"""
Adapted, and slightly modified, from:
http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style
"""


import os
import sys
import scipy.io
import scipy.misc
import tensorflow as tf
import numpy as np
def load_vgg_model(model_path, content_shape=(1, 300, 400, 3)):
    """
    Returns a model for the purpose of 'painting' the picture.
    Takes only the convolution layer weights and wrap using the TensorFlow
    Conv2d, Relu and AveragePooling layer. VGG actually uses maxpool but
    the paper indicates that using AveragePooling yields better results.
    The last few fully connected layers are not used.
    Here is the detailed configuration of the VGG model:
        0 is conv1_1 (3, 3, 3, 64)
        1 is relu
        2 is conv1_2 (3, 3, 64, 64)
        3 is relu    
        4 is maxpool
        5 is conv2_1 (3, 3, 64, 128)
        6 is relu
        7 is conv2_2 (3, 3, 128, 128)
        8 is relu
        9 is maxpool
        10 is conv3_1 (3, 3, 128, 256)
        11 is relu
        12 is conv3_2 (3, 3, 256, 256)
        13 is relu
        14 is conv3_3 (3, 3, 256, 256)
        15 is relu
        16 is conv3_4 (3, 3, 256, 256)
        17 is relu
        18 is maxpool
        19 is conv4_1 (3, 3, 256, 512)
        20 is relu
        21 is conv4_2 (3, 3, 512, 512)
        22 is relu
        23 is conv4_3 (3, 3, 512, 512)
        24 is relu
        25 is conv4_4 (3, 3, 512, 512)
        26 is relu
        27 is maxpool
        28 is conv5_1 (3, 3, 512, 512)
        29 is relu
        30 is conv5_2 (3, 3, 512, 512)
        31 is relu
        32 is conv5_3 (3, 3, 512, 512)
        33 is relu
        34 is conv5_4 (3, 3, 512, 512)
        35 is relu
        36 is maxpool
        37 is fullyconnected (7, 7, 512, 4096)
        38 is relu
        39 is fullyconnected (1, 1, 4096, 4096)
        40 is relu
        41 is fullyconnected (1, 1, 4096, 1000)
        42 is softmax
    """
    
    vgg = scipy.io.loadmat(model_path)

    vgg_layers = vgg['layers']
    
    def _weights(layer, expected_layer_name):
        """
        Return the weights and bias from the VGG model for a given layer.
        """
        wb = vgg_layers[0][layer][0][0][2]
        W = wb[0][0]
        b = wb[0][1]
        layer_name = vgg_layers[0][layer][0][0][0][0]
        assert layer_name == expected_layer_name
        return W, b

        return W, b

    def _relu(conv2d_layer):
        """
        Return the RELU function wrapped over a TensorFlow layer. Expects a
        Conv2d layer input.
        """
        return tf.nn.relu(conv2d_layer)

    def _conv2d(prev_layer, layer, layer_name):
        """
        Return the Conv2D layer using the weights, biases from the VGG
        model at 'layer'.
        """
        W, b = _weights(layer, layer_name)
        W = tf.constant(W)
        b = tf.constant(np.reshape(b, (b.size)))
        return tf.nn.conv2d(prev_layer, filter=W, strides=[1, 1, 1, 1], padding='SAME') + b

    def _conv2d_relu(prev_layer, layer, layer_name):
        """
        Return the Conv2D + RELU layer using the weights, biases from the VGG
        model at 'layer'.
        """
        return _relu(_conv2d(prev_layer, layer, layer_name))

    def _avgpool(prev_layer):
        """
        Return the AveragePooling layer.
        """
        return tf.nn.avg_pool(prev_layer, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

    # Constructs the graph model.
    graph = {}
    graph['input']   = tf.Variable(np.zeros((content_shape)), dtype = 'float32')
    graph['conv1_1']  = _conv2d_relu(graph['input'], 0, 'conv1_1')
    graph['conv1_2']  = _conv2d_relu(graph['conv1_1'], 2, 'conv1_2')
    graph['avgpool1'] = _avgpool(graph['conv1_2'])
    graph['conv2_1']  = _conv2d_relu(graph['avgpool1'], 5, 'conv2_1')
    graph['conv2_2']  = _conv2d_relu(graph['conv2_1'], 7, 'conv2_2')
    graph['avgpool2'] = _avgpool(graph['conv2_2'])
    graph['conv3_1']  = _conv2d_relu(graph['avgpool2'], 10, 'conv3_1')
    graph['conv3_2']  = _conv2d_relu(graph['conv3_1'], 12, 'conv3_2')
    graph['conv3_3']  = _conv2d_relu(graph['conv3_2'], 14, 'conv3_3')
    graph['conv3_4']  = _conv2d_relu(graph['conv3_3'], 16, 'conv3_4')
    graph['avgpool3'] = _avgpool(graph['conv3_4'])
    graph['conv4_1']  = _conv2d_relu(graph['avgpool3'], 19, 'conv4_1')
    graph['conv4_2']  = _conv2d_relu(graph['conv4_1'], 21, 'conv4_2')
    graph['conv4_3']  = _conv2d_relu(graph['conv4_2'], 23, 'conv4_3')
    graph['conv4_4']  = _conv2d_relu(graph['conv4_3'], 25, 'conv4_4')
    graph['avgpool4'] = _avgpool(graph['conv4_4'])
    graph['conv5_1']  = _conv2d_relu(graph['avgpool4'], 28, 'conv5_1')
    graph['conv5_2']  = _conv2d_relu(graph['conv5_1'], 30, 'conv5_2')
    graph['conv5_3']  = _conv2d_relu(graph['conv5_2'], 32, 'conv5_3')
    graph['conv5_4']  = _conv2d_relu(graph['conv5_3'], 34, 'conv5_4')
    graph['avgpool5'] = _avgpool(graph['conv5_4'])
    
    return graph
