from keras import backend as K
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Input, Dense, Activation
from keras.layers import Reshape, Lambda, BatchNormalization
from keras.layers.merge import add, concatenate
from keras.models import Model
from keras.layers.recurrent import GRU
from parameter import *
K.set_learning_phase(1)

# # Loss and train functions, network architecture
def ctc_lambda_func(args):
    y_pred, labels, input_length, label_length = args
    # the 2 is critical here since the first couple outputs of the RNN
    # tend to be garbage:
    y_pred = y_pred[:, 2:, :]
    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)


def get_Model(training):
    input_shape = (img_w, img_h, 1)     # (128, 64, 1)

    # Make Networkw
    inputs = Input(name='the_input', shape=input_shape, dtype='float32')  # (None, 128, 64, 1)

    # Convolution layer (VGG)
    inner = Conv2D(64, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(inputs)  # (None, 128, 64, 64)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(2, 2), name='max1')(inner)  # (None,64, 32, 64)

    inner = Conv2D(128, (3, 3), padding='same', name='conv2', kernel_initializer='he_normal')(inner)  # (None, 64, 32, 128)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(2, 2), name='max2')(inner)  # (None, 32, 16, 128)

    inner = Conv2D(256, (3, 3), padding='same', name='conv3', kernel_initializer='he_normal')(inner)  # (None, 32, 16, 256)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = Conv2D(256, (3, 3), padding='same', name='conv4', kernel_initializer='he_normal')(inner)  # (None, 32, 16, 256)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(1, 2), name='max3')(inner)  # (None, 32, 8, 256)

    inner = Conv2D(512, (3, 3), padding='same', name='conv5', kernel_initializer='he_normal')(inner)  # (None, 32, 8, 512)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = Conv2D(512, (3, 3), padding='same', name='conv6')(inner)  # (None, 32, 8, 512)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(1, 2), name='max4')(inner)  # (None, 32, 4, 512)

    inner = Conv2D(512, (2, 2), padding='same', kernel_initializer='he_normal', name='con7')(inner)  # (None, 32, 4, 512)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)

    # CNN to RNN
    inner = Reshape(target_shape=((32, 2048)), name='reshape')(inner)  # (None, 32, 2048)
    inner = Dense(64, activation='relu', kernel_initializer='he_normal', name='dense1')(inner)  # (None, 32, 64)

    # RNN layer
    gru_1 = GRU(256, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)  # (None, 32, 512)
    gru_1b = GRU(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)
    reversed_gru_1b = Lambda(lambda inputTensor: K.reverse(inputTensor, axes=1)) (gru_1b)

    gru1_merged = add([gru_1, reversed_gru_1b])  # (None, 32, 512)
    gru1_merged = BatchNormalization()(gru1_merged)
    
    gru_2 = GRU(256, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
    gru_2b = GRU(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
    reversed_gru_2b= Lambda(lambda inputTensor: K.reverse(inputTensor, axes=1)) (gru_2b)

    gru2_merged = concatenate([gru_2, reversed_gru_2b])  # (None, 32, 1024)
    gru2_merged = BatchNormalization()(gru2_merged)

    # transforms RNN output to character activations:
    inner = Dense(num_classes, kernel_initializer='he_normal',name='dense2')(gru2_merged) #(None, 32, 63)
    y_pred = Activation('softmax', name='softmax')(inner)

    labels = Input(name='the_labels', shape=[max_text_len], dtype='float32') # (None ,8)
    input_length = Input(name='input_length', shape=[1], dtype='int64')     # (None, 1)
    label_length = Input(name='label_length', shape=[1], dtype='int64')     # (None, 1)

    # Keras doesn't currently support loss funcs with extra parameters
    # so CTC loss is implemented in a lambda layer
    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) #(None, 1)

    if training:
        return Model(inputs=[inputs, labels, input_length, label_length], outputs=loss_out)
    else:
        return Model(inputs=[inputs], outputs=y_pred)