xmodar

import torch


def sinusoidal(positions, features=16, periods=10000):
    """Encode `positions` using sinusoidal positional encoding

    Args:
        positions: tensor of positions
        features: half the number of features per position
        periods: used frequencies for the sinusoidal functions

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import torch

__all__ = ['softmax_mask']


class SoftmaxMask(torch.autograd.Function):
    """Differentiable mask for logits before a softmax operation"""
    @staticmethod
    def forward(ctx, *args, **kwargs):
        inputs, = args

xmodar

import torch


def randg(*args, like=None, **kwargs):
    """Sample from Gumbel(location=0, scale=1)"""
    generator = kwargs.pop('generator', None)
    requires_grad = kwargs.pop('requires_grad', False)
    if like is None:
        samples = torch.empty(*args, **kwargs)
    else:

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import tempfile
import urllib.request
import importlib.util
from pathlib import Path


def import_from_url(url):
    """Import a module from a given URL"""
    with tempfile.TemporaryDirectory() as path:
        path = Path(path) / Path(url).name

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import gc
import math
import time
import datetime
from contextlib import contextmanager

import torch


class Monitor:

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import ssl
import urllib
from pathlib import Path

import torch
from torch.utils.data import Dataset
from torchvision.datasets.utils import extract_archive, check_integrity

import h5py
import pandas as pd

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xmodar

import math


def chunk_dim(tensor, chunks, dim=0):
    """Split a dimension of a tensor into two dimensions"""
    shape = list(tensor.shape)
    shape[dim] //= chunks
    shape.insert(dim, chunks)
    return tensor.view(shape)

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"""YOLOv3 object detector."""
import math
from pathlib import Path
from urllib.request import urlopen

from PIL import Image
from PIL import ImageColor, ImageOps

import matplotlib.pyplot as plt
import matplotlib.patches as patches

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import itertools
from typing import Tuple, Optional
from contextlib import contextmanager

import torch
from torch.utils import benchmark


# @torch.jit.script
def nearest_neighbors(