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import numpy as np
import hashlib

with open('names.txt', 'r') as f:
    names = f.read().splitlines()


def uid2name(string, num):
    hashobj = hashlib.sha256(string.encode('utf-8'))
    val_hex = int(hashobj.hexdigest(), 16)%(2**32-1)

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@torch.jit.script
def drloop(V: torch.Tensor,T: torch.Tensor) -> torch.Tensor:
    n = len(V)
    Ri = torch.zeros((3,), dtype=torch.float32, device=V.device)
    Ri[0] = -T[0][1]
    Ri[1] = T[0][0]
    Ri[2] = 0
    Ris = [Ri[None]]
    
    for i in range(n - 1):   

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import matplotlib.pyplot as plt
plt.switch_backend('tkagg')

import torch as th
import torch.nn.functional as F
import numpy as np
import cv2
from scipy.spatial.transform import Rotation

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"""
binary dilation in numba cuda
>> python cuda_binary_dilate_3d.py 512 512 512 1
>>> runtime: 0.001s on a Nvidia TitanXP. 
"""
import numba
import numpy as np
import time
from numba import cuda 

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"""
Local Convolutions in pytorch
"""
import torch
import torch.nn.functional as F


b,c,h,w = 2,3,32,32
d = 3
n = h*w

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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from ranger import Ranger

import numpy as np
import random
import cv2

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# Differentiable plasticity: binary pattern memorization and reconstruction
#
# Copyright (c) 2018 Uber Technologies, Inc.
#
# 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
#

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#!/usr/bin/python
from __future__ import print_function

"""
 * World To Camera & Back  (projective equations of pinhole cameras)    *
 * --------------------                                                 *
 * The OpenCV reference frame:                                          *
 * (or sometimes called "Camera Reference Frame"):                      *
 * Z points towards the direction of forward motion of the camera       *
 * X points right wrt Y                                                 *

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from __future__ import print_function
import time
import numpy as np
import cv2
from prophesee_utils.td_video import ChronoVideo

if __name__ == '__main__':
    delay = 1
    base_delay = 1
    stop = False

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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
import random


def bce_loss_with_logits(x, y):