python3.10
repos:
-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.4.0
    hooks:
    -   id: check-added-large-files
    -   id: check-toml
    -   id: check-yaml
        args:
        -   --unsafe
    -   id: end-of-file-fixer
    -   id: trailing-whitespace
-   repo: https://github.com/charliermarsh/ruff-pre-commit

And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.

* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

    with path.open("rb") as file:
        response = client.post("/files/", files={"file": file})
    assert response.status_code == 200, response.text
    assert response.json() == {"file_size": 14}


def test_post_large_file(tmp_path):
    default_pydantic_max_size = 2**16
    path = tmp_path / "test.txt"
    path.write_bytes(b"x" * (default_pydantic_max_size + 1))

    client = TestClient(app)
    with path.open("rb") as file:

    with path.open("rb") as file:
        response = client.post("/files/", files={"file": file})
    assert response.status_code == 200, response.text
    assert response.json() == {"file_size": 14}


def test_post_large_file(tmp_path):
    default_pydantic_max_size = 2**16
    path = tmp_path / "test.txt"
    path.write_bytes(b"x" * (default_pydantic_max_size + 1))

    client = TestClient(app)
    with path.open("rb") as file:

        response = client.post("/files/", files={"file": file})
    assert response.status_code == 200, response.text
    assert response.json() == {"file_size": 14}


@needs_py39
def test_post_large_file(tmp_path, client: TestClient):
    default_pydantic_max_size = 2**16
    path = tmp_path / "test.txt"
    path.write_bytes(b"x" * (default_pydantic_max_size + 1))

    with path.open("rb") as file:

This will be especially useful when you use it in a **large code base** where you use **the same dependencies** over and over again in **many *path operations***.

## To `async` or not to `async`

Import the `Response` class (sub-class) you want to use and declare it in the *path operation decorator*.

For large responses, returning a `Response` directly is much faster than returning a dictionary.

Starten Sie Ihre Anwendung und gehen Sie auf <a href="http://127.0.0.1:8000/static/redoc.standalone.js" class="external-link" target="_blank">http://127.0.0.1:8000/static/redoc.standalone.js</a>.

Sie sollten eine sehr lange JavaScript-Datei für **ReDoc** sehen.

Sie könnte beginnen mit etwas wie:

```JavaScript
/*!
 * ReDoc - OpenAPI/Swagger-generated API Reference Documentation

    ```

## Validierung von Zahlen: Floats, größer und kleiner

Zahlenvalidierung funktioniert auch für <abbr title="Kommazahl">`float`</abbr>-Werte.