* </ol>
     */
    void transitionService(Service service, State from, State to) {
      checkNotNull(service);
      checkArgument(from != to);
      monitor.enter();
      try {
        transitioned = true;
        if (!ready) {
          return;
        }
        // Update state.
        checkState(
            servicesByState.remove(from, service),

from typing import Annotated

from fastapi import Depends, FastAPI
from fastapi.security import OAuth2PasswordBearer
from pydantic import BaseModel

app = FastAPI()

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")


class User(BaseModel):
    username: str
    email: str | None = None
    full_name: str | None = None
    disabled: bool | None = None


def fake_decode_token(token):
    return User(

/**
 * Extracts the filename from the parameters as the content.
 *
 * <p>This extractor is useful when you want to index only the filename or resource name
 * without processing the actual file content. The input stream is validated but not
 * read - only the filename from the parameters is used as the extracted content.</p>
 *
 * <p>The filename is retrieved from the {@link ExtractData#RESOURCE_NAME_KEY} parameter.

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.

import time
from typing import Annotated

from fastapi import Depends, FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from sqlmodel import Field, Session, SQLModel, create_engine

engine = create_engine("postgresql+psycopg://postgres:postgres@localhost/db")


class User(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str


app = FastAPI()


def get_session():

from fastapi import FastAPI
from fastapi.responses import FileResponse
from pydantic import BaseModel


class Item(BaseModel):
    id: str
    value: str


responses = {
    404: {"description": "Item not found"},
    302: {"description": "The item was moved"},
    403: {"description": "Not enough privileges"},
}


app = FastAPI()


@app.get(
    "/items/{item_id}",
    response_model=Item,

from typing import Annotated

from fastapi import FastAPI, Path
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(
    item_id: Annotated[int, Path(title="The ID of the item to get", ge=0, le=1000)],
    q: str | None = None,
    item: Item | None = None,

                missingFiles.add("- The file '" + filename + "' from " + jsonFile.getName() + " was not found in the directory.");
            }
        }

        return missingFiles;
    }

    /**
     * Finds and collects all anchors from all .adoc files in a directory.
     */

from typing import Annotated, Union

from fastapi import Depends, FastAPI, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from pydantic import BaseModel

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {

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}