import logging
import secrets
import subprocess
import time
from collections import Counter
from collections.abc import Container
from datetime import datetime, timedelta, timezone
from math import ceil
from pathlib import Path
from typing import Annotated, Any

import httpx
import yaml
from github import Github
from pydantic import BaseModel, BeforeValidator, SecretStr
from pydantic_settings import BaseSettings

 * (unless there are {@link Double#NaN NaN} values, see below); otherwise, the result is the average
 * of the values which would appear at the indexes floor(x) and ceil(x) weighted by (1-frac(x)) and
 * frac(x) respectively. This is the same definition as used by Excel and by S, it is the Type 7
 * definition in <a

import static com.google.common.collect.CollectPreconditions.checkNonnegative;
import static com.google.common.collect.NullnessCasts.uncheckedCastNullableTToT;
import static java.lang.Math.ceil;
import static java.util.Collections.singletonMap;
import static java.util.Objects.requireNonNull;

import com.google.common.annotations.GwtCompatible;
import com.google.common.annotations.GwtIncompatible;

import static com.google.common.collect.CollectPreconditions.checkNonnegative;
import static com.google.common.collect.NullnessCasts.uncheckedCastNullableTToT;
import static java.lang.Math.ceil;
import static java.util.Collections.singletonMap;
import static java.util.Objects.requireNonNull;

import com.google.common.annotations.GwtCompatible;
import com.google.common.annotations.GwtIncompatible;

labels.reading=Odczyt
labels.roleTypeIds=ID roli
labels.scriptData=Skrypt
labels.scriptResult=Wynik
labels.scriptType=Typ wykonania
labels.segmentation=Segmentacja
labels.startTime=Godzina rozpoczęcia
labels.target=Cel
labels.token=Token
labels.synonymFile=Plik synonimów
labels.stopwordsFile=Plik stop-słów
labels.stemmerOverrideFile=Plik nadpisujący stemmer
labels.mappingFile=Plik mapowania
labels.protwordsFile=Plik protwords

    *   tf.optimizers => tf.keras.optimizers
    *   Add tf.keras.layers.AbstractRNNCell as the preferred implementation of
        RNN cell for TF v2. User can use it to implement RNN cell with custom
        behavior.
    *   Adding `clear_losses` API to be able to clear losses at the end of
        forward pass in a custom training loop in eager.

* Update example upgrade in docs main page. PR [#795](https://github.com/tiangolo/fastapi/pull/795) by [@cdeil](https://github.com/cdeil).
* Fix callback handling for sub-routers. PR [#792](https://github.com/tiangolo/fastapi/pull/792) by [@jekirl](https://github.com/jekirl).