import keras

from keras.models import Sequential, Model

from keras.layers import Input

from keras.layers.core import Dense, Activation, Dropout, Lambda, Reshape, Flatten, Permute

from keras.layers.recurrent import LSTM, SimpleRNN

from keras.layers.convolutional import Convolution1D

from keras.layers.pooling import MaxPooling1D

from keras.utils.data_utils import get_file

from keras.layers.wrappers import TimeDistributed

from keras.layers.merge import Concatenate

from keras.callbacks import Callback

from keras.optimizers import *

from keras.regularizers import l1,l2,l1_l2

import keras.backend as K

import dill

import re

import os,sys

import numpy as np

from copy import deepcopy

x = np.array(range(8))

num_signals = len(x)

x = np.atleast_2d(x)

x = np.reshape(x,(1,num_signals))

print(x.shape)

print(x)

indices_0d = np.array([0,1])

indices_1d = np.array([2,3,4,5,6,7])

num_1D = 2

pre_rnn_input = Input(shape=(num_signals,))

pre_rnn_1D = Lambda(lambda x: x[:,len(indices_0d):],output_shape=(len(indices_1d),))(pre_rnn_input)

pre_rnn_0D = Lambda(lambda x: x[:,:len(indices_0d)],output_shape=(len(indices_0d),))(pre_rnn_input)# slicer(x,indices_0d),lambda s: slicer_output_shape(s,indices_0d))(pre_rnn_input)

pre_rnn_1D = Reshape((num_1D,len(indices_1d)/num_1D)) (pre_rnn_1D)

pre_rnn_1D = Permute((2,1)) (pre_rnn_1D)

# for i in range(model_conf['num_conv_layers']):

# pre_rnn_1D = Convolution1D(num_conv_filters,size_conv_filters,padding='valid',activation='relu') (pre_rnn_1D)

# pre_rnn_1D = MaxPooling1D(pool_size) (pre_rnn_1D)

# pre_rnn_1D = Flatten() (pre_rnn_1D)

# pre_rnn = Concatenate() ([pre_rnn_0D,pre_rnn_1D])

model = Model(inputs = pre_rnn_input,outputs=pre_rnn_1D)

# x_input = Input(batch_shape = batch_input_shape)

# x_in = TimeDistributed(pre_rnn_model) (x_input)

# if return_sequences:

#x_out = TimeDistributed(Dense(100,activation='tanh')) (x_in)

# x_out = TimeDistributed(Dense(1,activation=output_activation)) (x_in)

# else:

# x_out = Dense(1,activation=output_activation) (x_in)

model.compile(loss='mse',optimizer='sgd')

y = model.predict(x)

print(model.layers)

print(x)

print(y)

print(y.shape)

print(y[0,:,0])

#bug with tensorflow/Keras