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import multiprocessing
import time

import numpy as np

import keras.backend as K
import keras.layers as KL
from keras import Model
from keras.callbacks import Callback
from keras.utils import Sequence

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import numpy as np

from keras.layers import Input, Lambda
from keras.models import Model
from keras.utils import Sequence


class SSEquence(Sequence):
    def __init__(self, value=1):
        self.value = value

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import multiprocessing
import threading
import time
from Queue import Queue


class Sequence():
    def __init__(self, my_list):
        self.my_list = my_list

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class weighted_categorical_crossentropy:
    def __init__(self, weights):
        self.weights = weights
        self.__name__ = 'wcentroid_loss'

    def __call__(self, y_true, y_pred):
        class0 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[0]
        class1 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[1]
        x = K.tf.where(y_true[:, :, :, 0] > 0, class0, class1)
        result = x * K.categorical_crossentropy(y_pred, y_true)

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import numpy as np
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable


class Layer(nn.Module):
    def __init__(self, in_, out, kernel, init_kernel='uniform', padding=1):
        super(Layer, self).__init__()

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import os
import time
from concurrent.futures import ProcessPoolExecutor
from itertools import cycle
from queue import Queue
from threading import Thread, Event

from keras.engine.training import GeneratorEnqueuer

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import operator
import threading
from functools import reduce

import keras
import keras.backend as K
from keras.engine import Model
import numpy as np
import tensorflow as tf
import time

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import tensorflow as tf
import numpy as np
import keras
import keras.backend as K
from keras.models import Model
import keras.callbacks as cbks
import threading
from keras.layers import Layer, Input, InputLayer, Dense

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import keras.backend as K
import tensorflow as tf
class Model:
  def __init__(self,batch_size):
    self.batch_size = batch_size
    
    def loss_DSSIS_tf11(self, y_true, y_pred):
        """Need tf0.11rc to work"""
        y_true = tf.reshape(y_true, [self.batch_size] + get_shape(y_pred)[1:])
        y_pred = tf.reshape(y_pred, [self.batch_size] + get_shape(y_pred)[1:])

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"""
Simple alarm clock to be awaken to the sound of your choice
"""
import datetime as dt
import time
import webbrowser
import argparse
import sys
from dateutil.relativedelta import relativedelta