from datetime import datetime, timedelta, timezone

import jwt
from fastapi import Depends, FastAPI, HTTPException, Security, status
from fastapi.security import (
    OAuth2PasswordBearer,
    OAuth2PasswordRequestForm,
    SecurityScopes,
)
from jwt.exceptions import InvalidTokenError
from pwdlib import PasswordHash
from pydantic import BaseModel, ValidationError

# to get a string like this run:
# openssl rand -hex 32

                "Method com.example.Source.foo(): From non-null returning to null-unmarked returning breaking change.",
                "Method com.example.Source.baz(): From non-null returning to null-unmarked returning breaking change.",
            )
            assertHasNoWarning()
            assertHasNoInformation()
        }
    }

    @Test
    fun `from null-unmarked returning to non-null returning is not breaking`() {

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 uuid

import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel


class MyUuid:
    def __init__(self, uuid_string: str):
        self.uuid = uuid_string

    def __str__(self):
        return self.uuid

    @property  # type: ignore
    def __class__(self):
        return uuid.UUID

    @property
    def __dict__(self):

from fastapi import FastAPI, Security
from fastapi.security import OAuth2PasswordBearer
from fastapi.testclient import TestClient
from inline_snapshot import snapshot

app = FastAPI()

oauth2_scheme = OAuth2PasswordBearer(
    tokenUrl="/token",
    description="OAuth2PasswordBearer security scheme",
    auto_error=False,
)


@app.get("/items/")
async def read_items(token: str | None = Security(oauth2_scheme)):
    if token is None:

   * close or flush either object.
   *
   * @param from the object to read from
   * @param to the object to write to
   * @return the number of characters copied
   * @throws IOException if an I/O error occurs
   */
  @CanIgnoreReturnValue
  public static long copy(Readable from, Appendable to) throws IOException {
    // The most common case is that from is a Reader (like InputStreamReader or StringReader) so

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

app = FastAPI()

security = HTTPDigest(auto_error=False)


@app.get("/users/me")
def read_current_user(
    credentials: HTTPAuthorizationCredentials | None = Security(security),
):
    if credentials is None:

import re
import warnings
from collections.abc import Sequence
from copy import copy
from dataclasses import dataclass, is_dataclass
from enum import Enum
from functools import lru_cache
from typing import (
    Annotated,
    Any,
    Literal,
    Union,
    cast,
    get_args,
    get_origin,
)

from fastapi._compat import lenient_issubclass, shared
from fastapi.openapi.constants import REF_TEMPLATE

from starlette.websockets import WebSocket as WebSocket  # noqa
from starlette.websockets import WebSocketDisconnect as WebSocketDisconnect  # noqa

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.