from typing import TYPE_CHECKING, Annotated

from fastapi import Depends, FastAPI
from fastapi.testclient import TestClient

from .utils import needs_py314

if TYPE_CHECKING:  # pragma: no cover

    class DummyUser: ...


@needs_py314
def test_stringified_annotation():
    # python3.14: Use forward reference without "from __future__ import annotations"
    async def get_current_user() -> DummyUser | None:
        return None

from a2wsgi import WSGIMiddleware
from fastapi import FastAPI
from flask import Flask, request
from markupsafe import escape

flask_app = Flask(__name__)


@flask_app.route("/")
def flask_main():
    name = request.args.get("name", "World")
    return f"Hello, {escape(name)} from Flask!"


app = FastAPI()


@app.get("/v2")
def read_main():
    return {"message": "Hello World"}

from typing import Annotated, Literal

from fastapi import FastAPI, Query
from pydantic import BaseModel, Field

app = FastAPI()


class FilterParams(BaseModel):
    model_config = {"extra": "forbid"}

    limit: int = Field(100, gt=0, le=100)
    offset: int = Field(0, ge=0)
    order_by: Literal["created_at", "updated_at"] = "created_at"
    tags: list[str] = []


@app.get("/items/")

    checkNotNull(from);
    checkNotNull(to);
    if (from instanceof FileChannel) {
      FileChannel sourceChannel = (FileChannel) from;
      long oldPosition = sourceChannel.position();
      long position = oldPosition;
      long copied;
      do {
        copied = sourceChannel.transferTo(position, ZERO_COPY_CHUNK_SIZE, to);
        position += copied;

from fastapi import FastAPI
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str | None = None
    description: str | None = None
    price: float | None = None
    tax: float = 10.5
    tags: list[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

            spec.getDisplayName().set("DSL");
            spec.getSourceRoots().from(extension.getKotlinDslSource());
            spec.getSourceRoots().from(runtimeExtensions.flatMap(GradleKotlinDslRuntimeGeneratedSources::getGeneratedSources));
            spec.getClasspath().from(extension.getClasspath());
            spec.getClasspath().from(runtimeExtensions.flatMap(GradleKotlinDslRuntimeGeneratedSources::getGeneratedClasses));

from fastapi import FastAPI
from fastapi.testclient import TestClient
from inline_snapshot import snapshot

app = FastAPI(
    servers=[
        {"url": "/", "description": "Default, relative server"},
        {
            "url": "http://staging.localhost.tiangolo.com:8000",
            "description": "Staging but actually localhost still",
        },
        {"url": "https://prod.example.com"},
    ]
)


@app.get("/foo")

1a) Send encrypted data key and master key ID to KMS.
1b) Receive decrypted data key.
2)  Decrypt encrypted object key with the KEK derived from the data key.
3a) Receive new plain data key from the KMS using the master key ID of the server config.
3b) Receive encrypted form of the data key from the KMS.
4)  Derive a new KEK from the new data key and re-encrypt the OEK with it.
5)  Store the encrypted OEK encrypted data key and master key ID in object metadata.

from typing import Annotated, Any

from annotated_doc import Doc
from pydantic import AfterValidator, BaseModel, Field, model_validator
from starlette.responses import StreamingResponse

# Canonical SSE event schema matching the OpenAPI 3.2 spec
# (Section 4.14.4 "Special Considerations for Server-Sent Events")
_SSE_EVENT_SCHEMA: dict[str, Any] = {
    "type": "object",
    "properties": {
        "data": {"type": "string"},