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import shutil

# Remove cache if it exists
try:
    shutil.rmtree("__pycache__")
except FileNotFoundError:
    pass

try:
    with open("/proc/cpuinfo") as f:

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#include <cuda_runtime.h>
#include <iostream>

#define CHECK_CUDA_ERROR(call)                                                      \
    do {                                                                            \
        cudaError_t err = call;                                                     \
        if (err != cudaSuccess) {                                                   \
            std::cerr << "CUDA error at " << __FILE__ << ":" << __LINE__ << " - "   \
                      << cudaGetErrorString(err) << " (" << err << ")" << std::endl; \
            exit(EXIT_FAILURE);                                                     \

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import cv2
import numpy as np
import urllib.request
import os

# Download Tsukuba stereo test images
urls = [
    "https://vision.middlebury.edu/stereo/data/scenes2001/data/tsukuba/scene1.row3.col1.ppm",
    "https://vision.middlebury.edu/stereo/data/scenes2001/data/tsukuba/scene1.row3.col2.ppm",
]

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Colors:
[[255, 128, 128], [255, 174, 128], [255, 221, 128], [213, 255, 128], [128, 255, 183], [128, 255, 253], [128, 208, 255], [128, 151, 255], [198, 128, 255], [255, 128, 202]]
RGB colors in unit range:
(1.00, 0.50, 0.50), (1.00, 0.68, 0.50), (1.00, 0.87, 0.50), (0.84, 1.00, 0.50), (0.50, 1.00, 0.72), (0.50, 1.00, 0.99), (0.50, 0.82, 1.00), (0.50, 0.59, 1.00), (0.78, 0.50, 1.00), (1.00, 0.50, 0.79)
Colors in hexadecimal:
['#ff8080', '#ffae80', '#ffdd80', '#d5ff80', '#80ffb7', '#80fffd', '#80d0ff', '#8097ff', '#c680ff', '#ff80ca']

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import numpy as np
import pyopencl as cl
from pymatting import KDTree
import pymatting
import time

source = """
typedef int int32_t;
typedef long int64_t;

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import blenderproc as bproc
import numpy as np

bproc.init()

# Create a simple object:
obj = bproc.object.create_primitive("MONKEY")

# Create a point light next to it
light = bproc.types.Light()

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import subprocess, getpass

def get_gpu_usage():
    """
    Returns a dict which contains information about memory usage for each GPU.

    In the following output, the GPU with id "0" uses 5774 MB of 16280 MB.
    253 MB are used by other users, which means that we are using 5774 - 253 MB.

    {

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import numpy as np
import matplotlib.pyplot as plt
import matplotlib

# ugly hack to embed fonts
matplotlib.rc("pdf", fonttype=42)

edgecolor = "black"

bar_scale = 0.8

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from z3 import *
import matplotlib.pyplot as plt
import random, math

# pip install z3-solver matplotlib
# do not install z3, that's a different library

random.seed(0)

def find_ordered_coloring(edges, n):

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import math

class Line:
    def __init__(self, x1, y1, x2, y2):
        self.x1 = x1
        self.y1 = y1
        self.x2 = x2
        self.y2 = y2

    def to_svg(self, style):