import static org.codelibs.fess.app.web.admin.backup.AdminBackupAction.getSearchLogNdjsonWriteCall;
import static org.codelibs.fess.app.web.admin.backup.AdminBackupAction.getUserInfoNdjsonWriteCall;

import java.io.BufferedWriter;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStreamWriter;
import java.io.Writer;
import java.util.List;

from .api_key import APIKeyCookie as APIKeyCookie
from .api_key import APIKeyHeader as APIKeyHeader
from .api_key import APIKeyQuery as APIKeyQuery
from .http import HTTPAuthorizationCredentials as HTTPAuthorizationCredentials
from .http import HTTPBasic as HTTPBasic
from .http import HTTPBasicCredentials as HTTPBasicCredentials
from .http import HTTPBearer as HTTPBearer
from .http import HTTPDigest as HTTPDigest
from .oauth2 import OAuth2 as OAuth2

package jcifs.internal.smb1;


import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import jcifs.Configuration;
import jcifs.RuntimeCIFSException;
import jcifs.SmbConstants;
import jcifs.internal.CommonServerMessageBlock;
import jcifs.internal.CommonServerMessageBlockRequest;
import jcifs.internal.CommonServerMessageBlockResponse;
import jcifs.internal.RequestWithPath;
import jcifs.internal.SMBProtocolDecodingException;

import javax.security.auth.kerberos.KeyTab;

import org.ietf.jgss.GSSException;
import org.junit.Assume;
import org.junit.Before;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
import org.junit.runners.Parameterized.Parameters;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import jcifs.CIFSContext;
import jcifs.CIFSException;
import jcifs.ResolverType;

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Image(BaseModel):
    url: str
    name: str


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()
    image: Image | None = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):

import org.gradle.workers.WorkQueue;
import org.gradle.workers.WorkerExecutor;

import javax.inject.Inject;
import java.io.File;
import java.net.URISyntaxException;
import java.security.CodeSource;
import java.security.ProtectionDomain;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

@CacheableTask

from fastapi.testclient import TestClient

from docs_src.additional_status_codes.tutorial001 import app

client = TestClient(app)


def test_update():
    response = client.put("/items/foo", json={"name": "Wrestlers"})
    assert response.status_code == 200, response.text
    assert response.json() == {"name": "Wrestlers", "size": None}


def test_create():
    response = client.put("/items/red", json={"name": "Chillies"})

from fastapi.testclient import TestClient

from docs_src.additional_status_codes.tutorial001_an import app

client = TestClient(app)


def test_update():
    response = client.put("/items/foo", json={"name": "Wrestlers"})
    assert response.status_code == 200, response.text
    assert response.json() == {"name": "Wrestlers", "size": None}


def test_create():
    response = client.put("/items/red", json={"name": "Chillies"})

from typing import Annotated, Union

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Annotated[
        Union[str, None],
        Query(
            title="Query string",
            description="Query string for the items to search in the database that have a good match",
            min_length=3,
        ),
    ] = None,
):

from contextlib import asynccontextmanager

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()