from typing import Annotated

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(x_token: Annotated[list[str] | None, Header()] = None):

from pathlib import Path

import pytest
from typer.testing import CliRunner

from scripts.translation_fixer import cli

data_path = Path(
    "scripts/tests/test_translation_fixer/test_code_blocks/data"
).absolute()


@pytest.mark.parametrize(
    "copy_test_files",
    [(f"{data_path}/en_doc.md", f"{data_path}/translated_doc_lines_number_gt.md")],
    indirect=True,
)
def test_gt(runner: CliRunner, root_dir: Path, copy_test_files):

from pathlib import Path

import pytest
from typer.testing import CliRunner

from scripts.translation_fixer import cli

data_path = Path(
    "scripts/tests/test_translation_fixer/test_code_blocks/data"
).absolute()


@pytest.mark.parametrize(
    "copy_test_files",
    [(f"{data_path}/en_doc.md", f"{data_path}/translated_doc_wrong_lang_code.md")],
    indirect=True,
)

    protected Map<String, String> globalParams = new HashMap<>();

    /** Generated web crawling configuration from parsed GSA config. */
    protected WebConfig webConfig = null;

    /** Generated file crawling configuration from parsed GSA config. */
    protected FileConfig fileConfig = null;

    /** Buffer to accumulate character data between XML tags. */

from fastapi import FastAPI, Security
from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
from fastapi.testclient import TestClient
from inline_snapshot import snapshot

app = FastAPI()

security = HTTPBearer()


@app.get("/users/me")
def read_current_user(credentials: HTTPAuthorizationCredentials = Security(security)):
    return {"scheme": credentials.scheme, "credentials": credentials.credentials}

// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of
// the op that produced it (or -1 if this tensor was watched using
// `GradientTape::Watch`.) The op_id is simply a unique index assigned to each
// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,

```{ .dockerfile .annotate }
# (1)!
FROM python:3.14

# (2)!
WORKDIR /code

# (3)!
COPY ./requirements.txt /code/requirements.txt

# (4)!
RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

# (5)!
COPY ./app /code/app

# (6)!
CMD ["fastapi", "run", "app/main.py", "--port", "80"]
```

1. Start from the official Python base image.

from functools import lru_cache
from pathlib import Path
from typing import Any

import material
from mkdocs.config.defaults import MkDocsConfig
from mkdocs.structure.files import File, Files
from mkdocs.structure.nav import Link, Navigation, Section
from mkdocs.structure.pages import Page

non_translated_sections = [
    "reference/",
    "release-notes.md",
    "fastapi-people.md",
    "external-links.md",
    "newsletter.md",

from fastapi import FastAPI, Form
from pydantic import BaseModel

app = FastAPI()


class FormData(BaseModel):
    username: str
    password: str


@app.post("/login/")
async def login(data: FormData = Form()):

from fastapi import FastAPI
from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    openapi_url: str = "/openapi.json"


settings = Settings()

app = FastAPI(openapi_url=settings.openapi_url)


@app.get("/")
def root():