import gensim.downloader
import torch.optim as optim
import torch.nn as nn
import torch
import numpy as np
from Net import NNet
#from timeit import default_timer as timer


def read_data(folder_name):
    with open(f'{folder_name}/in.tsv', encoding='utf-8') as file:
        x = [line.lower().split()[:-2] for line in file.readlines()]

    with open(f'{folder_name}/expected.tsv', encoding='utf-8') as file:
        y = [int(line.split()[0]) for line in file.readlines()]
    return x, y


def process_data(data, word2vec):
    processed_data = []
    for reddit in data:
        words_sim = [word2vec[word] for word in reddit if word in word2vec]
        processed_data.append(np.mean(words_sim or [np.zeros(100)], axis=0))
    return processed_data


def predict(folder_name, model, word_vec):
    with open(f'{folder_name}/in.tsv', encoding='utf-8') as file:
        x_data = [line.lower().split()[:-2] for line in file.readlines()]

    x_train = process_data(x_data, word_vec)

    y_predictions = []
    with torch.no_grad():
        for i, inputs in enumerate(x_train):
            inputs = torch.tensor(inputs.astype(np.float32)).to(device)

            y_predicted = model(inputs)
            y_predictions.append(y_predicted > 0.5)
    return y_predictions


def save_predictions(folder_name, predicted_labels):
    predictions = []
    for pred in predicted_labels:
        predictions.append(pred.int()[0].item())

    with open(f"{folder_name}/out.tsv", "w", encoding="UTF-8") as file_out:
        for pred in predictions:
            file_out.writelines(f"{str(pred)}\n")


device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)   #gpu is a bit faster here

word_vectors = gensim.downloader.load("glove-wiki-gigaword-100")

x_data, y_train = read_data('train')
x_train = process_data(x_data, word_vectors)

model = NNet().to(device)

criterion = nn.BCELoss()
optimizer = optim.SGD(model.parameters(), lr=0.005) #, momentum=0.9)

for epoch in range(2):
    running_loss = 0.0
    correct = 0.
    total = 0.
    for i, (inputs, label) in enumerate(zip(x_train, y_train)):
        inputs = torch.tensor(inputs.astype(np.float32)).to(device)
        label = torch.tensor(np.array(label).astype(np.float32)).reshape(1).to(device)

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        y_predicted = model(inputs)
        loss = criterion(y_predicted, label)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        correct += ((y_predicted > 0.5) == label).type(torch.float).sum().item()
        total += label.size(0)

        if i % 10000 == 9999:  # print every 10000 mini-batches
            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 10000:.3f}')
            print(f'Accuracy score: {100 * correct / total} %')
            running_loss = 0.0

predicted = predict('dev-0', model, word_vectors)
save_predictions('dev-0', predicted)

predicted = predict('test-A', model, word_vectors)
save_predictions('test-A', predicted)