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import keras.backend as K
import tensorflow as tf
from keras.layers import Layer

"""Not tested, I'll play around with GANs soon with it."""


class CoordConv2D(Layer):
    def __init__(self, channel, kernel, padding='valid', **kwargs):
        self.layer = Conv2D(channel, kernel, padding=padding)

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import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np

# Define custom py_func which takes also a grad op as argument:
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
    
    # Need to generate a unique name to avoid duplicates:
    rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
    

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import os
import fnmatch

def recursive_glob(rootdir='.', pattern='*'):
	"""Search recursively for files matching a specified pattern.
	
	Adapted from http://stackoverflow.com/questions/2186525/use-a-glob-to-find-files-recursively-in-python
	"""

	matches = []

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from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

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import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable, Function
from collections import defaultdict
import graphviz

"""
This is a rather distorted implementation of graph visualization in PyTorch.

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import torch
from torch.autograd import Variable
import torch.nn as nn


class Bottleneck(nn.Module):
    cardinality = 32  # the size of the set of transformations

    def __init__(self, nb_channels_in, nb_channels, nb_channels_out, stride=1):
        super().__init__()

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#
#  mnist_cnn_bn.py   date. 5/21/2016
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import numpy as np

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"""
Custom datasets from both in-memory and out-of-memory data
"""

import torch.utils.data as data

from PIL import Image
import os
import os.path

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from tf_logger import TFLogger


""" Example of using TFLogger to save train & dev statistics. To visualize
    in tensorboard simply do:
    
        tensorboard --logdir /path/to/summaries
        
    This code does depend on Tensorflow, but does not require that your model
    is built using Tensorflow. For instance, could build a model in Chainer, then