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Michael Jackson wore tape on his nose to get front pages, former bodyguard claims	Ehemaliger Bodyguard berichtet: Michael Jackson trug Klebeband auf seiner Nase, um auf die Titelseiten zu kommen
Michael Jackson's former bodyguard has claimed the late singer cultivated some of his eccentricities with the deliberate intention of riling up the media.	Der ehemalige Bodyguard von Michael Jackson behauptet, dass der verstorbene Sänger einige seiner exzentrischen Verhaltensweise extra aneignete, um die Medien anzustacheln.
Matt Fiddes, now a property developer and owner of a martial arts/dance chain, told Metro that Jackson believed the fascination around his persona would stop if he ceased to be a "mystery" in the public eye.	Matt Fiddes, jetzt ein Bauträger und Inhaber einer Kampfsport/Tanzverein-Kette, erzählte Metro, dass Jackson daran glaubte, dass die Faszination mit seinem Image schwinden würde, wenn er im öffentlichen Leben nicht mehr ein „Mysterium“ sein würde.
To get front pages, he would reportedly don su

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#!/usr/bin/env python3

import itertools
from argparse import ArgumentParser, FileType

import torch
import numpy as np
from tqdm import tqdm

from transformers import PrefixConstrainedLogitsProcessor, AutoTokenizer, AutoModelForSeq2SeqLM

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#!/usr/bin/env python3

import spacy

from statistics import mean
from argparse import ArgumentParser, FileType

def setup_argparse():
    parser = ArgumentParser()
    parser.add_argument("-m", "--model", type=str, default="en_core_web_lg", help="The spaCy model to use for evaluation")

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#!/usr/bin/env python3

from typing import Iterator
from itertools import cycle, islice, chain

class GetItemIterator(Iterator):
    def __init__(self, *it):
        self._len, it_copy = self._get_len(it)
        self._it = cycle(it_copy)
        self._curr_idx = 0

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#!/usr/bin/env python3

import torch
import torch.nn as nn

if __name__ == "__main__":
    V, max_seq, padding_idx, emb_dim, B = 10, 100, 1, 512, 32
    emb_layer = nn.Embedding(V, emb_dim, padding_idx=padding_idx)
    emb_bag = nn.EmbeddingBag.from_pretrained(emb_layer.weight, freeze=False, padding_idx=padding_idx)
    initial_weights = emb_layer.weight.detach()

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from transformers import PrefixConstrainedLogitsProcessor

def create_processor_fn(ref_tokens_by_segment):
    def inner(batch_id, _):
        return ref_tokens_by_segment[batch_id]
    return inner

# ...

with tokenizer.as_target_tokenizer():

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#!/usr/bin/env python3

import random

from typing import List, Optional

import torch

from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import Sampler, DataLoader, Dataset

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#!/usr/bin/env python3

import base64

from io import BytesIO
from typing import List
from pathlib import Path
from argparse import ArgumentParser

import torch

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-- Building for: Visual Studio 16 2019
-- Selecting Windows SDK version 10.0.18362.0 to target Windows 10.0.18363.
-- The CXX compiler identification is MSVC 19.27.29111.0
-- Check for working CXX compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.27.29110/bin/Hostx64/x64/cl.exe
-- Check for working CXX compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.27.29110/bin/Hostx64/x64/cl.exe - works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Looking for C++ include pthread.h

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from heapq import heapify, heappush, heappushpop

class MaxHeap:
    def __init__(self, top_n: int):
        self.h = []
        self.length = top_n
        heapify(self.h)

    def add(self, element):
        if len(self.h) < self.length: