import math
import os
import random
import string
import time
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import torch
from torch import nn

#sva slova abecede koja koristimo za rijeci, broj tih slova, broj ukupnih kategorija
all_letters = string.ascii_letters
n_letters = len(all_letters)



class AnimalDataset():
    def readLines(filename):
        lines = open(filename, encoding='utf-8').read().strip().split('\n')
        return [line for line in lines]

    def __init__(self, data_root):
        self.data_root = data_root
        self.animals = []
        self.animal_words = {}

        for animal in os.listdir(self.data_root):  # otvori Data direktorij

            self.animals.append(animal)

            animal_folder = os.path.join(self.data_root, animal)  # put poddirektorija
            filepath = animal_folder + "/" + str(os.listdir(animal_folder)[0])  # put seta podataka
            lines = open(filepath, encoding='utf-8').read().strip().split('\n')
            self.animal_words[animal] = lines
            # dodajemo uzorke u mapu: Zivotinja - rijec za zivotinju


# Find letter index from all_letters, e.g. "a" = 0
def letterToIndex(letter):
    return all_letters.find(letter)


# Turn a line into a <line_length x 1 x n_letters>,
# or an array of one-hot letter vectors
def lineToTensor(line):
    tensor = torch.zeros(len(line), 1, n_letters)
    for li, letter in enumerate(line):
        tensor[li][0][letterToIndex(letter)] = 1
    return tensor


dataset = AnimalDataset('/home/pero/Documents/Neuronske/Data/') #promjeniti na lokaciju dataseta
all_categories = dataset.animals
words = dataset.animal_words
n_categories = len(all_categories)

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()

        self.hidden_size = hidden_size

        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.softmax(output)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, self.hidden_size)


def categoryFromOutput(output):
    top_n, top_i = output.topk(1)
    category_i = top_i[0].item()
    return all_categories[category_i], category_i


n_hidden = 500
rnn = RNN(n_letters, n_hidden, n_categories)

inputs = lineToTensor('kamel')
hidden = torch.zeros(1, n_hidden)

output, next_hidden = rnn(inputs[0], hidden)
print(output) #test run za jedno ponavljanje mreže

print(categoryFromOutput(output))


def randomChoice(l):
    return l[random.randint(0, len(l) - 1)]


def randomTrainingExample():
    category = randomChoice(all_categories)
    line = randomChoice(words[category])
    category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long)
    line_tensor = lineToTensor(line)
    return category, line, category_tensor, line_tensor


for i in range(10):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    print('category =', category, '/ line =', line)

criterion = nn.NLLLoss()
learning_rate = 0.00045 #PARAM 1


def train(category_tensor, line_tensor):
    hidden = rnn.initHidden()

    rnn.zero_grad()

    for i in range(line_tensor.size()[0]):
        output, hidden = rnn(line_tensor[i], hidden)

    loss = criterion(output, category_tensor)
    loss.backward()

    # Add parameters' gradients to their values, multiplied by learning rate
    for p in rnn.parameters():
        p.data.add_(-learning_rate, p.grad.data)

    return output, loss.item()


n_iters = 70000 #PARAM 2
print_every = 500
plot_every = 100

# Keep track of losses for plotting
current_loss = 0
all_losses = []


def timeSince(since):
    now = time.time()
    s = now - since
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)


start = time.time()

for iter in range(1, n_iters):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output, loss = train(category_tensor, line_tensor)
    current_loss += loss

    # Print iter number, loss, name and guess
    if iter % print_every == 0:
        guess, guess_i = categoryFromOutput(output)
        correct = '✓' if guess == category else '✗ (%s)' % category
        print(
            '%d %d%% (%s) %.4f %s / %s %s' % (iter, iter / n_iters * 100, timeSince(start), loss, line, guess, correct))

    # Add current loss avg to list of losses
    if iter % plot_every == 0:
        all_losses.append(current_loss / plot_every)
        current_loss = 0

plt.figure()
plt.plot(all_losses)
# Keep track of correct guesses in a confusion matrix
confusion = torch.zeros(n_categories, n_categories)
n_confusion = 10000


# Just return an output given a line
def evaluate(line_tensor):
    hidden = rnn.initHidden()

    for i in range(line_tensor.size()[0]):
        output, hidden = rnn(line_tensor[i], hidden)

    return output


# Go through a bunch of examples and record which are correctly guessed
for i in range(n_confusion):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output = evaluate(line_tensor)
    guess, guess_i = categoryFromOutput(output)
    category_i = all_categories.index(category)
    confusion[category_i][guess_i] += 1

# Normalize by dividing every row by its sum
for i in range(n_categories):
    confusion[i] = confusion[i] / confusion[i].sum()

# Set up plot
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(confusion.numpy())
fig.colorbar(cax)

# Set up axes
ax.set_xticklabels([''] + all_categories, rotation=90)
ax.set_yticklabels([''] + all_categories)

# Force label at every tick
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

# sphinx_gallery_thumbnail_number = 2
plt.show()


def predict(input_line, n_predictions=3):
    print('\n> %s' % input_line)
    with torch.no_grad():
        output = evaluate(lineToTensor(input_line))

        # Get top N categories
        topv, topi = output.topk(n_predictions, 1, True)
        predictions = []

        for i in range(n_predictions):
            value = topv[0][i].item()
            category_index = topi[0][i].item()
            print('(%.2f) %s' % (value, all_categories[category_index]))
            predictions.append([value, all_categories[category_index]])


predict('pes')
predict('cucak')
predict('dva')
predict('treger')
a = input("Daj zivinu: ")

while a != 'kraj':
    predict(str(a))
    a = input("Daj zivinu: ")