import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import matplotlib.image as mpimg
import math

print("

")
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#
with tf.device("/gpu:0"):
    # Helper Function To Print Percentage
    def showPercent(num, den, roundAmount):
        print(  str( round((num / den) * roundAmount )/roundAmount ) + " % ", end="
")
    # Defince The Number Of Images To Get
    def getFile(dir, getEveryNthLine):
        allFiles = list(os.listdir(dir))
        fileNameList = []

        numOfFiles = len(allFiles)
        i = 0
        for fichier in allFiles:
            if(i % 100 == 0):
                showPercent(i, numOfFiles, 100)

            if(i % getEveryNthLine == 0):
                if(fichier.endswith(".png")):
                    fileNameList.append(dir + "/" + fichier[0:-4])
            i += 1
        return fileNameList

    # Other Helper Functions
    def init_weights(shape):
        init_random_dist = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float16)
        return tf.Variable(init_random_dist)
    def init_bias(shape):
        init_bias_vals = tf.constant(0.1, shape=shape, dtype=tf.float16)
        return tf.Variable(init_bias_vals)
    def conv2d(x, W):
        # x --> [batch, H, W, Channels]
        # W --> [filter H, filter W, Channels IN, Channels Out]

        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
    def max_pool_2by2(x):
        # x --> [batch, H, W, Channels]
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
    def convolutional_layer(input_x, shape):
        W = init_weights(shape)
        b = init_bias([ shape[3] ])
        return tf.nn.relu(conv2d(input_x, W) + b)
    def normal_full_layer(input_layer, size):
        input_size = int(input_layer.get_shape()[1])
        W = init_weights([input_size, size])
        b = init_bias([size])
        return tf.matmul(input_layer, W) + b

    print("Getting Images")
    fileNameList = getFile("F:cartoonset10k-small", 1000)
    print("
loaded " + str(len(fileNameList)) + " files")

    print("Defining Placeholders")
    x_ph = tf.placeholder(tf.float16, shape=[None, 400, 400, 4])
    y_ph = tf.placeholder(tf.float16, shape=[None])

    print("Defining Conv and Pool layer 1")
    convo_1 = convolutional_layer(x_ph, shape=[5, 5, 4, 32])
    convo_1_pooling = max_pool_2by2(convo_1)

    print("Defining Conv and Pool layer 2")
    convo_2 = convolutional_layer(convo_1_pooling, shape=[5, 5, 32, 64])
    convo_2_pooling = max_pool_2by2(convo_2)

    print("Define Flat later and a Full layer")
    convo_2_flat = tf.reshape(convo_2_pooling, [-1, 400 * 400 * 64])
    full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat, 1024))
    y_pred = full_layer_one # Add Dropout Later

    def getLabels(filePath):
        df = []
        with open(filePath, "r") as file:
            for line in list(file):
                tempList = line.replace("
", "").replace('"', "").replace(" ", "").split(",")
                df.append({
                    "attr": tempList[0],
                    "value":int(tempList[1]),
                    "maxValue":int(tempList[2])
                })
        return df

    print("
Splitting And Formating X, and Y Data")
    x_data = []
    y_data = []
    numOfFiles = len(fileNameList)
    i = 0
    for file in fileNameList:
        if i % 10 == 0:
            showPercent(i, numOfFiles, 100)
        x_data.append(mpimg.imread(file + ".png"))
        y_data.append(pd.DataFrame(getLabels(file + ".csv"))["value"][0])
        i += 1

    print("
Conveting x_data to list")
    i = 0
    for indx in range(len(x_data)):
        if i % 10 == 0:
            showPercent(i, numOfFiles, 100)
        x_data[indx] = x_data[indx].tolist()
        i += 1

    print("

Performing Train Test Split")
    train_x, test_x, train_y, test_y = train_test_split(x_data, y_data, test_size=0.2)

    print("Defining Loss And Optimizer")
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits_v2(
            labels=y_ph,
            logits=y_pred
        )
    )
    optimizer = tf.train.AdadeltaOptimizer(learning_rate=0.001)
    train = optimizer.minimize(cross_entropy)

    print("Define Var Init")
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        print("Checkpoint Before Initializer")
        sess.run(init)
        print("Checkpoint After Initializer")
        batch_size = 8
        steps = 1
        i = 0
        for i in range(steps):
            if i % 10:
                print(i / 100, end="
")

            batch_x = []
            i = 0
            for i in np.random.randint(len(train_x), size=batch_size):
                showPercent(i, len(train_x), 100)
                train_x[i]
            batch_x = [train_x[i] for i in np.random.randint(len(train_x), size=batch_size) ]
            batch_y = [train_y[i] for i in np.random.randint(len(train_y), size=batch_size) ]
            print(sess.run(train, {
                x_ph:train_x,
                y_ph:train_y,
            }))

import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():
    model = tf.keras.models.Sequential([
      tf.keras.layers.Flatten(input_shape=(28, 28)),
      tf.keras.layers.Dense(512, activation=tf.nn.relu),
      tf.keras.layers.Dropout(0.2),
      tf.keras.layers.Dense(10, activation=tf.nn.softmax)
    ])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model    

epoch = 3

print('GPU:')
with tf.device('/gpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)

print('
CPU:')
with tf.device('/cpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)