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use arrow::array::{make_array, ArrayData, Int64Array};
use arrow::pyarrow::PyArrowType;
use pyo3::prelude::*;

fn chunked_binary_search(chunks: Vec<&[i64]>, val: i64, left: bool) -> usize {
    let val = if left { val } else { val + 1 };

    let chunks: Vec<&[i64]> = chunks.into_iter().filter(|x| !x.is_empty()).collect();
    if chunks.is_empty() {
        return 0;

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import numpy as np


def estimate_eigeinvalues(A, X, extra=False):
    """From estimated eigenvectors X of A, compute estimated eigenvalues of A."""
    n, _ = A.shape
    R = np.eye(n) - X.T @ X
    S = X.T @ A @ X
    w = np.diag(S) / (1 - np.diag(R))
    if extra:

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import numpy as np

N = 200
L = 10_000_000
p = 0.2

r = np.arange(L / p, dtype=int)
s = [
    r[np.random.rand(r.shape[0]) > 1 - p]
    for _ in range(N)

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#include <algorithm>
#include <iostream>
#include <string>
#include <thread>
#include <vector>
#include <future>
#include <queue>
#include <condition_variable>
#include <mutex>
#include <functional>

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from pathlib import Path

import numpy as np
import pandas as pd


class RaggedIndexer:
    def __init__(self, counts):
        self.stops = np.r_[0, np.cumsum(counts)]
        self.starts = self.stops[:-1]

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# https://rockylinux.org/cloud-images/
# AMI 0951577d22b8e8dd4 rocky 9.1
# Instance p3.2xlarge

dnf install -y epel-release
crb enable

dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
dnf module install -y nvidia-driver:latest
reboot

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import numpy as np
from opt_einsum import contract

class DRE:
    """Deep Regression Ensemble

    Parameters
    ----------
    n_layers : int
        Number of hidden layers.

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import os

from multiprocessing import get_context
from concurrent.futures import ProcessPoolExecutor
from concurrent.futures.thread import _global_shutdown_lock as L

os.register_at_fork(
    before=L.acquire,
    after_in_child=L._at_fork_reinit,
    after_in_parent=L.release

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import heapq
import numpy as np

def generic_linkage(D, method='single'):
    N = D.shape[0]
    S = set(range(N))
    
    size = {i: 1 for i in range(N)}

    d = {}