"tax": None,
        },
        "user": {"username": "Dave", "full_name": None},
        "q": "somequery",
    }


def test_put_only_required(client: TestClient):
    response = client.put(
        "/items/5",
        json={
            "importance": 2,
            "item": {"name": "Foo", "price": 50.5},
            "user": {"username": "Dave"},
        },
    )
    assert response.status_code == 200

            "user": {"username": "Dave"},
        },
    )
    assert response.status_code == 200
    assert response.json() == {
        "item_id": 5,
        "importance": 2,
        "item": {
            "name": "Foo",
            "price": 50.5,
            "description": None,
            "tax": None,
        },
        "user": {"username": "Dave", "full_name": None},
    }

* ✏️ Fix typo in README installation instructions. PR [#13011](https://github.com/fastapi/fastapi/pull/13011) by [@dave-hay](https://github.com/dave-hay).
* 📝 Update docs for `fastapi-cli`. PR [#13031](https://github.com/fastapi/fastapi/pull/13031) by [@tiangolo](https://github.com/tiangolo).

### Translations

from pydantic_core import PydanticUndefinedType

from ._compat import (
    Url,
    is_pydantic_v1_model_instance,
)


# Taken from Pydantic v1 as is
def isoformat(o: Union[datetime.date, datetime.time]) -> str:
    return o.isoformat()


# Adapted from Pydantic v1
# TODO: pv2 should this return strings instead?
def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
    """

                            },
                            {
                                "name": "save-data",
                                "in": "header",
                                "required": True,
                                "schema": {"type": "boolean", "title": "Save Data"},
                            },
                            {
                                "name": "if-modified-since",

            "timestamp": "not a date",
        },
    )
    assert response.status_code == 422
    assert response.json() == {
        "detail": [
            {
                "loc": ["body", "timestamp"],
                "msg": "Input should be a valid datetime or date, invalid character in year",
                "type": "datetime_from_date_parsing",
                "input": "not a date",

    assert response.json() == {
        "detail": [
            {
                "type": "string_too_short",
                "loc": ["query", "q"],
                "msg": "String should have at least 3 characters",
                "input": "fa",
                "ctx": {"min_length": 3},
            }
        ]
    }


def test_openapi_schema(client: TestClient):

                            },
                            {
                                "name": "save-data",
                                "in": "header",
                                "required": True,
                                "schema": {"type": "boolean", "title": "Save Data"},
                            },
                            {
                                "name": "if-modified-since",

        "traceparent": "123",
        "x_tag": ["one", "two"],
    }


def test_header_param_model_no_underscore(client: TestClient):
    response = client.get(
        "/items/",
        headers=[
            ("save-data", "true"),
            ("if-modified-since", "yesterday"),
            ("traceparent", "123"),
            ("x-tag", "one"),
            ("x-tag", "two"),
        ],
    )
    assert response.status_code == 422

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement: