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import logging

import pyparsing as pp
pp.ParserElement.enablePackrat()

logging.basicConfig(format='%(message)s', level=logging.DEBUG)
LOG = logging.getLogger()

def unwrap(toks):
    if len(toks) == 1:

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# This file provides configuration information about non-Python dependencies for
# numpy.distutils-using packages. Create a file like this called "site.cfg" next
# to your package's setup.py file and fill in the appropriate sections. Not all
# packages will use all sections so you should leave out sections that your
# package does not use.

# To assist automatic installation like easy_install, the user's home directory
# will also be checked for the file ~/.numpy-site.cfg .

# The format of the file is that of the standard library's ConfigParser module.

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#!/usr/bin/env python

import os
import sys

FC = '/usr/local/bin/gfortran'

newargs = []
eat_next_arg = False
for arg in sys.argv[1:]:

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name: "ResNet_50_1by2_nsfw"
force_backward: true
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 224 dim: 224 } }
}
layer {
  name: "conv_1"

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#!/usr/bin/env python3

"""Total variation denoising."""

import argparse
import time

import numpy as np
from PIL import Image
from scipy.linalg import blas

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--[[ An experimental quasi-Newton optimizer.

Incorporates Hessian damping, momentum, and per-feature learning rate scaling.
Also implements optional polynomial-decay averaging (similar to ASGD).

ARGS:
- 'opfunc' : a function that takes a single input (X), the point
             of a evaluation, and returns f(X) and df/dX
- 'x'      : the initial point
- 'config` : a table with configuration parameters for the optimizer

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import numpy as np
from scipy.linalg import blas
from scipy.ndimage import zoom

# Machine epsilon for float32
EPS = np.finfo(np.float32).eps


# pylint: disable=no-member
def dot(x, y):

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name: "VGG_ILSVRC_16_layers_conv"
force_backward: true
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 224 dim: 224 } }
}
layer {
  bottom: "data"

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import argparse

from functools import partial, reduce
import math
from pathlib import Path
import random

from keras.layers import *
from keras.models import Model
from keras.utils import io_utils

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"""Converts colors from HSL to RGB and back."""

import numpy as np
from scipy import optimize


def tstack(a):
    """Stacks arrays in sequence along the last axis (tail)."""
    a = np.asarray(a)
    return np.concatenate([x[..., np.newaxis] for x in a], axis=-1)