from itertools import filterfalse
import sys
import os
import keras
from keras.layers import Activation
from keras.layers import BatchNormalization
from keras.layers import Conv2D
from keras import regularizers
from keras import optimizers
from matplotlib import pyplot
import numpy as np
import tensorflow

def bottleneck_residual_block(x,kernel_size,filters,reduce=False,s=2):
    F1, F2, F3 = filters
    x_shortcut=x

    if reduce:
        x_shortcut=Conv2D(filters=F3,kernel_size=(1,1),strides=(s,s))(x_shortcut)
        x_shortcut=BatchNormalization(axis=3)(x_shortcut)

        x=Conv2D(filters=F1,kernel_size=(1,1),strides=(s,s),padding='valid')(x)
        x=BatchNormalization(axis=3)(x)
        x=Activation('relu')(x)        
    else:
        x=Conv2D(filters=F1,kernel_size=(1,1),strides=(1,1),padding='valid',kernel_initializer = keras.initializers.glorot_uniform(seed=0))(x)
        x=BatchNormalization(axis=3)(x)
        x=Activation('relu')(x)

    x=Conv2D(filters=F2,kernel_size=kernel_size,strides=(1,1),padding='same',kernel_initializer = keras.initializers.glorot_uniform(seed=0))(x)
    x=BatchNormalization(axis=3)(x)
    x=Activation('relu')(x)

    x=Conv2D(filters=F3,kernel_size=(1,1),strides=(1,1),padding='valid',kernel_initializer = keras.initializers.glorot_uniform(seed=0))(x)
    x=BatchNormalization(axis=3)(x)

    x=keras.layers.Add()([x,x_shortcut])
    x=Activation('relu')(x)

    return x