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use crate::utils::{read_input, Result}; | |
#[derive(Clone, Copy)] | |
pub enum OperatorType { | |
Sum, | |
Product, | |
Min, | |
Max, | |
Gt, | |
Lt, |
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test.v2 0 1580115600 | |
test.v2 0 1580115660 | |
test.v2 1 1580115720 | |
test.v2 3 1580115780 | |
test.v2 2 1580115840 | |
test.v2 2 1580115900 | |
test.v2 10 1580115960 | |
test.v2 10 1580116020 | |
test.v2 12 1580116080 | |
test.v2 0 1580116140 |
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from __future__ import annotations | |
import array | |
import asyncio | |
from bisect import bisect_left | |
from dataclasses import dataclass | |
import logging | |
import os | |
import socket | |
import time |
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import React from "react"; | |
import Counter from "./Counter"; | |
const App = () => { | |
return ( | |
<div className="App"> | |
<Counter initialCount={100} /> | |
</div> | |
); | |
}; |
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from datetime import datetime | |
import json | |
from functools import lru_cache | |
from multiprocessing import Pool | |
import sys | |
from nltk.tokenize import word_tokenize, sent_tokenize | |
from nltk.corpus import stopwords | |
from nltk.util import ngrams | |
from nltk import WordNetLemmatizer, pos_tag |
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from datetime import datetime | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
from matplotlib.ticker import FuncFormatter | |
from nltk.sentiment.vader import SentimentIntensityAnalyzer | |
import praw | |
options = dict(client_id='', | |
client_secret='', | |
user_agent='') |
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, |
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from pathlib import Path | |
from typing import NamedTuple | |
from collections import defaultdict | |
from datetime import timedelta | |
from subprocess import call | |
from pycaption.srt import SRTReader | |
import lxml.html | |
import tensorflow as tf | |
import tensorflow_hub as hub | |
import numpy as np |
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