from itertools import filterfalse
import sys
import os

#from keras.layers.pooling import MaxPool2D
#from numpy.lib.shape_base import expand_dims 

#sys.path.append('c:/git/keras')
#sys.path.append('c:/git/absl')

# installed 
# py -mpip install numpy
# py -mpip show numpy 
# py -mpip install tensorflow
# py -mpip show tensorflow
# py -mpip install matplotlib
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages

import keras
#from keras.optimizers import adam_v2
#from tensorflow.keras.optimizers import Adam
#from keras.datasets import cifar10
#from keras.preprocessing.image import ImageDataGenerator
#from keras.models import Sequential
#from keras.utils import np_utils
#from keras.layers import Dense
from keras.layers import Activation
#from keras.layers import Flatten
#from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.layers import Conv2D
#from keras.layers import MaxPooling2D
#from keras.callbacks import ModelCheckpoint
#from tensorflow.keras.callbacks import EarlyStopping
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