def test_decimal_encoder_float():
    data = {"value": Decimal(1.23)}
    assert jsonable_encoder(data) == {"value": 1.23}


def test_decimal_encoder_int():
    data = {"value": Decimal(2)}
    assert jsonable_encoder(data) == {"value": 2}


def test_decimal_encoder_nan():
    data = {"value": Decimal("NaN")}
    assert isnan(jsonable_encoder(data)["value"])

from decimal import Decimal

from dirty_equals import IsOneOf
from fastapi import FastAPI
from fastapi.testclient import TestClient
from inline_snapshot import snapshot
from pydantic import BaseModel, condecimal

app = FastAPI()


class Item(BaseModel):
    name: str
    age: condecimal(gt=Decimal(0.0))  # type: ignore


@app.post("/items/")
def save_item_no_body(item: list[Item]):
    return {"item": item}

    results in failed round-tripping between encode and parse.
    Our Id type is a prime example of this.

    >>> decimal_encoder(Decimal("1.0"))
    1.0

    >>> decimal_encoder(Decimal("1"))
    1

    >>> decimal_encoder(Decimal("NaN"))
    nan
    """
    exponent = dec_value.as_tuple().exponent
    if isinstance(exponent, int) and exponent >= 0:
        return int(dec_value)
    else:
        return float(dec_value)

* `bytes`:
    * `bytes` estándar de Python.
    * En requests y responses se tratará como `str`.
    * El esquema generado especificará que es un `str` con "binary" como "format".
* `Decimal`:
    * `Decimal` estándar de Python.
    * En requests y responses, manejado igual que un `float`.

* `bytes`:
    * Встроенный в Python `bytes`.
    * В запросах и ответах будет рассматриваться как `str`.
    * В сгенерированной схеме будет указано, что это `str` в формате `binary`.
* `Decimal`:
    * Встроенный в Python `Decimal`.
    * В запросах и ответах обрабатывается так же, как и `float`.

* `bytes`:
    * O `bytes` padrão do Python.
    * Em requisições e respostas será representado como uma `str`.
    * O esquema gerado vai especificar que é uma `str` com o "formato" `binary`.
* `Decimal`:
    * O `Decimal` padrão do Python.
    * Em requisições e respostas será representado como um `float`.

            """
        ),
    ] = _Unset,
    decimal_places: Annotated[
        Union[int, None],
        Doc(
            """
            Maximum number of decimal places allowed for numbers.
            """
        ),
    ] = _Unset,
    examples: Annotated[
        Optional[list[Any]],
        Doc(
            """
            Example values for this field.

     * java.util.regex (where we want them in order to avoid catastrophic backtracking).
     */
    String decimal = "(?:\\d+#(?:\\.\\d*#)?|\\.\\d+#)";
    String completeDec = decimal + "(?:[eE][+-]?\\d+#)?[fFdD]?";
    String hex = "(?:[0-9a-fA-F]+#(?:\\.[0-9a-fA-F]*#)?|\\.[0-9a-fA-F]+#)";
    String completeHex = "0[xX]" + hex + "[pP][+-]?\\d+#[fFdD]?";

}

// Allowed cast types
const (
	castBool      = "BOOL"
	castInt       = "INT"
	castInteger   = "INTEGER"
	castString    = "STRING"
	castFloat     = "FLOAT"
	castDecimal   = "DECIMAL"
	castNumeric   = "NUMERIC"
	castTimestamp = "TIMESTAMP"
)

func (e *Expression) castTo(r Record, castType string, tableAlias string) (res *Value, err error) {
	v, err := e.evalNode(r, tableAlias)

				if !field.PrimaryKey &&
					(len(matches2) == 1 && matches2[0][1] != fmt.Sprint(length) && ok) {
					alterColumn = true
				}
			}
		}
	}

	// check precision
	if realDataType == "decimal" || realDataType == "numeric" &&
		regexp.MustCompile(realDataType+`\(.*\)`).FindString(fullDataType) != "" { // if realDataType has no precision,ignore
		precision, scale, ok := columnType.DecimalSize()
		if ok {