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def sample_gumbel(shape, eps=1e-20): 
  """Sample from Gumbel(0, 1)"""
  U = tf.random_uniform(shape,minval=0,maxval=1)
  return -tf.log(-tf.log(U + eps) + eps)

def gumbel_softmax_sample(logits, temperature): 
  """ Draw a sample from the Gumbel-Softmax distribution"""
  y = logits + sample_gumbel(tf.shape(logits))
  return tf.nn.softmax( y / temperature)

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from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

def sample_gumbel(shape, eps=1e-20):
    U = torch.rand(shape).cuda()
    return -Variable(torch.log(-torch.log(U + eps) + eps))

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Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

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"""
Usage:

    python convert.py xxx.rst xxx.py

To convert markdown to sphinx_gallery `.py`, use pandoc to generate a `.rst` text in advance:

    pandoc xxx.md --output xxx.rst
    python convert.py xxx.rst xxx.py

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# This part for jupyter notebook setting (if you wants to save, don't use this)
# %matplotlib inline
# %config InlineBackend.figure_format = 'svg'
# import numpy as np
# import matplotlib.pyplot as plt
# plt.rcParams["animation.html"] = "jshtml"


import networkx as nx
from networkx.algorithms import bipartite

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"""
This code was modified from the GCN implementation in DGL examples.

Simplifying Graph Convolutional Networks
Paper: https://arxiv.org/abs/1902.07153
Code: https://github.com/Tiiiger/SGC

SGC implementation in DGL.
"""
import argparse, time, math

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"""Training graphsage w/ fp16.

Usage:

python train_full.py --gpu 0 --fp16 --dataset

Note that GradScaler is not acitvated because the model successfully converges
without gradient scaling.

DGL's Message Passing APIs are not compatible with fp16 yet, hence we disabled

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import dgl
import dgl.ops as ops
import numpy as np
import torch as th
import torch.nn as nn

class FFN(nn.Module):
    def __init__(self, d_feat, d_ffn, dropout=0.1):
        super().__init__()
        self.linear_0 = nn.Linear(d_feat, d_ffn)

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#include <stdio.h>
char str[] = "#include <stdio.h>%cchar str[] = %c%s%c;%cint main() {%c    printf(str, 10, 34, str, 34, 10, 10, 10);%c}";
int main() {
	printf(str, 10, 34, str, 34, 10, 10, 10);
}

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prog = "prog = {:c}{}{:c}{:c}print(prog.format(34, prog, 34, 10))"
print(prog.format(34, prog, 34, 10))